Key Technologies of Internet of Things and Smart Grid (Advanced and Intelligent Manufacturing in China) 9819976022, 9789819976027

Citation preview

Advanced and Intelligent Manufacturing in China

Xianwu Zeng  Shuping Bao

Key Technologies of Internet of Things and Smart Grid

Advanced and Intelligent Manufacturing in China Series Editor Jie Chen, Tongji University, Shanghai, Shanghai, China

This is a set of high-level and original academic monographs. This series focuses on the two fields of intelligent manufacturing and equipment, control and information technology, covering a range of core technologies such as Internet of Things, 3D printing, robotics, intelligent equipment, and epitomizing the achievements of technological development in China’s manufacturing sector. With Prof. Jie Chen, a member of the Chinese Academy of Engineering and a control engineering expert in China, as the Editorial in Chief, this series is organized and written by more than 30 young experts and scholars from more than 10 universities and institutes. It typically embodies the technological development achievements of China’s manufacturing industry. It will promote the research and development and innovation of advanced intelligent manufacturing technologies, and promote the technological transformation and upgrading of the equipment manufacturing industry.

Xianwu Zeng · Shuping Bao

Key Technologies of Internet of Things and Smart Grid

Xianwu Zeng College of Information and Technology Qingdao University of Science and Technology Qingdao, Shandong, China

Shuping Bao College of Information and Technology Qingdao University of Science and Technology Qingdao, Shandong, China

ISSN 2731-5983 ISSN 2731-5991 (electronic) Advanced and Intelligent Manufacturing in China ISBN 978-981-99-7602-7 ISBN 978-981-99-7603-4 (eBook) https://doi.org/10.1007/978-981-99-7603-4 Jointly published with Chemical Industry Press The print edition is not for sale in China (Mainland). Customers from China (Mainland) please order the print book from: Chemical Industry Press. © Chemical Industry Press 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publishers, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publishers nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publishers remain neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore Paper in this product is recyclable.

Preface

The Internet of things (IoT) is a revolutionary field of information science and technology that has emerged in recent years. It represents a profound integration of various disciplines, including computer science, communication, and automatic control. Over time, the concept of IoT has evolved from mere theoretical abstraction to practical implementation. The IoT industry has witnessed significant growth, with engineering demonstrations, application services, scientific research, and related fields all contributing to its advancement. Presently, the IoT plays an increasingly vital role in society, spanning domains such as technology, economy, and daily life. The IoT stands as a milestone in the progression of information technology. It embodies the convergence of multiple technologies and scientific disciplines, such as information technology, communication technology, and computer technology. This includes the integration and expansive application of diverse fields like electronic technology, automatic control technology, and intelligent science. The IoT represents a significant leap forward, building upon the foundation laid by the Internet and pushing the boundaries of integration, expansion, and innovation. It signifies a new era of interconnectedness and transformative possibilities in the realm of technology and beyond. The IoT is a highly intricate system that encompasses various disciplines and fields. Scholars widely agree that the IoT can be classified into three distinct levels based on its overall structure: sensing (perception) control, transmission network, and comprehensive services. The sensing control layer assumes the primary role of acquiring comprehensive information and implementing relevant feedback control mechanisms. On the other hand, the transmission network layer is responsible for ensuring the secure and dependable transmission of information. Lastly, the comprehensive service layer handles information processing, storage, sharing, and the development of service applications. The defining characteristics of the IoT can be summarized as “comprehensive perception, reliable transmission, and intelligent processing.” The smart grid and IoT, being cutting-edge and emerging industries of strategic significance, have garnered immense global attention. Recognizing their importance, the Chinese government has elevated the IoT and smart grid to the status of national v

vi

Preface

strategies. Extensive measures have been taken, including the formulation of industrial policies, support for major scientific and technological projects, and the establishment of demonstration projects. By harnessing IoT technology, the smart grid is poised to create an extensive interconnected network built upon the power grid infrastructure. This network will span across urban and rural areas, encompassing users and electrical equipment, thus solidifying its position as a crucial infrastructure for “sensing China.” The integration and widespread application of the smart grid and IoT will lead to the effective amalgamation of communication infrastructure resources and power system infrastructure resources. This integration will, in turn, yield several significant benefits. It will facilitate energy conservation and emission reduction efforts while simultaneously enhancing the informatization, automation, and interactivity levels of the power grid. Additionally, it will elevate the operational capacity and service quality of the power grid. The advancements in the IoT and smart grid domain are instrumental in driving the structural transformation and industrial upgrading of the power industry. Moreover, the IoT technology will play a pivotal role in expediting the realization of a truly smart grid. It will enable functionalities such as equipment status prediction and regulation, support for decision-making in asset lifecycle management, and intelligent interaction between the grid and its users. The symbiotic relationship between the IoT and smart grid is poised to revolutionize the power industry, heralding a new era of efficiency, sustainability, and enhanced user experience. Comprised of seven chapters, this book delves into various aspects of the IoT and smart grid, taking into account their three-tier architecture. It comprehensively explores the essential technologies underpinning the IoT and smart grid, as well as the latest advancements that have gained prominence in recent years. These include pivotal topics such as big data and edge computing technology, 5G mobile communication technology, and emerging technologies like information intelligent analysis and processing technology, and microgrid integration. Chapter 1 is “Introduction.” First, the basic concept of the IoT is introduced, including the definition and vision of variety with respect to IoT, and the opportunities brought by the IoT. It is expected that by 2020, the IoT will create huge business opportunities in eight dominant fields. Second, the IoT architecture, elements, and standards are introduced and discussed. Third, as the hierarchical structure of the IoT, the IoT’s key technical domains are summarized, including the application domain, middleware domain, network domain and object domain technology, interoperability and integration technology, availability and reliability technology, and data storage, processing and visualization technology, scalability technology, management and self-configuration technology, modeling and simulation (including uniqueness of identification and security, and privacy technology). Fourth, the development and application fields of the IoT in China are introduced. Among them, the focus is on the smart grid—a very important area of the Internet of things. Chapter 2 is “The Infrastructure of Smart Grid.” Smart grid is developed upon the infrastructure of existing power system and its monitoring, control and management technology. Firstly, this chapter briefly introduces the physical infrastructure of the

Preface

vii

smart grid, namely power generation, transmission, distribution and consumption, as well as supporting monitoring, control and management systems. One of the significant differences between the smart grid and the traditional grid is the addition of distributed renewable energy sources, which requires power system operators to rethink the way the grid is managed in order to face unexpected and rapid dynamic changes. Secondly, the application of emerging information technology in smart grid is introduced and discussed. The emerging cloud computing, edge, and big data technologies are brand new information and communication technologies, which can reduce the communication load and improve the ability of rapid response. Thirdly, the application of big data in smart grid is introduced and discussed. The smart grid analysis architecture based on the big data platform can acquire, store, process, and query large amounts of data in nearly real time. The big data platform represented by Hadoop has powerful functions and can handle online smart grid applications that require real-time operations. The analysis of smart grid big data can bring many benefits, improve demand-side management capabilities, promote the integration of renewable energy, improve grid reliability, reduce peak load, and analyze and predict smart grids. Chapter 3 is “Sensing Technology.” In IoT, sensing devices play a key role in collecting information. Currently, sensor technology has developed toward the direction of intelligent sensors, while RFID technology has developed towards the direction of multi-function, multidimensional, and wireless. First, this chapter discusses the smart sensor, from its system composition and the structure and function of the soft system, and especially discusses its scheduling software and scheduling timing; second, it discusses the most promising chemical wireless sensor in the IoT. The combination of this type of sensor (chemical wireless sensor) with RFID and wireless sensor network brings bright prospects for its convenient application in the IoT. Third, smart meter technology is introduced and discussed, while harmonics, security and privacy issues are also discussed. Fourth, it introduces RFID and wireless sensor networks. Fifth, NB-IoT and LoRa wide-area communication technologies that are currently under development and have great application potential are introduced and discussed. Sixth, positioning technology is introduced and discussed, especially indoor positioning technology and 5G positioning technology are discussed. Chapter 4 is “The Communication and Security Technology of IoT.” First, the communication and security requirements of the IoT is discussed in this chapter; second, the wireless network technology and its classification are discussed; third, some important wireless communication standards of the IoT are introduced; fourth, some commonly used communication protocols of the IoT are introduced, especially the lightweight communication protocol for IoT terminals; fifth, the security issues of the IoT architecture are discussed, and the security issues of typical architectures widely used at present are also discussed in detail; sixth, the 5G with the IoT is introduced. As a mobile communication system that will be widely applied, 5G will provide an access platform with multiple advantages for the IoT with its characteristics of wide coverage, high speed, low latency, and low power consumption, in which the key technology is 5G IoT enabling Technology; seventh, the communication and security issues of the smart grid are discussed.

viii

Preface

The security challenge faced by the IoT is more severe than that of the traditional Internet, and it is a challenge to “entity information.” The adoption of more advanced security technologies to solve the security problems faced by the IoT in the Internet is necessary. In the underlying communication system of the IoT, because most of the massive IoT’s terminals use wireless communication, and wireless communication is open, susceptible to interference, easy to be intercepted, easy to counterfeit, and other vulnerabilities, it is more necessary to use enhanced wireless communication security technology to ensure its safety. As an application field of the IoT, the smart grid has a relatively complex communication infrastructure, and there are also variety of security threats, which require technical and non-technical solutions to ensure its security. Chapter 5 is “Big Data in Smart Grid and Edge Computing of IoT.” Firstly, the big data technology is introduced and discussed in this chapter. Smart grid, big data has many advantages, in which people can mine new laws, new knowledge, and new value from the big data generated by it, so as to better meet the needs of users, power demand, better response to the demand side, better scheduling, and management of the power grid. The key technologies for the analysis of power big data include multi-source data integration and storage, real-time data processing, data compression, visualization, data privacy, and security. The general used algorithms in machine learning and data mining have been widely used in the big data analysis of smart grid, mainly including k-means, support vector machine, logistic regression, linear regression, Gaussian discriminant analysis, BPNN naive Bayesian, and other nine kinds algorithm. Secondly, edge/fog computing technology is introduced and discussed, which is a supplement and extension of cloud computing, and it plays an important role in the IoT. The location of the edge/fog device is close to the generated data, which makes it perform outstandingly in terms of resource allocation, service delivery, and privacy, which can reduce the cost of information services for enterprises and improve the competitiveness of small- and medium-sized enterprises. Chapter 6 is “AMI and DR, Information Processing of Smart Grid.” In the era of smart grids, due to the increasing participation of users and the increasing penetration of distributed renewable energy sources into the power system with large fluctuations, it is difficult for traditional centralized information processing paradigms to meet the needs of smart grids. In practice, discuss and study the new mode of smart grid information processing to deal with the challenge of smart grid to the traditional information processing mode. Therefore, it is necessary to study the information processing mode of smart grid from the aspects of distributed computing, self-organizing sensor network, active control, and overall computing framework. The computing of smart grid will change from the traditional centralized computing to the decentralized, self-organized, active, and holistic computing paradigm, in which the significance aims to support rapid decision-making in data-rich but information-limited environments, and to enhance smart grid operations through a set of information services for knowledge discovery and data mining. Many important smart grid applications can benefit from the deployment of these information services, including online grid optimization, voltage control, security analysis, grid monitoring, real-time information sharing, energy price forecasting, and renewable energy forecasting. This AMI

Preface

ix

as an infrastructure directly related to user participation are discussed in this chapter firstly, based on which it discusses addressing demand response issues, and finally information processing enabling technologies for smart grids are discussed. Chapter 7 is “Renewable Energy and Microgrid.” The rapid development of the economy has put forward a huge demand for energy, and at the same time, the consumption of a large amount of energy has brought serious environmental problems. The emergence of smart grid, especially the emergence of microgrid as its main component, and the emergence of new power sources mainly based on renewable energy in microgrid make it possible to alleviate the double pressure. It is expected that renewable energy will gradually increase its share of energy consumption in the next ten years, reaching 19% in 2030 and 29% in 2050. Renewable energy, especially wind and solar, is growing faster than ever. China’s total potential for renewable energy is large, and it is critical to integrate renewable energy into China’s future energy system. It could be expected that China’s renewable energy will be further developed in the future and make more contributions to the low-carbon economy. The significant difference between the smart grid and the traditional grid is that the microgrid with renewable energy as the core is introduced into the power system. Through the integration and coordination between renewable energy and the power system, energy storage systems storing renewable energy can improve the reliability, security, and resilience of microgrid applications by providing ancillary services such as peak shaving. Integrating renewable energy into the power system will yield enormous socioeconomic and environmental benefits and minimize greenhouse gas emissions from conventional power plants. The status quo and development trend of renewable energy in China are introduced in this chapter firstly, then AD/CD microgrids are discussed, and finally, the problem of optimal operation is discussed. The IoT and the smart grid are developing, and various advanced and intelligent information and communication technologies will continue to emerge and will be applied to all aspects of society, economy, and daily life. It can be expected that the IoT technology that realizes the “Internet of Everything” will continue to develop in an open and intelligent way, and benefit mankind. During the writing process of this book, Prof. Zhiliang Wang of Beijing University of Science and Technology and all the teachers of Qingdao University of Science and Technology’s Internet of Things Engineering Teaching and Research Office have been greatly assisted. I would like to express my heartfelt thanks! At the same time, I would also like to thank the editor-in-chief Linru Jin of Chemical Industry Press for his great assistance. Qingdao, China July 2023

Xianwu Zeng Shuping Bao

Brief Introduction

The book consists of seven chapters. Based on the three-tier architecture of the Internet of things, it covers the key technologies of the Internet of things and smart grid, as well as new technologies that have been developed and widely used in recent years, including big data and edge computing technology, 5G mobile communication technology and emerging technologies such as information intelligent analysis and processing technology and microgrid. Chapter 1 serves as an initial gateway into the world of the Internet of things, where it lays the foundation by introducing fundamental concepts. It elucidates the intricate architecture, constituent elements, prevailing standards, and key technical domains associated with the Internet of things. This chapter particularly accentuates the significance of the smart grid, expounding on its core principles and applications. Chapter 2 ventures further into the physical underpinnings that shape the smart grid. It comprehensively explores various elements such as smart sensors, wireless sensor networks, wide-area communication technology, positioning technology, indoor positioning technology, and the cutting-edge 5G positioning technology. Through detailed discussions, this chapter unveils the pivotal role these elements play in establishing a robust and efficient smart grid infrastructure. Chapter 3 delves into the essential aspects of communication and security within the Internet of things. It delves into the meticulous examination of wireless communication standards that are pivotal for the Internet of things. Moreover, it highlights general application communication protocols within the Internet of things. This chapter also sheds light on the intersection of 5G technology with the Internet of things, underlining its potential and impact. Chapter 4 proceeds to explore the revolutionary technologies of big data and edge/fog computing. It offers detailed insights into these transformative domains, discussing their functionalities and implications within the context of the Internet of things. This chapter equips readers with a comprehensive understanding of how big data and edge computing drive innovation and efficiency in the Internet of things. Chapter 5 takes a deep dive into advanced metering infrastructure (AMI), demand response issues, and information processing technologies that enable smart grids. It delves into the intricacies of AMI, shedding light on its role in modernizing grid xi

xii

Brief Introduction

management. Additionally, this chapter tackles the challenges and solutions associated with demand response, an essential component of grid optimization. It further explores information processing technologies that empower smart grids to enhance performance and reliability. Lastly, Chaps. 6 and 7 present a detailed overview of China’s renewable energy landscape, illuminating the current state and future development trends. It also touches upon the concepts of AD/CD microgrids and optimal operation, offering insightful discussions on their applications and potential impact. The book encapsulates a comprehensive journey into the intricacies of the Internet of things, enabling readers to grasp the underlying technologies and their practical applications within the context of the smart grid.

Contents

1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Basic Concepts of the Internet of Things . . . . . . . . . . . . . . . . . . . . . . 1.1.1 The Vision and Definition of IoT . . . . . . . . . . . . . . . . . . . . . 1.1.2 Main Features of IoT and the Service-Oriented Vision of IoT Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1.3 The Opportunity of IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 The Architecture, Elements and Standards of IoT . . . . . . . . . . . . . . 1.2.1 IoT Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 The Element of IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4 IoT Common Standards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5 Key Technologies of the IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.5.1 Application Domain, Middleware Domain, Network Domain and Object Domain Technologies . . . . . 1.5.2 Interoperability and Integration Technologies . . . . . . . . . . 1.6 Availability and Reliability Technology . . . . . . . . . . . . . . . . . . . . . . . 1.6.1 Data Storage, Processing and Visualization Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6.2 Scalability Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.6.3 Management and Self-configuration Technology . . . . . . . 1.6.4 Modeling and Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.7 Uniqueness of Identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.8 Security and Privacy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.9 The Application and Development of the Internet of Things in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.9.1 Main Areas of IoT in China . . . . . . . . . . . . . . . . . . . . . . . . . 1.9.2 Development of IoT in China . . . . . . . . . . . . . . . . . . . . . . . . 1.10 Internet of Things and Smart Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.10.1 Basic Concepts of Smart Grid . . . . . . . . . . . . . . . . . . . . . . . 1.10.2 IoT in Smart Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.11 Key Technologies of IoT in Smart Grid . . . . . . . . . . . . . . . . . . . . . . . 1.11.1 IoT Architecture Layers Based on Smart Grid . . . . . . . . . .

1 1 1 3 5 6 6 9 12 16 16 18 21 22 23 24 26 27 27 29 29 30 33 34 35 38 39 xiii

xiv

Contents

1.11.2 Key Technologies of IoT in Smart Grid . . . . . . . . . . . . . . . 1.12 Emergence of Smart Grid and Policy Promotion . . . . . . . . . . . . . . . 1.12.1 Emergence of Smart Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.12.2 Policy Promotion for Smart Grid in Major Countries . . . . 1.13 Challenges for the Smart Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.13.1 Information and Communication . . . . . . . . . . . . . . . . . . . . . 1.13.2 Perception, Measurement, Control . . . . . . . . . . . . . . . . . . . . 1.13.3 Power Electronics and Power Storage . . . . . . . . . . . . . . . . . 1.14 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

40 42 42 43 46 47 49 52 56 57

2 The Infrastructure of Smart Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 2.1 The Composition of Power System . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 2.1.1 Power System Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 2.1.2 Power Generation and Grid Structure . . . . . . . . . . . . . . . . . 69 2.2 Power Automation System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 2.2.1 The Goals, New Technologies and Contents of Power Automation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 2.2.2 Integrated Automation of Substation . . . . . . . . . . . . . . . . . . 75 2.2.3 Power Dispatch Automation System . . . . . . . . . . . . . . . . . . 80 2.2.4 Distribution Automation System . . . . . . . . . . . . . . . . . . . . . 84 2.2.5 Automatic Meter Reading and Billing . . . . . . . . . . . . . . . . . 86 2.3 Application of Innovative ICT in Power System . . . . . . . . . . . . . . . . 87 2.3.1 State Estimation Architecture Based on Cloud and Edge Computing [4] . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 2.3.2 Big Data Analysis in Smart Grid . . . . . . . . . . . . . . . . . . . . . 98 2.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112 3 Sensing Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1 Smart Sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1.1 Composition of Smart Sensors . . . . . . . . . . . . . . . . . . . . . . . 3.1.2 Software System of Smart Sensor . . . . . . . . . . . . . . . . . . . . 3.1.3 MEMS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2 Wireless Biochemical Sensor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.1 Wireless Electrochemical Sensors . . . . . . . . . . . . . . . . . . . . 3.2.2 Wireless Electrical Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.3 Wireless Optical Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.4 Wireless Sensor Using Other Transduction Mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2.5 Challenges and Key Technologies of Wireless Chemical (Bio) Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Smart Meter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.1 The Development and Basic Composition of Smart Meter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3.2 Constituent Modules of Smart Meter . . . . . . . . . . . . . . . . . .

115 115 116 119 123 124 126 126 130 135 136 138 138 141

Contents

xv

3.3.3

The Impact of Harmonics on Smart Meter and the Security and Privacy of Smart Meter . . . . . . . . . . . 3.4 RFID and Wireless Sensor Network . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.1 RFID . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.2 Wireless Sensor Network . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.3 IEEE 802.15.4 Standard and ZigBee Protocol Specification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.4 Integration of RFID and WSN . . . . . . . . . . . . . . . . . . . . . . . 3.5 NB-IoT and LoRa Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.1 NB-IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5.2 LoRa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6 Positioning Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6.1 Introduction and Algorithm of Positioning Technology Based on Wireless Signal . . . . . . . . . . . . . . . . . 3.6.2 Indoor Positioning Technology Based on WLAN Channel State Information . . . . . . . . . . . . . . . . . . . . . . . . . . 3.6.3 The Positioning Technology of 5G . . . . . . . . . . . . . . . . . . . 3.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 The Communication and Security Technology of IoT . . . . . . . . . . . . . . 4.1 The Requirements for Communication and Security in IoT . . . . . . 4.1.1 Composition of IoT Communication System . . . . . . . . . . . 4.1.2 Requirements for Communication and Security . . . . . . . . 4.2 Wireless Network Technology and Its Classification . . . . . . . . . . . . 4.2.1 PHY (Physical Layer) Technology . . . . . . . . . . . . . . . . . . . 4.2.2 MAC Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2.3 Network Layer Technology . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Wireless Communication Standards of IoT . . . . . . . . . . . . . . . . . . . . 4.3.1 Ultra-Short Distance Communication Standard . . . . . . . . . 4.3.2 Standards for Short Range and Low Data Rate . . . . . . . . . 4.3.3 Short-Range Wi-Fi Standard . . . . . . . . . . . . . . . . . . . . . . . . . 4.3.4 Low-Power Wide Area Networks (LPWAN) . . . . . . . . . . . 4.3.5 Cellular Mobile Communication Standards . . . . . . . . . . . . 4.4 Some Communication Protocol for IoT . . . . . . . . . . . . . . . . . . . . . . . 4.4.1 Application Protocols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.2 Service Discovery Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.3 Infrastructure Protocol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4.4 Other . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Security of IoT Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5.1 Typical Commercial IoT Architecture . . . . . . . . . . . . . . . . . 4.5.2 Security Functions of the Typical Commercial IoT Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6 5G and IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.6.1 Capabilities and Requirements of 5G IoT . . . . . . . . . . . . . .

144 146 147 151 153 155 159 159 177 189 190 192 198 202 204 211 212 212 213 215 215 219 227 229 229 229 231 233 234 236 236 244 245 249 250 251 260 269 270

xvi

Contents

4.6.2 4.6.3

Key Enabling Technologies of 5G IoT . . . . . . . . . . . . . . . . Technical Challenges and Development Trends of 5G-IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.7 Communication Technology of Smart Grid . . . . . . . . . . . . . . . . . . . . 4.7.1 Communication System Structure of Smart Grid . . . . . . . 4.7.2 Wired Communication Technology of Smart Grid . . . . . . 4.7.3 Wireless Communication Technology . . . . . . . . . . . . . . . . . 4.8 Security Issues of Smart Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.8.1 Smart Grid Security Challenges, Objectives and Some Related Works . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.8.2 Sources and Countermeasures of Threats to Smart Grid Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.8.3 Framework of Reference for Clearing Threats to Smart Grid Security . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.9 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

271

5 Big Data in Smart Grid and Edge Computing of the IoT . . . . . . . . . . . 5.1 Smart Grid and Big Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.1 Big Data Sources of Smart Grid and the Benefits of Big Data Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1.2 Big Data Application in Smart Grid . . . . . . . . . . . . . . . . . . 5.2 Big Data Analysis Technology for Smart Grid . . . . . . . . . . . . . . . . . 5.2.1 Big Data Analysis Platform . . . . . . . . . . . . . . . . . . . . . . . . . 5.2.2 Big Data Analysis of Power and Its Key Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 Edge/Fog Computing in IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.1 Basic Concepts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3.2 The Architecture of FEC (Fog/Edge Computing) . . . . . . . 5.3.3 Services Provided by Fog/Edge Computing . . . . . . . . . . . . 5.4 Protocol and Enabling Technology in Fog/Edge Computing of IoT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.1 Technical Protocols in Fog/Edge Computing of IoT Domain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.4.2 Simulation Technology in FECIoT Domain . . . . . . . . . . . . 5.4.3 Security and Privacy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

301 301

6 AMI and DR, the Enabling Technologies for Information Processing in the Smart Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1 AMI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.1 Advanced Metering Infrastructure (AMI) and Smart Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.2 Subsystems of AMI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.1.3 Security of AMIs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

274 278 278 279 281 282 282 284 288 288 290

302 304 308 308 310 313 314 317 321 323 325 330 334 337 338 345 346 346 348 351

Contents

xvii

6.1.4 The Standards and Protocols Related AIM . . . . . . . . . . . . . Demand Response (DR) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.1 Basic Concepts and Benefits of DR . . . . . . . . . . . . . . . . . . . 6.2.2 User Classification and User Model in DR . . . . . . . . . . . . . 6.2.3 Demand Response Plan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.2.4 Enabling Smart Technology for Demand Response . . . . . 6.3 Enabling Technology of Information Processing in Smart Grid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.1 Problems and Challenges Faced by Information Processing Technology in Smart Grid . . . . . . . . . . . . . . . . . 6.3.2 The Optimization Model for Smart Grid . . . . . . . . . . . . . . . 6.3.3 Overview of Online Voltage Control . . . . . . . . . . . . . . . . . . 6.3.4 Overview of Online Security Analysis . . . . . . . . . . . . . . . . 6.3.5 Wide-Area Monitoring Protection and Control System (WAMPAC) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.6 Power Market Forecast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6.3.7 Adaptive Wind Power Forecasting . . . . . . . . . . . . . . . . . . . . 6.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

356 357 357 360 363 366

7 Renewable Energy and Microgrid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1 Development Trend of Renewable Energy in China . . . . . . . . . . . . . 7.1.1 Renewable Energy and Emission Reduction in China . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1.2 Hydropower Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1.3 Wind Power . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1.4 Solar Energy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1.5 Bioenergy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.1.6 Other Renewable Energy Sources . . . . . . . . . . . . . . . . . . . . 7.1.7 Prospects for China’s Renewable Energy Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.2 Integration of Smart Grid and Microgrid . . . . . . . . . . . . . . . . . . . . . . 7.2.1 Microgrid and Hybrid AC/DC Microgrid . . . . . . . . . . . . . . 7.2.2 Components and Models of Hybrid AC/DC . . . . . . . . . . . . 7.3 Optimal Operation of Microgrid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.3.1 Optimal Operation Model of Microgrid . . . . . . . . . . . . . . . 7.3.2 Overview of Microgrid Optimal Operation Model Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

383 384

6.2

369 369 370 372 373 374 375 376 377 378

384 385 386 387 387 388 388 389 390 391 398 398 404 405 406 409

Chapter 1

Introduction

1.1 Basic Concepts of the Internet of Things The Internet of Things (IoT) is an innovative paradigm in information technology that offers a multitude of new services and possibilities for technological advancements. With its vast array of applications, the IoT has the capability to seamlessly integrate the cyber realm with the physical world [1]. The IoT connects all “things” of physical entities and virtual entities to the Internet, where its exchanges information and interacts with agreed protocols, so as to achieve the goal of intelligent identification, positioning, tracking, monitoring and management of “things” [2]. The IoT acts as an Internet-based extension, expanding communication channels between people and various entities. Within the IoT paradigm, numerous objects in our surroundings will be interconnected in diverse ways, incorporating technologies such as Radio Frequency Identification (RFID), sensor technology, and other smart technologies into a wide range of applications.

1.1.1 The Vision and Definition of IoT As an emerging information technology, IoT does not currently have a universally accepted definition. However, there exist multiple definitions, prospects, and visions surrounding the IoT. The IoT (Internet of Things), IoE (Internet of Everything), M2M (Machine to Machine), CoT (Cloud of Things), and WoT (Web of Things) are related terms that are used by different authors, standardization bodies (such as ITU, ETSI, IETF, OneM2M, OASIS, W3C, NIST, etc.), alliances (such as IERC, IoT-i, IoT-SRA, MCMC, UK FISG, etc.), projects (such as IoT-A, iCore, CASAGRAS, ETP EPoSS, CERP, etc.), and related industries (such as CISCO, IBM, Gartner, IDC, Bosch, etc.).

© Chemical Industry Press 2023 X. Zeng and S. Bao, Key Technologies of Internet of Things and Smart Grid, Advanced and Intelligent Manufacturing in China, https://doi.org/10.1007/978-981-99-7603-4_1

1

2

1 Introduction

These terms may have overlapped or distinct meanings depending on the context in which they are used. IoE, coined by CISCO, encompasses the interconnectedness of people, things, data, and processing, providing a comprehensive framework. In contrast, IoT specifically focuses on the connectivity of “things” within this framework. Meanwhile, WoT is often regarded as having a similar concept to IoT, emphasizing the interconnectivity of devices. On the other hand, M2M denotes direct communication between objects or devices without requiring human intervention. The Internet of Things (IoT) is commonly defined from two [3] or three [4] primary perspectives: the “Internet-oriented” viewpoint, the “thing-oriented” viewpoint, and the “semantic-oriented” viewpoint. (1) Internet-oriented From an Internet-oriented perspective, the IoT is perceived as a worldwide infrastructure that facilitates seamless connectivity between virtual and physical objects. According to Recommendation ITU-T Y.2060, the IoT is defined as the global infrastructure of the information society, wherein the integration of advanced services is made possible by interconnecting physical or virtual “things” utilizing existing and evolving interoperable information and communication technologies [5]. ISO/IEC JTC also provides a similar definition of the IoT. According to their definition, the IoT is characterized as an infrastructure that interconnects various entities such as people, systems, information resources, and intelligent services. This interconnectedness empowers these entities to process and respond to information in both the physical and virtual realms [6]. (2) Object-oriented The object-oriented view of IoT recognizes “things” as either physical entities or virtual entities. Al-Fuqaha et al. [7] describe IoT as a technology that enables physical objects to perceive, analyze, share information, make decisions, and perform tasks. The IEEE special report defines the IoT as “a network of objects, each embedded with sensors, connected to the Internet.” Similarly, OASIS describes the IoT as “systems connected by the Internet to the physical world through ubiquitous sensors.” Many definitions associated with this viewpoint also refer to the term M2M (Machine-to-Machine) communication. ETSI defines M2M communication as the exchange of information between two or more entities without direct human intervention. (3) Oriented-semantic The IoT encompasses several critical components, including object description, analysis of IoT-generated data, utilization of semantic execution environments, adoption of architectures tailored to IoT requirements, implementation of scalable storage and communication infrastructure, and the application of suitable modeling solutions. The semantic-oriented perspective of the IoT originates from the IPSO (IP for Smart Objects) alliance. IPSO defines the IP stack as a high-level protocol that

1.1 Basic Concepts of the Internet of Things

3

establishes connectivity across diverse communication infrastructures and operates on resource-constrained embedded devices, even those powered by small batteries. The concept of semantic-oriented IoT emerges due to the immense scale of IoT objects, making it increasingly challenging to represent, store, interconnect, search, and organize the vast amounts of generated information [8]. Semantic technology is poised to play a crucial role in addressing these challenges. Another significant vision associated with the IoT is the “Web of Things,” where networking standards are adapted to connect and integrate everyday objects, such as embedded devices or computers, into the network. (4) Intelligent services and applications Based on the definitions mentioned earlier, we can define IoT as an Internet-based computing paradigm that facilitates seamless connectivity between physical and virtual objects. It enables adaptive function configuration and empowers the provision of intelligent services and applications.

1.1.2 Main Features of IoT and the Service-Oriented Vision of IoT Functions The IoT should possess the following three features [9]: (1) Comprehensive perception Sensing devices such as RFID, sensors, and other technologies are utilized to gather information about “things” at any time and location. This acquired information is then applied, while simultaneously integrating information and communication systems into our surrounding environment. The sensor network formed by these devices enables remote interaction with the real world, enabling individuals to engage with physical entities from a distance. (2) Reliable transmission Information about things can be obtained at any time through diverse radio networks, telecommunication networks, and the Internet. Communication technologies encompass a wide range of wired and wireless transmission technologies, switching technologies, network technologies, and gateway technologies. These technologies enable seamless data exchange and connectivity, facilitating the retrieval of information about things from various sources through different communication networks. (3) Intelligent processing Intelligently process IoT data to deliver timely information services to users. This can be achieved through centralized cloud technology or distributed fog computing methods.

4

1 Introduction

Given the diverse definitions of the IoT, it is challenging to provide a universal definition. However, it is important to consider the commonalities that exist. According to literature [1], the commonalities of the IoT can be described from a service-oriented perspective. These commonalities include: (a) Global IoT infrastructure The IoT serves as a global infrastructure, leveraging the strong foundation of the Internet for global information exchange. Built upon the Internet, the IoT offers a broader, more diverse, and complex range of information and services compared to its predecessor. (b) Seamless interconnection The IoT expands the capabilities and features of the Internet, particularly in terms of its seamless interconnection. This seamless interconnection is characterized by four key aspects: anytime, anywhere, anyone, and anything. This implies that individuals can be connected to other individuals, as well as to various objects, regardless of spatial or temporal limitations. (c) Heterogeneous and interactive network The IoT is characterized by its heterogeneous and interactive nature. Heterogeneity is evident in various aspects and levels, including the diversity of sensing and executing devices, short-distance communication technologies, as well as standards and protocols. This heterogeneity contributes to the overall diversity of the IoT. Furthermore, the IoT not only provides information services but also involves decision-making based on the acquired information and feedback to the executing entity, which can be a person, a virtual entity, or a physical actuator. Consequently, the IoT can be described as an interactive network, facilitating one-to-one, one-to-many, or many-to-many interactions. (d) Intelligent service The IoT offers intelligent services that rely on functions such as identification, sensing, networking, and processing. An example of an intelligent service based on identification is the current use of RFID technology within the IoT. Wireless sensor networks serve as another example of intelligent services that integrate sensing, networking, and processing functions. (e) Physical and virtual “things” with unique identities and addresses For the IoT to offer a diverse range of services, a fundamental requirement is that both physical and virtual “things” within the IoT possess unique identities and addresses. Identity represents the ontological attribute of “things,” while address signifies their spatial attributes. While “things” serve as the source and destination of information in the IoT, they are also unique entities in both time and space. Their distinctiveness is defined by their individual identity and address, encapsulating the characteristics that establish their uniqueness.

1.1 Basic Concepts of the Internet of Things

5

(f) Self-configuring agents with intelligent interfaces The IoT comprises numerous intelligent agents, which are entities possessing storage and computing capabilities, as well as computing, communication, and interaction functions. These agents are tasked with configuring their own functions based on varying service requirements. Additionally, they possess intelligent interfaces with interactive capabilities to fulfill service requirements, such as communication and interaction, in an intelligent manner. (g) Open and interactive standards The establishment of the IoT necessitates the presence of open and interactive standards. Openness ensures the continuous evolution of the IoT, while interoperability is essential for delivering IoT services. Open and interactive standards play a crucial role in enabling seamless communication and compatibility among diverse IoT devices and systems. (h) A technology stack that integrates multiple emerging technologies The IoT, as an emerging information technology, requires an organic integration of various emerging technologies. This integration forms a stack that encompasses multiple emerging technologies within the IoT ecosystem.

1.1.3 The Opportunity of IoT The IoT is poised to unlock vast opportunities across various industries, including equipment manufacturing and Internet services [10]. By the end of 2020, it is projected that IoT intelligent agents will be deployed in approximately 212 billion entities worldwide [11]. Furthermore, it is anticipated that machine-to-machine (M2M) traffic will constitute 45% of total Internet traffic by 2022 [10, 12, 13]. Supporting these projections, the McKinsey Global Institute reports a remarkable 300% growth in the number of networked machines (units) over the past five years [14]. The economic growth potential of IoT-based services is substantial for businesses. Specifically, healthcare applications and related IoT-based services, such as mHealth and remote care, offer significant business opportunities. These services encompass healthcare delivery, prevention, diagnosis, treatment, and monitoring, and are projected to generate business opportunities ranging from $11,000 billion to $2.5 trillion by 2025. The annual economic impact attributed to the IoT is estimated to be between $2.7 trillion and $6.2 trillion [14]. According to Wikibon [15], the Industrial Internet was predicted to generate a value of around US$1279 billion in 2020, with a significant increase in return on investment (ROI) to 149% compared to 13% in 2012. Additionally, Navigant [16]

6

1 Introduction

3% 1%

2% Manufacture

4%

Medicalcare

4%

4%

33%

Electric power Infrastructure

7% Security Resource exploit Agriculture 41%

Retail Automobile

Fig. 1.1 Projected market share of dominant IoT applications by 2025 [14]

reports that the Building Automation Systems (BAS) market is expected to experience substantial growth, expanding from $58.1 billion in 2013 to $100.8 billion in 2021, representing a 60% increase. The statistics indicate the immense potential and rapid growth expected in the IoT industry and related sectors in the coming years. This presents a valuable opportunity for traditional equipment manufacturers to evolve their products into “smart products.” Fig. 1.1 illustrates the projected market share of IoT applications expected to dominate by 2025.

1.2 The Architecture, Elements and Standards of IoT 1.2.1 IoT Architecture To enable the interconnection of billions or trillions of diverse objects through the Internet, the IoT requires a flexible layered architecture. This architecture provides the necessary framework for seamless integration and efficient communication among heterogeneous objects within the IoT ecosystem. Among the various models proposed, the most fundamental model in IoT architecture consists of three layers: the application layer, network layer, and perception layer [8, 15–17]. However, recent literature has introduced more abstracted models with additional layers [18–20]. Figure 1.2 illustrates a common architecture with five layers. We will now provide a brief overview of these five layers.

1.2 The Architecture, Elements and Standards of IoT

Application Layer

Network Layer Perception Layer (a) 3 Layers

7

Application Layer

Application Layer

Business Layer

Middleware Layer

Service Composition

Application Layer

Coordination Layer

Service Management

Service Management

Backbone Layer

Object Abstraction

Object Abstraction

Objects

Objects

(c) SOA

(d) 5 Layers

Existed alone Application System

Access Edge

(b) Middleware

Fig. 1.2 IoT architecture [18–20]

(1) Objects Layer The first layer, known as the Objects or Perception layer, encompasses the physical sensors of the IoT responsible for data collection and processing. This layer includes sensors and actuators that gather information related to various parameters such as location, temperature, weight, motion, vibration, acceleration, humidity, and more. To facilitate interoperability, a standardized plug-and-play mechanism is required to configure heterogeneous objects [15, 16]. The Object (Sensing) layer digitizes and transmits the acquired information data to the Object Abstraction layer through a communication channel. This layer generates a significant amount of IoT data, often referred to as “big data,” characterized by its large capacity, variety, high speed, and substantial application value. (2) Object Abstraction Layer The Object Abstraction layer is responsible for transmitting the data collected by the Object (Sensing) layer to the Service Management layer through a short-distance communication device. Various technologies such as RFID, cellular mobile communication (3G/4G/5G), wireless local area network, Bluetooth, infrared, and wireless sensor network (e.g., ZigBee) can be utilized to transmit data to the Service Management layer. Additionally, the Object Abstraction layer may incorporate functions related to cloud and edge computing technologies, as well as data management and processing [16]. (3) Service Management Layer The Service Management or Middleware layer plays a crucial role in matching requested services with the corresponding services based on addresses and names. This layer facilitates the integration of heterogeneous objects within IoT applications,

8

1 Introduction

regardless of their specific hardware platforms. Additionally, the Service Management layer processes the received data, makes decisions, and delivers the required services through network protocols [20]. (4) Application Layer The Application layer offers services to fulfill user service requests within the IoT architecture. For instance, in this layer, users can access various meteorological data specific to their location, including temperature, humidity, air pollution parameters, and more. The significance of this layer lies in its ability to provide users with high-quality intelligent services tailored to their requirements. The Application layer spans across numerous application domains, such as smart home, smart building, transportation, industrial automation, smart healthcare, and more [17, 19]. (5) Business Layer The Business layer oversees the overall activities and services of the IoT system. It is responsible for constructing business models and flowcharts based on the data received from the Application layer. This layer also handles the design, analysis, implementation, evaluation, monitoring, and development of IoT system elements. It facilitates decision-making processes through big data analysis and encompasses the monitoring and management of the lower four layers. Additionally, the Business layer compares the output of each layer with the expected output to improve services and uphold user privacy [18]. The traditional three-layer architecture model for the IoT does not adequately fit the actual IoT environment. It falls short in covering all the underlying technologies involved in data transmission to the IoT platform. Additionally, these models are designed with specific communication media in mind, such as wireless sensor networks. Moreover, the layers in these models may not be suitable for resourceconstrained devices, as processes like service composition in SOA-based architectures can be time and energy-consuming, hindering efficient communication and service integration. As a result, there is a need for more flexible and adaptive architectures that can better accommodate the diverse requirements and constraints of the IoT ecosystem. Ongoing developments aim to address these challenges and provide more effective solutions for IoT deployments. In the five-layer model, the Application layer serves as the interface for end users to interact with IoT systems. It facilitates advanced analytics, report generation, and control mechanisms for data access. With its comprehensive functionality, the five-layer architecture is considered the most suitable model for IoT applications. It enables seamless user interaction, advanced data analysis, and efficient control within the IoT ecosystem.

1.3 The Element of IoT

9

1.3 The Element of IoT The IoT system is composed of several interconnected modules that work together seamlessly. In the literature [10], the entire IoT system is categorized into six main elements: identification, perception, communication, computing, service, and semantics, as shown in Table 1.1. (1) Identification Identification plays a crucial role in the IoT system as it enables the matching of names and required services. Various identification methods are employed in IoT, such as the electronic product code (EPC) and ubiquitous code (uCode) [21]. Additionally, addressing IoT objects is essential to differentiate object IDs from their addresses. IPv6 and IPv4 are commonly used addressing methods for IoT objects. To address the needs of low-power wireless networks, 6LoWPAN [22, 23] introduces an IPv6 header-based compression mechanism to make IPv6 addressing suitable for such networks. Differentiating between object identification and address is crucial, as identification methods may not be globally unique, and addressing helps ensure unique identification of objects. Furthermore, objects within the network may use public IPs rather than private ones. Identification methods are utilized to provide distinct identities for each object within the network. (2) Sensing In the IoT, sensing refers to the process of gathering data from interconnected objects and transmitting it to a data store system. The collected data is then analyzed to facilitate specific actions based on the required services. IoT sensors encompass a variety of devices, including smart sensors, actuators, and wearable sensing devices. Table 1.1 IoT elements and common technologies Common technology

IoT element Identification Naming

EPC, uCode

Addressing IPv4, IPv6 Sensing

Smart sensors, wearable devices, sensing devices, embedded sensors, RFID tags

Communication

RFID, NF, UWB, Bluetooth, IEEE802.15.4, WiFi, Cellular mobile

Computation Hardware

Smart Agents, Arduino, Phidgets, Intel Galileo, Raspberry PI, Gadgeteer, BeagleBone, Cubieboard, Smartphone

Software

OS (Contiki、TinyOS、LiteOS、Riot OS、Android), Cloud Computing, etc.

Services

Identity-related services (e-commerce), information aggregation (smart grid), collaborative sensing services (smart home), universal services (smart city)

Semantics

RDF, OWL, EXI

10

1 Introduction

These sensors play a crucial role in capturing real-time data and enabling the IoT system to respond and interact with the environment effectively. (3) Communication Communication plays a vital role in the IoT as it connects heterogeneous objects to enable the delivery of specific smart services. In the presence of lossy and noisy communication links, IoT nodes (objects) typically operate using low power. Various communication technologies are employed in IoT, including Wireless Local Area Network (WiFi), Bluetooth, IEEE 802.15.4, and wireless cellular networks. Additionally, technologies such as RFID, Near Field Communication (NFC), and ultra-wide bandwidth (UWB) are utilized for specific applications within the IoT domain. These communication technologies facilitate seamless data transfer and enable efficient connectivity between IoT devices. (4) Computation Processing units, such as microcontrollers, microprocessors, system-on-chips (SOCs), FPGAs, and software with processing and computing capabilities, play a core role in the IoT ecosystem. These components enable the execution of complex algorithms and the processing of data collected from IoT devices. A wide range of hardware platforms has been developed for IoT applications, including Arduino, UDOO, Friendly ARM, Intel Galileo, Raspberry Pi, Gadgeteer, BeagleBone, Cubieboard, Z1, WiSense, Mulle, and T-Mote Sky [10]. In addition to hardware platforms, various software platforms play a crucial role in enabling IoT functionality. One key software component is the operating system, which serves as the core and foundation of the entire computing system. Realtime operating systems (RTOS) are commonly used in developing IoT applications. Contiki RTOS, for example, is widely utilized in the IoT domain, and its Cooja simulator enables simulation of IoT and wireless sensor network (WSN) applications [24]. Other lightweight operating systems designed for IoT environments include Tiny OS [25], Lite OS [26], and Riot OS [27]. Furthermore, in the automotive industry, the Open Auto Alliance (OAA) was established by industry leaders with the goal of incorporating new features into the Android platform to accelerate the adoption of the Internet of Vehicles (IoV) [28]. Table 1.2 provides a comparison of some features of these operating systems. Table 1.2 Common operating systems used in IoT environments OS

Language Supported

Minimum Memory (KB)

Event-based Programming

Multithreading

Dynamic Memory

Tiny OS

nesC

1

✓

Partial

✓

Contiki

C

2

✓

✓

✓

Lite OS

C

4

✓

✓

✓

Riot OS

C/C++

1.5

×

✓

✓

Android

Java

–

✓

✓

✓

1.3 The Element of IoT

11

Cloud platforms are another important computing part of IoT, which provide facilities for smart objects to send data to the cloud, process big data in real time, and provide knowledge extracted from big data to users. (5) Services Generally, IoT services can be categorized into Identity-related Services, Information Aggregation Service, Collaborative-Aware Service, and Ubiquitous Service [29]. Among these, identity-related services are fundamental and essential. In IoT applications, object recognition is crucial for converting real-world objects into the virtual world. Information Aggregation Services play a key role in collecting and aggregating raw data from sensors and devices. This data is then processed and reported to IoT applications for further analysis. Building upon the Information Aggregation Services, Collaborative-Aware Services utilize the collected data to make informed decisions and take appropriate actions. On the other hand, Ubiquitous Services aim to provide Collaborative-Aware Services on-demand, anytime and anywhere, ensuring seamless access for users in need. The ultimate goal of IoT applications is ubiquitous services, but achieving this goal is not without challenges. Existing applications primarily focus on identity-related, information aggregation, and collaborative-aware services. Examples of information aggregation applications include smart healthcare and smart grids, while smart home, smart buildings, intelligent transportation systems (ITS), and industrial automation are closer to the collaborative-aware category. However, significant difficulties and challenges need to be addressed to realize the ultimate goal of ubiquitous services in IoT. (6) Semantics Semantic in the context of IoT refers to the intelligent extraction of knowledge by machines to deliver the required services. This involves discovering and utilizing resources, modeling information, and analyzing data to make informed decisions and provide precise services. Semantic capabilities in IoT are supported by Semantic Web technologies like the Resource Description Framework (RDF) and the Web Ontology Language (OWL). In 2011, the World Wide Web Consortium (W3C) recommended the use of the Efficient XML Interchange (EXI) format to enhance the efficiency of data interchange in IoT systems. EXI plays a crucial role in optimizing XML applications for resource-constrained environments. It efficiently reduces bandwidth requirements while preserving the integrity of related resources. By converting XML messages into binary format, EXI minimizes storage size and conserves network bandwidth, enabling efficient communication and data transfer in IoT systems. The diverse range of standards, technologies, and the need for interoperability pose significant challenges for IoT application and development. With a multitude of IoT elements exhibiting heterogeneity, it is crucial to find comprehensive solutions that can enable seamless communication and integration among these elements. The establishment of common standards and protocols becomes paramount to ensure interoperability and facilitate the widespread adoption of IoT. By addressing these

12

1 Introduction

challenges, viable and complete solutions can be achieved, paving the way for the realization of ubiquitous IoT services that can benefit various industries and drive further innovation.

1.4 IoT Common Standards Numerous IoT standards have been proposed by various organizations to simplify the work of application programmers and service providers. Leading groups such as the W3C, IETF, EPCglobal, IEEE, ETSI, CCSA, and WGSN have made significant contributions to defining protocols for the IoT. Table 1.3 provides an overview of the notable protocols established by these groups. These protocols can be categorized into four main groups: application protocols, service discovery protocols, infrastructure protocols, and other influential protocols. While this subsection offers a brief introduction, a detailed discussion of these protocols will be provided in Chap. 4. (1) Application Protocols CoAP (Constrained Application Protocol) is an application layer protocol specifically designed for IoT applications, developed by the IETF Constrained RESTful Environments (CoRE) working group [32]. It utilizes the principles of Representational State Transfer (REST) on top of HTTP functions to provide a Web transfer protocol. REST is a lightweight approach for exchanging data between clients and servers over HTTP. CoAP, unlike traditional REST, is primarily bound to UDP (User Datagram Protocol) instead of TCP (Transmission Control Protocol), which makes it well-suited for resource-constrained IoT devices and networks. Table 1.3 Commonly used IoT standards [10] Application protocols

DDS

Service discovery protocols

mDNS

Infrastructure protocols

Routing Protocol

PRL

Network Layer

6LoWPAN

Link layer

IEEE802.15.4

PHY/ device layer

LTE

Other influential protocols

CoAP

AMQP

MQTT

MQTT-SN

XMPP

HTTP REST

DNS-SD

IPv4/IPv6

EPCglobal

IEEE1888.3, IPSec

IEEE802.15.4

Z-Wave

IEEE 1905.1

1.4 IoT Common Standards

13

MQTT (Message Queuing Telemetry Transport) is a messaging protocol developed in 1999 by Andy Stanford-Clark of IBM and Arlen Nipper of Arcom (now Eurotech). It was later standardized in 2013 by OASIS [34]. MQTT is designed to facilitate communication between embedded devices, networks, applications, and middleware. It supports various connection modes, including one-to-one, oneto-many, and many-to-many, making it suitable for IoT and M2M (Machine-toMachine) scenarios. The lightweight nature of MQTT enables efficient and optimized connectivity in resource-constrained environments. XMPP (Extensible Messaging and Presence Protocol) is an instant messaging (IM) standard developed by the IETF (Internet Engineering Task Force). It provides a framework for multi-party chatting, voice and video calling, and telepresence [36]. XMPP enables users to communicate with each other through instant messages over the Internet, regardless of the operating system they are using. It offers features such as authentication, access control, privacy measures, hop-by-hop and end-to-end encryption, and compatibility with other protocols. XMPP allows IM applications to establish secure and interoperable communication channels for seamless messaging experiences. AMQP (Advanced Message Queuing Protocol) is an open standard application layer protocol designed for message-oriented environments in the IoT [37]. It provides reliable communication by offering message delivery guarantees, including at-most-once, at-least-once, and exactly once delivery semantics. AMQP requires a reliable transport protocol, such as TCP, to ensure the exchange of messages between sender and receiver. DDS (Data Distribution Service) is a publish-subscribe protocol designed for real-time machine-to-machine (M2M) communications, developed by the Object Management Group (OMG) [38]. Unlike other publish-subscribe protocols such as MQTT or AMQP, DDS operates using a broker-less architecture and leverages multicasting to deliver excellent Quality of Service (QoS) and high reliability. This architecture is well-suited for IoT and M2M communications, as it accommodates real-time constraints. DDS supports 23 QoS policies, allowing developers to address various communication criteria such as security, urgency, priority, durability, and reliability, providing flexibility and fine-grained control over data transmission. Table 1.4 provides a brief comparison between the common IoT application protocols. The last column in the table indicates the minimum header size required by each protocol. (2) Service Discovery Protocols The high scalability of the IoT necessitates a resource management mechanism capable of registering and discovering resources and services in a self-configured, efficient, and dynamic manner. Two prominent protocols in this domain are multicast DNS (mDNS) and DNS Service Discovery (DNS-SD), which facilitate the discovery of resources and services provided by IoT devices. While these protocols were initially designed for resource-rich devices, there have been research efforts to adapt lightweight versions of them for IoT environments [41, 42]. These adaptations

14

1 Introduction

Table 1.4 Simple comparison of IoT application protocols [10] Protocol

RESTful

Transmission

Publish/ subscribe

Request/ response

Safety

QoS

Header size (byte)

COAP

✓

UDP

✓

✓

DTLS

✓

4

MQTT

×

TCP

✓

×

SSL

✓

2

MQTT-SN

×

TCP

✓

×

SSL

✓

2

XMPP

×

TCP

✓

✓

SSL

×

–

AMQP

×

TCP

✓

×

SSL

✓

8

DDS

×

TCP UDP

✓

×

SSL DTLS

✓

–

HTTP

✓

TCP

×

✓

SSL

×

–

aim to address the resource constraints and specific requirements of IoT devices while still enabling efficient resource discovery. (3) Infrastructure Protocols RPL (Routing Protocol for Low Power and Lossy Networks) is an IPv6-based routing protocol standardized by the IETF’s Routing Over Low Power and Lossy Links (ROLL) working group [45, 46]. It is specifically designed for resourceconstrained nodes in low-power and lossy networks. RPL aims to establish a reliable topology over unreliable and lossy links, enabling efficient communication among devices with minimal routing requirements. This protocol supports various traffic models, including multipoint-to-point, point-to-multipoint, and point-to-point, making it suitable for a wide range of IoT applications. 6LoWPAN (IPv6 over Low power Wireless Personal Area Networks) is a standard developed by the IETF’s 6LoWPAN working group in 2007 [48]. It addresses the unique characteristics of low-power Wireless Personal Area Networks (WPANs), such as limited packet size and low bandwidth. 6LoWPAN provides a mapping mechanism that enables the use of IPv6 packets within the constraints of IEEE 802.15.4, the underlying communication technology for many IoT devices [47–50]. It includes features such as header compression to minimize transmission overhead, fragmentation to accommodate the IPv6 Maximum Transmission Unit (MTU) requirement, and support for multi-hop delivery by forwarding packets at the link layer [50]. This adaptation layer ensures that IPv6-based networking can be maintained efficiently and effectively in low-power WPAN environments. The IEEE 802.15.4 protocol is designed to provide a sub-layer for Medium Access Control (MAC) and a physical layer (PHY) specifically for low-rate wireless private area networks (LR-WPANs) [64]. It is widely used in various applications including IoT, M2M, and wireless sensor networks (WSNs). The protocol offers several key features such as low power consumption, low data rate, low cost, and high message throughput. It supports reliable communication, interoperability across different platforms, and can accommodate a large number of nodes (up to 65,000). Additionally,

1.4 IoT Common Standards

15

IEEE 802.15.4 provides robust security features including encryption and authentication services. However, it does not offer Quality of Service (QoS) guarantees. The ZigBee protocol is built upon IEEE 802.15.4, leveraging its low-rate services for power-constrained devices and extending it to form a complete network protocol stack for WSNs. Bluetooth Low Energy (BLE), also known as Bluetooth Smart, utilizes a shortrange radio technology that enables devices to operate with minimal power consumption, allowing for extended battery life compared to previous versions of Bluetooth. BLE offers a range coverage of approximately 100 m, which is ten times greater than classic Bluetooth, and significantly reduces latency by 15 times [51]. The transmission power of BLE can range from 0.01 to 10 mW. These characteristics make BLE well-suited for IoT applications [52]. In terms of energy consumption and transmission efficiency per bit, BLE outperforms ZigBee [53, 54]. BLE is known for its energy efficiency, making it an attractive choice for low-power IoT devices. EPCglobal is the organization that is responsible for the development and management of the Electronic Product Code (EPC) and RFID technology standards. The EPC is a unique identification number that is stored on RFID tags and is primarily used in supply chain management to identify items. EPCglobal utilizes Internet-based RFID technologies, along with cost-effective RFID tags and readers, to enable the sharing of product information [55, 56]. It is widely adopted in IoT applications due to its open nature, scalability, interoperability, and reliability. EPCglobal’s architecture provides a framework for leveraging RFID technology in a variety of industries and applications, making it an essential component in the IoT ecosystem. LTE-A (Long Term Evolution—Advanced) is a set of cellular communication protocols that are well-suited for Machine-Type Communications (MTC) and IoT infrastructures, particularly in smart city environments where long-term durability of infrastructure is crucial [58]. LTE-A offers several advantages over other cellular solutions, including improved service cost and scalability. It provides enhanced features and capabilities that enable efficient and reliable communication for a wide range of IoT applications. Z-Wave is a popular low-power wireless communication protocol designed specifically for Home Automation Networks (HAN). It has gained widespread usage in remote control applications in smart homes and small commercial domains [59]. Originally developed by ZenSys (now Sigma Designs), Z-Wave has been further enhanced by the Z-Wave Alliance. The protocol supports point-to-point communication over a range of approximately 30 m. It is specifically designed for applications that require low data transmission, such as light control, household appliance control, smart energy and HVAC management, access control, wearable healthcare devices, and fire detection. Z-Wave’s focus on low-power consumption and its broad range of supported applications make it a popular choice for home automation and IoT deployments. Beyond the standards and protocols that define an operational framework for IoT applications, there are some other considerations like security and interoperability that should be considered. Exploiting protocols and standards that cover such considerations influence the acceptability of IoT systems.

16

1 Introduction

1.5 Key Technologies of the IoT The IoT is a vast and intricate system comprising various functional blocks. These blocks combine to enable diverse applications. The key technologies that constitute these blocks can be categorized as follows: sensing technology, recognition and identification technology, hardware, software and cloud platforms, communication technology and networks, software and algorithms, positioning technology, data processing solutions, and security mechanisms. The key technologies mentioned above can be categorized into four main domains, each encompassing various hardware, software, and technologies with specific functions and capabilities. These domains, when combined into an IP-based information architecture, enable the complete deployment of IoT technologies. To ensure seamless integration of diverse IoT key technologies, an IoT platform should possess the capability to connect objects to the network and facilitate the realization of IoT services [68].

1.5.1 Application Domain, Middleware Domain, Network Domain and Object Domain Technologies (1) Application Domain Technology IoT applications encompass a range of functions that can be categorized into four aspects based on their application: Monitoring: This aspect involves tracking and monitoring device status, environmental conditions, notifications, alarms, and other related parameters. Control: The control aspect focuses on managing and controlling device functions, enabling users to remotely operate and manipulate IoT devices. Optimization: In this aspect, the emphasis is on enhancing device performance, diagnosis, repair, and other optimization techniques to maximize efficiency and effectiveness. Autonomy: The autonomy aspect entails enabling devices to operate autonomously, making intelligent decisions and taking actions based on predefined rules and algorithms. By addressing these four aspects, IoT applications can provide comprehensive and diverse functionalities to cater to specific needs and requirements [69]. The application domain management typically delivers application services through the IoT middleware layer, where software and APIs are mapped to the application or middleware domains. To realize the functions within the application domain, commonly used embedded operating systems such as TinyOS, Contiki, LiteOS, Android, Riot OS, etc., are often employed. These operating systems are designed to support low-power Internet communications and operate efficiently

1.5 Key Technologies of the IoT

17

without requiring high computing resources. Software Development Kits (SDKs) associated with these operating systems provide a software framework that enables microcontroller firmware to run on IoT devices. The SDK framework facilitates application programming using various languages such as C, C++, C#, Java, and more. The software platform is a vital component of an IoT system, facilitating the seamless integration of IoT objects with network technologies through the utilization of diverse communication protocols. Furthermore, these platforms often offer additional capabilities, such as independent service development regardless of hardware constraints, as well as data storage and analysis functionalities. Developing IoT applications entails addressing several key issues, including: deployment, availability, management, reliability, interoperability, scalability (largescale deployment and integration), security (authentication, access control, configuration management, antivirus protection, encryption, etc.) and privacy. Addressing these key issues is crucial for the successful development and deployment of IoT applications, ensuring their effectiveness, reliability, and security in a rapidly evolving IoT ecosystem. (2) Middleware Domain Technology IoT middleware serves as a software and infrastructure layer, acting as an intermediary between IoT objects and the application layer. This domain of middleware provides technical support for a range of functionalities, including data aggregation, filtering, and processing of information received from IoT devices. It also facilitates tasks such as information discovery, machine learning, predictive modeling, and access control for application devices. Furthermore, many IoT middleware solutions offer operating system and API management, allowing IoT applications to communicate seamlessly through heterogeneous interfaces. These capabilities enable efficient interaction and integration between various IoT devices and applications, promoting interoperability and streamlining development processes. Cloud computing is a prevalent solution for implementing IoT middleware functions, offering support for IoT service development and data processing irrespective of hardware platforms. Several popular platforms, including Apache Hadoop, Apache Spark, Apache Kafka, Apache Storm, Apache Ambari, Apache HBase, Spark Streaming, Druid, Open TSDB, etc., can be leveraged to deploy IoT middleware. By utilizing these platforms, an efficient and scalable IoT platform can be constructed, enabling seamless integration and processing of IoT data [70]. Furthermore, fog computing, Mobile Edge Computing (MEC), Mobile Cloud Computing (MCC), and Cloudlet are emerging areas of research and development in the field of IoT middleware. These computing systems allocate resources, processing power, and services to data centers located closer to the edge of the network. This approach enhances the performance of IoT systems by reducing response time, improving throughput, and optimizing energy efficiency. Additionally, these technologies offer improved security and privacy for IoT applications, as data processing

18

1 Introduction

and storage can occur closer to the edge, reducing the need for transmitting sensitive information to distant cloud servers. (3) Network domain technology The network domain encompasses the hardware, software, technologies, and protocols that facilitate connectivity between objects and establish connections between objects and the global infrastructure, such as the Internet. IETF RFC 7452 [71] provides a comprehensive framework for the IoT communication model, outlining various communication modes and models. These include: Device-to-Device (D2D) communication, Device-to-Cloud communication, Deviceto-Gateway Model, Back-End Data-Sharing Model. Recommendation ITU-R M.2083-0 [72] emphasizes the significance of key capabilities in various usage scenarios within IoT systems. It highlights the need for IoT solutions to support diverse communication technologies and ensure their interoperability to meet the requirements of IoT deployments. As the volume of IoT traffic increases, it becomes increasingly challenging to meet the demands for reliable and efficient data transmission. Recommendation ITU-T Y.2060 [5] identifies several high-level requirements for IoT-enabled networks. These requirements include: identity-based connectivity, autonomous networking, autonomous service provisioning, location-based capabilities, security and privacy, quality of service, plug-and-play out-of-the-box, manageability. In addition to these requirements, there are several open issues related to the network domain in IoT, such as interoperability, scalability, reliability, mobility management, routing, coverage, resource usage control and management, selfconfiguration, energy efficiency, spectrum flexibility, bandwidth, and latency. (4) Object domain technology The object domain constitutes the endpoint layer of the IoT, encompassing both physical things (real-world entities) and virtual things (virtual entities). These objects possess diverse functionalities, including sensing, motion detection, recognition, data storage and processing, as well as the ability to connect and integrate with other objects and communication networks. IoT objects consist of embedded software, such as operating systems and on-board applications, and hardware components, which include electrical and mechanical elements with embedded sensors, processors, connected antennas, and other relevant features.

1.5.2 Interoperability and Integration Technologies Interoperability is the capability of multiple devices and systems to seamlessly work together, regardless of the specific hardware and software they employ. The vast array of standards, technologies, and solutions offered by different vendors in the IoT

1.5 Key Technologies of the IoT

19

space often leads to significant heterogeneity, resulting in interoperability challenges. Addressing interoperability issues is crucial at all layers of the IoT architecture. To overcome these challenges, the adoption of a layered framework with a standardized architecture is essential. ETSI White Paper No. 3 [73] proposes a framework that defines four levels of interoperability: technology interoperability, syntax interoperability, semantics interoperability, organization interoperability. By considering these four levels of interoperability, IoT systems can be designed and implemented with a focus on compatibility, effective communication, shared data understanding, and harmonized organizational processes. Technical interoperability in IoT systems often revolves around the communication infrastructure and protocols utilized. It is crucial for IoT systems to facilitate interoperability among diverse devices, networks, and communication protocols, including but not limited to IPv6, IPv4, 6LoWPAN/RPL, CoAP/CoRE, ZigBee, GSM/GPRS, Wi-Fi, Bluetooth, and RFID. One of the challenges arises from the fact that the existing Internet architecture does not inherently support seamless connectivity for heterogeneous devices, leading to complex integration issues [74]. This is particularly true when non-IPbased technologies are deployed in IoT systems. To achieve ubiquitous connectivity and overcome these challenges, a HetNet (Heterogeneous Networking) paradigm is necessary, which supports different Media Access Control (MAC) and Physical (PHY) layers. In addition, management and coordination mechanisms should be required. The IEEE 1905.1 standard [75] targets interoperability support and a standardized interface for home networking technologies. It introduces an abstraction layer that conceals the differences among various Media Access Control (MAC) protocols, promoting seamless interoperability. This standard enables interoperability, secure connectivity, network management, path selection, automatic configuration, extended network coverage, and end-to-end Quality of Service (QoS) support in home networks. To address interoperability challenges, solutions commonly rely on Application Programming Interfaces (APIs) and gateways. APIs establish standardized interfaces and protocols for consistent communication and data exchange. Gateways leverage virtual networks to integrate intelligent objects with limited resources into the Internet, facilitating interoperability [76]. Additionally, Software Defined Networking (SDN) methods are employed to enhance interoperability. SDN decouples the control plane from the underlying infrastructure, enabling dynamic resource management and flexible allocation of network resources [77]. Gateways address interoperability challenges by providing solutions such as protocol translation and centralized remote connections. They can be implemented at different layers, including the object and network layers. At the object layer, interoperable gateways support multiple interfaces, allowing devices to connect through different Access Networks (ANs). At the network layer, gateways facilitate the connection between various network technologies, bridging the gap between ANs and CNs.

20

1 Introduction

API-based interoperability solutions facilitate automatic conversion between application protocols and can be implemented in software and cloud platforms. Operating Systems such as Contiki, Riot OS, Android, TinyOS, and LiteOS provide open APIs, enabling modular design and application interoperability. IoT platforms improve interoperability by facilitating the connection of objects through communication technologies. They encompass hardware, software, and cloud platforms, offering functions like device management, data processing, and visualization. Hardware platforms support device connectivity and data processing outside the data center, providing gateways for connecting devices with different network technologies. These platforms ensure technical interoperability by developing and deploying IoT products across diverse devices and networks. Examples of hardware platforms include Arduino, Raspberry Pi, Gadgeteer, BeagleBoard, and pcDuino. In addition to the mentioned solutions, there are various other approaches that aim to provide technical interoperability at different levels. These solutions focus on dynamic, flexible, and automated network management and reconfiguration to simplify network design and management while addressing technical interoperability challenges. By implementing these solutions, organizations can overcome issues related to integrating heterogeneous technologies and ensure seamless communication and interoperability within their IoT systems. Vlacheas et al. [78] proposed a cognitive management framework for IoT to address the heterogeneity between devices and related services, offering a solution to improve interoperability. Another approach is the hub-based approach [79, 80], which enhances scalability and reliable communication while addressing interoperability issues. Software architectures like SOA (Service-Oriented Architecture) [81] reduce system integration challenges and improve interoperability among heterogeneous IoT devices by providing a robust framework for connectivity and component integration. SOA architecture aims to enhance IoT application interoperability and scalability at the service and application layers. However, there are still some challenges with SOA architecture, including the need for an intelligent and connectivity-aware framework to support interoperability. Syntactic interoperability plays a crucial role in understanding content and handling data integration in IoT applications. It involves data formats, syntax, and encodings such as XML and HTML. In order to effectively integrate data from diverse sources, new software architectures need to possess the ability to search, aggregate, and process data generated by heterogeneous devices. Standardized data formats, syntax, and encodings are essential for achieving syntactic interoperability. IoT middleware should incorporate mechanisms like API solutions to facilitate interoperability among different applications, services, and data formats. Semantic interoperability is crucial in enabling shared understanding of content among communicating parties in IoT systems [82]. In IoT, semantics refer to the ability to extract meaningful knowledge from raw sensor data, which can provide valuable services and insights based on data analysis. The development of semantic technologies facilitates data interoperability and advanced decision-making in IoT. Through semantic interoperability, IoT objects can acquire learning capabilities,

1.6 Availability and Reliability Technology

21

comprehend their surroundings, and understand the social and physical aspects of the environment. To achieve this, a high-level framework with appropriate architecture and technologies like data mining is required to extract meta-information from raw data and transform it into knowledge. Semantic technologies used in IoT include JSON, W3C, OWL (Web Ontology Language), RDF (Resource Description Framework), EXI (Efficient XML Interchange), and WSDL (Web Services Description Language) [83]. These technologies introduce heterogeneity at the semantic level, and one solution is to employ semantic models of XML and ontologies [84, 85]. These approaches help to establish a common understanding and facilitate interoperability at the semantic level in IoT systems. SDN is a network technology that offers centralized control and virtualized resources, which can help solve interoperability and integration issues in IoT systems. It allows for flexible network management and programmability, enabling the integration of heterogeneous devices and technologies. SDN has the potential to improve interoperability in the IoT ecosystem.

1.6 Availability and Reliability Technology The availability of IoT services is crucial for IoT applications. It involves making sure that authorized entities or objects can access and use IoT services anytime and anywhere. To achieve this, entities should be adaptive and intelligent, capable of dynamically adjusting to changing network conditions and maintaining continuous connectivity. By prioritizing availability, IoT systems can provide reliable and uninterrupted services to users. To ensure the availability of IoT services, it is important to have a network infrastructure that can maintain continuity of service despite mobility, dynamic changes in network topology, and the use of different technologies. Interoperability, handover, and recovery mechanisms are necessary for seamless operation, particularly in unattended scenarios. Robust monitoring systems, protocols, and self-healing mechanisms should be deployed to ensure system resilience. Certain communication technologies may experience intermittent issues that can disrupt service. To address this, techniques such as local data collection, processing, and control can be employed, reducing dependence on sending data to a remote computing infrastructure over the internet or other networks. Mobile Edge Computing (MEC), Mobile Cloud Computing (MCC), Cloudlet, and Fog computing are proposed as solutions to overcome such challenges. In scenarios where IoT devices are mobile and experience frequent changes in network topology, effective mobility management mechanisms are required. Mobile IPv6 (MIPv6) is a protocol specifically developed to support mobility in IPv6 networks. Additionally, IPSec (Internet Protocol Security) is often implemented to establish trust between local agents and mobile devices.

22

1 Introduction

Furthermore, in certain IoT deployments, devices need to be aware of their location and understand their environment. This information can be crucial for efficient operation and context-aware decision making. Routing plays a critical role in ensuring reliability in multi-hop mesh topologies. It needs to support dynamic topology changes, multi-hop routing, scalability, contextaware security, and quality of service (QoS) to maintain a robust and efficient network. Routing protocols in IoT require context awareness and energy awareness to optimize performance and reduce energy consumption. Huang et al. [87] proposed an algorithm for multicast routing trees, while RSVP and MPLS have addressed specific routing challenges. These approaches enhance reliability, efficiency, and resource utilization in dynamic and energy-constrained IoT environments. To address the trade-off between reliability and energy consumption, UDP is often adopted as the transport protocol in IoT. Effective upper layer protocols, both for transport and application, play a crucial role in providing end-to-end reliability. Furthermore, developing new protocol extensions such as MoMoRo (Mobility support layer for low-power wireless sensor networks) can offer additional solutions to enhance availability and reliability in IoT systems. These efforts aim to optimize energy efficiency while maintaining reliable communication.

1.6.1 Data Storage, Processing and Visualization Technology As the number of connected objects and data traffic in IoT systems continues to grow, there is a need for advanced calibration and analysis techniques. IoT systems generate a vast amount of data, requiring a general analytics platform to handle this big data. To extract valuable insights from the data, various data mining methods such as artificial intelligence (AI), machine learning, and intelligent decision-making algorithms are employed. These techniques enable computational processing to identify patterns and trends within large datasets, organizing raw data and extracting valuable information and knowledge from it. This helps in making informed decisions and optimizing IoT applications and services. To handle increasing data volumes in IoT systems, cloud technology is commonly used. However, transferring data from edge devices, the cost of data storage, data security, and privacy are important considerations. One solution is to develop a scalable and high-performance hybrid cloud platform that combines local edge computing and remote cloud resources [10]. Additionally, new algorithms are needed for filtering, selecting, abstracting, and aggregating raw data to optimize data processing and reduce the amount of data transmitted and stored [89, 90]. These approaches help address the challenges of data volume while ensuring efficiency and cost-effectiveness. Local infrastructure resources can be sufficient for data storage and processing, reducing the need for cloud computing. Computing paradigms such as MCC, MEC, Cloudlet, and fog computing extend cloud capabilities to the edge, improving IoT

1.6 Availability and Reliability Technology

23

properties like QoS, reliability, mobility, security, and privacy. However, these platforms may lack complex analysis and large-scale data storage capabilities, which are complemented by cloud computing. Basic calculations can be performed at the edge layer, while resource-constrained infrastructure may require forwarding data to the cloud. The integration of IoT and edge computing should address static and dynamic devices, leading to challenges in service migration, computational issues, collaboration, and synchronization among edge nodes. Mechanisms for data aggregation at gateways can manage data flow, but appropriate software deployment is necessary. The integration of IoT and edge computing alone may not fully address mobility, complex analysis, security, and data privacy challenges on edge devices [91]. Data visualization enable user interaction with the IoT environment. These applications rely on connections to cloud and network infrastructures, and they require user-friendly interfaces to control various devices securely and remotely. Data visualization tools facilitate real-time monitoring, interactive interfaces, and insights for effective management of IoT systems. Data visualization in IoT applications encompasses a range of visual elements, including charts, animations, maps, and tracked locations. These visualizations are presented through a Graphical User Interface (GUI), allowing users to view and interact with the collected sensor data. Web technologies like HTML5 offer effective solutions for visualizing IoT applications. Additionally, emerging technologies like touch screens and 3D displays have the potential to enhance navigation and extract meaningful insights from raw data, providing more efficient ways to interact with and understand the information.

1.6.2 Scalability Technology Scalability in the context of IoT refers to the system’s ability to accommodate the addition of new devices and services without negatively impacting the performance of existing services. An essential aspect of achieving scalability is ensuring support for a wide range of devices with different resource constraints such as memory, processing power, bandwidth, and other limitations [92]. To ensure efficient device discovery and promote interoperability, scalable mechanisms should be implemented in IoT systems. Scalability and interoperability can be achieved by employing layered frameworks and architectures [93]. Indeed, highly scalable cloud computing with suitable frameworks and architectures is a viable solution for storing and processing the massive amounts of data collected in IoT applications. By leveraging cloud computing, IoT can extend its capabilities as a global architecture, allowing for efficient data management and processing. Additionally, edge computing systems can be employed to extend cloud services to edge devices. This technology provides storage, computing, and networking services closer to the devices themselves, reducing latency and improving response times. However, edge computing systems may not have the same level of computational

24

1 Introduction

power or resources as the cloud, limiting their ability to perform complex data analysis. To address this limitation, a combination of cloud computing and edge computing can be used effectively. Seamless connectivity and easy implementation of new components and objects are crucial for addressing scalability challenges in IoT systems. Supporting topology changes and ensuring distributed scalability, mobility, and security are key considerations in this regard. A vision for next-generation network architectures, known as Content-Centric Networking (CCN), offers potential solutions to these issues [94]. CCN aims to enable automatic and application-agnostic caching, allowing data to be stored anywhere in memory, whether within the network or beyond. This approach holds promise for addressing scalability challenges by leveraging caching mechanisms and reducing the need for centralized data storage and processing. However, it’s important to note that this research is still in its early stages, and further exploration and development are needed to fully realize the potential of CCN in IoT systems. By adopting such innovative network architectures, IoT systems can potentially benefit from improved scalability, enhanced mobility support, and enhanced security measures. Nonetheless, it’s essential to closely monitor ongoing research and advancements in the field to effectively leverage emerging technologies and overcome the challenges associated with IoT scalability.

1.6.3 Management and Self-configuration Technology Managing IoT applications and devices is crucial for successful deployments. However, it poses challenges due to the complexity, heterogeneity, and large number of devices involved. Effective management functions like monitoring, control, and configuration are necessary. IoT software should be able to recognize and interact with various smart objects to enable efficient management and self-configuration capabilities. For example, self-configuring IoT systems can dynamically adapt to changes in the environment, leading to energy efficiency by turning off devices when there is no activity. Data management mechanisms in IoT applications need to fulfill various functions, including raw data aggregation, data analysis, data recovery, and security. They should support different types of reports, such as descriptive, diagnostic, and predictive. The IoT data management framework proposed in [95] consists of layers like application, query, aggregation, source, communication, and things to facilitate effective data management. These mechanisms should be adaptive, scalable, and trustworthy [96]. They need to employ innovative approaches for data aggregation and complex calculations to enable efficient and real-time decision-making. Additionally, there is a challenge of providing automated decision-making and selfconfiguring operations in complex, integrated, and open IoT systems. Objects within the IoT ecosystem should be able to acquire knowledge from collected data and perform situation-aware actions based on that knowledge.

1.6 Availability and Reliability Technology

25

Network management functions play a crucial role in effectively managing IoT deployments. These functions encompass various aspects such as network topology management, device synchronization, and flow and congestion control management. The design of new networks should incorporate effective management mechanisms to handle the challenges posed by a large number of connected devices, high data traffic loads, and diverse services with different quality of service (QoS) requirements. Monitoring the network infrastructure is essential to detect any changes or events that may impact the usage and security of network resources. In the dynamic IoT environment, a resource management solution is needed to allocate resources effectively, considering the uncertain nature of the IoT ecosystem. Several protocols have been developed to monitor and control network elements, including devices, gateways, and terminal servers. Protocols such as LNMP (LoWPAN Network Management Protocol) and SNMP (Simple Network Management Protocol) are used for specific network management functions in IPv6-based networks. Additionally, the TSMP (Time Synchronized Mesh Protocol) enables device synchronization in ad-hoc wireless networks. SDN (Software-Defined Networking) is an enabling technology for 5G systems [97] that provides dynamic, flexible, and automated network management and reconfiguration. It simplifies network design and management, and enables cost-effective scaling required for IoT services. SDN, along with NFV (Network Function Virtualization), offers a new approach to network management by virtualizing certain network functions and managing them through software. This paradigm allows for the management of heterogeneous devices with various deployments and use cases [99]. Device management mechanisms play a critical role in IoT deployments by providing monitoring and remote-control functions. These mechanisms enable actions such as activating or deactivating remote devices and performing firmware updates. Remote control requires the deployment of additional mechanisms, including device and service management protocols. Managing devices and ensuring seamless integration across various networks pose challenges for both hardware and software deployments. Addressing and optimization operations at the architectural and protocol levels are crucial in achieving efficient device management [100]. Identity management of devices and establishing a trusted environment are major concerns in IoT systems. The Open Mobile Alliance (OMA) and its device management working group have specified protocols and mechanisms for device and service management in resource-constrained environments, such as the Lightweight M2M (LWM2M) protocol [101]. Additionally, lightweight protocols like the NETCONF Light protocol [102] have been developed for device management. The challenges of device management are further complicated by the heterogeneity of devices and related services. Cognitive management framework solutions [78] and hardware platforms that facilitate the integration of objects and networks with management functions play a significant role in addressing these challenges. Device management mechanisms are essential for monitoring and remote control of IoT devices. The OMA and other organizations have developed protocols and mechanisms like LWM2M and NETCONF Light for efficient device management.

26

1 Introduction

The heterogeneity of devices and services requires tailored solutions, including cognitive management frameworks and hardware platforms that enable seamless integration and effective management.

1.6.4 Modeling and Simulation The development of IoT services faces challenges primarily due to the complexity and heterogeneity of the system architecture. The presence of diverse applications, devices, interfaces, and wireless technologies poses modeling problems for IoT systems. Currently, there is no standardized approach for modeling IoT-based systems [103]. While some literature [104–106] has proposed IoT modeling theories based on edge computing, there is still a lack of mathematical formulas to fully capture the integration and computing aspects of IoT system architecture. Simulation tools like Opnet, NS-3, and Cloudsim can be utilized to understand and model IoT systems. However, the complexity and heterogeneity of IoT scenarios make this process challenging [106]. Consequently, complex hybrid and multilevel modeling and simulation techniques are required [107, 108]. Additional challenges in modeling and simulation include the lack of integrated options for simulating network and cloud infrastructure, hindering a comprehensive evaluation of IoT system performance. Simulating IoT scenarios, which involve numerous heterogeneous devices with varying traffic loads and types, also remains an important issue. Addressing this challenge requires not only software tools but also substantial hardware resources such as CPU and RAM to handle the simulation demands. To effectively support the dynamic nature of IoT, real-time requirements, and the ever-increasing processing demands of heterogeneous technologies, simulation and modeling tools need to continuously evolve. These tools should provide robust support for the dynamic nature of IoT, consider real-time requirements, and be capable of handling the deployment of diverse technologies. In summary, the complexity and heterogeneity of IoT system architecture present significant challenges in the development of IoT services. Standardized approaches for modeling IoT-based systems are still lacking. Simulation and modeling tools, though available, require enhancements to address the intricacies of IoT scenarios, including integrated network and cloud infrastructure simulation. Moreover, the simulation of large-scale IoT scenarios necessitates substantial hardware resources. The ongoing development of simulation and modeling techniques is crucial to meet the dynamic and resource-intensive requirements of IoT deployments.

1.8 Security and Privacy

27

1.7 Uniqueness of Identification The unique identification of IoT objects is crucial for the success of IoT. Each object requires a distinct identifier like an IP address or URI (Uniform Resource Identifier). Proper identity management and efficient key distribution schemes are highlighted in the literature [109, 110] as important considerations. When each object has a unique identifier and is connected to the Internet, it becomes possible to monitor, control, and manage the object throughout its lifecycle. In the IoT, various identification methods and technologies exist for IoT objects. Some examples include: EPC (Electronic Product Code), uCode (Ubiquitous code), QR (Quick Response) or matrix barcode, etc., EPCglobal is a standardized, integrated EPC and RFID technology. IPv4, with its 32-bit addressing scheme, is limited in the number of unique network addresses it can provide. As the number of IoT devices continues to grow rapidly, IPv4 addresses are becoming scarce. This poses a challenge in providing unique network addresses for all IoT objects. However, IPv6, on the other hand, solves this problem by utilizing 128-bit addresses. With its significantly larger address space, IPv6 can provide an enormous number of unique addresses. This allows for the allocation of unique addresses to each IoT device, ensuring the scalability and continued growth of the IoT ecosystem. Due to the challenges of scalability, manual and static management of system resources is not a suitable solution for IoT. To overcome this issue, the use of service discovery protocols becomes essential. Protocols like DNS-SD, SSDP, SLP, mDNS, and APIPA are commonly employed for IoT service discovery. However, there are certain challenges when applying these protocols to IoT services, particularly with respect to the requirement of self-registration [111]. The discovery process needs to be dynamic and adaptable to accommodate the addition of new IoT devices to the network. Therefore, an effective IoT architecture should allow devices to seamlessly join or leave the IoT platform without disrupting the entire system.

1.8 Security and Privacy Security and privacy concerns are recognized as pivotal aspects of IoT technology [112], with a multitude of threats, vulnerabilities, and risks [113]. Various security models and threat classification models have been put forward to address IoT system security [114, 115]. A study conducted by Hewlett Packard Enterprise Research [116] reveals that privacy issues in numerous devices stem from factors such as inadequate authentication and authorization mechanisms, absence of transmission encryption, insecure web interfaces, and vulnerable software and firmware. To enhance IoT security, it is crucial to take into account the legal, social, and cultural dimensions of security and privacy. Security features should be embedded at every layer of the IoT architecture, and robust trust management systems must

28

1 Introduction

be implemented. This necessitates the development of diverse mechanisms aimed at enhancing IoT security and privacy [117, 118]. The mechanisms should encompass authentication, access control, data integrity, privacy, encryption, and other essential functions. Additionally, they should facilitate automatic data security based on user-defined policies and rules. It is imperative for these mechanisms to operate in real-time and be cost-effective and scalable, thereby minimizing complexity and maximizing availability. A significant number of security issues in IoT stem from communication threats, including but not limited to Malicious Code Injection, Sniffing Attacks, SpearPhishing Attacks, Denial-of-Service (DoS) Attacks, Sybil Attacks, Proxy Attacks, Sleep Deprivation Attacks, and more. To mitigate these attacks, it is crucial to deploy a range of mechanisms such as authorization, authentication, encryption, antivirus protection, and other relevant measures. While these mechanisms contribute to enhancing the security level of IoT systems, it is important to acknowledge that there are still several issues that need to be considered. For instance, proxy or man-inthe-middle attacks can potentially occur regardless of whether the transmitted signal is encrypted or not. IoT systems necessitate dependable and secure communication protocols across all layers of the protocol stack. At the application layer, there are three primary solutions for ensuring security. One approach involves leveraging standards and protocols like the Open Trust Protocol (OTrP) [119], which facilitates application installation, updates, removal, and security configuration management. Another solution is the utilization of IPSec (Internet Protocol Security). However, IPSec may not be suitable for all IoT applications. In such cases, it is more convenient to employ the Transport Layer Security (TLS) protocol [120], as it provides transparent connection-oriented channels. Certain IoT application protocols employ specific methods to bolster security, but the majority of security solutions rely on encryption protocols such as Secure Sockets Layer (SSL) and Datagram Transport Layer Security (DTLS). These protocols are typically implemented between the application layer and the transport layer of the TCP/IP protocol stack. TLS operates over a reliable transport channel, often using TCP. Consequently, a variant of TLS compatible with datagrams is necessary, which is fulfilled by DTLS [121]. DTLS is a TLS-based protocol that offers similar security guarantees specifically tailored for datagram protocols. Security protocols employ various mechanisms and standards, such as X.509 for managing digital certificates and public key encryption within TLS. For instance, Constrained Application Protocol (CoAP) utilizes DTLS, and a compressed version of DTLS is employed to provide lightweight security for CoAP in IoT. In terms of security, Extensible Messaging and Presence Protocol (XMPP) and Advanced Message Queuing Protocol (AMQP) utilize TLS and Simple Authentication and Security Layer (SASL). While the MQTT application protocol primarily relies on TLS/SSL, alternative solutions exist, such as OASIS MQTT employing a network security framework or a new secure MQTT mechanism known as Authenticated Publish & Subscribe (AUPS) [122]. When IoT systems utilize wireless communication technologies, the security risks become even more significant due to the open nature of the system and the potential

1.9 The Application and Development of the Internet of Things in China

29

physical accessibility to certain components like sensors. To address these risks, it is crucial to deploy mechanisms for detecting malicious activities and implementing recovery measures. IPSec offers end-to-end security at the network layer and can be used in conjunction with various transport protocols. To ensure linklayer security, secure communication should be established between devices, and suitable encryption algorithms must be employed. Encryption plays a vital role in safeguarding information security. However, encrypting a large volume of real-time data poses significant challenges as it requires transmitting a considerable amount of data. Moreover, encryption algorithms demand substantial computing resources and power consumption. Currently, research is underway to develop interoperable lightweight protocols and encryption algorithms to enhance security in IoT environments. In addition to employing various security mechanisms, it is essential to establish appropriate policies to protect privacy and ensure the overall security of IoT systems. As the IoT environment evolves, privacy policies need to accommodate dynamic changes. One of the key challenges arises from the openness and interoperability of the system with other systems, each of which may have its own privacy policy. In an IoT system, every object should have the capability to verify the compatibility of the privacy policy of the other party before sharing data. Privacy policies pertaining to both infrastructure and applications should be defined by users, who can be human entities (data owners) or physical entities (things). This ensures that users have control over specifying and managing the privacy settings and permissions associated with their data in the IoT.

1.9 The Application and Development of the Internet of Things in China China places significant emphasis on the advancement of the IoT and is actively accelerating its development [123]. The Chinese government recognizes the IoT as a new driver of economic growth and has consequently released the twelfth fiveyear plan for IoT development. This plan, spanning from 2011 to 2015, outlines a comprehensive roadmap for IoT advancement. It sets forth ambitious goals for future development and outlines several strategies and methods to achieve these objectives. Additionally, the plan introduces a series of supportive policies aimed at fostering and promoting the growth of the IoT industry in China.

1.9.1 Main Areas of IoT in China In China, the development of IoT applications is focused on nine key sectors. These sectors include: (1) industrial industry applications; (2) smart agriculture; (3) smart

30

1 Introduction

Table 1.5 Main application areas of IoT in China Areas

Typical application

Industry

Production process control, industrial environment monitoring, manufacturing supply chain tracking, product life cycle monitoring (PLM), safety in manufacturing, energy saving and pollution control in manufacturing

Agriculture

Utilization of agricultural resources, quality management in the production process of agricultural products, production and planting environment monitoring, quality management, safety and traceability of agricultural products

Smart logistics

Inventory control, distribution management, traceability and other modern logistics systems, public logistics service platforms covering different regions and fields, smart e-commerce and smart logistics

Smart transportation

Traffic status perception and notification, traffic guidance and intelligent control, vehicle positioning and scheduling, remote vehicle monitoring and service, vehicle and road coordination, integrated intelligent transportation platform

Smart grid

Monitoring of power utilities, smart substation, automatic power dispatch, smart power, smart dispatch, remote meter reading

Environmental protection

Pollution source monitoring, water quality monitoring, air quality monitoring, environmental information collection network and its information platform

Smart security

Social security monitoring, transportation of hazardous chemicals, food safety monitoring, early warning and emergency response of major bridges, buildings, railways, water supply and drainage and pipeline networks

Smart healthcare

Intelligent drug control, hospital management, collection of human physiological and medical parameters, telemedicine services for families and communities

Smart home

Home area network, home security, smart control of household appliances, smart meter reading, energy saving and low carbon, remote learning

logistics; (4) smart transportation; (5) smart grid; (6) smart environmental protection; (7) smart security; (8) Smart healthcare; (9) Smart home. These areas represent the targeted domains for IoT application development in China, as indicated in Table 1.5.

1.9.2 Development of IoT in China (1) The government’s promotion of the development of the IoT The research on IoT’s sensing network was first initiated in China in 1999. Recognizing its significance, the IoT was subsequently positioned as one of the strategic emerging industries and was explicitly mentioned in the government work report in March 2010. In November 2010, the State Council of China made a decisive move to

1.9 The Application and Development of the Internet of Things in China

31

accelerate the incubation and development of strategic emerging industries, explicitly stating its commitment to promoting research and application demonstrations of the IoT. This marked a significant milestone in the recognition and support of IoT development at the national level in China. In 2012, the Ministry of Industry and Information Technology of China released a comprehensive explanation of the national 12th Five-Year Plan (2011–2015) [124], which encompassed the development of the Internet of Things (IoT). This plan marked the first detailed roadmap for IoT development published by the Chinese government. The plan clearly outlined the development objectives for the IoT during the period from 2011 to 2015. By the end of 2015, substantial progress was expected to be achieved in the areas of IoT basic technologies, relevant applications, and standardization efforts. The plan served as a guiding framework to drive the advancement of IoT in China during that specific five-year period. The aforementioned 12th Five-Year Plan for the development of the IoT in China put forth eight primary tasks and specified five key projects to facilitate its implementation. These tasks and projects were as follows: key technology innovation projects, standardization acceleration projects, “10 industrial sectors and 100 new enterprises” industrial development and entrepreneurship projects, key sector application demonstration projects, and public services platform. To address unforeseen challenges and ensure the long-term development of the IoT, the State Council of China issued the “Guidelines for Tracking and Guiding the Development of the Internet of Things” [125]. These guidelines set development goals and provide strategic direction for the IoT. In September 2013, 14 government departments in China established the IoT Development Joint Conference and formed the IoT Development Expert Advisory Committee. Furthermore, 10 special development action plans were introduced [126], which encompassed various aspects of IoT development. These action plans included: (1) top-level design; (2) standard formulation; (3) technology development; (4) application promotion; (5) industry support; (6) business model; (7) security; (8) government support; (9) laws and regulations guarantee; (10) personnel training, all above issued by the joint meeting. In line with these action plans, the IoT Industrial Technology Innovation Strategic Alliance was established in October 2013 [127]. This alliance aimed to foster collaboration, knowledge exchange, and innovation among industry players to accelerate the development of IoT technologies in China. (2) R&D plan The Chinese central government has demonstrated its commitment to supporting the development of the IoT through the establishment of special funds for demonstration projects and research initiatives. In 2011, approximately 500 million yuan of special IoT funds were invested in various IoT-related fields, with about two-thirds of the funds allocated for research and development as well as practical applications. Since 2011, these funds have provided support to 381 companies involved in the IoT industry. Moreover, the Chinese government has actively supported 22 national major IoT application demonstration projects, which were officially announced by the National

32

1 Introduction

Development and Reform Commission of China in October 2013. These projects, conducted from 2014 to 2016, were designed as special regional pilot initiatives for organizing and implementing IoT advancements on a national scale. This demonstrates the government’s focus on promoting and showcasing IoT applications in various sectors to drive innovation and economic growth. In the realm of research and development, the Ministry of Industry and Information Technology in China has initiated several key technology research projects focusing on the architecture and application of Intelligent Transport Systems (ITS) and ehealth. These projects fall under the “New Generation Mobile Broadband” initiative. Through these projects, the ministry aims to explore advanced technologies and innovative applications within the IoT domain. Additionally, the Ministry of Science and Technology of China has undertaken a range of fundamental research activities in IoT construction, basic theory development, and design. These research endeavors align with the framework of the 973project, which is the National Key Basic Research and Development Program. This program serves as a foundation for driving crucial research efforts in key areas of national significance, including the IoT. In China, the research and development activities related to the Internet of Things (IoT) are distributed among various entities and regions. Operators, suppliers, and other enterprises play a significant role in providing IoT operations and system development. These entities are actively involved in developing and deploying IoT solutions and applications. Universities and research institutions in China primarily focus on conducting key technology research in the field of IoT. Standardization of the IoT is another crucial aspect, and it is overseen by standards organizations. These organizations work on developing and promoting industry standards and specifications for IoT technologies. The development of IoT-related industries in China is not centralized in one specific region. Instead, it is distributed across multiple regions, including the Bohai Bay region, the Yangtze River Delta, the Pearl River Delta, and the central and western regions. These regions have witnessed the emergence of IoT industry clusters, where companies and organizations specializing in IoT technologies, applications, and services are concentrated. This distributed approach to IoT research, development, and industry formation reflects the comprehensive and nationwide efforts in China to harness the potential of IoT and promote its growth across different sectors and regions. (3) Standardization Indeed, the IoT standard system encompasses a wide range of standards to address various aspects of IoT development and operation. Here are some key categories of standards within the IoT standard system: architecture standards, application requirements standards, communication protocol standards, identification standards, security standards, application standards, data standards, information processing standards and public service platform standards. In China, standardization work started in 2010. By establishing a comprehensive set of standards across these domains, the IoT

1.10 Internet of Things and Smart Grid

33

standard system aims to facilitate seamless integration, interoperability, and secure operation of IoT systems, fostering innovation, and promoting industry growth. The main standard organizations of China’s IoT are China Communications Standards Association (CCSA), China Sensor Network Standardization Working Group (WGSN), Electronic Label Standard Technical Committee, etc. These standard organizations lead China IoT standardization process. As part of the special IoT action plan, actions for IoT standardization include establishing a standard system, formulating common standards, key technical standards, and emergency industry standards, actively participating in the international standardization process, conducting standard verification and services, and improving organizational structure. During the standardization process, many research institutions and enterprises in China have also participated in the international standardization work of the third generation M2M of the International Organization for Standardization/International Electrotechnical Commission (ISO/IEC) and the ITU Telecommunication Standardization Sector (ITU-T). For 3GPP, China is one of the main countries in the ITU-T and ISO Working Group on Wireless Sensor Networks (WSN). CCSA is one of the sponsoring organizations of One M2M, and many companies are deeply involved in the development of MTC-related standards in 3GPP.

1.10 Internet of Things and Smart Grid The electricity systems are undergoing a significant transformation to facilitate the provision of clean and distributed energy, promoting sustainable global economic growth. At the forefront of this transformation is the Internet of Things (IoT), which plays a crucial role. Through real-time monitoring, enhanced situational awareness, intelligent control, and improved power grid security, the existing traditional power system is evolving into an intelligent power system. This transformation aims to enhance the efficiency, safety, reliability, flexibility, and sustainability of the power system [128]. By widely integrating IoT into power energy systems, numerous benefits are realized, including optimized management of distributed generation, reduction of energy waste, cost savings, and abundant opportunities for the growth and development of power companies. The concept of the smart grid was initially proposed by the Electric Power Research Institute (EPRI) in 2002. Since then, significant progress has been made globally in terms of research and construction of smart grid systems, with many countries formulating their comprehensive strategies, goals, and development approaches [129]. In the United States, the Smart Grid Initiative is dedicated to the development of a modern, reliable, and secure grid, addressing the challenges posed by aging electricity infrastructure. The initiative aims to improve demand-side efficiency while reducing electricity supply costs.

34

1 Introduction

In Europe, the Super Smart Grid initiative focuses on coordinating the development of renewable energy sources. This approach combines large-scale centralized systems with small-scale, local, and decentralized counterparts, facilitating the transition to a fully decarbonized electricity system. Australia places emphasis on renewable energy and energy efficiency in its smart grid development goals. The country specifically focuses on implementing smart meters and intelligent demand-side management techniques. In Japan, the smart grid primarily targets the construction of a renewable energy grid suitable for large-scale solar power generation. This objective aims to address the challenges of limited land availability, energy shortages, and economic development. South Korea’s smart grid research concentrates on integrating power systems with smart green cities, aiming to create sustainable urban environments that prioritize energy efficiency and environmental conservation.

1.10.1 Basic Concepts of Smart Grid A traditional grid primarily focuses on power generation, transmission, distribution, and control. It typically has electromechanical structures, one-way communications, centralized generation, fewer sensors, manual recovery, manual inspection/testing, and limited customer choices. In contrast, a smart grid is an intelligent grid that enhances the efficiency, sustainability, flexibility, reliability, and security of power systems. It achieves this by making the grid observable, controllable, automated, and fully integrated. A smart grid features a digital structure, two-way communication, distributed generation, multi-sensor capabilities, self-monitoring, self-healing, remote inspection/testing, pervasive control, and provides customers with more choices. Furthermore, smart grids offer a myriad of opportunities and applications that have significant implications. They enable the safe integration of a greater amount of renewable energy sources, electric vehicles, and distributed generators into the grid. With demand response mechanisms and comprehensive control and monitoring capabilities, smart grids provide more efficient and reliable power supply. Automatic grid reconfiguration is another valuable feature of smart grids, helping prevent or recover swiftly from outages. Additionally, smart grids empower consumers to have better control over their power consumption and actively participate in power markets. The concept of the smart grid lacks a universally accepted definition, but it stands as a visionary and highly discussed topic within modern power systems. Simply put, the smart grid represents an intelligent power system, an intelligent grid. It goes beyond the traditional grid’s functions of power transmission and distribution, incorporating additional capabilities such as energy storage, communication, and decision-making. In essence, the smart grid inherits the core functions of the traditional grid while embracing new technological advancements and possibilities. A smart grid has the transformative ability to enhance the cooperative nature, responsiveness, and organic operation of a traditional grid [130]. According to the

1.10 Internet of Things and Smart Grid

35

Strategic Deployment Document for Europe’s Electricity Networks of the Future, a smart grid is characterized by intelligent integration of all stakeholders within the power network: generators, consumers, and those who fulfill both roles. The objective is to efficiently provide a sustainable, economical, and secure power supply [131]. The South Korea Smart Grid Roadmap 2030 defines a smart grid as a next-generation power grid that incorporates information technology into the existing infrastructure. Its aim is to optimize energy efficiency through real-time two-way exchange of power information between suppliers and consumers [132]. The National Institute of Standards and Technology (NIST) describes a smart grid as a power grid system that integrates multiple digital computing and communication technologies and services into the power system infrastructure. This goes beyond smart meters in homes and businesses, enabling bi-directional energy flow and bi-directional communication and control capabilities, thus introducing new functionalities [133]. The State Grid Corporation of China Electric Power Research Institute defines the concept of a smart grid as a highly integrated grid that combines modern advanced sensing and measurement technology, information technology, communication technology, control technology, and physical power system. A smart grid is characterized by its strong self-healing capabilities, compatibility, economy, and comprehensiveness [134]. Smart grids serve as platforms that maximize reliability, availability, efficiency, economic performance, and security against both attacks and natural power outages [135]. To gain a deeper understanding of smart grids, it is helpful to compare them to traditional grids. Yu et al. provide a comprehensive comparison of these two grid structures [136]. Table 1.6 in their work presents an overview of the key characteristics that differentiate smart grids from traditional grids. In summary, this book aligns with the concept put forth by the Electric Power Research Institute of the State Grid Corporation of China, which defines a smart grid as an integration of sensing and measurement technology, information technology, communication technology, and control technology. The book emphasizes the importance of strong self-healing properties, compatibility, economy, and comprehensive power system within the context of a smart grid.

1.10.2 IoT in Smart Grid The power system comprises power generation, transmission, distribution networks, and the power consumption grid. Traditionally, it has operated as a one-way system, transmitting electric energy to power users. However, traditional power systems face several challenges, including fuel mix balancing, transmission reliability, asset visibility, identifying new revenue streams, an aging workforce, knowledge acquisition, and technology integration [128, 138]. To address these challenges, the unidirectional power system is being transformed into a bidirectional smart grid. The smart grid is characterized as an automated, flexible, intelligent, robust, and consumer-centric grid that facilitates bidirectional power

36

1 Introduction

Table 1.6 Comparison of traditional grid and smart grid [137] Traditional grid

Smart grid

Mechanization

Digitization

One communication way

Two real-time communication way

Centralized power generation

Distributed generation

Radial grid

Decentralized grid

Involves a small amount of data

Involving big volumes of data

Few sensors

Lots of sensors and monitors

Little or no automatic monitoring

Extensive automatic monitoring

Manual control recovery

Automatic control recovery

Less involved in security and privacy

Easy involved in security and privacy

Concerned about system outages

Adaptive protection

Power is produced and consumed simultaneously

Use of electrical energy storage systems

Limited control

Extensive control system

Response Slowly in emergencies and less choice for user

Response fast in emergency, more choice for user

and data flow. Energy storage plays a vital role in enabling this two-way power flow and empowers customers to actively participate in buying and selling power. The smart grid integrates grid security technology, smart device communication, and highly permeable distributed energy generation into all aspects of power generation, transmission, distribution, and consumption. The goal is to establish a flexible, reliable, safe, and sustainable energy delivery network. Smart grids offer numerous advantages, including increased energy efficiency, cost reduction, improved demand–supply response, and reduced transmission and distribution losses [139]. The application of the Internet of Things (IoT) technology in the smart grid has played a significant role in its evolution. Key IoT technologies currently utilized in smart grids include advanced metering infrastructure (AMI) and supervisory control and data acquisition (SCADA) systems [140, 141]. Incorporating IoT technology into the smart grid offers various benefits [128]: (1) improved reliability, resilience, adaptability, and energy efficiency; (2) reduced number of communication protocols; (3) networked operation and enhanced information operational capabilities [142]; (4) improved control over home appliances; (5) on-demand information access and end-to-end service provisioning; (6) improved perception capabilities; (7) enhanced scalability and interoperability [143]; (8) reducing damage caused by natural disasters; (9) reducing physical attacks on the power system through continuous real-time monitoring of the physical assets of the grid.

1.10 Internet of Things and Smart Grid

37

The application of IoT technology in the smart grid aims to achieve intelligent solutions for power generation, transmission, distribution, and consumption, bringing about greater efficiency and effectiveness. (1) Digitalization and intellectualization power generation Distributed generation is emerging as a new infrastructure for generation, complementing traditional plants. It involves optimizing the integration of distributed generation with existing plants, as well as improving operation, maintenance, and optimization processes. The deployment of IoT technology in distributed generation enables enhanced overall generation efficiency [138]. To achieve optimal generation combination and operation, real-time data from the transmission and distribution grid needs to be collected and analyzed using IoT devices such as smart meters, smart feeders, and microphasor measurement units (PMUs) [144]. This data enables load and state forecasting, as well as distributed control of the power system, improving efficiency and reliability. The strategic placement of plants plays a pivotal role in the realm of renewable energy, where the production of power can be influenced by various factors, such as fluctuations in solar intensity caused by cloud cover (for solar plants) and the ever-changing wind patterns (for wind plants). To address this challenge, innovative Internet of Things (IoT) solutions have emerged, leveraging cutting-edge technologies like cloud computing, sophisticated load management systems, and advanced weather simulation algorithms. By harnessing the power of these IoT solutions, new energy operations can effectively optimize their performance and ensure a more reliable and efficient generation of clean power. (2) Digitalization and intellectualization for transmission and distribution grid Existing transmission and distribution grids face various challenges, including delayed blackout response, power loss, data theft, and integrating distributed renewable energy sources. The solution lies in digitizing these grids with IoT technology. IoT enables intelligent monitoring and control of transmission and distribution grids, empowering operators to proactively address power outages, customer concerns, and seamlessly integrate renewable energy sources. Additionally, IoT helps reduce power loss and prevent data theft by adjusting electrical parameters and tracking theft sources in real time. Examples of IoT devices and technologies for these grids include smart meters, smart inverters, ADMS, and distribution grid sensors [138]. Implementing IoT in transmission and distribution grids enhances efficiency, reliability, and security, ensuring an optimized power supply. (3) Digitalization and intellectualization for power consumption IoT devices and sensor technologies play a crucial role in driving the advancement of microgrids/nanogrids, intelligent home appliances, power transmission, and distributed energy storage systems. This progress has also transformed electricity consumers, empowering them to access electric energy from local distributed renewable sources while maintaining the option to draw power from the grid to meet their

38

1 Introduction

energy demands. Moreover, consumers now have the opportunity to engage in power exchange with the grid. Smart loads utilize IoT sensors to deliver valuable generation and consumption data to power consumers, enabling them to optimize power usage, minimize waste, and manage costs effectively. Distributed energy storage systems, including batteries and electric vehicles, surpass centralized storage systems in terms of flexibility, control, scalability, and reliability [145]. These systems play a vital role in managing fluctuations in renewable energy generation. When generation levels fall below demand, batteries and electric vehicles can compensate for the deficit. Conversely, any surplus power generated from renewable resources can be stored in batteries or electric vehicles or fed back into the grid. (4) IoT’s smart home power monitor IoT-based smart home power monitors have the capability to track the energy consumption of individual household appliances and devices [146]. By utilizing these monitors, homeowners can gain better insight into their energy usage patterns, make adjustments to reduce costs and energy waste, and ensure that all appliances and devices are operating efficiently without excessive power consumption. There are four primary types of power monitors available, each with its own features and functionalities. These include readout and historical monitors (such as Wattvision Power Monitors), instant readout monitors (such as Blue Line PowerCost monitors), plug-in monitors (such as Kill a Watt EZ Power Monitor), and circuit monitors that provide historical tracking and instant readout of measurements (e.g., eMonitor) [147].

1.11 Key Technologies of IoT in Smart Grid The smart grid represents a digital and intelligent evolution of the conventional power grid, with the IoT playing a crucial role in its advancement. The transformation and development of the smart grid should align with the principles of IoT architecture. In their work, Rekaa et al. [148] proposed an IoT architecture comprising four layers: the application layer, management service layer, network layer (including gateway), and perception layer, specifically tailored for the smart grid. This architecture incorporates numerous IoT technologies and serves as a foundational technology for constructing a smart grid infrastructure.

1.11 Key Technologies of IoT in Smart Grid

39

1.11.1 IoT Architecture Layers Based on Smart Grid Figure 1.3 illustrates the architecture layers of the IoT within the context of the smart grid. (1) Application Layer The application layer provides various applications for the smart grid, including smart meter reading, smart home, electric vehicles, renewable energy, demand side management, demand side response modeling, fault monitoring, etc. (2) Management Service Layer The management service layer provides various smart grid service functions, including security control, electrical equipment management, load data flow management, system monitoring data management, distributed data processing management, plan management, pricing management, power market management, user files management etc. (3) Network Layer The network layer is the infrastructure for building the IoT’s information data transmission and interaction, including the existing Internet, gateways accessing the Internet, short-distance wireless communication technologies (such as Bluetooth, WiFi, WiMax, etc.), cellular mobile communications (such as 3G/4G/5G), 6LoWPAN, Zwave, PLC, etc. Fig. 1.3 Architecture layers of IoT based on smart grid

Application Layer Smart meter reading, demand side management etc

Management Service Layer Security control, monitoring and pricing, etc

Network Layer 6LoWPAN PLC, etc. Sensing Layer Sensor RFID, etc.

40

1 Introduction

(4) Sensing Layer The sensing layer provides basic data for the operation, monitoring, and control of the smart grid, including various sensors, wireless sensor network (WSN), FRFID, video surveillance, barcode, etc.

1.11.2 Key Technologies of IoT in Smart Grid To fulfill the primary objectives of the smart grid, including reliability, two-way power exchange, security, and improved power quality, the implementation of IoT technology is necessary. The key technologies that play a significant role in achieving these goals include: (1) Cloud Computing Cloud computing offers robust computing resources that greatly benefit smart grids. It enables the execution of various advanced applications within the smart grid domain, including energy management, power distribution management, equipment management, asset management, planning management, demand-side management, power market operations, fault management, and more. Cloud computing offers small and medium-sized grids the advantage of accessing flexible computing resources on demand. These resources encompass information system hardware, platform resources (such as operating systems, databases, etc.), and general application services (such as SCADA, DMS, meter reading data analysis, and statistics). The operation of the smart grid generates a substantial volume of data, including real-time power grid operation data, power grid asset data, and more. These data necessitate robust storage systems with large capacity and extensive computing resources. Cloud computing offers the ideal solution, providing ample storage capacity, flexibility, scalability, and redundancy. With cloud storage capabilities, including backup and other functions, it becomes the optimal platform for data storage and processing within the smart grid domain. (2) Big Data Big data refers to a system that encompasses storage, processing, analysis, mining, and other functionalities for handling massive amounts of structured, semi-structured, and unstructured data. Big data exhibits key characteristics such as large volume, variety, and high velocity in terms of data generation. In the context of the smart grid, operational data consists of real-time collection, static (or historical) grid asset data, as well as process data generated during analysis and processing. These types of data possess distinct big data characteristics, necessitating the support of big data systems within the smart grid infrastructure.

1.11 Key Technologies of IoT in Smart Grid

41

(3) M2M Machine-to-machine (M2M) communication is a crucial technology in smart grids. Real-time adjustment of the grid’s operating status necessitates the exchange of information among smart devices within the grid to facilitate decision-making and enable timely status adjustments. Examples of M2M communication in smart grids include the operation of reclosing systems with smart devices installed and the interaction between gateway meters and sub-meters in automatic meter reading systems. (4) Edge Computing (or Fog Computing) The smart grid confronts challenges related to local decision-making and control, two-way power exchange, and bidirectional information interaction. In traditional power grids, local control relies on global decision-making, posing significant communication challenges. However, in the smart grid, local decisions can be accomplished through edge computing (also known as fog computing). Edge computing involves delegating decision-making calculations to the computing resources closest to the point of execution and control, such as gateways, data concentrators, routers, and wireless coordinators of sensor networks. By leveraging edge computing, the communication requirements can be reduced, and the responsiveness and execution speed can be improved, thereby addressing these challenges effectively. (5) Middleware Middleware is a software system designed to facilitate the connection and information exchange between heterogeneous systems or devices. In the context of the smart grid, where numerous diverse devices and systems coexist, middleware plays a crucial role in enabling their seamless connectivity. By employing middleware, these disparate components within the smart grid system can effectively interact and communicate with one another, promoting efficient and reliable operation of the entire system. (6) Smart Sensor and Actuator Smart sensors play a pivotal role in the collection, processing, and communication of operational data within the power grid. They provide essential functions such as data collection, data processing, and communication capabilities. In recent times, smart sensors and actuators have been integrated, leading to the development of smart devices [149]. (7) Wireless LAN and WAN Wireless LAN and WAN technologies encompass various options, including NBIoT (Narrowband Internet of Things), LoRa (Long Range), and cellular mobile technologies such as 4G and 5G. These technologies provide wireless connectivity and communication capabilities for IoT devices within local area networks (LAN) and wide area networks (WAN). NB-IoT is designed specifically for low-power, wide-area IoT applications, while LoRa enables long-range communication with low power consumption. Cellular mobile technologies like 4G and 5G offer robust connectivity and high-speed data transmission, making them suitable for a wide range of IoT applications.

42

1 Introduction

(8) Security of Smart Grid With the rapid development and widespread application of smart grids, the associated security issues are becoming more prominent. Physical attacks, cyber attacks, and natural disasters are the primary forms of threats to smart grids, which can result in infrastructure failures, power outages, energy theft, customer privacy breaches, and risks to the safety of operating personnel. Consequently, robust security technologies are essential to safeguard the power grid.

1.12 Emergence of Smart Grid and Policy Promotion 1.12.1 Emergence of Smart Grid The precise emergence date of the smart grid remains uncertain [137]. It is an evolutionary process that began almost simultaneously with the distribution of power through the grid itself. As the transmission and distribution systems evolve, it becomes essential to have a comprehensive understanding of real-time power consumption, pricing, service quality, reliability, and energy efficiency. These factors constitute fundamental requirements for a well-functioning grid. Moreover, countries worldwide are transitioning towards renewable energy sources to combat greenhouse gas emissions, address climate change concerns, and ensure a sustainable energy future. In this context, grid modernization efforts play a pivotal role in addressing climate change and promoting sustainable energy development. Smart grid projects have frequently been linked to the deployment of smart meters. These meters, which initially provided information to power utilities in the 1970s [150], saw widespread adoption in the 1980s. Notably, Pacific Gas and Power Co. implemented two-way communication capabilities with customers, enabling them to monitor their energy consumption [151]. Sensor and control technology play a significant role in the context of the smart grid. While sensors and controllers were introduced as early as the 1930s, the development of wireless sensor networks (WSNs) took shape in the 1950s with the Sound Surveillance System (SOSUS) created by the U.S. military. This pioneering wireless network served as a foundation for the subsequent use of WSNs in various applications such as specialized factory automation, wastewater treatment, and the distribution [152]. Enel’s Telegestore project in Italy is widely regarded as the pioneering commercial application of smart grid technology at the household level. Enel, the largest power company in Italy and the second largest in Europe in terms of installed capacity, spearheaded this project. Telegestore represents a cutting-edge smart meter reading application on the global stage. The project encompasses a vast system consisting of 32 million electricity meters, over 350,000 data concentrators, and an extensive network of secondary substations spanning kilometers [153].

1.12 Emergence of Smart Grid and Policy Promotion

43

1.12.2 Policy Promotion for Smart Grid in Major Countries (1) United States The energy policy of the United States focuses on achieving a secure energy supply, maintaining low energy costs, and protecting the environment. To achieve these goals, the US government is committed to enhancing energy efficiency, increasing domestic production of traditional energy sources, and promoting the development of renewable energy and renewable fuel technologies. Despite not being a member of the Kyoto Protocol, the US has set carbon reduction targets and has made substantial investments in renewable energy. The country has also initiated the modernization of its energy infrastructure. According to the 2012 Global Smart Grid Consortium report, the US has a nonbinding target to reduce emissions by approximately 17% from the 2005 levels by 2020, as stated in the “Copenhagen Accord”. Notably, in 2010, 663 utilities in the US had already deployed 20,334,525 units of smart metering infrastructure, resulting in a national smart meter penetration rate of 14% annually. The U.S. Department of Energy recognized the need for grid modernization and energy infrastructure development and established the Office of Electricity Transmission and Energy Reliability. This office played a crucial role in formulating the “GRID 2030” plan, which outlines a national vision for the future of electricity in the United States. The plan aims to guide the transformation of the grid and address the challenges and opportunities that lie ahead in the second century of electricity. By setting a clear vision and strategic direction, the “GRID 2030” plan aims to ensure a resilient, reliable, and sustainable electricity system for the nation. (2) South Korea South Korea has implemented energy policies focused on sustainable development, driven by national security and economic growth objectives. To enhance energy self-sufficiency and diversify the energy supply mix, the country has enacted laws promoting low-carbon growth and green energy initiatives. South Korea has set a voluntary emission reduction target of 30% by 2020 and has embarked on an ambitious plan to deploy smart meters in half of Korean households by 2016, with the aim of replacing all old meters by 2020 [154]. In 2010, the Low Carbon Growth and Green Growth Basic Law was enacted, allocating 2% of the country’s gross domestic product towards green business and projects, as well as reducing greenhouse gas emissions. South Korea has also implemented a comprehensive renewable energy standard, which mandates that 2% of total energy production should come from renewable sources starting in 2012. This target is set to increase to 10% by 2022, promoting the use of renewable energy from large-scale generators [155]. In its pursuit of energy self-sufficiency, South Korea has adopted a strategic approach of exporting green technology and providing development aid in exchange for energy resources. The country has established the Korea Smart Grid Promotion

44

1 Introduction

Act, which serves as a framework for the development, deployment, and commercialization of sustainable smart grid projects. South Korea has emerged as a leader in smart grid technologies, exemplified by initiatives like the Jeju Smart Grid Demonstration Project. A noteworthy aspect of South Korea’s efforts is the high level of coordination between the government and industry in achieving its green innovation goals. The Korea Smart Grid Association plays a crucial role in mediating between the various stakeholders, facilitating smart grid development, promoting standardization, and fostering valuable research and development activities in the field. (3) Europe The European Union (EU) has set ambitious goals to transition towards a more sustainable energy future. One of the key objectives is to derive 20% of its energy from renewable sources by 2020. This target serves the purpose of reducing greenhouse gas emissions and decreasing reliance on imported energy [156]. In 2007, the European Council adopted the 20:20:20 targets, which encompass three main goals to be achieved by 2020. The first goal is to reduce greenhouse gas emissions by 20% compared to 1990 levels. The second goal is to increase the share of renewable energy in the overall energy mix to 20%. Lastly, the EU aims to improve energy efficiency by 20%, which involves reducing energy consumption and promoting energy-saving practices and technologies. The Electricity Directive 2009/752/EC mandates EU member states to install smart meters in 80% of households by 2020. However, this necessitates a rigorous cost–benefit evaluation. Since power sectors differ among member states, the implementation and associated expenses need to be addressed on a case-by-case basis. Additionally, the European Commission has launched the European Grid Programme, a nine-year initiative focused on research, development, and innovation in smart grid technology and market advancements [157]. (4) Australia Australia’s target is to incorporate 20% renewable energy into its grid by 2020. As a federal parliamentary democracy with states and territories, energy policy is coordinated nationally but falls under state jurisdiction. The Council of Australian Governments (COAG) establishes the framework for energy policy. Despite the higher costs involved, the deployment of smart meters was pursued after experiencing energy shortages in 2006 and 2007. Smart meter installations are still ongoing in New South Wales and Victoria. Australia’s commitment to smart grids is evident through the efforts of Smart Grid Australia, a non-partisan organization leading the modernization of electrical systems and supporting the government in implementing smart grid initiatives, including the Smart Grid, Smart Cities initiative. Additionally, Australia is actively working on enhancing incentives for smart grid investments and implementing measures to address demand-side regulation and tariffs. Priorities include demand management, energy security, and energy efficiency.

1.12 Emergence of Smart Grid and Policy Promotion

45

(5) Canada Despite Canada’s withdrawal from the Kyoto Protocol in 2011, it remains a party to the Copenhagen Accord. The country’s voluntary target is to reduce greenhouse gas emissions by 17% below 2005 levels by 2020. While this goal is not legally binding, the federal government is actively supporting green initiatives through initiatives such as the Clean Energy Fund and the EcoEnergy Innovation Initiative. Several provinces, including Quebec and Ontario, have implemented carbon taxes, demonstrating their commitment to green energy. Pilot programs for smart grids are being carried out in these provinces and others. Utilities are also engaged in projects aimed at modernizing the grid infrastructure. Smart Grid Canada, an association consisting of various stakeholders and academia, plays a key role in raising awareness about smart grids, supporting research and development of new energy technologies, and making policy recommendations to facilitate smart grid advancements [154]. Smart grid development in Canada is backed by various government entities. Natural Resources Canada plays a vital role in overseeing the energy sector, including supporting smart grid initiatives. The National Energy Board, an independent federal agency, regulates the international and interprovincial aspects of oil, gas, and electric utilities, thereby contributing to the advancement of smart grids. The National Smart Grid Technology and Standards Task Force serves as a coordination entity, responsible for aligning and harmonizing different aspects of smart grid development. In addition to these federal bodies, provincial governments also actively participate in supporting the growth and implementation of smart grids [157]. (6) Japan Japan’s 2010 energy strategic plan prioritizes several key areas, including energy security, environmental conservation, efficient supply, economic growth, and structural reform in the energy industry. The plan sets ambitious targets for 2030, which include increasing the energy independence ratio to 70%, achieving a zero-emission power supply ratio of approximately 70%, reducing CO2 emissions in the residential sector by half, ensuring that the industrial sector maintains and improves its energy efficiency to be among the highest globally, and securing or expanding its position as a leading player in the global market for energy-related products and systems [158]. Following the Fukushima nuclear disaster, the Japanese government recognized the importance of demand-side management and introduced smart meters as a solution. Tokyo Electric Power Co. (TEPCO), the largest utility company in Japan, played a significant role by installing around 27 million residential smart meters for customers in 2014. To further enhance the benefits of smart meters, a service utilizing this technology was scheduled to be launched in July 2015. This service enables remote meter reading and offers users detailed information on their power consumption patterns [159]. The Ministry of Economy, Trade and Industry (METI), in charge of energy policy formulation in Japan, actively encourages the development of smart grids and their expansion abroad as part of Japan’s aspiration to be a global energy leader.

46

1 Introduction

To advance this objective, Japan has initiated the concept of “eco-model cities,” which are designed to showcase next-generation energy and social systems incorporating low-carbon technologies. Noteworthy examples include Kansai City, which emphasizes the use of home electric vehicles (EVs) and photovoltaics, Yokohama, where photovoltaic installations, electric vehicles, and real-time energy management systems for homes and buildings are integrated, and Toyota City, which integrates electric vehicles with a demand response center [160]. (7) China China’s energy policy centers around several key principles. These include prioritizing environmental protection, utilizing domestic resources, promoting diversified development, safeguarding the environment, fostering scientific and technological innovation, advancing reform, expanding international cooperation, and enhancing the well-being of the population [160]. The implementation of smart grids in China has been aligned with the country’s energy objectives, which encompass enhancing energy efficiency, increasing the proportion of renewable energy sources, and reducing carbon intensity. Smart grid development plays a crucial role in achieving these priorities and ensuring a more sustainable and efficient energy infrastructure in China. The Chinese government has entrusted several agencies with specific responsibilities related to smart grid development. The National Development and Reform Commission is responsible for supervising smart grid development plans, controlling power prices, and granting approvals for smart grid projects. The National Energy Administration formulates and implements national energy policies and development plans. The State Electricity Regulatory Commission oversees the daily operations of power generation and distribution companies. The China Electricity Council assists in formulating power policies and advocates for the national smart grid plan. The Ministry of Science and Technology is responsible for research and development efforts in the field of smart grid technology. Notably, smart grid technology has been given significant priority in the 12th five-year plan for national science and technology development, reflecting China’s strong commitment and attention to the advancement of smart grid initiatives [161].

1.13 Challenges for the Smart Grid The research, deployment, and application of smart grid introduce various key challenges, including information and communication, sensing, measurement, control, power electronics, and electric energy storage.

1.13 Challenges for the Smart Grid

47

1.13.1 Information and Communication The data traffic generated by sensors and transmitted between smart meters and data centers necessitates a robust information and communication infrastructure. To optimize latency, frequency range, data rate, and throughput specifications in line with the communication requirements of every smart grid component, an integrated, flexible, interoperable, reliable, scalable, and secure bidirectional communication backbone is essential [162]. Securing the transmission and storage of information is crucial to prevent cyberattacks in the smart grid. To achieve this, numerous encryption protocols, privacypreserving billing protocols, authentication mechanisms, encryption/decryption techniques, and key management schemes have been proposed as cybersecurity solutions to safeguard smart grid networks and devices [163]. Smart grid communication can be categorized into three main areas based on the geographical coverage: home area network, neighborhood area network, and wide area network. Power line communications (PLC), wireless communications, cellular communications, and internet-based virtual private networks (VPNs) serve as the infrastructure for smart grids. However, the application of these technologies in the smart grid poses several challenges that need to be addressed. (1) PLC Power line communication (PLC) is a technology primarily used for transmitting data by utilizing existing power cables to carry carrier communication signals. PLC operates within different frequency ranges, namely 0.3–3 kHz for ultra-narrowband, 3–500 kHz for narrowband, and 1.8–250 MHz for wideband applications [164]. However, PLC faces several challenges in its implementation. The time-varying nature of the channel, interruptions in communication at open ends, reflections caused by impedance mismatch, the strong low-pass characteristic of the communication channel, signal attenuation and distortion, channel congestion, interference, and noise are among the main problems encountered in PLC systems [165]. Moreover, for PLC to effectively support smart grid establishment, it needs to address additional requirements. These include providing real-time communication capabilities, high-speed data transmission, efficient coexistence mechanisms to mitigate interference, robust security mechanisms to ensure data protection, and support for IP protocols [166]. Overcoming these challenges is crucial for the successful integration and utilization of PLC technology in the smart grid context. (2) Wireless communication technology ZigBee (IEEE 802.15.4), Wi-Fi (IEEE 802.11), and WiMAX (IEEE 802.16) are widely utilized wireless communication technologies in the context of smart grids [167]. These technologies differ in terms of their coverage area and data rates. ZigBee has a coverage area of 50 m with a data rate of 250 kb/s, Wi-Fi covers an area of 100 m with a data rate of 150 Mb/s, and WiMAX can cover up to 100 km with a data rate of 288.8 Mb/s [167].

48

1 Introduction

However, wireless communication in general faces certain challenges. Interference caused by industrial, scientific, and medical frequency bands poses a significant issue, as they have lower bandwidth compared to wired communication technologies and can suffer from limited penetration range unless a substantial amount of transmit power is consumed. Furthermore, wireless communications encounter challenges related to downlink communications, quality of service (QoS) differentiation and configuration, self-healing capabilities, multicast support, cluster-based routing, and network design considerations. Additionally, the presence of high-voltage electrical equipment can negatively impact or completely disrupt wireless communications, thereby posing further challenges to overcome. Cellular communication technology faces certain challenges in the context of smart grids. When the base station of a cell is damaged, it can result in the loss of some or all services within that cell due to the tree topology of the cellular network. This vulnerability highlights the need for robust backup systems and redundancy measures to ensure uninterrupted communication. Furthermore, the performance of cellular networks is constrained by inter-cell interference, making it insufficient to rely solely on increasing signal power to meet the demands of big data and highrate transmission in smart grids [168]. Alternative approaches and optimizations are necessary to address this limitation effectively. Moreover, several specific challenges arise in cellular communication for smart grids, particularly in machine-to-machine (M2M) communication. These challenges include smart antenna configuration, cross-layer optimization, efficient signaling protocols and overhead management, adapting to multi-cell environments, and enabling cooperative communication [169]. These aspects require careful consideration and innovation to ensure reliable and efficient M2M communication within the cellular network. (3) Internet An Internet-based virtual private network (VPN) converts a public network into a private network for secure data transmission. VPNs can be implemented using technologies like IPsec and MPLS, based on coverage and peer-to-peer VPN architectures. The coverage model requires a full mesh network for optimal routing and faces challenges in determining inter-block circuit capacity. The peer-to-peer model requires complex filters, and VPN routes are carried within the service provider’s IGP. IPsec has scalability issues with large VPNs and lacks robust connections due to unpredictable communication performance. This gives rise to the coverage channel barrier problem in VPNs [170]. (4) Security Smart grids encounter various security challenges, stemming from evolving communication technologies, potential threat attacks, and inadvertent compromises. Information security is of utmost importance for enabling secure two-way communication between generators, substations, customers, and power equipment [171, 172].

1.13 Challenges for the Smart Grid

49

One challenge is the identification of denial-of-service (DoS) attacks, where the “profile-then-detect” approach may result in increased detection time. Errors in physical layer authentication can occur due to the dynamic nature of communication channels [173]. Additionally, the use of long key lengths in public key encryption can lead to performance degradation in data transmission. Key distribution processes also carry the risk of key compromise in symmetric key cryptography, particularly due to the use of short-lived keys [174]. Ensuring host security for devices such as PLCs, RTUs, and IEDs typically relies on device manufacturers, leaving end users unable to install security software. Moreover, exposed application programming interfaces utilized in big data analytics engines can introduce potential security vulnerabilities, including wrapping attacks, phishing attacks, metadata spoofing, and injection attacks [175]. Addressing these security challenges is crucial to safeguard the integrity, confidentiality, and availability of data in smart grids. Robust security measures and protocols are necessary to protect against threats and vulnerabilities, ensuring the reliability and trustworthiness of the smart grid infrastructure.

1.13.2 Perception, Measurement, Control (1) Smart Meter A smart meter is an advanced energy meter that provides real-time display of energy usage, price information, and dynamic tariffs. It incorporates two-way communication and remote connect/disconnect capabilities. Smart meters offer various functionalities, including automatic control of electrical appliances, power quality measurement, load management, demand-side integration, outage notification, and power theft detection [176]. However, there are specific challenges associated with smart meter networks. Each component and device in the network requires unique IDs, making the integration of new devices complex as the number of customers increases. Additionally, the integration of supplementary memory to store data logs adds to the deployment cost of smart meters [177]. Power usage data collected by smart meters can reveal valuable customer information, such as occupancy patterns and device usage. This raises concerns regarding the privacy and security of customer data. Manipulation of energy costs, unauthorized control of smart meters, and potential vulnerabilities in smart meter readings represent security risks [178]. Attackers can exploit intercepted data to manipulate calculations, clone meters, share encryption keys, execute unauthorized code through host interfaces, and gain control over security applications. Consequently, smart meters with sensing and measurement capabilities also face security challenges that must be addressed to ensure the integrity and privacy of customer data and the overall security of the smart grid system.

50

1 Introduction

(2) Demand side management Demand side management (DSM) enables customers to make informed decisions about their power usage, helping power providers in reducing peak load demand and achieving a more balanced load distribution [179]. DSM strategies involve demand task scheduling, utilization of stored power, and real-time pricing to optimize power consumption. While DSM can increase the complexity of power system operations and redistribute the load, it does not necessarily result in an overall reduction in total power consumption [180]. The effectiveness of DSM depends on the capacity of the power system. In scenarios where the system is operating at maximum capacity, the value of DSM is high as it helps alleviate strain. However, in systems with spare capacity, the value of DSM is relatively low [181]. Therefore, ensuring an adequate grid generation capacity becomes a significant challenge for implementing DSM strategies [181]. Furthermore, the implementation of DSM can vary across regions due to uneven regional development, making it difficult to establish a unified standard policy system in some countries [182]. Another consideration is the potential rebound in energy usage following high price signals, which could lead to larger spikes in DSM and undermine its intended benefits [183]. (3) Measurement Error Substation automation systems in smart grids involve various equipment such as current and voltage transformers, intelligent electronic devices (IEDs), remote terminal units (RTUs), and rack controllers. However, certain challenges exist with these components. Iron core current transformers can introduce measurement errors due to factors like magnetizing currents, flux leakage, magnetic saturation, and eddy current heating. Similarly, electromagnetic voltage transformers can be affected by leakage reactance, winding resistance, core permeability, and core loss, which can impact measurement accuracy [184]. IEDs play a critical role in substation automation by independently performing functions like protection, control, monitoring, metering, fault recording, and communication. However, the sheer volume of data generated by IEDs can overwhelm utilities, making it nearly impossible to perform routine analysis manually. As a result, automated analysis tools are required to provide decision support. The limited processing power of IEDs necessitates the use of simple and potentially insecure service protocols, limiting the implementation of robust host-based and distributed intrusion detection systems [185]. Furthermore, the vendor-specific hardware and hardware-related software used in IEDs can restrict their flexibility. This can pose challenges when it comes to customization, interoperability, and the integration of advanced functionalities.

1.13 Challenges for the Smart Grid

51

(4) Control The integration of distributed energy resources (DERs) into a smart distribution management system (SDMS) presents certain challenges. As DERs are interconnected, the intelligent control function of the SDMS becomes more complex. Smallscale DERs may not have access to two-way communication networks and remote command capabilities [186, 187]. Moreover, the inclusion of small power generation loads on the customer’s side adds complexity to power flow analysis, emergency analysis, and emergency control within the SDMS. One key challenge in the SDMS is achieving waveform parameter matching between the grid and distributed generation sources. Ripple control systems used in the SDMS can switch distributed generation sources in or out during peak loads. However, this can result in delays in signal propagation and potential equipment damage in the distribution network [188]. Energy management systems (EMS) primarily focus on informing consumers about their power usage and sharing this information with energy suppliers. However, EMS often overlooks active user participation in energy management strategies and fails to consider the automation of power consumption control for home appliances. In microgrids, centralized control may not be compatible with plug-and-play functionality and can require high computational costs. On the other hand, decentralized control requires synchronization, and achieving consensus among local agents may be time-consuming [189]. (5) PMU Phase measurement units (PMUs) play a crucial role in enhancing the monitoring, protection, and control of the power grid by compensating for phase delays caused by anti-aliasing filters and enabling simultaneous phase measurements within the system [190]. This allows for more accurate and synchronized data acquisition. However, it’s important to note that PMU data has limitations. For instance, PMUs may not be able to capture electromagnetic transients, which are short-duration disturbances in the power system. These transients can provide valuable insights into system behavior and performance. Furthermore, the integrity of PMU data can be compromised by potential GPS spoofing attacks. GPS spoofers have the capability to manipulate the timestamps of PMU measurements by injecting fake GPS signals into the PMU’s time-reference receiver [191]. This can lead to inaccurate synchronization and potentially impact the reliability and effectiveness of grid monitoring and control. Intelligent fault diagnosis plays a critical role in outage management and service restoration within power systems. Different methods for fault location in transmission lines typically rely on specific measurements taken from one or more ends of the line. However, in some cases, digital fault recorders, which provide recorded

52

1 Introduction

measurements, are only available in critical substations. This limitation prevents the retrieval of measurements from either end of the faulted line when relying solely on this data source. In the context of fault localization methods in distribution feeders, there are additional challenges to consider. The inhomogeneity of the line, along with the presence of laterals, load taps, and low-grade meters in the distribution system, can significantly impact the accuracy of fault localization [192]. These factors introduce complexities and uncertainties that must be accounted for when attempting to pinpoint the location of faults in distribution feeders.

1.13.3 Power Electronics and Power Storage (1) Power electronics interface equipment Power electronics interface devices play a crucial role in the efficient control and management of active power, reactive power, and terminal voltage in distributed and micro-generators. These versatile devices find extensive application in the grid integration of photovoltaic and energy storage systems, enabling the conversion of DC to AC power for seamless integration. Moreover, in the realm of renewable energy, hydro turbines are utilized to elegantly decouple turbine speed from the wind grid frequency, ensuring optimal performance. The wide-scale deployment of power electronic interfaces necessitates the installation of cutting-edge FACT (Flexible AC Transmission) and High Voltage DC (HVDC) devices, forming the backbone of a smart power grid infrastructure [193]. These advanced technologies empower the grid by enhancing power transfer capability across existing transmission lines while exerting precise control over steady-state and dynamic power flow. HVDC systems employ a range of sophisticated controllers, including current source inverters, voltage source inverters, multilevel inverters, and multimodule inverters, all meticulously engineered to facilitate seamless power transmission. Similarly, FACT devices, such as Static VAR Compensator (SVC), Static Synchronous Compensator (STATCOM), Thyristor-Controlled Series Compensator (TCSC), Thyristor-Switched Series Compensator (TSSC), Static Synchronous Series Compensator (SSSC), Unified Power Flow Controller (UPFC), Interline Power Flow Controller (IPFC), and Static Phase Shifter (SPS), stand at the forefront of FACT control technology, enabling precise regulation of power flow and grid stability in a dynamic operational environment. The utilization of power electronics in grid systems brings forth certain challenges that need to be effectively addressed. One such challenge lies in the injection of harmonics into the grid, potentially leading to voltage distortion issues [194]. In the case of a multilevel inverter with a split DC link configuration, voltage imbalances among the integrated capacitors can arise, necessitating careful management and control strategies [194]. Additionally, forced commutation inverters employed in

1.13 Challenges for the Smart Grid

53

variable speed wind turbines have been identified as sources of internal harmonics, demanding precise filtering and mitigation techniques [195]. When it comes to current source inverters and voltage source inverters, distinctive characteristics and considerations emerge. Inductors utilized for energy storage in current source inverters exhibit higher conduction losses and lower energy storage efficiency compared to the DC link capacitors in voltage source inverters [196]. Moreover, the lifetime of a standard voltage source PWM inverter, connected to the utility through an LCL filter, is impacted by the performance and longevity of the electrolytic capacitors employed for power decoupling between the PV modules and the utility [197]. In the context of high-power systems, such as two-stage three-phase voltage source PWM inverters, specific issues come to the forefront. Large switching losses and unbalanced voltages can be observed in these inverters due to the series-connected switch strings, necessitating robust control techniques and effective voltage balancing strategies [198]. Additionally, the utilization of multi-pulse diode rectifiers with center-tapped current transformers aims to ensure equal ripple currents for both rectifiers. However, an unequal current problem can arise as a result of the uneven DC voltage outputs of the two rectifiers, requiring careful attention to achieve balanced operation [198]. The operation of DC/DC converters involves certain challenges that must be carefully managed to ensure optimal performance. When employing hard switching techniques, precise control is essential to maintain accurate bipolar waveforms or current mode control. Failure to achieve such precision can lead to transformer saturation, resulting in additional losses and decreased efficiency [199]. Additionally, in VSCbased multi-terminal DC systems, the converter blocking technique is utilized for protection purposes, interrupting the fault current in the line-commutated converter. However, this protective measure can inadvertently disrupt the control of the VSC, necessitating sophisticated control strategies to ensure reliable operation [199]. Fault diagnosis of power electronics systems poses significant difficulties due to the extremely short time interval between fault occurrence and potential catastrophic failure. Detecting and diagnosing faults in such a limited timeframe requires advanced monitoring and diagnostic techniques, as well as real-time analysis to prevent further damage or system failure [199]. Traditional HVDC systems face limitations in separating reactive power control from active power control. However, HVDC systems based on voltage source converters offer the advantage of independent control of both active and reactive power, enabling the integration of multiple converters into a common DC bus [200]. Nevertheless, VSC-HVDC systems introduce nonlinear impedance to the power generation system, leading to the generation of harmonic currents and adverse effects such as low power factor, increased electromagnetic interference, and voltage distortion [200]. In VSC-HVDC systems, the clearance of faults is typically achieved using AC side circuit breakers. However, in multi-terminal systems, this process can be timeconsuming and may not be suitable for traditional protection methods. Furthermore, breaking the current in a DC circuit presents additional challenges as there is no zero

54

1 Introduction

point for current and voltage reference. Consequently, addressing protection issues during DC faults in multiterminal VSC-HVDC systems requires tailored approaches based on the specific power electronic converter topology employed [201]. It is worth noting that HVDC systems based on current source converters primarily utilize thyristor technology. The inability of thyristors to directly switch off the gate signal poses unique challenges, as does the requirement for reactive power to be supplied to the thyristor valves in the converter [201]. Careful design and control strategies are necessary to address these limitations and ensure efficient and reliable operation in current source converter-based HVDC systems. Thyristor-controlled reactors and thyristor-switched capacitors play vital roles in power system compensation by providing continuous inductance and discontinuous capacitance compensation, respectively. The use of Static Var Compensators (SVC) allows for both inductive and capacitive continuous compensation. However, it’s important to note that the current compensation capability of SVC diminishes at voltages below the rated voltage, which can lead to the generation of 3rd, 5th, and 7th harmonic currents within the power system [202]. When comparing SVC and Static Synchronous Compensator (STATCOM), STATCOM offers advantages in terms of stability margin and response time. However, it comes with higher costs, increased losses, and a more intricate control strategy. On the other hand, Thyristor-Controlled Series Compensator (TCSC), Thyristor-Switched Series Compensator (TSSC), and Static Synchronous Series Compensator (SSSC) are effective in controlling power flow in transmission lines and improving the dynamic characteristics of the power system. Nevertheless, it’s important to acknowledge that series compensation methods cannot address the transmission angle issue, as the main transmission angle may not align with the specific requirements of a given power line and may vary with daily or seasonal system loads [203]. Unified Power Flow Controller (UPFC), although capable of controlling power flow in a single transmission line, offers a more flexible topology and can be utilized to manage the power flow of a group of power lines [204]. In the realm of Flexible AC Transmission Systems (FACT), two common types of thyristor-controlled static phase shifters are the Phase Angle Regulator (PAR) and the Quadrature Boosting Transformer (QBT). While PAR is a symmetric device with its position having no effect on power flow characteristics, QBT is a symmetric device whose position does impact power flow characteristics. To overcome interference between active power modulation and shaft torsional mode oscillations, a power oscillation damping controller can be integrated into the reactive power control loop. However, it’s important to note that these controllers may exhibit oscillations in terminal voltage for a period before the terminal voltage settles [205]. As the competitive electricity market continues to evolve, establishing an appropriate pricing methodology for reactive power becomes crucial in providing voltage control ancillary services. Addressing this aspect is paramount to ensure efficient and reliable voltage control in power systems operating under market conditions [206].

1.13 Challenges for the Smart Grid

55

(2) Electric energy storage Energy storage applications in power systems aim to provide short-term power compensation for power quality, voltage, and frequency, and provide energy for renewable generation smoothing, power time-shifting, and end-user energy management over longer periods of time. Each of these energy storage technologies presents a unique set of advantages and disadvantages, making them suitable for different applications depending on specific requirements and considerations [207]. For example, lithium-ion (Li-ion) batteries offer a wider operating temperature range compared to flow batteries, enhancing their versatility in diverse environmental conditions. Superconducting Magnetic Energy Storage (SMES) systems thrive in low-temperature settings, whereas Sodium-Sulfur (NaS) batteries perform optimally at higher temperatures. When it comes to self-discharge rates, flywheels, Electric Double-Layer Capacitors (EDLC), NaS, Sodium-Nickel Chloride (NaNiCl), and SMES systems exhibit relatively higher self-discharge rates, whereas lithium-ion and lead-acid batteries demonstrate the lowest daily self-discharge rates, ensuring longer-term energy storage capabilities. The efficiency of Pumped Hydroelectric Storage (PHES) systems surpasses that of Compressed Air Energy Storage (CAES) systems, contributing to improved overall energy utilization. Considering the lifespan of energy storage systems, PHES and CAES systems boast the longest lifetimes, while lead-acid, Nickel-Metal Hydride (NiMH), and Zinc-Bromine (ZnBr) batteries have more limited lifespans. In terms of cost, PHES systems and lithium-ion batteries tend to have higher upfront investment costs due to their power capacity, whereas NaNiCl, SMES, and EDLC systems offer more economical options. However, when considering energy capacity costs, flywheels, SMES, and EDLC systems involve higher investment costs, while hydrogen storage and CAES systems present more cost-effective alternatives. In terms of physical characteristics, lithium-ion batteries exhibit a compact size with high energy and power density, making them suitable for space-constrained applications. On the other hand, PHES, CAES, lead-acid, Nickel–Cadmium (NiCd), and ZnBr batteries have higher environmental impacts and potential hazards, whereas flywheel systems demonstrate the least environmental impact. Charging times vary across different energy storage technologies, with lithiumion batteries requiring the shortest charging time, while lead-acid and sodium-salt batteries necessitate longer charging durations. In terms of recyclability, lead-acid, NaS, NaNiCl, and ZnBr batteries exhibit high recyclability rates, contributing to a more sustainable approach, whereas hydrogen storage presents challenges in terms of recyclability. It is important to carefully consider these factors when selecting an energy storage technology, taking into account the specific requirements, performance expectations, and environmental considerations of each application.

56

1 Introduction

1.14 Summary This chapter provides an introduction to the fundamental concepts of the Internet of Things (IoT). It begins by defining the IoT and exploring its vision across various aspects, highlighting the opportunities it brings. It is anticipated that by 2020, the IoT will unlock significant business prospects in eight prominent domains. Next, the chapter delves into the architecture, elements, and standards of the IoT. The IoT architecture encompasses four main structures: three-tier architecture, middleware-based architecture, SOA-based architecture, and five-tier architecture. Each structure has its own specific background and application environment. The elements of the IoT revolve around six key components: identification, perception, communication, computing, service, and semantics. While the IoT lacks universally recognized standards, existing standards proposed by various organizations are tailored for specific environments. These standards can be categorized into application protocols, service discovery protocols, infrastructure protocols, and others. Drawing from the hierarchical structure of the IoT, the chapter further presents an overview of the crucial technical domains that underpin its functioning. These domains include the application domain, middleware domain, network domain, and object domain technology. They encompass essential aspects such as interoperability, integration, availability, reliability, data storage, processing, visualization, scalability, management, self-configuration, modeling, simulation, identification uniqueness, and security and privacy. The IoT has experienced rapid development in China, driven by governmental initiatives, societal support, and enterprise engagement. In China, IoT research and applications span nine key areas: smart industry, smart agriculture, smart logistics, smart transportation, smart grid, smart environmental protection, smart security, smart healthcare, and smart home. Of particular significance within the IoT landscape is the smart grid, which serves as the foundation for the digital and intelligent evolution of traditional power systems. By leveraging IoT technology, the smart grid enables intelligence in power generation, transmission, distribution, and consumption. The IoT-based smart grid comprises four layers: the application layer, management service layer, network layer (including gateways), and perception layer. Key technologies within the smart grid-based IoT ecosystem encompass cloud computing, big data, machine-to-machine (M2M) communication, edge computing (or fog computing), middleware, smart sensors and actuators, wireless local and wide area networks, as well as smart grid security measures. The global adoption of smart grid technology is an evolutionary process, with major countries formulating policies to promote its development and deployment. The primary objectives of these initiatives revolve around energy conservation, emission reduction, and sustainable energy development.

References

57

Nevertheless, the smart grid faces significant challenges across multiple fields, including information and communication, perception, measurement, control, power electronics, and power storage. Addressing these challenges is crucial for advancing the capabilities and effectiveness of the smart grid within the IoT framework.

References 1. Colakovic A, Hadžialic M (2018) Internet of Things (IoT): a review of enabling technologies, challenges, and open research issues. Comput. Netw. 144:17–39. https://doi.org/10.1016/j. comnet.2018.07.017 2. Stankovic JA (2014) Research directions for the Internet of Things. IEEE Internet Things J 1:3–9 3. Atzori L, Iera A, Morabito G (2010) The internet of things: a survey. Comput Netw54:2787– 2805 4. Gubbi J et al (2013) Internet of Things (IoT): a vision, architectural elements, and future directions. Fut Gener Comput Syst 1645–660 5. Global Information Infrastructure (2012) Internet protocol aspects and next-generation networks, next generation networks–frameworks and functional architecture models: overview of the internet of things, ITU-T recommendation Y.2060 Series Y 6. Internet of Things (IoT) ISO/IEC JTC 1 information technology, 2014 Preliminary Report 7. Al-Fuqaha A et al (2015) Internet of things: a survey on enabling technologies, protocols, and applications. IEEE Commun Surv Tutor 17:2347–2376 8. Zeng X, Bao S (2016) Introduction to internetwork of things. Publishing House of Electronics Industry, Beijing 9. Liu T, Lu D (2012) The application and development of IoT. In: Proceedings of international symposium on information technology in medicine and education (ITME), vol 2, pp 991–994 10. AL-Fuqaha et al (2015) IoT: survey on enabling technologies, protocols, and applications. IEEE Commun Surv Tutor 17(4):2347–2379 11. Gantz J, Reinsel D (2007) The digital universe in 2020: Big data, bigger digital shadows, and biggest growth in the far east, IDC iView, 2012, IDC Anal. Future, pp 1–16 12. Evans D (2011) The Internet of things: how the next evolution of the Internetis changing everything, CISCO, San Jose,CA, USA,White Paper 13. Taylor S (2013) The next generation of the Internet revolutionizing the waywe work, live, play, and learn, CISCO, San Francisco, CA, USA, CISCO Point of View 14. Manyika J et al (2013) Disruptive technologies: advances that will transform life, business, and the global economy, San Francisco, CA. McKinsey Global Instit, USA 15. Floyer D (2013) Defining and sizing the industrial Internet, Wikibon, Marlborough, MA, USA; Commercial Building Automation Systems (2013) Navigant Consulting Res., Boulder, CO, USA; Khan R et al (2012) Future Internet: the internet of things architecture, possible applications and key challenges. In: Proceedings of 10th International Conference FIT, pp 257–260 16. Yang Z et al (2011) Study and application on the architecture and key technologies for IOT. In: Proceedings of ICMT, pp 747–751 17. Wu M et al (2010) Research on the architecture of Internet of Things. In: Proceedings of 3rd ICACTE, pp V5-484–V5-487 18. Atzori L et al (2010) The internet of things: a survey. Comput Netw 54:2787–2805 19. Tan L, Wang N (2010) Future Internet: the internet of things. In: Proceedingsof 3rd ICACTE, pp V5-376–V5-380 20. Chaqfeh MA, Mohamed N (2012) Challenges in middleware solutions for the Internet of Things. In: Proceedings of international conference on CTS, pp 21–26

58

1 Introduction

21. Koshizuka N, Sakamura K (2010) Ubiquitous ID: standards for ubiquitous computing and the internet of things. IEEE Pervasive Comput 9(4):98–101 22. Kushalnagar N et al (2007) IPv6 over low-power wireless personal area networks (6LoWPANs): overview, assumptions, problem statement, and goals, Internet Eng. Task Force (IETF), Fremont, CA, USA, RFC4919, vol 10 23. Montenegro G et al (2007) Transmission of IPv6 packets over IEEE 802.15. 4 networks, Internet Eng. Task Force (IETF), Fremont, CA, USA, Internet Proposed Std. RFC 4944 24. Dunkels A et al (2004) Contiki—a lightweight and flexibleoperating system for tiny networked sensors. In: Proceedings of 29th annual IEEE international conference on local computer networks, pp 455–462 25. Levis P et al (2005) TinyOS: an operating system for sensor networks, ambient intelligence. Springer, New York, pp 115–148 26. Cao Q et al (2008) The lite OS operating system: towards Unix-like abstractions for wireless sensor networks. In: Proceedings of international conference on IPSN, pp 233–244 27. Baccelli E et al (2013) RIOT OS: towards an OS for the Internet of Things. In: Proceedings of IEEE Conference on INFOCOM WKSHPS, pp 79–80 28. Open Auto Alliance. http://www.openautoalliance.net/ 29. Gigli M, Koo S (2011) Internet of things: Services and applications categorization. Adv Internet Things 1:27–31 30. Barnaghi P et al (2012) Semantics for the internet of things: early progress and back to the future. In: Proceedings of IJSWIS, vol 8, pp 1–21 31. Kamiya T, Schneider J (2011) Efficient XML Interchange (EXI) Format 1.0, World Wide Web Consortium, Cambridge, MA, USA, Recommend. REC-Exi-20110310 32. Bormann C et al (2012) CoAP: an application protocol for billions of tiny Internet nodes. IEEE Internet Comput 16:62–67 33. Lerche C et al (2012) Industry adoption of the Internet of Things: a constrained application protocol survey. In: Proceedings of IEEE 17th Conference on ETFA, pp 1–6 34. Locke D (2010) MQ telemetry transport (MQTT) v3. 1 protocol specification, IBM developer Works, Markham, ON, Canada, Tech. Lib. http://www.Ibm.com/Developerworks/Web services/Library/Ws-Mqtt/Index.html 35. Hunkeler U et al (2008) MQTT-S—a publish/subscribe protocol for wireless sensor networks. In: Proceedings of 3rd international conference on COMSWARE, pp 791–798 36. Saint-Andre P (2011) Extensible messaging and presence protocol (XMPP): Core, Internet Eng. Task Force (IETF), Fremont, CA, USA, Request for Comments: 6120 37. OASIS Advanced Message Queuing Protocol (AMQP) (2012) Version 1.0, Adv. Open Std. Inf. Soc. (OASIS), Burlington, MA, USA 38. Data Distribution Services Specification (2015) V1.2, Object Management Group (OMG), Needham, MA, USA. http://www.omg.org/spec/DDS/1.2/ 39. Esposito C et al (2008) Performance assessment of OMG compliant data distribution middleware. In: Proceedings of IEEE IPDPS, pp 1–8 40. Thangavel D et al (2014) Performance evaluation of MQTT and CoAP via a common middleware. In: Proceedings of IEEE 9th international conference on ISSNIP, pp 1–6 41. Jara AJ et al (2012) Light-weight multicast DNS and DNS-SD (lmDNS-SD): IPv6-based resource and service discovery for the web of things. In: Proceedings of 6th international conference on IMIS Ubiquitous Computer, pp 731–738 42. Klauck R, Kirsche M (2012) Chatty things—making the internet of things readily usable for the masses with XMPP. In: Proceedings of 8th international conference on CollaborateCom, pp 60–69 43. Cheshire S, Krochmal M (2013) Multicast DNS, Internet Eng. Task Force (IETF), Fremont, CA, USA, Request for Comments: 6762 44. Krochmal M, Cheshire S (2013) DNS-based service discovery, Internet Eng. Task Force (IETF), Fremont, CA, USA, Request for Comments: 6763 45. Vasseur J et al (2011) RPL: the IP routing protocol designed for low power and lossy networks, Internet Protocol for Smart Objects (IPSO) Alliance. CA, USA, San Jose

References

59

46. Winter T et al (2012) RPL: IPv6 routing protocol for low-power and lossy networks, Internet Eng. Task Force (IETF), Fremont, CA, USA, Request for Comments: 6550 47. Clausen T et al (2011) A critical evaluation of the IPv6 routing protocol for low power and lossy networks (RPL). In: Proceeding of IEEE 7th international conference on WiMob, pp 365–372 48. Palattella MR et al (2013) Standardized protocol stack for the Internet of (important) things. IEEE Commun Surv Tuts 15(3):1389–1406 49. Ko J et al (2011) Connecting low-power and lossy networks to the Internet. IEEE Commun Mag 49(4):96–101 50. Hui JW, Culler DE (2008) Extending IP to low-power, wireless personal area networks. IEEE Internet Comput 12(4):37–45 51. Frank R et al (2014) Bluetooth low energy: an alternative technology for VANET applications. In: Proceedings of 11th annual conference on WONS, pp 104–107 52. Decuir J (2014) Introducing Bluetooth smart: Part 1: a look at both classic and new technologies. IEEE Consum Electron Mag 3(1):12–18 53. Mackensen E et al (2012) Bluetooth low energy (BLE) based wireless sensors. IEEE Sens 1–4 54. Siekkinen M et al (2012) How low energy is bluetooth low energy? Comparative measurements with ZigBee/802.15.4. In: Proceedings of IEEE WCNCW, pp 232–237 55. Jones EC, Chung CA (2011) RFID and Auto-ID in planning and logistics: a practical guide for military UID applications. CRC Press, Boca Raton 56. Minoli D (2013) Building the internet of things with IPv6 and MIPv6: the evolving world of M2M communications, New York, NY. Wiley, USA 57. Grasso J (2004) The EPCglobal network: overview of design, benefits, and security, EPCglobal Inc, Position Paper, vol 24 58. Hasan M et al (2013) Random access for machine-to-machine communication in LTEadvanced networks: issues and approaches. IEEE Commun Mag 51(6):86–93 59. Gomez C, Paradells J (2010) Wireless home automation networks: a survey of architectures and technologies. IEEE Commun Mag 48(6):92–101 60. Bagci I et al (2012) Codo: confidential data storage for wireless sensor networks. In: Proceedings of IEEE 9th international conference on MASS, 1–6 61. Raza S et al (2012) Secure communication for the Internet of Things—a comparison of link-layer security and IPsec for 6LoWPAN. Secur Commun Netw 7(12):2654–2668 62. Raza S et al (2011) Securing communication in 6LoWPAN with compressed IPsec. In: Proceedings of international conference on DCOSS, pp 1–8 63. Srivatsa M, Liu L (2005) Securing publish-subscribe overlay services with EventGuard. In: Proceedings of 12th ACM conference on computer and communications security, pp 289–298 64. Corman AB et al (2007) QUIP: a protocol for securing content in peer-to-peer publish/ subscribe overlay networks. In: Proceedings of 13th Australasian conference on computer science, vol 62, pp 35–40 65. Raza S et al (2013) Lithe: lightweight secure CoAP for the Internet of Things. IEEE Sens J 13(10):3711–3720 66. MQTT NIST Cyber Security Framework. https://www.oasis-open.org/committees/download. php/52641/mqtt-nist-cybersecurity-v1.0-wd02.doc 67. IEEE Standard for a Convergent Digital Home Network for Heterogeneous Technologies (2013) IEEE Std. 1905. 1–2013, pp 1–93 68. H2020–UNIFY-IoT Project. Supporting Internet of Things Activities on Innovation Ecosystems, 2016 Report on IoT platform activities. http://www.unify-iot.eu/wp-content/uploads/ 2016/10/D03_01_WP02_H2020_UNIFY-IoT_Final.pdf 69. Gluhak A et al (2011) A survey on facilities for experimental Internet of Things research. IEEE Commun Mag 49(11):58–67 70. Díaz M et al (2016) Challenges, and open issues in the integration of Internet of Things and cloud computing. J Netw Comput Appl 67:99–117 71. Architectural Considerations in Smart Object Networking (2015) IETF RFC 7452 (March)

60

1 Introduction

72. MT Vision (2015) Framework and overall objectives of the future development of IMT for 2020 and beyond, ITU-R Recommendation M.2083-0 73. van der Veer H, Wiles A (2008) Achieving technical, interoperability-the ETSI approach. ETSI White Paper No. 3. http://www.etsi.org/images/files/ETSIWhitePapers/IOP%20whitepaper% 20Edition%203%20final.pdf 74. Upadhyaya B et al (2011) Migration of SOAP-based services to RESTful services. In: Proceedings of 13th IEEE international symposium on web systems evolution (WSE), pp 105–114 75. Ngu AHH et al (2016) IoT middleware: a survey on issues and enabling technologies. IEEE Internet Things J 99:1–1 76. Ishaq I et al (2012) Internet of Things virtual networks: bringing network virtualization to resource-constrained devices. In: Proceedings of IEEE international conference on green computing and communications, pp 293–300 77. Qin Z et al (2014) A software defined networking architecture for the Internet-of-Things. In: Proceedings of IEEE network operations and management symposium (NOMS), pp 1–9 78. Vlacheas P et al (2013) Enabling smart cities through a cognitive management framework for the Internet of Things. IEEE Commun Mag 51(6):102–111 79. Blackstock M, Lea R (2014) IoT interoperability: a hub-based approach. In: Proceedings of international conference on the internet of things (IoT), pp 79–84 80. Talavera LE et al (2015) The mobile hub concept: enabling applications for the Internet of Mobile Things.In: Proceedings of IEEE international conference on pervasive computing and communication workshops (PerCom Workshops), pp 123–128 81. Chen I-R, Guo J, Bao F (2016) Trust management for SOA-based IoT and its application to service composition. IEEE Trans Serv Comput 9(3):482–495 82. Kiljander J et al (2014) Semantic interoperability architecture for pervasive computing and Internet of Things. IEEE Access 2(August):856–873 83. Ganzha M et al (2016) Semantic technologies for the IoT—an inter-IoT perspective.In: Proceedings of IEEE first international conference on internet-of-things design and implementation (IoTDI), pp 271–276 84. Huang Y, Li G (2010) A semantic analysis for Internet of Things. Proc Int Conf Intell Comput Technol Autom 1:336–339 85. Song Z et al (2010) Semantic middleware for the Internet of Things. In: Proceedings of IoT, pp 1–8 86. Choi S-I, Koh S-J (2016) Use of proxy mobile IPv6 for mobility management in CoAP-based Internet-of-Things networks. IEEE Commun Lett 20(11):2284–2287 87. Huang J et al (2017) Multicast routing for multimedia communications in the Internet of Things. IEEE Internet Things J 99:1–1 88. Lamaazi et al (2017) RPL-based networks in static and mobile environment: a performance assessment analysis. J King Saud Univ Comput Inf Sci 1–14 89. Abdelwahab S et al (2016) Cloud of things for sensing-as-a-service: architecture, algorithms, and use case. IEEE Internet Things J3(6):1099–1112 90. Roopaei M et al (2017) Cloud of Things in smart agriculture: intelligent irrigation monitoring by thermal imaging. IEEE Cloud Comput 4(1) 91. Chiang M, Zhang T (2016) Fog and IoT: an overview of research opportunities. IEEE Internet Things J3(6):854–864 92. Pereira C, Aguiar A (2014) Towards efficient mobile M2M communications: survey and open challenges. Sensors 14(10):19582–19608 93. Sarkar C et al (2014) DIAT: a scalable distributed architecture for IoT. IEEE Internet Things J2(3):230–239 94. Mosko M et al. CCNx 1.0 Protocol architecture, A Xerox Company, Computing Science Labaratory PARC. http://www.ccnx.org/pubs/CCNxProtocolArchitecture.pdf 95. Abu-Elkheir M et al (2013) Data management for the Internet of Things: design primitives and solution. Sensors 13(October):15582–15612

References

61

96. Chen I-R et al (2016) Trust management for SOA-based IoT and its application to service composition. IEEE Trans Serv Comput 9(3):482–495 97. Cho HH et al (2014) Integration of SDR and SDN for 5G. IEEE Access 2(September):1196– 1204 98. Santos MAS et al (2014) Decentralizing SDN’s control plane. In: Proceedings of 39th annual IEEE conference on local computer networks, pp 402–405 99. Bizanis N, Kuipers FA (2016) SDN and virtualization solutions for the Internet of Things: a survey. IEEE Access 4(September):5591–5606 100. Bandyopadhyay D, Sen J (2011) Internet of Things: applications and challenges in technology and standardization. Wirel Pers Commun 58(1):49–69 101. OMA LightweightM2M (LWM2M), OMA (Open Mobile Alliance) Specification. http://tec hnical.openmobilealliance.org/Technical/technical-information/release-program/current-rel eases/oma-lightweightm2m-v1-0-2 102. Perelman V et al. Network configuration protocol light (NETCONF Light). IETF Network Working Group. https://tools.ietf.org/html/draft-schoenw-netconf-light-01 103. Batool K, Niaz MA (2017) Modeling the Internet of Things: a hybrid modeling approach using complex networks and agent-based models. Complex Adapt Syst Model 5(4):1–19 104. Li W et al (2017) System modelling and performance evaluation of a three-tier cloud of things. Future Gener Comput Syst 70(May):104–125 105. Sarkar S, Misra S (2016) Theoretical modelling of fog computing: a green computing paradigm to support IoT applications. IET Netw5(2):1–7 106. Fortino G et al (2017) Modeling and simulating Internet-of-Things systems: a hybrid agentoriented approach. Comput Sci Eng19(5):68–76 107. D’Angelo G et al (2017) Modeling the Internet of Things: a simulation perspective. In: Proceedings of high performance computing & simulation (HPCS), pp 18–27 108. Kecskemeti G et al (2017) Modelling and simulation challenges in Internet of Things. IEEE Cloud Comput 4(1):62–69 109. Trnka M, Cerny T (2016) Identity management of devices in Internet of Things environment. In: Proceedings of 6th international conference on IT convergence and security (ICITCS), pp 1–4 110. Roman R, Najera P, Lopez J (2011) Securing the Internet of Things. Computer 44(9):51–58 111. Jara AJ et al (2012) Light-weight multicast DNS and DNS-SD (lmDNS-SD): IPv6-based resource and service discovery for the web of things. In: Proceedings of sixth international conference on innovative mobile and internet services in ubiquitous computing, pp 731–738 112. Babovic ZB et al (2016) Web performance evaluation for Internet of Things applications. IEEE Access 4(October):6974–6992 113. Qi J et al (2014) Security of the Internet of Things: perspectives and challenges. Wirel Netw20(8):2481–2501 114. Babar S et al (2010) Proposed security model and threat taxonomy for the Internet of Things (IoT). Proceedings of communications in computer and information science, pp 420–429 115. Kalra S, Sood SK (2015) Secure authentication scheme for IoT and cloud servers. Pervasive Mob Comput 24(December):210–223 116. Internet of Things Research Study (2015) Hewlett Packard Enterprise Report. http://www8. hp.com/us/en/hp-news/press-release.html?id=1909050#.WPoNH6KxWUk 117. Yan Z, Zhang P, Vasilakos AV (2014) A survey on trust management for Internet of Things. J Netw Comput Appl 42:120–134 118. Kang K et al (2014) An interactive trust model for application market of the Internet of Things. IEEE Trans Ind Inf 10(2):1516–1526 119. Pei M et al (2016) The open trust protocol (OTrP), IETF. https://tools.ietf.org/html/draft-peiopentrustprotocol-00 120. Dierks T et al. The TLS protocol version 1.0, IETF RFC 2246. https://www.ietf.org/rfc/rfc 2246.txt 121. Rescorla E, Modadugu N (2006) Datagram transport layer security. IETF RFC 4347. https:// tools.ietf.org/html/rfc4347

62

1 Introduction

122. Rizzardi A et al (2016) AUPS: an open source authenticated publish/subscribe system for the Internet of Things. Inf Syst 62(December):29–41 123. Shanzhi C et al (2014) A vision of IoT: applications, challenges, and opportunities with China perspective. IEEE Internet Things J 1(4):349–359 124. Ministry of Industry and Information Technology of China (2012) The National 12th FiveYear Plan Including IoT Development(2011–2015). http://www.gov.cn/zwgk/2012-02/14/con tent2065999.htm 125. State Council of China (2013) Guidance on tracking and ordering for promoting the development of IoT. http://www.gov.cn/zwgk/2013-02/17/content2333141.htm 126. Ministry of Industry and Information Technology of China (2013) Special Development Action Plans for IoT. http://www.miit.gov.cn/n11293472/n11293832/n11293907/n11 368223/15649701.html 127. Ministry of Science and Technology of China (2013) The strategic alliance for industrial technology innovations of IoT. http://www.most.gov.cn/kjbgz/201309/t20130904109120.htm 128. Bedi G et al (2018) Review of Internet of Things (IoT) in electric power and energy systems. IEEE Internet Things J 5(2):847–870. https://doi.org/10.1109/JIOT.2018.2802704 129. SGCC (2012) The roadmaps for smart grids of developed countries. J SGCC 2:73–75 130. Litos Strategic Communication (2008) The smart grid: an introduction. Washington D.C. 131. The European Technology Platform Smart Grids. Smart grids: strategic deployment document for Europe’s electricity networks of the future. http://www.smartgrids.eu/documents/SmartG rids_SDD_FINAL_APRIL2010.pdf 132. Korean Smart Grid Institute, Korea’s smart grid roadmap2030. www.smartgrid.or.kr/Ebook/ KoreasSmartGridRoadmap.PDF 133. Office of the National Coordinator for Smart Grid Interoperability (2012) NIST framework and road map for smart grid interoperability standards 134. Hu XH (2009) Smart grid—a development trend of future power grid. Power Syst Technol 33(14):1–5 135. Keyhani A (2011) Design of smart power grid renewable energy systems, Hoboken, NJ. Wiley, USA 136. Yu Y, Yang J, Chen B (2012) Smart grids in China—a review. Energies 5(13):21–38. https:// doi.org/10.3390/en505132 137. Tuballa ML et al (2016) A review of the development of Smart Grid technologies. Renew Sustain Energy Rev 59:710–725. https://doi.org/10.1016/j.rser.2016.01.011 138. Annunziata M et al. Poweringthe future: leading the digital transformation of the power industry, GE Power Digit. Solutions, Boston, MA, USA. https://www.gepower.com/content/ dam/gepowerpw/global/en_US/documents/industrial%20internet%20and%20big%20data/ powering-the-future-whitepaper.pdf 139. Fang X et al (2012) Smart grid—the new and improved power grid: a survey. IEEE Commun Surv Tuts 14(4):944–980 140. Jain S et al (2014)Survey on smart grid technologies-smart metering, IoT and EMS. In: Proceedings of IEEE Stud. Conf. Elect. Electron. Computer Science (SCEECS), Bhopal, India, pp 1–6 141. Young R et al. The power is on: howIoT technology is driving energy innovation. Deloitte Center Energy Solutions, Houston, TX, USA. http://dupress.com/articles/internet-of-thingsiot-in-electricpower-industry/ 142. Yun M, Yuxin B (2010) Research on the architecture and key technology of Internet of Things (IoT) applied on smart grid. In: Proceedings of International Conference on Advances in Energy Engineering (ICAEE), Beijing, China, pp 69–72 143. Bui N et al (2012) The Internet of energy: a web-enabled smart grid system. IEEE Netw 26(4):39–45 144. Gore R, Valsan SP (2016) Big data challenges in smart grid IoT (WAMS) deployment. In: Proceedings of 8th international conference on communication systems and network (COMSNETS), Bengaluru, India, pp 1–6

References

63

145. Lounsbury D. Weighing the advantages of distributed and centralized energy storage. http://www.renewableenergyworld.com/articles/2015/04/weighing-theadvantagesof-distributed-energy-storage-and-centralized-energy-storage.html 146. Lee W et al (2016) Automatic agent generation for IoT-based smart house simulator. Neurocomputing 209:14–24. http://www.sciencedirect.com/science/article/pii/S09252 3121630580X 147. Energy Monitoring, Green Step, Gaithersburg, MD, USA. http://www.greensteptoday.com/ energy-monitoring 148. Rekaa SS et al (2018) Future effectual role of energy delivery: a comprehensive review of Internet of Things and smart grid. Renew Sustain Energy Rev 91:90–108. https://doi.org/10. 1016/j.rser.2018.03.089 149. Sanchez-Ayala G et al (2013) Current trends on applications of PMUs in distribution systems. In: Innovative smart grid technologies (ISGT), 2013 IEEEPES. IEEE, pp 1–6 150. IEEE Global History Network, The history of making the grid smart. http://www.ieeeghn. org/wiki/index.php/The_History_of_Making_the_Grid_Smart 151. Pacific Gas & Electric Company, Understanding radio frequency (RF). http://www.pge.com/ en/safety/systemworks/rf/index.page 152. Silicon Laboratories Inc, The evolution of wireless sensor networks. http://www.silabs.com/ SupportDocuments/TechnicalDocs/evolution-of-wireless-sensor-networks.pdf 153. International Confederation of Energy Regulators (2012) Experiences on the regulatory approaches to the implementation of smart meters, Annex 4–smart meters in Italy 154. Smart Grid Canada (2012) Global smart grid federation report 155. US Energy Information Administration, Independent statistics and analysis. http://www.eia. gov/countries/analysisbriefs/South_Korea/south_korea.pdf 156. The Worldwatch Institute, Vital signs, vol 20. http://books.google.com.ph/books?/Electrici tyDirective2009/75 157. Popescu S, Roberts C, Bento J. Canada—smart grid developments. https://www.smartgridscre.fr/media/documents/Canada_SmartGridDevelopments.pdf 158. Ministry of Economy Trade and Industry Japan (2010) The strategic energy plan of Japan 159. Watanabe C (2013) TEPCO aims to install smart meters 3 years earlier than planned. Bloomberg. http://www.bloomberg.com/news/2013-10-28/tepco-aims-to-installsmart-meters-3-years-earlier-than-planned.htm 160. China Internet Information Center, Policies and goals of energy development. http://www. china.org.cn/government/whitepaper/201210/24/content_26893107.htm 161. Innovation Center Denmark Shanghai (2013) Smart grid in China—a R&D perspective 162. Ancillotti E, Bruno R, Conti M (2013) The role of communication systems in smart grids: architectures, technical solutions and research challenges. Comput Commun 36(17– 18):1665–1697 163. Li W, Zhang X (2014) Simulation of the smart grid communications: challenges, techniques, and future trends. Comput Electr Eng 40(1):270–288 164. Galli S, Scaglione A, Wang Z (2011) For the grid and through the grid: the role of power line communications in the smart grid. Proc IEEE 99(6):998–1027 165. Usman A, Shami SH (2013) Evolution of communication technologies for smart grid applications. Renew Sustain Energy Rev 19:191–199 166. Yigit M et al (2014) Power line communication technologies for smart grid applications: a review of advances and challenges. Comput Netw 70:366–383 167. Su H, Qiu M, Wang H (2012) Secure wireless communication system for smart grid with rechargeable electric vehicles. IEEE Commun Mag 50(8):62–68 168. Akyildiz IF, Estevez DMG, Reyes EC (2010) The evolution to 4G cellular systems: LTEadvanced. Phys Commun 3(4):217–244 169. Mumtaz S et al (2014) Smart direct-LTE communication: an energy saving perspective. Ad Hoc Netw 13:296–311 170. Rossberg M, Schaefer G (2011) A survey on automatic configuration of virtual private networks. Comput Netw 55(8):1684–1699

64

1 Introduction

171. National Institute of Standards and Technology. NIST framework and roadmap for smart grid interoperability standards. http://www.nist.gov/public_affairs/releases/upload/smartgrid_int eroper-ability_final.pdf 172. Metke AR, Ekl RL (2010) Security technology for smart grid networks. IEEE Trans Smart Grid 1(1):99–107 173. Wang W, Lu Z (2013) Cyber security in the smart grid: survey and challenges. Comput Netw 57(5):1344–1371 174. Cyber Security Working Group (2010) Introduction to NISTIR 7628 guidelines for smart grid cyber security. http://csrc.nist.gov/publications/nistir/ir7628/introduction-to-nistir-7628.pdf 175. Schuelke-Leech BA et al (2015) Big data issues and opportunities for electric utilities. Renew Sustain Energy Rev 52:937–947 176. Benzi F, Anglani N, Bassi E, Frosini L (2011) Electricity smart meters interfacing the households. IEEE Trans Ind Electron 58(10):4487–4494 177. Kantarci ME, Mouftah HT (2013) Smart grid forensic science: applications, challenges, and openissues. IEEE Commun Mag 51(1):68–74 178. Amin SM (2011) Smart grid: overview, issues and opportunities. Adv Challenges Sens Model Simul Optim Control Eur J Control 17(5–6):547–567 179. Logenthiran T, Srinivasan D, Shun TZ (2012) Demand side management in smart grid using heuristic optimization. IEEE Trans Smart Grid 3(3):1244–1252 180. Khodayar ME, Wu H (2015) Demand forecasting in the smart grid paradigm: features and challenges. Electr J 28(6):51–62 181. Saad W, Han Z, Poor HV, Basar T (2012) Game-theoretic methods for the smart grid: an overview of microgrid systems, demand-side management, and smart grid communications. IEEE Signal Process Mag 29(5):86–105 182. Ming Z, Li S, Yanying H (2014) Status, challenges and countermeasures of demand- side management development direction. Sustain Cities Soc 11:22–30 183. Gelazanskas L, Gamage KAA (2015) Demand side management in smart grid: a review and proposals for future in China. Renew Sustain Energy Rev 47:284–294 184. Bayliss CR, Hardy BJ (2012) Current and voltage transformers. In: Transmission and distribution electrical engineering, 3rd edn, USA, Newnes, Massachusetts pp 157–159 185. Premaratne UK et al (2010) An intrusion detection system for IEC 61850 automated substations. IEEE Trans Power Deliv 25(4):2376–2383 186. Song IK et al (2013) Operation schemes of smart distribution networks with distributed energy resources for loss reduction and service restoration. IEEE Trans Smart Grid 4(1):367–374 187. Song IK, Yun SY, Kwon SC, Kwak NH (2013) Design of smart distribution management system for obtaining real-time security analysis and predictive operation in Korea. IEEE Trans Smart Grid 4(1):375–382 188. Muttaqi KM, Nezhad AE, Aghaei J, Ganapathy V (2014) Control issues of distribution system automation in smart grids. Renew Sustain Energy Rev 37:386–396 189. Su W, Wang J (2012) Energy management systems in micro-grid operations. Electr J 25(8):45– 60 190. Ree JDL, Centeno V, Thorp JS, Phadke AG (2010) Synchronised phasor measurement applications in power systems. IEEE Trans Smart Grid 1(1):20–27 191. Shepard DP, Humphreys TE, Fansler AA (2012) Evaluation of the vulnerability of phasor measurement units to GPS spoofing attacks. Int J Crit Infrastruct Prot 5(3–4):146–153 192. Kezunovic M (2011) Smart fault location for smart grids. IEEE Trans Smart Grid 2(1):11–22 193. Benysek G, Strzelecki R (2011) Modern power-electronics installations in the Polish electrical power network. Renew Sustain Energy Rev 15(1):236–251 194. Shafiullah GM, Amanullah MTO, Shawkat Ali ABM et al (2013) Potential challenges of integrating large-scale wind energy into the power grid—a review. Renew Sustain Energy Rev 20:306–321 195. Carrasco JM, Franquelo LG, Bialasiewicz JT et al (2006) Power-electronic systems for the grid integration of renewable energy sources: a survey. IEEE Trans Ind Electron 53(4):1002–1016

References

65

196. Hosseini SH, Kangarlu MF, Sadigh AK (2009) A new topology for multi-level current source inverter with reduced number of switches. In: Proceedings of ELECO, Turkey, Bursa, pp 273–277 197. Blaabjerg F, Chen Z, Kjaer SB (2004) Power electronics as efficient interface in dispersed power generation systems. IEEE Trans Power Electron 19(5):1184–1194 198. Hansen S, Nielsen P (2002) In: Kazmierkowski MP, Krishnan R, Blaabjerg F (eds) Power quality and adjustable speed drives, control in power electronics: selected problems. Academic Press, California, USA, pp 461–482 199. Colak I, Fulli G, Bayhan S et al (2015) Critical aspects of wind energy systems in smart grid applications. Renew Sustain Energy Rev 52:155–171 200. Hajibeigy M, Farsadi M, Asl KB (2012) A modified structure of hybrid active dc filter in HVDC system. Int J Tech Phys Probl Eng 4(10):11–16 201. Xu L, Yao L (2011) DC voltage control and power dispatch of a multi-terminal HVDC system for integrating large offshore wind farms. IET Renew Power Gener 5(3):223–233 202. Kulkarni DB, Udupi GR (2010) ANN-based SVC switching at distribution level for minimalinjected harmonics. IEEE Trans Power Deliv 25(3):1978–1985 203. Gavrilovic A, Williams WP, Thanawala HL, Young DJ (2003) In: Laughton MA, Warne DJ (eds) Reactive power plant and FACTS controllers. Electrical engineer’s reference book. Newnes, Massachusetts, USA, pp 1–40 204. Jiang S, Gole AM, Annakkage UD, Jacobson DA (2011) Damping performance analysis of IPFC and UPFC controllers using validated small-signal models. IEEE Trans Power Deliv 26(1):446–454 205. Eltigani D, Masri S (2015) Challenges of integrating renewable energy sources to smart grids: a review. Renew Sustain Energy Rev 52:770–780 206. Kolenc M, Papiˇc I, Blažiˇc B (2014) Coordinated reactive power control to achieve minimal operating costs. Int J Electr Power Energy Syst 63:1000–1007 207. Palizban O et al (2014) Microgrids inactive network management—Part I: hierarchical control, energy storage, virtual power plants, and market participation. Renew Sustain Energy Rev 36:428–439

Chapter 2

The Infrastructure of Smart Grid

The smart grid is built upon the foundation of the existing power system, utilizing advanced monitoring, control, and management technologies. It aligns with the “smarter power system” vision outlined in the International Energy Agency’s “Smart Grid Technology Roadmap” [1]. The smart grid represents the progressive development and enhancement of the current transmission control center and distribution control center. Consequently, the power system itself, along with its automation system, plays a pivotal role in facilitating the evolution of the smart grid.

2.1 The Composition of Power System 2.1.1 Power System Structure The generator plays a crucial role in the energy conversion process within the power system. It transforms various primary energy sources, such as petrochemical energy, water energy, nuclear energy, wind energy, and solar energy, into electrical energy. This electrical energy is then transmitted and distributed to users through a network of transformers, converters, and power lines. Users can consume this power for various purposes. The power system is a comprehensive entity consisting of interconnected components such as generators, transformers, converters, power lines, and various electrical equipment [2]. This unified system is responsible for generating, transforming, transmitting, distributing, and facilitating the consumption of electric energy. Figure 2.1 visually represents the structure and interconnections of these components within the power system. The power system is divided into four interconnected systems: generation, transmission, distribution, and consumption. These systems work together to ensure the reliable supply of electrical power. © Chemical Industry Press 2023 X. Zeng and S. Bao, Key Technologies of Internet of Things and Smart Grid, Advanced and Intelligent Manufacturing in China, https://doi.org/10.1007/978-981-99-7603-4_2

67

68

2 The Infrastructure of Smart Grid Power System Grid

Fan

500kV

Water

Water

±500kV

110kV

Fire

Fire

220kV

Nuclear

110kV

Fire

Fire

110kV

Fire

10kV 35kV 380/220V S.H.P 380/220V

Microgrid Pho

E.S.

6kV

Legend Generator Phaser Electric motor Autotransform Appliance

双绕组变压器 三绕组变压器 Steam Turbine/Water Turbine Inverter

Fig. 2.1 Power system and grid structure [1]. Note SHP: Small Hydropower; Fire: Thermal power; Water: hydropower; Nuclear: Nuclear energy; Hho: Photovoltaic; E.S.: energy storage

With the advancement of green renewable energy sources like wind energy and photovoltaics, renewable energy power generation has become a significant component of the power system. This transition towards renewable energy is a notable development within the system. Furthermore, the emergence of energy storage technology has led to a shift from traditional one-way power flow to two-way power exchange. This two-way power exchange is a distinctive characteristic of the smart grid, distinguishing it from the traditional grid structure. To ensure the safe and efficient operation of the power system, continuous monitoring is essential. Power automation systems, such as generator set control, power transmission and distribution automation, and power consumption management systems, are required to enable secure, efficient, and scientifically informed monitoring, control, scheduling, and management of the grid. These systems play a crucial role in maintaining the overall stability and performance of the power system.

2.1 The Composition of Power System

69

2.1.2 Power Generation and Grid Structure (1) Generation The power generation system is responsible for producing electric energy that is utilized by users. It encompasses various methods of generating electricity based on different primary energy sources. The primary energy power generation can be categorized into several types: thermal power, hydropower, nuclear power, geothermal power, wind power, tidal power and photovoltaic (solar) power. Thermal power uses the heat energy of coal, oil, natural gas and other fuels to drive steam turbines and drive generators to generate electricity. The power generated by gas turbines and diesel generators also belongs to thermal power generators, which have fast start-up and can meet the requirement of peak loads. Hydropower uses water energy to generate power, and the power of hydroelectric power is proportional to the potential energy generated by flow and head. Hydropower plants (systems) include run-of-river power plants, dam power plants and pumped storage power plants. Tidal power and wave power are also a type of hydropower. Tidal power plants generally build dikes in bays or river mouths to store water. When the tide is high, seawater is introduced, and the water level difference inside the dam is used to generate power; when the tide is ebbing, the water level difference between the inside and outside of the dam is used to discharge water to generate electricity. Wave letting is a method of generating power that converts ocean waves into electrical energy. These two power generation methods are less stable. Nuclear power is the use of nuclear energy to generate power, and nuclear power is a clean energy source. In order to improve the economy and safety of nuclear power, nuclear power generation generally operates at a constant power load. And the wind power is the use of natural wind to generate power. There are two ways of solar power generation, one is light/heat/electricity conversion, which is to convert the heat energy collected by the sun into steam to drive a turbo generator, the other is to convert light into electrical energy, namely photovoltaic. Currently commonly used solar energy refers to photovoltaic power generation, and its most important device is a solar cell. Solar power is currently the cleanest and most widely used green energy. (2) Grid structure Indeed, the grid system often consists of multiple interconnected sub-grids, forming a large-scale grid network. As a result, the grid structure can be categorized into different layers, each serving specific functions. The general division of the grid includes transmission network, secondary transmission network, high and medium voltage distribution network and low voltage distribution network, as depicted in Fig. 2.2.

70

2 The Infrastructure of Smart Grid

Large Power Plant Other Power System

Primary Transmission network above 220kV

Extra-large User

Small and Medium Power Plants

Secondary transmission network

Secondary transmission network Medium and high voltage distribution network

Large User

Medium load users

Low voltage distribution network 380/220V

Fig. 2.2 Hierarchical structure of grid

The transmission network consists of high-voltage power lines, such as those operating at 220 kV or higher in China. These power lines form the primary transmission network, serving as the core infrastructure. They connect large power plants, high-capacity users, and neighboring power grids. The secondary transmission network operates at a lower voltage compared to the primary transmission network, typically ranging from 110 to 220 kV in China. It is interconnected with the upper side of the primary transmission network and linked to the high and medium voltage distribution network on the lower side. This network forms a regional grid and connects regional power plants and large consumers. On the other hand, the distribution network supplies power to medium-load and small-load users. In China, the distribution network refers to the transmission network

2.2 Power Automation System

71

operating at voltages of 10–35 kV. This network serves as the interface between the secondary transmission network and the end-users, facilitating the distribution of electricity within specific regions. Furthermore, the low-voltage distribution network in China refers to the transmission network operating below 1 kV. It directly provides power energy to users, serving as the final stage of distribution where electricity is delivered to individual households, businesses, and other low-power consumers.

2.2 Power Automation System 2.2.1 The Goals, New Technologies and Contents of Power Automation (1) The Goals and new technologies The power automation system is responsible for controlling and managing the power grid. Its primary objective is to ensure the smooth operation of the entire power system, delivering reliable and cost-effective power to users while maintaining high quality standards. In the event of a power system failure, the automation system aims to swiftly identify and isolate the fault, and make efforts to restore the normal operation of the system promptly. The power automation system plays a vital role in ensuring the smooth functioning of the power grid. The power system consists of numerous devices involved in power generation, transmission, transformation, and distribution. These devices are interconnected through power lines of varying voltage levels, creating a vast and intricate system. Consequently, effectively managing and controlling these devices to ensure their safe, high-quality, and cost-effective operation poses a significant challenge. To address this challenge, automatic monitoring devices and automation systems are employed to monitor and control the power system. By utilizing automation, the power system can be managed more efficiently, enhancing safety, quality, and cost-effectiveness. The automation of the power system refers to the use of various devices or systems with automatic detection, decision-making and control functions, through the signal system and data transmission system to automatically monitor, control and adjust each component, partial system or the whole system of the power system on-site or remotely, to ensure the safely, reliably and economically operation of the power system and provide qualified electric energy to users [3]. Managing a power system involves monitoring and regulating various parameters, including system frequency, node voltages, line currents, and power levels. Since the power system is a complex interconnected network, it is necessary to not only adjust individual components but also optimize the entire system or specific subsystems.

72

2 The Infrastructure of Smart Grid

This requires information exchange, functional complementarity, and optimization techniques to achieve efficient and reliable power system operation. Faults in the power system occur randomly, making the isolation and control of these faults complex. The consequences of system instability can be catastrophic. To address this, real-time monitoring and accurate measurement using automation systems are essential. These systems enable quick detection and control of disturbances or faults, preventing system instability. Additionally, automation systems play a crucial role in restoring normal operation swiftly after a system failure or power outage. Their use ensures the reliability and stability of the power system, minimizing downtime and maximizing power supply to users. The current power automation system exhibits several key characteristics. Firstly, the integration of automation systems within substations is widespread. This means that a single set of automation systems or devices can perform the tasks previously accomplished by multiple single-function systems or devices. Furthermore, the implementation of software with real-time online analysis capabilities for power systems has become prevalent. Dispatching systems now possess the ability to conduct telemetry, remote signaling, remote control, and remote adjustment functions. This integration of functions allows for efficient management and control of the power system. The application of new information and communication technologies has played a significant role in facilitating information sharing among dispatching centers at various levels. This ensures coordinated operations and improved communication between different entities involved in power system management. The current rapid development of power automation technology and the emergence of new technologies are primarily evident in the following aspects: (a) Intelligent Control The control of the power system has progressed from traditional single-input and single-output control to intelligent control methods. This includes linear optimal control, nonlinear control, and coordinated control of multi-machine systems. Intelligent control is characterized by its ability to handle nonlinear and variable dynamics in large-scale power systems, optimize multiple objectives, and ensure robustness under different operating and fault modes. It also emphasizes the need for coordination among local controllers and controllers in different locations. These characteristics enable intelligent control to effectively manage the complexities of power system dynamics and ensure stable and efficient operation. Intelligent control is used to solve complex control problems that traditional methods struggle with. It is particularly effective for systems with model uncertainty, strong nonlinearity, and robustness requirements. Intelligent control offers adaptive, self-learning, and self-organizing capabilities. Design methods include expert systems, artificial intelligence, fuzzy control, machine learning, and autonomous control. These methods enable the control system to handle uncertainties and optimize control strategies. Intelligent control provides flexible and robust control solutions for various applications.

2.2 Power Automation System

73

(b) FACTS and DFACTS FACTS (Flexible Alternative Current Transmission System) is an important part of the power transmission system. It utilizes power electronic devices to regulate and control key parameters such as voltage, phase difference, and reactance. By doing so, FACTS enables more reliable and controllable power transmission with higher efficiency. FACTS is an integrated technology that combines power electronics, microprocessors, and control technologies to improve the reliability, stability, and power quality of high-voltage transmission systems. Some of the existing FACTS devices include Advanced Static Var Generator (ASVG), Thyristor Controlled Series Compensation (TCSC), Unified Power Flow Controller (UPFC), Static Var Compensator (SVC), Thyristor Controlled Phase Angle Regulator (TCPAR), Solid-State Circuit Breaker, Subsynchronous Resonance (SSR) Controller, Superconducting Magnetic Storage System, and Battery Energy Storage System. DFACTS is the FACTS technology used in power distribution systems. This technology provides a variety of integrated solutions for power quality, and can be applied to medium and high voltage distribution networks and low voltage distribution networks (user grid). (c) EMS and dynamic safety monitoring system with unified clock. (2) The Contents of Power Automation Power automation is an integral component of a power system, functioning as a secondary system that primarily encompasses the automatic monitoring and dispatching of power equipment and systems, be it the entire system or specific segments. This intricate framework comprises several subsystems, with each one dedicated to accomplishing one or multiple functions. In terms of effective power system operation management, the power automation system can be categorized into distinct areas, namely Grid Dispatching Automation, Power Plant Integrated Automation, Substation Integrated Automation, and Distribution Integrated Automation. (a) Grid dispatching Automation System The functions of power automation can be succinctly summarized as follows: firstly, it facilitates the dispatching of the overall operation mode of the power system, ensuring the safe, high-quality, and cost-effective provision of power energy to users during normal operations. Additionally, it undertakes the crucial responsibility of managing loads during power shortages, efficiently addressing faults, and swiftly restoring normal power supply in fault states. Specifically, the role of grid dispatching automation is to achieve automated power dispatching, effectively carrying out dispatching tasks with utmost efficiency. The dispatch automation system primarily consists of two key components: the master station (MS) system, installed within the dispatch center, and the remote unit (RUT), strategically positioned within power plants and substations. The basic structure of this system is visually depicted in Fig. 2.3.

74

2 The Infrastructure of Smart Grid

Dispatch Center Superior MS

Subordinate MS MS

RTU

RTU

RTU

RTU

Fig. 2.3 Basic structure of scheduling automation

The Remote Terminal Unit (RTU) serves as a pivotal component responsible for on-site data collection in power plants and substations. It executes commands issued by the master station and acquires vital operating parameters and statuses of power equipment, encompassing voltage, current, active and reactive power, frequency, circuit breaker status (on/off), relay protection information, and more. This comprehensive data is then transmitted back to the master station for analysis and monitoring. Moreover, the RTU acts as the recipient of dispatching commands from the master station, such as circuit breaker control signals, power adjustment signals, and equipment setting values. Following the execution of these commands, the RTU relays the operation information back to the master station, ensuring effective communication and coordination within the automation system. (b) Substation integrated automation system The Substation Integrated Automation System encompasses various subsystems, including substation monitoring, protection, and voltage and reactive power control. The substation monitoring system performs critical functions such as data collection of analog quantities, status quantities (switching quantities), and power parameters. It also includes features like Sequence of Events (SOE) recording and analysis, fault recording and fault location identification, harmonic analysis, monitoring of substation operations, human–machine interaction, and data exchange with communication and dispatching systems. The protection system plays a crucial role in safeguarding transformers, busbars, and other equipment within substations. It ensures the timely detection and appropriate response to any abnormal or fault conditions to prevent potential damage and ensure the reliability and integrity of the substation. Integrated control of voltage and reactive power focuses on automating the regulation of voltage and reactive power within the substation. This involves automatic adjustment of the tap position of on-load tap changers to maintain desired voltage levels. It also encompasses the automatic control of switching and operation of reactive power compensation equipment, ensuring optimal reactive power management and stability of the substation’s electrical parameters.

2.2 Power Automation System

75

(c) Distribution Automation Distribution automation comprises several essential components, including distribution grid scheduling automation, substation (spot) automation, feeder automation (FA), equipment management, geographic information system (GIS), power management automation, distribution system management, and distribution grid analysis system.

2.2.2 Integrated Automation of Substation The substation is a vital component of the power system, responsible for voltage transformation, power exchange, and energy distribution. It consists of primary equipment (such as transformers, circuit breakers, and busbars) and secondary equipment (including monitoring instruments, control systems, and relay protection devices). The primary equipment ensures stability, reactive power balance, and overvoltage protection with the help of additional components like synchronous condensers and shunt reactors. The secondary equipment enables monitoring, control, and protection of the substation. The substation automation system includes integrated automation, remote measurement and control, relays protection and other intelligent technologies. Substation integrated automation involves the strategic integration of secondary equipment within the substation through optimized design and functional combination. By leveraging computer technology, electronic technology, communication technology, and signal processing technology, this automation system enables automatic monitoring and control of primary equipment, transmission lines, and distribution systems. It encompasses various tasks such as measurement, control, protection, and information exchange with the dispatch center. Substation integrated automation involves collecting analog, status, pulse, and non-power signals from protection and automation devices, as well as remote equipment like RTUs. The collected data is processed and optimized, and then regrouped over a local area network or communication bus. This system enables integrated monitoring, control, and scheduling of substations according to predetermined procedures. It functions as an information processing and automatic control system with features like integrated functions, hierarchical structure, computerized operation monitoring, networked communication, intelligent management, and digital measurement. (1) Functions of Substation Integrated Automation The substation integrated automation system serves a range of essential functions aimed at ensuring the smooth and safe operation of the substation. These functions encompass the collection of electrical quantities within the substation, as well as the monitoring, control, and adjustment of electrical equipment. The primary goal is to maintain the normal functioning and safety of the substation. In the event of a fault, specialized devices such as relays protection and fault recording systems come into

76

2 The Infrastructure of Smart Grid

play, collecting and monitoring transient electrical quantities. These devices work swiftly to isolate the fault and facilitate the recovery operation following the incident. Substation integrated automation has the following specific functions: (a) Relays Protection The main protections are: transmission lines, power transformers, busbars, capacitors, automatic line selection for small current grounding systems, automatic reclosing, etc. (b) Monitoring Real-time data acquisition and processing: including analog quantity, status quantity (switch quantity), pulse and digital quantity, etc. The analog quantities to be collected mainly include: voltage, line voltage, current, active power, and reactive power of each section of bus; current, active and reactive power of the main transformer; capacitor current, reactive power, current, voltage, and Power, frequency, phase, power factor, etc.; main transformer oil temperature, DC power supply, station transformer voltage, etc. The status quantities (switching quantities) to be collected including: substation circuit breaker position status, isolating switch position status, relays protection action status, synchronous detection status, on-load transformer tap position status, primary equipment operation alarm signal, grounding signal, etc. The operation monitoring function is mainly to automatically monitor the operating conditions and equipment status of the substation, that is, to monitor the displacement of various switching values and various analog quantities of the substation. By monitoring the displacement of the switch, it is possible to monitor the position and action status of circuit breakers, isolating switches, grounding switches, and transformer taps in substations, monitor the action status of relays protection and automatic devices, and their action sequences, etc. Monitored analog quantities is divided into normal measurement and over-limit alarm, recall of analog quantity changes during accidents, etc. Fault recording and ranging functions. Since the voltage level of the transmission line is high, the transmission line is also long, and the fault affects a large area, when a line fault occurs, the fault point should be found out as soon as possible, so as to shorten the maintenance time, restore power supply as soon as possible, and reduce losses. Therefore, it is necessary to use fault recording and fault distance measurement to solve the problem of fault finding and processing. Fault sequence record and fault recall function. The fault sequence record is the record of the action sequence of relays protection, segmentation device, circuit breakers, etc. in the substation when an fault occurs. The time at which events are recorded should be very precise, usually on the order of milliseconds. Fault sequence records are very important for analyzing faults, evaluating relays protection and automatic devices, and the action status of circuit breakers. Faults recall is to continuously measure and record some main analog quantities in substations (such as lines, current on each side of the main transformer, active power, main bus voltage, etc.) within a period of time before and after the fault. This record can understand the status of the

2.2 Power Automation System

77

system or a certain circuit before and after the accident, and plays a very important auxiliary role in the analysis and handling of the accident. Lockout function for control and safe operation. Locally or remotely perform on/ off operations for circuit breakers and isolating switches, to adjust transformer taps, and perform on/off operations to capacitor banks. All the above operations can be mutually operated and blocked locally or remotely. Data processing and Recording functions. The generation and storage of historical data is the main objective of data processing. To meet the requirements of relays protection and substation management, statistics and processing must be carried out on the records of main transformer, busbar, circuit breaker action control operation and revision value. (c) Automatic Control Device The substation integrated automation system is equipped with a range of automatic control devices. These devices serve specific purposes within the system and contribute to its overall performance. Some key automatic control devices commonly include voltage and reactive power integrated control devices, low-frequency load control devices, backup power self-input control devices, and small current grounding line selection devices. The integrated control to substation voltage and reactive power uses on-load transformers and busbar reactive power compensation capacitors and reactors to automatically adjust local voltage and reactive power compensation, so that the voltage deviation of the load-side busbar is within a limited range. When the frequency of the power system drops due to the shortage of active power caused by a fault, the low-frequency load shedding device should be able to automatically disconnect part of the load immediately to prevent further frequency reduction and ensure the stable operation of the power system. When the working power fails to supply power due to failure, the automatic device should be able to quickly switch on the backup power automatically or switch the user to the backup power. Typical backup automatic switching devices include single-bus incoming line backup switching, segmentation circuit breaker backup switching, transformer backup switching, incoming line and bridge circuit breaker backup switching. When single-phase grounding occurs in the small current grounding system, the grounding protection should be able to correctly select the grounding line or busbar and grounding phase, and give an alarm in time. (d) RTU and Data Communication The communication function of substation integrated automation includes internal on-site communication and communication between automation system and superior dispatching. The integrated automation system must have all the functions of RTU, and can transmit the collected analog and status quantities, event sequence records and other information to the dispatch center, and can also receive various operations, controls, and modified values etc. issued by the dispatch center.

78

2 The Infrastructure of Smart Grid

(2) Composition of Substation Integrated Automation System From a structural perspective, the evolution of the substation integrated automation system has followed a progression from the process of centralized, hierarchical distributed, decentralized and centralized, and completely decentralized and distributed. In the centralized integrated automation system, the protection devices, monitoring hosts, and data acquisition systems are meticulously configured based on the scale of the substation. These components are installed within the main control room, adhering to a centralized structure. Through the use of cables, critical information regarding the main transformer, incoming and outgoing lines, and the operating status of all electrical equipment is transmitted to the protection devices or monitoring hosts in the main control room. These systems also establish communication with the main host at the dispatch center, facilitating seamless data exchange. Moreover, the local host computer located in the substation plays a significant role by providing essential functions such as local display and control. However, it is important to acknowledge the limitations of the centralized integrated automation system. These drawbacks encompass the necessity for dual-machine redundancy, a complex software structure that poses challenges for modification, upgrading, and debugging, and a lack of flexibility in software configuration. Additionally, the system’s design specificity restricts its versatility, making it primarily suitable for small and medium-sized substations. (a) Hierarchical distribution As per the IEC61850 substation communication network and system protocol, the substation communication system is structured into three layers: the substation layer, isolation layer, and equipment layer. Within the substation integrated automation system, various functions such as relays protection, reclosing, fault recording, and fault ranging are often combined to form a protection unit. Similarly, measurement and control functions are combined to form a control unit. These protection and control units are referred to as isolation layer units, which operate at the intermediate layer. The equipment layer primarily encompasses the primary equipment found in the substation, including transformers, circuit breakers, isolating switches, and associated auxiliary contacts, as well as voltage and current transformers. This layer forms the foundation of the substation’s operational infrastructure. A hierarchical distribution structure is depicted in Fig. 2.4, illustrating the arrangement of these layers within the substation integrated automation system. The isolation layer in the substation integrated automation system is organized based on the primary equipment, specifically divided according to the circuit breaker isolation. It comprises three main parts: measurement, control, and relays protection. Within the isolation layer, the measurement and control components have distinct responsibilities. The measurement part is accountable for gathering data, monitoring operations, controlling circuit breakers, and implementing interlocking mechanisms. Additionally, it records event sequences for future analysis. The protection part is

2.2 Power Automation System

79

local monitoring host Communication host (front-end processor)

dispatch center Substation layer Isolation layer DAU

ICU

DAU

ICU

Fig. 2.4 Layered distribution structure of substation integrated automation system. Note DAU: Data Acquisition Unit; ICU: Integrated Control Unit

dedicated to safeguarding and recording waveforms for transformers and capacitors under its jurisdiction. To facilitate communication and coordination, the different units within the isolation layer are interconnected with the substation layer through a communication bus. The substation layer is composed of one or more hosts for monitoring, control and other operations. (b) Combination of Decentralization and Centralization This structural approach is specifically designed for the electrical primary circuit or for achieving electrical isolation, such as connecting an outgoing line with a transformer. It involves integrating various data acquisition, monitoring units, and protection mechanisms within the isolation layer, all housed in the same chassis and employed for adjacent primary equipment. Through this design, each isolation unit operates independently and can communicate with the host computer via a cable. Typically, the functionalities provided within this isolation layer are not reliant on the communication network, resulting in a decentralized structure. Figure 2.5 illustrates the structural diagram depicting the combination of decentralization and centralization. This approach offers several key advantages, including simplified configuration of the secondary part of the substation, reduced workload during construction, installation, and commissioning, streamlined interconnection between secondary equipment, and a reliable, high-performance system. Furthermore, it enables flexible configuration options, facilitates maintenance procedures, and allows for convenient future modifications. (c) Completely decentralized distributed A fully decentralized hardware structure in a system refers to a setup where primary equipment, such as transformers, circuit breakers, and busbars, serve as the installation units. Various units, including protection, control, input/output, and locking mechanisms, are scattered and installed on the switchgear of the primary equipment. The main control unit, situated in the main control room, establishes communication with the distributed units through a field bus. Additionally, the main control unit is connected to the monitoring host via a network.

80

2 The Infrastructure of Smart Grid

local monitoring host Communication host (front-end processor)

Dispatch Center

DPMCU

DPMCU

switch compartment

MTPMCU

HVLPU

RPC smart meter

smart meter

Master Control Room

Fig. 2.5 Structural diagram of automation system combining decentralization and centralization [3]. Note DPMCU: Distributed protection measurement and control unit; MTPMCU: Main transformer protection measurement and control unit; HVLPU: High Voltage Line Protection Unit; RPC: reactive power control

The implementation of this completely decentralized integrated automation system can be achieved in two modes. The first mode involves relatively independent protection, where each unit functions independently for protection purposes. The second mode encompasses the integration of measurement and control, where units collaborate and combine their functions for comprehensive measurement and control operations. (3) Communication Protocol The communication in the substation integrated automation system requires a communication protocol, and the commonly used protocols can be divided into a cyclic telecontrol protocol and a question-and-answer telecontrol protocol. At present, IEC has formulated the substation communication network and system standard IEC61850, which provides strict regulations on the communication network and communication protocol of the substation automation system, and China also implements this standard. For the substation automation system, IEC has published the IEC60870-5-103 communication protocol for substation protection devices. In addition, IEC has also promulgated two telecontrol communication protocols, which are IEC6087-5-101 and IEC6087-5-104. China also has corresponding relevant standards have been established, and these standards have been widely used.

2.2.3 Power Dispatch Automation System Power dispatch plays a vital role in coordinating the functioning of power generation, transmission, transformation, and distribution equipment within the power system. It encompasses crucial tasks such as managing operations, resolving issues, and ensuring the secure and cost-effective operation of the power system. By diligently

2.2 Power Automation System

81

carrying out these responsibilities, power dispatch ensures the uninterrupted supply of high-quality electrical energy to users, meeting stringent quality standards. The task of power dispatching can be summarized as follows: monitoring and controlling the operational state of the entire power system to ensure that it meets the requirements for safe, high-quality, and cost-effective power supply to consumers under normal conditions. In addition, power dispatching is responsible for efficient load management during power shortages and swiftly addressing and restoring power in case of accidents or emergencies. The power dispatch automation system seamlessly integrates computer technology, measurement and control systems, and communication technology to achieve the automation of power system dispatching management. The main tasks of power dispatching include load forecasting, power generation planning, switching operations, fault handling, and economically dispatching power resources. Power dispatching also follows a hierarchical control strategy, where different levels of dispatchers coordinate and manage the power system. (1) Functions of power dispatch automation system Power dispatching automation systems are designed to efficiently dispatch, control, and manage the power grid. These systems can vary in specifications and functions depending on the specific composition and conditions of the power grid. The main functions of a power dispatching automation system typically include: • • • • • • • • • •

Supervisory Control and Data Acquisition (SCADA) State Estimation Network Topology Analysis Load Forecast Load Flow Optimum Security Analysis Var/Voltage Control Automatic Generation Control Ecnomical Dispatching Dispatcher Training Simulator.

The above functions have be implemented by software, and the software of the master station of the power dispatching automation system is shown in Table 2.1. Table 2.1 Master station software of power dispatching automation system Energy management system (EMS) Other advanced application (PAS (power analysis system)) Network topology analysis State estimation (SE) SCADA Support software (DataBase, HMI, API) System software (OS, software operating environment)

82

2 The Infrastructure of Smart Grid Dispatch Centre

C.N.

P.E.

A.D

O.S.a.P. RTU

C.N.

RTU O.S.a.P.

RTU O.S.a.P.

P.E.

P.E. C.a.A.P. Power Plant

C.a.A.P.

C.a.A.P.

A.D.

Substation

A.E.

Fig. 2.6 Structural diagram of power dispatching automation system. Note C.N.: Communication Network; P.E.: Power equipment; A.D.: Automatic Device; O.S.a.P.: Operating Status and Parameters; C.a.A.P.: Control and Adjustment Parameters

(2) Configuration and Structure of Dispatch Automation System The power dispatch automation system consists of three main parts: the master station system (computer information system), the communication system, and the remote terminal unit (RTU). The RTU gathers operational data, the communication system facilitates data exchange between the master station and RTU, and the master station system processes the information for various dispatching functions, shown as Fig. 2.6. In traditional dispatch automation systems, the communication network typically consists of a dedicated system. This network enables data communication, including the exchange of operating status and parameters of power equipment, control and adjustment parameters, as well as voice and image communication. It’s important to note that the communication network used by substations and power plants is specific to the power system and is separate from other communication networks. (3) Dispatch Center The dispatch center, also known as the main station system, primarily consists of a computer system and data communication infrastructure. In most cases, RTUs located in power plants and substations engage in bidirectional data communication with the computer master station through point-to-point communication. The master station system typically comprises multiple computers and forms a dedicated computer network system for monitoring, control, and scheduling purposes. With technological advancements, the main station system has evolved from a centralized system consisting of single or dual machines to a distributed system with multiple computers. Each computer within the master station system performs specific functions, such as data storage and processing in data server systems, as well as various workstations for tasks like load forecasting, power flow calculation, monitoring, control, and adjustment.

2.2 Power Automation System

83

To ensure the synchronization of the power grid, the master station system is usually equipped with a GPS (or Beidou) system, which helps maintain accurate time uniformity throughout the network. (4) RTU RTU, or Remote Terminal Unit, is an essential automation device used to monitor and control power equipment in power plants and substations within power systems. It collects various parameters that represent the operating status of the power system in the plant or substation and transmits this data to the master station. Additionally, it executes control and adjustment commands sent by the master station to the plant or substation. Modern RTUs are considered as computer-based measurement and control devices. They are equipped with multiple input/output channels, possess built-in software functions, offer robust data processing capabilities, and can maintain, set parameters, and adjust functions locally or remotely. From a hardware perspective, RTUs have evolved from single CPU configurations to multi-CPU distributed systems, enabling enhanced functionality and performance. Some key functions of RTUs include: (a) Four Tel-functions Tele-measurement (China call it as YC): This involves remote measurement and collection of various analog operating parameters in power plants or substations. Tele-indication (China call it asYX): It includes the remote measurement of status signals from equipment in plants or substations, such as switch and circuit breaker status, protection relay actions, and automatic device status. A single RTU can collect multiple status signals, often numbering in the hundreds or even thousands. Tele-command (China call it as YK): This refers to the execution of remote commands. Based on dispatching commands received from the master station, the RTU executes commands to change the equipment’s status, such as turning equipment on/off, or opening and closing switches/circuit breakers. Tele-adjusting (China call it as YT): This involves remote adjustment. The RTU executes commands received from the master station to change parameters related to the operation of equipment, such as adjusting the tap position of a transformer. (b) Data Communication The RTU performs data communication with the master station based on a specific communication protocol. (c) Local functions, self-diagnosis functions, etc.

84

2 The Infrastructure of Smart Grid

2.2.4 Distribution Automation System In the power system, the distribution grid serves as the final segment for power generation, transmission, transformation, and distribution. The distribution system is responsible for transmitting the generated electric energy from the power grid to endusers. The distribution system comprises primary distribution equipment, including feeders, step-down transformers, circuit breakers, and various switches. Additionally, the distribution system incorporates secondary equipment, such as protection devices, automation devices, measuring and metering instruments, as well as communication and control devices. In the power system of China, the distribution grid operates at voltage levels generally below 110 kV. The distribution grid can be categorized based on different voltage levels. A distribution grid with a voltage level above 35 kV is referred to as a high-voltage distribution grid. A distribution grid with a voltage level of 10 kV is known as a medium-voltage distribution network. Lastly, a distribution grid with a voltage level of 0.4 kV (380/220 V) is commonly referred to as a low-voltage distribution grid. Due to the distinct characteristics and disparities between urban and rural areas, significant differences exist in the distribution grid’s capacity, number of transformers, load distribution, reliability of power supply, and recovery time after a fault. Therefore, it is necessary to employee distribution automation to improve the reliability and quality of power supply. Improving the quality of power supply requires enhancing the equipment and operation of the power system. A well-designed distribution grid structure and the automation of protection, monitoring, and control systems are crucial for reliable and high-quality power supply. Leveraging information and communication technology plays a key role in monitoring and managing the grid effectively, leading to improved reliability and quality of power supply. Distribution automation, also known as a power distribution management system (DMS), is an automation system that utilizes electronic technology, communication technology, and information technology to enable the monitoring, protection, control, power consumption, and distribution management of the power distribution system under normal and fault conditions. The DMS system shares a fundamental structure similar to an Energy Management System (EMS), with the distinction that it does not monitor or manage power plants. The DMS system encompasses the following essential functions: (1) SCADA In the distribution grid, SCADA (Supervisory Control and Data Acquisition) plays a crucial role in collecting real-time data from monitoring devices, processing the data, and monitoring and controlling the distribution grid. The monitoring devices typically consist of RTUs (Remote Terminal Units) in substations, TTUs (Transformer Terminal Units) for monitoring distribution transformers, and FTUs (Feeder Terminal Units) for feeder terminal devices. The main functions of the distribution SCDA system include data acquisition, telemetry, remote signaling, remote

2.2 Power Automation System

85

control, remote adjustment, alarm, event sequence recording, statistical calculation and reports, etc. (2) Distribution Substation Automation Distribution substation automation plays a critical role in achieving real-time data collection, monitoring, and control of distribution substations, as well as facilitating communication with the distribution dispatch center through SCADA. (3) Feeder Automation Feeder automation focuses on automating the distribution lines within the power system. It involves monitoring the status of sectional switches and tie switches, as well as real-time monitoring of the current and voltage of the feeder. Feeder automation enables remote or local opening and closing operations of line switches. In the event of a fault, record the fault information, and automatically identify and isolate the faulty section of the feeder, and quickly restore power to the non-faulty area. (4) User Automation User automation encompasses two important aspects: load management and power consumption management. Load management involves controlling and managing the energy consumption of users according to specific needs and requirements, and enables dispatchers to formulate effective load control strategies and plans to ensure the optimal utilization of available power resources. Power consumption management focuses on the accurate and efficient measurement of user energy consumption, as well as the associated billing processes. (5) Advanced Application of distribution Advanced applications in distribution systems encompass various important functionalities that help optimize system operations and enhance overall performance. Some of these applications include load forecasting, network topology analysis, state estimation, power flow calculation, line loss analysis and calculation, voltage/ var optimization, etc. (6) Distribution Geographic Information System The distribution geographic information system (GIS) plays a crucial role in the distribution automation system, primarily due to the spatial nature of the distribution grid and the need to manage and operate equipment in relation to their geographical locations. The distribution GIS typically encompasses three key components: Automatic Mapping (AM), Facility Management (FM), and Geographic Information System (GIS), commonly referred to as AM/FM/GIS.

86

2 The Infrastructure of Smart Grid

2.2.5 Automatic Meter Reading and Billing The Automatic Meter Reading (AMR) system is a key component of the power system that utilizes communication technology and information technology to transmit and collect power consumption data recorded by electric energy meters installed at user locations. This system has significantly enhanced the management of power consumption. By employing automatic meter reading technology, the AMR system improves labor productivity and ensures accurate meter readings. It enables transmission of real-time and precise power consumption data to both users and the business department. As information integration continues to advance, users can conveniently pay their power bills, and with the development of the power market, tiered pricing structures can be implemented. This leads to improved power consumption patterns, optimized load balancing, and reduced overall energy costs. Automatic meter reading encompasses various methods such as local automatic, mobile, prepaid, and remote automatic meter reading. Among these, remote automatic meter reading is currently the prevailing approach. It utilizes communication methods such as low-voltage distribution lines, telephone networks, wireless technologies, and multiple serial bus and field bus communication systems to upload power consumption data to the power billing information system. This enables the accurate measurement and billing of power consumption data from remote locations. (1) Composition of Remote AMR system The remote Automatic Meter Reading (AMR) system consists of several key components, including an electric energy meter equipped with automatic meter reading capabilities, a meter reading concentrator, a meter reading switcher, and a meter reading information processing system. The remote AMR system consists of an electric energy meter with automatic meter reading, a meter reading concentrator, a meter reading switcher, and a meter reading information processing system. The meter reading concentrator connects multiple energy meters, processing their data centrally with communication and data processing functions. Multiple concentrators can be networked through the switcher, allowing integration with other information systems. The meter reading information processing system, typically a computer system, handles data processing, computation, and storage. (2) Electric Energy Meter There are two types of electric energy meters commonly used in remote automatic meter reading systems: pulse energy meters and smart energy meters. Pulse electric energy meters can be further categorized into voltage type and current type based on the pulse output method. Voltmeter-based meters provide a level signal output and use a three-wire transmission method, suitable for shorter transmission distances.

2.3 Application of Innovative ICT in Power System

87

Smart energy meters utilize communication interfaces to transmit data remotely in coded form, offering high accuracy and reliability. Smart meters can be classified into two types based on the communication interface: RS-485 interface type and low-voltage distribution line carrier interface type. (3) Meter Reading Concentrator and Meter Reading Switcher The meter reading concentrator serves as a centralized device in the remote automatic meter reading system, responsible for collecting data from electric energy meters. It can gather data through various means such as buses and power line carriers. The meter reading concentrator is capable of processing pulse output signals from pulse electric energy meters and reading data from smart energy meters through the RS485 communication bus. It is equipped with multiple communication interfaces to facilitate data exchange with external systems. The meter reading switcher functions as a secondary centralized device in the remote meter reading system. It aggregates data received from the meter reading concentrator and transmits the collected data to the meter reading information processing system using different communication methods. The meter reading switcher establishes communication with the meter reading concentrator through various communication interfaces or power line carriers and can exchange data with external systems through its communication interface. (4) Meter Reading Information Processing System The meter reading information processing system, also known as the electric energy billing center, is a component of the remote meter reading system. It comprises a computer network and specialized information processing software designed to handle tasks such as electricity billing, data statistics, and charging. (5) Communication The communication mode of the remote automatic meter reading system offers great flexibility, supporting various options such as wired connections (e.g., optical fiber, cable), wireless technologies, and telecommunication networks (e.g., telephone network, mobile communication network). These diverse communication technologies allow for efficient data transmission between the meter reading devices and the central monitoring system, ensuring reliable and accurate collection of metering data.

2.3 Application of Innovative ICT in Power System The ongoing transition from the traditional grid to the smart grid marks a significant transformation in the power industry. One of the key distinctions between the smart grid and the traditional grid lies in the integration of distributed energy sources [4]. This integration necessitates a fundamental reevaluation of grid management

88

2 The Infrastructure of Smart Grid

practices, enabling power system operators to effectively respond to unexpected and rapid dynamic changes [5]. To meet these evolving demands, power system operators have made substantial advancements in deploying new measurement devices, such as phasor measurement units (PMUs) and smart meters (SMs). These devices play a crucial role in collecting valuable information that enables the monitoring of grid operating parameters and enhances grid control capabilities. To effectively manage and control both traditional grids and smart grids, it is essential to have an advanced and forward-looking information and communication system that meets the evolving needs of grid operators [6–8]. With the continuous integration of new measurement and control devices, the frequency of grid measurements is increasing, resulting in a significant surge in data generation, which is expected to escalate further in the near future. Consequently, the infocommunication infrastructure must be capable of efficiently handling these high-speed measurement data streams in a scalable manner to support the widespread deployment of smart grids. However, addressing this challenge goes beyond merely expanding transmission capacity. The infocommunication infrastructure should possess the ability to dynamically sense the grid’s state, determine the appropriate destinations for data transmission and storage, and make this data accessible for various applications. Considering these factors, the infocommunication infrastructure of the power system should exhibit the following characteristics [4]: (1) Scalability and elasticity, which should be adapted to large data streams and related storage and computing. (2) Flexibility, which should be used to allocate and redistribute grid functions and data flow control strategies at runtime. (3) Information pools composed of measured data from underlying physical devices should be virtualized in a context-aware manner so that they could be reused for more than one application independent of underlying communication and electrical details. (4) Future-proof, so that new functions and devices could be added, removed or replaced in a modular fashion without rethinking the entire infocomm infrastructure from scratch. For some of above characteristics, cloud computing should be a key technology to meet these characteristics. Both traditional cloud computing and edge computing models could be leveraged in this context, where the latter represents the devices configured in grid edge to be evolving into micro-cloud servers, enabling them not only manage certain grid functions, but also manage some application modules, and enabling them better accommodate predictable levels of quality of service and workload/traffic, enabling them be more nearly users and/or clients. Furthermore, a distributed architecture consisting of massively deployed edge nodes is inherently more scalable than cloud computing approaches. Therefor Cloudbased solutions could address important tasks related to storage, real-time computing, and optimization of the predictable large volumes of data. Using techniques of IoT [9], combining cloud and edge computing attributes in a virtualized environment, the remaining requirements should be met. Through

2.3 Application of Innovative ICT in Power System

89

resource virtualization [10], the critical data processing and communication requirements for smart grids, which are common features of recent IoT architecture solutions, can be properly addressed. Two relevant application cases are given below.

2.3.1 State Estimation Architecture Based on Cloud and Edge Computing [4] Prominent smart grid research initiatives, such as those undertaken by the European Union [10] and the United States [11], are actively exploring the integration of information and communication infrastructure with smart grids. A key focus of their research is to ensure that the information and communication infrastructure can flexibly meet the requirements of smart grids. Cloud computing emerges as a promising virtual solution to address this requirement, enabling the efficient analysis and distribution of data from all nodes within the smart grid, regardless of time and location. To achieve interoperability and integration across a wide range of applications, it is essential to establish a level of abstraction for connected grid components and actors. This abstraction layer serves as a common framework, enabling smooth communication and interaction between various grid components. By defining standardized interfaces and protocols, diverse applications can seamlessly integrate with the smart grid, facilitating comprehensive grid management. The abstraction layer ensures interoperability among different systems and devices, enabling effective coordination and control of the entire smart grid infrastructure. Given the significant volume and high-speed generation of data from smart meters, particularly Phasor Measurement Units (PMUs), state estimation for the grid necessitates robust information and communication infrastructure. This infrastructure should encompass high-speed computing capabilities, ample storage capacity, and low-latency resources for transmission and communication. Additionally, in certain scenarios such as fault states, the state estimation process requires rapid calculations to enable swift grid control. This calls for localized execution of calculations using a smaller dataset for quick responses. To effectively address these two cases, a dynamic adjustment of information and communication resources is essential to meet the resource requirements of both global and local state estimation. Cloud computing proves capable of meeting the global demands by providing scalable computing resources, while edge computing is well-suited for fulfilling local requirements. A comprehensive framework that combines cloud computing, virtualization, edge computing, adaptive bandwidth strategies, and PMU technologies has been proposed in literature [4]. This framework focuses on state estimation for the IEEE 34 bus system and provides an evaluation of its performance. The framework, as depicted in Fig. 2.7, highlights the integration of cloud-based and edge computing approaches to support accurate and efficient state estimation processes.

90

2 The Infrastructure of Smart Grid

Fig. 2.7 State estimation framework based on cloud and edge computing [4]

The aforementioned framework encompasses the entire communication link from physical devices to applications, considering the integration of both ICT and nonICT components within the smart grid. While certain components, such as PMUs, already possess communication capabilities, other grid elements like electromechanical switches or traditional wind turbines necessitate ICT interfaces to enable seamless communication within the network. A key concept introduced in the framework is the notion of Virtual Objects (VOs), which serve as virtual counterparts to physical devices. VOs are virtualized entities that inherit the characteristics of one or more physical devices. They facilitate authorized user access to resources and functions in a reusable and interoperable manner, without requiring knowledge of the specific devices and protocols involved. For instance, a PMU can directly handle communications at the transport level and establish connections with individual hosts. By employing VOs, enhanced communication functions are enabled, making data available to multiple hosts. These VOs can be positioned as physical entities at the edge of the communication network (within the same subnet as the physical device) or within the cloud. In the proposed framework, VOs are located at the edge of the communication network. This placement decision takes into account the high data load generated by PMUs when operating at their maximum rate. While the maximum rate is crucial for detecting node dynamics, it may not always be feasible to transmit all data remotely during near-steady states of the network. Architecturally, VOs at the edge receive data at the maximum rate to fulfill tasks with strict latency constraints, which require fine-grained temporal information. VOs also implement context-aware local policies to determine whether to send data remotely based on the actual state of the power grid. Security is an important consideration, and by residing within the local network,

2.3 Application of Innovative ICT in Power System

91

VOs ensure proper protection. This framework enables security-critical information to be processed and acted upon locally, enhancing network reliability by providing essential information remotely in the event of a cyber attack. Local security against external threats can be delegated to firewalls, safeguarding the security of the local area and eliminating the need for extensive encryption of data exchanged between VOs and physical devices. Time-consuming and resource-intensive tasks can be performed using local resources, aligning with IEC recommendations for actions with strict timing constraints [13]. When data needs to be transmitted remotely, employing Representational State Transfer Application Programming Interfaces (REST APIs) can ensure network security by utilizing protocols such as SSL/TLS (Secure Socket Layer/Transport Layer Security). Virtual Objects offer resources and functions that are leveraged by Micro Engines (MEs). A ME can be described as a cognition-enabled combination of VOs created to accomplish specific advanced tasks. MEs provide a unified interface at the application level and are independent of the underlying resources available. MEs are typically connected at the edge of the communication network, yielding benefits such as reduced traffic load, decreased latency, and a faster response system for critical applications. MEs operate behind firewalls to enhance security. Remote MEs offer the advantage of integration within a cloud infrastructure, providing maximum computing and storage capacity that can dynamically adapt to application changes. They offer global visibility, enabling the utilization of VO services across different remote locations, distinct from the local MEs. In the presented framework, the primary role of MEs is to perform state estimation for specific grid segments. They also monitor the bandwidth of associated VOs to determine actions necessary to prevent overload in terms of bandwidth usage or storage. MEs can interface with other MEs (e.g., for multi-region state estimation) and at the application level, allowing interested parties to access grid details such as the number and locations of installed PMUs or the grid’s topology. For optimal functionality, MEs are ideally located within cloud instances, offering elasticity in computing resources while providing geographical backup for improved reachability [14]. The topmost layer of the proposed architecture comprises applications that utilize one or more underlying MEs to implement high-level functionalities. The location of these applications depends on their specific requirements. For instance, a visualization application can be easily implemented as a web service hosted in the cloud. The following section outlines a partial implementation of the proposed state estimation architecture. (1) Physical Equipment for State Estimation The Phasor Measurement Unit (PMU) stands as a paramount component within the Smart Grid (SG), holding both immense importance and demanding considerable resources. This physical device assumes the responsibility of capturing synchrophasor measurements at a predetermined sampling rate, typically set at 50–60 frames per second (fps). In the synchrophasor standards, the most recent references

92

2 The Infrastructure of Smart Grid

are the esteemed IEEE C.37.118.1-2011 [15] and IEEE C.37.118.1a-2014 [16]. These standards meticulously outline the precise definitions pertaining to synchrophasors, encompassing frequency and frequency rate of change during various operational conditions, notably the measurement of Rate of Change of Frequency (ROCOF). Complementing these standards, IEEE C.37.118.2 [17] meticulously establishes a protocol for the real-time exchange of synchrophasor measurement data among diverse power system devices. Consider a distributed grid, where N P PMUs are deployed. These PMUs can measure N Q quantities of electricity, such as voltage and current phasors and frequencies at a given sampling frequency. Since the way the PMU is built, the sampling rate of the physical PMU cannot be changed at runtime, it is fixed and therefore must be set before the start of the data transfer, which corresponds to be received the command “turn on the transfer of data frames” by the PMU. If the rate should be changed, the PMU must be stopped. The collected measurements are sent to the configured receivers with GPS synchronized time stamps. The data was received by the corresponding VO, which operated as a receiver of a PMU at the edge of the communication network in the proposed architecture. Each PMU creates a TCP socket with a corresponding VO and sends measurement data at the maximum reporting rate according to [17]. To test the PMU-based architecture in a practical way, [4] used a real PMU prototype of an automatic measurement system implemented by National Instruments CompactRIO modular technology. Synchronization of each PMU is achieved via a GPS receiver (providing one pulse signal per second, PPS, with 100 ns accuracy), and each prototype can be used as a fully equipped PMU for fine signal acquisition, or as a PMU hardware emulator. In the latter case, the PMU prototype computes synchronization measurements or simulates expected measurement outputs starting from pre-stored signals. PMU emulators are particularly suitable for testing dynamic operating conditions in a controlled environment to compare different algorithms and configurations using the exact same signal from the network. (2) Virtual Objects (VO) VO enriches the functions of physical devices. For the specific case of this case, once the VO receives data from the PMU, it performs ] processing to determine [ the relevant whether the received measured value of q ∈ 1, . . . , N Q should be sent to the ME according to the given quotas, or no further processing. In this case, the quantity q can be considered as a voltage, and the data is sent by p ∈ [1, . . . , N P ]. | | |m pq (t) − M pq (t)| > T pq (2.1) M pq (t) where m pq (t) is the q measured by the PMU p at time t, M pq (t) is the value representing the memory that the VO has at time t, referring to the previously measured value of q, and T pq is the threshold value of capable of transmitting the value q. The memory value can be calculated according to all measurements received from the

2.3 Application of Innovative ICT in Power System

93

PMU or only those sent by the VO. Without loss of generality, assume that M pq (t) is the last measurement sent by the considered VO before time t (remark: (2.1) equation is used to calculate the “dead zone”) M pq (t) = m pq (t − Tlms )

(2.2)

where, Tlms is the time interval between transmission of consecutive measurement from VO to ME. Following, T pq is set to 1%, since 1% is the accuracy limit for the total vector error (TVE), thus the phase magnitude error. So, a measurement received at time t is sent if and only if its value differs by more than 1% from the last sent measurement. This value takes into account the high accuracy of PMU measurements that are actually available under steady-state conditions. Equation (2.2) can also be generalized to the past N M measurements sent by the PMU, defined as M pq (t) =

NM ∑

w pq (i ) · m pq (t − i · Ts )

(2.3)

i=1

where, N M is the number of past measurements considered, Ts is the sampling rate of the PMU, and w pq (i ) is the weight associated with the measurements m pq (t − i Ts ). The difference between (2.2) and (2.3) is as follows: the former focuses on the degree of difference between the last sent measurement value and the actual measurement value, that is, how much the measurement value used for state estimation changes; the latter focuses on the entire data flow and subsequent measurements are averaged to avoid false positives due to noise. The equation defined in (2.2) is used below. In the monitoring process, in addition to dynamic detection, VO also periodically LS as the sending time of the sends updated values under static conditions. Define t pq last measured q, then the new measurement data will be sent at the following time LS t = t pq + TS · R pq

(2.4)

where, R pq represents the subsampling factor used by the VO. For example, if under static conditions only 1 out of 50 measurements per second received from the PMU is sent by the VO, then R pq = 50. Therefore, TS · R pq is the actual sampling interval received by the state estimator. In the testbed, each VO is hosted in a CapeDwarf installation in a local server, which is a PaaS (Platform as a Service), an open-source implementation of Google App Engine (GAE). Actually, VO is a process that runs locally at the edge of the grid, but can be moved to the cloud if latency permits and more computing power is required.

94

2 The Infrastructure of Smart Grid

(3) ME (Micro Engine) In the context of the Distributed System State Estimation (DSSE), the Measurement Engine (ME) leverages the measured data received from the Virtual Observer (VO) in conjunction with predictions based on historical information of loads and generators. These predictions, often referred to as pseudo-measurements. The ME employs a sophisticated calculation process to estimate the state of the grid. It takes into account the measured data received from the VO, which includes information such as voltage phasors for each node and current phasors for each branch. Additionally, the ME utilizes the pseudo-measurements derived from historical load and generator data. By combining the actual measurements with the predicted values, the ME performs an intricate analysis to estimate the grid state. It determines the voltage phasor term for each node, providing valuable insights into the voltage levels and their associated phase information throughout the distributed grid. Furthermore, the ME estimates the current phasors for each branch, enabling the assessment of the current flow and distribution across the system. Distributed grids are characterized by a significantly larger number of nodes with varying loads and voltage levels. These grids exhibit diverse configurations, including both three-phase and two-phase/single-phase setups. Additionally, the presence of asymmetrical loads and the integration of distributed energy generation (DER) and distributed generation (DG) introduce significant variations and complexities [18, 19]. Several methods for DSSE have been proposed in [20], mainly based on weighted least squares (WLS). Recently, a new branch current DSSE (BC-DSSE) was proposed that demonstrated the same accuracy as the node voltage DSSE but performed faster, which is the method applied in this case to estimate the running conditions, also including classical χ-squared and normalized residual tests for bad data detection and identification. When new measurement data is received by the ME, a new estimation is triggered by the timestamp indicated in the received packet. If no measurement with the same timestamp is found for monitoring other nodes, the latest measurement is used for estimation. Once DSSE has been performed, new measurements with the same timestamp can also be received. In this case, the ME will recalculate the grid state based on the measurements available at that time to obtain a more accurate estimate associated with the corresponding time stamp. DSSE calculations are fast and refinement of estimates is automatic. DSSE output does not depend on previous estimates to avoid the risk of poor performance under dynamic conditions. While this may seem inefficient from a computational point of view, it also provides an important advantage for applications, and applications can benefit from the last system SE even before all measurements are received. Also, note that the implemented solution allows parsing the grid state even if some packets are dropped or lost due to packet loss. In this case, the DSSE instance is implemented in ME in the cloud, thus providing the necessary computational and storage elasticity required by the proposed system. In fact, ME is hosted in Google Cloud App Engines in class B 8, which runs on a

2.3 Application of Innovative ICT in Power System

95

4.8 GHz processor, but can clone itself if resources for instantiation are insufficient. If multiple instances are created to meet the demand for more computing resources, this process is not perceived externally, and the internal management is consistent. In particular, when a new measurement is received, the data is first written to the shared memory cache, and then the interesting data for state estimation is read from the same memory cache shared between instances, which guarantees consistency when multiple instances operate in parallel on a shared dataset. At present, Google has deployed more than 15 data centers around the world, and it is expected that there will be more data centers in the future. However, although Google Cloud is used in this case, the proposed architecture can easily use cloud services from other cloud providers such as Amazon. VO acts as an interface between the local subnet and the cloud, and the communication between VO in the edge and ME in the cloud is done using REST API, which ensures the interoperability and protocol-independent interface between VO and ME. Furthermore, using HTTP offers the possibility to implement secure protocols on top of HTTP, such as TLS/SSL, which cannot be exploited in direct PMU-tostate estimator communication since the PMU communication capability stops at the transport level. While this adds latency to data transfers, it is limited to purposes that do not have strict time constraints. In the proposed framework, communication takes place in PUSH mode, and data is automatically sent from VO to ME via HTTP POST. The data at the ME is parsed, processed, stored and sent to the application for visualization to the estimated state of the grid. In specific cases, an additional task is given to the ME receiving PMU data in order to show context awareness at the ME level [21], which is based on global rather than local information, such as VO situations. Specifically, the ME should dynamically update the R pq value at VO in order to guarantee that the average bandwidth utilization on the network tends towards the target value Dobj which is chosen by the smart grid operator based on internal quality goals and network configuration. This update passes the measured value to the VO via the VO in response to an HTTP POST. Here, Dobj is expressed as the amount of data received and stored in the ME normalized over time (i.e. [B/ s]). Now, calculate the average rate as the accumulated data normalized over time generated by any pth PMU and any quantity q from the beginning of the system until time t as: [ ] ∑ p,q,tx D pq (tx ) (2.5) Davg (t) = t For convenience, tx refers to each moment when VO sends measurement data. Davg (t) can be implemented with two counters: the duration since system startup, and the total amount of data received. Each time the ME receives a new measurement, the value of R pq at time t is updated as LS R pq (t) = [(1 + Δ(t)) · R pq (t pq )]

(2.6)

96

2 The Infrastructure of Smart Grid

Fig. 2.8 Test system: IEEE34 node test feeder [23]

where Δ(t) = α ·

Davg (t) − Dobj Davg (t) + Dobj

(2.7)

0 < α ≤ 1 is the sensitivity parameter of the model, here we choose ( L S ) α = 0.1. , depending Therefore, the updated value R pq (t) is an increase or decrease of R pq t pq on the target bandwidth and the total history of data received by the ME up to time t (as described in Eqs. (2.5) and (2.7)). (4) Test The results presented below were obtained on a three-phase test system based on IEEE 34 bus test feeders derived from an actual system in Arizona with two onload tap-switch and two capacitor banks, the actual system Features: long lines and certain load systems. The system is of particular interest because it has been shown to be very sensitive to DG (distributed generation) effects [22], shown as Fig. 2.8. Details on line parameters (line length and impedance), rated load and generator can be found in [23]. A photovoltaic power station with a capacity of 2 MW is installed at node 34. The photovoltaic power station is modeled as PQ injection (where the specified active power is P and reactive power is Q), and the PQ injection is restricted by the second-order transfer function. The first order transfer function is tuned to mimic the interconnect filter behavior of the power plant converter. Several radiation variation conditions have been considered to create the test cases. The system was simulated using Opal-RT. Opal-RT is a digital real-time simulator for electrical and electromechanical systems. By using analog and digital I/O, it is possible to interface the simulator with external hardware (hardware-in-the-loop) so that the measurement device operates when connected to the real electrical system. The presented results were obtained using three PMUs measuring the voltage phasors at bus numbers 3, 9 and 31 of the grid (Fig. 2.8). Four test operating conditions were considered, each run 3 × 102 times:

2.3 Application of Innovative ICT in Power System

97

Case 1: The photovoltaic power plant is turned on at t = 25.3 s, actual power P = 2 MW. Case 2: The photovoltaic power station is turned on at t = 24.3 s, the actual power is P = 2 MW, then it is turned off after 2 s. Case 3: The photovoltaic power station injects power (1–1.5 MW) rapidly, starting from t = 20 s and injecting within 10 s. Case 4: The slow injection of the PV plant from 0 to 2 MW starts at t = 0.3 s and ends at t = 10.3 s. (5) Accuracy Estimation Two accuracy indices were used to compare the estimates given by the fixed-step estimator to those computed by the described variable-rate system. The absolute mean value of the node voltage magnitude estimation error (denoted by MAVE below) is M AV E φ =

T −1 N | 1 ∑ ∑|| Vφ,V R (n, i ) − Vφ,F R (n, i )| N T i=0 n=0

(2.8)

where, Vφ,F R (n, i ) was the estimated value per unit of the voltage amplitude of node n whose phase was φ obtained by each PMU at time i TS with a fixed maximum report rate of 50fps, TS = 20 ms; Vφ,V R (n, i ) was the estimation performed by variable DSSE obtained by the proposed estimation system. N and T were the number of nodes and the number of times considered, respectively. Since ME only computed DSSE (at TV R time) for the measurement with timestamp received from VO, for comparison purposes, the estimation of the proposed system is used for comparison with the timestamped value. This choice gave the worst case for the comparison, but finer interpolation methods also could be used. The proposed architecture was variable rate, an index could also be used for comparison. Dynamic time warping distance (DTW) measures the similarity between two time series that may vary in time or velocity and is often used for pattern comparison. Each point of the sequence was represented by an N-dimensional point of the estimated voltage magnitude, and the distance considered was Euclidean based. Thus, DTW is a cumulative index of nodes and time instants and has been normalized with respect to duration to give a better measure. In order to calculate the performance in terms of reduced bandwidth, the Bandwidth Saving Ratio (BSR) is defined as the ratio between the amount of data sent to the ME by all considered VOs and the total data sent at the full rate which was fixed at 50 fps. In the test scenario, the size of the HTTP POST sent to ME was constant, equal to D pq = 424 B (including the payload and header of the communication stack layer). And Dobj was set to 4.24 kB/s, so Dobj /D pq = 10 fps. In the case of sending all generated data, Dobj /D pq = 150 fps because three PMUs are used. So BSR is asymptotically equal to BSR = 0.0667. For the 4 cases considered, and considering that the full rate data rate was Dobj = 63.6 kB/s, this reduction is quite large.

98

2 The Infrastructure of Smart Grid

The introduction of distributed power resources has necessitated rapid improvements in power grid security management. To address this, intelligent devices with information communication capabilities, such as PMUs, are essential. These devices enable the monitoring system to quickly detect dynamic changes in the power grid and respond promptly. Achieving this requires an IoT architecture with a dynamic and resilient monitoring grid, enabling real-time estimation of the grid state in the presence of distributed power systems. This architecture ensures effective information and communication resources to support efficient grid state estimation. PMUbased wide-area measurement systems have successfully utilized the virtualization capability of IoT, the advantages of edge computing, and the computing power and flexibility of the cloud to enhance their capabilities.

2.3.2 Big Data Analysis in Smart Grid The worldwide adoption of smart meters, replacing traditional meters, has led to the automatic collection of power data. However, managing the vast amount of data generated by smart grid instruments for monitoring and control poses significant challenges. The efficient, reliable, and sustainable operation of smart grids relies on effectively handling this data deluge. To address this challenge, advanced information technology and robust network infrastructure are required to process and analyze the large volumes of data. The unprecedented amount of data generated by smart grids necessitates the development of an effective platform to leverage the potential of big data analytics. This platform plays a crucial role in enabling smart grids to thrive in the era of big data. In this section, we will introduce a smart grid big data analysis framework outlined in [24]. This framework is implemented on a secure cloud platform and demonstrates its effectiveness in two specific scenarios. Firstly, it visualizes electricity consumption within a smart grid comprising more than 6000 smart meters. This visualization offers insights into energy usage patterns, allowing for informed decision-making and optimization. Additionally, the framework enables dynamic demand response, facilitating real-time adjustments to electricity demand based on consumption patterns and grid conditions. This capability enhances grid stability, energy efficiency, and customer satisfaction. (1) Core Components of Smart Grid Big Data Analysis Figure 2.9 illustrates the layered architecture of the core components utilized in the smart grid big data analytics framework. Let’s delve into the functionalities of the data acquisition, data storage and processing, data query, and data analysis components. By adopting the Hadoop platform, the data storage and processing components ensure efficient and scalable handling of the extensive smart grid datasets.

2.3 Application of Innovative ICT in Power System

99

Fig. 2.9 The layered architecture of core component Hadoop-based for smart grid big data analysis [24]

(a) Data Acquisition Components Flume is a distributed system developed by Apache that specializes in efficiently acquiring, aggregating, and transferring large volumes of log data from diverse sources to a centralized storage system. It provides a reliable and scalable solution for ingesting massive amounts of streaming data, including social media feeds and sensor data, into Hadoop’s Distributed File System (HDFS). (b) Distributed Data Storaging and Processing Components Hadoop is an open-source software platform that revolutionized the storage and processing of massive amounts of data. It eliminates the need for expensive proprietary hardware by enabling distributed data processing on commodity server clusters. Hadoop’s core components include the Hadoop Distributed File System (HDFS) for data storage and MapReduce for data processing. Hadoop has transformed the way big data is handled. It offers cost-effective and scalable solutions for storing, processing, and analyzing massive datasets, making it a critical component in any big data architecture. (c) Data Querying Component Hive and Impala are SQL-like languages for big data analysis. Hive utilizes MapReduce for batch processing, while Impala provides real-time interactive querying by leveraging in-memory processing and parallel execution. Hive is suitable for structured and semi-structured data analysis, while Impala offers faster query response times and near-real-time results. (d) Data Analysis Component Mahout is a data mining library built on top of Hadoop, specifically designed for batch processing of machine learning tasks. It offers a range of scalable and highperformance algorithms for machine learning applications. SAMOA (Scalable Advanced Massive Online Analysis) is a distributed streaming machine learning framework. It provides programming abstractions for distributed streaming algorithms, making it suitable for common data mining and machine learning tasks in streaming data environments.

100

2 The Infrastructure of Smart Grid

Fig. 2.10 Framework for processing smart grid big data for visual analysis [24]

Tableau is an interactive data visualization tool that empowers users to analyze, visualize, and share information and dashboards. It offers a user-friendly interface and a wide range of visualization options, allowing for effective exploration and communication of data insights. (2) Big Data Analysis Framework for Smart Grid Figure 2.10 illustrates the framework for analyzing smart grid big data, encompassing the complete lifecycle of data in the smart grid. This framework facilitates data generation, data analysis, and the formation of a learning and response loop. (a) Data Acquisition The acquisition of smart grid data could be decomposed into three sub-tasks, namely data acquisition, data transmission and data preprocessing. First, the generated data is actively collected by centralized/distributed agents, and then the collected data is transferred to the master nodes in the Hadoop cluster. After raw data is collected, it is delivered to the data storage infrastructure for subsequent processing. Since different data sources, the acquired data may have different formats and means, so data preprocessing is required. Data integration techniques aim to combine data from different sources and provide a unified view. In this framework, the data is transported to a commaseparated value (CSV) file format. The data attributes include information such as timestamp, smart meter ID, generation/consumption, and location. During the data preprocessing stage, inaccurate and incomplete data are modified or removed to enhance data quality. Flume serves as the data acquisition tool in this framework. It can efficiently collect, aggregate, and transfer large volumes of data generated from various sources

2.3 Application of Innovative ICT in Power System

101

to the Hadoop master nodes. When Flume receives data, it stores it in one or more Channels, which are passive stores that hold events until they are received by Flume. Flume sinks remove events from the channel and store them in an external repository. For this framework, the data files need to be ingested into an external Hadoop Distributed File System (HDFS) repository. The HDFS sink in Flume enables data to be written to the HDFS repository in the desired format by utilizing a plug-in Serializer. The Serializer converts and reassembles the Flume data into the desired format. This preprocessing step helps achieve a unified view of the data. By leveraging Flume’s capabilities for data acquisition and ingestion into HDFS, the framework enables efficient data preprocessing and facilitates the creation of a unified data view for further analysis and insights. (b) Data Storage and Processing After acquiring smart grid data, the next stage involves processing the stored data using Hadoop’s Hadoop Distributed File System (HDFS). An HDFS cluster comprises a single NameNode responsible for managing file system metadata, and multiple DataNodes that store the actual data. The smart grid data received is divided into blocks and stored across a set of DataNodes. Hadoop YARN serves as the computing core for big data analysis. Both HDFS and YARN operate on the same set of nodes, allowing tasks to be processed on nodes already present for smart grid data processing. Currently, there is an opensource Apache Hadoop release available that includes essential components such as MapReduce, HDFS, and more. Popular open-source Hadoop distributions include Cloudera, MapR, and Hortonworks. By utilizing HDFS for data storage and YARN for distributed computing, the framework can efficiently process and analyze the smart grid data. (c) Data Querying In this framework, Hive and Impala play a crucial role in accessing and analyzing smart grid data stored in the Hadoop Distributed File System (HDFS) repository. They enable the selection, analysis, and generation of data of interest for various purposes. For instance, one can query and retrieve information such as the power consumption of a specific region or the total power generated by a wind farm. By utilizing Hive and Impala, users can extract valuable insights from the smart grid data. These tools provide the flexibility to perform various data analysis tasks, and their integration within the Hadoop cluster ensures efficient and fast processing of queries. Additionally, the framework allows for the utilization of other data elements in queries, enabling comprehensive analysis and exploration of the smart grid data. (d) Data Analytics To enhance the efficiency of the smart grid, it is important to share the acquired smart grid data. This data sharing can serve multiple purposes, including analysis for generating recommendations, providing valuable insights to researchers and data mining experts, as well as empowering consumers with relevant information. However, it is crucial to strike a balance between data security and data sharing.

102

2 The Infrastructure of Smart Grid

Various tools are available for performing data analysis on the shared smart grid data. For instance, Tableau is a powerful tool for visualizing big data, enabling users to create interactive and informative visualizations of the smart grid data. Tools like Mahout and SAMOA are specifically designed for mining big data, providing advanced algorithms and techniques for extracting meaningful patterns and insights from the data. In the data analysis phase, two main objectives are learning and responding. Sharing the state of the grid between utilities and consumers can contribute to the overall stability of the smart grid. Active participation of users in grid stability can be facilitated through a visual dashboard portal accessible via the internet or mobile applications. These portals provide real-time visualizations of the smart grid’s status, allowing users to monitor and respond to changing conditions. For instance, dynamic power pricing and incentives to reduce load during peak periods can be announced through these platforms. (3) Implementing on Cloud Computing Platform (a) Cloud Platform Cloud computing, particularly Infrastructure as a Service (IaaS) cloud, is well-suited for implementing the proposed smart grid big data framework. Cloud infrastructure offers the necessary scalability, accessibility, and resource management capabilities required for handling big data. One feasible option is to utilize cloud service providers such as Amazon AWS or Google Cloud Platform. In the case of implementing the framework on the Google Cloud Platform, a cluster consisting of six machines can be deployed. Five machines running the CentOS Linux operating system would be dedicated to hosting the Hadoop platform, while the remaining machine(s) would run the Windows operating system for visual analysis tasks [24]. In a Hadoop cluster, the machines are divided into a master node and multiple slave nodes. To ensure proper communication and coordination within the cluster, each node’s IP address and hostname should be specified in the /etc./ hosts file of all nodes. Whenever a new node is added to the cluster, it must be defined in the /etc./ hosts file of all other cluster nodes to enable seamless integration and communication between them. By leveraging cloud infrastructure, specifically IaaS, the smart grid big data framework can benefit from flexible resource allocation, easy scalability, and efficient management of computational resources. Cloud service providers like Amazon AWS and Google Cloud Platform offer robust infrastructure and services to support the deployment and operation of the framework in a scalable and cost-effective manner. In the smart grid big data analysis, it is common practice to harness vast amounts of data from numerous sources, some of which may be unchecked. Consequently,

2.3 Application of Innovative ICT in Power System

103

it becomes imperative to comprehend security and governance strategies applicable to the diverse smart grid data sources. Furthermore, protecting and effectively managing the remaining data assumes great significance. Hence, the establishment of a well-defined security policy becomes a prerequisite. It is worth emphasizing that security measures necessitate regular updates, given the constant evolution of existing technologies. Additionally, data encryption emerges as a crucial consideration, particularly when transmitting smart grid data to a cloud provider. To establish a secure cluster environment for a smart grid framework, the adoption of the Secure Shell version 2 (SSH) protocol proves invaluable. SSH serves as an encrypted network protocol that enables the safe operation of network systems even in unsecured environments. It facilitates a secure encrypted link in a client–server architecture, connecting clients with servers. Moreover, SSH provides authentication, encryption, and data integrity, bolstering the protection of network communications. Through SSH configuration, various operations can be performed within the cluster, including initiation, termination, and distribution of tasks across nodes. It establishes a secure connection between slave and master nodes, ensuring the safeguarding of resources. Furthermore, SSH offers methods for specifying secure node-to-node connectivity and utilizing the resultant secure connection to access resources on other nodes. An advantageous aspect of employing a well-known cloud service provider’s implementation framework lies in their compliance with Cloud Security Alliance (CSA) standards. These standards advocate the adoption of best practices to guarantee security in cloud computing. To implement SSH within a cloud cluster, the recommended approach is to utilize public key authentication. Public key authentication is widely regarded as a robust authentication method, employed for authenticating cluster nodes. To accomplish this, public and private keys are manually generated on the nodes. The public key is then distributed to all cluster nodes requiring authentication, and any data encrypted with the public key can be decrypted using the corresponding private key. Consequently, each cluster node possesses a file encompassing the complete list of other nodes’ keys. While authentication is based on private keys, the keys themselves are never transmitted over the network during the authentication process. SSH solely verifies that the node providing the public key possesses the corresponding private key, thus preventing unauthorized nodes from connecting as authenticated entities (thus preventing eavesdropping). Following the SSH configuration, the public key (id_rsa.pub) is copied to each node, ensuring that every node in the cluster possesses a comprehensive list of other nodes’ keys. To implement the framework, the essential components such as Flume, Hadoop, Hive, and Impala were installed on the cluster. The chosen framework for this implementation was Cloudera’s distributed Hadoop (CDH). During the CDH setup process, master and slave nodes were identified using their respective hostname or IP address. The master node assumes the responsibility of running the master “daemon,” which possesses information regarding the location of the slave nodes and their available resources. As the master node performs several services, the most vital one is the Resource Manager, which governs the allocation of resources. Slave nodes, on the

104

2 The Infrastructure of Smart Grid

other hand, report themselves to the Resource Manager and periodically send heartbeat signals to maintain communication. Each slave node contributes resources to the cluster, with resource capacity determined by factors such as memory and the number of cores. At runtime, the Resource Manager efficiently allocates and manages these resources based on the requirements and workload of the cluster. (b) Flum Upon acquisition from the smart meter, the data is directed to the local node. Leveraging cloud services provides a notable advantage, enabling data transmission from any location with an internet connection, granted that the cloud’s security protocols permit it. Within the implemented system, a node can function as a Flume agent, serving as either a master node or a slave node. In this particular implementation, the Flume agent is responsible for receiving data in a csv file format. The attributes of the file encompass a range of relevant information, including the timestamp (date-time) denoting when the data was recorded, the ID of the smart meter associated with the data, the power generation (Gen) or consumption (Cons) levels, and location-specific details such as the zip code (Zip). These attributes contribute to a comprehensive dataset, providing valuable insights for further analysis and processing. In this particular implementation, the Flume agent is responsible for receiving data in a csv file format. The attributes of the file encompass a range of relevant information, including the timestamp (date-time) denoting when the data was recorded, the ID of the smart meter associated with the data, the power generation (Gen) or consumption (Cons) levels, and location-specific details such as the zip code (Zip). (c) Hadoop In this particular implementation, the chosen Hadoop platform was Cloudera Manager Hadoop Release. This selection aimed to provide a user-friendly development environment for individuals less familiar with the CentOS operating system, while still requiring similar tools for smart grid application development. Cloudera Manager simplifies the setup process by automatically installing the basic configuration, handling debugging tasks, setting up the SQL database, and installing the CDH agent along with other essential components. During the installation phase, cluster nodes were identified and specified using both their IP addresses and hostnames. Furthermore, each node was assigned a specific role within the cluster, distinguishing between master and slave nodes. It is important to note that a cluster can have multiple master nodes, with each master node responsible for running specific tasks. For instance, nodes dedicated to executing MapReduce tasks and nodes responsible for Hive tasks were explicitly designated. Cluster nodes possess the flexibility to perform multiple tasks, and Cloudera Manager facilitates monitoring their status and resource utilization. This monitoring capability empowers decision-makers to adjust the number of nodes in the cluster and monitor the reliability of each node accordingly. The Flume component plays a pivotal role in storing data into the HDFS (Hadoop Distributed File System) repository. HDFS manages storage across the cluster by

2.3 Application of Innovative ICT in Power System

105

dividing incoming files into blocks and redundantly storing each block on different slave servers. Redundancy is achieved by default, as HDFS stores three full copies of each file, ensuring reliability. By default, the chunk size is set to 128 MB, although it can be adjusted to meet specific application requirements. However, reducing the block size may result in a larger number of blocks spread throughout the cluster, consequently increasing the metadata management burden on the master node. The CDH processing component, known as YARN, effectively harnesses the distributed data by distributing analysis workloads across multiple nodes. Each node processes the data blocks assigned to it within the file, and the results from each node are aggregated and consolidated into a final outcome. CDH continuously monitors job execution and, if necessary, automatically restarts any jobs that were lost due to node failures, ensuring the uninterrupted progress of data analysis tasks. (d) Hive Hive, with its SQL-like interface, plays a crucial role in facilitating the reading, writing, and management of data stored within HDFS repositories. Within this framework, Hive is utilized to process smart grid data files residing in the HDFS repository and generate relevant data tables. Specifically, there is a requirement to construct a table that includes essential information such as the timestamp, smart meter ID, and smart meter usage. To fulfill this requirement, SQL-like queries are employed. These queries are designed to retrieve data from the ‘user/flume/sgf’ directory, and whenever new data is added to this directory, the designated “ConsumptionsTable” automatically updates itself, ensuring the table remains up-to-date with the latest data. (e) Impala Similar to Hive, Impala provides the capability to read, manipulate, and manage data stored in HDFS repositories using SQL-like queries. However, there are notable differences in how Hive and Impala execute queries and the syntax used for writing SQL statements. In this implementation, an example query focuses on processing individual house smart meter data, specifically including the power generated from solar panels (photovoltaic power) and residential wind turbines (wind power). To achieve this, the query involves creating a table that incorporates several key fields, including the timestamp, electricity usage, photovoltaic power, wind power, and Zip code. The creation of this table enables efficient data organization for subsequent analysis. It is important to note that after the table creation, an “Invalidate metadata” statement is required to update the metadata. This step ensures that Impala has the most up-to-date information regarding the data and tables being queried directly by the client. For Impala to effectively respond to queries, it needs to maintain current metadata about the data and tables it accesses. Therefore, if any modifications are made to the data tables used by Impala, it is crucial to update the cached information within Impala. This ensures consistency and accuracy when querying the data. In scenarios where data may experience delays due to factors such as power outages, Flume, in conjunction with HDFS storage, ensures that the data is stored

106

2 The Infrastructure of Smart Grid

securely until it becomes available. Once the data is present in the designated “’user/ flume/sgf’” location, Impala or Hive will automatically update the associated table, incorporating the newly arrived data. To further refine the query results, an “ORDER BY” statement can be added. In this case, the data will be reordered based on the timestamp, providing a chronological view of the information for analysis and presentation purposes. (f) Visualized Analytics In this implementation, the visualization and analysis of smart grid big data are using Tableau software. Tableau offers interactive and dynamic data visualization capabilities by leveraging SQL queries, which are sent to Hive or Impala to retrieve and process the relevant data for visualization. To establish a connection between Tableau and Hive/Impala, the Open Database Connectivity (ODBC) interface is employed. ODBC enables applications to access data stored in a database management system (DBMS) using SQL as the standard querying language. In this case, the computers running the Windows operating system were utilized to set up Tableau, and the Hive/Impala ODBC driver provided by Tableau was installed. Once the Tableau software is configured, the user can select the option to connect to the Hadoop server and specify the appropriate machine and port for the connection. With the successful establishment of the connection, Tableau gains access to the required data stored in the HDFS. Visualizations in Tableau are constructed by generating SQL-like queries specific to Hive or Impala through the Tableau interface. These queries are executed by Hive or Impala on the Hadoop platform, leveraging its distributed processing capabilities. This allows for efficient and high-speed analysis of large-scale datasets, enabling swift and responsive visualizations in Tableau. (4) Practical Application The implemented cloud platform and Hadoop cluster framework were deployed on the IaaS Google Cloud Platform, utilizing a total of six computers. Among these, five computers were configured to form a Hadoop cluster comprising one master node and four slave nodes. The master node was a 2.6 GHz computer with 7.5 GB RAM, running a 64-bit Linux operating system. On the other hand, the slave nodes were equipped with 2.6 GHz processors, 3.75 GB RAM, and running a 64-bit Linux operating system. The remaining computer was dedicated to running Tableau for visualization tasks, featuring a 2.6 GHz processor, a 64-bit Windows operating system, and 3.75 GB RAM. To ensure secure communication and data exchange within the cluster, a robust encryption mechanism was established using the Secure Shell (SSH) protocol. This protocol facilitated the creation of a secure and encrypted link between the different nodes of the cluster, bolstering the overall security of the framework. In the first scenario, the framework was applied to individual houses, focusing on efficient power management to reduce consumption and support the seamless operation of the grid. In addition to conventional household appliances, these houses were

2.3 Application of Innovative ICT in Power System

107

equipped with micro-generators such as residential wind turbines and rooftop photovoltaic (PV) solar panels. Furthermore, electric vehicles (EVs) were also integrated into the scenario. The power consumption data for these households was sourced from the UCI database [26], encompassing metrics such as global active power and readings from three submeters. The first submeter reading pertained to the kitchen, encompassing essential appliances such as the dishwasher, oven, and microwave. The second submeter reading was associated with the laundry room, covering the washer, dryer, refrigerator, and lights. Lastly, the third submeter reading was linked to the water heater and air conditioner. To compute the power output of wind turbines and photovoltaic solar panels, relevant data on wind speed, temperature, and irradiance were sourced from literature [27]. The specific wind turbine under consideration was a 3 kW residential turbine. Calculations for the power output of wind turbines relied on geo-specific weather data (e.g., wind speed and air density) and reference tables for wind turbines [28]. In this study, there were 10 rooftop photovoltaic solar panels [29]. The charging behavior of electric vehicles (G2V) was derived from a study on EV drivers’ charging habits in North East England [30], while the discharging behavior (V2G) was sourced from the literature [31], which indicated that 10% of EV energy could be discharged to the grid. Data for all submeters in this application was generated at a rate of one batch per minute. Figure 2.11a displayed power consumption and generation at a oneminute resolution, utilizing the Tableau visualization tool. Figure 2.11b exhibited a real-time dashboard illustrating the power generation status of the aforementioned house, updated every minute. Power consumption (household loads and G2V) was denoted in red, while aggregated generation from PV panels, wind turbines, and V2G was represented in blue (Fig. 2.11b). Furthermore, an additional pie chart could be incorporated to indicate the distribution of average consumption/generation power across different houses (Fig. 2.11c). This visual representation could be extended to other houses on the map if the necessary data was available. For the second scenario, the framework’s feasibility was tested using the Smart Metering Electricity Behavior dataset obtained from the Irish Social Science Data Archive [32]. This dataset comprised 6436 Irish households and businesses, covering the period from July 2009 to December 2010, with a time resolution of 30 min. Throughout this period, each smart meter generated approximately 25,730 power consumption sequence readings, resulting in over 165 million power consumption readings overall. The data generation rate for each smart meter was 30 min, leading to the ingestion of data from 6436 smart meters every 30 min. Each observation included a timestamp, smart meter ID number, and power consumption. While utility companies had access to additional customer data such as location and square footage, this information was typically unavailable to third-party apps. The data was ingested individually into the master nodes of the Hadoop cluster using Flume. Once collected, the data was stored within the HDFS infrastructure. The power consumption readings from the 6436 smart meters were queried in both Hive and Impala, as each had distinct capabilities, in order to determine which performed faster. Over a significant period of time, in the Hadoop cluster, both Hive and Impala

108

2 The Infrastructure of Smart Grid

Fig. 2.11 Power status dashboard. a Cumulative consumption and generation with one-minute temporal resolution. b The power condition of the house. c Consumption (red) and power generation from the house are plotted in a pie chart

2.3 Application of Innovative ICT in Power System

109

Fig. 2.12 Impala query execution time detail to generate/update incoming 6436 smart meter readings

successfully generated and updated spreadsheets with the newly stored data from the 6436 smart meters, including the timestamp, smart meter ID number, and power consumption every 30 min. Figure 2.12 provided detailed information regarding the temporal execution of Impala queries utilized for generating and updating smart meter readings. For this application, a comprehensive dashboard was developed to visualize the smart grid’s status for Demand Response (DDR) purposes, utilizing the Tableau visualization software. Tableau software seamlessly connected to the Hadoop clusters via Hive or Impala queries, enabling near real-time visualization. Notably, Impala outperformed Hive in updating the smart grid status visualization, suggesting that Hive was more suitable for big data batch processing, while Impala adequately met the requirements of near real-time big data processing. Figure 2.13a portrayed a dashboard illustrating the state of the smart grid, displaying the total electricity consumption for the 6436 Irish households and businesses. Consequently, through careful analysis, DDR strategies could be implemented during peak load periods. By proactively identifying anomalies, utilities could take preventive measures to minimize outages and enhance grid reliability. Moreover, the implementation of dynamic power pricing and incentives to encourage load shedding during peak periods could be announced, facilitating efficient energy management. The framework discussed in Sect. 2.3.2 showcased its competence in handling the ingestion, storage, processing, and querying of substantial data volumes in near realtime. Both Hive and Impala, with their distinctive functionalities, demonstrated their ability to generate and update the newly stored data from the 6436 smart meters within a comparable timeframe. It was observed that Hive could be effectively employed for big data batch processing, while Impala excelled in meeting the demands of near real-time big data processing. The robust Hadoop platform, capable of executing and managing online smart grid applications, further amplified its potential for real-time operations. The analysis of big data in smart grids yields several notable benefits. Firstly, it facilitates the seamless integration of renewable energy sources by employing demand-side management techniques to address fluctuations in power supply. Secondly, leveraging customers as virtual power resources during peak hours enhances grid reliability. This approach mitigates the need for constructing new

110

2 The Infrastructure of Smart Grid

Fig. 2.13 Dashboard of power status in smart grid. a Power consumption by 6436 Irish households and businesses, updated every 30 min. b Power consumption of 11 selected smart meters

power plants and reduces the load by actively curbing consumption during peak periods through effective advertising and communication. Furthermore, employing data mining tools like SAMOA enables the analysis and forecasting of near realtime smart grid data streams. User-facing applications, including push notifications through smartphones, empower end-users to make informed decisions regarding their electricity consumption, leading to cost savings. Lastly, producer-oriented applications provide valuable insights into user habits, allowing for the implementation of customer rewards programs or data aggregation for further analysis. In summary, the analysis of big data in smart grids brings about a range of advantages, including efficient integration of renewable energy, enhanced grid reliability,

2.4 Summary

111

load reduction, forecasting capabilities, user empowerment, and valuable insights for producers.

2.4 Summary The development of the smart grid builds upon the existing power system and its monitoring, control, and management technology. It represents the evolution of the transmission control center and distribution control center, which form the foundation of the smart grid. This chapter provides an overview of the physical aspects of the smart grid, including power generation, transmission, distribution, and consumption, as well as the supporting monitoring, control, and management systems. The power automation system plays a critical role in controlling and managing the power grid. Its primary objective is to ensure the normal operation of the power system and provide users with reliable and cost-effective electricity. In the event of a power system failure, the automation system swiftly identifies and isolates faults to restore normal operation as quickly as possible. The power automation system, as a secondary system, encompasses various subsystems responsible for functions such as automatic monitoring, dispatching of power equipment and systems, and integration at different levels, including grid dispatching, plant, substation, and distribution automation. The ongoing evolution from the traditional grid to the smart grid introduces significant changes. One notable difference is the integration of distributed energy sources, which necessitates a rethinking of grid management to address unforeseen and rapid dynamic changes. To meet these demands, power systems are deploying new measurement devices that collect information about the smart grid, monitor operating parameters, and make decisions based on the collected data. Efficient and future-proof information and communication systems are crucial for the management and control of both conventional and smart grids. As new measurement and control devices are continuously added, the frequency of grid measurements increases, leading to the generation of large amounts of data. Therefore, the ICT infrastructure must be capable of handling high-speed measurement data streams in a scalable manner to accommodate the expected widespread deployment of smart grids. Beyond transmission capacity, the ICT infrastructure should be adaptive, sensing the grid’s state, determining data routing and storage, and making the data available for various applications. To meet the measurement and control requirements of smart grids, emerging technologies such as cloud computing, edge computing, and big data are at the forefront. Edge computing transforms power grid edge devices into micro-cloud servers, enabling the management of power grid functions and application modules, reducing communication load, and enhancing rapid response capabilities. Cloudbased solutions address essential tasks related to storage, real-time computing, and optimization of the vast amounts of data expected in smart grids.

112

2 The Infrastructure of Smart Grid

This chapter also presents two application cases that demonstrate the utilization of cloud computing with edge technology and big data technology in the smart grid. The first case focuses on estimating the state of the power grid using cloud computing and edge computing, enabling dynamic and flexible monitoring to detect changes promptly while conserving communication and computing resources. The second case showcases the application of big data analysis technology to analyze large volumes of power consumption data, providing valuable insights and facilitating demand-side management, renewable energy integration, grid reliability improvement, peak load reduction, and smart grid analysis and prediction. In conclusion, the analysis of big data in smart grids brings numerous benefits, enhancing various aspects of the power system, including demand-side management, renewable energy integration, grid reliability, peak load reduction, and analysis and prediction capabilities for smart grids.

References 1. International Energy Agency, Smart Grid Technology Roadmap. http://www.nea.gov.cn/201106/17/c_131086173.htm 2. Zhenxiang H (2013) Power system analysis, 5th edn. Zhejiang University Press, Hangzhou 3. Fu Z (2006) Automation of power systems. China Electric Power Press, Beijing 4. Meloni A et al (2018) Cloud-based IoT solution for state estimation in smart grids: exploiting virtualization and edge-intelligence technologies. Comput Netw 130:156–165. https://doi.org/ 10.1016/j.comnet.2017.10.008 5. Wang W, Xu Y, Khanna M (2011) A survey on the communication architectures in smart grid. Comput Netw 55(15):3604–3629. https://doi.org/10.1016/j.comnet.2011.07.010 6. Gungor VC et al (2013) A survey on smart grid potential applications and communication requirements. IEEE Trans Ind Inf 9(1):28–42. https://doi.org/10.1109/TII.2012.2218253 7. Kuzlu M et al (2014) Communication network requirements for major smart grid applications in HAN, NAN and WAN. Comput Netw67:74–88. https://doi.org/10.1016/j.comnet 8. Mocanu D et al (2016) Big IoT data mining for real-time energy disaggregation in buildings. In: IEEE international conference on systems, man, and cybernetics, pp 9–12 9. Farris I et al (2015) Social virtual objects in the edge cloud. IEEE Cloud Comput 2(6):20–28. https://doi.org/10.1109/MCC.2015.116 10. Nitti M et al (2016) The virtual object as a major element of the Internet of Things: a survey. IEEE Commun Surv Tutor 18(2):1228–1240. https://doi.org/10.1109/COMST.2015.2498304 11. Strategic research agenda for Europe’s electricity networks of the future. https://www.nist.gov/ smartgrid/upload/NIST-SP-1108r3.pdf 12. NIST. Framework and roadmap for smart grid interoperability standards, re-lease 3.0 13. IEC TC57 (2007) Power systems management and associated information exchange data and communications security. Part 6: security for IEC 61850 14. Pamies-Juarez L et al (2011) Towards the design of optimal data redundancy schemes for heterogeneous cloud storage infrastructures.Comput Netw 55(5):1100–1113. https://doi.org/ 10.1016/j.comnet.2010.11004 15. IEEE (2011) IEEE standard for synchrophasor measurements for power systems. https://doi. org/10.1109/IEEESTD.2011.6111219 16. IEEE (2014) IEEE standard for synchrophasor measurements for power systems-amendment 1: modification of selected performance requirements. https://doi.org/10.1109/IEEESTD.2014. 6804630

References

113

17. IEEE (2011) IEEE standard for synchrophasor data transfer for power systems. https://doi.org/ 10.1109/IEEESTD.2011.6111222 18. Giustina DD et al (2014) Electrical distribution system state estimation: measurement issues and challenge. IEEE Instrum Meas Mag 17(6):36–42. https://doi.org/10.1109/MIM.2014.696 8929 19. Primadianto A, Lu CN (2017) A review on distribution system state estimation. IEEE Trans Power Syst 99. https://doi.org/10.1109/TPWRS.2016.2632156.1-1 20. Pau M, Pegoraro PA, Sulis S (2013) Efficient branch-current-based distribution sys-tem state estimation including synchronized measurements. IEEE Trans In-strum Meas 62(9):2419– 2429. https://doi.org/10.1109/TIM.2013.2272397 21. Donohoe M et al (2015) Context-awareness and the smart grid: requirements and challenges. Comput Netw79:263–282. https://doi.org/10.1016/j.comnet.2015.01.007 22. Silva JA, Funmilayo HB, Bulter-Purry KL (2007) Impact of distributed generation on the IEEE 34 node radial test feeder with overcurrent protection. In: 39th North American Power Symposium (NAPS), pp 49–57. https://doi.org/10.1109/NAPS.2007.4402285 23. IEEE PES distribution test feeders. http://ewh.ieee.org/soc/pes/dsacom/testfeeders/ 24. Munshi AA et al (2017) Big data framework for analytics in smart grids. Electric Power Syst Res 151:369–380. https://doi.org/10.1016/j.epsr.2017.06.006 25. Serrano N, Gallardo G, Hernantes J (2015) Infrastructure as a service and cloud technologies. Softw IEEE 32(2):30–36 26. Lichman M. UCI machine learning repository, p 201. http://archive.ics.uci.edu/ml 27. Solar Radiation Research Laboratory (BMS) (2017) http://www.nrel.gov/midc/srrlbms 28. Kingspan wind, KW3 Small Wind Turbines, RAL 9005, Datasheet 29. SUNPOWER (2011) E20/435 Solar Panel, SPR-435NE-WHT-D Datasheet 30. Robinson AP et al (2013) Analysis of electric vehicle driver recharging demand profiles and subsequent impacts on the carbon content of electric vehicle trips. Energy Policy 61:337–348 31. Alonso M et al (2014) Optimal charging scheduling of electric vehicles in smart grids by heuristic algorithms. Energies 7(4):2449–2475 32. Irish Social Science Data Archive (ISSDA) (2017). www.ucd.ie/issda

Chapter 3

Sensing Technology

In the of the Internet of Things (IoT), particularly in smart grids, sensing devices assume a pivotal role in gathering essential information. These devices possess the capability to acquire not only physical, chemical, and biological data but also embedded information, including RFID tags and barcodes. The technologies employed by sensing equipment can be classified into three categories: sensor technology, RFID technology, and barcode technology, all of which contribute to the integration of valuable information. Furthermore, measuring equipment, consisting of sensors as sensitive components, forms an integral part of the IoT infrastructure. Complementing these technologies is image recognition, which employs image sensors to capture visual data, enabling multi-dimensional information processing. Thus, the crux of sensing technology lies in the amalgamation of sensor technology and information implantation technology. Currently, sensor technology is advancing towards the realm of smart sensors, while information implantation technology, spearheaded by RFID and barcode systems, is progressing towards wireless, multi-functional, and multi-dimensional capabilities.

3.1 Smart Sensor Smart sensors possess a range of functionalities, including data acquisition, conversion, analysis, and even decision-making capabilities [1]. With the proliferation and rapid advancement of the IoT, the market for smart sensors is anticipated to experience a remarkable growth rate, projected at an average annual rate of approximately 36%. By 2020, this market is expected to reach a value of 10.4 billion US dollars [2]. Smart sensors primarily consist of silicon materials and are manufactured utilizing microfabrication and CMOS integrated circuit technology. Based on their manufacturing processes, smart sensors can be categorized into three main types: MicroElectro-Mechanical System (MEMS), Complementary Metal Oxide Semiconductor

© Chemical Industry Press 2023 X. Zeng and S. Bao, Key Technologies of Internet of Things and Smart Grid, Advanced and Intelligent Manufacturing in China, https://doi.org/10.1007/978-981-99-7603-4_3

115

116

3 Sensing Technology

(CMOS), and Spectroscopy. MEMS and CMOS technologies enable cost-effective, large-scale production, while integrating sensitive components, bias and conditioning circuits, and integrated circuits within the same substrate or package. This integration empowers smart sensors with multiple sensing functions and intelligent data processing capabilities [3]. Looking ahead, the future of sensor development lies in intelligence, miniaturization, and biomimicry. Currently, smart sensors have found widespread applications in various aspects of social life, particularly in domains such as smartphones, smart homes, wearable devices, industrial control equipment, smart buildings, medical devices, and military applications.

3.1.1 Composition of Smart Sensors

Sensor 2

Sensor n

Multiplexer

Sensor1

Programming Control Amplifier

A/D

Keyboard/ Monitor/Print

Microprocessor

A smart sensor represents a sensor equipped with a microprocessor at its core, endowing it with the capabilities of detection, analysis, and information processing. A smart sensor, illustrated in Fig. 3.1, represents a computerized detection system with a microprocessor serving as its core component. Smart sensors exist in two primary forms: intellectualization and intelligence. Intellectualized sensors utilize microprocessors or microcomputer systems to expand and enhance the functionality of traditional sensors. In this case, the sensor and microprocessor may function as independent units. The sensor’s output signal is amplified, converted, and then processed further by the microprocessor. On the other hand, intelligent sensors are smart sensors that incorporate semiconductor technology to integrate the conductor (or transducer) component, signal amplification and processing circuits, and microprocessors into a single chip, forming a large-scale integrated circuit. Smart sensors offer multiple functions, demonstrate high reliability, boast compact sizes, enable high integration, facilitate mass production, are user-friendly, and provide cost-effective solutions. These characteristics make them an inevitable direction for sensor development. Presently, the widespread adoption of intelligent sensors is achieved through the intellectualization of sensors.

RAM ROM Analog output A/D C.I

Fig. 3.1 Block diagram of smart sensors. Note Communication Interface = C.I

3.1 Smart Sensor

117

(1) Sensor In the aforementioned Fig. 3.1, the sensor primarily comprises sensitive elements and conversion elements. The sensitive element serves as the central component of the sensor, and Table 3.1 illustrates the commonly used sensitive elements. Smart sensors, in general, have the capability to integrate multiple sensors, enabling them to achieve awareness across various scenarios. The sensitive components play a crucial role in transforming the sensed parameters into electrical parameters, which are then further processed through the conversion elements. (2) Multiplexer The primary function of a multiplexer is to facilitate the input of multiple electric signals from multiple sensors into a subsequent processing system, which typically consists of amplification, analog-to-digital (A/D) conversion, and a microprocessor. This input is accomplished through time-division multiplexing, where the multiplexer sequentially switches between the different sensor signals. By employing this approach, the processing system can effectively handle and process the information captured by multiple sensitive components in a corresponding manner. (3) Programming Control Amplifier In smart sensors, the integrated analog signal amplifier can be dynamically controlled through programming, allowing for adjustable amplification performance. As the analog signals from multiple switches in the sensor may have varying amplitudes or power levels, it becomes necessary to amplify them in different ways. By implementing program control over the amplifier, the flexibility and adaptability of the amplification process are significantly enhanced. (4) A/D Converter The amplified analog signal is subsequently converted into a digital signal to facilitate processing by the microprocessor. The accuracy of the analog-to-digital (A/D) converter plays a crucial role in determining the overall precision and accuracy of the sensor. Consequently, high-precision A/D conversion is essential. It is worth noting that the number of bits in the converter directly affects the conversion precision. Generally, a higher number of bits results in higher conversion precision. However, it also implies increased cost. Therefore, there exists a tradeoff between the desired level of conversion precision and the associated cost implications. Finding the right balance becomes crucial in designing a cost-effective and accurate sensor system. (5) Microprocessor The microprocessor serves as the central component of a smart sensor. In many cases, an 8-bit microprocessor is sufficient to meet the processing power requirements. However, to fully support the functionality of the smart sensor, various software components are necessary. These include scheduling software (or an operating system), signal processing software, communication software, control software, as well as sensor performance compensation and calibration software. The software system running on the microprocessor is crucial for achieving high-performance smart sensors.

118

3 Sensing Technology

Table 3.1 Main sensiing elements of conventional sensors Function

Main sensitive elements

Force (pressure) to displacement conversion Elastic elements (rings, beams, cylinders, diaphragms, capsules, bellows, spring tubes) Displacement sensing

Potentiometer, inductor, capacitor, differential transformer, eddy current coils, capacitive grids, magnetic grids, inductive synchros, Hall elements, gratings, code discs, strain gauges, optical fibers, gyroscopes

Force sensing

Semiconductor piezoresistive element, piezoelectric ceramic, quartz crystal, piezoelectric semiconductor, polymer piezoelectric bodies, piezoelectric magnetic element

Thermal sensing

Metal thermal resistance, Semiconductor thermistor, PN junction, pyroelectric device, Thermal wire probe, Strong magnetic body, Strong dielectric

Photo sensing

Phototube, Photomultiplier Tube, Photodiode, Color-sensitive triode, Optical fiber, CCD, Pyroelectric device

Magnetic sensing

Hall elements, Semiconductor magnetoresistive elements, Ferromagnetic metal thin film magnetoresistive elements (superconducting devices)

Acoustic sensing

Piezoelectric vibrator

Ray sensing

Scintillation counter, Ionization chamber, Geiger counter, PN diodes, Surface barrier diodes, PIN diodes, MIS diodes, Channel photomultiplier tubes

Gas sensing

MOS gas sensor, thermal conduction element, Semiconductor gas sensor element, Concentration battery, Infrared absorption gas sensor

Moisture sensiing

MOS moisture sensor, Electrolyte humidity sensor, Polymer capacitive humidity sensor, Polymer resistive humidity sensor, thermistor, CFT humidity sensor

Mass-sensitive

Solid phase enzyme membrane, Periphase microbial membrane, Animal and plant tissue membrane, Ion sensitive field effect transistor

(6) Storage, Display/Print and Keyboard The memory component of a smart sensor consists of Random Access Memory (RAM) and Read-Only Memory (ROM). RAM is responsible for temporary data storage during sensor operation, while ROM stores software and instructions that are essential for the sensor’s functioning. The memory system is tightly integrated with the microprocessor and forms an integral part of the overall microprocessor system.

3.1 Smart Sensor

119

In most cases, smart sensors do not have local display capabilities. However, certain applications, such as smart meters, may require local display or printing functionalities. These smart meters incorporate visual displays to present relevant information to users, and they may also include keyboards or other input/output devices for user interaction. (7) A/D and Communication Interface If an analog signal output is required from the smart sensor, the signal processed by the microprocessor needs to undergo digital-to-analog (D/A) conversion. The precision of the D/A converter can be adjusted through program control to meet specific requirements. On the other hand, if the output needs to be in digital format, it can be achieved through the communication interface. The communication interface not only facilitates data output but also serves additional functions such as downloading sensor parameters and updating the software system. In the present, sensors, especially smart sensors, are increasingly integrated with wireless sensor networks and technologies like Radio Frequency Identification (RFID) or Near Field Communication (NFC). Consequently, communication interfaces take various forms to support such integration. Common forms of communication interfaces include serial ports, USB ports, IP (RJ45) interfaces, and bus interfaces.

3.1.2 Software System of Smart Sensor The software system of a smart sensor encompasses several essential components that collectively enable its effective and reliable operation. These components include: scheduling software (or OS), signal processing software, communication software, control software, and sensor performance compensation and calibration software, which support the effective and reliable operation of the entire sensor. (1) Scheduling Software (OS) A smart sensor requires scheduling software (or an operating system) to ensure coordinated and orderly execution of its functions. This software acts as a simplified operating system, managing tasks and processes within the sensor. It ensures efficient and secure operation from data collection to output, display, and transmission. (a) Data Acquisition The data acquisition task in a smart sensor involves initiating and managing the multiplexer, starting and configuring the amplifier, and initiating A/D conversion. This task enables the sensor to capture measured quantities from the target object effectively.

120

3 Sensing Technology

(b) Data Reading and Processing The process within the smart sensor is responsible for scheduling and managing the I/O port of the microprocessor. It reads data from external sources and saves processed data to the memory. Additionally, it dispatches (calls) the signal processing software when data processing is required. This task includes read-in processes for data input and the calling of information processing processes for data analysis and manipulation. (c) Memory Management The process within the smart sensor is responsible for managing memory in the microprocessor. The memory is divided into two parts: temporary storage memory for processing information and the memory data table (or memory database) for data management. This task involves several processes, including: • Allocation, use, and recovery of temporary memory: The process handles the allocation of temporary memory for storing and processing information. It ensures efficient utilization of the memory and releases it when it is no longer needed, allowing for optimal memory management. • Initial allocation, locking, and read–write control of the memory data table: This process is responsible for the initial allocation of memory for the data table. It also manages the locking mechanism to ensure data integrity and concurrency control. Additionally, it controls the read and write operations on the memory data table, providing secure and controlled access to the stored data. (d) Priority Management The process within the smart sensor manages the priority of each task based on their importance. The tasks are assigned priorities in the following order, from high to low: communication process, control process, signal processing process, parameter loading process, display and keyboard process. (e) Display and Keyboard Management The process manages the display of processed data and manage local keyboard input. (2) Signal Processing Software The signal processing software mainly perform the process of denoising and filtering, computation, and decision-making of the acquisition data. (a) Denoising and Filtering After the data from the sensor undergoes amplification and analog-to-digital conversion, it enters the microprocessor for further processing. However, the collected data often contains unwanted noise or unnecessary signals, requiring appropriate processing.

3.1 Smart Sensor

121

The collected signals are typically low-speed and the data volume is not excessively large. As a result, digital signal processing methods such as Fourier analysis, spectrum analysis, and other techniques can be employed to effectively denoise, filter, and extract the desired signals from the data. (b) Computation, and Decision-making After the collected information is denoised and filtered, the resulting data is typically in the form of raw, unprocessed data. To derive meaningful engineering data from this raw data, appropriate calculation formulas and algorithms are applied. These formulas and algorithms are designed to perform specific computation conversions, transforming the raw data into the desired engineering parameters or measurements. Additionally, certain types of information collected by the smart sensor may require decision-making processes to determine their states or conditions. This could include parameters that indicate positive or negative results for biochemical analysis, for example. Decision-making software is employed within the smart sensor to analyze the processed data, apply predefined criteria or rules, and make decisions based on the specific requirements or conditions set for the sensor. The decision-making software utilizes computational algorithms and logical rules to interpret the processed data and make informed decisions or determinations. It enhances the functionality of the smart sensor by enabling it to autonomously analyze and interpret the data, providing valuable insights or actions based on the desired outcomes or predefined criteria. By incorporating calculation formulas and decision-making software, the smart sensor can transform raw data into meaningful engineering parameters and make intelligent decisions based on the analyzed information. This enhances the sensor’s capabilities and usefulness in various applications. (3) Control Software The control software is mainly responsible for controlling the work and operation of A/D conversion, multi-way switch, amplifier, display and keyboard. (4) Calibration Software The calibration software is mainly responsible for calibrating the smart sensors to ensure that the data collected by each sensor has high accuracy. Calibration software generally requires to be calibrated according to the static curve and dynamic curve of the sensor sensitive element. The static and dynamic curve can be downloaded to the local micro-processing or received through the communication interface from the upper-level transmission curve for calibration. (5) Communication Software In order to transmit processed information and receive commands from a superior entity, smart sensors rely on communication interfaces. Additionally, downloading sensor parameters also requires data transmission through these interfaces. To ensure

122

3 Sensing Technology

the security and reliability of data transmission and information exchange, appropriate communication protocols are necessary. These protocols facilitate error-free transmission of data and protect against unauthorized access or data breaches. With the integration of sensors and technologies like wireless sensor networks, RFID, NFC, and Bluetooth, it becomes crucial to consider communication protocols that are adaptable and compatible with multiple versions. These protocols should be capable of meeting the evolving needs of the IoT. In essence, a smart sensor can be viewed as an IoT terminal, encompassing not only data acquisition capabilities but also communication functions. One potential optimal choice for smart sensors is the 6LoWPAN (IPv6 over Low-Power Wireless Personal Area Network) protocol. This protocol is specifically designed to align with the development of the IoT. It enables smart sensors to efficiently connect to the IoT infrastructure, facilitating seamless communication and integration within the network. By utilizing the 6LoWPAN protocol, smart sensors can achieve interoperability, scalability, and secure data transmission, enhancing their effectiveness and compatibility in the IoT environment. (6) Scheduling Timing of Scheduling Software (Operating System) Indeed, the scheduling software or operating system within a smart sensor functions as a controlled real-time system. The task scheduling within the smart sensor is typically executed based on superior commands or orders. This approach is adopted due to the fact that information collection from sensing objects does not always need to occur continuously. The scheduling software ensures that tasks are performed at specific times or as directed by external commands, optimizing resource utilization and power efficiency. Furthermore, it is essential to consider the integration of smart sensors with external systems such as wireless sensor networks, RFID, NFC, and Bluetooth. These systems often operate under power-constrained conditions, emphasizing the need for smart sensors to operate efficiently and consume minimal power. By aligning the power consumption of smart sensors with these external systems, overall system energy efficiency can be achieved. The scheduling sequence of the scheduling software within a smart sensor can be visualized using Fig. 3.2. This sequence outlines the order and timing of tasks within the software, ensuring coordinated and efficient execution of various processes and functions. By following this scheduling sequence, the smart sensor can effectively carry out data collection, processing, communication, and other operations, while minimizing power consumption and meeting the requirements of the integrated external systems.

Sleep interval

Data acquisition

Denoising filtering, computing, decision-making

scheduling cycle

Fig. 3.2 Scheduling sequence

storage

Sleep

communication

Time

3.1 Smart Sensor

123

Upon the start of the scheduling software, the first step is to initialize various components and interfaces within the smart sensor. This includes initializing the multiplexer, A/D conversion module, communication interface, display, keyboard (if applicable), and memory. These initializations ensure that the system is prepared for subsequent operations. Once the initialization is complete, the scheduling software begins scanning the communication interface regularly to check for incoming commands. This includes checking for commands related to parameter download or data output. Regular scanning ensures that the smart sensor can promptly respond to external commands and perform the requested actions. As the acquisition data time counter reaches zero, indicating the designated time for data acquisition, the sensor initiates the tasks involved in the data acquisition process. These tasks typically include data collection, denoising, filtering, calculation, decision-making, and storage. The sensor executes these tasks to process the collected data and extract meaningful information. Upon completing the required tasks, the system enters a sleep state, conserving power while awaiting the arrival of the next communication command or triggering event. This sleep state allows the smart sensor to minimize power consumption during periods of inactivity, effectively managing energy resources. By following this scheduling approach, the smart sensor can perform its designated tasks, respond to external commands, and conserve power during idle periods, ensuring efficient and effective operation.

3.1.3 MEMS MEMS (Micro-Electro-Mechanical System) technology refers to the integration of micro-mechanical components, sensors, actuators, signal processing circuits, and other functionalities on a single chip. It has been made possible by advancements in semiconductor fabrication and precision machining techniques. MEMS devices are characterized by their miniaturization, integration, and mass-production capabilities, making them an important field of study [4]. Micromachining technology plays a key role in MEMS processing. It involves the creation of various structures such as holes, grooves, cones, and hemispheres on silicon chips to form mechanical components like diaphragms, cantilever beams, bridges, and mass blocks. By combining these elements, complex micro-mechanical systems can be realized. This technology enables the integration of valves, springs, vibrators, nozzles, regulators, and sensors on silicon chips to create micro-mechanical systems. The sensors manufactured using MEMS processing technology are known as MEMS sensors or micro sensors.

124

3 Sensing Technology

MEMS sensors offer several advantages over traditional sensors. Firstly, their performance is greatly enhanced by on-chip signal amplification, reducing interference and improving the signal-to-noise ratio. Integration of feedback and compensation circuits improves linearity, frequency response, temperature stability, and sensitivity. Secondly, MEMS sensors can be arrayed on a single chip, allowing for the integration of multiple identical sensitive elements and facilitating parallel sensing. Thirdly, MEMS sensors are highly compatible with microelectronic devices, making integration and packaging straightforward. Finally, MEMS sensors can be massproduced using established silicon microfabrication techniques, resulting in low-cost production. One prominent application of MEMS sensors is in miniature inertial devices, which are highly valuable in defense applications. Micro silicon gyroscopes and micro silicon accelerometers are examples of micro inertial devices. Micro-silicon acceleration sensors, produced through micro-machining techniques, are characterized by their simple structure, small size, low power consumption, massproducibility, and affordability. They find application in various fields such as satellite microgravity measurements, miniature inertial measurement systems, automotive safety systems, inclination measurement, impact measurement, and more. Overall, MEMS technology has revolutionized the field of sensor development, enabling the creation of highly compact, integrated, and cost-effective sensors with improved performance characteristics.

3.2 Wireless Biochemical Sensor Over the past two decades, there has been a remarkable advancement in the field of wireless chemical sensors, encompassing a wide range of disciplines and application domains. The need for distributed chemical (biological) sensing has surged in areas such as healthcare, security, food chain management, sports, and mobile communication technology advancements. Furthermore, the continuous progress in miniaturization techniques and the development of advanced sensing methods have played pivotal roles in propelling the growth of wireless biochemical sensor technology. Wireless chemical sensors serve as vital components and prerequisites for the advancement of IoT. Through the integration of IoT, individuals can interact and communicate with both their surroundings and the local environment, all while automatically responding to the sensed information. Projections suggest that by the year 2025, the global count of connected objects and devices is expected to surpass an astonishing 7 trillion, encompassing a wide array of applications, including diverse biochemical sensors [5]. In the field of IoT-enabled smart medical care, real-time monitoring of personal physiological information is crucial. This necessitates the utilization of biochemical sensors designed to sense and track human physiological data. Simultaneously, the expansion and widespread implementation of IoT in environmental monitoring call for the deployment of biochemical sensors to effectively monitor environmental

3.2 Wireless Biochemical Sensor

Biochemical Sensor

Wireless Device

125

Wireless Device

Analysis and processing

Fig. 3.3 Basic structure of wireless biochemical sensor

information. Throughout the evolutionary trajectory of IoT, encompassing the development of diverse short-range wireless communication technologies and cellular mobile communication technologies, biochemical sensors have become deeply integrated with these advancements, resulting in a broad spectrum of applications. Notably, this integration has given rise to wearable devices, which play a pivotal role in advancing the field of smart medicine. Wireless chemical (biological) sensors are capable of gathering biochemical data from their immediate surroundings. These sensors subsequently employ wireless technology to transmit the acquired data to remote devices [6], as depicted in Fig. 3.3. Wireless chemical (biological) sensors have the capability to establish various network topologies, including point-to-point, point-to-multipoint, and mesh configurations, thereby creating a wireless biochemical sensor network. This network operates in a manner similar to traditional wireless sensor networks, facilitating the transmission of collected data from multiple sources to an information processing center. Moreover, the integration of chemical (biological) sensor networks with personal area network technology and smartphones presents an opportunity to enhance wearable devices and cater to the demands of smart medical care, thereby elevating the overall level of healthcare provision. Chemical (biological) sensors are commonly categorized based on their underlying conversion principles. In wireless chemical sensors, the transducing elements predominantly consist of electrochemical, optical, electrical, and mass-sensitive devices [7, 8]. Electrochemical sensors operate by converting the electrochemical reactions of analytes, which are attached to electrodes, into voltage or current signals. These sensors can be further classified into potential sensors and current sensors. Electrochemical sensors are typically straightforward, as the analytical signal originates from alterations in the electrical properties of the material. For instance, a change in conductivity of a metal oxide semiconductor or an organic semiconductor occurs due to chemical interactions with the analyte. Optical chemical sensors rely on the measurement of optical phenomena resulting from analyte-receptor interactions. These sensors can directly measure the absorbance of an analyte or an analyte-sensitive indicator molecule, as well as the luminescence intensity emitted by an indicator molecule in the receptor layer. Mass-sensitive chemical sensors detect changes in mass by monitoring alterations in analyte mass on the sensor surface due to chemical reactions.

126

3 Sensing Technology

3.2.1 Wireless Electrochemical Sensors Electrochemical sensors are the most common type of chemical sensors, employing potentiometry and amperometry as the primary transduction methods.1 Potentiometry sensors offer a compelling advantage for integration into wireless chemical (biological) sensors due to their advantageous features, including low power consumption, straightforward operation, and a wide dynamic range. A notable example of this integration is the utilization of smart wireless RFID tags, which leverage the potential conduction mechanism to incorporate chemical sensors for detecting pH electrodes and ISE pH values [9]. For instance, Novell et al. successfully developed a solid-contact ISE based on plasticized PVC to measure K+ and Mg2+ concentrations, integrating them with a low-power RFID platform [10]. These wireless electrochemical sensors exhibited remarkable analytical performance in terms of sensitivity, linearity, limit of detection (LOD), and drift. A comprehensive overview of the current noteworthy wireless chemical (biological) sensors, employing potential conduction as the underlying principle, can be found in Table 3.2.

3.2.2 Wireless Electrical Sensors Conductometric sensors are widely recognized as the most popular type of electrical sensors, primarily utilized for wireless gas detection systems due to their conductometric transduction capabilities. (1) MOx Metal oxide semiconductor (MOx) gas sensors have gained significant popularity in commercial applications due to their exceptional sensitivity. These sensors have also found integration into wireless sensors designed for the detection of greenhouse gases [11] and combustible gases such as hydrogen (H2 ) and methane (CH4 ) [12]. However, an inherent challenge with MOx sensors is their requirement for high operating temperatures, resulting in substantial power consumption that limits the battery life of these devices. In an alternative approach, gas sensing can be achieved using different semiconductor materials that operate at room temperature, leading to reduced power consumption. Steinberg et al. developed an RFID-based conductometric sensor by coating interdigitated gold electrodes with the conducting polymer PEDOT:PSS. This novel sensor configuration holds great potential for applications in smart agriculture [13, 14], offering a more energy-efficient solution for gas sensing compared to high-temperature MOx sensors.

1

The conduction method refers to the conversion of non-electrical quantities into electrical quantities so that they can be further measured.

3.2 Wireless Biochemical Sensor

127

Table 3.2 Current typical wireless electrochemical chemical sensors [6]

Analyte pH K+ , Mg2+ K+ pH NO3 − Na+ Na+ Na+ Na+ Na+ , K+ , glucose, lactate pH, Na+ , lactate pH

Recognition element Glass electrode ISE: Ionophore in polymer membrane ISE (FET) ISE: Ionophore in polymer membrane ISE

Transduction mechanism Potentiometry Potentiometry

Wireless system RFID RFID

Potentiometry Potentiometry

ISE: Ionophore in polymer membrane) ISE: Ionophore in polymer membrane ISE; Ionophore in polymer membrane ISE: Ionophore in polymer membrane ISE (Na+ , K+ ), GOx, LOx ISE (pH, Na+ )

Potentiometry

Bluetooth General ISM/SRD Environmental, Water quality monitoring GPRS Environmental, Water quality monitoring Bluetooth Sports, sweat monitoring

Potentiometry

ZigBee

Potentiometry

Bluetooth Sports, sweat monitoring

Potentiometry

RFID/ NFC

Potentiometry, Amperometry Potentiometry, Amperometry Potentiometry

Bluetooth Sports, sweat monitoring Bluetooth Sports, sweat monitoring Bluetooth Implantable, blood CO2

Glucose and lactate Glucose

GOx

Glucose

GOx

L-lactic acid

LOx

Glucose

General General

Potentiometry

Poly (phenylene diamine) ISE: Ionophore Potentiometry in polymer membrane GOx on ZnO Potentiometry nanowires GOx/LOx Amperometry

pH, K+ , Na+ , Cl−

Application

Sports, sweat monitoring

Sports, sweat monitoring

Bluetooth Implantable, Home-care system GSM Healthcare

ISM/SRD Implantable, Brain glucose and lactate Amperometry Bluetooth Wearable, (Microchip interstitial fluid electrophoresis) metabolites Amperometry ISM/SRD Implantable, fish monitoring (stress) Amperometry ISM/SRD Implantable, fish monitoring (stress) (continued)

128

3 Sensing Technology

Table 3.2 (continued)

Analyte

Recognition element Cholesterol oxidase

Transduction mechanism Amperometry

Uric acid

Uricase

Amperometry

K3 Fe(CN)6 glucose Uric acid

Direct Amperometry reduction, GOx Uricase Amperometry

Uric acid

Uricase

Amperometry

Glucose

Glucose oxidase

Amperometry

Cholesterol

Lactate Ethanol

LOx Alcohol oxidase CO, SO2 , NO2 Direct oxidation/ reduction Fe(CN)6 4−/3− (model) Au electrode Dopamine Carbon nanofiber array electrodes pH, Cd2+ , Pb2+ Polyglycine, ZnO-graphene

Amperometry Amperometry Amperometry CV Fast scan cyclic voltammetry Potentiometry, CV, DPSV, SWASV

Wireless Application system ISM/SRD Implantable, fish monitoring (metabolism) ISM/SRD Implantable, bird monitoring (metabolism) RFID Gerenal RFID

Wound monitoring Bluetooth Healthcare, saliva monitoring Custom Healthcare, saliva monitoring NFC Sports Bluetooth Alcoholism control Bluetooth Environmental, pollution Bluetooth Food quality Bluetooth Implantable, neurological monitoring RFID Environmental, natural water monitoring

(2) CNT The utilization of carbon nanotubes (CNTs) combined with Bluetooth technology has been successfully applied in methanol detection sensors. The operating principle relies on the adsorption of reducing gases, such as methanol, onto CNTs, resulting in a decrease in CNT conductivity [15]. This change in conductivity enables the detection of the concentration of these gases. CNT-based conductometric sensors can be further enhanced by functionalization techniques to augment their sensitivity and selectivity towards specific gases. For instance, the introduction of carbonyl groups on the outer walls of multi-walled carbon nanotubes (MWCNTs) through oxygen plasma treatment enhances their sensitivity and selectivity towards NO2 [16]. By integrating this technology into a battery-assisted passive RFID-based NO2 sensor, remarkable attributes are achieved, including low cross-sensitivity to common interfering gases and minimal power consumption. The calculated operational lifespan of this sensor reaches an impressive 10 years while sampling at a rate of 12 samples per hour.

3.2 Wireless Biochemical Sensor

129

(3) SWCNT Single-walled carbon nanotubes (SWCNTs) have been effectively modified using single-stranded DNA, resulting in a significant 300% increase in their response towards specific explosives [17]. The DNA molecules self-assemble onto the surface of SWCNTs through non-covalent interactions, preserving the electronic properties of the nanotubes. This modified sensor, integrated with a ZigBee module, demonstrated its capability to detect dinitrotoluene (a TNT model) with high sensitivity and accuracy. The integration of both carbon nanotubes (CNTs) and conducting polymers within the same sensor yields a synergistic effect. CNTs enhance the stability of the polymer layer, while the conducting polymer improves the selectivity of the bare CNTs. In a study by Gou et al., a pH sensor was developed by immobilizing SWCNTs on gold electrodes and subsequently electropolymerizing poly(1-amino anthracene) on top [18]. This passive RFID sensor showcased a Nernstian pH response across a wide pH range (2–12) and maintained its sensitivity for over 120 days. Another notable example involves a multi-walled carbon nanotube (MWCNT)/ polypyrrole sensor designed for the detection of sevoflurane, a harmful anaesthetic agent commonly found in surgical wards [19]. Lorwongtragool et al. devised a wearable electronic nose capable of real-time monitoring of volatile organic compounds (VOCs) using a ZigBee network [20]. This innovative system demonstrates the potential of combining CNTs and conducting polymers in developing advanced sensors for various applications. (4) Silver Electrode Inkjet Printing technology A notable development in sensor technology involves the inkjet printing of silver electrodes on a flexible substrate made of polyethylene naphthalate, which are then modified with MWCNT/polymer ink. This advancement has led to the creation of a fully-printed environmental sensor based on battery-assisted passive (BAP) RFID technology, enabling simultaneous detection of humidity, temperature, and NH3 [21]. The sensor comprises a capacitive humidity sensor, consisting of cellulose acetate butyrate on interdigitated silver electrodes, and a conductometric NH3 sensor utilizing polyaniline/carbon nanocomposite. Both sensors are inkjet printed on a flexible PET substrate, while the circuitry and antenna are screen printed. The wireless system boasts a lifespan of 57 days when measurements are taken once per hour [22]. The sensor array comprises eight sets of interdigitated capacitors, each coated with different polymers such as PHEMA, PDMS, or PMMA. These polymers exhibit varying sensitivities to sorption of water, ethanol, and ethyl acetate. To facilitate operation, graphene serves as an electron-transferring carrier, while ZnO acts as a catalytic oxidant. The sensor can be conveniently operated via a smartphone application using Bluetooth technology [23]. For further reference, Table 3.3 provides an overview of commonly used wireless electrochemical sensors.

130

3 Sensing Technology

Table 3.3 Common application wireless, electrochemical sensors [6] Analyte

Recognition element

Transduction mechanism

Wireless system

Application

CO, CO2 ,SOx , NOx , O2

MOx

Conductometric

ZigBee

Environmental, pollution

H2 , CH4

Fe2 O3

Conductometric

ZigBee

Security

Ethanol

PEDOT:PSS

Conductometric

RFID

General

Methanol

CNT

Conductometric

Bluetooth

General

NO2

Oxygen plasma modified MWCNT

Conductometric

RFID

General

DNT

DNA-modified SWCNT

Conductometric

ZigBee

Security

pH

poly(1-amino anthracene) with SWCNT

Conductometric

RFID

General

Sevoflurane

MWCNT-loaded Polypyrrole

Conductometric

ISM/SRD

General

VOCs

MWCNT/polymer Conductometric

ZigBee

Environmental Wearable

NH3 humidity

Polyaniline/ carbon nanocomposite

Conductometric capacitive

RFID

Environmental

VOCs (EtOH, EtOAc,湿度)

PDMS, PBMA, PHEMA

Capacitive

ZigBee

Environmental (industrial)

VOCs

ZnO-graphene modified electrodes

Impedimetric

Bluetooth

Sports (acetone), other

3.2.3 Wireless Optical Sensors Optical sensors present an enticing prospect for integration with wireless platforms due to their ease of miniaturization, elimination of reference electrodes, and the availability of diverse indicator chemistries based on simple absorption and fluorescence principles. Certain modes of operation, such as fluorescence, offer exceptionally high sensitivity. However, a key concern for many wireless biochemical applications is the power consumption of light sources. While high-power laser sources can provide the highest sensitivity and limit of detection (LOD) in critical applications, they come with increased costs and power requirements, making them impractical for most wireless sensor implementations.

3.2 Wireless Biochemical Sensor

131

Light emitting diodes (LEDs) offer a viable solution for optical sensors in wireless applications. LEDs are cost-effective, reliable, and cover a broad electromagnetic spectrum ranging from ultraviolet (UV) to infrared (IR). They provide satisfactory analytical performance while minimizing power consumption, making them the preferred light source for most wireless optical sensors. (1) The Passive RFID tag employing dual wavelength optical absorption A dual-wavelength absorbing passive RFID tag wireless sensor can be created by incorporating two LEDs and a photodiode (PD), along with a sol–gel silicate film as the sensitive element [24]. The sol–gel silicate film serves as the platform for immobilizing a pH-sensitive dye, such as bromocresol green, enabling accurate pH detection within the range of 5.2–8.3 pH. By replacing the sensitive element with a valinomycin optode membrane, the sensor can be configured to detect potassium ions [25]. The valinomycin optode membrane exhibits specific selectivity towards potassium ions, enabling their accurate detection. This dual-wavelength absorbing passive RFID tag wireless sensor demonstrates the versatility and adaptability of sol–gel silicate films as sensitive elements, allowing for the detection of various analytes by simply modifying the immobilized dye or membrane. (2) Absorbance sensor Absorbance-based measurements can be implemented in various sensor designs, utilizing LEDs and photodiodes as light sources and detectors. Several examples of absorbance-based sensors are outlined below: Shepherd et al. developed an absorbance-based pH sensor for monitoring environmental gas acidity [26]. This sensor employs a paired emission-detection diode (PEDD) configuration, where an LED serves as both the light source and detector, with reverse biasing. The photocurrent is indirectly measured by monitoring the discharge time of the detector. Ethyl cellulose doped with bromophenol blue is coated on both LEDs, and multiple sensors were deployed to monitor plume movement. An ambient acidity sensor was created by incorporating a sol–gel silicate membrane containing a pH indicator dye (3’-3”dichlorophenol-sulfonephthalein) [26]. This sensor utilizes an LED light source and a photodiode (PD) detector, providing pH accuracy to 0.1 units. It is integrated with a ZigBee module for cultural heritage protection applications. A NO2 concentration sensor employs a sensitive element consisting of porous glass doped with diazo coupling reagents [26]. The sensor selectively reacts with NO2 to produce an azo dye, and the absorbance of the dye is measured using an LED and photodiode setup. Schyrr et al. developed a Bluetooth fiber-optic sensor for measuring wound pH [27]. The sensing data is obtained by coating a PMMA fiber with a sol–gel film containing a pH-sensitive dye. This portable pH detection device features an optocoupler and LED to measure changes in light absorption within the coatings.

132

3 Sensing Technology

An optical wireless smart bandage for wound pH detection was also developed [28]. A pH indicator dye is covalently bound to cellulose particles dispersed in a biocompatible hydrogel, enabling the creation of pH-sensitive layers on various types of wound dressing materials. This RFID-based bandage provides noninvasive monitoring of temporal pH changes in wounds during the healing process. The absorbance-based sensors utilizing LEDs and photodiodes offer versatile solutions for pH monitoring, environmental gas detection, and wound pH measurement in various applications. (3) Fluorescent Sensor Martinez-Olmos et al. developed a passive RFID-based fluorescent oxygen (O2 ) sensor designed for monitoring the freshness of packaged food [29]. The sensor system features a screen-printed RFID antenna on one side of a polyester substrate, along with an RFID chip, microcontroller, UV LED source, and RGB detector. On the opposite side, a polystyrene film containing platinum octaethylporphyrin (PtOEP) is placed. The fluorescence of PtOEP is quenched by oxygen, and this change in fluorescence can be detected through the polyester packaging. The system has been expanded to include additional colorimetric sensors for carbon dioxide (CO2 ), ammonia (NH3 ), and humidity, which are crucial analytes for monitoring food quality. Mortellaro et al. have developed a subcutaneously implanted glucose sensor that utilizes a fluorescent, non-enzymatic hydrogel with a bis-boronic acid derivative [30]. The hydrogel, composed of pHEMA, immobilizes the fluorescent bis-boronic acid derivative. Binding of glucose disrupts the intramolecular photoinduced electron transfer, resulting in increased fluorescence intensity. The sensor employs a UV LED and two photodiodes (PDs) to detect and measure the fluorescence changes. This passive sensor communicates through near-field communication (NFC) with a body-worn reader, equipped with a Bluetooth module for transmission over longer distances. Unlike enzyme-based systems, this sensor is not limited by stability issues and experiences less biofouling since the signal is derived from the entire hydrogel bulk rather than just the surface. For monitoring gastrointestinal bleeding, a swallowable wireless capsule has been developed, equipped with a built-in fluorimeter [31]. The fluorimeter detects the presence of a leaked fluorescent dye, previously injected into the bloodstream, within the gastrointestinal tract. The capsule transmits data to an external unit using the IEEE 802.15.4 protocol and can operate for several days, depending on the measurement and transmission rates. Lastly, a luminescent wireless chemical sensor is introduced, featuring a passive RFID tag integrated with a photodiode for measuring luminescence within a microfluidic channel [32]. The sensor was utilized for the detection of thyroid-stimulating hormone (TSH) in a sandwich microfluidic immunoassay. The sensor consumes minimal power, but the microfluidics are operated separately, and the measurement procedure involves multiple reagent additions and washing steps, resulting in a relatively lengthy process (Table 3.4).

Wireless system

Reflectance Fluorescence

Bromophenol blue

GJM 534

pH

pH

Absorbance

Bis-boronic acid fluorescent indicator

Glucose

Fluorescence

Pt octaethylporphyrin

α-naphtholphthalein, bromophenol blue, Absorbance, Fluorescence crystal violet, PdTFPP

O2

CO2 ,NH3 , humidity, O2

Absorbance

Reflectance

Bromocresol purple

7-Hydroxyphenoxazone

Reflectance

pH

Bromocresol purple

pH

Absorbance

Absorbance

Absorbance

pH

Bromocresol purple

pH

IR Absorbance

Direct absorbance

KMnO4 (model)

Humidity

Diazo coupling reagents

NO2

KMnO4 (model)

Absorbance

3’-3”-dichlorophenolsulfonephthalein

NH3 vapor

Application

Food quality

NFC

(continued)

Implantable, glucose monitoring

Food quality

RFID NFC

Wearable, wound monitoring

Wearable, wound monitoring

Sports, sweat monitoring

Sports, sweat monitoring

Sports, sweat monitoring

Environmental, water quality

General

General

Environmental, air quality

Environmental, Cultural Heritage conservation

Environmental, gas

General

General

RFID

Bluetooth

Bluetooth

Bluetooth

ISM/SRD

Bluetooth

Bluetooth

ZigBee

ISM/SRD

ZigBee

RFID ISM/SRD

Absorbance

Valinomycin, N Octadecanoyl-Nile blue Absorbance

Bromophenol blue

Acetic acid vapor

RFID

Model dye, K+

Transduction mechanism Reflectance

Recognition element

Bromocresol green

Analyte

pH

Table 3.4 Common application optical wireless chemical sensors [6]

3.2 Wireless Biochemical Sensor 133

Recognition element

Fluorescent labelled analyte binding protein

Direct flfluorimetry

Antibody (sandwich immunoassay)

Analyte

Glucose

Fluorescein (marker)

Thyroid-stimulating hormone TSH

Table 3.4 (continued)

Chemiluminescence

Fluorescence

Fluorescence

Transduction mechanism

RFID

Zigbee

ISM/SRD

Wireless system

Healthcare, POC diagnostics

Swallowable, Gastrointestinal bleeding

Implantable, glucose monitoring

Application

134 3 Sensing Technology

3.2 Wireless Biochemical Sensor

135

3.2.4 Wireless Sensor Using Other Transduction Mechanisms Mass-sensitive transducers are commonly employed for analog transmission of analytical data, relying on the measurement of analyte-induced resonant frequency changes. The micro-quartz tuning fork is currently utilized as the mass-sensitive element in such transducers. The changes in its resonance frequency, reflecting the mass variations caused by analyte interactions, are converted into digital information for output. This enables mobile phones to read and interpret the data through Bluetooth connectivity. (1) Ozone Sensor The ozone concentration sensor operates by monitoring the resonant frequency change of a tuning fork, which is induced by the oxidation of polybutadiene coating applied to it [33]. This sensor has been utilized as part of a portable environmental analyzer, which incorporates additional components such as a sampler/ preconcentrator, a separation unit (gas chromatography column), and an array of quartz tuning fork detectors functionalized with distinct molecularly imprinted polymers. With this analyzer, it is possible to repeatedly detect various volatile organic compounds (VOCs) including Benzene, Toluene, Ethylbenzene, and Xylene at parts per billion (ppb) levels. This enables accurate measurement of pollution levels in urban environments. (2) Ethanol Sensor Cheney et al. conducted a study on alcoholism and developed an implantable ethanol sensor for this purpose [34]. The sensor utilizes a microcantilever array that is coated with an ethanol-sensitive material called methyl phenyl mercaptopropyl silicone. To enhance selectivity, a hydrophobic nano-membrane with vapor permeability was incorporated into the sensor design. The functionality of the sensor was evaluated by monitoring the concentration of ethanol in the interstitial fluid of rats. According to the study, the sensor demonstrated a lifespan of up to 6 weeks, making it suitable for long-term monitoring of ethanol levels in the context of alcoholism research. (3) Other Wireless Chemical Sensors Other wireless chemical sensors commonly employ sensitive elements such as surface acoustic wave (SAW) [35, 36], magnetoelastic [37, 38], and resonancebased LC [39] elements. These elements, when combined with RFID tags and NFC technology, have enabled the development of various low-cost passive sensors for (bio)chemical sensing [40–43]. These sensitive elements have found applications in different types of sensors, including conductometric gas sensors [44], hydrogel-based pH sensors [45], fruit ripeness monitoring devices [46], bacteria detection sensors [47], amperometric glucose sensors [48], and circulating Anticancer Drug Monitors [49]. However, the stability of the sensing membrane and the sensitive element remains a limitation for these sensors.

136

3 Sensing Technology

3.2.5 Challenges and Key Technologies of Wireless Chemical (Bio) Sensors Wireless chemical (biological) sensors have made significant advancements and have found wide applications in various fields such as environmental monitoring, security and military applications, healthcare, exercise physiology, and food and agriculture. However, several challenges still need to be addressed, and breakthroughs in key technologies are necessary to overcome these challenges. Some of the prominent challenges and key technologies include: (1) Selectivity, Stability and Lifespan In the wireless chemical (bio)sensors, a significant hurdle to overcome is the attainment of satisfactory selectivity for the target analyte, coupled with the essential requirement of sensor stability and a suitable operational lifetime during field deployment. This challenge stems from the presence of a myriad of interfering species that exist within the complex and unrefined measurement matrices encountered in practical applications. These matrices encompass a wide range, including environmental waters, gas mixtures, as well as vital biological fluids such as blood, interstitial fluid, wound fluid, sweat, saliva, and tears. To address the crucial issue of selectivity, researchers have explored various approaches and methodologies. Enzymes have proven invaluable in enhancing specificity towards target analytes [50], while the development of analyte-specific conducting polymers [51] has showcased promising results in improving selectivity. Furthermore, functionalizing transducers using oxygen plasma treatment [52] and incorporating DNA or other biomolecules [53] as part of biochemical solutions have demonstrated enhanced selectivity capabilities. In addition, the construction of multisensor arrays and the application of advanced multivariate analysis techniques [17] have contributed significantly to boosting selectivity levels in wireless chemical (bio)sensors. (2) Stability and Lifespan In many environmental and healthcare applications, long-term stability is of utmost importance as sensors may need to operate continuously for extended periods, spanning months to years, without regular maintenance. Additionally, these sensors might be subjected to harsh environmental conditions during storage before their single-time use. Throughout their extended usage, sensors are exposed to complex sensing mediums that can lead to biofouling, thereby impacting sensor response and necessitating frequent re-calibration or maintenance, which is impractical. To address the challenges posed by biofouling, one common approach is to physically separate the sensing area from the sample through the incorporation of a (micro)fluidic system. However, this solution comes at the cost of increased complexity, higher power demands, and elevated production costs [54]. Alternatively, biofouling can be mitigated by employing surface chemistry modifications

3.2 Wireless Biochemical Sensor

137

and utilizing highly hydrophobic materials [55], which helps reduce the adhesion of unwanted substances. Innovative transduction mechanisms, such as fluorescent-based glucose sensing, are also being explored to overcome the limitations associated with poor stability of enzymatic components [56]. Moreover, the integration of novel physical and chemical transduction mechanisms with information processing technologies holds great potential in addressing the challenges related to sensor lifetime, sensitivity, and calibration issues. (3) Power Consumption Power consumption is a persistent challenge in the development of chemical sensors and their wireless transceivers, greatly impacting their practicality and applicability in various fields. Efforts are being made to tackle this issue by focusing on reducing the power requirements of peripheral components associated with sensor operation. One area of research focuses on minimizing the power consumption of fluid handling equipment used in conjunction with the sensors. Advances in this direction include the development of soft polymer actuators [57], which offer efficient and low-power mechanisms for fluid manipulation. Additionally, the design and implementation of low-power micro-pumps and valves [58] contribute to reducing overall energy consumption. Another promising development is the integration of fully passive textile micro-fluidic systems [59], which enable fluid control without the need for external power sources. In wearable devices, where power supply is a critical consideration, novel biofuel cells have emerged as a potential solution. These biofuel cells can harvest energy from sweat and tears, leveraging the body’s own resources to generate power for the sensor and its wireless transceiver [60, 61]. This approach offers a sustainable and selfsufficient energy solution, alleviating the reliance on traditional power sources and addressing the power supply challenge in wearable chemical sensing applications. (4) Flexible and Stretchable Substrates The utilization of flexible and stretchable substrates represents a crucial technological advancement in the field of wearable chemical sensors [62, 63]. These substrates offer unique benefits by enabling the fabrication of sensors that can conform to irregular surfaces, adapt to body movements, and provide enhanced wearer comfort. Flexible substrates are highly compatible with various established fabrication techniques, allowing for cost-effective production of wearable chemical sensors without compromising their performance [64]. Techniques such as screen printing and inkjet printing [65] have proven to be effective in producing sensors on flexible substrates, enabling large-scale and low-cost manufacturing. In addition, the integration of dyed textiles with indicator compounds has been explored for the development of optical sensors [66], further expanding the range of possibilities for wearable chemical sensing. The development of flexible wireless sensors based on flexible and stretchable materials holds significant potential in the field of smart medicine and healthcare

138

3 Sensing Technology

[67]. These sensors can be seamlessly integrated into wearable devices, allowing for real-time monitoring of various biochemical parameters. They have the capability to revolutionize personalized healthcare by providing continuous and non-invasive monitoring of vital signs, metabolites, and other relevant biomarkers.

3.3 Smart Meter The smart meter is a fundamental component of the smart grid within the context of the IoT. As a sensing device, the smart meter encompasses the essential characteristics of a sensing control layer in the IoT ecosystem. It is equipped with the capabilities to sense and monitor various performance parameters of the electrical grid, while also possessing computational and communication functions.

3.3.1 The Development and Basic Composition of Smart Meter (1) The Development of Smart Meter The development of smart meters has been progressing rapidly to meet the demands of implementing smart grids worldwide. These modern smart meters exhibit higher transmission rates and possess powerful and flexible application performance. As a result, traditional electricity meters, such as the watt-hour meter, are gradually being replaced by smart meters on a large scale [68]. The evolution of watt-hour meters has seen three generations of development. The initial generation featured analog electromechanical watt-hour meters, which had limited accuracy and could only measure active energy. In the 1990s, with advancements in microprocessor technology and fast analog-to-digital converters, watt-hour meters entered the second generation, where electronic components replaced the electromechanical counters for energy measurement. At the beginning of the twenty-first century, with further progress in electronic technology, instrumentation, communication, and data processing, the watt-hour meter reached its third generation [69]. This third-generation meter expanded its capabilities to measure various parameters, including: • Instantaneous parameters: voltage, current, power, power factor, and more. • Billing parameters: kilowatt-hour (kWh), reactive power (kVArh), maximum demand, load curves, and others. By integrating technologies like mobile communication and power line communication with these third-generation watt-hour meters, additional value-added services can be provided to users [70].

3.3 Smart Meter

139

Smart meters offer a range of intelligent features that distinguish them from traditional electricity meters. These features include dynamic pricing, demand response, remote power connection/disconnection, outage management, and network security. Smart meters provide higher accuracy at a lower cost and require less power, making them more efficient and cost-effective compared to their predecessors. By deploying smart meters equipped with high-speed bi-directional communication capabilities throughout the grid, it becomes possible to establish a dynamic and interactive infrastructure with advanced energy management capabilities, commonly known as the smart grid [71]. Overall, the ongoing development and deployment of smart meters are transforming the energy sector by enabling improved grid management, enhanced consumer engagement, and the integration of renewable energy sources. The transition to smart meters represents a significant step toward building a more efficient, sustainable, and responsive energy system for the future. (2) Basic Composition of Smart Meter A smart meter is composed of several essential modules that work together to enable its functionalities [72]: (a) Microcontroller Unit (MCU) The MCU is a key component that houses the integrated core with built-in flash memory. It provides flexibility for configuration, information processing, and interfaces with the host through suitable interface types or data input/output pins. (b) Analog-to-digital converter (ADC) The ADC is responsible for digitizing the acquired voltage and current signals, enabling digital signal processing. It can accurately measure phase current and voltage within a cycle. Advanced metering ICs with high-resolution 64-bit ADCs are commonly used in modern smart meters [73]. (c) Analog-Front End (AFE) The AFE module consists of a multiplexer, ADC converter, and voltage reference. It collects and calculates single-phase and multi-phase voltage, current, power, kilowatt-hour (kWh) readings, and power quality disturbances such as harmonics. (d) Liquid Crystal Display (LCD) Diver The LCD driver controls and displays the information on the LCD screen, enabling localized display output. If a local display function is not required, this module may not be included in the configuration. (e) Real-Time Clock (RTC) Smart meters often incorporate a real-time clock (RTC) to support time-based billing. The RTC ensures accurate timekeeping for billing purposes, especially for time-ofuse rates where electricity rates vary based on peak and off-peak periods. In some cases, if the smart meter is used for real-time phase monitoring and synchronization

140

3 Sensing Technology

with Phasor Measurement Units (PMUs), a GPS clock is used to ensure precise timing. In addition to these basic hardware modules, smart meters also require software components, security measures, and communication protocols: (f) Safety Smart meters incorporate safety features to protect against physical tampering events, ensuring mechanical and electronic tamper resistance. These measures help safeguard user data and privacy [74]. (g) Wireless/Wired Communication Protocol Stack Smart meters support various communication protocols based on the requirements of the concentrator and Advanced Metering Infrastructure (AMI). The communication protocol can be flexibly configured to accommodate different communication technologies and facilitate data exchange between the smart meter and the utility or other systems. (3) Smart Meter Solution Smart meter solutions can be categorized into three main types, each offering distinct advantages and features: (a) Based on Analog Front End (AFE) This solution focuses on the analog front-end of the smart meter. According to the latest metrology standards, smart meters with AFE should meet the requirements for conversion range and signal-to-noise ratio (SNR). Advanced meter ICs with high SNR and built-in automatic gain control (AGC) mechanisms in the analog-to-digital converter (ADC) can achieve accurate wide-range current measurement with better than 0.5 class accuracy [75]. They also incorporate high-speed synchronous serial interfaces for four-quadrant and three-channel measurements [76]. (b) Metering System-on-Chip (SoC) Solution This solution is based on a comprehensive system-on-chip (SoC) design specifically tailored for metering applications. It combines highly accurate metering capabilities, multiple layers of security, and efficient handling of communication protocols. (c) Smart-Application-on-Chip (SaoC) Solution The SaoC solution primarily focuses on the communication interface of the smart meter. Different regions and manufacturers may adopt various communication protocols based on their specific requirements. For instance, American companies often choose ZigBee as the communication interface, while in Europe, power companies and manufacturers tend to focus on power line communication (PLC). Recent developments in SaoC platforms such as STCOMET [77] and ASM221 [78] offer programmable and firmware-upgradeable solutions that integrate narrowband power line communication (NBPLC) standards like PRIME, IEC61334-5-1, G3-PLC, METERS&MOREs, and P1901.2. These platforms support high-precision metering and provide essential safety functions within a single chip.

3.3 Smart Meter

141

3.3.2 Constituent Modules of Smart Meter A smart meter is composed of several essential modules, each serving a specific function, shown in Fig. 3.4. The following are the key modules of a smart meter: (1) Signal Acquisition The core function of smart meters is to accurately and continuously obtain electrical parameters for further processing and communication. The fundamental electrical parameters required for measurement include voltage, frequency, current magnitude, and current phase relative to the voltage. These parameters serve as the basis for computing additional quantities such as power factor, active/reactive power, and total harmonic distortion (THD). To acquire the current and voltage signals, smart meters utilize sensors such as current transformers (CTs) and voltage transformers (VTs). CTs and VTs are commonly employed in the signal acquisition module to transform the current and voltage signals into appropriate levels. The signals obtained after passing through CTs and VTs should be low-amplitude signals, suitable for subsequent stages of signal conditioning and analog-to-digital conversion (ADC). (2) Signal Conditioning The signal conditioning stage in a smart meter plays a crucial role in preparing the input signal for the subsequent analog-to-digital conversion (ADC) process. This stage involves a series of essential operations, including attenuation/amplification and filtering, aimed at optimizing the input signal. Depending on the specific implementation, signal conditioning can be realized using discrete components or integrated with the ADC section of an integrated circuit. Alternatively, these processes can be integrated into a System-on-Chip (SoC) along with various other functions, offering a more streamlined solution. In many instances, the input signal requires adjustments such as attenuation, amplification, or offset addition/subtraction to ensure that its maximum amplitude falls within the input range of the ADC stage. This step is crucial for preventing signal distortion or loss of information. To further enhance accuracy and mitigate issues related to aliasing, it is essential to eliminate signal components that exceed the sampling frequency. This is achieved by applying a low-pass filter to the signal prior to its input into the ADC stage. The selection of the sampling frequency is determined by the specific functionality of the smart meter. For instance, if the meter is designed to measure fundamental frequency parameters like current, voltage, and

Signal Acquisition

signal conditioning

Fig. 3.4 Smart meter constituent modules

ADC

Computing

communication

142

3 Sensing Technology

power, as well as harmonic measurements, the sampling frequency should be set sufficiently high to ensure accurate capture of the harmonic components. By implementing robust signal conditioning techniques and appropriate filtering, smart meters can effectively optimize the input signal for accurate and reliable digital conversion. (3) Digital-to-Analog Conversion (ADC) The Digital-to-Analog Conversion (ADC) stage in a smart meter is responsible for converting the sampled current and voltage signals obtained from the sensors into digital form. In the case of a single-phase meter, where both current and voltage signals are present, a single ADC can be used with the help of a multiplexer that sequentially sends the signals to the ADC. The ADC performs the crucial task of converting the continuous analog signal from the sensors into discrete digital values. This discretization is necessary due to the limited number of levels available for analog-to-digital conversion. The resolution of an ADC is defined by the formula: Resolution = Voltage Range/2n , where ‘n’ represents the number of bits in the ADC. It is evident that a higher number of bits leads to increased resolution and decreased quantization error. (4) Computing Computation in a smart meter encompasses various tasks and functions, including arithmetic operations on input signals, time stamping of data, data preparation for communication or output peripherals, processing of routines related to irregular inputs such as payments and tamper detection, data storage, system updates, and collaboration. These functions are illustrated in Fig. 3.5, which represents the functional blocks associated with the calculation function of a smart meter. Given that the measured parameters often require extensive arithmetic operations, it is advisable to utilize a microprocessor with digital signal processor (DSP) capabilities. This combination of processing power enables efficient and accurate computation of the required electrical quantities, as outlined in Table 3.5. Apart from routine arithmetic operations, smart meters handle a multitude of other programs, including those related to payments, tamper detection, system updates, and user interaction. Additionally, they perform various routine tasks such as communicating billing information. Therefore, it is essential to have a high-performance system that can handle parallelism effectively. Parallelism refers to the ability to Fig. 3.5 Overall diagram of the computing part

MP

RTC

I/O

System Bus Memory

System Controller Firmware

3.3 Smart Meter

143

Table 3.5 Arithmetic operations required for different parameters Required Parameters

Operation type

Instantaneous voltage

Multiplication

Instantaneous current

Multiplication

Peak voltage/current

Ratio

System frequency

Zero-crossing detection, Fourier analysis

RMS voltage/current

Multiplication

Phase shift

Zero-crossing detection, ratio

Power factor

Trigonometric functions

Instantaneous apparent power

Multiplication

Instantaneous active power

Multiplication

Instantaneous reactive power

Multiplication

Power usage/production

Integral

Harmonic Voltage Distortion

Fourier analysis

Total Harmonic Distortion

Multiplication and addition

perform multiple tasks simultaneously, involving the same dataset. Furthermore, buffering, which allows for temporarily suspending arithmetic operations to process other demands, is also important for efficient task management. In the computing aspect of smart meters, both volatile and non-volatile memory play crucial roles. Volatile memory, which loses its stored information when power is lost, is utilized to temporarily store data to support the processor during operations. The amount of volatile memory required depends on factors such as the number, rate, and complexity of computations, as well as the communication rate of the ports. On the other hand, non-volatile memory is essential for storing specific information that needs to be retained even when power is disconnected. This includes data such as the unit serial number and maintenance access key codes. Additionally, data related to power consumption should be preserved until successful communication with the billing company. To ensure meaningful analysis of the acquired data, a time reference must be associated with each sample and/or computing parameter. To achieve this, a realtime clock (RTC) is necessary. The RTC provides accurate timekeeping, allowing for proper timestamping of data. However, it’s important to note that the accuracy of an RTC can be influenced by temperature variations. To address this, the RTC can be synchronized with a global positioning system (GPS) to ensure consistent and accurate timekeeping, regardless of temperature fluctuations. (5 Input/Output A key feature of a smart meter is its display, which allows users to access information in both text and graphical formats. Common display technologies used in smart meters include liquid crystal displays (LCDs) and light emitting diodes (LEDs).

144

3 Sensing Technology

These displays provide a user-friendly interface for viewing important data and readings. Furthermore, smart meters often incorporate additional input mechanisms to enable human–computer interaction. This can include a keypad or touch screen, allowing users to interact with the meter and perform actions such as adjusting settings or selecting specific options for meter control. To ensure accurate measurements and compensate for any variations in sensor tolerance or system gain errors, smart meters are equipped with a calibration input. This allows the meter to be calibrated against a reference voltage, ensuring precise and reliable readings. In some cases, smart meters also offer remote calibration and control capabilities through communication links, allowing for convenient adjustments and maintenance without physical access to the meter. (6) Communication The smart meter use various network adapters for communication. Wired options include Public Switched Telephone Network (PSTN), Power Line Carrier, Cable Modem and Ethernet, and wireless options include ZigBee, Infrared and Cellular. (7) Metering Integrated Circuits (ICs) Metering ICs (Integrated Circuits) offer a streamlined and efficient approach to smart meter design. These integrated circuits incorporate various functions as depicted in Fig. 3.5, such as metering AFE (Analog Front-End), metering SoC (System-on-Chip), and metering SaoC (System-at-a-Chip), which greatly simplify the development process of smart meters. One notable example of a metering IC is the 78M6613, which represents the industry’s pioneering SoC solution designed for AC/DC power measurement. It boasts a customized firmware that enables precise measurement of electrical parameters in all four quadrants. This integrated circuit provides a comprehensive and accurate solution for power measurement within smart meters [79]. Another noteworthy metering IC is the EM773, which incorporates a 32-bit MCU (Microcontroller Unit) known for its exceptional performance and minimal power consumption. Equipped with a 32-bit Reduced Instruction Set Computing (RISC) MCU, this metering IC offers 128–256 KB flash memory, enabling efficient metering and monitoring applications even with limited resources. It serves as an efficient and resource-conscious option for smart meter implementations [80].

3.3.3 The Impact of Harmonics on Smart Meter and the Security and Privacy of Smart Meter (1) The Impact of Harmonics on Smart Meter A significant challenge faced in the practical application of smart meters is the presence of harmonics, which introduce errors between real power and apparent power

3.3 Smart Meter

145

measurements. This challenge is further compounded by the growing complexity of highly non-resistive and nonlinear loads, such as rectifiers, inverters, industrial power electronics, and power transmission systems, within the distribution grid [81]. Distributed energy systems like solar and wind energy often exhibit high levels of total harmonic distortion (THD) in voltage and current. Harmonics can cause significant distortion in load currents, making it essential to measure the harmonics of distributed energy sources. The measurement of higher harmonics, particularly the 15th harmonic, has been suggested as a means to address this issue [82]. Additionally, the phasor harmonic index (IPH) serves as a crucial parameter for harmonic measurement in distribution networks, particularly for energy storage, electric vehicles, and DER (Distributed Energy Resources) systems. The IPH takes into account both the amplitude and phase angle of the waveform, enabling a comprehensive assessment of harmonic characteristics [83]. In low-voltage grids, such as residential photovoltaic systems and various types of loads, higher harmonics are generated, impacting the accuracy of metering [84]. These higher harmonics can lead to overheating of power transformers, circuit breakers, reactive power compensators, and neutral conductors. Hence, the power industry must employ precise measurement and analysis of harmonic energy. Currently, metering ICs equipped with harmonic analysis capabilities can effectively characterize the state of loads or power supplies [85, 86]. (2) Security and privacy of Smart Meter With the widespread deployment of smart meters and the continuous expansion of Advanced Metering Infrastructure (AMI), the security of AMI, including smart meters, has become increasingly critical. Attacks targeting smart meters pose significant threats to the security and privacy of end users, surpassing the scope of AMI defense [87]. Therefore, smart meters need to adhere to comprehensive security measures throughout their lifecycle, meeting four essential security requirements: • Device Authenticity: Access to smart meters’ functions, such as manufacturing testing and software debugging, should only be permitted through authorized mechanisms. • Data Confidentiality: This involves the secure creation, transfer, processing, and storage of user data, including dynamically generated data like readings and power consumption profiles. Customer data, such as metering and usage information, is a valuable asset for utility providers. On the AMI side, user profiles must remain confidential, accessible only to authorized systems, while protecting against eavesdroppers [88]. • Data Authenticity and Integrity: Ensuring that user data and transactions are genuine and complete. • User Privacy and Security: Smart meters should employ robust security mechanisms to prevent unauthorized access, ensuring that encrypted meter data can only be decrypted by authorized requesters. Considering these security requirements, the flexibility of smart meter security measures is crucial to effectively address future security threats.

146

3 Sensing Technology

(3) Types of Attack on Smart Meter Attacks on smart meters can be categorized into two main types: physical attacks and cyber-physical system (CPS) attacks. (a) Physical Attacks Smart meters are susceptible to physical security vulnerabilities, allowing attackers to manipulate records or data through various means, such as exploiting their interfaces. Physical attacks include replacing dedicated metering ICs with counterfeit ICs, software cloning, and abusing host interfaces. (b) Cyber-Physical System (CPS) Attacks As smart meters are interconnected within networks, the communication software must possess robust security measures to prevent unauthorized operations that can alter software configurations, compromise recorded data, or manipulate calibration data. AMI security relies on authentication mechanisms, communication technologies, and routing protocols. CPS attacks can be further classified into three categories: • Denial-of-Service (DoS) Attacks: These attacks aim to disrupt the functioning of smart meters or the AMI network by overwhelming the system with excessive requests or malicious actions. • Man-in-the-Middle (MITM) Attacks: In MITM attacks, an unauthorized entity intercepts communication between smart meters and other components, gaining unauthorized access to sensitive data or manipulating communication. • Data Integrity Attacks: These attacks seek to compromise the integrity of data transmitted between smart meters and the AMI system, potentially leading to inaccurate readings, unauthorized modifications, or data tampering. By addressing these potential attack vectors, implementing robust security measures, and ensuring the integrity and privacy of data, smart meters can effectively mitigate security risks and safeguard the interests of end users.

3.4 RFID and Wireless Sensor Network RFID (Radio Frequency Identification) and wireless sensor networks are two crucial technical domains within the IoT [89]. Both technologies play significant roles the IoT applications. RFID systems serve as sensing systems capable of reading and writing information. This means that information can be encoded within RFID tags, and these tags can be accessed and manipulated to retrieve or modify the embedded information. RFID technology finds applications in various sectors such as inventory management, supply chain tracking, access control, and asset tracking.

3.4 RFID and Wireless Sensor Network

147

Wireless sensor networks are short-range wireless communication systems with built-in sensing capabilities. These networks are formed by integrating sensing devices, such as sensors, and enable the collection, transmission, and fusion of data. In recent years, there has been a trend toward the convergence of RFID and wireless sensor networks. This convergence aims to leverage the communication capabilities of wireless sensor networks to enable the acquisition and manipulation of embedded information based on sensor perception and RFID systems. By combining the strengths of both technologies, it becomes possible to create more sophisticated and versatile IoT solutions.

3.4.1 RFID RFID technology, originally developed for military purposes during World War II, has evolved to become one of the key technologies in the field of identification [90]. It relies on wireless communication, particularly through radio frequency, between tags attached to objects and RFID readers. Compared to other identification systems like barcodes, RFID systems offer advantages in product identification. RFID tags do not require direct visual contact and can be placed within boxes, containers, embedded in objects, or even injected into animals [91]. As a result, RFID systems have gained popularity and are widely utilized in various industries, including logistics, identification, toll roads, pharmacy, item tracking, pallet tracking, and animal tracking. There are currently over 3000 known typical use cases for RFID technology [92]. Agriculture and food industry represent one of the most promising domains for RFID application. Traceability, in particular, is a significant application of RFID in this field, ensuring food safety and quality. RFID enables the tracking and monitoring of animals, agricultural products, food items, and their ingredients throughout the supply chain, providing valuable information on various historical links [93]. To enhance the capabilities of RFID systems, sensors have been integrated with RFID technology, leading to the development of wireless biochemical sensors. (1) RFID System Composition RFID systems are utilized for product identification and automatic collection of item information, and they can be categorized into three types: passive, active, and semi-passive, each operating at different frequencies [94]. The RFID system comprises several key components, including RFID antennas, chips (tags), readers, and information systems consisting of computers. RFID Antenna: The antenna is designed based on the specific application requirements, aiming for low cost, non-invasiveness, and environmental friendliness. The impedance of the antenna should be matched with the chip to ensure maximum transmission of wireless signal energy. Chip (Tag): The chip, also known as the tag, contains the Electronic Product Code (EPC), which consists of encoded bits representing product information. The EPC

148

3 Sensing Technology

is unique to each tag and is recorded during production. The chip can access data in two modes: read-only and read-write. The type of data access determines whether the information can be modified, changed, or deleted. Additional memory can be configured in the chip to incorporate features like a “kill” or access password. User memory can also be added to store supplementary information such as sensor data. Electronic Product Code (EPC): The EPC protocol was developed by EPCglobal in collaboration with Auto-ID Laboratories [95]. The EPC can be a 64-bit or 96-bit code with different functions. It includes fields such as the header, EPC manager, object class, and serial number [96]. The next-generation EPC is the electronic product code information service (EPCIS), which governs the information contained within the EPC (e.g., label ID, manufacturing date, country of origin, production batch, and shipment). EPCIS allows companies and partners to determine how data is accessed and facilitates the exchange of EPC data among partners. Manufacturers, shippers, warehouses, and retailers can track product history and movement, enabling better product identification and tracking [97]. Reader: The reader uses radio frequency electromagnetic waves to communicate with the RFID tag and retrieve information stored in the tag. It detects and identifies the tag’s identification information using the tag ID provided by the chip manufacturer. Information System: The information system is responsible for storing and utilizing the data collected from the reader. It also controls the operation of the reader itself [98]. Together, these components form a complete RFID system that enables the identification and tracking of products, providing valuable information for various applications in industries such as logistics, inventory management, and supply chain tracking. (2) Type of Tag RFID tags can be classified into three types: active tags, passive tags, and semi-passive tags. (a) Passive Tag Passive tags do not have their own power source and rely on the electromagnetic waves emitted by the reader to power the chip. The read range of passive tags is typically up to 10 m, depending on the operating frequency. The data transmission rate is determined by the frequency, and multiple tags can be read simultaneously. The data on passive tags is typically write-once, read-many (WORM). Passive tags are widely used due to their low cost, long lifespan, small size, and lightweight nature. (b) Semi-Passive Tag Semi-passive tags are powered by batteries that only provide power to the chip. The energy for tag communication still comes from the radio frequency signal emitted by the reader. The battery on semi-passive tags remains in a sleep state most of the

3.4 RFID and Wireless Sensor Network

149

time to conserve power and extend the tag’s lifespan. The addition of a power supply in semi-passive tags increases the operating range compared to passive tags. In some cases, the battery on a semi-passive tag can also power a sensor that interfaces with the chip, enabling sensing capabilities. (c) Active Tag Active tags have their own power source, usually a battery, to power the chip and communication. Active tags have a longer read range compared to the other two types, typically up to 30 m or even longer. They have a fast data transmission rate, and multiple tags can be read simultaneously by the reader. Active tags support multiple read and write operations on the chip. However, active tags are generally more expensive and larger in size compared to passive and semi-passive tags. The lifespan of active tags depends on the battery life. (3) Operating Frequency Table 3.6 presented the communication frequency bands and their characteristics, showcasing RFID’s reliance on electromagnetic waves. Two interaction modes exist between readers and tags: inductive coupling using magnetic fields and radiative coupling utilizing electromagnetic waves. Communication rate determines data transmission speed from tag to reader, while read distance indicates the maximum range for tag identification and reading. The low frequency (LF) band spans from 125 to 134 kHz, offering RFID tags within this range a distinct advantage: minimal interference with liquids and metals. This makes LF tags well-suited for applications where proximity to such materials is common. Moving to the high frequency (HF) band with a center frequency of 13.56 MHz, a broader reading range and faster tag reading speed are achieved compared to LF. HF technology provides enhanced capabilities for various applications. Stepping up to the ultra high frequency (UHF) band, which ranges from 860 to 960 MHz, UHF tags exhibit even better read range and improved data transmission. Table 3.6 Communication frequency band of RFID tags LF

HF

UFH

SHF

Frequency

125–134 kHz

13.56 MHz

860–960 MHz 860 MHz (Europe) 915 MHz (US)

2.45 and 5.8 GHz

Coupling type

Inductive (near field)

Inductive (near field)

Radiating (far field)

Radiating (far field)

Communication rate

Several kb/s

Several kb/ s–100 kb/s

Hundreds kb/s

Hundreds kb/s

Reading distance

20–100 cm

0.1–1.5 m

3–15 m

3–30 m

Application

Animal tracking

Cold chain monitoring

Identification/ Transportation

Charge, access control

150

3 Sensing Technology

However, they can be affected by the presence of water and metal, requiring careful consideration in environments where these factors come into play. Reaching the super high frequency (SHF) band at 2.45 GHz or 5.8 GHz, the pinnacle of frequency ranges is attained. SHF frequencies offer the highest data transfer rates and efficient identification capabilities. Nevertheless, tags operating at these frequencies tend to have higher costs, and it’s important to note that electromagnetic waves in this range struggle to penetrate metal and water, limiting their applicability in certain scenarios. (4) Standard The emergence of RFID standards since 2000 has played a crucial role in ensuring the security and interoperability of tags and RFID readers across countries. These standards encompass various aspects, including the format, protocol, content of the Electronic Product Code (EPC), and the operational frequency utilized by tags and readers. Two prominent organizations, ISO and EPCglobal, collaborate to establish a unified scheme for RFID standards, shaping the landscape of RFID systems worldwide. The operating frequency of RFID systems adheres to standardization set forth by the Federal Communications Commission (FCC) in the United States. This regulatory body defines four public operating frequencies, as outlined in Table 3.6. Notably, standards like ISO15693 and ISO14443 govern smart RFID cards, enabling their operation within a range of up to 1 m. UHF RFID tag standards are defined by ISO 18000-6A and ISO 18000-6B. When integrating sensors with RFID technology, compatibility with standards such as the ISO15693 RFID protocol becomes paramount. Thankfully, this protocol offers flexibility to accommodate the seamless integration of sensor data streams. Additional standards serve specific purposes within the realm of RFID technology. ISO11784 and ISO11785 are instrumental in animal identification, ensuring efficient and reliable tracking. ISO8402 defines traceability as the ability to retrieve historical information and track the usage or location of an activity through the identification of records. The ISO18000 protocol standard defines the operational frequency range for RFID systems, with ISO18000-6 specifically covering the frequency range of 860–960 MHz. Other standards delineate aspects such as access modes (read and write operations), memory types, memory organization, data transmission rates, tag read speeds, read ranges, tag capacities, and tag types (passive or semi-passive). While standards for RFID tags have been well-established, the same cannot be said for RFID readers. A lack of standardized protocols for readers has resulted in potential interference, especially when multiple RFID readers simultaneously interrogate a single RFID tag [99].

3.4 RFID and Wireless Sensor Network

151

3.4.2 Wireless Sensor Network Wireless Sensor Networks (WSNs) play a vital role in the IoT, serving as a critical technology for information acquisition from “things” and enabling communication between “things” within the sensing control layer of the IoT. WSNs can be considered a special manifestation of the IoT, representing its embryonic form and serving as a foundational element for the ubiquitous network [4]. At their essence, WSNs bestow upon itself capability of real-time monitoring [100]. These networks consist of an assemblage of sensor nodes, each endowed with limited storage, processing, and energy resources [101]. Tasked with collecting data from their surrounding environments, these sensor nodes orchestrate wireless communication, forming a multi-hop self-organizing network system [102]. Moreover, they exhibit a remarkable collaborative nature, allowing sensor nodes to perform data aggregation and fusion functions, thereby harmonizing and amalgamating the processed information [103]. However, energy limitation is one of the main challenges of WSNs [104], since in most cases, these nodes are deployed in locations that are inaccessible to people and thus cannot replace the batteries that power them [105]. WSNs exhibit characteristics such as limited power supply, communication, and computing abilities. They operate on a large scale with wide distribution, showcasing self-organization and dynamic network capabilities. Data-centricity and application-related challenges arise when WSNs cooperate with other IoT terminals, including network heterogeneity, interconnection, and large-scale data fusion under heterogeneous conditions. (1) The Node Structure [4] A wireless sensor node typically comprises four main components: a sensor module, a processor module, a wireless communication module, and a power module, as depicted in Fig. 3.6. The sensor module serves the purpose of gathering information within the monitoring area. It converts the collected data into a digital format, typically by converting original analog signals, and different sensors acquire distinct types of information. The processor module, usually an embedded system, handles the processing and storage of data obtained by the sensor. It oversees the operation of various components

Sensor Module

Processor Module

wireless communication module

Processor

Sensor

A/D

Network

Memory

MAC

Tx/Rx

Power Supply Module Fig. 3.6 Wireless sensor node structure. Note Wireless Transmitter/Receiver = Tx/Rx

152

3 Sensing Technology

within the collaborative sensor node and controls the power supply to optimize energy efficiency. Additionally, the processor module is responsible for processing data received from other nodes. The wireless communication module plays a vital role in transmitting data output by the processor to other nodes or a central sink through wireless channels. It is designed with low power consumption characteristics and facilitates short-distance communication. The power supply module ensures a reliable energy source for the sensor nodes and is typically powered by a micro battery. Regarding the wireless communication module, when sending data, the information is transmitted from the network layer to the data link layer. From there, it is further forwarded to the physical layer, where it is converted into binary signals and transmitted as radio waves. Upon receiving data, the receiver demodulates the wireless signal, passes it up to the MAC layer, and then to the network layer. Finally, the data reaches the processor module for further processing. (2) Wireless Sensor Network Protocol Architecture The wireless sensor network protocol encompasses the definition and description of the network’s functional aspects and its constituent components. It comprises network communication protocols, sensor network management, and application support technologies, as depicted in Fig. 3.7. The layered structure of the network communication protocol resembles the traditional TCP/IP protocol architecture, consisting of the physical layer, data link layer, network layer, transport layer, and application layer. The physical layer encompasses tasks such as channel selection, wireless signal monitoring, and signal transmission and reception. Wireless sensor networks employ various transmission modes, including wireless, infrared, and light waves. The

Distributed Web Service Interface

DLL PHY

MAC Radio

Topology Infrared

Topology

Remote Management

Routing Protocol

Mobility Control

Network Layer

Network security

Transmission Control

Network Management

Transport Layer

QoS Management

Positioning Tec.

Energy Management

Application Layer Time Sync.

Distributed Network Management Interface

Optic

Layered Network Protocol

Sensor Network Management Technology

Fig. 3.7 Wireless sensor network protocol architecture. Note Time Sync. = Time Synchronization, Positioning Tec. = Positioning Technology, DLL = Data Link Layer, PHY = Physical Layer

3.4 RFID and Wireless Sensor Network

153

primary objective of the physical layer is to achieve a higher link capacity while minimizing energy loss. The data link layer’s main responsibility is to establish error-free communication links. This layer typically comprises the media access control (MAC) sublayer and the logical link control (LLC) sublayer. The MAC layer governs channel resource sharing among different users, while the LLC layer provides agreed service interfaces to the network layer. The network layer assumes tasks such as packet routing and network interconnection, ensuring efficient data transfer across the network. The transport layer is responsible for controlling the transmission of data flow and offering reliable and efficient data transmission services. Network management technology focuses on managing both the sensor nodes themselves and the sensor network as a whole. The network management module encompasses functionalities such as network fault management, billing management, configuration management, and performance management. Other modules include network security, mobile control, and remote management. Additionally, application support technology in the sensor network provides users with various application support services, including time synchronization, node positioning, and coordination application service interfaces.

3.4.3 IEEE 802.15.4 Standard and ZigBee Protocol Specification ZigBee, a notable technology in Wireless Sensor Networks (WSN), is built upon the IEEE 802.15.4 standard. It directly utilizes the physical layer and MAC layer of IEEE 802.15.4, establishing a close relationship between the two. (1) Main performance of IEEE802.15.4 The IEEE 802.15.4 standard is designed for Wireless Personal Area Networks (WPAN) to enable short-distance wireless communication. It specifies the protocol and interface for devices within a Personal Area Network (PAN). The standard utilizes CSMA/CA (Carrier Sense Multiple Access with Collision Detection) as the media access control method and supports point-to-point and star network topologies. The IEEE 802.15.4 standard primarily focuses on the physical layer and MAC layer standards, with communication distances typically within tens of meters. The physical layer serves as the foundation for communication in Wireless Sensor Networks (WSN). The MAC layer handles access to the physical layer, ensuring tasks such as beacon synchronization, PAN association and de-association, reliable connections between MAC entities, and channel access. Following the ISO/OSI reference model, the IEEE 802.15.4 standard employs a layered structure and defines multiple physical layers alongside a single MAC layer. Some key properties of the standard include:

154

3 Sensing Technology

(a) Frequency Band, Data Transmission Rate, and Number of Channels: • 868 MHz band: 20 kbit/s transmission rate, 1 channel • 915 MHz band: 40 kbit/s transmission rate, 10 channels • 2.4 GHz band: 250 kbit/s transmission rate, 16 channels (b) Communication Range: • Indoor: 250 kbit/s transmission rate at a distance of 10 m. • Outdoor: 40 kbit/s transmission rate at distances ranging from 30 to 75 m, and 20 kbit/s transmission rate at a distance of 300 m. (c) Topology and Addressing Mode: • Supports point-to-point and star network topologies • Supports up to 65,536 network nodes • Utilizes a 64-bit IEEE address and an 8-bit network address. (2) ZigBee Protocol Specification (Fig. 3.8). The ZigBee protocol stack architecture consists of multiple layers, including the Application Layer, Application Aggregative Layer, Network Layer, Data Link Layer, and Physical Layer. Here is a summary of each layer: (a) Application Layer, Application Aggregative Layer, and Network Layer: • The application layer defines various application services and is the top layer user of the protocol stack. • The application aggregative layer maps different applications to the ZigBee network layer and handles security, authentication, data aggregation, device discovery, and service discovery. • The network layer manages topology, MAC, routing, and security. (b) Data Link Layer: • The data link layer includes the Logical Link Control (LLC) sublayer and the Medium Access Control (MAC) sublayer. • The LLC sublayer ensures reliable data transmission, packet segmentation and reassembly, and sequential data transmission. Fig. 3.8 ZigBee protocol stack architecture

Application Layer Application Aggregative Layer Network Layer LLC Data Link Layer MAC Physical Layer

3.4 RFID and Wireless Sensor Network

155

• The MAC sublayer handles wireless link establishment, maintenance, and dismantling, confirmation frame transmission and reception, channel access control, frame verification, time slot management, and broadcast information management. (c) Physical Layer and MAC Layer: • ZigBee utilizes the physical layer and MAC layer of the IEEE 802.15.4 standard. • Three frequency bands are used: 868 MHz in Europe, 915 MHz in the United States, and 2.4 GHz worldwide. • Direct Sequence Spread Spectrum (DSSS) is employed, with different modulation methods for each frequency band (DPSK for 868 MHz and 915 MHz, and Q-QPSK for 2.4 GHz). • IEEE 802.15.4 offers low-speed short-distance wireless communication, with data transmission rates of 250kbit/s in 2.4 GHz, 20kbit/s in 868 MHz, and 40kbit/s in 915 MHz. (d) Network Layer: • ZigBee supports star, tree, and mesh topologies. • The ZigBee coordinator controls the network in a star topology, while in tree and mesh topologies, the coordinator starts the network and selects key parameters. • Routing management is a core function of the network layer, with hierarchical routing in tree networks and peer-to-peer communication in mesh networks. • The network layer ensures the normal operation of the ZigBee MAC layer and provides suitable service interfaces for the application layer. (e) Application Specification: • The application layer includes the Application Support Layer (APS) and ZigBee Device Object (ZDO). • APS and ZDO specify endpoints, bindings, service discovery, device discovery, and other application-related functions. Overall, the ZigBee protocol stack combines the IEEE 802.15.4 standard’s physical layer and MAC layer with ZigBee Alliance-developed layers to provide a comprehensive solution for low-speed, short-distance wireless communication with application-specific support.

3.4.4 Integration of RFID and WSN The integration of RFID systems and WSN offers a wealth of possibilities for expanding IoT applications. While RFID systems excel at object identification, WSN

156

3 Sensing Technology

nodes possess the capability to perceive information and establish a coordinated multi-hop wireless network for aggregating and transmitting data. There have been four proposed modes of integrating RFID and WSN systems [105–108], each bringing its unique advantages. The first mode involves combining sensing devices with RFID tags, allowing for the simultaneous capture of environmental data and RFID identification. The second mode focuses on interconnecting sensor nodes and RFID tags, enabling seamless communication between the two systems. The third mode establishes a link between sensor nodes and RFID readers, facilitating data exchange between WSN nodes and the RFID infrastructure. Lastly, the fourth mode integrates RFID components and sensors through application-specific approaches. Considering the various integration modes, this section will explore the mode that connects RFID readers to wireless sensor nodes (type 3), as it offers a costeffective solution for diverse IoT applications while maintaining deployment efficiency. However, it’s important to note that this integration introduces additional power consumption to the WSN nodes. Thus, specific protocols need to be devised to monitor node conditions and optimize power usage. To realize this integration mode, a device known as a Reader Sensor (RS) node [106] is employed. The RS node combines the functionalities of a wireless sensor node and an RFID reader. Not only does it perform environmental sensing, but it also tracks and identifies RFID tags. The acquired data is then transmitted to a central node through multi-hop communication. RS nodes can be designed with two different architectures. The first architecture equips RS nodes with a single communication interface, enabling both sensor data transmission and RFID tag communication. In the second architecture, RS nodes feature two independent communication interfaces, one dedicated to sensor data transmission and the other to RFID tag communication. (1) RS Nodes with Software-Defined Radio Architecture The RS nodes employing the RS-SDR (Reader Sensor with Software-Defined Radio) architecture utilize a single communication interface to facilitate message exchange between WSN and RFID components. The RS-SDR architecture leverages SoftwareDefined Radio (SDR), which refers to a radio device equipped with a transceiver that can dynamically modify its operating parameters, such as frequency, modulation type, or maximum output energy, through software control, without requiring any hardware changes for radio frequency transmission [109]. This flexible configuration enables seamless integration of WSN and RFID functionalities within a single RS node. However, it’s important to note that when utilizing the RS-SDR architecture, sensor nodes and RFID tags cannot communicate simultaneously. The system operates in a predefined sequence, as depicted in Fig. 3.9. During the period when a reader node is acquiring information from RFID tags, it is unable to receive messages from sensors or other reader nodes present within the network. This temporal constraint is inherent to the RS-SDR architecture and should be taken into consideration when designing and deploying systems that employ this integration approach.

3.4 RFID and Wireless Sensor Network Fig. 3.9 RS-SDR operation timing of a communication port

157 Change RF Parameters

RFID Off

RFID On

RFID Off

WSN On

WSN Off

WSN On t

Running Cycle T

(2) RS Node with Dual Radio Architecture RS nodes with Dual Radio (RS-DR) architecture have two independent communication interfaces. The first communication interface is used to exchange messages between RS nodes and sensor nodes. The CC2420 chip [111] can be used to perform this task. The second interface is responsible for communication between RS nodes and RFID tags. Depending on the specification of the tag to be identified, a CC1000 chip [112] can be used to perform reader-to-tag communication. RS-DR can communicate with WSN and RFID components at the same time because it has two independent communication interfaces. The running sequence of the system is shown in Fig. 3.10. (3) Application scenarios Figure 3.11 shows the scenario of integrated RFID and WSN for IoT applications, where an integrated system of RFID and WSN is applied. It includes the following elements: (a) Reader to Sensor Node They are the elements that connect RFID readers and sensor nodes in a WSN. Their tasks include acquiring data stored in RFID tags, sensing the surrounding environment, and providing multi-hop communication with other network elements. RS nodes can have DR or SDR architecture. (b) Sensor Nodes They are the elements that sense the environment and provide multi-hop communication with other network nodes. Considering that in potential applications, some nodes do not need to perform the reading task, and these nodes do not have the Fig. 3.10 RS-SDR running timing of two communication ports

On/Off RFID RFID Off

RFID On

RFID Off

WSN On

WSN On

WSN On

Running Cycle T

t

158

3 Sensing Technology

Fig. 3.11 Network Scenario Example of WSN and RFID Integration

ability to acquire information from RFID tags. However, they are used to acquire environmental data and serve as a path for tag delivery data obtained from RS nodes. (c) RFID Tags These elements identify items and/or people with an ID. They communicate with RS nodes to notify the data recorded in their memory. (d) Sink Node It is a differentiated network element that typically does not present energy constraints. These nodes have greater storage and processing power to process information obtained by other nodes. To share the data acquired by the network, the sink node can be connected to the Internet, and then it acts as a gateway. (4) Key technologies to be solved In the integrated system of RFID reader and WSN, it is necessary to solve the RFID conflict problem and the routing problem. RFID readers can use an anticollision protocol to coordinate response messages sent from tags [113]. One of the most commonly used RFID anti-collision protocols is EPC Global UHF Class-1 Generation-2 Standard (C1-G2) [114]. The solution to the routing problem needs to dynamically select routes according to the energy status of WSN nodes, channel status, and packet error and packet loss, so energy-aware routing protocols can be used in this system.

3.5 NB-IoT and LoRa Technology

159

3.5 NB-IoT and LoRa Technology Based on the transmission distance, IoT wireless communication technology can be classified into two main categories [115]. The first category is short-distance communication technology, which includes popular technologies like Zigbee, WiFi, and Bluetooth. These technologies are designed for communication over relatively short distances within a confined area, typically ranging from a few meters to a few hundred meters. The second category is wide-area communication technology, often referred to as Low-Power Wide-Area Network (LPWAN). LPWAN is specifically designed to provide long-range communication capabilities with low power consumption, enabling devices to communicate over large geographic areas. Wide-area communication technology can be further divided into two subcategories based on the working frequency band. The first subcategory operates in unlicensed frequency bands and includes technologies such as Lora [116] and Sigfox [117]. These technologies operate on nonstandard and custom frequency bands, and they are known for their long-range communication capabilities while consuming minimal power. They are suitable for applications that require wide coverage, such as smart cities, agriculture, and environmental monitoring. The second subcategory operates in licensed frequency bands and encompasses technologies like 2G, 3G, LTE, LTE-A, and 5G cellular mobile communication technologies. These technologies, such as GSM, CDMA, and WCDMA, follow international standards defined by organizations like 3GPP [118]. They provide widearea coverage and high-speed data transmission capabilities, making them ideal for applications requiring seamless connectivity over large regions, including mobile communication networks and industrial IoT deployments. The choice of wireless communication technology depends on factors such as the required transmission distance, power consumption, data rate, and regulatory considerations. By leveraging the strengths of both short-distance and wide-area communication technologies, IoT applications can be tailored to meet specific requirements, ensuring efficient and reliable connectivity across various use cases.

3.5.1 NB-IoT (1) Characteristics and Design Goals of NB-IoT NB-IoT, a wireless communication technology proposed by 3GPP based on LTE (Long Term Evolution), is specifically designed for low-power wide-coverage IoT applications. It operates on a system bandwidth of 180 kHz, which is equivalent to one Physical Resource Block (PRB) in LTE [119]. Due to its narrow bandwidth, it is referred to as Narrowband Internet of Things (NB-IoT).

160

3 Sensing Technology

As a LPWA technology standardized by 3GPP, NB-IoT offers significant advantages over unlicensed frequency band technologies. It can leverage existing mobile communication operator networks without requiring additional site or transmission resources. Mobile operators can deploy and maintain NB-IoT networks, enabling plug-and-play terminals and facilitating large-scale deployments. Moreover, NBIoT operates in licensed frequency bands, which enhances reliability and security [120]. NB-IoT employs ultra-narrowband, repeated transmission, and simplified network protocol designs. While sacrificing certain rate, delay, and mobility performance, it achieves the necessary capacity for LPWA IoT and meets the requirements of low-power, wide-coverage designs. The design goals of NB-IoT include [121]: • Low cost: Less than $5 per terminal, with future projections as low as $2 per terminal. • Large capacity: Each cell can support a minimum of 50,000 NB-IoT devices. • Wide/depth coverage: Offers approximately 20 dB stronger coverage than GPRS, expanding the coverage area by about seven times. • Low power consumption: Battery life exceeds 10 years. • Low latency sensitivity: Latency is less than 10 s. The NB-IoT terminal transmission bandwidth is narrow, reaching 3.75 kHz. Compared to the 180 kHz terminal bandwidth of 2G/3G/LTE systems, NB-IoT achieves an uplink power spectral density increase of approximately 17dBm with the same terminal transmission power. In practical applications, the maximum transmission power of NB-IoT is 23dBm, and the Power Spectrum Density (PSD) of NB-IoT terminals is approximately 7 dB higher than that of GSM GPRS. Additionally, NB-IoT can achieve 6-16 dB additional gain through repeated transmission and coding. Consequently, NB-IoT boasts coverage that is about 20 dB stronger than GPRS [122]. NB-IoT’s deep coverage characteristics are well-suited for specific application scenarios. In environments with poor signal conditions, NB-IoT terminals supporting deep coverage can maintain data communication in harsh conditions. With a single NB-IoT cell supporting over 50,000 terminals, the terminal density per unit area is relatively high. However, the access behavior of IoT terminals is random and sudden. When a large number of terminals simultaneously wake up and initiate access requests, access conflicts inevitably occur. NB-IoT adopts a new frequency hopping preamble scheme [123], which does not support code division multiplexing. Under a 180 kHz channel bandwidth, each time slot supports a maximum of 48 available preambles. The limited preamble resources significantly constrain the access performance of a large number of terminals. If numerous NB-IoT terminals trigger access simultaneously, the limited preambles cannot handle the volume of access requests, leading to congestion and severe delays. This challenge represents a crucial aspect that NB-IoT needs to address. (2) NB-IoT deployment method and Physical Channel parameters NB-IoT offers three deployment methods [123]. The first method is stand-alone deployment, wherein a seamless transition occurs by replacing a GSM carrier with

3.5 NB-IoT and LoRa Technology

161

an NB-IoT carrier. To ensure smooth operation, a 10 kHz guard bandwidth must be added on both sides of the NB-IoT carrier, safeguarding against interference. The second method, known as in-band deployment, entails occupying a Physical Resource Block (PRB) within an existing LTE carrier. Notably, this deployment method does not necessitate additional protection bandwidth between carriers. It is crucial to highlight that NB-IoT communication remains independent of existing LTE common channels, such as downlink synchronization/broadcast channels and cell common pilots, preserving their integrity. The third method, guard-band deployment, involves ingeniously inserting NBIoT carriers into the LTE guard interval. Remarkably, this deployment strategy eliminates the need for guard bandwidth between LTE PRBs and NB-IoT carriers. Both guard-band deployment and in-band deployment can be achieved through software and hardware upgrades to the LTE network, ensuring compatibility and smooth integration. For a comprehensive understanding of the physical channel parameters governing NB-IoT’s uplink and downlink transmission, please refer to Table 3.7, which provides a concise overview of these vital parameters. In the context of NB-IoT Release 13, it’s important to note that mobility management in the connected state, encompassing measurements, measurement reports, handover, and related functionalities, is not supported. This deliberate design choice aims to optimize terminal power consumption, a critical consideration for NB-IoT deployments. By limiting mobility-related activities, NB-IoT terminals can conserve power and extend their operational lifespan. Additionally, it is theoretically discouraged for NB-IoT terminals to engage in frequent data transmission. There are two primary reasons for this cautionary approach. Firstly, frequent data transmission places increased demands on the terminal’s power consumption, potentially depleting its energy reserves more rapidly. Considering the intended long lifespan of NB-IoT devices, minimizing power consumption is crucial for ensuring their prolonged operation. Secondly, transmitting data too frequently can lead to network congestion, negatively impacting overall network performance and causing unacceptable delays. Given the nature of NB-IoT’s low-power, wide-area coverage, network efficiency and reliability are paramount. By avoiding excessive data transmission rates, the network can maintain optimal performance levels and ensure timely delivery of critical information. Table 3.7 NB-IoT uplink and downlink physical channel parameters NB-IoT

Uplink

Downlink

Control interface

• FDMA, GMSK • SC-FDMA

OFDMA

Subcarrier space

Single-tone

Slot

Multi-tone

3.75 kHz

15 kHz

15 kHz

2 ms

0.5 ms

0.5 ms

15 kHz 0.5 ms

162

3 Sensing Technology

Therefore, the limitations on mobility management and the recommendation to avoid frequent data transmission in NB-IoT deployments are carefully balanced considerations aimed at maximizing power efficiency, minimizing network congestion, and ensuring an acceptable level of service quality. (3) Large-scale Access method of NB-IoT NB-IoT, as a Machine-to-Machine (M2M) communication technology, encounters access congestion issues commonly associated with M2M deployments in cellular networks. To address this challenge, 3GPP has put forward several viable solutions [124], with the following six being commonly recognized [125–130]. The first solution is the Back-off Mechanism, which reduces access collision rates and eases cell access load in LTE systems. The network configures Back-off indices, indicating the maximum time for a user to wait before sending the next preamble signal. While this method effectively mitigates temporary access congestion under normal load, it suffers from significant time delays. However, when a large number of devices attempt to connect simultaneously, postponing access requests proves less effective. The second solution is the Slot Access Mode. In this mode, terminals send preambles only within specific subframes and Random Access (RA) slots while entering sleep mode during other periods. This mode follows a non-contention access approach, where each terminal initiates access within its allocated slot without occupying slots assigned to other users. The system broadcasts the RA period, typically a multiple of the frame, with the number of RA slots proportional to the RA period. However, when the number of devices exceeds the available RA slots, the Narrowband Physical Random Access Channel (NPRACH) becomes overloaded. Extending the RA cycle can reduce preamble collisions but results in significant delays when sending RA requests. The third solution is the Pull-based mode, which triggers the base station to schedule target devices through the M2M server. This centralized control mode enables devices to send access requests based on received paging information. The base station regulates the number of pages considering the PRACH load status and available resources. In contrast to Pull-based mode, the mode where devices actively send access requests is called Push-based mode. In Push-based mode, devices are not aware of the current network status and therefore actively transmit access requests, which may contribute to increased access congestion. The fourth solution is the PRACH resource division mode. This mode considers allocating orthogonal PRACH resources for Human-to-Human (H2H) and M2M communication. PRACH resources include RA slots and preamble sequences, which are allocated in two steps. First, mutually orthogonal RA slots are assigned for M2M and H2H. Second, a portion of the available preambles is allocated to M2M. The fifth solution is the PRACH resource allocation mode, where the base station dynamically assigns additional PRACH resources based on the overall PRACH load. By promptly reacting to overload situations, the base station effectively mitigates PRACH overload.

3.5 NB-IoT and LoRa Technology

163

The sixth solution is the ACB mode (Access Class Barring). In this mode, the base station broadcasts a restriction factor α (ranging from 0 to 1). Each terminal generates a random access parameter p (between 0 and 1), and eligible terminals can select and send a preamble only if their random access parameter is less than α. This method limits the number of terminals selecting preambles, reducing the preamble collision rate. ACB allows for access requests to be initiated by the source control terminal, regulating load peaks and reducing the likelihood of core network signaling congestion overload. However, directly restricting the number of devices sending RA requests can lead to significant RA delays. Enhanced Access Barring (EAB) improves upon ACB by employing similar principles. To mitigate access conflicts caused by high concurrent access requests, research can be conducted from three perspectives [115, 131, 132]: Firstly, increasing access resources by adding additional NB-IoT channels or enhancing the utilization of access channel bandwidth through spectrum resource multiplexing. Secondly, adopting decentralized access approaches, such as configuring business cycles within vertical industry centers to avoid simultaneous periodic uploads by business terminals, or establishing heterogeneous networks that allow terminals to connect to adjacent micro-base stations, thereby reducing access pressure on macro base stations. Lastly, implementing selective access strategies. When a burst of access requests occurs, the core network and access network need to respond promptly by selectively allowing devices to access, preventing a large number of devices from competing for limited NPRACH resources simultaneously, and avoiding network congestion. The ACB and EAB schemes proposed by 3GPP exemplify typical selective access approaches. (4) NB-IoT Coverage Extended and its Architecture 3GPP has categorized NB-IoT terminals into three Coverage Extended (CE) levels based on the Maximum Coupling Loss (MCL, MCL = transmit power-receiver sensitivity + receiver processing gain) of their operating environment [133]. These levels provide an indication of the channel environment and its impact on terminal performance. Level 0 (CE0) signifies a channel environment with a maximum coupling loss below 144 dB, indicating a favorable signal environment. Level 1 (CE1) represents a channel environment with a maximum coupling loss ranging between 144 and 154 dB, indicating a relatively better signal environment. Level 2 (CE2) denotes a harsh channel environment with a maximum coupling loss of 164 dB, which is the maximum limit allowed by NB-IoT technology. This level typically refers to environments with poor signal conditions, such as underground garages, sewers, or stairwells. In practical deployments, wireless signals are influenced not only by the distance from the base station but also by factors like building obstructions and other obstacles. The NB-IoT system architecture consists of five primary components [134], as depicted in Fig. 3.12.

164

3 Sensing Technology

Fig. 3.12 NB-IoT system architecture [135]

Terminal Side: This includes devices equipped with NB-IoT communication capabilities, which access nearby eNBs (base stations) using NB-IoT technology. Access Side: The Access Side comprises a cluster of eNBs that support NBIoT. These eNBs can undergo necessary software and hardware upgrades based on existing LTE base stations or be newly built as dedicated NB-IoT base stations. The eNBs communicate with each other through the X2 interface to facilitate signaling and data interaction. The eNB communicates with NB-IoT terminals through the air interface (Uu interface) and connects to the core network EPC (Evolved Packet Core) through the S1 interface. Core Network: The Core Network comprises essential elements such as the MME (Mobile Management Entity), S-GW (Serving Gateway) responsible for IoT access services, and P-GW (PDN Gateway) for the IoT private network. These core network elements can be upgraded to support NB-IoT-related features or can be built as independent NB-IoT core network devices. The core network facilitates the connection between eNBs and the cloud platform. Cloud Platform: The NB-IoT cloud platform plays a crucial role in data processing. It handles various tasks such as application layer protocol stack adaptation, big data analysis, and service scheduling. The cloud platform processes data and forwards the results to vertical business applications or the corresponding NB-IoT terminals. Vertical Business: The Vertical Business component encompasses application servers tailored for different industries. These servers receive data from NB-IoT terminals, control terminal services, and facilitate vertical-specific services and applications.

3.5 NB-IoT and LoRa Technology

165

Fig. 3.13 NB-IoT network overall architecture

(5) NB-IoT Network Architecture The network architecture of the NB-IoT system shares similarities with that of the LTE system, as both are based on the Evolved Packet System (EPS). EPS comprises three key components: the Evolved Packet Core (EPC), base station (eNB), and User Equipment (UE). The overall network architecture of NB-IoT can be visualized in Fig. 3.13. In the NB-IoT network architecture, it includes NB-IoT terminal, E-UTRAN base station (ie., eNode B), Home Subscriber Server(HSS), Mobility Management Entity (MME), Serving Gateway (SGW), PDN Gateway(PGW), Service Capability Exposure Function (SCFE), third-party service capability server (Service Capabilities Server, SCS) and third-party application server (Application Server, AS). (a) Evolved Packet Core (EPC) The Evolved Packet Core (EPC) serves as the backbone of the core network in the NB-IoT system, offering a comprehensive range of IP-based service capabilities. The EPC consists of several key components, including the Mobility Management Entity (MME), Serving Gateway (SGW), Packet Data Network (PDN) Gateway (PGW), and Home Subscriber Server (HSS) [136]. The MME plays a crucial role in EPC signaling processing, specifically for mobility control functions within the network. It handles tasks such as authentication, tracking area updates, and handover management. The SGW is responsible for data processing within the EPC. It handles routing and forwarding of data packets between the UE and external networks or services. The SGW ensures efficient data transmission and facilitates seamless connectivity. The PGW serves as the border gateway of the evolved core system network. It provides essential functions such as access to non-3GPP users, data forwarding, session management, and IP address assignment. The PGW enables communication between the NB-IoT network and external networks, ensuring seamless connectivity and efficient data transfer.

166

3 Sensing Technology

The HSS introduces access restrictions for UEs that are signing up for NB-IoT services. It configures Non-IP default Access Point Name (APN) settings for UEs and verifies non-IP data delivery authorization, among other functions. The HSS plays a critical role in managing access and ensuring the security and integrity of NB-IoT communications. The UE, or User Equipment, engages in negotiations with the network regarding its supported NB-IoT capabilities during the attachment and Tracking Area Update (TAU) processes. The UE is required to support the Control Plane (CP) mode and can optionally support the User Plane (UP) mode. Once the MME or PGW provides uplink rate control information to the UE, the UE must comply with the instructions and perform uplink transmission control for small packets. In comparison to the LTE system, the NB-IoT network system introduces the Service Capability Exposure Function (SCEF) to the network architecture to support non-IP data transmission and the CP mode. During the actual deployment of the network, the MME, SGW, and PGW can be combined to form lightweight core network elements, reducing the physical network elements required. This combination of lightweight core network elements is referred to as the C-SGN (CIoT Service Gateway Node), streamlining network infrastructure while supporting NB-IoT services effectively. (b) Base Station (eNodeB, eNB) The eNodeB base station plays a crucial role in the access network, also referred to as the E-UTRAN or radio access network [137]. It forms the wireless access network for NB-IoT and consists of one or more base stations (eNB) that communicate with User Equipment (UE) through the Uu interface, which represents the air interface. The eNB provides the UE with protocol termination points for both the user plane (PDCP/RLC/MAC/PHY) and the control plane (RRC). To address handover between different eNB base stations, the eNBs are directly interconnected through the X2 interface. This interface enables efficient communication and handover management for UEs as they move between different coverage areas. The access network and the core network are connected via the S1 interface. The eNB base station establishes a connection with the Evolved Packet Core (EPC) through the S1 interface. The eNB utilizes the S1-MME interface to establish a connection with the Mobility Management Entity (MME) and the S1-U interface to connect with the Serving Gateway (SGW). The connections between the MME/SGW and eNB are established in a multito-multi fashion through the S1 interface. A single eNB can establish connections with multiple MME/SGWs, and conversely, multiple eNBs can connect to the same MME/SGW simultaneously [138]. (c) User Equipment (UE) In NB-IoT technology, the User Equipment (UE) is equipped with various capabilities and features that enhance its interaction with the network. Some of these features include:

3.5 NB-IoT and LoRa Technology

167

NB-IoT Capabilities Negotiation: During the Attach and Tracking Area Update (TAU) processes, the UE can inform the network about its supported NB-IoT capabilities. This includes indicating whether it supports not establishing a Packet Data Network (PDN) connection during attachment, whether it supports Control Plane (CP) optimized transmission scheme, User Plane (UP) optimized transmission scheme, and SMS based on CP optimization scheme. The network, represented by the Mobility Management Entity (MME), responds by providing feedback on the NB-IoT capabilities supported by the network. This negotiation allows the UE to select the appropriate transmission scheme for subsequent uplink data transmission based on the negotiated capabilities. Control Plane Optimization Process: Both the UE and the network must support the control plane optimization process in NB-IoT. This process enables the UE to transmit small uplink data packets through signaling during the Radio Resource Control (RRC) connection establishment process. NAS (Non-Access Stratum) data packets are carried in the wireless signaling bearer SRB (Signaling Radio Bearer), and IP and non-IP data to be sent by the UE are encapsulated within these NAS data packets. Similarly, small downlink data packets issued by the network can also be received through signaling during the RRC connection establishment process. User Plane Optimization Process: The user plane optimization process is optional in NB-IoT and requires support from the UE. If the UE supports this procedure, it must also support RRC connection recovery and RRC connection suspension procedures, which optimize the transmission of user plane data. Handoff between Control Plane and User Plane Optimization: While the UE and the network can support both control plane optimization and user plane optimization modes simultaneously, the UE can only utilize one mode at a time. However, the UE has the flexibility to switch from the control plane optimization mode to the user plane optimization mode when necessary. Uplink Rate Control: The MME and the PGW (PDN Gateway) can generate rate control information based on network conditions and service settings. This rate control information includes both uplink and downlink parameters. After receiving uplink rate control information from the MME and PGW, the UE adjusts its uplink small data transmission accordingly to adhere to the specified rate control parameters. (6) NB-IoT Protocol Stack (a) NB-IoT Network Interface Protocol The radio interface, known as the Uu interface or air interface, serves as the connection between the User Equipment (UE) and the access network [138]. In NB-IoT technology, this Uu interface acts as an open interface, enabling devices from different manufacturers to communicate with each other as long as they adhere to the NB-IoT standard. Within the E-UTRAN radio interface protocol architecture of NB-IoT, the protocol stack is organized into three layers: the physical layer (L1), data link layer (L2), and network layer (L3).

168

3 Sensing Technology

The NB-IoT protocol layer incorporates two data transmission modes: Control Plane (CP) mode and User Plane (UP) mode. The CP mode is mandatory, while the UP mode is optional. (i) Control Plane Protocol On the UE side, the Control Plane (CP) protocol stack takes charge of managing and controlling the Uu interface [139]. It comprises various sublayer protocols responsible for different tasks, including the Radio Resource Control (RRC) sublayer protocol, the Packet Data Convergence Protocol (PDCP) sublayer protocol, the Radio Link Control (RLC) sublayer protocol, the Media Access Control (MAC) sublayer protocol, the PHY physical layer protocol, and the Non-Access Stratum (NAS) control protocol. The CP mode is a mandatory requirement for both the NB-IoT UE and the network. Regardless of whether it involves IP data or Non-IP data, these data types are encapsulated into NAS data packets using NAS layer security and header compression techniques. When the UE transitions into the idle state (RRC_Idle), it disconnects from the Access Stratum (AS), and upon re-establishing a connection, it needs to initiate a new RRC connection establishment request. The control plane protocol stack is illustrated in Fig. 3.14. The NAS protocol handles the transmission of information between the UE and the Mobility Management Entity (MME). NAS messages exchanged on the control plane include Connection Management (CM), Mobility Management (MM), Session Management (SM), and GPRS Mobility Management (GMM), among others. The RRC sublayer, which serves as the third layer information, facilitates the processing of control plane communications between the UE and the eNB. It carries all the necessary parameters for establishing, modifying, and releasing layer 2 and

Fig. 3.14 Control plane protocol stack

3.5 NB-IoT and LoRa Technology

169

PHY layer protocol entities. The RRC sublayer plays a pivotal role in control signaling between the UE and the E-UTRAN, encompassing the transmission of NAS signaling as well. Its primary functions include transmitting relevant signaling messages, allocating radio resources, establishing radio bearers, and configuring RRC signaling control between the eNB and the UE. (ii) User Plane Protocol (UP) The User Plane (UP) protocol stack consists of the Packet Data Convergence Protocol (PDCP) sublayer protocol, the Radio Link Control (RLC) sublayer protocol, the Media Access Control (MAC) sublayer protocol, and the PHY physical layer protocol. These protocols collectively handle various tasks such as header compression, encryption, scheduling, Automatic Repeat reQuest (ARQ), and Hybrid Automatic Repeat reQuest (HARQ). Figure 3.15 illustrates the organization of the user plane protocol stack. The data link layer utilizes the services provided by the PHY sublayer for data transmission. The PHY sublayer is responsible for establishing the transmission channel, while the MAC sublayer provides the logic channel service for the RLC sublayer. The PDCP sublayer is positioned within the second layer of the Uu protocol stack and serves multiple purposes. On the User Plane, it receives IP data packets from the upper layer, processes them accordingly, and then forwards them to the RLC sublayer. On the Control Plane, the PDCP sublayer handles the transmission of signaling messages for the RRC sublayer. It performs encryption and integrity protection for the signaling messages, as well as decryption and integrity checks for received RRC signaling messages.

Fig. 3.15 User plane protocol stack

170

3 Sensing Technology

(iii) Coexistence of Control Plane and User Plane CP mode in NB-IoT is primarily designed for transmitting small data packets, whereas UP mode is better suited for handling large data packets. In situations where there is a requirement for transmitting large data packets while using CP mode, the UE or the network can initiate a transition from CP mode to UP mode. During the Idle state, the transition from CP to UP can be initiated by the user through the service request process. Upon receiving the service request from the UE, the Mobility Management Entity (MME) is responsible for deleting the S1-U information and IP header compression related to CP mode and establishing a user plane channel for data transmission. In the connected state, the transition from CP mode to UP mode can be triggered by the UE through the Tracking Area Update (TAU) process or directly by the MME. When the MME receives a TAU message carrying the activation flag from the UE or detects the presence of large downlink data packets, it will remove the S1-U information and IP header compression associated with CP mode and establish a user plane channel for data transmission. For NB-IoT UEs that only support CP mode, user data is carried within the NAS layer, and the PDCP protocol is not utilized. On the other hand, for NB-IoT UEs that support both CP mode and UP mode, the PDCP protocol remains unused until access layer security is activated. (b) Physical Layer of NB-IoT At the bottom layer of the radio interface protocol stack in NB-IoT, we have the PHY layer. The PHY layer is responsible for facilitating data transmission over the physical medium and providing information services to higher layers, including the MAC layer. In NB-IoT, the PHY layer has undergone significant simplification and modifications, encompassing various aspects such as multiple access methods, operation frequency bands, frame structures, modulation and demodulation techniques, antenna ports, cell searching, synchronization processes, power control, and more [140]. One of the notable modifications in the NB-IoT PHY layer is the redefinition of the Narrowband Primary Synchronization Signal (NPSS) and Narrowband Secondary Synchronization Signal (NSSS). These signals play a crucial role in simplifying the design of UE receivers [141]. The time domain positions of NPSS and NSSS within the radio frame are depicted in Fig. 3.16. NPSS is transmitted on subframe 5 of each radio frame, with a transmission period of 10 ms. On the other hand, NSSS is transmitted on subframe 9 of even-numbered radio frames, with a transmission cycle of 20 ms. (i) NPSS Given that NB-IoT primarily caters to low-cost terminals, if there are multiple NPSS signals, the complexity of terminal synchronization detection will be doubled, so there is only one NPSS sequence for NB-IoT [142]. NPSS is designed based on ZC short sequences.

3.5 NB-IoT and LoRa Technology

171

Fig. 3.16 Time domain locations of NPSS and NSSS

To simplify the UE receiver, different transmission modes employ a unified synchronization signal. Therefore, to avoid the first three Orthogonal Frequency Division Multiplexing (OFDM) symbols, NPSS only occupies 11 symbols in one subframe, and each symbol occupies 11 subcarriers. NB-IoT generates the main synchronization signal sequence from the Zadoff-Chu sequence in the frequency domain, as follows: d1 (n) = s(1) × e− j

πun(n+1) 11

(3.1)

where, the value range of n is an integer from 0 to 10, and each symbol carries a Zadoff-Chu sequence with 11 long roots of 5, and different symbols carry different masks (Cover Code), and the cyclic prefix is defined as shown in the Table 3.8 shown. NPSS occupies 0 ~ 10, a total of 11 subcarriers in frequency domain. And NPSS always uses the fifth subframe in each radio frame, and starts from the fourth symbol in the subframe in time domain. (ii) NSSS The design idea of the NSSS sequence is basically the same as that of the NPSS, the only difference is that the NPSS sequences sent by all cells are the same. The NPSS sequences sent on the two subframes are conjugated to each other, while the NSSS needs to indicate the cell ID information and frame timing. Frame timing information and different cell IDs are indicated by a combination of root indices of different Zadoff-Chu sequences used on two subframes. NSSS is a frequency-domain Zadoff-Chu sequence of length 131 and is scrambled by Hadamard matrix. The 504 cell IDs are indicated by the root of Zadoff-Chu and 4 Table 3.8 The definition of cyclic prefix Length of cyclic prefix

s3

s4

s5

s6

s7

s8

s8

s9

s10

s11

s12

s13

Regular value

1

1

1

1

−1

−1

1

1

1

1

−1

1

172

3 Sensing Technology

Hadamard matrices, and the sequence of frames within 80 ms is indicated by 4 time domain cyclic shifts. The Zadoff-Chu sequence in the frequency domain is also used to generate the NB-IoT secondary synchronization signal sequence, as follows: d(n) = b p (m)e− j2πθ f n e− j

πun ' (n ' +1) 131

(3.2)

Ncell ' where, [ nN =]0, 1, · · · , 131; n = nmod131; m = nmod128; u = N I D mod126 + 3; N I Dcell q = 126 . πun ' (n , +1)

Zadoff-Chu sequence is e− j 131 , whose length can be expended to 132 by cyclic shift, cyclic shift is as follows: θf =

33 ( n f ) mod4 132 2

(3.3)

That is, the four cyclic shift intervals are 0/132, 33/132, 66/132, and 99/132, respectively. The scrambling sequence b p (m) is four 132-length Hadamard sequences. The PCID of the NB-IoT cell is determined by the combination of the root index of the Zadoff-Chu sequence and the scrambling sequence index by the following two formulas: u = mod(PCI, 126) + 3 q=

PCI 126

(3.4) (3.5)

Since NSSS is only sent in even frames, four cyclic shifts can determine the position of NSSS within 80 ms. NSSS occupies all 12 subcarriers in frequency domain. NSSS is only sent in even frame numbers, starting from the fourth symbol in the subframe in time domain. (7) System Message The system message of NB-IoT includes a master information block (MIB-NB) and multiple system information blocks (SIB). In 3GPP Release 13, the SIB include SIB1-NB, SIB2-NB, SIB3-NB, SIB4-NB, SIB5-NB, SIB14-NB and SIB16-NB. The SIB blocks except SIB1-NB constitute several SI messages that are carried by Narrowband Physical Downlink Shared Channel (NPDSCH). (a) MIB-NB MIB-NB needs to be sent frequently, so its size is strictly limited, and only contains the most critical information. Its main contents include:

3.5 NB-IoT and LoRa Technology

173

• The upper four bits and lower six bits of the system frame number SFN are carried by the secondary synchronization channel and MIB-NB encode. • The 2 lower bits of the super system frame number H-SFN. • AB-enabled access control enable (1bit): Access barring is enabled indication switch. • SIB1-NB scheduling information (4bit): Used to indicate the TBS and repetition times of SIB1-NB. • System information Value Tag (5bit): The UE uses the system information Value Tag to detect whether the system information has been updated. • Configuration information related to Operation Mode, which is used to distinguish “In-band/same PCI”, “In-band/different PCI”, and “Stand-alone” operation modes and indicate the required other necessary information. To employ CRC check bits, channel coding, rate matching, scrambling, segmentation, modulation and resource mapping functions, the Narrowband Physical Broadcast Channel (NPBCH) is divided into 8 durations with 80 ms and independently decodable block. NPBCH perform the following tasks: • Adding CRC check bits: 16-bit check bits are computed based on the 34-bit payload. • Channel coding: TBCC coder is employed. • Rate matching: The output bit is 1600bits. • Scrambling: Use the cell-specific scrambling code scrambling sequence to scramble the rate-matched bits, where the scrambling code sequence is initialized through PCID in the radio frame that satisfies SFN mod 64 = 0. • Segmentation: The scrambled bits are divided into 8 coded sub-blocks with a size of 200 bits. • Modulation: For each coded sub-block, QPSK modulation is used. • Resource mapping: The modulation symbols corresponding to each coded subblock are repeatedly transmitted 8 times and extended to a interval of 80 ms. (b) SIB1-NB The content of SIB1-NB includes: • • • •

Cell access and Cell selection information. The high 8 bits of H-SFN and the low 2 bits of H-AFN are indicated in MIB-NB. Scheduling message of SI message. Downlink Bitmap: It is used to indicate effective subframes for downlink transmission (effective subframes refer to subframes that can be used for SI message and PDSCH, etc.). If this parameter is not configured, all downlink subframes except those occupied by NPSS, NSSS, NPBCH and SIB1-NB are valid subframes.

The scheduling period of SIB1-NB is fixed at 2560 ms, to avoid SIB1-NB transmission interference between adjacent cells, SIB1-NB starts to send radio frames and PCIDs related to the physical cell identity of the cell in the scheduling period,

174

3 Sensing Technology

that is, adjacent cells set different SIB1-NB message start frames to stagger the time domain transmission resources. (c) SI message The scheduling method of SI message in NB-IoT adopts a semi-static scheduling method, that is, PDCCHless scheduling. In NB-IoT, multiple SIBs of the same period (Periodicity) can form an SI message, which is scheduled with units of SI messages. The system configures the sending window for each SI message, that is, the SIWindow, based on the period of the SI message, so that the SI-Window of different SI messages does not overlap each other, and the starting position of the SI-Window of different SI messages is calculated by the following formula: (HSFN ∗ 1024 + SFN) ∗ modT = FLOOR(x/10) + offset

(3.6)

where, T is the period of SI message; offset is the starting offset of SI-Window; x = (n − 1)∗w, w is SI-Window length; n is the order of the sequence of System Information Block Type1-NB in the SIBI-NB cell. The lengths of Periodicity, Offset, and SI-Window are all configured within SIB1-NB of (3.6). The SI message is repeatedly sent several times in the SI-Window configured for it, and the number of repetitions is jointly determined by the repetition mode configured for each SI in the SIB1-NB and the length of the SI-Window. In SIB1-NB, the repetition mode of the SI message is defined as sending its repetition once in the first effective radio subframe of every 2nd, or 4th, or 8th, or 16th radio frame. Depending on the SI message transmission block size (TBS), a repeated transmission of the SI message requires 8 radio subframes or 2 radio subframes to perform. The SI message is sent from the first effective subframe of the radio frame defined by the repetition pattern, and continuously occupies the effective radio subframe until a whole repetition is sent. If there are not enough radio subframes in the radio frame specified by the repetition pattern cell frame, the insufficient part occupies the effective subframe of the subsequent radio frame. (8) Paging Process In a communication system, the paging mechanism is used to notify users in an idle state of system message have been changed and notify users of arrival of downlink data. The diagram of paging is shown in Fig. 3.17. When the core network needs to send data to the user, it will send a paging message to the base station through the SI interface via the MME, and the paging message Fig. 3.17 Paging process

3.5 NB-IoT and LoRa Technology

175

will include user ID, TAI list and other information. The base station (eNodeB) receives the paging message, reads the content therein to obtain the user’s TAI list information, and then performs paging in the cells in the TAI list. NB-IoT adopts E-UTRAN paging related configuration, the main differences with E-UTRAN are as follows: • For NB-IoT, eDRX is configured only by BCCH. • When using eDRX in idle state, the maximum DRX cycle is 2.91 h. • When the UE is in the RRC_Idle idle state, it receives paging on the anchor carrier. Common user paging optimization mechanism, that is, the MME optimizes the paging message sending range according to the base station list information in the paging assistance message reported by the base station and the preset optimization strategy. When sending a paging message, the MME can select one or more base stations to send the message, while the cell list information in the paging assistance message will not be processed by the MME, and will be directly sent to the base station along with the paging message for processing by the base station, which can be used to determine the delivery range of the air interface paging message. The cell list information in the paging assistance information includes the cell global identifier and dwell time. The base station list in the paging assistance message contains the global ID of the base station, and for the home base station, it may be connected to the MME through the home base station gateway, so the MME needs to use the TAI information to identify and route the paging message to the home base station gateway. At the same time, the paging message on the SI interface also includes paging attempt count and planned paging attempt times information, and may optionally include next paging range indication information. For the paging of the current UE, the number of attempts will be accumulated after a paging message occurs, and the indication information of the next paging range indicates that the MME plans to change the current paging range during the next paging. If the UE transitions from Idle to Connected, the number of paging attempts is reset. (9) BC95: NB-IoT terminal communication module [135] Quectel’s BC95 is a NB-IoT communication module with a wide operating voltage of 3.1–4.2 V. At the same time, BC95 also supports the UDP protocol, which is oriented to small devices and suitable for transmitting a small amount of data at a time. The BC95 module is a module that integrates the RF chip, Baseband chip and NB-IoT protocol stack on one PCB, and provides hardware pins and software interfaces to the outside. The module has three models: BC95-B8, BC95-B5, BC95-B20, where, BC95-B8 and BC95-B20 are used in the operator network of China Mobile and China Unicom, while BC95-B5 is used in the telecom network. The BC95 module is mainly composed of NB-IoT Baseband controller, flash memory, RF module, power module, antenna interface and other commonly used interfaces. The functional block diagram of BC95 is shown in Fig. 3.18.

176

3 Sensing Technology

Fig. 3.18 Functional block diagram of BC95

The BC95 module has the characteristics of low power consumption and high performance. Its size is 19.9 × 23.6 × 2.2 mm, which can greatly meet the requirements of small size, low power consumption and low cost of UE equipment. The BC95 module can communicate with many terminal equipment connection, each cell supports up to 100,000 users. It is often used in smart cities, smart transportation, remote meter reading, smart logistics, smart buildings, agriculture and environmental monitoring, etc., to provide comprehensive SMS and data transmission services. The main performance of the BC95 module is shown in Table 3.9. The BC95 module has a total of 94 pins, 54 of which are LCC pins, and the remaining 40 are LGA pins. The interface functions of the module include: power Table 3.9 Main performance of BC95 module Parameter

Explanation

Power Supply

VBAT supply voltage range: 3.1– 4.2 V; typical supply voltage: 3.6 V

Transmit Power

23d Bm±2 dB

Temperature

− 30±75 °

USIM

Only support 3.0 V external USIM card

Mater serial port

The baud rate for AT command transmission and data transmission is 9600 bps The baud rate for software upgrade is 115,200 bps

Debug serial port

For software debugging and setting baud rate

3.5 NB-IoT and LoRa Technology

177

supply, serial port, analog-to-digital conversion interface, USIM card interface, network status indication interface, and RF interface. The SIM interface of NB-IoT module BC95 provides 5 interface signal lines: • • • • •

SIM_VCC: SIM card power supply SIM_DATA: SIM card data signal line SIM_RST: SIM card reset interface SIM_CLK: SIM card clock signal interface GD: SIM card ground wire interface. These signal interfaces are respectively connected to the SIM cards.

3.5.2 LoRa (1) Characteristics of LoRa technology and LoRa WAN network Architecture LoRa, a revolutionary chip for data transmission introduced by Semtech in August 2013. Operating within the frequency band below 1 GHz, typically utilizing a 125 kHz bandwidth, LoRa harnesses the advantages of low-frequency transmission. This strategic choice enables it to achieve unparalleled distances while utilizing the same transmission power, thus ensuring extensive coverage at a fraction of the cost. A testament to its exceptional performance, LoRa technology exhibits an astounding receiving sensitivity of up to -148dBm, outshining other advanced subGHz chips by a staggering 20 dB. This heightened sensitivity guarantees steadfast network connections, instilling confidence in its reliability [143]. The hallmark of its low power consumption lies in its adaptive rate mechanism, adeptly utilizing higher transmission rates whenever channel conditions permit. By minimizing the transmitter’s continuous transmission time, this intelligent approach conserves energy, ultimately reducing power consumption. Moreover, LoRa incorporates cutting-edge transmission technologies, such as forward error correction coding and spread spectrum techniques. These innovations effectively mitigate data packet errors, curtailing the need for retransmissions, and adeptly suppress burst errors arising from multipath fading. LoRa’s spread spectrum factor spans an impressive range of 6–12. Once the modulator and demodulator are configured, the data sequence undergoes a remarkable transformation. Each bit is divided into 64–4096 chips, depending on the chosen spread spectrum factor. The higher the spread spectrum factor, the more efficiently LoRa can extract valuable information from ambient noise. Additionally, LoRa technology significantly enhances the link budget when compared to traditional spread spectrum modulation techniques. This improvement empowers LoRa to counter inband interference, extend the range of radio links, and fortify the overall robustness of the network. These inherent qualities make LoRa particularly suitable for scenarios requiring long-distance coverage, low power consumption, and low-rate data requirements, making it an ideal choice for LPWAN applications.

178

3 Sensing Technology

When comparing LoRa with NB-IoT technology, it becomes evident that LoRa offers several distinct advantages [144]: • The cost of LoRa is lower. NB-IoT uses authorized frequency bands, and the cost of frequency band authorization is relatively high. • LoRa is transmitted in the sub-GHz radio frequency band, which makes it easier to achieve long-distance communication with lower power consumption. • The lower data rate of LoRa prolongs battery life and increases the capacity of the network. The data rate range of NB-IoT is 160–250 kbps, while the data rate range of LoRa is 0.25–5 kbps. • LoRa signal has very strong penetration and obstacle avoidance. As of June 2017, the deployment of LoRa networks had garnered significant attention and participation from the mobile communication industry. A total of 42 mobile communication operators had publicly announced their deployment of LoRa networks, with 30 operators joining the LoRa Alliance. This widespread adoption was accompanied by the establishment of over 250 LoRa test networks and urban commercial networks. Furthermore, the LoRa Alliance had grown to include more than 480 members [145]. In 2015, a momentous milestone was achieved for LoRa with the collaborative efforts of Semtech, Actility, IBM, and other organizations. Together, they formulated LoRa WAN, an end-to-end technical standard specification for LoRa. This significant development took place during the Mobile World Congress held in Barcelona. The LoRa WAN network architecture, depicted in Fig. 3.19, encompasses three key components: terminal devices, gateways, and a central server. The gateway serves as a relay node, connecting the terminal devices to the central server. The communication between the gateway and the central server utilizes the standard IP protocol, while the communication between the terminal devices and the gateway employs a single-hop communication method. (2) Typical LoRa Chip and Transmission mode Semtech has developed a range of six LoRa radio chips, namely SX1272, SX1273, SX1276, SX1277, SX1278, and SX1279. These chips are equipped with halfduplex low-IF transceivers and feature both standard FSK and LoRa spread-spectrum modems [146]. The key differences among these chips lie in three main aspects: the supported frequency bands (broadband, low-frequency, or high-frequency), receiving sensitivity, and spread spectrum factor. Each chip has its own unique performance characteristics, leading to variations in pricing. Among these chips, SX1276, SX1277, and SX1278 are the most widely utilized, with their key parameters outlined in Table 3.10. While the bandwidth remains consistent across the three chips, SX1277 shares the same frequency range as SX1276 but with a smaller spread spectrum factor. On the other hand, SX1278 shares the same spread spectrum factor as SX1276 but with a narrower frequency range. Consequently, SX1276, which supports a range of 137 MHz to 1020 MHz, has emerged as the preferred choice. This chip encompasses

3.5 NB-IoT and LoRa Technology

179

Fig. 3.19 LoRa WAN network Architecture [144]

Table 3.10 LoRa chip comparison No

Frequency (MHz)

SSF

Bandwidth (k Hz)

Effective bit rate (kbps)

Sensitivity

SX1276

137–1020

6 –12

7.8–500

0.018–7.5

− 111 to − 48d Bm

SX1277

137–1020

6–9

7.8–500

0.11–37.5

− 111 to − 39d Bm

SX1278

137–525

6–12

7.8–500

0.018–37.5

– 111 to − 48d Bm

Note SSF = spread spectrum factor

major license-free frequency bands such as 920 MHz in the United States, 868 MHz in Europe, and 433 MHz in Asia. Additionally, SX1276 exhibits an impressive receiving sensitivity of up to – 136 dBm. SX1276 employs software spread spectrum technology, enabling high sensitivity of up to – 148 dBm at low transmission rates. However, when the transmission rate reaches a certain threshold, its performance resembles that of FSK, losing the distinct advantages of LoRa. The spread spectrum communication utilized by SX1276 is a method capable of operating effectively under negative signal-to-noise ratios, thus showcasing remarkable resistance to interference. Existing LoRa technology applications are built on the LoRa WAN protocol, which encompasses three different transmission modes tailored for specific scenarios: Class A, Class B, and Class C. Class A serves as the default mode in the network, primarily suited for scenarios involving significant uplink communication. In Class A mode, as depicted in Fig. 3.20, the terminal device node must initiate the transmission process. Following

180

3 Sensing Technology

Fig. 3.20 The diagram of Class A receiving window [144]

the uplink communication, two receiving windows, RX1 and RX2, are available for receiving downlink data from the gateway node. The size of the receiving window is adjusted based on specific communication requirements. Class B mode, in addition to the RX1 and RX2 receiving windows found in Class A, introduces a receiving window called Slot to accommodate frequent reception of gateway commands. As shown in Fig. 3.21, in this mode, the gateway broadcasts a Beacon frame containing synchronization time. Terminal nodes use this reference for periodically opening the Slot receiving window. Class C mode, illustrated in Fig. 3.22, maintains the terminal node in a constant receiving state except during uplink transmission time. Compared to Class A and B, Class C mode consumes more energy due to its continuous reception. (3) Transmission Parameters of LoRa The three transmission parameters of LoRa are: spreading factor (SF), coding rate (CR) and bandwidth (BW). These three parameters and their impact on transmission performance are discussed below.

Fig. 3.21 The diagram of Beacon and Ping Slot [144]

3.5 NB-IoT and LoRa Technology

181

Fig. 3.22 The diagram of Class C receiving time slots [144]

(a) Spreading factor (SF) Lora modulation adopts Chirp spread spectrum technology which has the characteristics of anti-multipath fading and Doppler effect. A Chirp signal is defined as shown in Eq. 3.8. (3.7) where ϕ(t) is the Chirp signal’s phase. LoRa uses multiple orthogonal spreading factors (between 6 and 12). SF makes a compromise between data rate and transmission distance. Choosing a higher spreading factor can increase the transmission distance, but it will reduce the data rate. On the contrary, A low spreading factor will reduce the transmission distance and increase the data rate. When the link state is good, a lower spreading factor can be used to transmit at a higher rate. When the link state is poor, the sensitivity can be improved by increasing the spreading factor. Table 3.11 shows typical spreading factors. (b) Coding Rate (CR) LoRa employs Forward Error Correction (FEC) to further increase receiver sensitivity. The coding rate (or information rate) defines the number of FECs. LoRa Table 3.11 LoRa spreading factor (SF) Spreading factor (Regmodulationcfg)

Spreading factor (chirp/symbol)

LoRa demodulator S/N (dB) −5

6

64

7

128

− 7.5

8

256

− 10

9

512

− 12.5

10

1024

− 15

11

2048

− 17.5

12

4096

− 20

182 Table 3.12 Data overhead at different encoding rates

3 Sensing Technology

CR (RegTxCfg1)

Cyclic coding rate

Overhead ratio

1

4/5

1.25

2

4/6

1.5

3

4/7

1.75

4

4/8

2

provides a CR value from 0 to 4, where CR is 0, which means that FEC is not used. The cyclic coding rates corresponding to CR 1, 2, 3, and 4 are shown in Table 3.12. The cyclic coding rate is the proportion of useful parts in the data stream. Using FEC will add transmission overhead, so as the CR value increases, the lower the cyclic coding rate, the effective data rate per channel decreases. (c) Signal Bandwidth (BW) LoRa is a half-duplex system, and the uplink and downlink operate in the same frequency band. This will increase the signal bandwidth and increase the data rate, but the receiver sensitivity will be reduced. The LoRa bandwidth options are shown in Table 3.13. Currently, most LoRa chips support a system bandwidth of 2 MHz, which consists of 8 channels, each with a fixed bandwidth of 125 kHz. To ensure proper channel separation, a guard band of 125 kHz is required between each fixed bandwidth channel. Thus, the total system bandwidth requirement amounts to at least 2 MHz. Within each fixed bandwidth channel of 125 kHz, there is considerable flexibility in selecting the data rate. The data rate can be chosen from a wide range, spanning from 250bit/s to 5kbit/s. This expansive range allows for accommodating different application requirements and optimizing data transmission within the LoRa system. (4) LoRaWAN The LoRa Alliance was formed in June 2015 and released the first open standard, LoRa WAN R1.0. This standard enabled multiple LoRa terminals to communicate through gateways, providing a physical access control mechanism. Table 3.13 LoRa bandwidth option

BW (kHz)

SF

CR

Nominal bit rate (bps)

7.8

12

4/5

18

10.4

12

4/5

24

15.6

12

4/5

37

20.8

12

4/5

49

31.2

12

4/5

73

41.7

12

4/5

98

62.5

12

4/5

146

125

12

4/5

293

250

12

4/5

586

500

12

4/5

1172

3.5 NB-IoT and LoRa Technology

183

(a) LoRa Frame Structure The LoRa frame structure begins with the preamble, where the encoded sync word is located. This sync word serves to differentiate LoRa networks operating on the same frequency band [147]. If the decoded sync word does not match the pre-configured one, the terminal ceases to listen to the transmission. Following the preamble is the optional header (header) that provides information such as payload size (2–255 Byte), the data rate used for transmission (0.3–50 kbit/ s), and the presence of a payload CRC at the end of the frame. The PHDR_CRC field validates the header, and if it is found to be invalid, the packet is discarded. Figure 3.23 illustrates the LoRa frame structure. The MAC header (MHDR) indicates the type of MAC message (MType) and the LoRaWAN version number, with RFU (Reserved for Future Use) designated as a reserved field. LoRa WAN defines six types of MAC messages, including joinrequest and join-accept messages for Over-The-Air Activation (OTAA), as well as four data messages that can carry MAC commands, application data, or a combination of both. Confirmed data messages require a response from the receiving end, while unconfirmed data messages do not.

Fig. 3.23 LoRa frame structure [148]

184

3 Sensing Technology

The MAC Payload represents the “data frame” and its maximum length (M) varies depending on the region. The Frame Header (FHDR) consists of four components: device address, frame control (FCtrl, differing for uplink and downlink), frame counter (FCnt), and frame options (FOpts). The FRM Payload carries specific application data or MAC commands and is encrypted using AES-128. The content of FCtrl differs for uplink and downlink. The Adaptive Data Rate (ADR) is utilized to adjust the terminal’s data rate, optimizing battery life and maximizing network capacity. FPending is a frame suspension used exclusively for downlink, indicating that the gateway has additional information to send to the terminal, prompting the terminal to promptly transmit an uplink frame and open the receiving window. In Class B, the RFU bit is repurposed to indicate that the terminal has entered Class B mode. FOptsLen indicates the actual length of FOpts. FCnt counts only new transmissions, distinguishing between FCntUp and FCntDown. Each time the terminal sends an uplink frame, FCntUp is incremented by “1”, while each time the gateway sends a downlink frame, FCntDown is incremented by “1”. FOpts is employed to carry MAC commands within data frames. FPort represents the port field. If the FRM Payload is not empty, FPort must be present, and when FPort is included, four possibilities arise. MIC (Message Integrity Code) serves to verify the message’s integrity and is calculated based on MHDR, FHDR, FPort, and the encrypted FRMPayload. (b) LoRaWAN Classes LoRaWAN defines three distinct classes of terminals: Class A, Class B, and Class C, each with its own characteristics and operational modes (as depicted in Figs. 3.20, 3.21 and 3.22). A brief overview of each class is provided below: Class A terminals feature a communication pattern where each uplink transmission is followed by two short downlink receiving windows: RX1 and RX2. Typically, RX2 is activated approximately 1 s after RX1. Class A terminals autonomously schedule their transmission time slots based on their own communication requirements, utilizing a random time reference (ALOHA protocol) for fine-tuning. In Class A, the traffic process is initiated by the terminal itself. When a gateway intends to send a downlink transmission, it must wait for the terminal to transmit an uplink data frame. Class A is the fundamental and mandatory terminal type in LoRa networks, and all connected terminals must support Class A. While a terminal can opt to switch to Class B or Class C based on specific requirements, it must remain compatible with Class A. The decision to switch to Class B mode is made by the terminal’s application layer in response to certain requirements. Initially, the gateway broadcasts a beacon to provide time synchronization for the terminals. Subsequently, the terminal periodically opens an additional receiving window called a “ping slot,” during which the gateway can initiate downlink transmissions (pings). If the terminal detects a change

3.5 NB-IoT and LoRa Technology

185

in the beacon’s ID or changes its own ID, it must send an uplink frame to inform the gateway and update the downlink routing table. Failure to receive the beacon within a given time frame results in the terminal losing synchronization with the network, triggering a notification from the MAC layer to the application layer to return to Class A mode. If the terminal still intends to enter Class B mode, it must initiate the process anew. Class C terminals have an “always open” receiving window unless they are actively transmitting an uplink frame. Class C offers the lowest transmission delay but consumes the most energy. It’s important to note that Class C is not compatible with Class B. Class C terminals continuously listen for downlink transmissions on RX2, provided they are not sending or receiving data on RX1. For this purpose, the terminal opens a short receiving window (the first RX2 in Fig. 3.22) between the uplink transmission and RX1, adhering to the parameter settings of RX2. Once RX1 is turned off, the terminal immediately switches to RX2 and remains active until another uplink transmission is initiated. (c) LoRa Connection To establish network connectivity, terminals in LoRaWAN must undergo an activation process. LoRaWAN offers two methods for activation: Over-The-Air Activation (OTAA) and Activation By Personalization (ABP). In the case of OTAA, the terminal initiates the join-procedure. It broadcasts a join-request message containing the APPEUI, DevEUI, and DevNonce, which are preconfigured in the terminal by the device manufacturer. Upon receiving the join-request message, the gateway responds with a join-accept message, signaling that the terminal is granted access to the network. If multiple gateways transmit join-accept messages, the terminal selects the network with the strongest signal quality for connectivity. Upon receiving the join-accept message, the terminal sets both FCntUp and FCntDown to 0. Once activated, the terminal stores four essential pieces of information: DevAddr, APPEUI, NwkSkey, and AppSKey. If no join-request message is received, the gateway takes no further action. Alternatively, terminals can be activated using ABP, which bypasses the joinprocedure. With ABP, the terminal is directly provisioned with DevAddr, NwkSKey, and AppSKey, providing it with the necessary access information specific to a particular LoRa network. Terminals activated via ABP must initially operate in Class A mode and can switch to other modes as required. In summary, LoRaWAN provides the flexibility of two activation methods: OTAA, which involves the join-procedure, and ABP, which directly configures the terminal with access information. Both methods enable terminals to establish connectivity with LoRa networks, with the option to switch to different operational modes when needed. (d) MAC Command To facilitate network management, LoRaWAN incorporates several MAC commands that allow for the configuration and modification of terminal parameters. MAC

186

3 Sensing Technology

commands can be included either in the FOpts field or the FRMPayload field (when FPort = 0), but not in both simultaneously. When MAC commands are included in the FOpts field, their length must not exceed 15 bytes, and encryption is not required. On the other hand, if MAC commands are included in the FRMPayload field, their length must not exceed the maximum length allowed for FRMPayload, and encryption is mandatory. MAC commands are composed of a command identifier (CID) that spans 1 byte, followed by specific commands associated with that identifier. Each MAC command request has a corresponding answer, resulting in seven pairs of commands. Among these pairs, only the link check request (LinkCheckReq) is initiated by the terminal and responded to by the gateway. The remaining requests are initiated by the gateway and responded to by the terminal. Table 3.14 presents commonly used MAC commands, providing an overview of their functionalities and purposes. (5) Typical Chinese LoRa Module and Applications The LoRa module serves as a wireless communication solution for IoT terminals, integrating with the data acquisition and control subsystem and controlled by the IoT terminal’s microprocessor [149]. When selecting a LoRa communication module, it is important to choose one that incorporates the LoRaWAN protocol stack, making development easier. Additionally, the module should exhibit characteristics such as Table 3.14 Commonly used MAC commands Command

Function

LinkCheckReq

Checks for network connectivity, does not carry payload

LinkCheckAns

Estimate the signal reception quality and return the estimated value to the terminal back

LinkADRReq

The terminal is required to adopt Adaptive Data Rate (ADR, Adaptive Data Rate)

LinkADRAns

Notifies the gateway whether to perform rate adjustment

DutyCycleReq

Limit the maximum aggregate transmission duty cycle of the terminal

DutyCycleAns

Response to DutyCycleReq without payload

RXParamSetupReq

Change the frequency and data rate of RX2; set the deviation between the uplink frame and the data rate of RX1

RXParamSetupAns

Response to RXParamSetupReq without payload

DevStatusReq

Check terminal state without payload

DevStatusAns

Notify the gateway the battery level and demodulation signal-to-noise ratio of the terminal

NewChannelReq

Set the center frequency of the channel and the available data rate of the channel to modify the existing channel parameters or create a new channel

NewChannelAns

Response to NewChannelReq without payload

RXTimingSetupReq Set the delay between the terminal of uplink transmission and the opening of RX1 RXTimingSetupAns Response to RXTimingSetupReq without payload

3.5 NB-IoT and LoRa Technology

187

low power consumption, long transmission distance, and strong anti-interference capabilities. One example of a LoRa module is the LSD4WN-2N717M91 [150], which embeds the LoRaWAN protocol stack and complies with the LoRaWAN Specification 1.01 Class A\C application standard by the LoRa Alliance and the CLAA application specification by the China LoRa Application Alliance. This module utilizes a serial interface to communicate with the microcontroller, facilitating data and instruction exchange and providing users with fast LoRaWAN network access and wireless data services. The LSD4WN-2N717M91 module supports both Class A and Class C working modes. Operating at a voltage range of 2.5 V to 3.6 V, the module offers a transmission power of 19 ± 1dBm(maximum) and boasts ultra-high receiving sensitivity of − 136 ± 1dBm. It enables effective long-distance communication of up to 5 km in urban road environments and non-wilderness areas. Figure 3.24 illustrates the module application diagram of LSD4WN-2N717M91. The LoRa module features a total of 22 pins, with pins P0 to P4 designated as subsequent expansion function configuration interfaces. The VCC pin serves as the power supply input, accepting an operational voltage range of 2.5 V to 3.6 V. The NF pin functions as the radio frequency outlet, connected to an external antenna. The following section focuses on the WAKE, STAT, BUSY, RST, TX, and RX pins. The WAKE pin enables the selection of two operation states for the LoRa module, as outlined in Table 3.15. In the sleep state, the module operates in a low-power mode without performing data transmission or other operations. Upon waking up (when the user sets the WAKE pin to a high level to enter the active state), the user can engage in operations such as LoRaWAN network data transmission and reception. The MODE pin allows for the selection of two operation modes for the LoRa module, as presented in Table 3.16. The activation state encompasses two modes:

Fig. 3.24 The application diagram of Module

188 Table 3.15 State control

3 Sensing Technology

Function pin

Description

WAKE = 1 hold low level

Activation state

WAKE = 0 hold low level

Sleep state

transparent transmission mode and command mode. When the MODE pin is set to a low level, the module operates in transparent transmission mode. By pulling the MODE pin to a high level, the module enters command mode, enabling users to retrieve the current operation state of the LoRa module or configure it using AT commands. Upon initial use of the module, the user must configure relevant parameters such as serial port communication rate, LoRaWAN network parameters, gateway server ID, and key. Before sending data through the serial port, it is important for the user to check the status of the BUSY signal. If the BUSY signal is low (indicating the module is busy), the user should halt sending data. Once the BUSY signal returns to a high level, the user can resume sending data. During transparent transmission, the LoRa module should be set to transparent transmission mode, following the steps outlined below: • Pull the WAKE pin to a high level to wake up the module and activate it. • Set the MODE pin to a high level to enter transparent transmission mode, and wait for the BUSY signal to reach a high level. • When the BUSY pin is in a high level state, the user can transmit serial data. • If the complete data frame sent by the user is within the maximum frame data size set by the module and no data is sent for a period of time, it indicates the end of the current data frame. At this point, the BUSY pin automatically returns to a low level, and the module transmits the data. • The module sends uplink data, automatically attempting retransmission if the initial transmission fails. • If the server has downlink data, the module receives it. • The module then transmits the received data to the microcontroller via the serial port. If there are any abnormalities in the current communication data, the STAT pin output will be set to a low level, indicating an issue. In such cases, the user should wait for the BUSY signal to reach a high level, enter command mode, and retrieve more detailed status information through relevant registers. If no exceptions occur, the STAT pin remains at a high level. Once the user data interaction is complete, the Table 3.16 Mode control

Function pin

Description

MODE = 1 hold low level

Command mode

MODE = 0 hold low level

Transparent transmission mode

3.6 Positioning Technology

189

user can set the WAKE pin to a low level as needed to put the module back into sleep mode.

3.6 Positioning Technology The IoT utilizes various positioning technologies, including GPS/Beidou, Mobile Cellular measurement, WLAN, Short-Range wireless measurement, WSN, UWB (ultra-wideband), and others. Each positioning technology has its own characteristics and suitable application scenarios. Table 3.17 provides a comparison of these positioning technologies based on coverage, reliability, coexistence, mobility, cost, and application [151]. In terms of positioning range, satellite positioning systems like GPS offer the widest coverage, followed by cellular-based localization services. Bluetooth and RFID have the smallest positioning range. GPS positioning excels in outdoor environments, but its signal attenuation indoors and susceptibility to interference from other radio systems limit its effectiveness for indoor positioning. Cellular positioning relies on cellular mobile technology, particularly CDMA. However, the positioning accuracy of cellular systems is affected by environmental factors and multipath effects, which reduce its precision. Technologies like WiFi and UWB have relatively smaller positioning ranges and are commonly used for local area relative positioning. WiFi, ZigBee, and Bluetooth operate in the open ISM frequency band, making them susceptible to interference. Table 3.17 Comparison of main positioning technology features Feature

GPS

蜂窝

WiFi

UWB

ZigBee

Blutooth

RFID

Coverage

★★★★

★★★

★★

★★

★★

★

★

Reliability

★★

★★★

★★★

★★★

★★

★★

★★

Coexistence

★

★★

★★

★★★★

★★

★★

★★

Mobility

★★★

★★★

★★

★★★★

★★★★

★★★★

★★★★

Flexibility

★★★

★★★

★★★

★★

★★

★★

★

Cost

★

★★

★★★

★★

★★★

★★

★★★★

Response

★

★★★

★★

★★★

★★★

★★

★★★★

Precision

< 50 m

20 m

10 m

< 0.3 m

1∼3 m

>3m

—

Relative Accuracy

★★★

★

★★

★★★

★★

★

—

Energy Consumption

★

★

★★

★★

★★★

★★★

★★★

Application

Outdoor

3G/4G

Indoor

Industry

Indoor

Smart Device

Material Management

190

3 Sensing Technology

On the other hand, UWB, with its broad bandwidth and low power spectral density, exhibits excellent coexistence performance. Flexibility and system cost are related. GPS and cellular base station positioning have higher operational costs but provide extensive coverage, compensating for their lack of flexibility. WiFi, UWB, and ZigBee support mobility and offer high flexibility. UWB technology has the potential for achieving the highest theoretical positioning accuracy. While GPS may not offer high positioning accuracy (without considering differential correction), it excels in relative positioning accuracy. ZigBee provides efficient and cost-effective networking capabilities, making it suitable for network positioning and monitoring. RFID has low implementation costs and focuses less on position accuracy for personnel identification and positioning. However, by combining methods like signal strength detection and deploying reference nodes extensively, improved positioning performance can be achieved. GPS receivers are more expensive and slower to respond, but the Chinese Beidou system has significantly improved positioning response speed, with the initial GPS positioning taking around 1 min and the Beidou system requiring approximately 1–3 s. This section primarily discusses recent advancements in indoor positioning technology and 5G positioning technology.

3.6.1 Introduction and Algorithm of Positioning Technology Based on Wireless Signal The positioning technologies based on wireless signals encompass several approaches, each with its own principles and considerations. The following is a brief description of these positioning techniques: (1) Time of Arrival (TOA) This technology calculates the relative distance between a transmitter and receiver by knowing the propagation rate of the signal and the time taken by the signal to travel. Highly synchronized clocks between the transmitter and receiver are required for accurate measurements. (2) Time Difference of Arrival (TDOA) TDOA involves transmitting two signals with different propagation speeds and determining the distance between the transmitter and receiver based on the time difference between the arrival of the two signals. Clock synchronization between the transmitter and receiver is not necessary. (3) Angle of Arrival (AOA) AOA calculates the coordinates of a node by measuring the angle between the signal’s propagation direction and the horizontal plane where the positioning node is located.

3.6 Positioning Technology

191

This technique requires angle sensors or receiving arrays to accurately measure the arrival angles of neighboring beacon nodes, making it less practical due to hardware requirements and sensitivity to external factors. (4) Received Signal Strength (RSS) RSS positioning analyzes and determines the position of a node by measuring the signal strength received at the positioning node from a beacon. Two main algorithms are used: one estimates distance based on path loss models and applies trilateration, while the other utilizes fingerprint recognition algorithms (pattern matching) to obtain coordinate information. (5) Bluetooth Low Energy (BLE) Bluetooth is a short-range, low-power wireless technology used for small-scale positioning. Signal strength detection and path loss models are employed to estimate the position of a user. However, accuracy may be compromised in complex environments and due to interference from noise signals. (6) RFID Positioning Technology RFID positioning utilizes signal strength analysis and clustering algorithms to calculate the distance between beacons and positioning nodes, enabling spatial positioning. This technology is characterized by small, cost-effective beacons but limited positioning range and security. (7) Wireless Sensor Network Positioning Technology Networks composed of wireless sensor nodes can locate unknown nodes by leveraging known beacon nodes. Distance-free positioning algorithms like the centroid algorithm, DV-Hop (Distance Vector-Hop) algorithm, and DV-Distance algorithm are commonly used. (8) Ultra-Wide Bandwidth (UWB) UWB technology transmits data using extremely narrow pulses with durations in nanoseconds. It offers precise indoor positioning with advantages such as strong penetration, low power consumption, anti-multipath effects, high security, low system complexity, and high accuracy. However, it requires precise clock synchronization, is relatively costly, and less suitable for commercial applications. (9) Other Positioning Technologies Additional positioning technologies include intelligent LED lamp technology, ultrasonic technology, indoor positioning based on frequency modulation, infrared indoor positioning technology, and more. (10) Positioning Algorithm Table 3.18 provides a summary of wireless signal-based positioning algorithms from various sources, including IEEE documents over the past decade [152].

192

3 Sensing Technology

Table 3.18 Summary of some commonly used indoor positioning algorithms Wireless /Positioning Algorithm

Precision Real-Time

Robustness Cost

WLAN RSS, MPL, SVW, etc 3 m

Middle

Unidirectional UWB, TDOA, Least Squares

Response frequency 0.1–1 Hz Poor

< 0.3 m

Good

Low Higher

Auxiliary GPS, TDOA

5–50 m

High

Poor

Middle

UHF TDOA, Least squares/ residual weighting

2–3 m

Middle

Good

Low

Unidirectional UWB, TDOA 15 cm + AOA, Least squares

Response frequency 1–10 Hz

Poor

Higher

QDMA, Self-positioning Algorithm

Delay 1 s

Good

Middle

10 m

Image Processing

10 cm

Middle

Poor

High

TOA, Least squares

10 cm

Middle

Good

High

Mobile devices, SAR, radio 17 cm frequency positioning, stereo vision

Middle

Good

High

Range finder/panoramic camera, EM algorithm

30 cm

Middle

Good

High

Multi-sensor, Co-location

50 cm

RSS/INS, human kinematics, 2 m phase detection

High

Good

High

Middle

Good

Middle

3.6.2 Indoor Positioning Technology Based on WLAN Channel State Information Utilizing the path loss model as a foundation for analyzing wireless signals, the Received Signal Strength Indicator (RSSI) has been commonly employed for positioning in WLAN systems. However, the practical application of RSSI presents numerous limitations, including unstable measurement values and susceptibility to factors such as multipath interference and environmental changes. Consequently, enhancing the positioning accuracy based on RSSI within a WLAN context proves to be a challenging endeavor. With the advent of the IEEE 802.11n series standards for WLAN, a remarkable evolution in wireless transmission has occurred, with the incorporation of advanced technologies such as Multiple Input Multiple Output (MIMO) and Orthogonal Frequency Division Multiplexing (OFDM) at the physical layer. These groundbreaking advancements have facilitated the estimation of wireless transmission characteristics, enabling the estimation of the wireless transmission channel itself. This estimation process provides invaluable insight through the extraction of Channel State Information (CSI), which has found widespread application, particularly in indoor positioning [153, 154]. By harnessing CSI, it becomes possible to capture

3.6 Positioning Technology

193

crucial properties of the physical environment, including scattering, environmental attenuation, and power attenuation. Compared to the traditional RSSI, CSI goes beyond mere signal strength measurements, offering more detailed information regarding the channel’s frequency response and encompassing a broader range of characteristic parameters. Leveraging these comprehensive physical parameters, significant improvements in positioning accuracy can be achieved, effectively addressing challenges such as multipath interference. (1) Channel State Information (CSI) In wireless communication systems, the quality of signals is significantly inferior to that of wired signals due to path loss, multipath effects, and the dynamic nature of channels during transmission. For a multi-antenna wireless communication system, the signal model can be expressed through the following equation: Y = HX + N

(3.8)

where, Y and X represent the vectors of the received signal and the transmitted signal respectively, H is the channel matrix, and N is the noise vector. The channel matrix comprehensively characterizes the channel’s properties and can be estimated through computational methods. In the context of employing OFDM and MIMO technologies for wireless signal reception, accurate channel estimation becomes crucial as it directly influences system performance. Various methods exist for channel estimation in WLAN protocols [155]. Typically, frequency domain features, also known as Channel Frequency Response (CFR), can be directly obtained and utilized. Alternatively, the Channel Impulse Response (CIR) can be employed to describe the multipath effect and represent the channel. Assuming linear time invariance, the channel impulse response is described as follows: h(τ ) =

N ∑

ai e− j θi δ(τ − τi )

(3.9)

i=1

where, ai , θi and τi represent the amplitude, phase and time delay of the i-th path of signal propagation respectively, N is the number of paths of multipath, and δ(τ ) is the Dirichlet function. Under the assumption of unlimited bandwidth, the channel frequency response and the channel impulse response are equivalent, representing Fourier transforms of each other. The Channel State Information (CSI) obtained during WLAN signal processing can be viewed as a subset of CFR, encompassing specific frequency responses of the current channel. With CSI, CIR with a certain level of accuracy can be obtained and applied to certain positioning algorithms. Some existing wireless network cards also integrate OFDM and MIMO technologies, enabling the direct acquisition of underlying channel state information through software. The channel state information for each antenna pair can be directly obtained via the wireless network card, typically encompassing 30 sub-carrier frequencies. In

194

3 Sensing Technology

a typical 3 × 3 antenna structure, each acquisition provides 3 × 3 × 30 channel state information values, as indicated by the following equation: H ( f k ) = ∥H ( f k )∥e jθ

(3.10)

where, H ( f k ) is a complex value, indicating the channel state information corresponding to the subcarrier frequency with the center frequency f k , which can be expressed by the amplitude ∥H ( f k )∥ and the phase angle θ , that is, the attenuation amplitude and phase shift of the signal. However, due to factors such as the accuracy of channel estimation, there may exist certain deviations between the CSI values and the actual channel conditions. Therefore, preprocessing is necessary before applying the CSI to various methods to mitigate major errors. The feature quantities corresponding to different antennas and sub-carrier frequencies exhibit distinct differences. Although the channel is not fully described, the richness of CSI data is already sufficient for positioning estimation. In recent years, numerous positioning systems based on CSI fingerprint matching, angle measurement, and distance measurement principles have emerged, resulting in improved accuracy. (2) CSI-based Fingerprint Matching Positioning Method Fingerprint matching technology is widely utilized in indoor positioning, where radio signal strength and location-related physical quantities serve as fingerprint information. In most WLAN-based fingerprint matching and positioning technology systems, RSSI data is initially used to construct a fingerprint database. RSSI data, which reflects the physical quantity of the channel state between the transmitting and receiving terminals, is easily obtainable. However, RSSI only characterizes the total received energy of the channel and lacks detailed information about environmental characteristics such as multipath effects. Therefore, positioning methods based on CSI fingerprint matching hold greater potential for application. CSI fingerprint matching positioning systems typically require at least one transmitter (base station) and one receiver. Each time the receiver receives a data packet, it can generate a CSI matrix that corresponds to the channel characteristics between the base station and the receiver. The fingerprint positioning method generally involves two stages: offline establishment of the fingerprint database and online matching. The establishment process of the fingerprint database includes collecting original data and performing calibration. Even if only considering a fingerprint matching system with a single base station, multiple CSI matrices can be obtained for each fingerprint point within a short time frame based on the data packet interval settings during database establishment. Similar to other methods of establishing fingerprint libraries, multiple sets of data are collected at once, followed by calibration to obtain a representative record that best reflects the characteristics, which is then stored in the fingerprint library. During online matching, since CSI data can be obtained in multiple batches, the CSI value from a single data packet can be directly used for matching with

3.6 Positioning Technology

195

the fingerprint library according to performance requirements. Alternatively, after collecting a certain amount of data, the processed CSI data can be used for matching. The structure of a typical CSI fingerprint positioning system is depicted in Fig. 3.25, while Fig. 3.26 illustrates a diagram of the typical CSI fingerprint matching process. A digram of a typical CSI fingerprint matching process is shown in Fig. 3.26. In 2014, Chapre et al. [156] presented their implementation of a CSI-MIMO fingerprint localization system. This system takes into account the spatial diversity of CSI measurement values and the frequency response diversity by using multiple sub-carrier frequencies of WLAN to construct a fingerprint database. During the construction of the fingerprint library, the system first records the original CSI values collected at each fingerprint point, followed by further processing. For a system with p transmit antennas and q receive antennas, the obtained CSI values are summed to produce a 1 × 30 matrix, expressed as: CSIavg =

p q ∑ ∑

csinm

(3.11)

m=1 n=1

Fig. 3.25 Structure of CSI Fingerprint Positioning System

BS

BS

Fingerprint Library BS

Fig. 3.26 Typical CSI fingerprint matching process [153]. Note BS = Base Station; BbP = Baseband Processing; Md CSI CP = Multidimensional CSI calibration processing; MO = Match Online; WfMsCSID = Waiting for multiple sets of CSI data; FL = Fingerprint Library; Get Po. = Get Posistion)

Receiver

BS Signal BbP Get CSI No Md CSI CP

MO

Yes

one matching

WfMsCSID

FL

Fingerprint Matching Algorithm

Get Po.

196

3 Sensing Technology

Then, the amplitude and phase corresponding to each subcarrier frequency are derived from the aggregated CSI. Recognizing that different sub-carrier frequencies are affected by the environment to varying degrees, CSI-MIMO calculates the differences between adjacent sub-carrier frequency measurements to mitigate environmental influences and obtain the fingerprint. After performing actual matching, the positioning accuracy increases by 57%. Compared to traditional RSSI, CSI provides information in two dimensions: amplitude and phase. For systems with multiple antennas and multiple frequency responses, these amplitude and phase features can further correspond to distances and angles. In theory, within the confines of a single base station, a fingerprint library constructed using CSI can represent at least a two-dimensional geographic space. By utilizing high-dimensional CSI information, a rich fingerprint library can be constructed, leading to improved positioning accuracy in fingerprint matching systems. (3) Using CSI to measure the angle AOA (Angle of Arrival) is a widely used positioning technology that can benefit from the multi-antenna capabilities and CSI of MIMO systems to enhance angle measurement accuracy, thereby improving positioning accuracy. To explain the principle of CSI-based AOA measurement, let’s consider an example with a base station having n antennas and a terminal system with a single antenna. Assuming the base station antennas are linearly arranged at equal distances, the measured phase difference at the receiving terminal can be expressed as: Δϕi = 2πid cos θ f /c, i = 1, . . . , n

(3.12)

where, f is the carrier frequency, c is the speed of light, θ is the angle of arrival, and d is the distance between each antenna of the base station. In theory, a set of phase differences Δϕ can be obtained from the CSI measurement values. Combined with the known antenna spacing d and other information, the angle of arrival θ can be directly calculated. However, due to the presence of interference such as noise and multipath, the above formula cannot be directly solved to obtain an accurate θ . For the signal parameter estimation problem involving multiple sensors, algorithms like MUSIC (Multiple Signal Classification) and ESPRIT (Estimation of Signal Parameters via Rotational Invariance Techniques) [157] are typically employed. Using the MUSIC algorithm, after the base station receives the data packet and obtains the CSI values, it can further derive the correlation matrix: ) ( RX X = E X XH

(3.13)

where, X is the output of the receiving matrix, which can be obtained from the CSI data, and X H is the X conjugate matrix. Decompose R X X into eigenvalues, and consider the noise subspace E N obtained from the signal source, then the spectral distribution of AOA is:

3.6 Positioning Technology

197

P(θ ) =

1 aH (θ ) E

N

E HN a(θ )

(3.14)

where, The direction vector a(θ ) can be obtained from the antenna spacing d. The source AOA (ie. θ ) can be obtained by looking for the spectral peak of P(θ ). An implementation called Array Track [158] was introduced in 2013, which utilized more than 8 antennas on a customized hardware platform and achieved a positioning accuracy with an average error of 38 cm. Estimating AOA based on CSI requires the use of multiple antennas at the base station, as there is a clear relationship between the phase difference among antennas and AOA. The presence of multipath and other factors make the calculated angle of arrival more accurate with an increased number of antennas. However, due to considerations such as cost and form factor, existing commercial WLAN devices typically have a limited number of antennas. With the development of WLAN protocols and advancements in antenna design and technology, the availability of a greater number of antennas in future devices will further improve the accuracy of the AOA method. Additionally, the AOA method relies on a strict mathematical model, and the accuracy of the phase information used directly affects the accuracy of the results. Therefore, it is necessary to apply a certain level of error processing to the phase extracted from the CSI to enhance the overall accuracy. (4) CIS Ranging The FILA system [159] utilized CSI for positioning and established a model correlating CSI amplitude with distance based on the principles of signal propagation. Experimental results showed that the CSI-based attenuation model was more robust than the RSSI-based model. However, model-based ranging methods still rely on energy evaluation and are susceptible to environmental disturbances. For wireless signals, measuring propagation time to obtain distance provides a more direct and accurate ranging method. In a radar system, for instance, the distance of a target can be calculated by measuring the time difference (Δt) between the emission of an electromagnetic wave and the reception of its echo and using the speed of light (c): S = 21 cΔt. This approach is commonly known as round-trip time (RTT) measurement or time-of-flight (ToF) measurement. Some methods achieve distance measurements by improving WLAN terminals, such as synchronizing transceivers and measuring signal ToF or measuring RTT using request-response mechanisms with data packets [153]. However, these approaches require firmware and even hardware modifications to WLAN equipment, which disrupts the existing network infrastructure, and achieving high accuracy is challenging due to various factors. Therefore, they cannot be widely applied. The CSI phase component carries physical meaning, as different carrier frequencies correspond to different phase values, mainly resulting from the combination of the carrier frequency and ToF. Theoretically, ToF measurement can also be realized based on CSI data. However, due to the limited bandwidth of wireless signals, achieving accurate measurement is difficult.

198

3 Sensing Technology

The Chronos system [160], implemented in 2016, achieved sub-nanosecond ToF measurement accuracy on commercial WLAN equipment. Chronos employed a special frequency hopping protocol to measure CSI data on discontinuous and unequally spaced multiple WLAN frequency bands simultaneously, integrating them to create an equivalent of a wide-bandwidth signal measurement. Instead of directly superimposing CSI measurement values from multiple frequency bands, Chronos first obtained the phase angle (∠h i ) corresponding to different frequencies ( f i ). The phase angle is related to the ToF (τ ) as follows: ∠h i = −2π f i τ mod2π

(3.15)

For n different carrier frequencies, a congruence group can be obtained, ∀i ∈ {1, 2, . . . , n}, τ = −∠h i /2π f i , mod 1/ f i

(3.16)

The τ can be obtained by solving the congruence, namely ToF. In fact, there have been research results on ToF estimation of OFDM wireless systems [161]. Similar algorithms can be applied to estimate ToF of WLAN signals using OFDM technology based on available CSI data. The wider the signal bandwidth, the higher the theoretical ranging accuracy that can be achieved. The Chronos system achieves a higher bandwidth by integrating measured values from different frequency bands, but this integration method may introduce new errors, including phase offset and frequency offset, and disrupt the normal data communication process. In the future, with hardware upgrades and WLAN protocol advancements, signal measurement accuracy will improve, bandwidth will expand further, and ToF methods will achieve higher accuracy. CSI provides a new perspective for WLAN signal positioning indoors. Compared to traditional RSSI or other positioning methods, the CSI-based positioning method is more flexible and does not require changes to the existing network structure. It holds significant application potential and can meet the requirements of opportunistic location services.

3.6.3 The Positioning Technology of 5G In the 5G concept white paper, the 5th Generation Mobile Communication System Promotion Group eloquently delineated the four fundamental technical scenarios that define this revolutionary wireless technology [162]. These scenarios encompass continuous wide-area coverage, high-capacity hotspots, large-scale connections with minimal power consumption, and low-latency, high-reliability communication. Furthermore, the emergence of 5G has not only revolutionized traditional mobile communication but has also birthed novel opportunities for the advancement of high-precision positioning technology [163].

3.6 Positioning Technology

199

Unquestionably, the pursuit of an exceptional user experience in the 5G era necessitates an enhanced level of positioning accuracy, fostering comprehensive improvements in information delivery. Consequently, prominent organizations in the realm of mobile communication have presented increasingly stringent demands for high-precision positioning capabilities. While the 3GPP R15 standard has already laid the groundwork by supporting “radio access technology independent” (RATindependent) positioning, the forthcoming 3GPP R16 standard is poised to explore “RAT-dependent” and hybrid positioning techniques, with the sole aim of further augmenting positioning accuracy. European consortium 5GPPP, in their comprehensive research report on autonomous driving, set forth the audacious target of achieving a remarkable positioning accuracy of 10 cm, while the esteemed NGMN Alliance, in their illuminating white paper on 5G enhanced services, emphasized the need for a positioning accuracy of 10 m, with an 80% probability, and an impressive 1 m accuracy for indoor networking design. Thus, the ongoing development of key 5G technologies unlocks new frontiers for the evolution of positioning technology, indispensably aligning with the requirements of the four principal technical scenarios of 5G. As being widely application for 5G key technologies such as large-scale antenna arrays, new multiple access technologies, and dense network integration, 5G positioning methods could also be divided from the perspective of cooperation and noncooperation, from the perspective of whether to integrate other devices and other communication networks. (1) Non-Cooperative Positioning Non-cooperative positioning, a technique that relies on communication between devices and base stations for determining location, can be implemented on singlemode terminals or devices that don’t support 5G, as well as non-cooperating users who prefer not to disclose their location information. This positioning technology utilizes various parameters such as Time of Arrival (TOA), Time Difference of Arrival (TDOA), Angle of Arrival (AOA), Frequency Difference of Arrival (FDOA), Received Signal Strength (RSS), and fingerprints. (a) TOA/TDOA In TOA-based position estimation, the base station calculates the arrival time of the signal to estimate the target’s location. By utilizing information from at least three base stations, a circle is constructed with a radius equal to the distance (TOA multiplied by the speed of light) and the base station as the center. The overlapping area obtained from these circles allows for the estimation of the unknown position using methods such as least squares. Accurate TOA measurement requires the inclusion of time stamp information in the transmitted and received signals, along with strict time synchronization between the transmitter and receiver. In cases where synchronization is challenging, a round trip time (RTT) protocol can be designed to achieve synchronization. Since TOA/TDOA measurements provide optimal performance in direct path conditions, distinguishing between direct and non-direct paths is crucial. Eliminating

200

3 Sensing Technology

non-direct paths or employing methods like semi-positive definite programming can improve positioning accuracy by minimizing the impact of non-direct paths on TDOA positioning errors. The measurement of signal arrival time relies on obtaining a reference signal. Commonly, a pilot channel is used in 3G mobile communication systems, while LTE (4G) employs a positioning reference signal (PRS). In the context of 5G positioning research, the design of reference signals becomes pivotal in enhancing the accuracy of TOA/TDOA-based positioning [163]. Researchers have made progress in this area, and millimeter wave communication, a key 5G technology, contributes to improved multipath resolution and precision in TOA/TDOA measurements due to its high frequency and bandwidth characteristics. (b) AOA AOA positioning involves utilizing directional antennas installed on base stations to estimate the direction of incoming signals. By forming rays with the base station as the endpoint, and intersecting these rays from multiple base stations, the transmitter’s position can be estimated. AOA methods solely rely on accurately measuring the incoming wave direction, without requiring signal time synchronization. Driven by the advancements brought by 5G, such as large-scale antenna technology and millimeter-wave beamforming, the accuracy of AOA-based positioning has significantly improved. Large-scale antenna arrays enhance angular resolution, while millimeter-wave beamforming technology offers excellent directivity, both contributing to enhanced target positioning accuracy. (c) RSS (RSSI) RSS is a parameter related to the distance between transceivers. Combined with a path loss model of the environment, the distance between transceivers can be estimated. The commonly used path loss model is the logarithmic shadow fading model and its improved form, where shadow fading is usually modeled as a Gaussian random variable with mean zero and variance delta: ri = Pt + K i − 10γi log

di − ϕi d0

(3.17)

where, Pt is the transmit power, K i is the gain of the omnidirectional antenna used at distance of d0 from the AP (Access Point) in free space, γi is the path loss factor from the transmitter to the i-th AP, and di is the path loss factor from the transmitter to the i-th AP, d0 is the reference distance, and ϕi is a random variable representing shadow fading (which often presents a normal distribution). Although Eq. (3.17) can be applied to direct and non-direct path environments as long as the channel parameters that meet the environment of the transceiver are selected, it is very difficult to accurately select channel parameters in multi-path and non-direct path environments, so compared with the direct path positioning accuracy is poor. Use Eq. (3.17) to estimate the distance between the three base stations and the transmitter, and then use the trilateral positioning rule to estimate the position of the

3.6 Positioning Technology

201

target. Since the influence of measurement noise, there is a large error in position estimation. For this reason, algorithms such as nonlinear least mean square, weighted least mean square, maximum likelihood estimation, and convex optimization are commonly used for estimation [163]. Establishing an accurate channel model is the fundamental method to improve the positioning accuracy based on RSS, and the 5G communication system is an open and integrated system. Due to differences in carrier and modulation methods, the channel characteristics of different applications are also diverse. The RSS-based positioning method requires to accurately model the 5G channel, which is inseparable from a large amount of measured data, and the positioning accuracy depends on the measurement of the direct path in space. (d) Hybrid Method The hybrid method uses more than two signal features to estimate the position of the transmitter. Through cross-validation, the position error caused by the limitation of its own features can be reduced to a certain extent, so as to obtain better positioning accuracy. Under the open and integrated communication architecture of 5G, its infrastructure supports the integration of multiple positioning methods. Although this method improves the positioning accuracy, because it focuses on the fusion of observation results, it cannot get rid of the inherent limitations of various algorithms, especially the dependence on the direct path. (e) Fingerprinting Due to the variety of wireless communication in the 5G system, its rich signal characteristics have brought about the expansion of the fingerprint library. In order to reduce the database storage and search costs, the commonly used methods for the compression of the location fingerprint library include based on the path loss model, based on Fingerprint clustering, matrix filling-based, and sparse representation based on compressed sensing and RSS measurements. (2) Collaborative Positioning Collaborative positioning mainly refers to the sharing of positioning results between different networks or different devices to improve positioning accuracy. Since the 5G system is a heterogeneous system composed of multiple wireless systems, it can support cooperative positioning. Collaborative positioning can be divided into two types: network collaborative and device collaborative positioning. (a) Network Collaborative Positioning Network cooperative positioning mainly uses the positioning results of multiple base stations in the network, or the positioning results of different networks to estimate the target position. Among them, the cooperative positioning of multiple base stations mainly performs data fusion on the basis of non-cooperative positioning results. Fusion methods under 5G ultra-dense networks have been the focus of research in recent years. Mainly include: the extended Kalman filter algorithm

202

3 Sensing Technology

aimed at improving the calculation efficiency of the joint estimation of TOA and AOA of a single base station, which can solve the problems of time deviation between users and base stations and synchronization errors between base stations; EKF-based synchronization and positioning mechanism, the mechanism achieves sub-meter positioning accuracy in the 5G Internet of Vehicles scenario; in addition, the density of network access points is directly related to positioning accuracy, and the connection information between the target and the access point can be used for positioning. Cooperative positioning between different networks mainly refers to the integrated positioning of communication network and satellite network. This integration defines the positioning protocol LPP (LTE positioning protocol) in 3GPP LTE Release 9, which supports mixed positioning of A-GNSS (assisted-global navigation satellite system) and OTDOA. In the research of 5G network cooperative positioning, the new trend is the research on the fusion architecture of communication network and satellite network targeting positioning. The architecture of satellite navigation and 5G hybrid positioning has been proposed, which provides a design reference for the deep integration of communication network and satellite network [164]. (b) Device Collaborative Positioning With the rapid development of the IoT, the 5G system will have a large number of connected devices, and the location information of the devices can provide the necessary support for optimizing data transmission. For dense networking, a large number of access points requir to be deployed to improve positioning accuracy. The cooperative positioning of devices in 5G uses the location information between terminals to obtain higher positioning accuracy. As the cost of IoT devices, this type of positioning is generally measured by RSS-based methods, and the positioning equation is solved by trilateration. However, limited by the processing capability of the device, there is general a certain error in the parameter result, but as the location information is shared among the devices, the error will accumulate.

3.7 Summary In the IoT, sensing devices hold a paramount role in acquiring crucial information. These devices have the remarkable ability to capture not only physical, chemical, and biological data but also implanted information, including RFID tags and barcodes. Presently, sensor technology is advancing towards intelligent sensors, while information implantation technologies like RFID and barcodes are evolving into multifunctional, multidimensional, and wireless solutions. This chapter delves into intelligent sensors, commencing with a comprehensive exploration of their system composition and the structure and functionality of the flexible systems they encompass. Special attention is given to their scheduling software and timing mechanisms, which contribute significantly to their efficacy.

3.7 Summary

203

Furthermore, the discussion shifts towards chemical wireless sensors, offering the most promising application prospects within the IoT. Recent advancements in electrochemical, electron chemical, and photochemical sensors are introduced, showcasing their fusion with RFID and wireless sensor networks. The seamless integration of these sensor types presents a bright future for their convenient implementation in the IoT. Next, the focus turns to smart meter technology, unveiling various design solutions categorized into three groups: those based on analog front-end, metering system-onchip, and metering smart application chip solutions. These solutions provide robust support for the rapid growth of smart meters. Additionally, the chapter explores the impact of harmonics on smart meter measurements and sheds light on the security and privacy concerns surrounding these devices. Intricacies such as smart meter attacks are discussed, accompanied by an overview of different attack types. The subsequent section introduces RFID and wireless sensor networks, currently the most widely adopted technologies within the IoT. The chapter emphasizes the prevalent integration and fusion of these technologies, presenting the concept of a reader sensor (RS) node. An integration scheme featuring two communication interfaces and operating modes is detailed, showcasing the versatility and possibilities arising from combining RFID and wireless sensor networks. Moving forward, the chapter introduces and examines two emerging wide-area communication technologies with immense potential: NB-IoT and LoRa. NB-IoT, a 3GPP standardized LPWA technology, offers upgradeability and seamless compatibility with existing mobile communication operator networks, eliminating the need for additional site or transmission resources. It facilitates plug-and-play terminals, facilitating large-scale deployment, while operating on licensed frequency bands to ensure higher reliability and robustness. On the other hand, LoRa, characterized by its low speed, low power consumption, cost-effectiveness, and long transmission distance, operates in the sub-1 GHz frequency band. Leveraging a 125 kHz bandwidth, it enables cost-effective long-distance transmission and broader coverage. Furthermore, the chapter delves into positioning techniques, encompassing an examination of various technologies. It highlights the most widely adopted indoor positioning technology within the IoT realm, and briefly touches upon the 5G positioning technology poised for widespread implementation. While using WLAN RSSI for positioning poses challenges in terms of accuracy due to inherent limitations, the emergence of IEEE802.11n WLAN standards incorporating MIMO and OFDM transmission technologies has revolutionized the estimation of wireless signal transmission characteristics. By utilizing the estimated channel state information and the finer-grained channel frequency response information, positioning accuracy can be enhanced, and challenges like multipath interference can be mitigated. Leveraging millimeter-wave technology, large-scale array antennas, and other novel 5G network features, the fifth-generation technology offers a promising avenue for the development of high-precision positioning techniques. In summary, this chapter provides a comprehensive exploration of intelligent sensors, chemical wireless sensors, smart meter technology, RFID and wireless

204

3 Sensing Technology

sensor networks, NB-IoT and LoRa communication technologies, and positioning techniques within the IoT domain. These advancements and innovations pave the way for a future where seamless connectivity, accurate measurements, and precise positioning shape the landscape of the Internet of Things.

References 1. Yin Y (2018) An overview of smart sensors technology developing. Microelectronic 48(4):504–507, 519 2. Smart sensor industry report. http://www.Marketsandmarkets.com 3. Ko WH, Fung CD (1982) VLSI and intelligent transducers. Sens Actuators 2(3):239–250 4. Xianwu ZENG, Shuping BO (2016) Introduction to Internet of Things. Publishing House of Electronics Industry, Beijing 5. Borgia E (2014) The Internet of Things vision: key features, applications and open issues. Comput Commun 54:1–31 6. Kassal P et al (2018) Wireless chemical sensors and biosensors: a review. Sens Actuators B 266:228–245 7. Hulanicki A, Glab S, Ingman F (1991) Chemical sensors: definitions and classification. Pure Appl Chem 63:1247–1250 8. Farre M et al (2009) Sensors and biosensors in support of EU Directives. Trac-Trends Anal Chem 28:170–185 9. Kassal P et al (2013) Wireless smart tag with potentiometric input for ultra low-power chemical sensing. Sens Actuators B-Chem 184:254–259 10. Novell M et al (2013) A novel miniaturized radiofrequency potentiometer tag using ionselective electrodes for wireless ion sensing. Analyst 138:5250–5257 11. Kumar A, Hancke GP (2014) Energy efficient environment monitoring system based on the IEEE 802.15.4 standard for low cost requirements. IEEE Sens J 14:2557–2566 12. Song K et al (2011) A wireless electronic nose system using a Fe2 O3 gas sensing array and least squares support vector regression. Sensors 11:485–505 13. Steinberg MD et al (2014) Wireless smart tag with on-board conductometric chemical senso. Sens Actuators B-Chem 196:208–214 14. Steinberg MD et al (2016) Towards a passive contactless sensor for monitoring resistivity in porous materials. Sens ActuatorsB-Chem 234:294–299 15. Jang CW et al (2013) A wireless monitoring sub-nA resolution test platform for nanostructure sensors. Sensors 13:7827–7837 16. Clement P et al (2015) Oxygen plasma treated carbon nanotubes for the wireless monitoring of nitrogen dioxide level. Sens Actuators B-Chem 208:444–449 17. Liu Y et al (2013) SWNT based nanosensors for wireless detection of explosives and chemical warfareagents. IEEE Sens J 13:202–210 18. Gou P et al (2014) Carbon nanotube chemiresistor for wireless pH sensing. Sci Rep 4 19. Chavali M et al (2008) Active 433 MHz-W UHFRF-powered chip integrated with a nanocomposite m-MWCNT/polypyrrolesensor for wireless monitoring of volatile anesthetic agent sevoflurane. Sens Actuat A: Phys 141:109–119 20. Lorwongtragool P et al (2014) A novel wearable electronic nose for healthcare based on flexible printed chemical sensor array. Sensors 14:19700–19712 21. Quintero AV et al (2016) Smart RFID label with a printed multisensor platform for environmental monitoring. Flexible Printed Electron 1:025003 22. Oikonomou P et al (2016) A wireless sensing system for monitoring the workplace environment of an industrial installation. Sens Actuators B-Chem 224:266–274 23. Liu L et al (2017) Smartphone-based sensing system using ZnO and graphene modified electrodes for VOCs detection. Biosens Bioelectron 93:94–101

References

205

24. Steinberg IM, Steinberg MD (2009) Radio-frequency tag with optoelectronic interface for distributed wireless chemical and biological sensor applications. Sens Actuators B-Chem 138:120–125 25. Steinberg MD et al (2014) Miniaturised wireless smart tag for optical chemical analysis applications. Talanta 118:375–381 26. Shepherd R et al (2007) Monitoring chemical plumes in an environmental sensing chamber with a wireless chemical sensor network. Sens Actuators B-Chem 121:142–149 27. Schyrr B et al (2014) Development of a polymer optical fiber pH sensor for on-body monitoring application. Sens Actuators B-Chem 194:238–248 28. Kassal P et al (2017) Smart bandage with wireless connectivity for optical monitoring of pH. Sens Actuators B-Chem 246:455–460 29. Escobedo P et al (2017) Flexible passive near field communication tag for multigas sensing. Anal Chem 89:1697–1703 30. Mortellaro M, DeHennis A (2014) Performance characterization of an abiotic and fluorescentbased continuous glucose monitoring system in patients with type 1 diabetes. Biosens Bioelectron 61:227–231 31. Nemiroski A et al (2015) Swallowable fluorometric capsule for wireless triage of gastrointestinal bleeding. Lab Chip 15:4479–4487 32. Yazawa Y et al (2014) System-on-fluidics immunoassay device integrating wireless radiofrequency-identification sensor chips. J Biosci Bioeng 118:344–349 33. Wang R et al (2009) Real-Time ozone detection based on a microfabricated quartz crystal tuning fork sensor. Sensors 9:5655–5663 34. Cheney CP et al (2009) In vivo wireless ethanol vapor detection in the Wistar ra. Sens Actuators B-Chem 138:264–269 35. Luo J et al (2013) A new type of glucosebio sensor based on surface acoustic wave resonator using Mn-doped ZnOmultilayer structure. Biosens Bioelectron 49:512–518 36. Sivaramakrishnana S et al (2008) Carbon nanotube-coated surface acoustic wave sensor forcarbon dioxide sensing. Sens Actuators B-Chem 132:296–304 37. Gao X, Cai Q (2013) Kinetic analysis of glucose with wireless magnetoelasticbio senso. Asian J Chem 25:8681–8684 38. Lu Q et al (2009) Wireless, remote-query, and high sensitivity escherichia coli O157:H7 biosensor based on the recognitionaction of concanavalin. Anal Chem 81:5846–5850 39. Song SH, Park JH, Chitnis G, Siegel RA, Ziaie B (2014) A wireless chemical sensor featuring iron oxide nanoparticle-embedded hydrogels. Sens Actuators B: Chem 193:925–930 40. Potyrallo RA, Morris WG (2007) Multianalyte chemical identification and quantitation using a single radio frequency identification sensor. Anal Chem 79:45–51 41. Manzari S, Marrocco G (2014) Modeling and applications of a chemical-loaded UHF RFID sensing antenna with tuning capability. IEEE Trans Antennas Propag 62:94–101 42. Manzari S et al (2014) Development of an UHF RFID chemical sensor array for battery-less ambient sensing. IEEE Sens J 14:3616–3623 43. Azzarelli JM et al (2014) Wireless gas detection with a smartphone via rf communication. Proc Natl Acad Sci USA 111:18162–18166 44. Fiddes LK, Yan N (2013) RFID tags for wireless electrochemical detection of volatile chemicals. Sens Actuators B-Chem 186:817–823 45. Sridhar V, Takahata K (2009) A hydrogel-based passive wireless sensor using a flex-circuit inductive transducer. Sens Actuators A-Phys 155:58–65 46. Tao H et al (2012) Silk-Based conformal, adhesive, edible food sensors. Adv Mater 24:1067– 1072 47. Mannoor MS et al (2012) Graphene-based wireless bacteria detection on tooth enamel. Nat Commun 3 48. Ahmadi MM, Jullien GA (2009) A wireless-implantable microsystem forcontinuous blood glucose monitoring. IEEE Trans Biomed Circuits Syst 3:169–180 49. Baj-Rossi C et al (2014) Full fabrication and packaging of an implantablemulti-panel device for monitoring of metabolites in small animals. IEEE Trans Biomed Circuits Syst 8:636–647

206

3 Sensing Technology

50. Kotanen CN, Moussy FG, Carrara S, Guiseppi-Elie A (2012) Implantable enzymeamperometric biosensors. Biosens Bioelectron 35:14–26 51. Steinberg MD et al (2014) Wireless smart tag with on-board conductometric chemical sensor. Sens Actuators B-Chem 196:208–214 52. Clement P et al (2015) Oxygen plasma treated carbon nanotubes for the wireless monitoring of nitrogen dioxide levels. Sens. Actuators B-Chem. 208:444–449 53. Potyrailo RA, Surman C, Nagraj N, Burns A (2011) Materials and transducers toward selective wireless gas sensing. Chem Rev 1117315–1117354 54. Byrne R et al (2006) Characterisation and analytical potential of a photo-responsive polymericmaterial based on spiropyran. Biosens Bioelectron 26:1392–1398 55. Zhuiykov S (2012) Solid-state sensors monitoring parameters of water quality for the next generation of wireless sensor networks. Sens Actuators B-Chem 161:1–20 56. Mortellaro M, DeHennis A (2014) Performance characterization of an abiotic and fluorescentbased continuous glucose monitoring system in patients with type 1 diabetes. Biosens Bioelectron 61:227–231 57. Benito-Lopez F et al (2009) Spiropyran modified micro-fluidic chip channels as photonically controlledself-indicating system for metal ion accumulation and release. Sens Actuators BChem 140:295–303 58. Camarillo-Escobedo RM et al (2013) Micro-analyzer with optical detection and wireless communication. Sensor Actuat A: Phys 199:181–186 59. Curto VF et al (2012) Concept and development of an autonomous wearable micro-fluidic platform for real time pH sweat analysis. Sens Actuators B-Chem 175:263–270 60. Bandodkar AJ, Jia W, Wang J (2015) Tattoo-Based wearable electrochemical devices: a review. Electroanalysis 27:562–572 61. Pankratov D et al (2016) Tear based bioelectronics. Electroanalysis (n/a-n/a) 62. Windmiller JR, Wang J (2013) Wearable electrochemical sensors and biosensors: a review. Electroanalysis 25:29–46 63. Malon Radha SP et al (2017) Recent developments in microfluidic paper-, cloth-, and threadbased electrochemical devices for analytical chemistry. Rev Anal Chem https://doi.org/10. 1515/revac-2016-0018 64. Ochoa M, Rahimi R, Ziaie B (2014) Flexible sensors for chronic wound management. IEEE Rev Biomed Eng 7:73–86 65. Ihalainen P, Maattanen A, Sandler N (2015) Printing technologies for biomoleculeand cellbased applications. Int J Pharm 494:585–592 66. Mohr GJ, Mueller H (2015) Tailoring colour changes of optical sensor materials by combining indicator and inert dyes and their use in sensor layers, textiles and non-wovens. Sens Actuators B-Chem 206:788–793 67. Xu S et al (2014) Soft microfluidic assemblies of sensors, circuits, and radios for the skin. Science 68. Smart meter. http://en.wikipedia.org/wiki/Smart_meter 69. Cavdar IH (2004) A solution to remote detection of illegal electricity usage via power line communications. IEEE Trans Power Deliv 19:1663–1667 70. Yute C, Jeng HK (2006) A reliable energy information system for promoting voluntary energy conservation benefits. IEEE Trans Power Deliv 21(1):102–107 71. Majchrak M et al (2007) Single phase electricity meter based on mixed-signal processor MSP430FE427 with PLC modem. In: Proceedings of the 17th international conference, Radioelektronika, pp 1–4 72. Sharma K, Mohan Saini L (2015) Performance analysis of smart metering for smart grid: an overview. Renew Sustain Energy Rev 49:720–735. https://doi.org/10.1016/j.rser.2015.04.170 73. ASM221: ASMGRID2TM system-on-chip for smart meter PLC communication single and polyphase metrology, communication and application processing. http://www.accent-soc. com/products/ASM221_abridged_datasheet.pdf 74. Application note 5631 ensuring the complete life-cycle security of smart meter. http://www. maximintegrated.com/en/app-notes/index.mvp/id/5631

References

207

75. PS2100 single phase power meter soc. http://uk.alibaba.com/product/123927057-PS2100-Sin gle-Phase-Power-Meter-SOC.html 76. Luo Z-K et al (2012) Design of harmonic energy meter based on TDK+DSP+MCU. In: Proceedings of the 2012 international conference on measurement, information and control (MIC), vol 2, pp 874–878 77. Smart meter and powerline communication system-on-chip. http://www.st.com/stwebui/sta tic/active/en/resource/technical/document/data_brief/DM00097094.pdf 78. ASM221: ASMGRID2TM system-on-chip for smart meter PLC communication single and polyphase metrology, communication and application processing. http://www.accent-soc. com/products/ASM221_abridged_datasheet.pdf 79. 78M6613 single-phase ac power measurement IC. http://datasheets.maximintegrated.com/ en/ds/78M6613.Pdf 80. Porcarelli D et al (2013) Perpetual and low-cost power meter for monitoring residential and industrial appliances. In: Proceedings of the design, automation & test in Europe conference & exhibition (DATE), pp 1155–1160 81. Suslov KV et al (2013) Smart grid: effect of high harmonics on electricity consumers in distribution networks. In: Proceedings of the international symposium on electromagnetic compatibility (EMC EUROPE), pp 841–845 82. Lee PK, Lai LL (2009) A practical approach of smart metering in remote monitoring of renewable energy applications. In: Proceedings of the IEEE power & energy society general meeting(PES ’09), pp 1–4 83. Reza A et al (2013) Harmonic interactions of multiple distributed energy resources in power distribution networks. Electric Power Syst Res 105:124–133 84. Cataliotti A et al (2009) Static meters for the reactive energy in the presence of harmonics: an experimental metrological characterization. IEEE Trans Instrum Meas 58(8):2574–2579 85. De Capua C, Romeo E (2007) A smart THD meter performing an original uncertainty evaluation procedure. IEEE Trans Instrum Meas 56(4):1257–1264 86. Makram EB et al (1992) Effect of harmonic distortion in reactive power measurement. IEEE Trans Ind Appl 28(4):782–787 87. Kaplantzis S et al (2012) Security and smart metering. In: Proceedings of the 18th European wireless conference, EW, pp 1–8 88. Li H et al (2012) Efficient and secure wireless communications for advanced metering infrastructure in smartgrids. IEEE Trans Smart Grid 3(3):1540–1551 89. Sheng Z, Yang S, Yu Y, Vasilakos et al (2013) A survey on the ietf protocol suite for the internet of things: standards, challenges, and opportunities. IEEE Wirel Commun 20(6):91–98 90. Kumari L, Narsaiah K, Grewal MK et al () Application of RFID in Agri-food sector—a review. Trends Food Sci Technol. https://doi.org/10.1016/j.tifs.2015.02.005 91. Finkenzeller K (2010) RFID handbook fundamentals and applications. Wiley 92. Realini CE, Marcos B (2014) Active and intelligent packaging systems for a modern society. Meat Sci 98(3):404–419. https://doi.org/10.1016/j.meatsci.2014.06.031 93. Costa C, Antonucci F, Pallottino F et al (2012) A review on agri-food supply chain traceability by means of RFID technology. Food Bioprocess Technol 6(2):353–366 94. Aguzzi J, Sbragaglia V, Sarria D et al (2011) A new laboratory radio frequency identification (rfid) system for behavioural tracking of marine organisms. Sensors 11(10):9532–9548 95. Xiao Y, Yu S, Wu K et al (2007) Radio frequency identification: technologies, applications, and research issues. Wirel Commun Mobile Comput 7(4):457–472. https://doi.org/10.1002/ wcm.365 96. Kumari L, Narsaiah K, Grewal MK et al (2015) Application of RFID in Agri-food sector—a review. Trends Food Sci Technol. https://doi.org/10.1016/j.tifs.2015.02.005 97. Finkenzeller K (2010) RFID handbook fundamentals and applications. Wiley 98. Roberts CM (2006) Radio frequency identification (RFID). Comput Secur 25(1):18–26. https://doi.org/10.1016/j.cose.2005.12.003 99. Fabien B, Carole G, Nathalie G et al (2017) A review: RFID technology having sensing aptitudes for food industry and their contribution to tracking and monitoring of food products. Trends Food Sci Technol 62:91–103. https://doi.org/10.1016/j.tifs.2017.01.013

208

3 Sensing Technology

100. Gluhak A, Krco S, Nati M et al (2011) A survey on facilities for experimental internet of things research. IEEE Commun Mag 49(11):58–67 101. Rashid B, Rehmani MH (2016) Applications of wireless sensor networks for urban areas: a survey. J Netw Comput Appl 2016(60):192–219 102. Akyildiz IF, Su W, Sankarasubramaniam Y et al (2002) Wireless sensor networks: a survey. Comput Netw 38(4):393–422 103. Yick J, Mukherjee B, Ghosal D (2008) Wireless sensor network survey. Comput Netw 52(12):2292–2330 104. Lin Y, Zhang J, Chung et al (2012) An ant colony optimization approach for maximizing the lifetime of heterogeneous wireless sensor networks. IEEE Trans Syst Man Cybernet Part C Appl Rev 42(3):408–420 105. Kulkarni RV, Forster A, Venayagamoorthy GK (2011) Computational intelligence in wireless sensor networks: a survey. IEEE Commun Surv Tutor 13(1):68–96 106. José VV et al (2018) A framework for enhancing the performance of Internet of Things applications based on RFID and WSNs. J Netw Comput Appl 107:56–68. https://doi.org/10. 1016/j.jnca.2018.01.015 107. Liu H, Bolic M, Nayak A, Stojmenovic I (2008) Taxonomy and challenges of the integration of rfid and wireless sensor networks. IEEE Netw 22(6):26–35 108. Hussain S, Schaffner S, Moseychuck D (2009) Applications of wireless sensor networks and rfid in a smart home environment. In: 2009 Seventh annual communication networks and services research conference (CNSR). IEEE, 2009, pp 153–157 109. López TS, Kim D, Canepa GH, Koumadi K (2009) Integrating wireless sensors and rfid tags into energy-efficient and dynamic context networks. Comput J 52(2):240–267 110. Dillinger M, Madani K, Alonistioti N (2005) Software defined radio: architectures, systems and functions. Wiley 111. Instruments T (2016) cc2420: 2.4 ghz ieee 802.15. 4/zigbee-ready rf transceiver, http://www. ti.com/lit/gpn/cc2420. Visited on 25 June 2016 112. Instruments T (2018) Cc1000: single chip very low power Rf transceiver, reference SWRS048. Rev A 113. Klair DK, Chin K-W, Raad R (2010) A survey and tutorial of rfid anti-collision protocols. Commun Surv Tutor IEEE 12(3):400–421 114. Wang C, Daneshmand M, Sohraby K, Li B (2009) Performance analysis of rfid generation-2 protocol IEEE Trans Wireless Commun 8(5):2592–2601 115. Wenyan L (2018) Research on massive access in narrowband Internet of Things. Thesis Submitted to Nanjing University of Posts and Telecommunications for the Degree of Master of Engineering 116. Bor M, Vidler J, Roedig U (2016) LoRa for the Internet of Things. In: International conference on embedded wireless systems and networks. Junction Publishing, pp 361–366 117. Vejlgaard B et al (2017) Interference impact on coverage and capacity for low power wide area Io T networks. In: IEEE International conference on wireless communications and networking, pp 1–6 118. Benny V, Mads L, Huan N et al (2017) Coverage and capacity analysis of Sigfox, LoRa, GPRS, and NB-IoT. IEEE international conference on vehicular technology conference: Vtc2017Spring, pp 1–5 119. Ruki H, Ray-Guang C, Chia-Hung W et al (2017) Optimization of random access channel in NB-IoT. IEEE Internet of Things J PP(99):1–1 120. Chen Bol, Gan Z-H (2016) Research on commercial value and networking solution of NB-IoT Network. Mobile Commun 40(13):42–46 121. Mangalvedhe N, Ratasuk R, Ghosh A (2016) NB-Io T deployment study for low power wide area cellular IoT. In: IEEE International symposium on personal, indoor, and mobile radio communications, pp 1–6 122. Wang YPE, Lin X, Adhikary A et al (2016) A primer on 3GPP narrowband Internet of Things (NB-IoT). IEEE Commun Mag 55(3)

References

209

123. Ratasuk R, Vejlgaard B, Mangalvedhe N et al (2016) NB-IoT system for M2M communication. IEEE International conference on wireless communications and networking conference, pp 428–432 124. 3GPP TR 37.868 V0.7.0 (2010) Study on RAN improvements for machine type communications 125. Xu Y, Zhang J, Zhang Y et al (2017) Research on access network overload control of mixed service for LTE network. In: IEEE International conference on computer and communications, pp 2955–2959 126. Wei CH, Cheng RG, Lin YS (2015) Analysis of slotted-access-based channel access control protocol for LTE-Advanced networks. Wireless Pers Commun 85(1):1–19 127. Shih MJ, Wei HY, Lin GY (2016) Two paradigms in cellular Io T access for energy-harvesting M2M devices: push-based versus pull-based. Iet Wirel Sens Syst 6(4) 128. Duan S, Shah-Mansouri V, Wang Z et al (2016) D-ACB: Adaptive congestion control algorithm for bursty M2M traffic in LTE networks. IEEE Trans Veh Technol 65(12):9847–9861 129. Duan S, Shah-Mansouri V, Wong VWS (2013) Dynamic access class barring for M2M communications in LTE networks. IEEEGLOBECOM Workshops 4747–4752 130. Wang Z, Wong VWS (2015) Optimal access class barring for stationary machine type communication devices with timing advance information. IEEE Trans Wireless Commun 14(10):5374–5387 131. Militano L, Orsino A, Araniti G et al (2016) Trusted D2D-based data uploading in inband narrowband-IoT with social awareness. In: IEEE International symposium on personal, indoor, and mobile radio communications 132. Petrov V, Samuylov A, Begishev V et al (2017) Vehicle-Based relay assistance for opportunistic crowd sensing over Narrowband Io T (NB-IoT) IEEE Internet of Things J PP(99):1–1 133. R1-157409 (2015) Narrowband Io T–random access evaluation in Inband deployment. Source Ericsson, TSG-RAN1 #83 134. Xing Y, Zhang L, Hu Y (2016) The evolution research of mobile cellular communication technology for IoT. Des Tech Posts Telecommun 11:87–92 135. Zeng L (2018) Research and application based on NB-IoT data transmission. Master thesis of An Hui University of Science and Technology 136. Xiaoxue Z (2015) Research and implementation of volte technology based on IMS. Harbin Institute of Technology, Dissertation for the Master Degree in Engineering 137. 3GPP TS 23.401 (2014) General packet radio service (GPRS) enhancements for evolved universal terrestrial radio access network (E-UTRAN) access 138. Yan H (2013) The evolution and deployment of LTE core. Mondern Sci Technol Telecommun 43(11):12–16 139. Luo S (2010) Research of LTE system RRC connection setup procedure. Inf Commun 23(03):19–21+25 140. Zhang W, Lu T, Gao Y (2017) System status and development of NB-IoT. ZTE Technol J 23(01):10–14. https://doi.org/10.3969/j.issn.1009-6868.2017.01.003 141. Huang T, Liu Y, Zhang N (2017) Analysis on capacity performance in the mode of NBIoT stand-alone deployment. Mobile Commun 41(17): 78–84. https://doi.org/10.3969/j.issn. 1006-1010.2017.17.015 142. Lu B (2016) Analysis on NB-PDCCH resource scheduling in NB-IoT. Mobile Commun 40(23):17–20. https://doi.org/10.3969/j.issn.1006-1010.2016.23.003 143. Sartori B, Bezunartea N, Thielemans S (2017) Enabling RPL multihop communications based on LoRa. In: 2017 IEEE 13th international conference on wireless and mobile computing, networking and communications (Wi Mob) 144. Chen F (2018) Research on narrowband wireless ad hoc network routing protocol based on LoRa. Master Thesis of Xidian University 145. Sornin N, Luis M, Eirich T (2015) LoRa WAN specification 146. Semtech Corporation (2013) SX1272/3/6/7/8Lo Ra modem design guide 147. LoRa Alliance (2016) V1. 0.1-2016 LoRa WANTM specification

210

3 Sensing Technology

148. Zhao W (2017) Technical overview on LoRa physical layer and MAC layer. Mobile Commun 41(17):66–72. https://doi.org/10.3969/j.issn.1006-1010.2017.17.013 149. Wei M (2018) Design of the intelligent monitor using LoRa technology. Master Thesis of XIDIAN University 150. LSD4WN-2N717M91 (2017) Lo Ra WAN End Node, Lierda 151. Zhu X, Dong W, Fa L et al (2013) The research of the IoT positioning and location aware. Sci China (Inf Sci) 43:1265–1287 152. Gongxu LIU, Lingfeng SH (2018) An overview about development of indoor navigation and positioning technology. J Navig Positioning 6(2):7–14 153. CHEN Ruizhi, YE Feng (2018) An overview of indoor positioning technology based on WI-FI channel state information. Geomatics Inf Sci Wuhan Univ 43(12):2064–2070 154. Halperin D, Hu W, Sherth A et al (2011) Tool release: Gathering 802.11n traces with channel state information. ACM Sigcomm Comput Commun Rev 41(1):53 155. Zheng ZW (2010) Channel estimation and channel equalization for the OFDM-based WLAN system. In: International conference on e-business and e-government. IEEE 156. Chapre Y, Ignjatovic A, Seneviratne A et al (2014) CSI-MIMO: indoor Wi-Fi fingerprinting system. In: 39th Annual IEEE conference on local computer networks, Edomonton, AB, Canada 157. Zhang X (2013) Matrix analysis and applications, 2nd edn. Tsinghua University Press 158. Xiong J, Jamieson K (2013) Array track: a fine-grained indoor location system. In: Usenix Conference on networked systems design and implementation. USENIX Association, Lombard, IL 159. Wu K, Xiao J, Yi Y et al (2012) FILA: fine-grained indoor localization. IEEE INFOCOM, Oriando, USA 160. Kumar S, Vasisht D, Katabi D (2016) Decimeter-Level localization with a single Wi-Fi access point. In: USensix conference on networked systems design and implementation, USENIX Association, Sata Clara,CA, USA 161. Li X, Pahlavan K (2004) Super-Resolution TOA estimation with diversity for indoor geolocation. IEEE Trans Wirel Commu 3(1):224–234 162. IMT-2020 (2015) 5G concept white paper. IMT-2020 Working Group, Beijing. http://www. imt-2020.org.cn/zh/documents/1 163. Ping Z, Hao C (2018) A survey of positioning technology for 5G. J Beijing Univ Posts Telecommun 41(5):1–12. https://doi.org/10.13190/j.jbupt.2018-185 164. Chen S, Wang H, Chen D (2018) 5G oriented high-precision fusion positioning architecture and key technologies. ZTE Technol J 99:1–9

Chapter 4

The Communication and Security Technology of IoT

From the perspective of the three-layer architecture of the IoT comprising the sensing and control layer, transmission network layer, and comprehensive application layer, the communication system can be divided into two primary categories: the high-level communication system based on the Internet and the underlying communication system that integrates with the Internet through gateways. The high-level communication system primarily relies on the Internet, utilizing the IP communication protocol for information transmission. It serves as a global IP communication infrastructure, facilitating the exchange of data. However, the underlying communication system is a diverse and heterogeneous network that caters to IoT terminals, encompassing sensing and control devices. These IoT terminals are characterized by their vast numbers and field-oriented applications. Consequently, they exhibit heterogeneity in terms of their sensing and control functionalities, communication methods, and protocols. Wireless communication is the prevalent choice for these diverse IoT terminals. One of the significant challenges faced by the IoT’s high-level communication system, akin to the Internet, is the issue of security. However, the security challenges encountered by the IoT on the Internet are even more critical compared to the traditional Internet. This is due to the fact that the IoT deals with not only virtual information but also physical entity information. Consequently, advanced security technologies are essential to address the security concerns posed by the IoT. In the underlying communication system of the IoT, where wireless communication is predominantly employed by the vast number of IoT terminals, there is a heightened need for enhanced wireless communication security measures. This is necessary to safeguard against vulnerabilities such as interference, interception, and counterfeiting, which are inherent in open wireless communication channels. Furthermore, the smart grid, being a significant application field of the IoT, also requires robust communication infrastructure for efficient information transmission and interaction. Similar to other IoT domains, the smart grid faces multifaceted security threats. To ensure the safety of the smart grid, a comprehensive approach

© Chemical Industry Press 2023 X. Zeng and S. Bao, Key Technologies of Internet of Things and Smart Grid, Advanced and Intelligent Manufacturing in China, https://doi.org/10.1007/978-981-99-7603-4_4

211

212

4 The Communication and Security Technology of IoT

combining technical and non-technical solutions is imperative. These solutions aim to address potential vulnerabilities and mitigate security risks associated with the smart grid’s communication infrastructure.

4.1 The Requirements for Communication and Security in IoT 4.1.1 Composition of IoT Communication System The Internet of Things (IoT) relies on diverse wireless communication networks and infrastructures to fulfill its requirements [1, 2]. These networks encompass a range of communication standards and protocols that are integrated at each layer, alongside the utilization of IoT gateways, to establish a comprehensive communication system [3, 4]. Figure 4.1 illustrates the composition of the IoT communication system. The global Internet follows TCP/IP and/or IPv6 protocols and provides the infrastructure for high-speed wired and wireless communications. (1) The Underlying Communication System of the IoT The Area Network interconnects various IoT terminals with IoT gateways, and then interconnects with IoT high-level communication systems through gateways. It is characterized by short transmission distances, flexible transmission way, and diverse and complex communication protocols. Several Area Network constitute the underlying communication system of the IoT. The main communication mode of the Area Network is to employ short-distance, low-power wireless communication technology, which is a communication system with limited communication resources and

Internet High-level communication system Convey Network

IoT gateway

Area Network

Area Network

Fig. 4.1 Composition of IoT communication system

IoT gateway

Underlying communication system Area Network

4.1 The Requirements for Communication and Security in IoT

213

energy resources, and which includes Bluetooth, infrared, wireless sensor networks, low-power wireless communication technology etc. (2) The High-level Communication System of the IoT The high-level communication system of the IoT consists of two key components: the Convey Network and the Internet. The Internet encompasses a vast array of network devices, including switches, routers, and hosts, which collectively form the backbone of global connectivity. On the other hand, the Convey Network comprises a diverse range of public communication infrastructures, encompassing public fixed networks, public mobile communication networks, public data networks, and private networks. The public fixed and mobile communication networks, along with public data networks, serve as the primary components of the Convey Network. These networks include established technologies such as the Public Switched Telephone Network (PSTN), cellular mobile communication systems like 3G/4G/5G, Digital Data Networks (DDN), Asynchronous Transfer Mode (ATM), and FRAME-RELAY (FR). They function as critical data convey platforms, providing essential communication facilities and forming the fundamental infrastructure of the Internet. The Internet, as a global information infrastructure, offers a wide range of basic services crucial to the functioning of the IoT. It serves as a repository for information storage, a medium for information transmission, and a platform for information processing. The Internet not only supports the comprehensive application layer of the IoT but also acts as a carrier for various specialized fields within the IoT [5].

4.1.2 Requirements for Communication and Security (1) Requirements for communication The communication of the IoT relies on a heterogeneous system, but it primarily operates on the IP protocol. Thus, the underlying communication system of the IoT must address the requirements of the PHY, MAC, and network layers. Considering the wireless nature of IoT, it is crucial to select a wireless technology that fulfills these layer-specific communication needs. Within the IoT communication, several critical considerations emerge for each layer. The physical layer (PHY) demands careful attention to wireless technology coverage, data rate, and capacity. The medium access control layer (MAC) encompasses channel access mechanisms, collision avoidance technologies, and introduced delay management. Meanwhile, at the network layer, a comprehensive understanding of all network nodes becomes paramount to determine the most efficient route between sender and receiver—a vital aspect known as the “fastest route” determination. Moreover, given the resource-constrained nature of IoT devices, it becomes crucial to meet requirements pertaining to cost, complexity, and power consumption. For a holistic overview of these communication requirements, refer

214

4 The Communication and Security Technology of IoT

to Table 4.1, which encapsulates the essential criteria to consider when selecting a wireless technology for IoT applications. (2) Requirements for Safety Security plays a vital role in the IoT, with authenticity, confidentiality, and integrity serving as key components. Confidentiality ensures that only authorized users can access data, shielding it from prying eyes and unauthorized interception. Integrity safeguards data against tampering or corruption during transmission, guaranteeing its accuracy and reliability. Authenticity verifies the legitimacy of both communicating nodes and the transmitted data, establishing trust and preventing impersonation or malicious attacks. In the pursuit of robust IoT security, contemporary approaches leverage a combination of spread spectrum techniques and physical layer security (PLS) mechanisms to counteract potential data leakage by eavesdroppers. These techniques, when integrated with encryption algorithms employed in medium access controls (MACs), fortify the overall security posture, offering enhanced protection against various threats. To gain a comprehensive understanding of the security requirements for IoT deployments, refer to Table 4.2. This tabulated resource presents a concise overview of the essential security considerations when designing and implementing the IoT. Table 4.1 Requirements for communication [1] Layer

Requirements

PHY

Maximum wireless link coverage Maximum coupling loss including attenuation Maximum power loss when transmitting a signal Maximum data rate System capacity for various traffic patterns PHY security and related methods

MAC

Time required for media access Methods of handling urgent business

Network

Information about all nodes in the network

All three layers

Device power, cost and complexity

Table 4.2 Requirements for security

Layer

Requirements

PHY

Secure data dissemination technology Physical layer security (PLS) techniques

All three layers

Authenticity, confidentiality and Integrity

4.2 Wireless Network Technology and Its Classification

215

4.2 Wireless Network Technology and Its Classification The design of IoT systems typically relies on existing technologies and established standards. In this approach, the selection of the most suitable standard is determined by the specific requirements of the application at hand, followed by the subsequent design steps. During the design process, a delicate balance must be struck between the adaptability of required functions and features and adherence to the limitations set by the chosen standard. Given the diverse applications of IoT systems, varying needs may arise, such as the requirement for wider bandwidth, extended battery life, or broader coverage. To effectively evaluate and compare wireless technologies, they can be classified using various criteria. In this section, we provide a brief classification of wireless networks and outline the technologies and architectures associated with different systems. To enhance clarity and organization, these technologies will be categorized based on the mechanisms defined within the Open Systems Interconnection (OSI) model layers. By employing this layered approach, the classification of wireless networks becomes more structured and facilitates a comprehensive understanding of the underlying technologies and their respective functionalities across different layers of the IoT communication stack.

4.2.1 PHY (Physical Layer) Technology All wireless communication technologies rely on the utilization of radio waves. Figure 4.2 presents a fundamental block diagram of a wireless communication system, illustrating the various components involved. In this system, the transmitter plays a crucial role by converting information into a carrier radio frequency (RF) signal, each with its distinct propagation properties, bandwidth availability, and signal attenuation characteristics. These RF properties essentially determine the purpose, coverage, and throughput of the communication system. For achieving higher throughput, it is ideal to have a wider available bandwidth with minimal attenuation. However, obtaining a larger bandwidth requires the use of higher RF frequencies, which, in turn, results in higher signal attenuation, limiting the maximum coverage of wireless transmission. Consequently, striking a balance between high throughput, bandwidth, and range becomes a challenge, necessitating trade-offs based on the specific application requirements.

Transmitter Information signal

Encoder

Modulator

Receiver Demodulator

Fig. 4.2 Basic diagram of a wireless communication system

Decoder

receive signal

216

4 The Communication and Security Technology of IoT

The majority of wireless technologies operate within the microwave spectrum ranging from a few megahertz (MHz) to 6 gigahertz (GHz). For extensive communication coverage, lower frequency spectrums are employed, whereas higher RF frequency bands are utilized for high-speed short-range communication. Most of the RF spectrum is licensed and regulated by the International Telecommunication Union (ITU), enabling licensed bands to operate at higher transmit power. On the other hand, unlicensed spectra only need to adhere to specified transmission power regulations, which can result in potential interference. Two widely used license-exempt frequency bands worldwide are the 2.4 GHz ISM band and the 5 GHz band. As microwave frequencies suffer from limited bandwidth, frequencies above 30 GHz are employed for wide-bandwidth communications [6]. However, signals at these frequencies face significant free-space signal attenuation of up to 20 decibels (dB). Moreover, they struggle to penetrate obstacles in the line-of-sight, further exacerbating signal attenuation during transmission. To overcome these challenges, antenna arrays can be employed to enhance receiving gain, with the total gain of the wireless signal reliant on the antenna array. Subsequently, we will delve into modulation techniques, narrowband and broadband communications, as well as physical layer security, exploring their significance in wireless communication systems. (1) Modulation Technology Modulation techniques play a vital role in determining the data transmission rate, bandwidth, coverage, and spectral efficiency of wireless systems. They even influence the implementation of physical layer security (PLS) techniques. In terms of hardware, modulation greatly impacts the size, power, and complexity of devices. Each modulation technology possesses its unique advantages and disadvantages, making the choice of modulation technique pivotal in wireless communication. From a bandwidth perspective, modulation techniques can be broadly categorized into two groups: narrowband and broadband communications. (2) Narrowband Communication Narrowband communication employs carrier signals that carry the message signal based on amplitude or phase modulation. Frequencies below 1 GHz are typically utilized, and the required bandwidth primarily depends on the symbol rate. As the transmitted signal is concentrated within a limited frequency spectrum, a narrower band-limiting filter is employed. However, due to the amplitude or phase modulation and the limited spectrum range, narrowband communication is highly susceptible to interference. Additionally, fading channels can affect narrowband modulation [7]. Furthermore, when the signal rate exceeds the sampling frequency, inter-symbol interference (ISI) can degrade the original information [8]. Common modulation techniques employed in narrowband communication include amplitude modulation (AM), frequency modulation (FM), single sideband (SSB), and binary phase shift keying (BPSK). (3) Broadband Communication Broadband communication operates with a wider bandwidth, thereby enabling higher data transmission rates. Typically, two prominent technologies are employed in

4.2 Wireless Network Technology and Its Classification

217

broadband communication. The first is Orthogonal Frequency Division Multiplexing (OFDM), which employs multiple adjacent carrier signals that collectively form a wide frequency band, effectively mitigating ISI. The second technology is direct sequence spread spectrum (DSSS), which spreads the signal over a broader bandwidth through spread spectrum techniques. DSSS not only facilitates information encryption but also helps in avoiding various forms of interference. However, the coverage of broadband communication is limited. Modulation techniques commonly used in broadband communications encompass OFDM, offset quadrature phase-shift keying (OQPSK), Gaussian minimum shift keying (GMSK), quadrature amplitude modulation (QAM), as well as frequency hopping (FH). (4) Physical Layer Security While spread spectrum technology can enhance the anti-interference capability and signal-to-noise ratio (SNR) of the receiver, it may not effectively address security threats targeting the physical layer (PHY). To counter these security threats, the following methods can be employed: (i) Information Theory-Based Security Approach This approach relies on Shannon’s information-theoretic secrecy criterion, which states that the maximum information transmission rate from a transmitter to a receiver is dependent on the information accessible to a rogue receiver. Full-duplex communication can effectively enhance the confidentiality rate. [9] proposes an approach to improve secrecy rates where a full-duplex transceiver sends a noise signal upon receiving information, leading to higher secrecy rates. To maximize secrecy rates, [10] introduces an alternate concave difference programming approach to optimize the covariance matrix of transmitted information, approximating the optimal secrecy rate through optimization. Another method proposed in [11] focuses on maximizing the secrecy rate by realizing multiple-input multiple-output (MIMO) technology, thereby maximizing the transceiver’s secrecy degree of freedom. The author also suggests formulating precoding matrices and optimizing antenna allocation for this purpose. Although the security approach based on information theory can provide an appropriate security level and optimize it to some extent, it may not guarantee the security of the communication system as it does not rely on knowledge of the communication channel’s characteristics. (ii) Channel-based Approach This approach enhances security by utilizing channel attributes. Techniques such as RF fingerprinting, channel decomposition precoding, and randomization of transmission coefficients can be employed to improve security. RF fingerprinting involves obtaining the unique identification of each radio transmitter by measuring its external characteristics and storing these identifications in a feature library. Authentic wireless signals can be identified by matching received wireless fingerprints with those stored in the library. In [12], the author employs

218

4 The Communication and Security Technology of IoT

multidimensional permutation entropy to derive RF fingerprints from the transmitter, observing improved classification accuracy under different signal-to-noise ratios (SNRs) and indirectly enhancing trusted wireless signal identification. In the channel decomposition precoding technique, each symbol is modulated by a complex code vector and then dispersed across a multipath channel. The multipath nature of the transmission makes it challenging for an intruder to decode the original message even if the code vector is obtained. In [12], the authors explore a transmitter with precoding-assisted spatial modulation and find that it significantly improves privacy in communication. Randomized transmission coefficient approaches introduce random constellation mapping and standard spatial modulation (SM) techniques to the transmission coefficients, thereby achieving secrecy. Results from [13] indicate that SM’s symbol-toantenna mapping, combined with random shifting on each modulation symbol, can provide the desired level of secrecy. (iii) Encoding Approach This approach utilizes error correction and spread spectrum coding to enhance security. Error correction codes introduce redundancy to the message bits, preventing eavesdroppers from intercepting the message. In [14], the authors propose a security scheme based on the bit error rate (BER), providing high-speed operation and low-cost encoders and decoders. DSSS and FH are primarily employed in broadband communication. While these techniques can obfuscate information, intruders can still employ blind estimation methods to estimate the original information. To address this issue, [15] proposes a novel chaotic DSSS approach that modifies the symbol period according to a chaotic sequence and multiplies the changed symbol with the chaotic sequence, generating variable symbols with a periodic spread. (iv) Power-based Approach This approach leverages directional antennas or introduces artificial noise (AN) to enhance the security of wireless communication. Directional antennas have a larger coverage area, and M-PSK modulation technology based on directional modulation, as described in [16], improves confidentiality. Due to the directional modulation, legitimate receivers receive M-PSK symbols with the proper phase, while eavesdroppers cannot receive them. As a result, eavesdroppers only obtain symbols with a high symbol error rate, thus enhancing confidentiality. Another approach involves introducing artificial noise (AN) generated and spread over the receiver channel. AN can disrupt the eavesdropper’s wireless channel while having no effect on the legitimate receiver’s channel. In [17], a solution for training and data transmission utilizing AN in WSN is proposed. Research shows that the technique is effective at high SNR, but its advantages are diminished at low SNR. (v) Physical Layer Encryption Physical Layer Encryption (PLE) encrypts message bits using a generated key. This technology boasts high hardware efficiency and is increasingly popular in IoT applications. In [18], the author presents a design scheme for an IEEE 802.15.4 transceiver

4.2 Wireless Network Technology and Its Classification

219

with Simeck32/64 cipher, replacing stream ciphers with more secure block ciphers. To reduce the data rate, message bits are first accessed in parallel, followed by encryption and modulation. Research results demonstrate the design’s advantages of low power consumption and reduced hardware complexity. The BER results of the noisy design indicate improved performance compared to DES and AES algorithms. While PLE holds promise, further research and consideration of other related aspects are necessary, particularly regarding IoT traffic, which serves massive energyconstrained IoT devices with sparse transmitted data and complex channel environments. Additionally, achieving minimum power consumption and addressing the coexistence of various wireless technologies are crucial areas for further research in the field of PLE and IoT security.

4.2.2 MAC Technology In wireless communication, the sharing of the transmission medium plays a crucial role. With a multitude of nodes (IoT terminals) connected to the network, it becomes imperative to efficiently distribute medium access among these nodes. The MAC layer, apart from facilitating medium access, serves various other functions including networking, synchronization between nodes, handshake signals, priority management, and reliable data transmission. By overseeing media allocation and node sleep time, the MAC layer directly governs energy consumption, latency, and throughput. Two modes of operation exist within the MAC layer: Beacon-enabled mode and non-Beacon enabled mode. The non-Beacon mode is relatively simple, allowing any node to directly access the medium using either the ALOHA or non-slot carrier sense multiple access/collision avoidance (CSMA-CA) algorithm. This strategy proves effective for small-scale networks. However, in larger networks, the simultaneous access attempts by numerous nodes can lead to collisions and subsequent energy loss. Consequently, a more structured approach, such as the Beacon-enabled pattern, is preferred in such cases. In the Beacon-enabled mode, synchronization among nodes is commonly achieved through the transmission of beacons. This mode adopts the superframe structure depicted in Fig. 4.3 and employs the slotted CSMA-CA algorithm as its channel access mechanism (see Fig. 4.4). Moreover, well-defined handshaking mechanisms and allocated time slots for each operation ensure collision avoidance even during peak phases of data transmission. Each superframe exists between two beacons, and it is characterized by the macBeaconOrder (BO) and macSuperframeOrder (SO), which also define active and inactive periods. This superframe structure allows for collision-free data transmission in large networks, making the Beacon-enabled mode the preferred choice for IoT applications.

220

4 The Communication and Security Technology of IoT

Fig. 4.3 Superframe structure [1] (Note: CAP: Contention Access Period; BO/SO: Beacon/ Superframe Order; CFP: Contention Free Period; GTS: Guaranteed Time Slot; BI: Beacon Interval; SD: Superframe Duration)

Within the Beacon-enabled mode, each device supports three variables—NB, CW, and BE—before attempting a transmission. Initially set to zero, NB represents the number of backoff attempts required by the CSMA-CA algorithm for the current transmission. CW denotes the contention window length, determining the number of backoff cycles that must be completed before transmission commences. It is initialized to CW0. Lastly, BE represents the backoff index dictating the number of backoff cycles the device should wait before performing channel assessment. Its initial value is the }macMinBE. A random backoff interval is generated within the range of { setBto 0, 2 E − 1 , and nodes must adhere to this interval during the backoff phase. After the backoff, the node performs a Clear Channel Assessment (CCA). If the medium is found to be free, CW is decremented by one, and the node repeatedly performs CCA until CW reaches zero, at which point the packet transmission commences. However, if the channel is found to be busy during CCA, CW is reset to CW0, NB and BE are incremented by 1, and the node must wait for a random backoff time. This process continues until the channel is found to be free. BE increases until it reaches macMaxBE and remains at that value, while NB increases up to macMaxCSMABackoffs. Once NB exceeds macMaxCSMABackoffs, the packet transmission is unblocked, and the entire process restarts. To enhance the MAC protocol used in IoT applications, various approaches have been proposed [1], categorized into two types: low-power approaches and low-latency approaches (see Fig. 4.5). Let’s delve into a brief description of each approach. (1) Low Energy Approach The primary objective of low energy approaches is to minimize the energy consumption of sensor nodes by employing various techniques such as duty cycle variation, multi-layer approach, relay nodes, and fast handover between different modes.

4.2 Wireless Network Technology and Its Classification

221

Fig. 4.4 CSMA-CA algorithm [1]

(i) Duty Cycle-Based One widely adopted approach for reducing energy consumption is duty cycle management, where the activation and sleep intervals are controlled by adjusting the values of SO (Sleep On) and BO (Backoff) parameters. While this approach enables low energy operation, it comes at the expense of reduced quality of service (QoS) [19–22]. (ii) Multi-layer Approach The multi-layer approach involves dividing the sensor nodes into groups called layers, which interact and perform the communication process collectively. By applying optimization techniques to these layers, power resources can be utilized more efficiently [23, 24].

222

4 The Communication and Security Technology of IoT

Fig. 4.5 MAC approaches for IoT applications [1]

(iii) Frame Size Control Based This approach optimizes energy consumption by adjusting the size of the MAC (Media Access Control) frame without altering the original MAC protocol, thereby avoiding additional optimization overhead. Chen et al. [25] proposed a non-slotted CSMA/CA (Carrier Sense Multiple Access with Collision Avoidance) channel access mechanism for star-shaped IPv6 lowpower wireless personal area networks (6LowPAN). They modeled the random behavior of target nodes as an M/G/1 queuing system and found that the probability of packet loss increases with larger frame lengths and higher traffic, leading to delays in packet transmission. Additionally, they observed that the energy efficiency improves with longer MAC frame lengths up to a finite threshold, beyond which it starts to decline. Thus, finding the optimal frame length becomes crucial for efficient transmission. Mohammadi et al. [26] focused on optimizing the MAC frame length to minimize energy requirements. They proposed two methods: channel prediction and slow start mechanism. The channel prediction method predicts the channel traffic based on historical data and selects an appropriate frame size accordingly. The slow start mechanism increases the MAC frame size after each successful transfer and decreases it after failures. Simulation results demonstrated that the channel prediction mechanism, despite being slightly more complex, is more energy efficient. (iv) Self-configuration-Base In the self-configuration-based approach, power consumption is minimized by altering the network configuration. Although not inherently energy-efficient, this approach is easy to use and implement as it requires only minor algorithmic changes and comes with low cost. It has shown promising performance improvements [27–29].

4.2 Wireless Network Technology and Its Classification

223

(2) Low Latency Approach In real-time applications with stringent latency requirements, continuous data generation and transactions necessitate efficient techniques to reduce latency and minimize power consumption. Considerable progress has been made in research on low-latency MAC technology, and several notable approaches are described below. (i) Parameter Tuning-Base The primary focus of this approach is to reduce latency and enhance the stability of the MAC layer operation. However, these approaches also contribute to low energy consumption. Changes to frame synchronization, sleep mechanisms, and acknowledgments can improve overall quality of service (QoS) [30]. Park et al. [30] proposed a distributed adaptive algorithm that utilizes a Markov chain model to accurately and precisely tune MAC parameters, thereby reducing power consumption and improving packet reliability and latency. Rao et al. [31] introduced S-MAC (Sensor-MAC), an energy-efficient MAC protocol for wireless sensor networks. The S-MAC state machine model consists of four different states: receive, transmit, radio, and channel states. Latency and stability are addressed through the adaptive listening mechanism of the sleep mechanism and adjustments to the duty cycle. However, multi-hop nodes may experience additive delays and affect stability. For real-time IoT applications, networks with fewer hops are more beneficial in terms of latency. Marn et al. [32] proposed the LL-MAC (Low-Latency MAC) protocol specifically designed for low-latency applications. It utilizes a synchronous sleep scheduling mechanism to avoid collisions. The LL-MAC control interval consists of three sub-intervals: Node Advertisement, Sub-Application Request, and SubAcknowledgment. Based on the number of nodes, it is further divided into M slots, ensuring synchronization across the system and collision avoidance without the need for Request-To-Send/Clear-To-Send (RTS/CTS) exchanges. Mahmood et al. [33] focused on the ACK (Acknowledgment) and non-ACK modes of the CSMA/CA (Carrier Sense Multiple Access with Collision Avoidance) mechanism in the IEEE 802.15.4 standard. They simulated and analyzed the nonACK mode, comparing it with the ACK mode. The results showed that the nonACK mode increases the probability of successful transmission by dealing with less control overhead and higher channel access probability. Thus, for applications that don’t require an acknowledgment for each transmission, the non-ACK mode can outperform the ACK mode. However, time-sensitive applications may still benefit from the lower packet loss rate provided by the ACK mode. (ii) Priority-Based In the priority-based approach, nodes are assigned predetermined priorities, enabling nodes with higher priority to access the medium more easily than others. This approach enhances latency and throughput by improving the allocation scheme for guaranteed time slots (GTS) or introducing modifications to the superframe structure.

224

4 The Communication and Security Technology of IoT

Zhan et al. [34] proposed the GTS Size Adaptation Algorithm (GSAA), which continuously monitors the device’s data size and adjusts the GTS size accordingly, instead of keeping it fixed. Xi et al. [35] introduced the Adaptive and Real-Time GTS Allocation Scheme (ART-GAS) to differentiate GTS allocation for time-sensitive and high-traffic devices, thereby optimizing bandwidth utilization. Shabani et al. [36] suggested modifications to the Zigbee/IEEE 802.15.4 MAC protocol’s superframe structure to accommodate multi-hop mesh networks. Collotta et al. [37] proposed a mechanism to manage GTS allocation for industrial Distributed Process Control Systems (DPCS) through WSN (Wireless Sensor Networks), considering periodic traffic, control traffic, and network management. Lu et al. [38] presented a priority-based CSMA/CA mechanism based on the IEEE 802.15.4 standard. The sensor nodes are classified, and different contention parameters are set for nodes with different priorities. Through modeling using a Markov chain, it was found that the proposed mechanism improves the performance of high-priority nodes in terms of throughput and energy consumption. (iii) Variable Backoff-Based The variable backoff-based approach involves dynamically adjusting the backoff interval based on the current traffic situation. By changing the values of BE (Backoff Exponent) or CW (Contention Window), improvements can be observed in throughput, packet transfer rate, and energy efficiency. Jung et al. [39] proposed the Adaptive Collision Resolution (ACR) algorithm, which consists of two phases. The first phase adjusts BE based on previous successful/ failed transmissions and avoids unnecessary Clear Channel Assessment (CCA) with a backoff index adaptation mechanism. The second phase employs backoff period adaptation, where the backoff period is increased by a certain value considering ongoing transmissions to avoid unnecessary backoff expirations. Frame start detection, achieved by appending a small preamble header to the data frame, helps accurately estimate the channel. ACR demonstrates significant improvements in energy efficiency, packet transfer rate, and throughput. Wang et al. [40] introduced a novel backoff algorithm for high-density wireless sensor networks based on channel traffic and packet collision rate. It utilizes a twolevel information feedback (TLIFB) scheme, where the busy rate (an estimate of how many nodes want to access the channel) is employed instead of relying solely on collision rates. Based on these parameters, the backoff window size is adjusted to maximize channel utilization. Liu et al. [41] proposed the Collision-Aware Backoff (CABEB) algorithm, which dynamically adjusts the backoff period considering the collision probability. A parameter, k, dependent on the collision probability, is used to find the backoff period by multiplying it with the maximum backoff period. The results demonstrate that CABEB provides higher throughput and less energy consumption and latency compared to the original CSMA/CA. The advantages and disadvantages of each approach are given in Table 4.3. The duty-cycle-based approach is one of the main energy-saving methods; the multi-layer

4.2 Wireless Network Technology and Its Classification

225

approach is only suitable for large networks, so, as termer of the IoT applications, this method should only be applied when the number of nodes is higher. Table 4.3 Comparison of MAC approaches Type

Subtype

Advantages

Disadvantages

Low energy approach

Duty cycle based

1. Reduce power consumption by simply changing SO and BO 2. very little overhead 3. Suitable for low power applications

1. QoS downgrade 2. For adaptive changes, the frame needs to be changed

Multi-layer approach

1. The nodes are divided Increased end-to-end into multiple layers, latency and jitter and one layer is activated at a time 2. Energy saving compared to duty cycle method 3. Suitable for large networks

低时延法

Frame size control based 1. Simple 2. Tradeoff between energy efficiency and packet collisions

1. Frame size error can cause collision fluctuations 2. Energy efficiency improve a little

Self-configuration-base

Need to add some overhead

complex

Parameter tuning-base

1. The standard 1. Not suitable for parameters without to latency-critical be changed applications 2. performance 2. Easy implementing improvements a little 3. Not much extra overhead

Priority-based

More suitable for time-sensitive transactions

Variable backoff-based

1. Useful for emergency 1. The channel access services mechanism changes per node, and the 2. No extra overhead coordinator has to remember it 2. With higher priority, complex

1. Managing GTS complicates the system 2. Performance varies with different duty cycles

226

4 The Communication and Security Technology of IoT

By properly handling success and failure acknowledgments, frame size controlbased approach can be integrated into current WSN systems without any other changes. Employing GSAA, GTS can be efficiently processed, further reducing latency and improving throughput and energy efficiency. For this, the GTS feature frame format requires to be changed. Different priorities can be assigned according to traffic scenarios or the importance of data packets, traffic estimation can be performed based on successful and failed acknowledgments, therefore, frame control and priority-based approaches can be integrated to improve MAC low power consumption and low latency performance more effectively. (3) MAC Security MAC Security in WSNs involves providing confidentiality, integrity, and authenticity of data transmitted over the network. The statement highlights the usage of Advanced Encryption Standard (AES) with different security modes in WSN technologies. Here are the key points regarding MAC security: Symmetric Encryption and AES: Most WSN technologies support symmetric encryption, and AES is commonly used to enhance security. AES employs 128-bit keys for encryption and decryption, making it highly secure. Security Modes Provided by AES: Table 4.4 likely provides information about the different security modes supported by AES. These modes include AES in Cypher Block Chaining (AES-CBC) for applications requiring authenticity and integrity, AES in counter (AES-CTR) for applications focusing on confidentiality, and AESCCM (Combined Counter Mode) for systems that require both confidentiality and authenticity. Optional Security: Security features in WSNs are typically optional, meaning that there are specifications in wireless standards to opt-out of using security mechanisms. This flexibility allows for tailored security implementations based on the specific requirements and constraints of the application. Preference for AES-CCM: AES-CCM is a popular security mode in most WSNs. It combines confidentiality and authenticity, providing a comprehensive security solution for data transmission in the network. Lightweight Encryption: While AES is a widely trusted encryption algorithm, lightweight encryption algorithms are gaining importance in the context of IoT due to resource-constrained devices. DESL [42], Simon, and Speck [43] are mentioned Table 4.4 Security type of AES Security mode

Data encryption

Data authentication

No security

No

No

AES-CBC-MAC-32/64/128

No

Yes

AES-CTR

Yes

No

AES-CCM-32/64/128

Yes

Yes

4.2 Wireless Network Technology and Its Classification

227

as examples of lightweight ciphers that can be used in the MAC layer. However, it is noted that further research is needed to establish their trustworthiness compared to AES. In summary, MAC security in WSNs involves the use of AES with different security modes to ensure confidentiality, integrity, and authenticity of data. While AESCCM is commonly preferred, lightweight encryption algorithms are being explored for resource-constrained IoT devices, but more research is required to establish their reliability and trustworthiness.

4.2.3 Network Layer Technology The fundamental structures of wireless networks encompass the Star, Tree, and Mesh topologies, each serving as a cornerstone in establishing robust connectivity. Among these topologies, the Star structure embodies simplicity, featuring straightforward communication routes. However, its reliability may be compromised. On the contrary, the other two topologies, namely Tree and Mesh, exhibit greater complexity with intricate communication routes and an abundance of available paths, thereby offering heightened communication reliability. Operating at the network layer, the pivotal function of this layer involves the addressing of IoT nodes (or terminals) and the routing of transmitted data packets. Currently, there exist several standard protocols designed to fulfill these functions effectively. (1) IPv4 IPv4 stands as one of the most widely employed network layer protocols. Its distinctive address format comprises 32 bits, divided into two components: the network address and the host address. IPv4 employs a classification system, consisting of five distinct classes, namely A, B, C, D, and E, allocated according to the number of bits assigned to network and host addresses. However, due to the limited 32-bit address space, the pool of IPv4 addresses—capable of accommodating up to 232 (4,294,967,296) devices—will eventually be exhausted. Consequently, the development of a new version is imperative to cater to the burgeoning number of devices seeking connectivity. (2) IPv6 IPv6 represents an evolution of its predecessor, IPv4, boasting 128-bit addresses to overcome the limitations inherent in IPv4’s address space. This enhanced version, with its expanded address format, resolves the constraint of limited addresses and ushers in a new era for the Internet of Things (IoT). Standard protocols such as 6LoWPAN, coupled with the TSCH mode of IEEE 802.15.4e (6TiSCH), synergistically harness the power of IPv6, rendering it ideally suited for IoT applications.

228

4 The Communication and Security Technology of IoT

(i) 6LoWPAN 6LoWPAN, an Internet Engineering Task Force (IETF) standardized network layer protocol, is custom-tailored for low-power and resource-constrained devices [44]. Widely implemented in diverse domains such as smart homes and smart cities, 6LoWPAN transmits IPv6 data packets at the MAC layer. To accommodate resourcelimited applications, it employs segmentation techniques to break down large IPv6 data packets into smaller fragments. These fragments are buffered, forwarded, and processed, demanding orderly reassembly at the receiving node to restore the original data. However, the process poses certain security threats [45], which can be mitigated by incorporating frame counters and message integrity codes (MICs) into each frame. It is crucial to safeguard this information, as its compromise could lead to the facile deciphering of the original data. (ii) RPL routing protocol To address the low-power requirements of nodes, the IETF has developed the RPL (Routing protocol for low-power and lossy network) protocol based on 6LoWPAN, enabling the construction of efficient routing schemes and information dissemination among nodes. RPL supports various traffic types, including point-to-point, point-to-multipoint, and multipoint-to-point communications. Employing a distance vector routing protocol, RPL stores routing information in the form of a Directed Acyclic Graph (DAG), thus creating a Destination Oriented Directed Acyclic Graph (DODAG). DODAG provides valuable information about one-hop neighbor nodes, aiding in the routing of packets. Leveraging this information, RPL performs estimations of throughput, latency, and load, facilitating the selection of optimal routes. (iii) 6TiSCH Emerging as a novel protocol within the IETF, 6TiSCH (IPv6 over the time-slotted channel hopping mode of IEEE 802.15.4e) strives to integrate the attributes of IEEE 802.15.4e standard with 6LoWPAN. It prioritizes enhancements in various performance metrics, such as jitter, latency, reliability, scalability, and low-power operation. The 6TiSCH architecture outlines strategies for routing IPv6 packets using limited resources [46]. Additionally, it addresses critical concerns pertaining to security and web link management, fortifying the overall system.

4.3 Wireless Communication Standards of IoT

229

4.3 Wireless Communication Standards of IoT In the IoT applications, there exists a plethora of wireless communication standards, each offering distinct advantages and capabilities. In the subsequent section, we shall provide a concise overview of these standards, shedding light on their unique features and functionalities.

4.3.1 Ultra-Short Distance Communication Standard The ultra-short-range communication standard is a technology developed for lowcost and ultra-short-range communication. Notable examples of this standard include NFC (Near-field communication) and RFID, which find extensive employment in various sectors. These sectors encompass healthcare [47], smart environmental protection [48], data exchange and sharing applications [49], mobile payment, ticketing, and loyalty evaluation [50], entertainment [51], social networking [52], education, and other domains. Comprising two fundamental elements, namely the tag and the reader, NFC and RFID systems offer remarkable versatility. Tags are available in two variants: active and passive, each with its distinctive functionality. The primary distinction between NFC and RFID lies in their operating frequency ranges. NFC primarily operates within mid-frequency bands, whereas RFID operates within high-frequency bands.

4.3.2 Standards for Short Range and Low Data Rate This section will delve into the discussion of short-range, low-data-rate technologies that are utilized in various applications. Typically, these standards employ the 2.4 GHz ISM frequency band for wireless communication. The subsequent passage will introduce several widely adopted IoT standards, including ZigBee, which was previously discussed in Chap. 3 and will not be reiterated here. (1) Bluetooth Bluetooth, initially designed by SIG (Bluetooth Special Interest Group) and subsequently incorporated into the IEEE 802.15.1 standard, operates within the 2.4 GHz ISM band. To mitigate coexistence interference, Bluetooth utilizes frequency hopping spread spectrum (FHSS) technology, offering a total of 79 channels, each with a bandwidth of 1 MHz. Communication is facilitated through time division duplex (TDD). Typically, Bluetooth networks adopt a star topology. Bluetooth has different classes based on communication distance and transmission power, as depicted in Table 4.5. Recent advancements have led to the introduction of multiple

230

4 The Communication and Security Technology of IoT

versions of Bluetooth 5, each augmenting the functionality of its predecessor. Bluetooth Low Energy (BLE) and Bluetooth 5 versions primarily cater to low-power communication and IoT applications. (i) BLE BLE [53], a subset of Bluetooth 4, is specifically designed for low-energy applications. It features two implementation modes: single and dual. In the single-mode implementation, there is a single protocol stack dedicated to low-energy operations, whereas dual-mode integrates low-power features with classic Bluetooth. Gaussian Frequency Shift Keying (GFSK) modulation is employed to enhance communication range. Power optimization techniques such as low baud rates, reduced channel usage time, and shorter wake-up times are implemented to optimize power consumption. (ii) Bluetooth 5 Bluetooth 5 introduces specifications that govern parameters such as communication range, data transmission rate, packet length, and broadcast message capacity [54]. Out of the 40 available physical channels, 37 are designated for data interaction, while the remaining 3 are utilized for transmitting advertising packets. Bluetooth 5 exhibits an 800% improvement in message broadcasting capability compared to previous versions, along with extended communication range and higher data rates, making it highly suitable for IoT applications. Moreover, it boasts improved battery life and coexistence capabilities with other networks like Wi-Fi. Additionally, a scalable advertisement model has been introduced, featuring random access advertisements and bounded payload delivery capabilities [55]. (2) Z-Wave Z-Wave [56], developed by ZenSys, is primarily employed in home automation scenarios, adhering strictly to the ITU-T G.9959 specification. It commonly adopts a mesh network topology to communicate with various home appliances. With a data rate ranging from 9 to 40 kbps and a communication range of up to 40 m [57], ZWave utilizes frequency shift keying (FSK) narrowband modulation. In Europe, the operating frequency is 868.42 MHz, while in the United States, it is 908.42 MHz. ZWave employs the CSMA-CA Low Power MAC protocol. The interoperability layer of Z-Wave enables nodes to exchange information with any other node in the network via neighboring nodes. Only the network controller can add or remove nodes within Table 4.5 Bluetooth class Bluetooth class

Communication rage (m)

Transmit power (mW)

Class 1

100

100

Class 2

10

2.5

Class 3

0.1

1

Class 4

0.05

0.5

4.3 Wireless Communication Standards of IoT

231

the network. Each network possesses a unique network ID, allowing communication solely among nodes with the same network ID. Most nodes in the network remain in sleep mode and awaken only for specific functions. (3) EnOcean EnOcean [58] is a low-power wireless communication system designed for home power automation, encompassing sensors, switches, and battery-free nodes integrated with micro-energy converters. It boasts a communication range of up to 30 m within a building. EnOcean devices employ multiple sub-packets transmitted with randomly selected backoff periods to access the medium without collisions. Although this approach reduces throughput to below 30%, it significantly enhances the success rate of accessing the media [59]. EnOcean utilizes small data packets, typically 14 bytes in length, and transmits at a data rate of 125 kbps. To conserve RF power, only “1"s are transmitted. (4) WirelessHART WirelessHART [60], a protocol built upon the Highway Addressable Remote Transducer (HART), adheres to the IEEE 802.15.4 standard and operates within the 2.4 GHz ISM radio band. WirelessHART offers enhanced security and reliability for end-to-end and peer-to-peer communication, employing advanced encryption methods. However, when coexisting with one or more WLAN networks, it experiences a relatively high packet loss rate [61]. (5) DECT ULE DECT ULE (Digital Enhanced Cordless Telecommunications Ultra Low Energy) is a low-power, cost-effective air interface technology developed for home automation, incorporating various control and security technologies. DECT utilizes GFSK modulation, providing a maximum data rate of 32 kbps. ULE networks adopt a star topology, with a central control device known as the “base” connecting all nodes. The indoor communication range of ULE spans 50 m, while the outdoor range extends to 300 m. Repeaters can be utilized to extend the communication range. The dedicated radio frequency band used in Europe, Asia, Australia, and South America is 1880–1900 MHz, while other regions utilize the 1920–1930 MHz frequency band. In the United States, the frequency band is 1.9 GHz [62]. Due to the utilization of dedicated channels, DECT ULE exhibits superior congestion management, interference resistance, and stability compared to Zigbee, Z-Wave, Bluetooth, and similar standards.

4.3.3 Short-Range Wi-Fi Standard Wi-Fi, an extensively utilized technology in public spaces like residences, offices, and industrial settings, adheres to the specifications outlined in the IEEE 802.11 standard. Renowned for its user-friendly nature and seamless integration with existing

232

4 The Communication and Security Technology of IoT

technologies and IoT devices, Wi-Fi operates within the 2.4 and 5.6 GHz ISM bands. Table 4.6 provides a comprehensive overview of the various IEEE 802.11 standards (a/b/g/n), each introducing notable enhancements in bandwidth utilization, data rates, and communication range. At the Media Access Control (MAC) layer, Wi-Fi employs the Carrier Sense Multiple Access with Collision Avoidance (CSMA-CA) mechanism to effectively evade collisions. In an effort to conserve power, the duty cycle is adjusted by increasing sleep time. However, excessive sleep times can result in packet loss if not adequately controlled. A research study [63] proposes an energy-aware deep-sleep scheme, wherein low-energy devices are granted a higher probability of accessing the channel. Leveraging this scheme, power consumption can be reduced by an impressive 70%. Additional power savings can be achieved through the implementation of collision avoidance techniques, power control mechanisms, and reduced idle listening [64]. In the context of the IoT, Wi-Fi introduces a novel standard to complement the existing ones. IEEE 802.11 s, a modified protocol derived from IEEE 802.11, caters to more complex networks such as mesh and wireless ad hoc networks. Furthermore, IEEE 802.11ah, also known as Wi-Fi HaLow, specifically targets IoT and Wireless Sensor Networks (WSN). Operating within the licensed 900 MHz frequency band, Wi-Fi HaLow requires transmission power comparable to Bluetooth but boasts a larger communication range. Recent advancements in Wi-Fi have also seen the development of power-efficient Physical (PHY) transceivers. Protocol revisions in the PHY contribute to expanding wireless communication range to 1 km within sub-gigahertz frequency bands. Moreover, multi-hop routing and duty cycle adjustments at the MAC layer aid in reducing power consumption. The following two vital protocols are briefly introduced: (1) IEEE 802.11ad Operating within the unlicensed 60 GHz frequency band, IEEE 802.11ad facilitates high-speed data transmission of up to 7 Gbps, all while ensuring low-power operation and high-performance interoperability. However, the communication range is inherently limited due to the inability of high-frequency signals to penetrate walls. This limitation can be partially mitigated through beamforming techniques, which extend the communication range up to 10 m. Beamforming also enhances security by Table 4.6 Wi-Fi standards IEEE 802.11

a

b

g

n

Tadio spectrum (GHz)

5.6

2.4

2.4

2.4 和 5.6

Data rate (Mbps)

6–54

1–11

6–54

54–600

Bandwidth (MHz)

20

22

20

20–40

Modulation

OFDM

DSSS

OFDM

MIMO-FDM

Indoor communication range (m)

35

35

38

70

Outdoor communication range (m)

120

140

140

250

4.3 Wireless Communication Standards of IoT

233

enabling frequency reuse, thereby ensuring the confidentiality of communications. Intel recently introduced a 27.8 Gb/s 11.5pJ/b 60 GHz transceiver using polarized Multiple-Input Multiple-Output (MIMO) technology, employing energy-efficient communication practices. Applications of this transceiver span across system interfaces for computer peripherals, as well as display interfaces for HDTVs and projectors [65]. (2) IEEE 802.11p Designed specifically for vehicular communication systems, IEEE 802.11p serves as an improved iteration of IEEE 802.11. It supports high-speed vehicle-to-vehicle and vehicle-to-roadside infrastructure data communications. Since the devices utilized in this context are either roadside or in-vehicle devices, the standard’s authentication mechanisms and data confidentiality measures are not employed. Operating within the licensed 5.85–5.925 GHz frequency band with a channel bandwidth of 10 MHz, IEEE 802.11p offers data rates ranging from 3 to 27 Mbps, with a communication range of up to 1000 m. Toll collection systems, vehicle security services, and automotive cargo transportation are among the applications that benefit from this protocol [66].

4.3.4 Low-Power Wide Area Networks (LPWAN) The standards of LPWAN mainly include: LoRaWAN, Sigfox (discussed in Chap. 3, not reiterated here), and: (1) Weightless Weightless is a collection of LPWAN technologies that handle data communication between base stations and thousands of nodes. Weightless SIG has introduced three different configurations of LPWAN standards: Weightless-W, Weightless-N, and Weightless-P [67]. Weightless-W operates in the TV white space spectrum and utilizes its propagation characteristics. Weightless employs various modulation schemes such as 16-QAM and DBPSK. The data transmission rates range from 1 kbps to 10 Mbps. Narrowband is used for transmitting data to the base station to reduce power consumption. As TV white space cannot be used for shared access, Weightless-SIG introduced two additional configurations: (1) Weightless-N, which supports one-way communication from terminal devices to the base station, making it the most energy-efficient Weightless standard. It operates in the sub-GHz frequency range using DBPSK modulation. (2) Weightless-P, which provides bidirectional communication, operates in the sub-GHz frequency range with a channel bandwidth of 12.5 GHz, offering data rates of up to 100 kbps. It utilizes both GMSK and QPSK modulation schemes for different applications.

234

4 The Communication and Security Technology of IoT

Table 4.7 Comparison of various LPWAN technologies LPWAN

Spectrum

Data Rate

Bandwidth

Range(km)

LoRaWAN

> 1 GHz

300 bps- 37.5 kbps

(125, 250, 500) kHz

15

Sigfox

> 1 GHz

100 bps

100 Hz

30

Weightless-W

TV Free-Space

1 kbps-10 Mbps

5 MHz

5

Weightless-N

> GHz

100 bps

200 Hz

3

Weightless-P

> GHz

100 bps

12.5 kHz

2

DASH7

> GHz

167 kbps

(25 or 200) kHz

5

(2) DASH7 DASH7 is an open-source LPWAN protocol based on the ISO/IEC 18,000–7 standard. It uses narrowband two-level GFSK modulation and operates in the sub-1 GHz frequency band. It incorporates a low-power wake-up mechanism to reduce energy consumption, helping to extend battery life for several years [68]. It has a coverage range of up to 2 km and supports a data rate of 167 Kbps. DASH7 primarily adopts a tree network topology. To minimize latency, terminal devices periodically monitor the downlink transmission. Additionally, forward error correction and symmetric key cryptography enhance the security features of DASH7. A comparison of LPWAN technologies is shown in Table 4.7.

4.3.5 Cellular Mobile Communication Standards The cellular mobile communication has reached the pinnacle of the fifth generation, with 2G/3G/4G/5G all enabling the deployment of IoT applications. Chap. 3 has extensively discussed NB-IoT, while the forthcoming subsection will delve into the virtualization of 5G and its integration within the IoT landscape. Within this subsection, we shall introduce pivotal standards such as 3GPP, which play a fundamental role in shaping the future of this domain. (1) 4G The fourth generation, also known as Long-Term Evolution (4G-LTE), boasts impressive data transmission rates of 150 Mbps for downlink and 50 Mbps for uplink. However, its high power consumption and cost render it unsuitable for the requirements of the IoT, which necessitates low power consumption and cost-effectiveness. Consequently, 3GPP Release 12 introduced a new category exclusively designed for the IoT, named Cat0. This category imposes limitations on both uplink and downlink data transfer rates, capping them at 1 Mbps. Despite operating within the LTE band, Cat0 reduces the bandwidth to a mere 1.4 MHz.

4.3 Wireless Communication Standards of IoT

235

(2) 3GPP Cellular Standard Series Considered a roadmap for IoT applications, the Third Generation Partnership Project Long-Term Evolution (3GPP LTE) series caters to the overarching needs of wide coverage, affordability, security, and dedicated spectrum. It encompasses diverse solutions to bolster M2M applications and extends comprehensive support through 4G broadband networks, including UMTS (universal mobile telecommunications system). Furthermore, it serves as a foundation for M2M communication and paves the way for 5G development, all while keeping a vigilant eye on the future demands of IoT applications. In the current standardization, namely Release-13, 3GPP has proposed three key standards that will propel the IoT forward, including M2M communication (refer to Table 4.8), along with ancillary services like smart grid, smart home, and smart city. (i) eMTC 3GPP Release-13 introduced eMTC (Enhanced Machine Type Communication), also referred to as LTE Cat-M1 or Cat-M [69]. Its primary objective is to curtail power consumption, system cost, and complexity. Operating within a restricted bandwidth of 1.4 MHz, eMTC devices utilize 1.08 MHz for their operation, allocating the remaining bandwidth as a safeguard band. The use of narrowband channels aids in reducing device costs and complexities while enabling a maximum throughput of 1 Mbps. The eMTC device offers two power levels: 23 dBm or 20 dBm, and incorporates extended discontinuous reception (eDRX) technology to minimize power consumption. When powered by a 5Wh battery, its battery life can extend up to an impressive 10 years. (ii) EC-GSM-IoT 3GPP Release-13 proposed EC-GSM-IoT (Extended Coverage GSM for IoT), which leverages Enhanced General Packet Radio Service (eGPRS) to ensure wider communication range and enhanced communication capacity, all while maintaining low energy consumption [69]. It introduces optimization technologies to the existing Table 4.8 Comparison of 3GPP releases 3GPP releases

eMTC

NB-IoT

EC-GSM-IoT

Uplink rate

1 Mbps

250 kbps (multi- tone) 20 kbps (single tone)

474 kbps (EDGE) 2 Mbps (EGPRS2B)

Downlink rate

1 Mbps

250 kbps

474 kbps (EDGE) 2 Mbps (EGPRS2B)

Bandwidth

1.08 MHz

180 kHz

200 kHz

Delay

10–15 ms

1.6–10 s

700–2 s

Duplex mode

Full or Half duplex

Half duplex

Half duplex

Transmission power (dBm)

20–23

20–23

23–33

236

4 The Communication and Security Technology of IoT

GSM framework, bolstering communication range and extending battery life. ECGSM-IoT adopts the eDRX standardization, thereby improving energy efficiency and Quality of Service (QoS). It employs two modulation techniques, namely 8PSK and GMSK, to facilitate variable data transfer rates.

4.4 Some Communication Protocol for IoT Section 1.2.3 provides a concise overview of prevalent IoT standards that have been put forth by various organizations and institutions. These standards aim to streamline and expedite the endeavors of IoT developers and service providers. The proposed IoT protocols can be categorized into four distinct groups: application protocols, service discovery protocols, infrastructure protocols, and other protocols. For a comprehensive breakdown, please refer to Table 1.3, which outlines the specific classification of these protocols.

4.4.1 Application Protocols (i) Constrained Application Protocol (CoAP) The CoAP protocol, developed by the IETF’s CoRE (Constrained RESTful Environments) working group, is an application layer protocol specifically designed for IoT applications [70]. CoAP builds upon the principles of REST, serving as a REST-based web transport protocol layered on top of HTTP functionality. Figure 4.6 illustrates the system structure composed of the CoAP protocol. REST can be seen as a connection protocol that supports caching and relies on a stateless client–server architecture. It is commonly used in mobile and social networking applications, employing HTTP methods such as GET, POST, PUT, and DELETE to disambiguate interactions. REST facilitates the exposure and consumption of Web services like the Simple Object Access Protocol (SOAP). In contrast to REST, CoAP is primarily bound to UDP (instead of TCP), making it more suitable for IoT applications. Additionally, CoAP incorporates modifications

REST Internet

CoAP Communication

CoAP Server HTTP

REST-CoAP Proxy CoAP Environment Fig. 4.6 Composition of CoAP

CoAP Client

4.4 Some Communication Protocol for IoT Client

Server

Client

Server

CON[0x7d34]

237

Client

Server Client

Server

CON[0xbc90]

CON[0xbc91]

ACK[0xbc90]

ACK[0xbc91]

Server

CON[0x7d10] ACK[0x7d10]

NON[0x01a0] ACK[0x7d34]

Client

CON[0x23bb] ACK[0x23bb]

(a) confirmable

(b) non-confirmable

(c) Mixed response

(d) Alone Response

Fig. 4.7 CoAP message types

to certain HTTP functions to fulfill the specific requirements of the IoT domain, such as low power consumption and operation in the presence of lossy and noisy links. Due to its foundation on REST, the transformation between these two protocols within the REST-CoAP agent is straightforward. CoAP aims to enable RESTful interactions with resource-constrained devices that possess limited power, computing capabilities, and communication functionalities. CoAP consists of two sublayers: the message passing sublayer and the request/ response sublayer. The message passing sublayer employs an exponential backoff mechanism to detect duplicates and ensures reliable communication over the UDP transport layer. On the other hand, the request/response sublayer handles the REST communication. CoAP employs four types of messages: confirmable, non-confirmable, reset, and acknowledgment. Reliability in CoAP is achieved through a combination of confirmable and non-confirmable messages. The protocol also incorporates four response modes, as depicted in Fig. 4.7. The separate response mode is utilized when the server needs to delay its response to the client for a specified period. In CoAP’s non-confirmable response mode, the client transmits data without waiting for an acknowledgment, and the message ID is used for duplicate detection. In cases where a message is missed or a communication problem arises, the server responds with a reset (RST) message. Similar to HTTP, CoAP employs methods like GET, PUT, POST, and DELETE to implement create, retrieve, update, and delete (CRUD) operations. For instance, the server can utilize the GET method to query the client for the temperature, using a mixed response pattern. If the temperature exists, the client sends it back; otherwise, it responds with a status code indicating that the requested data could not be found. CoAP adopts a compact message format for message encoding. Each message begins with a fixed four-byte header. Subsequently, a token value, ranging in length from 0 to 8 bytes, may be present. Token values are used to correlate requests and responses. Optional fields such as Options and Payload follow the token. A typical CoAP message ranges between 10 and 20 bytes. Figure 4.8 illustrates the message format of a CoAP packet. The header fields in a CoAP message are as follows: Ver represents the CoAP version, T indicates the type of transaction, OC denotes the Option count, and Code specifies the request method (1–10) or response code (40–255). For instance, GET,

238

4 The Communication and Security Technology of IoT

Fig. 4.8 Message format

POST, PUT, and DELETE have the codes 1, 2, 3, and 4, respectively. The header also includes a Transaction ID, which serves as a unique identifier used to match responses. CoAP provides several important functions [70, 71]: • Resource observation: It enables dynamic subscription to monitor specific resources of interest using a publish/subscribe mechanism. • Block-wise resource transport: CoAP allows the exchange of large data blocks between the client and server without requiring the entire data to be updated, thereby reducing communication overhead. • Resource discovery: The server offers resource discovery capabilities for clients using URI paths based on the Web link field of the CoRE link format. • Interacting with HTTP: To facilitate flexible communication with multiple devices, CoAP interacts with HTTP through a proxy, leveraging the common REST architecture. • Security: CoAP prioritizes security by relying on datagram transport layer security (DTLS), ensuring the integrity and confidentiality of exchanged messages. (2) MQTT MQTT is specifically designed to facilitate the connection between embedded devices and applications/middleware within a network. Its connection mechanism utilizes a routing system, making MQTT the leading choice for IoT and M2M (Machine-to-Machine) communication in the present time. To ensure flexibility and ease of implementation, MQTT adopts the publish/ subscribe pattern, as depicted in Fig. 4.9. This pattern allows for efficient data transformation while maintaining simplicity. Moreover, MQTT is particularly well-suited for devices with limited resources, operating in environments with unreliable or low-bandwidth links. The protocol is built on top of the TCP protocol and offers three levels of Quality of Service (QoS) for message delivery. MQTT encompasses two main specifications: MQTT v3.1 and MQTT-SN [72] V1.2 (formerly known as MQTT-S). The latter is specifically tailored for sensor networks and incorporates a UDP mapping for MQTT, along with added broker support for topic name indexing. The specification covers three essential elements: connection semantics, routing, and endpoints. MQTT comprises just three fundamental components: Subscribers, Publishers, and Brokers. Interested devices register as Subscribers to specific topics, allowing the Broker to notify them when a Publisher publishes data on a topic of interest. Publishers serve as generators of relevant data, transmitting information to interested

4.4 Some Communication Protocol for IoT

239

Fig. 4.9 MQTT architecture

entities (Subscribers) through the Broker. Additionally, the Broker ensures security by verifying the authorization of both Publishers and Subscribers. MQTT finds application in various domains, including healthcare, monitoring, and smart meters. As a result, MQTT serves as an ideal messaging protocol for IoT and M2M communications, offering efficient routing for small, cost-effective, low-power, and low-memory devices operating in vulnerable and low-bandwidth networks. The Publish/Subscribe process employed by MQTT is illustrated in Fig. 4.10, while the message format utilized by the MQTT protocol is demonstrated in Fig. 4.11. The fixed headers occupy the first two bytes of the message format. The message type field specifies the type of message, such as CONNECT (1), CONNACK (2), PUBLISH (3), SUBSCRIBE (8), and more. The DUP flag indicates whether the message is a duplicate, providing information to the recipient who may have received the message previously. The QoS level field identifies the three levels of QoS available for guaranteed delivery of published messages. The “Retain” field instructs the server to retain the most recent “Publish” message and deliver it as the initial message to new subscribers. Lastly, the remaining length field indicates the length of the optional part of the message, denoting the remaining message length. Fig. 4.10 MQTT publish/ subscribe process

Publisher

Subscriber (Destination) Subscribe (topic)

Broker

(source) Publish (topic, info)

Publish (topic, info)

240

4 The Communication and Security Technology of IoT

Fig. 4.11 MQTT message format

(3) XMPP The Extensible Messaging and Presence Protocol (XMPP) serves as an instant messaging (IM) standard for multiparty chat, voice and video calling, and telepresence, as defined by the IETF [73]. Originally developed by the Jabber open-source community, XMPP aims to establish an open, secure, spam-free, and decentralized messaging protocol. XMPP enables users to communicate with each other through the exchange of instant messages over the Internet. It empowers IM applications to implement various essential features, including authentication, access control, privacy measures, hopby-hop and end-to-end encryption, as well as compatibility with other protocols. Figure 4.12 illustrates the overall architecture of the XMPP protocol, showcasing the potential for gateways to act as bridges connecting external messaging networks. Operating in a decentralized manner, XMPP runs on diverse Internet-based platforms and relies on a stream of XML stanzas to establish connections between clients and servers. XML stanzas serve as units of communication and are divided into three distinct parts: message, presence, and iq (information/query), as depicted in Fig. 4.14. The message stanza specifies the source and destination addresses, message type, and ID. It includes the message subject and body fields, which contain the title and content of the message, respectively. The presence stanza is responsible for displaying

Fig. 4.12 XMPP communication

4.4 Some Communication Protocol for IoT

241

and notifying the client about the updated status, typically indicating authorization. Lastly, the iq stanza facilitates the exchange of information and queries between message senders and receivers (Fig. 4.13). (4) AMQP The Advanced Message Queuing Protocol (AMQP [74]) is an open-standard application-layer protocol designed for the IoT, with a specific focus on messageoriented environments. Its primary objective is to enable reliable communication, offering different message delivery guarantee primitives such as at-mostonce, at-least-once, and exactly-once delivery. AMQP requires a dependable transport protocol like TCP to facilitate the exchange of messages. Interoperability between AMQP implementations is achieved through the definition of a wire-level protocol. The communication within AMQP is facilitated by two key components: the Exchange and the Message Queue, as illustrated in Fig. 4.14. The Exchange component is responsible for routing messages to their appropriate queues. This routing process is based on predefined rules and conditions. Messages

Fig. 4.13 Structure of an XMPP stanza

Fig. 4.14 Subscribe/publish mechanism of AMQP

242

4 The Communication and Security Technology of IoT

Fig. 4.15 AMQP message format

can be stored in a message queue and subsequently forwarded to their intended recipients. In addition to supporting peer-to-peer communication, AMQP also includes support for a publish/subscribe communication model. Within AMQP, there exists a messaging layer that builds upon the transport layer and handles messaging functionality. AMQP defines two types of messages: Bare Messages, which are provided by the sender, and Annotated Messages, which are observed by the receiver, as depicted in Fig. 4.15. The header of this message format contains delivery parameters such as durability, priority, time to live, first acquirer, and delivery count. The transport layer of AMQP provides the necessary extension points for the messaging layer. Communication within this layer is frame-oriented, with each frame having a specific structure, as shown in Fig. 4.16. The first four bytes of an AMQP frame represent the frame size, while the “DOFF” (Data Offset) field indicates the position of the subject within the frame. The “Type” field specifies the format and purpose of the frame. (5) DDS The Data Distribution Service (DDS) is a publish-subscribe protocol developed by the Object Management Group (OMG) [74] specifically designed for real-time machine-to-machine (M2M) communication. In contrast to other publish-subscribe Fig. 4.16 AMQP frame format

4.4 Some Communication Protocol for IoT

243

application protocols like MQTT or AMQP, DDS adopts a brokerless architecture and utilizes multicast to provide superior quality of service and high reliability for its applications. The agentless publish-subscribe architecture of DDS makes it particularly suitable for IoT and M2M communications. DDS offers support for 23 different Quality of Service (QoS) policies, allowing developers to address various communication standards such as security, urgency, priority, persistence, and reliability. The DDS architecture is defined by two main layers: the Data-Centric PublishSubscribe (DCPS) layer and the Data-Local Reconstruction Layer (DLRL). The DCPS layer is responsible for delivering information to subscribers, while the DLRL layer serves as an optional interface to DCPS functions, enabling the sharing of distributed data among distributed objects [75, 76]. Within the DCPS layer, five key entities participate in the data flow: (1) Publisher, responsible for transferring data; (2) DataWriter, which is utilized by an application program to interact with a Publisher and learn about specific values and changes related to a particular type of data. The association between DataWriter and Publisher indicates that the application program intends to publish the specified data in the provided context; (3) Subscriber, who receives the published data and sends it to the application; (4) DataReader, employed by subscribers to access received data; and (5) topics, which are identified by data types and names. Topics establish the association between DataWriters and DataReaders. Data transmission is permitted within a DDS domain, which serves as a virtual environment for connected publish and subscribe applications. Figure 4.17 illustrates the architecture of the DDS protocol (Fig. 4.18).

Fig. 4.17 DSS conceptual model

244

4 The Communication and Security Technology of IoT

Fig. 4.18 Print service discovery with DNS-SD

4.4.2 Service Discovery Protocol The scalability demands of IoT necessitate the presence of a resource management mechanism that can efficiently and dynamically register and discover resources and services. Two primary protocols used for this purpose are multicast DNS (mDNS) and DNS Service Discovery (DNS-SD). These protocols enable the discovery of resources and services offered by IoT devices. (1) mDNS The primary function of multicast DNS (mDNS) is to facilitate the resolution of domain names for essential services in certain IoT applications, such as chat applications. mDNS serves as a service capable of performing tasks typically associated with unicast DNS servers [77]. It offers great flexibility due to its utilization of a local DNS namespace, which requires no additional configuration or cost. This makes mDNS highly adaptable and well-suited for embedded Internet-based devices. Furthermore, mDNS operates without the need for centralized infrastructure, eliminating the requirement for manual reconfiguration or additional administrative efforts to manage these devices. It can continue to function even in the event of infrastructure failure. The process of name resolution in mDNS involves the client sending IP multicast messages to all nodes within the local domain. Through this query, the client requests the device with the specified name to respond. Upon receiving the query, the target host multicasts a response message containing its IP address. All devices in the network that receive the response message then update their local cache with the corresponding name and IP address, enabling efficient name resolution within the network. This decentralized approach of mDNS allows for communication and automatic discovery of devices without relying on a central DNS server.

4.4 Some Communication Protocol for IoT

245

(2) DNS-SD DNS-based service discovery (DNS-SD) is the essential pairing function that enables clients to discover and access required services in a specific network using standard DNS messages. Similar to mDNS, DNS-SD allows hosts to connect without the need for external management or configuration [78]. The protocol works by utilizing mDNS to transmit DNS packets over UDP to specific multicast addresses. The process of handling service discovery involves two main steps: first, finding the hostname of the desired service (e.g., a printer), and second, associating the IP address with its corresponding hostname using mDNS. This approach is crucial because IP addresses may change, but names remain constant. By multicasting network attachment details (such as IP addresses) and port numbers to the relevant hosts, the pairing function ensures the establishment of connections for desired services. DNS-SD ensures that instance names in the network remain as stable as possible, thereby increasing trust and reliability. This stability is significant in IoT architectures, where devices should be able to join or leave the platform without affecting the overall system’s behavior. The seamless integration of mDNS and DNS-SD facilitates this development approach. However, it’s important to note that mDNS and DNS-SD have a main drawback: the need to cache DNS entries, especially when resource-constrained devices are involved. To address this issue, timing the cache for a specific interval and draining it when necessary can effectively mitigate the problem. This approach ensures that the cache remains up-to-date while optimizing the utilization of limited resources in IoT environments.

4.4.3 Infrastructure Protocol (1) RPL The Routing Protocol for Low Power and Lossy Networks (RPL) is an IETF routing standardization protocol designed for resource-constrained nodes in IPv6 networks [79, 80]. It is specifically developed to support routing requirements in environments with lossy links and constrained devices. RPL can handle various traffic models, including multipoint-to-point, point-to-multipoint, and point-to-point. At the core of RPL is the Destination Oriented Directed Acyclic Graph (DODAG), which represents the routing graph of nodes. The DODAG is a directed acyclic graph with a single root, as depicted in Fig. 4.19. Each node in the DODAG knows its parent nodes but does not possess information about its child nodes. RPL ensures that each node reserves at least one path to the root node and prioritizes parent nodes to establish faster paths for improved performance (Fig. 4.20).

246

4 The Communication and Security Technology of IoT

Fig. 4.19 DODAG topology

Fig. 4.20 RFID system

To maintain the routing topology and keep routing information up to date, RPL utilizes four types of control messages. The most significant among them are DODAG Information Objects (DIOs), which maintain the current rank of nodes, determine the distance of each node from the root, and select preferred parent paths. Destination Advertisement Objects (DAOs) support uplink and downlink communication by broadcasting destination information to selected parent nodes. DODAG Information Solicitation (DIS) messages allow nodes to obtain DIO messages from reachable adjacent nodes. DAO Acknowledgment (DAO-ACK) messages serve as responses to DAO messages, acknowledging their receipt [81]. The construction of a DODAG begins when the root node starts sending DIO messages containing its location to all Low-power Lossy Network (LLN) ranks. At each rank, the receiving router registers parent paths and participating paths for each node, gradually building up the entire DODAG. The preferred parent node obtained by a router becomes the default upward route toward the root node. The root node may store destination prefixes obtained from other routers’ DIO messages in its own DIO messages to facilitate upward routing. Downward routing is achieved by routers sending DAO messages via unicast through parent nodes, identifying corresponding nodes and route prefixes.

4.4 Some Communication Protocol for IoT

247

RPL routers operate in one of two modes of operation (MOP): non-storing or storing mode. In non-storing mode, RPL routes messages to lower ranks based on IP source routing. In storing mode, downward routing is based on destination IPv6 addresses, enabling more efficient routing decisions. (2) 6LowPAN Low-power Wireless Personal Area Networks (WPANs), such as those based on IEEE 802.15.4, have specific characteristics that distinguish them from previous link-layer technologies. These characteristics include limited packet sizes, various address lengths, and low bandwidth [82–84]. To enable the use of IPv6 data packets over such low-power WPANs, an adaptation layer that conforms to the IEEE 802.15.4 specification is necessary. The IETF 6LoWPAN working group developed a standard in 2007 to address this requirement. 6LoWPAN, which stands for IPv6 over Low-Power Wireless Personal Area Networks, provides the necessary mapping service specifications for maintaining IPv6 networks over low-power WPANs. The standard incorporates several techniques to optimize the transmission of IPv6 packets over these networks. These techniques include header compression to reduce transmission overhead, fragmentation to meet IPv6 Maximum Transmission Unit (MTU) requirements, and forwarding to the link layer to support multi-hop transmission. A datagram in 6LoWPAN is composed of a combination of headers. These headers fall into four types, each identified by a two-bit identifier: (00) NO6LoWPAN header, (01) Dispatch Header, (10) Mesh Addressing, and (11) Fragmentation. The NO6LoWPAN header is used to drop packets that do not conform to the 6LoWPAN specification. The Dispatch Header is used to specify IPv6 header or multicast compression. The Mesh Addressing header identifies IEEE 802.15.4 packets that need to be forwarded to the link layer. For datagrams longer than a single IEEE 802.15.4 frame, the Fragmentation header is used. 6LoWPAN removes a significant amount of IPv6 overhead, allowing small IPv6 datagrams to be sent over a single IEEE 802.15.4 hop in the best-case scenario. It achieves this by compressing IPv6 headers to as little as two bytes, enabling efficient transmission over low-power WPANs. (3) BLE Bluetooth Low Energy (BLE), also known as Bluetooth Smart, is a short-range wireless technology that offers several advantages over previous versions of the Bluetooth protocol. BLE is designed to have a longer battery life, extended coverage range (up to approximately 100 m), and reduced latency compared to traditional Bluetooth [85]. It operates with a transmit power range of 0.01mW to 10mW, making it well-suited for IoT applications. Additionally, BLE exhibits improved energy efficiency and a higher ratio of transmitted energy per transmitted bit when compared to ZigBee. The network protocol stack of BLE consists of several layers [86]: • Physical (PHY) Layer: This layer handles the transmission and reception of data streams.

248

4 The Communication and Security Technology of IoT

• Link Layer: It provides services such as media access control, connection establishment, error control, and flow control. • Logical Link Control and Adaptation Protocol (L2CAP): L2CAP offers multiplexing for data channels and supports segmentation and reassembly for larger packets. • Generic Attributes Protocol (GATT): GATT enables efficient data acquisition from sensors and defines how data is organized and exchanged between devices. • Generic Access Profile (GAP): GAP allows applications to configure and operate in different modes, such as advertising, scanning, connection initiation, and management. In a BLE network, devices can operate as masters or slaves in a star topology. Slave devices send advertisements on dedicated advertising channels, and masters scan these channels to discover and connect to slaves. When devices are not actively exchanging data, they enter sleep mode to conserve power, resulting in low power consumption in idle states. (4) EPCglobal The Electronic Product Code (EPC) is a unique identification code used to identify items, and it is stored on RFID tags. EPCglobal, the organization responsible for EPC development, manages EPC and RFID technologies and standards. The EPCglobal architecture utilizes internet-based RFID technology, inexpensive RFID tags, and readers to enable the sharing of product information [87, 88]. It is widely used in IoT applications due to its openness, scalability, interoperability, and reliability. EPCs are categorized into four types: 96-bit, 64-bit (I), 64-bit (II), and 64-bit (III). The 64-bit EPC types can support approximately 16,000 companies with unique identities, covering 1 to 9 million types of products, and 33 million serial numbers for each type. The 96-bit EPC type can support around 268 million companies with unique identities, 16 million product categories, and 68 billion serial numbers per category. RFID systems consist of two main components: tags and tag readers. Tags consist of a chip for storing the object’s unique identification and an antenna for radio communication. The reader generates a radio frequency field to communicate with the tag and retrieve the object’s information. RFID operates by using radio waves to transmit the tag’s code to a tag reader. The reader then passes this code to a specific computer application called Object-Naming Services (ONS), which looks up the tag details from the database. The EPCglobal system comprises five components: EPC, ID system, EPC middleware, discovery service, and EPC information service. The EPC serves as the unique identifier for the object and consists of four parts, as shown in Fig. 4.21. The ID system uses the EPC reader through the middleware to associate the EPC code with the corresponding database. The discovery service utilizes ONS to locate the required data based on the tag information. The second generation of EPC tags, known as Gen 2 tags, were introduced in mid2006. These tags are designed to cover a wide variety of company products globally.

4.4 Some Communication Protocol for IoT

(Header)8bit

(EPC Manager)28bit

249

(Object Class)24bit

(Serial Number)36bit

Fig. 4.21 EPC 96bit tag

Gen 2 tags offer improved services compared to the first generation of passive RFID tags. They provide interoperability across different objects, high performance to meet various requirements, high reliability, and cost-effective tags and readers.

4.4.4 Other In addition to the established standards and protocols that define the operational framework for IoT applications, there are other crucial considerations that should be taken into account, such as security and interoperability. (1) Safety The unique functionalities and mechanisms of IoT require security protocols beyond the traditional ones used on the Internet. Furthermore, the introduction of new protocols and architectures to support IoT brings forth new security concerns that must be addressed across all layers of the IoT, ranging from the application layer to the infrastructure layer. This includes safeguarding data within resource-constrained devices. To ensure secure data storage, Codo [89] presents a solution for file system security specifically designed for Contiki OS. By employing data caching for bulk encryption and decryption, Codo enhances the performance of security permissions. At the link layer, the IEEE 802.15.4 security protocol offers mechanisms to protect communication between adjacent devices [90]. Moving to the network layer, IPSec serves as a mandatory security protocol for the IPv6 network layer. Considering the multi-hop nature of 6LoWPAN networks and the large size of messages transmitted, IPsec proves to be a more efficient communication mechanism than the IEEE 802.15.4 security protocol [91]. Since IPSec operates at the network layer, it can cater to any upper layer, including all application protocols based on TCP or UDP. Additionally, Transport Layer Security (TLS) is a widely recognized security protocol employed to establish a secure transport layer for TCP communications. For securing UDP communications, Datagram TLS (DTLS) is the counterpart protocol. At the application layer, there is a scarcity of viable security solutions, and most of them rely on the security protocols of the transport layer, specifically TLS or DTLS. Examples of such solutions that support encryption and authentication include EventGuard [92] and QUIP [93]. Consequently, application protocols possess their own security considerations and methods. [94] introduces Lithe, a secure CoAP implementation using DTLS and a compressed version of CoAP. While many MQTT

250

4 The Communication and Security Technology of IoT

Fig. 4.22 IEEE 1905.1 network stack

security solutions are project-specific or rely solely on the TLS/SSL protocol, the OASIS MQTT Security Subcommittee is actively developing a standard for securing MQTT messaging using the MQTT Web Security Framework [95]. XMPP utilizes the TLS protocol to secure data streams and employs a specific profile of the Simple Authentication and Security Layer (SASL) protocol for stream authentication. Similarly, AMQP utilizes TLS sessions and SASL negotiation to secure the underlying communication. In addition to encryption and authentication services for IoT communications, wireless attacks within the 6LoWPAN network and on the Internet can exploit several other vulnerabilities. In such cases, the implementation of an Intrusion Detection System (IDS) becomes imperative. (2) Interoperability (IEEE 1905.1) In an IoT environment, various devices rely on different networking technologies, necessitating interoperability between these underlying technologies. The IEEE 1905.1 standard was specifically designed to facilitate the convergence of digital home networks and heterogeneous technologies [96]. It provides an abstraction layer that conceals the diverse media access control topologies depicted in Fig. 4.22, all without requiring modifications to the underlying layers. This protocol establishes an interface for common home networking technologies, allowing combinations of data link and physical layer protocols such as IEEE 1901 over power lines, WiFi/ IEEE 802.11 over RF bands, Ethernet over duplex or fiber cables, and MoCA 1.1 over coaxial cables to seamlessly coexist with each other.

4.5 Security of IoT Architecture The issue of security and privacy in the IoT has garnered significant attention, leading to the exploration of various solutions [97]. Yang et al. [98] have undertaken an extensive investigation of security and privacy concerns within the IoT, approaching the topic from four distinct perspectives. Initially, the authors shed light on the inherent limitations of implementing robust security measures in IoT devices, taking into account factors such as battery life and computing power. In response,

4.5 Security of IoT Architecture

251

they propose innovative solutions, including the implementation of lightweight encryption schemes specifically designed for embedded systems. Furthermore, the authors present a comprehensive classification of IoT attacks, encompassing physical, remote, and local threats, among others. In addition, they focus their attention on the development and implementation of mechanisms and architectures designed to authenticate and authorize users within the IoT. Finally, the study delves into an analysis of security issues across different layers of the IoT architecture, ranging from the physical layer to the network layer. In a similar vein, Kumar [99] et al. and Vikas [100] et al. have devoted their efforts to address security and privacy concerns within each layer of a three-layered IoT architecture [101]. Their investigation uncovers a multitude of vulnerabilities that permeate the IoT landscape, arising from the diverse communication technologies and networks employed by wireless sensors. To ensure controlled access and exclusively permit authorized and legitimate users, Bouij-Pasquier et al. [102] have proposed an authorized access model as a robust security framework for the IoT. Meanwhile, Fremantle et al. [103] have undertaken a comprehensive review of the challenges surrounding IoT middleware security and the proposed approaches to tackle them. Many existing systems inherit their security properties from middleware frameworks. In light of this, the authors analyze and evaluate various middleware approaches, taking into consideration well-known security and privacy threats. They elucidate how each approach addresses security concerns and establish a set of requirements necessary for securing IoT middleware. While the aforementioned studies and surveys have extensively examined aspects of IoT security such as network protocols and middleware, there remains a pressing need to evaluate the security features of commercial IoT architectures in order to effectively address IoT security concerns. Subsequently, we will delve into a comprehensive discussion regarding the security aspects of several typical commercial IoT architectures.

4.5.1 Typical Commercial IoT Architecture (1) AWS IoT Amazon has introduced AWS IoT [104], an IoT cloud platform designed to facilitate secure connectivity and interaction between smart devices, the AWS cloud, and other connected devices. This framework enables utilization of various AWS services, such as Amazon DynamoDB [105], Amazon S3 [106], and Amazon Machine Learning [107], offering users a wide range of options. Notably, AWS IoT provides the capability for applications to communicate with devices even in offline scenarios. The AWS IoT architecture comprises four primary components: Device Gateway, Rules Engine, Registry, and Device Shadow [108]. Acting as a mediator, the Device Gateway facilitates communication between connected devices and cloud services,

252

4 The Communication and Security Technology of IoT

leveraging the MQTT protocol. Moreover, the device gateway supports WebSockets and HTTP 1.1 protocols [109], enabling flexible connectivity options. The Device Gateway collaborates with the Rules Engine, which processes incoming published messages. These messages undergo transformation and are subsequently delivered to other subscribing devices or AWS cloud services. Furthermore, AWS Lambda [110] allows seamless integration with non-AWS services for advanced processing and analysis. To uniquely identify each connected device, regardless of type, vendor, or connection method, the Registry assigns a distinct ID. Additionally, the Registry stores essential metadata associated with connected devices, including device name, ID, and attributes, providing comprehensive tracking capabilities. AWS IoT introduces the concept of Device Shadows, which are virtual images representing each connected device. These Device Shadows persistently reside in the cloud, ensuring constant availability and accessibility for managing and interacting with devices. (2) ARM mbed IoT ARM mbed IoT, a platform centered around ARM microcontrollers [111], serves as a comprehensive development platform for IoT applications. This platform caters to the needs of both standalone and network-based IoT applications within the ARM ecosystem. With its integration of mbed tools and services, ARM microcontrollers, mbed OS, mbed device connector, and mbed Cloud, the ARM mbed IoT platform strives to offer a scalable, secure, and interconnected environment for IoT devices. At the core of the ARM mbed IoT platform, several essential building blocks are instrumental in its functionality. These include mbed OS, the mbed client library, mbed cloud, mbed device connector, and ARM microcontroller-based hardware devices. mbed OS, being an open-source full-stack operating system specifically designed for ARM Cortex-M microcontrollers, forms the foundation of the platform [112]. It simplifies the architecture of the ARM mbed IoT platform by providing a streamlined operating system that facilitates driver interfacing with the hardware layer, simplifies security and device management functions, offers standard communication protocols, and multiple APIs for integration and interaction. Operation with mbed Device Connector, mbed Device Server, and mbed Client ensures a comprehensive IoT solution. The mbed device interface layer supports a wide array of communication protocols, including Bluetooth Low Energy (BLE), WiFi, Ethernet, ZigBee IP, 6LoWPAN, and more. Notably, the TLS/DTLS sublayer incorporates the mbed TLS security module, ensuring end-to-end security across communication channels. Furthermore, the architecture supports multiple application protocols such as CoAP, HTTP, and MQTT. The mbed client library plays a crucial role in facilitating communication with the upper layers of the architecture. It encapsulates a subset of mbed OS functionality, enabling physical devices to connect with the mbed device connector service.

4.5 Security of IoT Architecture

253

mbed Cloud [113], a Software-as-a-Service (SaaS) solution, serves as a comprehensive IoT device management platform. It offers secure device updates, configuration management, and secure device connectivity. The platform incorporates robust security measures such as cryptographic modules, trusted zones, and key management. At the top layer of the mbed IoT architecture, developers can implement thirdparty applications, including web and smart applications, that leverage REST APIs to manage cloud-connected IoT devices. (3) Azure IoT Suite Microsoft’s Azure IoT Suite [114] is a comprehensive platform comprising a suite of services that empower end users to interact with their IoT devices, receive data from these devices, perform various data operations such as aggregation, multidimensional analysis, and transformation, and visualize the data in a suitable manner for commercial applications. Figure 4.23 illustrates the Azure IoT architecture [115]. IoT devices communicate with the Azure cloud through predefined cloud gateways. The data collected from these IoT devices is stored in the cloud for further processing and analysis using Azure cloud services like Azure Machine Learning and Azure Stream Analytics. Alternatively, the data can be utilized for real-time analysis. Azure IoT Hub [116] is a web service that facilitates bidirectional communication between devices and cloud backend services, ensuring the fulfillment of all security requirements. It includes an Identity Registry that stores identity and authentication-related information for each device. Additionally, it features a Device Identity management unit responsible for managing all connected and authenticated devices. Fig. 4.23 Azure IoT Framework [115]

254

4 The Communication and Security Technology of IoT

The architecture distinguishes between two classes of IoT devices: IP-enabled and PAN-enabled. IP-enabled devices can directly communicate with Azure IoT Hub using prescribed communication protocols [117]. Azure IoT Hub natively supports communication via AMQP, MQTT, or HTTP protocols. However, Azure IoT Protocol Gateway [118] can also accommodate other protocols as required. Gateways play a vital role in protocol adaptation. The Protocol Gateway facilitates direct communication with Azure IoT Hub using MQTT/AMQP protocols and can additionally support various communication protocols based on the specifications of the connected devices. A Field Gateway serves as an aggregation point specifically for PAN (Personal Area Network) devices. Due to their limited capacity to run a secure HTTP session, these constrained devices transmit data to the Field Gateway. The Field Gateway securely aggregates, stores, and forwards the data to Azure IoT Hub. The IoT solution backend layer encompasses a wide range of Azure cloud services [119], including Azure Machine Learning, Azure Stream Analytics, and more. At the top layer of the Azure IoT architecture, the Presentation layer enables users to display their data and access business intelligence (BI) services [120]. (4) Brillo/Weave Google introduced the Brillo/Weave platform to facilitate the rapid implementation of IoT applications, which comprises two main components: Brillo and Weave [121, 122]. Brillo serves as an Android-based operating system designed for developing embedded low-power devices, while Weave acts as a communication framework for interaction and messaging purposes. Weave plays a crucial role in device registration via the cloud and facilitates the exchange of remote commands. Together, Brillo and Weave form a comprehensive IoT framework. Although initially focused on the smart home, Brillo/Weave has expanded its support to encompass general IoT devices. Figure 4.24 presents the Brillo/Weave architecture, which encompasses two subarchitectures associated with Brillo and Weave, respectively. Brillo functions as a lightweight embedded operating system entirely implemented in the C/C++ programming language, devoid of any Java framework or runtime. At the underlying layer, the platform represents the IoT devices. The kernel layer resides atop the hardware layer and relies on Linux as its foundation. It takes responsibility for providing fundamental architectural models such as system resource management, process scheduling, and communication with external devices as required. Additionally, it offers drivers and libraries to control various physical device resources like displays, cameras, power, WiFi, keyboards, and more. Android HAL (Hardware Abstraction Layer) serves as middleware that bridges the gap between hardware and software. It facilitates communication between Android applications and hardware-specific device drivers by handling system calls between the kernel and the primary Android-based layers. Since the architecture lacks a display, Brillo employs the Binder IPC mechanism [123] to interact with Android system services within the application framework.

4.5 Security of IoT Architecture

255

Fig. 4.24 Brillo/Weave architecture

The OTA update component represents a radio service [124] specifically designed for bulk installation and software version updates over the airwaves. The underlying device periodically checks with the OTA server for updates. Furthermore, the OTA server promptly notifies all connected devices whenever a new update becomes available. While Brillo caters to the low-level aspect of the architecture (the operating system), Weave operates at a higher level. Weave serves as a suite of communication protocols and APIs that enable seamless communication between smartphones, IoT devices, and the cloud. It also provides services for authentication, discovery, configuration, and interaction. Weave adopts the JSON format for its operations. As mentioned earlier, the Weave module is incorporated into the Brillo OS as a crucial component of the top layer of the Brillo architecture. Weave enhances the user experience by offering the ability to connect devices either directly or through the cloud, facilitated by a common language (Weave) exposed across all Brillo-powered devices. Moreover, Weave exists as a mobile SDK for smartphones and a cloudbased web service. The mobile SDK can be implemented on Android or iOS phones, enabling mobile apps to connect with Brillo-enabled IoT devices. Once connected, mobile apps can utilize local APIs (if on the same network) or cloud APIs to control and manage connected IoT devices. Weave supports multiple communication and application protocols. The final three layers represent the operating system, while the top layer comprises core services encompassing OTA updates, organization, metrics, and analysis services.

256

4 The Communication and Security Technology of IoT

(5) Calvin Calvin is an open-source IoT platform developed by Ericsson [125], designed to create and manage distributed applications that enable devices to communicate with each other. It is built on the concept of Flow-based Programming (FBP), which treats applications as networks of asynchronous processes communicating through message passing using structured data chunks called packets. Figure 4.25 illustrates the architecture of Calvin, where the bottom two layers establish the foundation of the operating environment. The base layer represents the hardware or physical devices, while the second layer encapsulates the operating system exposed by the hardware. The top layer consists of Calvin’s platformdependent runtime layer. This layer handles various types of communication between different operating environments, such as IoT devices. It provides abstractions for hardware functions (e.g., I/O operations), supports multiple transport layer protocols (such as WiFi, Bluetooth, and i2c), and presents platform-specific functionality to the platform-independent runtime layers in a unified manner (e.g., sensors and actuators). The platform-dependent runtime layer sits on top of the platform-specific runtime layers. The platform-independent runtime layer serves as the interface for actors. Actors can be configured to grant access to different resources based on their role within the application. Actors execute asynchronously and autonomously and can encapsulate protocols like REST or SQL queries and device-specific I/O functions. Connections between actors are not explicitly specified but are handled dynamically by different runs. Proxy actors [126] are an essential feature provided by Calvin. They enable integration of different systems by managing communication and converting data into messages or tokens that both systems can understand. Proxy actors allow Calvinbased applications to scale and interoperate with non-Calvin applications, facilitating interoperability between heterogeneous systems. (6) HomeKit HomeKit is an IoT framework developed by Apple [127], specifically designed for connected devices within the home. The HomeKit architecture consists of Fig. 4.25 Calvin architecture

4.5 Security of IoT Architecture

257

several core components, including the HomeKit configuration database, HomeKit Accessory Protocol (HAP), HomeKit API, and HomeKit-enabled devices. Figure 4.26 illustrates a simplified HomeKit architecture. The base layer represents IoT devices or accessories in the home. However, not all connected devices in a home can directly integrate with the HomeKit platform. HomeKit requires certain hardware specifications and compatibility. For devices that do not meet these requirements, HomeKit Bridges act as intermediate devices or proxy gateways between iOS apps and non-HomeKit-compatible home automation devices, enabling them to connect to the HomeKit platform. The backbone of the HomeKit architecture is the HomeKit Accessory Protocol (HAP) layer. HAP is a proprietary protocol that operates over HTTP and utilizes the Bonjour architecture [128]. Bonjour is an Apple framework used for networking purposes, providing functions such as service discovery, address assignment, and hostname resolution, making it easier to discover devices. Within HAP, messages between iOS apps and HomeKit-compatible devices are exchanged using the JSON format. The HomeKit API layer provides interfaces for third-party developers, simplifying the development of smart applications and abstracting the underlying complexities. Developers can leverage the HomeKit API to integrate their apps with HomeKit and control HomeKit-enabled devices. The top layer of the HomeKit architecture is the application layer, which provides a consistent user interface across all Apple devices associated with the same user Fig. 4.26 HomeKit Simplified Architecture

258

4 The Communication and Security Technology of IoT

account. This synchronization is achieved by storing data in a shared database using iCloud [129], ensuring that users have a seamless experience when interacting with their HomeKit-enabled devices. With the introduction of the HomeKit framework to tvOS 10 [130], Apple expanded the capabilities of Apple TV. Apple TV can act as a hub for running home automation setups, allowing users to control their devices remotely. Additionally, Apple TV supports additional controls and functions for shared users, enabling users to share control of their devices with others, manage access permissions, and make configuration changes within the home using their Apple ID to invite other users. (7) Kura Kura is an open-source project under the Eclipse IoT umbrella, aiming to provide a Java/OSGi-based framework for IoT gateways running M2M (machine-to-machine) applications [131, 132]. The Kura framework enables the management of interactions between a local network of physical IoT devices and the public internet or cellular network. It abstracts and isolates developers from the complexities of hardware, networking subsystems, and software development, providing APIs for smooth access and management of the underlying hardware components. Figure 4.27 shows the architecture of Kura. Kura is specifically designed to run on Linux-based devices and offers a remotely manageable system that includes core services and a device abstraction layer for accessing the gateway’s hardware [133]. The Device Abstraction Layer allows developers to abstract hardware using OSGi services, providing access to various devices for serial, USB, and Bluetooth communication. Communication APIs for devices connected via GPIO, I2C, or PWM enable system integrators to utilize custom hardware as part of the gateway [134]. The Gateway Basic Services layer provides configurable OSGi services that allow applications to interact with fundamental gateway functionality. These services include watchdog, clock, GPS location, embedded database, process, and device profile services. The network management layer provides configurable OSGi services for accessing and managing the current network configuration, including functionalities such as DHCP, NAT, and DNS. It interacts with the Linux system to configure network interfaces, including WiFi access points and PPP connections. The connectivity and delivery layer simplifies the development of telemetry M2M applications that interact with remote cloud servers [135]. It provides capabilities for communication, data transmission, and integration with cloud-based services. The Remote Management layer offers features such as remote configuration, remote software updates, remote system commands, remote log retrieval, device diagnostics, and remote VPN access. These functionalities enable the remote management and maintenance of IoT gateways. Lastly, the management GUI provides a graphical interface for accessing and utilizing the various services and functionalities offered by Kura.

4.5 Security of IoT Architecture

259

Fig. 4.27 Kura architecture

(8) SmartThings SmartThings is an IoT platform developed by Samsung, primarily focused on smart home applications [136]. It allows developers to create applications that enable users to manage and control home appliances through their smartphones. The SmartThings ecosystem consists of several key components: the SmartThings Cloud Backend, the SmartThings Hub (home controller), the SmartThings mobile client application (friend application), and IoT devices (Smart Devices). Figure 4.28 illustrates the architecture of the SmartThings platform. The SmartThings Hub serves as a gateway between IoT devices (Smart Devices) and cloud services. It connects to the internet and supports various communication protocols such as ZigBee, Z-Wave, WiFi, and BLE. The SmartThings Hub can perform certain functions locally, even without a connection to the cloud. However, events and updates are still sent to the cloud to reflect the current state of the home and enable other cloud-based services. Communication between all connected parties is encrypted using the SSL/TLS protocol to ensure security. Smart Devices can connect to the SmartThings cloud directly via WiFi/IP protocol, bypassing the need for a gateway. This direct connection allows for quicker and more efficient communication between Smart Devices and the cloud. SmartDevices can be categorized as Hub-connected, LAN-connected, or Cloudconnected devices [137], depending on their connectivity and interaction capabilities within the SmartThings ecosystem.

260

4 The Communication and Security Technology of IoT

Fig. 4.28 SmartThings Architecture

SmartApps, which are developed by third-party developers, can interact with Smart Devices through two methods: method calls and event subscriptions. With method calls, SmartApps can execute actions on Smart Devices. With event subscriptions, SmartApps can subscribe to events generated by other SmartApps or Smart Devices, enabling them to react to changes or trigger actions based on specific events. The SmartThings Cloud serves as the management layer, responsible for maintaining persistent and secure connections between connected devices (such as hubs) and cloud services. The device type handler layer simplifies scalability by managing instances or virtual images for each type of Smart Device. End users interact with physical Smart Devices indirectly through instances hosted in the cloud. The SmartThings Cloud has two essential functions: hosting and running Smart Apps in a closed-source environment and running virtual images of physical smart devices, providing an abstraction layer and web services to support the application layer [138]. Overall, SmartThings offers a comprehensive platform for building and managing smart home applications, allowing users to control and monitor their home appliances through a unified interface.

4.5.2 Security Functions of the Typical Commercial IoT Architecture (1) Security Functions of AWS IoT Amazon has implemented multi-layer security architecture for AWS IoT, incorporating security measures at every layer of the technology stack. The security mechanism is exemplified in Fig. 4.29, showcasing the integration of Message Broker services with security and identity services. Features provided include:

4.5 Security of IoT Architecture

261

Fig. 4.29 AWS IoT security mechanism

(i) Certification To establish a connection between a new IoT device and the AWS IoT Cloud, the device must undergo authentication. AWS IoT offers support for mutual authentication across all connection points, ensuring the source of transmitted data is always identifiable. Generally, AWS IoT provides three authentication methods: • X.509 certificates [139]; • AWS IAM users, groups and roles [140]; • AWS Cognito Identity [141]. The most commonly employed authentication technique within AWS IoT is through X.509 certificates. These digital certificates rely on public key cryptography and should be issued by a trusted certification authority (CA). Within the AWS IoT cloud, the Security and Identity unit acts as the CA. SSL/TLS-based X.509 certificates are utilized for secure authentication. Through the SSL/TLS protocol’s authentication mode, AWS IoT verifies the certificate’s validity by requesting the client’s ID (e.g., an AWS account) and the corresponding X.509 certificate. Additionally, AWS IoT requires the client to demonstrate ownership of the private key associated with the provided public key in the certificate. Alternatively, users can leverage certificates issued by their preferred CAs, as long as they are registered within the certificate registry. For HTTP and WebSocket requests sent to AWS IoT, authentication is accomplished using AWS Identity and Access Management (AWS IAM) [142] or AWS Cognito [143], both of which support AWS authentication through AWS Signature Version 4 (SigV4) [144]. While HTTP allows the flexibility to choose authentication methods, MQTT connections solely rely on X.509 certificates for authentication. WebSocket connections, on the other hand, are limited to authentication using SigV4. In summary, each IoT device connected to AWS IoT undergoes authentication using one of the aforementioned methods chosen by the end user. The Message Broker handles authentication and authorization for all operations associated with user accounts. Specifically, it authenticates all connected devices, securely extracts device data, and enforces user-defined access policies to regulate device permissions.

262

4 The Communication and Security Technology of IoT

(ii) Authorization and Access Control The authorization process in AWS IoT is based on policies, which can be applied through mapping rules and policies to individual certificates or by utilizing IAM policies. This ensures that only specified devices or applications can access the corresponding devices associated with these certificates. Achieving this level of control is facilitated by a rules engine, which operates under the principle of least privilege. The rules engine securely accesses and transfers data to the designated destination based on predefined rules/policies within the AWS access management system. Owners of cloud-connected devices can leverage the rules engine to define authorization rules, granting access to specific devices or applications while blocking others. The utilization of AWS policies or IAM policies grants users full control over their devices and specifies the privileges granted to others for accessing device functions and performing actions [145]. (iii) Secure Communication All traffic to and from AWS IoT is encrypted using the SSL/TLS protocol. TLS guarantees the confidentiality of application protocols supported by AWS IoT, such as MQTT and HTTP. For both MQTT and HTTP protocols, TLS encrypts the connection between the device and the Message Broker. AWS IoT offers support for numerous TLS cipher suites, including ECDHE-ECDSA-AES128-GCM-SHA256, AES128GCM-SHA256, AES256-GCM-SHA384, and more. Furthermore, AWS IoT incorporates Forward Secrecy, a property of secure communication protocols that ensures compromised long-term keys do not impact temporary session keys. Additionally, within the AWS IoT cloud, each legitimate user is assigned a private home directory where all private data is stored in an encrypted format using symmetric key encryption (e.g., AES128). (2) Security Functions of ARM mbed IoT The security framework of the mbed IoT platform is implemented across three distinct levels: (1) the device itself, encompassing both hardware and mbed OS; (2) the communication channel; and (3) the lifecycle management of embedded and smart applications, including device management and firmware updates. The security architecture is visualized in Fig. 4.30 [146], key components of the security architecture include: • mbed uVisor [147]: This device-end security solution provides effective isolation of various software components and operating systems from one another. • mbed TLS [148]: It ensures secure communication, confidentiality, and authentication. The aforementioned security components offer the following security function: (i) Certified ARM mbed IoT provides various encryption standards, key exchange mechanisms, certificate-based signatures, and symmetric and public/private key encryption through the utilization of the mbed TLS software module.

4.5 Security of IoT Architecture

263

Fig. 4.30 The security architecture of ARM mbed IoT

(ii) Authorization and Access control ARM mbed IoT devices support multiprogramming, wherein memory is organized into distinct blocks rather than being an unprotected space. This organization significantly enhances security. To regulate resource access and maintain authorization levels, the mbed IoT platform leverages the architecture of ARMv7-M, which includes the Memory Protection Unit (MPU) and uVisor components. The MPU, a hardware module, enforces memory isolation, while uVisor acts as an independent software hypervisor, forming the foundation of the mbed OS security architecture kernel. Operating as a sandbox, uVisor establishes an isolated security domain within the microcontroller itself (Cortex-M3, M4, or M7) using the MPU. This isolation domain effectively safeguards sensitive system components, ensuring that each part resides in a separate memory section. Consequently, the application consists of non-intersecting parts, meaning that an attack or security breach in one part does not impact other parts. This isolation provides robust protection against potential bugs or security vulnerabilities. In summary, uVisor protects software running on Cortex-M3, Cortex-M4, and Cortex-M7 processors by dividing memory into secure (private) and unsecure (public) memory spaces based on the MPU. (iii) Secure Communication The mbed OS employs the TLS/DTLS protocol as a fundamental cornerstone to ensure end-to-end security among all entities involved in the communication channel. By implementing TLS/DTLS, the mbed OS guarantees secure and encrypted communication, thwarting eavesdropping, tampering, and message forgery while ensuring message integrity.

264

4 The Communication and Security Technology of IoT

Within mbed OS, mbed TLS provides a comprehensive suite of security mechanisms, including support for Transport Layer Security (TLS) and its related protocol Datagram TLS (DTLS). These mechanisms effectively safeguard against various security threats, enabling secure and trustworthy communication. Additionally, mbed TLS encompasses software implementations of popular cryptographic primitives, secure key management, certificate handling, and other cryptographic functions. ARM also takes advantage of hardware encryption modules available in certain microcontrollers to further enhance the security of sensitive data sections. (3) Security Functions of Azure IoT Suite Azure IoT leverages the built-in security and privacy features of the Azure platform, along with the Security Development Lifecycle (SDL [149]) and Operational Security Assurance (OSA [150]) processes, to establish a secure development and operational environment. In Azure IoT’s comprehensive architecture, security is deeply ingrained at every layer and meticulously enforced across all components of the ecosystem. This commitment to security is manifested through a range of robust features, including: (i) Certification To establish a secure connection between an IoT device and Azure IoT Hub, a stringent mutual authentication process is employed. The Transport Layer Security (TLS) protocol is utilized to encrypt the handshake procedure, ensuring confidentiality. In addition, cloud services are authenticated by presenting an X.509 certificate as proof of identity to the targeted IoT device. Each device deployed in Azure IoT is assigned a unique device identity key, enhancing individual authentication. Subsequently, the device securely authenticates itself to Azure IoT Hub by transmitting a token comprising an HMAC-SHA256 signed string, which is a fusion of the generated key and the user-selected device ID. (ii) Authorization and Access Control Azure IoT capitalizes on the robust capabilities of Azure Active Directory (AAD) [151] to facilitate a policy-based authorization model for cloud-stored data, streamlining access, management, and auditing. This model enables seamless and immediate revocation of access to both cloud-stored data and connected IoT devices. Azure IoT Hub employs a comprehensive set of access control rules to govern permissions granted or denied to IoT devices and intelligent applications. System-level authorization ensures swift revocation of access credentials and permissions, empowering the activation and deactivation of any IoT device’s identity. (iii) Secure Communication Secure Socket Layer/Transport Layer Security (SSL/TLS) protocols are employed to encrypt communications, ensuring data integrity and confidentiality throughout the ecosystem. The identity registry within Azure IoT Hub offers secure storage for device and security key identities, bolstering the overall security posture. Moreover,

4.5 Security of IoT Architecture

265

data is securely stored in either DocumentDB [152] or SQL databases, safeguarding privacy and preserving confidentiality. (4) Security Functions of Brillo/Weave The security architecture of the Brillo/Weave framework incorporates essential components such as secure enablement, signed radio updates, OS-level timely patches, and SSL/TLS utilization. Within this framework, various security functions are implemented, including: (i) Certification Weave fulfills crucial roles in device and user discovery, provisioning, and authentication. Authentication is achieved through the utilization of the OAuth 2.0 protocol and digital certificates. Regardless of the chosen Weave-enabled cloud server, Google serves as the authentication server, ensuring a reliable and secure authentication process. (ii) Authorization and Access Control The Linux kernel plays a pivotal role in enforcing access control within the framework. The Security-Enhanced Linux (SELinux) module is responsible for implementing robust access control security policies. IoT device owners have the ability to apply multi-level access controls according to their specific requirements. Access control is enforced by assigning explicit permissions (read, execute, write) to each user or user group. Moreover, the Linux-based framework leverages sandboxing technology, specifically regarding User ID (UID) and Group ID (GID), to provide an enhanced mechanism for enforcing information separation based on the confidentiality and integrity needs of individual configuration files. (iii) Secure Communication Weave ensures secure communication channels, while the SSL/TLS protocol provides link-level security. The Linux kernel further supports disk encryption, safeguarding data integrity. Additionally, Brillo leverages Trusted Execution Environments (TEEs) and Secure-enabled features to protect both code and data stored within IoT devices, ensuring confidentiality. The presence of TEE enables essential functionalities such as key storage and key management [153], with support from connected device hardware. (5) The Safety Features of Calvin The Calvin platform employs a range of techniques to implement security measures at various levels [154]. These measures include: (i) Certification User authentication within Calvin can be achieved through three different methods. The first method is local authentication, where hashes of usernames and passwords are stored in JSON files within a directory on the same machine. Authentication is performed by comparing the hashes of the entered credentials with the stored

266

4 The Communication and Security Technology of IoT

records. The second method involves utilizing an external machine that acts as an authentication server, handling the authentication process. The third method utilizes a RADIUS server to authenticate the username and password, leveraging the subject attribute for response. (ii) Authorization and Access Control Authorization is supported either through local or external procedures. In local authorization, policies are stored in JSON files within a directory on the local machine. External authorization involves employing another runtime as an authorization server. For external authorization, a digital certificate adhering to the X.509 standard is required to verify the signed JSON Web Token containing the authorization request/response. The authorization process is performed after successful authentication, utilizing the returned subject attributes as input. Access control for actors or entities is activated through a property-based configuration file. In the Calvin framework, the activation of a functionality is achieved by adding a feature with a corresponding attribute value. The framework does not provide sandboxing or virtualization technologies, as Ericsson does not maintain its own cloud infrastructure. (iii) Secure Communication IoT devices within the Calvin platform interact with each other and with smart applications. They connect to the M2M gateway through a short-range radio protocol. The devices and gateways are integrated with the mobile network to access the cloud, enabling end users to communicate with the cloud and obtain information about authorized IoT devices. Authentication and authorization processes are required for IoT devices to connect to the cloud through an M2M gateway. Since M2M gateways lack a user interface for entering usernames and passwords, Calvin relies on the capabilities of the mobile network. All M2M gateways are equipped with SIM cards and utilize their SIM-based identity for authentication to cloud services using the 3GPP standardized Generic Bootstrapping Architecture (GBA). The transmitted and received data can be secured using the TLS/DTLS protocol. Within the TLS suite, the Calvin framework implements the Elliptic Curve Cryptographic (ECC) algorithm to encrypt communications and provide digital signatures. ECC is chosen due to its limited overhead compared to other protocols such as RSA. The Calvin framework can be integrated with any public cloud system, as it does not rely on the Ericsson cloud as a major component of its ecosystem. However, the details regarding objectlevel security within the cloud are not provided by Calvin. (6) HomeKit Security Features HomeKit leverages many features of the iOS [155] security architecture, as it consists of software, hardware, and services designed to work together in a secure manner, where end-to-end security must be guaranteed. This means that the entire ecosystem is affected by the security policies and mechanisms enforced by the tight integration of hardware and software in iOS devices. HomeKit security features include:

4.5 Security of IoT Architecture

267

(i) Certification Authentication is a crucial aspect of the HomeKit ecosystem, ensuring secure communication between HomeKit-connected accessories and iOS devices. The authentication process utilizes public–private key signatures based on Ed25519 [156]. The keys are securely stored in Shield Keychain and synced across devices using iCloud Keychain. During authentication, keys are exchanged through a secure remote cryptographic protocol, where the user enters an 8-digit code provided by the accessory manufacturer via the iOS device’s user interface. To enhance security, the exchanged key is encrypted using the ChaCha20Poly1305 AEAD algorithm, and the key itself is derived using HKDF-SHA-512. Additionally, the accessory’s MFi certification is verified during the setup process. These keys are long-term keys. For each communication session, a temporary session key is generated using a station-to-site protocol. This session key is then encrypted with a per-session Curve25519 key [157], derived using HKDF-SHA-512. The same authentication and encryption mechanisms apply to configuring Apple TV for remote access and adding new shared users. (ii) Authorization and Access Control Applications must explicitly request permission from users to access their family data within the HomeKit framework. Additionally, all applications are subject to security measures designed to prevent collisions and mutual compromise. Each application can only access its own data, which is stored in a unique home directory randomly assigned during the application’s installation process. System data on iOS is isolated from third-party apps, and users have no authority to modify it under any circumstances. Address Space Layout Randomization (ASLR) technique [158] is employed to mitigate buffer overflow memory-based attacks. (iii) Secure Communication The iOS security architecture incorporates core components, such as Secure Boot, which ensures that only trusted code can run on Apple devices. Data encryption is efficiently enabled through the application of the AES-256 encryption protocol in an engine built into the DMA path between each device’s flash memory and main system memory. Each Apple device possesses a unique Device ID, represented by an AES256-bit key injected into the processor during manufacturing. This cryptographic binding ensures that data is exclusively accessible by a specific device, making it inaccessible and indecipherable if the memory chip is moved to another device. The system’s random number generator (RNG) utilizing the CTR_DRBG algorithm [159] creates all encryption keys. Communication utilizing the HTTP protocol is secured through TLS/DTLS with AES-128-GCM and SHA-256. In HomeKit, the long-term keys used for communication are exclusively stored on the user’s device. Even Apple cannot decrypt these keys if the communication passes through an intermediary device or service. Furthermore, HomeKit employs forward secrecy, generating a new session key for each communication session between an Apple user’s device and HomeKit-enabled

268

4 The Communication and Security Technology of IoT

accessory to ensure privacy and confidentiality. This session key is discarded upon completion, safeguarding the communication process in the event of device compromise and exposure of the long-term key, making it virtually impossible for an attacker to decrypt the communication using only the long-term key. (7) Security Features of Kura The Kura framework, enhanced by Eurotech’s ESF [160, 161], provides a security architecture for securing communications with IoT devices and gateways. ESF adds advanced security features, VPN remote access, vertical-specific application diagnostics, and bundling capabilities to Kura. The Eclipse Foundation has also contributed several security components to the Kura framework, including Security Services, Certificate Services, SSL Manager, and Cryptographic Services. The security features of Kura include: (i) Authorization and Access Control. The Security Services component in Kura allows the management of security policies and enables script compliance. The Certificate Services API is used for retrieving, storing, and verifying certificates for SSL, device management, and bundle signing. (ii) Security Manager The Security Manager component regularly checks the integrity of the environment to prevent malicious users from tampering with files. Runtime policies can be enforced to restrict execution of specific services or import/export of specific packages, making it more challenging for hackers to access sensitive information. (iii) Secure Communication The SSL Manager in Kura manages SSL certificates, trust stores, and public/private keys. All communications are secured using the SSL/TLS protocol. The Crypto API is used for encryption, decryption, and retrieving the master password. (8) Security functions of SmartThings. SmartThings has a security framework in place to ensure the secure integration and operation of SmartDevices and SmartApps. The security features of SmartThings include: (i) Certification When integrating new SmartDevices into the SmartThings environment, the OAuth/ OAuth2 protocol is utilized for authentication. This process verifies the identity of the SmartDevice and authorizes the SmartThings platform to access its functions, ensuring that only trusted and authorized devices can interact with the platform.

4.6 5G and IoT

269

(ii) Authorization and Access Control Access to SmartDevices through SmartApps is governed by policies defined in the SmartThings Capability model. This model specifies the capabilities and permissions that a SmartApp can have when interacting with SmartDevices. It ensures that SmartApps can only access authorized functions and services of the designated SmartDevices. The SmartThings ecosystem executes all SmartApps, whether on a closed-source cloud or SmartThings Hub. To ensure security and isolation, the environment adopts the Kohsuke sandboxing technique [162]. This technique utilizes Groovy to create a controlled execution environment that isolates untrusted code. Only predefined method calls included in a whitelist, stored in the restricted operating system, are allowed. Developers cannot create their own classes or load external libraries within this sandboxed environment. Once a SmartApp or SmartDevice is released, it is allocated a dedicated isolated data store to maintain data separation and privacy. (iii) Secure Communication The SmartThings Hub, which acts as a central control point, supports security measures for communication. When adding a secure Z-Wave device to the Hub network, communications are encrypted using 128-bit AES. This encryption ensures the confidentiality and integrity of the data transmitted between the Hub and the Z-Wave device. Similarly, for ZigBee-enabled products, the Hub provides the same level of security guarantees. Communication between the various components within the SmartThings ecosystem, including SmartApps, SmartDevices, and the Hub, typically occurs over the SSL/TLS protocol. This protocol adds an additional layer of encryption and authentication to secure the communication channels.

4.6 5G and IoT The prevailing 4G cellular mobile communication network has gained extensive usage in the IoT and is continually evolving to meet the demands of future IoT applications. Currently, the 5G mobile communication network is poised for massive expansion into the IoT domain, aiming to address the security and network challenges inherent in IoT systems while propelling the future advancement of the Internet [163]. For the proliferation of IoT devices on a massive scale, forthcoming IoT applications and business models necessitate novel performance benchmarks encompassing aspects like extensive connectivity, security, reliability, wide coverage, ultra-low latency, ultra-high throughput, and unparalleled dependability [164]. To cater to these requirements, LTE and 5G technologies are anticipated to offer fresh connectivity interfaces for future IoT applications. The ongoing development of the next-generation “5G” technology is currently in its nascent stages and focuses on novel radio access technology (RAT), advanced antenna technology, utilization of higher frequency bands, and re-architecting of networks [165, 166]. According to Gartner’s projections, the number of IoT devices connected via machine-to-machine (M2M) communication reached a staggering 8.4 billion in 2017,

270

4 The Communication and Security Technology of IoT

and this figure is predicted to surge to 20.4 billion by the year 2020 [167, 168]. The advent of 5G-IoT (5G Internet of Things) will facilitate the connection of an enormous number of IoT devices, catering to the burgeoning market demand for wireless services, thereby fueling fresh economic and societal development [169]. The new requirements stemming from future IoT applications, coupled with the advancements in 5G wireless technology, serve as pivotal driving forces for the ongoing progress of 5G IoT. The advent of 5G will provide connectivity services for large-scale IoT deployments, empowering billions of smart devices (or IoT terminals) to establish connections with the Internet. Moreover, 5G will furnish flexible, rapid, and high-quality network access services for the IoT, achievable through the implementation of wireless softwaredefined networking (WSDN) [170]. Numerous WSDN solutions tailored for 5G have been proposed, including notable ones like SoftAir [171], CloudRAN [172], and CONTENT [173], among others.

4.6.1 Capabilities and Requirements of 5G IoT (1) Enhanced Capabilities Offered by the 5G-IoT Architecture The 5G-IoT architecture encompasses a multitude of advanced capabilities, catering to the ever-evolving demands of modern applications. These capabilities empower users with real-time, on-demand, and online experiences, while ensuring adaptability and fostering social connectivity. To achieve this, the 5G-IoT architecture is designed to exhibit end-to-end coordination capabilities, characterized by agility, automation, and intelligent operations at each stage [174]. The 5G-IoT architecture will provide the following capabilities: • Provision of logically independent networks tailored to meet specific application requirements, allowing for a customizable and optimized network experience. • The radio access network (RAN) undergoes a revolutionary transformation through the implementation of cloud-based radio access network (CloudRAN) technology. This innovative approach facilitates the establishment of largescale connectivity, accommodating multiple standards. Such connectivity can be deployed on demand, ensuring that the RAN functions required by 5G are readily accessible. • The 5G-IoT architecture simplifies operations and enables the dynamic configuration of network functions as per the needs of the moment. This on-demand configurability ensures efficient utilization of network resources while adapting to changing circumstances. In the pursuit of these goals, the International Mobile Telecommunications (IMT) framework outlines the capabilities provided by 5G mobile communication networks. These capabilities encompass three primary dimensions: enhanced mobile broadband

4.6 5G and IoT

271

(eMBB) communication, ultra-reliable and low-latency communication (uRLLC), and massive machine type communication (mMTC). (2) Requirements of 5G IoT The IoT is revolutionizing our daily lives, enabling a wide range of new applications for an ecosystem of smart and highly heterogeneous devices. For this reason, some requirements are proposed for the 5G IoT, mainly including: • High data transmission rate: Future IoT applications, such as high-definition video streaming, virtual reality (VR) or augmented reality (AR), etc., require higher data transmission rate (about 25 Mbps) to provide acceptable performance [174]. • High scalability and fine-grained network: To improve network scalability, 5G-IoT requires higher scalability to support fine-grained fronthaul network1 decomposition through NFV. • Low latency: Some applications of the IoT require the system to have low latency, such as tactile Internet, AR, video games, etc., which require a low latency of about 1 ms. • Reliable resilience: 5G-IoT requires IoT devices and application users to have a wide coverage area and high handoff efficiency. • Security: In future IoT mobile payment and digital wallet applications, unlike general security policies that protect connections and user privacy, 5G IoT requires improved security policies to improve the security of the entire network. • Long battery life: 5G-IoT supports billions of low-power and low-cost IoT devices, and 5G IoT requires low-energy solutions. • High connection density: A large number of devices will be connected together in 5G-IoT, which will require that 5G should be able to support efficient messaging within a certain time and area. • Mobility: 5G-IoT should be able to support a large number of device-to-device connections with high mobility. In addition, all raw data generated by IoT devices will be uploaded and stored in the cloud, and cloud servers will process the data to extract useful knowledge through data analysis such as data mining, machine learning, and big data analysis [163].

4.6.2 Key Enabling Technologies of 5G IoT 5G IoT includes many key enabling technologies from physical communications to IoT applications. [163, 175] summarizes the main key enabling technologies related to 5G-IoT. Here, we group these key enabling technologies into the following five broad categories: (1) 5G-IoT architecture; (2) Virtualization of wireless network functions; (3) Heterogeneous network; (4) Device-to-device directly communication; 1

Fronthaul network: refers to the network that does not include the wireless access network in the mobile communication network, such as the optical fiber backbone network.

272

4 The Communication and Security Technology of IoT

(5) Spectrum sharing and interference management. This section will give a brief introduction to the above five key enabling technologies. (1) 5G-IoT Architecture Figure 4.31 presents a smart home prototype integrating with 5G infrastructure, where 5G-IoT employs multiple wireless communication protocols to bridge many resource-constrained IoT devices to remote cloud-based applications. 5G-IoT will be mainly based on 5G wireless systems, so the architecture generally includes two planes [171]. • Data plane: focus on data perception through software-defined fronthaul network. • Control plane: Consists of network management tools and reconfigurable service (application) providers. The 5G-IoT architecture should satisfy the following service requirements: • Scalability, cloudification/network function virtualization (NFV). • Network virtualization function. • Advanced network management: including mobility control, access control and resource-efficient network virtualization.

Fig. 4.31 Example of 5G-IoT architecture [171, 174]

4.6 5G and IoT

273

• Smart Services: The architecture should be able to provide smart services based on big data analysis. (2) Wireless Network Function Virtualization (WNFV) As a supplement to the 5G mobile communication network, WNFV will realize the virtualization of the entire network function to simplify the deployment of 5G-IoT, where NFV will no longer focus on hardware and underlying network functions but will focus on cloud services [170]. NFV can decompose the physical network into multiple virtual networks, as shown in Fig. 4.32, where devices can be reconfigured according to the requirements of the application, thereby constructing multiple networks. NFV will provide real-time processing capabilities for 5G-IoT applications by optimizing speed, capacity, and coverage in logically sliced networks to meet application requirements. NFV can dynamically construct networks, such as 5G, device networks and 4G networks. Therefore, NFV with 5G will change the way to construct networks in 5G-IoT, thereby to provide scalable and flexible network functions. For application service requirements, in the highly heterogeneous 5G-IoT, network densification will be able to increase density of 5G infrastructure with multi-RAT (radio access technology) connections. (3) Heterogeneous Network (HetNet) HetNet is a new network paradigm designed to meet the on-demand requirements of service-driven 5G IoT. HetNet enables 5G-IoT to provide the required information transmission rate on demand [177, 178]. A large number of resource-constrained devices are deployed in 5G-IoT, and for the QoS of these devices, massive MIMO links can be used to dynamically reconfigure the network [177]. For resourceconstrained M2M communication, mobile devices can be used as their mobile gateways [179, 180] to further improve the deployment of M2M applications in 3GPP LTE/LTE-A networks and improve their QoS performance.

Fig. 4.32 5G NFV technology [176]

274

4 The Communication and Security Technology of IoT

MTC applications have a large number of devices to join, broadband network access and strict QoS requirements in emergencies, which requires MTC to have a high data transmission rate and throughput, and 5G/HetNet can meet the rapidly growing data traffic requirements of MTC devices, and allocate network resources on demand. (4) Directly Communication of Device-to-Device (D2D) In HetNet, traditional macrocell base stations (MBS) cooperate with each other to provide base stations with low power consumption and on-demand allocation of network resources. However, the short-distance directly communication between two devices (D2D) has also been proposed as a new method of data transmission. The use of the directly communication method will help reduce the power consumption of 5G-IoT, balance the load, and enable edge users to obtain better QoS. D2D realizes data exchange between user equipment without using a base station, so it is considered the “cell layer” in 5G-IoT. In addition, in the IoT, D2D is used in cooperation with mobile NB-IoT user equipment, and D2D can be used as an extension of NB-IoT uplink, which can establish cellular link routing through NB-IoT [181]. (5) Spectrum Sharing and Interference Management In the 5G-IoT architecture shown as Fig. 4.31, a large number of 5G IoT devices will be densely deployed in many cases. For this reason, spectrum sharing and interference management have become key enabling technologies for 5G-IoT. And HetNet is a promising solution for interference management in 5G IoT. Massive MIMO is the core to achieve higher spectral efficiency. Recently, many advanced MIMO techniques have been proposed, including multi-user MIMO (MUMIMO), very large MIMO (VLM), etc., 3GPP LTE-A has included MU-MIMO, and these techniques all utilize more antennas on the base station [182]. (6) Other Enabling Technologies Other key enabling technologies include optimization methods in 5G IoT, including convex optimization, heuristic methods, evolutionary algorithms (EA), machine learning methods, and artificial neural networks (ANN). These approaches will have an increasing impact on key 5G-IoT enabling technologies.

4.6.3 Technical Challenges and Development Trends of 5G-IoT The functions provided by 5G can meet the requirements of the future IoT, but it also faces a series of interesting research challenges for 5G-IoT architecture, trusted communication between devices, security issues, etc. 5G-IoT have integrated many

4.6 5G and IoT

275

technologies and is having a significant impact on applications in the IoT. In this subsection, we will discuss the potential technical challenges and future trends of 5G-IoT. (1) Technical Challenges Although much research has been done on 5G-IoT, technical challenges remain, including: (i) 5G-IoT architecture Although many architectures have been proposed with many advantages, there are still many challenges faced architecture design, including: • Scalability and network management: In 5G-IoT, due to the large number of IoT devices and their increasing number, network scalability will be a major issue. In addition, managing the status information of a large number of IoT devices is also a problem to be considered [183, 184]. • Interoperability and Heterogeneity: Seamless interconnection between heterogeneous networks is a major challenge. Connecting a large number of IoT devices through communication technologies to communicate with other intelligent networks or applications to transmit and collect important information [185, 186]. • Security and privacy, security and cyber-attacks, add to the privacy concerns. (ii) Software Defined Networking (SDN) The effectiveness of SDN for 5G data networks remains a challenge. While bringing about scalability, there is still a need to close the technology gap in SDN. (1) In order to provide high flexibility to the core network, scalable SD-CN is a challenge for network scalability. (2) The separation of control and data planes is difficult for most SDNs. (iii) NFV and SDN are highly complementary, but not interdependent In the past few years, many NFV solutions have been developed, including SoftAir, OpenRoads, CloudMAC, SoftRAN [187], etc. However, 5G-IoT still needs to solve several technical challenges: energy-saving cloud network; security and privacy, VNF runs on a third-party public cloud, so security and privacy become a big problem; VNF management, efficient VNF The switching system and the interface provided by the VNF are two technical challenges in NFV. (iv) Good spectrum resource and interference management scheme. D2D communication will provide high throughput for 5G-IoT. In D2D, energy and spectral efficiency are two challenges. The success of D2D requires good spectrum resources and interference management solutions to maximize the high reliability of device-to-device communication.

276

4 The Communication and Security Technology of IoT

(v) Application deployment of the IoT Deploying IoT applications faces for challenges due to large-scale, resourceconstrained IoT devices and heterogeneous environments. Many existing IoT applications include overlapping deployments of IoT device networks, where neither the devices nor the applications can interact and share information with each other. At the same time, the ability and efficiency to collect and transmit data in the physical world faces challenge [188]. In addition, the dense heterogeneous network deployment in the IoT, 5G and multiple access technologies other than 5G, and simultaneous full-duplex transmission also face challenges. (2) Security and Privacy Issues In 5G-IoT, key new security functions will be required at the device and network levels to address complex applications, including smart cities, smart networks, and more. Security is very complex in diverse 5G-IoT systems. The designers must consider not only remote software intrusion, but also local intrusion of the device itself [189], meanwhile, security assurance must consider avoiding weak security links. Challenges include: identification, authentication, assurance, key management, encryption algorithms, mobility, storage, and backward compatibility, and so on. (3) Standardization Due to the heterogeneity of networks and devices in 5G-IoT, IoT systems and applications lack consistency and standardization. There are still many obstacles and challenges to implementing these solutions. The obstacles to 5G IoT standardization mainly include the following four aspects: • IoT devices and platforms, including the form and design of IoT products, big data analysis tools, etc. • Connectivity, including communication networks and protocols to connect IoT devices. • Business model, that is expected to meet the requirements of e-commerce, vertical, horizontal and consumer markets. • Advanced applications, including control functions, data acquisition and analysis functions. The standardization of 5G-IoT involves two types of standards: (1) technical standards, including wireless communication, network protocols, and data aggregation standards; (2) regulatory standards, including data security and privacy, such as the General Data Protection Regulation, Security Solutions, cryptographic primitives, etc. The challenges faced in the adoption of standards in 5G-IoT are unstructured data, security and privacy issues and data analysis protocols, etc.

4.6 5G and IoT

277

(4) Development Trend The evolving 5G-IoT is still in its early stages. In addition to addressing the above challenges, attention should also be paid to the following development trends: (i) NDN In 5G-IoT, applications will continue to expand and more and more devices will be connected. 5G-IoT applications drive the requirement for viable network architectures, leading to NDN (named data networking, NDN) [190], which supports high-density IoT applications. In addition, due to the diversity of IoT, 5G-IoT will become more and more decentralized, so more complex management (such as NVF) technology needs to be developed to manage 5G-IoT. (ii) Edge Computing Edge computing is another key technology of 5G-IoT. This is because: the requirement for data analysis makes edge computing the core of the IoT; edge computing in 5G-IoT will significantly enhance high computing-related applications, such as VR/ AR or countless data-intensive smart city plans, storage, etc. (iii) Convergence of 5G, AI, data analytics and IoT The combination of these four key technologies is expected to transform 5G-IoT and will enhance user experience in communication, applications, digital content, and commerce [191]. AI will enable 5G-IoT to enable new applications of cognition, such as connected cars, consumer IoT, connected smart homes, wearables and mutable entities. 5G will make the future IoT smart. (iv) Spectrum and Energy Harvesting Efficient research on spectrum and energy harvesting will be another key development trend in spectrum-sharing 5G-IoT systems [192]. Low-energy IoT devices will greatly expand the scalability of IoT, while spectrum solutions will enable 5G technology to enhance wireless network coverage and fast handoff. (v) Security and Privacy The security and privacy of 5G solutions in IoT will cover all layers of 5G-IoT, including end-to-end protection mechanisms. The urgent requirements for 5G-IoT security require active research on new methods and technologies including security infrastructure, trust models, service delivery models, privacy issues, and threat situation judgments. (vi) The middleware Solutions of Context-aware IoT In high-density device scenarios, context-aware solutions are expected to increase the scale, mobility, and heterogeneity of entities in IoT that can be autonomous and automatically adapt to dynamic changes in context.

278

4 The Communication and Security Technology of IoT

4.7 Communication Technology of Smart Grid 4.7.1 Communication System Structure of Smart Grid A smart grid is a modern transmission and distribution grid that harnesses the power of two-way data communication, distributed computing technologies, and intelligent sensors. Its primary goal is to enhance power delivery capabilities while concurrently enhancing the safety, reliability, and efficiency of power consumption [193]. By integrating advanced information processing and communication technology infrastructure, the smart grid possesses the capability to fully leverage its distributed generation system and optimize the energy efficiency of the entire power system. Consequently, the smart grid functions as an advanced data communication network, facilitating flexible interoperability among diverse power elements through the support of power management equipment, thus enabling the streamlined operation of the entire power system [194–197]. In essence, the smart grid can be categorized into three distinct components: the home area network (HAN), the neighborhood area network (NAN), and the wide area network (WAN), as depicted in Fig. 4.33. (1) HAN The primary purpose of the HAN is to establish communication network that connects smart meters, home appliances, and plug-in electric vehicles. This network empowers users to access valuable information regarding their power usage patterns and associated costs, conveniently displayed on home display devices. Given the relatively lower bandwidth demands of the HAN, it necessitates the utilization of cost-effective communication technologies such as Wi-Fi, Bluetooth, and ZigBee. (2) NAN The NAN plays a vital role in establishing a communication network between data concentrators and smart meters within close proximity. This network enables the acquisition of measurement data from smart meters and facilitates its transmission

WAN

Generation side

NAN

Transmission side

Distribution side

HAN

User side

Smart Grid System Fig. 4.33 Structural architecture of smart grid from power generation to user side

4.7 Communication Technology of Smart Grid

279

to the data concentrators. To achieve this, short-range communication technologies such as Wi-Fi and radio frequency network technologies are commonly employed. (3) WAN The WAN serves as a communication network that establishes a connection between the data center of the service provider (typically the power supply company) and the data concentrators within the smart grid infrastructure. It operates as a bi-directional communication network with ample bandwidth and high throughput capabilities, enabling efficient monitoring and control of the smart grid system, as well as facilitating long-distance data transmission. To ensure cost-effectiveness and broad coverage, the WAN employs communication technologies such as 3G/4G-LTE/5G, optical fiber, and power line communication networks [195, 197]. To establish a standardized framework for the communication structure of the smart grid, the IEEE 2030-2011 standard plays a pivotal role. This standard serves as a crucial reference for comprehending and designing smart grid applications and communication infrastructure with a hierarchical structure [198].

4.7.2 Wired Communication Technology of Smart Grid Wired communication technology is employed in data communication within smart grids due to its reliability and anti-interference capabilities [199, 200]. Among the various wired communication technologies, Power Line Communication (PLC) stands as the most prevalent method. Additionally, fiber optics and digital subscriber line (DSL) are also utilized in smart grid data communication. Digital DSL, in particular, possesses the capacity to support high-speed data transmission ranging from 10 Mbps to 10 Gbps. Furthermore, coaxial and optical cables enable high-speed data transmission rates ranging from 155 Mbps to 160 Gbps [201]. These diverse wired communication technologies provide smart grids with the means to establish stable and efficient data transmission channels, ensuring seamless communication and facilitating the exchange of information critical to the functioning of the smart grid infrastructure. (1) PLC Power Line Communication (PLC) is a data communication technology that leverages transmission and distribution lines as the medium for transmitting information. Initially, in 1997, the focus of PLC applications was on Internet access and providing services through PLCs in Europe. However, the concept of PLC-based Internet access faced disruption due to interference issues. Consequently, at the beginning of the twenty-first century, the interest of people shifted towards industrial communication and home applications. This shift was primarily driven by several industry alliances such as the HomePlug Powerline Alliance, Universal Powerline Association (UPA), High-Definition PLC (HD-PLC) Alliance, and HomeGrid Forum [202].

280

4 The Communication and Security Technology of IoT

PLC encounters several technical challenges due to the unpredictable nature of transmission and distribution line propagation characteristics. The detrimental effects and interference resulting from propagation irregularities often concentrate in electromagnetic environments such as transformers [203]. To overcome these challenges, two PLC technologies with different bandwidths have been developed: narrowband PLC (NB-PLC) and broadband PLC (BB-PLC) [202]. NB-PLC was initially used for data transmission ranging from a few bits per second to several kilobits per second. Subsequently, bandwidth technology emerged, enabling data transmission rates exceeding 10 kbps, and currently reaching up to 500 kbps. NB-PLC can be employed on both low-voltage and high-voltage lines, with an impressive communication range of 150 km or more. In contrast, BB-PLC achieves data transmission rates of up to 200 Mbps on the high-frequency band ranging from 2 to 30 MHz [202]. The success of NB-PLC has propelled the development of BBPLC, particularly in the realm of Internet services and home area network (HAN) applications. Over the past decade, researchers have successfully implemented data communication over power lines using various standards and technologies such as TIA-1113, ITU-T G.hn, IEEE 1901 FFT-OFDM, and IEEE 1901 Wavelet-OFDM [204, 205]. This progression has led to advancements in transmission bandwidth, starting with 14 Mbps (HomePlug 1.0) on the physical layer (PHY), followed by 85 Mbps (HomePlug Turbo), and currently reaching 200 Mbps (HomePlug AV, HD-PLC, UPA) transmission bandwidth. (2) Fiber and DSL Apart from Power Line Communication (PLC), the communication system of the smart grid incorporates other wired communication systems such as optical fiber and DSL. These systems offer higher data transmission rates compared to PLC. Optical fiber communication holds a significant advantage as it provides Gigabit per second (Gbps) transmission bandwidth and exhibits exceptional resistance to electromagnetic interference. It enables the smart grid to achieve high-speed data transfer with remarkable reliability. Optical fiber cables, including special variants like optical power ground cables, further enhance data transfer rates and support long-distance communications. DSL, on the other hand, facilitates the transmission of digital data over telephone lines. In DSL technology, asymmetric DSL (ADSL) offers a downlink data transmission rate of 8Mbps. ADSL2 + provides a maximum downlink rate of 24Mbps, while very high bit-rate DSL (VDSL or VHDSL) enables a downlink transmission rate of up to 52Mbps over copper lines. These DSL variants empower the smart grid with efficient data transmission capabilities, leveraging existing telephone infrastructure to deliver reliable and high-speed communication within the grid. In summary, alongside PLC, the smart grid’s communication system incorporates optical fiber and DSL communication technologies, which provide higher data transmission rates. Optical fiber offers Gbps transmission bandwidth with strong electromagnetic interference resistance, while DSL variants like ADSL, ADSL2 +

4.7 Communication Technology of Smart Grid

281

, and VDSL enable significant downlink transmission rates over telephone lines. These diverse wired communication systems contribute to the effective and reliable data exchange within the smart grid infrastructure.

4.7.3 Wireless Communication Technology Wireless communication networks have emerged as a significant area of study in the context of the smart grid. While wireless networks offer advantages in terms of installation and coverage, they do have limitations such as limited bandwidth and susceptibility to strong electromagnetic interference [206]. The wireless network in the smart grid is typically structured as a hierarchical mesh network, which is well-suited for Advanced Metering Infrastructure (AMI) to establish Neighborhood Area Networks (NAN) and Home Area Networks (HAN). This setup enables the creation of a cost-effective communication infrastructure. The communication between the NAN and Data Management Points (DMPs), which can be wired or wireless, can span several kilometers. Each DMP can connect and manage numerous Smart Meters (SMs), with the coverage area extended through mesh networking or repeating DMPs. Communication networks, constructed using wireless sensor networks (WSNs), play a critical role in the operation of smart grids. WSNs must meet the requirement of providing a reliable infrastructure by minimizing latency [206]. For NAN, the latency requirement is typically less than 1 s, while HAN, which focuses on energy management and demand planning within a local area, allows for a latency of less than 5 s. Technologies such as WiMAX, UMTS, LTE, IEEE 802.22, Wi-Fi, and WPAN based on IEEE 802.11 and IEEE 802.15 can be employed in NAN. WiMAX, based on the IEEE 802.16 standard for Metropolitan Area Networks (MANs), is commonly used to establish connectivity between DMPs and SMs. WiMAX utilizes Orthogonal Frequency Division Multiple Access (OFDMA), a multi-user adaptive technology that schedules subsets of multiple subcarriers to specific clients. This allows for simultaneous data transmission to multiple clients at low data rates, reducing interference and improving spectral efficiency [207, 208]. The IEEE 802.15.4 standard, known as WPAN, provides a reference for the Physical Layer (PHY) of low data rate, low power consumption, and cost-effective networks. In a star topology, WPAN can achieve a data rate of 256 kbps, with a coverage range of 10 m to 1600 m. When configured in a single hop, cluster tree, or multi-hop fashion, WPAN can form a mesh topology with wide coverage. Cellular mobile communication technology offers an alternative solution for NAN coverage, providing advantages such as broader coverage compared to other wireless networks. Technologies like 4G/5G and NB-IoT offer different transmission bandwidth solutions for smart grids, addressing the communication requirements of the NAN effectively.

282

4 The Communication and Security Technology of IoT

4.8 Security Issues of Smart Grid 4.8.1 Smart Grid Security Challenges, Objectives and Some Related Works (1) Security threats and Challenges of Smart Grid The smart grid is vulnerable to a wide range of security threats and challenges, including theft, cyber-attacks, deliberate sabotage, and natural disasters. Any compromise to the smart grid’s security can lead to severe consequences, such as power outages, failures in the information and communication infrastructure of the grid, cascading failures, damage to power equipment, chaos in the energy market, and even jeopardizing personal safety [209]. To understand the security challenges faced by the smart grid, it is essential to analyze them from various perspectives: (i) Security Level: The smart grid encounters security threats and challenges concerning authentication, authorization, and privacy. Ensuring a robust security level in these areas is crucial. (ii) Technical and Non-Technical Threats: Security threats to the smart grid can originate from different sources, both technical and non-technical in nature. These threats require comprehensive attention and countermeasures. (iii) Man-Made and Non-Man-Made Threats: Depending on the cause of the threat, security challenges can arise from either man-made factors or non-man-made factors. Both types of threats need to be addressed to safeguard the smart grid. (iv) Safety Threats: Failures in generation, transmission, distribution, or substation operations can pose safety threats, which must be considered in the overall security framework. (v) Natural and Unnatural Causes: Natural disasters and other unforeseen events can also pose security threats to the smart grid. Safeguarding against these risks is essential for maintaining grid resilience. (vi) Organized Crime: Organized crime activities, including hacker attacks, riots, terrorism, cybercrime, energy theft, sabotage, coercion, and service interruption, are significant sources of security threats. Addressing these challenges requires a comprehensive approach involving technical, management, and legal measures. Given the multitude of factors that threaten the smart grid, it becomes imperative to establish clear security goals for the grid. Employing a range of technical, management, legal, and other strategies becomes crucial to ensure the security and integrity of the smart grid infrastructure. Proactive measures and robust security practices are vital to mitigate risks and protect the smart grid from potential threats. (2) The Security Goals for Smart Grid To ensure a reliable and resilient power supply system in smart grids, it is essential to establish clear security goals that prioritize the effective and safe operation of the

4.8 Security Issues of Smart Grid

283

grid, along with its expansion, technical improvement, and integration of renewable energy and distributed generators [210, 211]. The security goals for the smart grid can be defined as follows [212–220]: • Authentication, Availability, and Confidentiality: The smart grid should ensure the authenticity of users and devices, maintain high availability of services, and uphold the confidentiality of sensitive information. • Integrity and Efficiency: The smart grid must maintain a high level of integrity to ensure the accuracy and reliability of data and operations. It should also strive for optimal efficiency in energy delivery and consumption. • Authentication, Acceptability, and Reliability: The smart grid should establish robust authentication mechanisms, gain acceptability among stakeholders, and ensure reliable performance in all operational aspects. • Robustness, Flexibility, and Resilience: The smart grid needs to be robust, allowing it to withstand and recover from various threats and disruptions. It should also exhibit flexibility to adapt to changing conditions and promote resilience through self-healing capabilities. • Adaptability to Mature Electricity Markets: The smart grid should be adaptable to mature electricity markets, supporting the effective integration of renewable energy sources and distributed generators. It should enhance system performance while reducing operation and maintenance costs, enabling efficient system planning and future expansions. • Accessibility and Observability: The smart grid should provide high levels of accessibility and observability, enabling efficient monitoring, control, and management of grid operations. This facilitates real-time decision-making and optimization. Various organizations, such as the US Department of Energy (DOE), National Institute of Standards and Technology (NIST), Institute of Electrical and Electronics Engineers (IEEE), Electric Power Research Institute (EPRI), and industry leaders like Google, Microsoft, General Electric (GE), North American Electric Reliability Corporation (NERC), Federal Energy Regulatory Commission (FERC), ANSI, and others, have proposed their own security recommendations and standards to ensure the security of the smart grid [221–223]. These initiatives aim to coordinate multiple security objectives, enhance the maturity and flexibility of relevant security standards, and strive for commonly recognized security objectives. (3) Introduction to some Smart Grid Security Technologies Researchers worldwide are actively exploring various technologies to address the security challenges and enhance the resilience of smart grids. Here are some notable studies as the following: Calderaro et al. [224] utilized Petri Net (PN) modeling to analyze and identify faults in the smart grid’s protection systems. This approach focuses on fault detection in distribution systems with distributed generation.

284

4 The Communication and Security Technology of IoT

De Santis et al. [225] developed a system for detecting faults in the medium voltage grid in Rome, Italy. They employed an evolutionary learning and clustering hybrid approach using comprehensive fault data to identify and classify faults. Cross-authentication methods are employed to improve the reliable operation of key components in the grid, such as the Advanced Metering Infrastructure (AMI). Public key infrastructure (PKI) technology is used for AMI, and encryption methods are applied to ensure secure communication and protect user privacy [209]. Rahnamay-Naeini [226, 227] investigated the interdependencies within critical infrastructure and developed optimization techniques to minimize cascading effects and improve resilience against faults and targeted attacks in interdependent grids. Shahidehpour et al. [228] leveraged microgrids to enhance grid resilience by providing backup power to key loads during extreme events when utility power is unavailable. Panteli et al. [229] proposed well-structured and defensible microgrid islands to improve overall grid resilience. Communication infrastructure plays a vital role in smart grids, and utilities aim to enhance security and enable new applications by leveraging intelligent communication infrastructures [230, 231]. Anderson [232] proposed the use of 4G cellular mobile communication, such as WiMax, to ensure reliable operation of the smart grid. Emerging technologies like microgrids, virtual power plants (VPPs), distributed smart technologies, smart metering infrastructure, demand response technologies, and distributed and renewable energy sources contribute to a more resilient, decentralized, and flexible grid against threats [233, 234]. Ensuring customer privacy is another key aspect of network security. Yip et al. [235] proposed the use of incremental hash functions to improve customer data privacy and prevent unauthorized access without compromising data integrity. Staff [236] proposed inconsistency robustness techniques that involve marking and protecting computer memory over an extended period to reduce vulnerabilities in data centers. These research efforts contribute to the development of robust security solutions, enhance the resilience of smart grids, and safeguard critical infrastructure and customer data privacy in the face of evolving security challenges.

4.8.2 Sources and Countermeasures of Threats to Smart Grid Security To ensure grid system security, it is essential to identify and define threats, study their sources, and develop corresponding countermeasures. (1) Technology Sources of Threats to the Smart Grid From a technical perspective, the threats to the smart grid predominantly originate from three key areas: infrastructure security, technical operation security, and system data management security.

4.8 Security Issues of Smart Grid

285

(a) Infrastructure Security The smart grid infrastructure is a remarkably intricate system that spans across geographical, logical, and economic dimensions. It serves as a comprehensive network that connects users, power plants, power supply enterprises, power transmission and distribution systems, substations, transformers, as well as advanced metering infrastructure (AMI) and related ICT equipment like wireless, optical fiber, and PLC technologies. This integration enables the smart grid to function as a highly intelligent system, making the security of its infrastructure a paramount concern. In their study, Goel et al. [237] highlighted several potential threats to the grid infrastructure, including network security breaches, cascading faults, and blackouts, which can lead to significant failures within the system. Due to its central role in smart grid operations, the Advanced Metering Infrastructure (AMI) is particularly susceptible to attacks. Recognizing this vulnerability, the AMI Security Working Group (AMI-SEC) has taken proactive measures by developing relevant standards and security guidelines for the implementation of robust AMI security solutions. These standards and guidelines encompass various aspects, ranging from the Meter Data Management System (MDMS) to the Smart Meter interface and other interconnected components [238]. (i) Security of Advanced Metering Infrastructure (AMI) AMI serves as an essential component of the smart grid infrastructure, encompassing smart meters, communication networks, and data management systems. It facilitates bi-directional communication between power supply companies and customers, enabling seamless exchange of information. The collected data is integrated into software application platforms, allowing for precise control responses and dynamic pricing mechanisms [239]. Moreover, AMI enables near real-time monitoring of consumer power consumption [240]. Ensuring the security of AMI is crucial, particularly in relation to the privacy of the entire system, its personnel, and third parties involved. To address these concerns, the Cyber Security Coordination Task Group (CSCTG), in collaboration with the Advanced Security Acceleration Project-Smart Grid (ASAP-SG) and NIST, has developed standardized frameworks that offer comprehensive security measures for different architectural setups [222, 240]. The architecture of AMI presents increased vulnerability to cyber attacks due to its network of sensors, meters, devices, and computers responsible for data recording and analysis. Smart meters often become the primary target for such attacks, aiming to perpetrate power theft or unauthorized access. While the deployment of smart meters within the AMI framework has partially mitigated the issue of power theft by enabling detection of offline meters during complete or intermittent bypass scenarios, the primary concern remains cyber attacks, which aim to manipulate data [240]. In recent times, hackers have indeed launched attacks on smart meters, primarily exploiting vulnerabilities within these devices. These attacks not only compromise data integrity and confidentiality but also expose user privacy to unauthorized access.

286

4 The Communication and Security Technology of IoT

Consequently, it becomes imperative to establish comprehensive security guidelines governing the acquisition, transmission, storage, and maintenance of power consumption data to safeguard against these threats. (ii) Cyber Attacks The prevalence of cyber attacks poses a significant threat to smart grids, as failure to adequately protect against these attacks can lead to a complete breakdown of the system [241]. Research conducted by Dennis [242] and McDaniel [243], among others, has demonstrated that major network attacks can manipulate power supply companies into making erroneous decisions regarding power consumption and generation capacity. These attacks can potentially overwhelm the companies, rendering them incapable of effectively dealing with subsequent and continuous attacks. Maintaining the confidentiality, authentication, and privacy of data is of paramount importance to ensure the reliability and efficiency of the grid. It is essential to establish robust measures to prevent unauthorized modification of data through the grid infrastructure. In addition to cyber attacks, there are other common threats to smart grid operations and security, including physical sabotage, infrastructure theft, and power theft through deliberate or unintentional actions [244]. (iii) Operation Security Ensuring the safe operation of the complex grid infrastructure necessitates the implementation of appropriate solutions. While some aspects of power system operation can be performed under computer control, certain operations still require the presence of operators at control centers, particularly during emergency situations. Consequently, security concerns can arise in terms of technical operations. Technical operation security encompasses various elements such as infrastructure installation and operating procedures, control start-up (whether manual or automatic), system status control, operational reliability and resilience, system intelligence level, system data and analysis, personnel qualifications and technical skills, as well as regular routine inspection and maintenance programs. To enhance the level of operational safety, the following methods can be employed: • In a resilient grid system, the timely identification and diagnosis of conditions that may lead to faults are critical in preventing the spread of disturbances. By utilizing advanced methods, analytical tools, and techniques rooted in the fields of computing, control, and communication, local self-regulating and automatic reconfiguration solutions can be implemented to address failures, threats, or disturbances, ensuring uninterrupted grid operation and infrastructure resilience. • Since protection devices themselves may fail, it is essential to design self-healing systems within the power grid. This involves ensuring acceptable fault tolerance and providing the necessary redundancy to guarantee reliable operational safety [245]. The concept of self-healing and distributed control in power systems

4.8 Security Issues of Smart Grid

287

entails enabling interaction, monitoring, and collaboration among every component in a substation. Each component functions as an independent intelligent agent, working collectively to optimize operational performance. (iv) System Data Management Security This aspect encompasses several crucial elements, including real-time recording, monitoring, and storage of essential data and information. It also involves ensuring the security of data against attacks, adhering to rules and regulations governing data policies, maintaining privacy compliance for operators, and prioritizing customer satisfaction regarding privacy protection. Collecting, analyzing, and simulating key data related to generation, transmission, distribution networks, consumer load, performance parameters, and control equipment functions are imperative for assessing system reliability and maintainability. This is especially critical when considering new installations and upgrades to existing systems. Thorough research must be conducted to identify all potential security threats, vulnerabilities, and corresponding security regulations to effectively safeguard the grid infrastructure. Concerns have been growing regarding the potential leakage of customer data privacy by power supply companies, which remains a significant concern for customers. While smart meters have transformed the nature of data fraud or attacks, compromising meters through remote exfiltration and gaining control over recorded and stored data can serve as the foundation for highly sophisticated attacks. These attacks have the potential to introduce subtle alterations to customer data or launch large-scale attacks on the main grid. To mitigate these risks, it is essential to establish robust measures to protect customer data privacy, ensure the integrity and confidentiality of recorded and stored data, and bolster the overall security of the grid infrastructure. This requires a comprehensive approach that encompasses technological advancements, regulatory frameworks, and proactive monitoring to detect and respond to potential threats promptly. By prioritizing data security and privacy, the trust of customers can be maintained while mitigating the risks associated with data breaches and sophisticated attacks. (2) Non-technical sources of smart grid threats Non-technical sources of threats to smart grids primarily stem from natural or humaninduced environmental hazards. These hazards encompass events such as earthquakes, floods, fallen trees, and wildfires, which can potentially disrupt the smooth functioning of the grid. The unpredictable nature of these natural occurrences poses significant challenges to grid resilience and reliability.

288

4 The Communication and Security Technology of IoT

4.8.3 Framework of Reference for Clearing Threats to Smart Grid Security In their study, [209] introduced a reference framework aimed at mitigating security threats to smart grids, illustrated in Fig. 4.34. The framework primarily focuses on the technical aspects of tracking threat sources, employing machine learning algorithms to extract, classify, identify, and group the relevant features required for effective system cleaning and recovery. The reference input stage involves cross-checking data from various sources such as the Advanced Metering Infrastructure (AMI), sensors, or other alerts with the control and support system units to identify potential threats. During the central processing phase, which resembles a control center, the necessary characteristics are extracted, classified, identified, and grouped to facilitate effective system cleaning and recovery. The Threat Clearance and Feedback phase is responsible for outputting data and providing it for further analysis. This data also serves as a reference input to the Reference Control Input and Support System (RCISS). It is important to note that capturing all system conditions prior to operation or during a single operation is not feasible. Instead, the framework relies on intelligent data collection and analysis over time to enhance its effectiveness. As a result, the proposed framework is expected to undergo continual modifications and improvements to adapt to evolving threat landscapes and optimize its performance. By leveraging machine learning algorithms and incorporating feedback loops, this reference framework offers a systematic approach to identify and address security threats in smart grids. It provides a foundation for ongoing research and development, aiming to enhance the resilience and security of smart grid systems in the face of evolving threats.

4.9 Summary In this chapter, various aspects of communication and security in the Internet of Things (IoT) were discussed. The chapter began by addressing the communication requirements of the IoT, followed by an exploration of wireless network technologies and their classifications. Additionally, important wireless communication standards and commonly used protocols in the IoT were introduced, with a focus on lightweight communication protocols for IoT devices. Furthermore, the security issues surrounding the IoT architecture were thoroughly examined, specifically highlighting the security concerns in widely used architectures. The chapter also delved into the integration of 5G and the IoT, emphasizing the enabling technology of 5G-IoT and the advantages it brings to the IoT, such as wide coverage, high speed, low latency, and low power consumption. The discussion then shifted to the communication and security challenges in the context of the smart grid. The smart grid, being an application field of the

4.9 Summary

289

Fig. 4.34 Conceptual framework for threat source-based smart grid security threat identification and clearance [209]

IoT, possesses a complex communication infrastructure and faces multiple security threats. Both technical and non-technical solutions are necessary to ensure the security of the smart grid. It was noted that the IoT’s communication system consists of high-level communication systems based on the Internet and bottom-level communication systems that connect to the Internet through gateways. The Internet serves as the highlevel communication system, transmitting information using the IP communication

290

4 The Communication and Security Technology of IoT

protocol. On the other hand, the underlying communication system is heterogeneous, with numerous IoT terminals connecting to the Internet via IoT gateways, thus achieving the vision of the “Internet of Everything.“ The security challenges in the IoT are more pronounced than those in the traditional Internet, particularly regarding the security of entity information. Consequently, advanced security technologies must be adopted to address these challenges and ensure the security of IoT devices and networks. Given that wireless communication is extensively employed in the IoT’s underlying communication system, which introduces vulnerabilities such as susceptibility to interference, interception, and counterfeiting, it is imperative to employ enhanced wireless communication security technologies to ensure the safety of IoT deployments. In conclusion, the IoT presents unique communication and security challenges that require comprehensive and advanced solutions. This is particularly relevant in the context of the smart grid, where a complex communication infrastructure and multiple security threats must be addressed to ensure the grid’s security and reliability.

References 1. Bhoyar P, Sahare P, Dhok SB et al (2019) Communication technologies and security challenges for internet of things: a comprehensive review. Int J Electron Commun (AEÜ) 99: 81–99. https://doi.org/10.1016/j.aeue.2018.11.031 2. Liu Y, Zhou G (2012) Key technologies and applications of internet of things. In: 2012 Fifth international conference on intelligent computation technology and automation (ICICTA), IEEE, pp 197–200 3. Gazis V, Görtz M, Huber M et al (2015) A survey of technologies for the internet of things. In: 2015 international wireless communications and mobile computing conference (IWCMC), IEEE, pp 1090–1095 4. Chilamkurti et al (2013) Next-generation wireless technologies, computer communications and networks, London: Springer-Verlag, 2013. (Chapter 10 Communication Issues in the Internet of Things (IoT) (Oladayo B., Sherali Z.). https://doi.org/10.1007/978-1-4471-51647_10 5. Zeng X, Bao S (2016) Introduction to Internet of Things. Publishing House of Electronics Industry, Beijin 6. Nakamura T, Masuda T, Washio K et al (2012) A push-push vco with 13.9-ghz wide tuning range using loop-ground transmission line for full-band 60-ghz transceiver. IEEE J Solid-State Circ. 47(6):1267–77 7. Wang Q, Xu K, Ren K (2012) Cooperative secret key generation from phase estimation in narrowband fading channels. IEEE J Sel Areas Commun 30(9):1666–1674 8. Prlja A, Anderson JB (2012) Reduced-complexity receivers for strongly narrowband intersymbol interference introduced by faster-than-nyquist signaling. IEEE Trans Commun 60(9):2591–2601 9. Zheng G et al (2013) Improving physical layer secrecy using full-duplex jamming receivers, IEEE Trans Sig Proc 61(20):4962–74. https://doi.org/10.1109/TSP.2013.2269049 10. Li Q, Zhang Y, Lin J, Wu SX (2017) Full-duplex bidirectional secure communications under perfect and distributionally ambiguous eavesdropper’s csi. IEEE Trans Signal Process 65(17):4684–4697. https://doi.org/10.1109/TSP.2017.2709270

References

291

11. Li L et al (2017) Mimo secret communications against an active eavesdropper. IEEE Trans Inf Forensics Secur 12(10):2387–401. https://doi.org/10.1109/TIFS.2017.2705618 12. Wu F, Zhang R, Yang L-L, Wang W (2016) Transmitter precoding-aided spatial modulation for secrecy communications. IEEE Trans Veh Technol 65(1):467–471 13. Taha H, Alsusa E (2018) Secret key exchange and authentication via randomized spatial modulation and phase shifting. IEEE Trans Veh Technol 67(3):2165–2177 14. Kim I-M, Kim B-H, Ahn JK (2016) Ber-based physical layer security with finite codelength: combining strong converse and error amplification. IEEE Trans Commun 64(9):3844–3857 15. Quyen NX, Duong TQ, Vo N-S et al (2016) Chaotic direct-sequence spreadspectrum with variable symbol period: a technique for enhancing physical layer security. Comput Netw 109:4–12 16. Kalantari A, Soltanalian M, Maleki S et al (2016) Secure m-psk communication via directional modulation. In: 2016 IEEE international conference on acoustics, speech and signal processing (ICASSP), IEEE, pp 3481–3485 17. Liu T-Y, Lin S-C, Hong Y-WP (2017) On the role of artificial noise in training and data transmission for secret communications. IEEE Trans Inf Forensics Secur 12(3):516–531 18. Bhoyar P, Dhok S, Deshmukh R (2018) Hardware implementation of secure and lightweight simeck32/64 cipher for ieee 802.15. 4 transceiver, AEU-Int J Electron Commun 90:147–54 19. Huang Y-K, Pang A-C, Hung H-N (2009) A comprehensive analysis of low-power operation for beacon-enabled ieee 802.15. 4 wireless networks. IEEE Trans Wirel Commun 8(11) 20. Rasouli H, Kavian YS, Rashvand HF (2014) Adca: adaptive duty cycle algorithm for energy efficient ieee 802.15. 4 beacon-enabled wireless sensor networks. IEEE Sens J 14(11):3893– 902 21. Gragopoulos I, Tsetsinas I, Karapistoli E et al (2008) Fp-mac: a distributed mac algorithm for 802.15. 4-like wireless sensor networks. Ad Hoc Netw 6(6):953–69 22. de Paz Alberola R, Pesch D (2012) Duty cycle learning algorithm (dcla) for ieee 802.15. 4 beacon-enabled wireless sensor networks, Ad Hoc Netw 10(4):664–79 23. Thalore R, Sharma J, Khurana M et al (2013) Qos evaluation of energy-efficient mlmac protocol for wireless sensor networks. AEU-Int J Electron Commun 67(12):1048–1053 24. Chandra A, Biswas S, Ghosh B et al (2014) Energy efficient relay placement in dual hop 802.15. 4 networks. Wirel Personal Commun75(4):1947–67 25. Chen S-n, Yang L-x, Zhao Y-j et al (2013) Impact of mac frame length on energy efficiency in 6lowpan. J China Univ Posts Telecommun 20(4):67–72 26. Mohammadi MS, Zhang Q, Dutkiewicz E (2014) Channel-adaptive mac frame length in wireless body area networks. In: 2014 International symposium on wireless personal multimedia communications (WPMC), IEEE, pp 584–558 27. Fourty N, Van Den Bossche A, Val T (2012) An advanced study of energy consumption in an ieee 802.15. 4 based network: everything but the truth on 802.15. 4 node lifetime. Comput Commun 35(14):1759–67 28. Cuomo F, Abbagnale A, Cipollone E (2013) Cross-layer network formation for energyefficient ieee 802.15.4/zigbee wireless sensor networks. Ad Hoc Netw 11(2):672–686 29. Gao D-Y, Zhang L-J, Wang H-C (2011) Energy saving with node sleep and power control mechanisms for wireless sensor networks. J China Univ Posts Telecommun 18(1):49–59 30. Park P, Di Marco P, Fischione C et al (2013) Modeling and optimization of the ieee 802.15. 4 protocol for reliable and timely communications. IEEE Trans Parallel Distrib Syst 24(3):550– 564 31. Rao Y, Cao Y-m, Deng C et al (2016) Performance analysis and simulation verification of s-mac for wireless sensor networks Comput Electric Eng 56:468–484 32. Marín I, Arias J, Arceredillo E et al (2008) Ll-mac: a low latency mac protocol for wireless self-organised networks. Microprocess Microsyst 32(4):197–209 33. Mahmood D, Khan Z, Qasim U et al (2014) Analyzing and evaluating contention access period of slotted csma/ca for ieee802. 15.4, Procedia Comput Sci 34:204–211 34. Zhan Y, Xia Y, Anwar M (2016) Gts size adaptation algorithm for ieee 802.15. 4 wireless networks. Ad Hoc Netw 37:486–498

292

4 The Communication and Security Technology of IoT

35. Xia F, Hao R, Li J et al (2013) Adaptive gts allocation in ieee 802.15. 4 for real-time wireless sensor networks. J Syst Architect 59(10):1231–1242 36. Shabani H, Ahmed MM, Khan S et al (2013) Smart zigbee/ieee 802.15. 4 mac for wireless sensor multi-hop mesh networks. In: 2013 IEEE 7th international power engineering and optimization conference (PEOCO). IEEE, 282–287 37. Collotta M, Gentile L, Pau G et al (2014) Flexible ieee 802.15. 4 deadline-aware scheduling for dpcss using priority-based csma-ca. Comput Ind 65(8):1181–1192 38. Lu Z, BAI G-w, Hang S et al (2013) Priority-based ieee 802.15. 4 csma/ca mechanism for wsns. J China Univ Posts Telecommun 20(1):47–53 39. Jung K-H, Lee H-R, Lim W-S et al (2014) An adaptive collision resolution scheme for energy efficient communication in ieee 802.15. 4 networks. Comput Netw 58:39–57 40. Wang L, Mao J, Fu L et al (2015) An improvement of ieee 802.15. 4 mac protocol in highdensity wireless sensor networks. In: 2015 IEEE international conference on information and automation, IEEE, 1704–1707 41. Liu Q, Li P (2014) Backoff algorithm optimization and analysis for ieee 802.15. 4 wireless sensor networks. In: 2014 9th international symposium on communication systems, networks and digital signal processing (CSNDSP), IEEE, 411–416 42. Kitsos P, Sklavos N, Parousi M et al (2012) A comparative study of hardware architectures for lightweight block ciphers. Comput Electric Eng 38(1):148–160 43. Beaulieu R, Treatman-Clark S, Shors D et al (2015) The simon and speck lightweight block ciphers. In: 2015 52nd ACM/EDAC/IEEE design automation conference (DAC), IEEE, pp. 1– 6 44. Shelby Z, Bormann C (2011) 6LoWPAN: the wireless embedded Internet, vol 43. John Wiley & Sons 45. Winter T Rpl: Ipv6 routing protocol for low-power and lossy networks (6550). https://doi. org/10.17487/RFC6550 46. Thubert P, Watteyne T, Palattella MR et al (2013) Ietf 6tsch: Combining ipv6 connectivity with industrial performance. In: 2013 Seventh international conference on innovative mobile and internet services in ubiquitous computing (IMIS), IEEE, pp. 541–546 47. Xu G, Zhang Q, Lu Y et al (2017) Passive and wireless near field communication tag sensors for biochemical sensing with smartphone. Sens Actuat B: Chem 246:748–755 48. Volpentesta AP (2015) A framework for human interaction with mobiquitous services in a smart environment. Comput Hum Behav 50:177–185 49. Arunan T, Won E-T (2015) Near field communication (nfc) device and method for selectively securing records in a near field communication data exchange format (ndef) message, uS Patent 9032211 50. Rodrigues H, José R, Coelho A et al (2014) Ticketing and couponing solution based on nfc. Sensors 14(8):13389–13415 51. Briggs RA (2016) Interactive entertainment using a mobile device with object tagging and/ or hyperlinking, uS Patent 9480913 52. Ngai EW, Moon K-l K, Lam SS et al (2015) Social media models, technologies, and applications: an academic review and case study. Ind Manage Data Syst 115(5):769–802 53. Gomez C, Oller J, Paradells J (2012) Overview and evaluation of bluetooth low energy: an emerging low-power wireless technology. Sensors 12(9):11734–11753 54. Ray PP, Agarwal S (2016) Bluetooth 5 and internet of things: potential and architecture. In: 2016 International conference on signal processing, communication, power and embedded system (SCOPES), IEEE, pp 1461–1465. 55. Di Marco P, Skillermark P, Larmo A et al (2017) Performance evaluation of the data transfer modes in bluetooth 5. IEEE Commun Stand Mag 1(2):92–97 56. Z-wave devices and standards. http://www.z-wavealliance.org/ 57. Knight M (2006) How safe is z-wave?[wireless standards]. Comput Control Eng 17(6):18–23 58. Enocean devices and standards. http://www.enoceanalliance.org/en/home/. 59. Ploennigs J, Ryssel U, Kabitzsch K (2010) Performance analysis of the enocean wireless sensor network protocol. In: 2010 IEEE conference on emerging technologies and factory automation (ETFA), IEEE, pp 1–9

References

293

60. Kim AN, Hekland F, Petersen S et al (2008) When hart goes wireless: Understanding and implementing the wirelesshart standard. In: IEEE international conference on emerging technologies and factory automation, pp 899–907 61. Chen D, Nixon M, Mok A (2010) Why wirelesshart. Springer, pp 195–199 62. Das K, Havinga P (2012) Evaluation of dect-ule for robust communication in dense wireless sensor networks. In: 2012 3rd International conference on the Internet of Things (IOT), IEEE, pp 183–190 63. Lin H-H, Shih M-J, Wei H-Y et al (2015) Deep-sleep: Ieee 802.11 enhancement for energyharvesting machine-to-machine communications, Wirel Netw 21(2):357–370 64. Rajandekar A, Sikdar B (2015) A survey of mac layer issues and protocols for machine-tomachine communications. IEEE Internet Things J 2(2):175–186 65. Saha SK, Siddiqui T, Koutsonikolas D et al (2017) A detailed look into power consumption of commodity 60 ghz devices. In: 2017 IEEE 18th international symposium on a world of wireless, mobile and multimedia networks (WoWMoM), IEEE, pp 1–10 66. Jiang D, Delgrossi L (2008) Ieee 802.11 p: towards an international standard for wireless access in vehicular environments, Vehicular Technology Conference, 2008, VTC Spring 2008, IEEE, pp 2036–2040 67. Weightless. http://www.weightless.org/ 68. Weyn M, Ergeerts G, Berkvens R et al (2015) Dash7 alliance protocol 1.0: low-power, mid-range sensor and actuator communication. In: 2015 IEEE conference on standards for communications and networking (CSCN), pp 54–59. https://doi.org/10.1109/CSCN.2015.739 0420 69. Oyj N (2016) Lte evolution for iot connectivity. Nokia Corporation White Paper, pp 1–18 70. Bormann C et al (2012) CoAP: an application protocol for billions of tiny Internet nodes. IEEE Internet Comput 16(2(Mar/Apr)):62–67 71. Lerche C et al (2012) Industry adoption of the internet of things: a constrained application protocol survey. In: Proceeding of IEEE 17th conference ETFA, pp 1–6 72. Hunkeler U et al (2008) MQTT-S—A publish/subscribe protocol for wireless sensor networks. In: Proceeding of 3rd international conference. COMSWARE, pp 791–798 73. Saint-Andre P (2011) Extensible messaging and presence protocol (XMPP): core. In: Internet engineering task force (IETF), Fremont, CA, USA, request for comments: 6120 74. OASIS Advanced Message Queuing Protocol (AMQP) (2012) Version 1.0. Adv Open Std Inf Soc (OASIS), Burlington, MA, USA 75. Data distribution services specification, V1.2, Object Manage. Group (OMG), Needham, MA, USA, Apr. 2, 2015. http://www.omg.org/spec/DDS/1.2/ 76. Esposito C et al (2008) Performance assessment of OMG compliant data distribution middleware. Proc IEEE IPDPS, pp 1–8 77. Cheshire S, Krochmal M (2013) Multicast DNS, Internet Eng. Task Force (IETF). Fremont, CA, USA, Request for Comments: 6762 78. Krochmal M, Cheshire S (2013) DNS-based service discovery, Internet Eng. Task Force (IETF), Fremont, CA, USA, Request for Comments: 6763 79. Vasseur J et al (2011) RPL: The IP routing protocol designed for low power and lossy networks, Internet Protocol for Smart Objects (IPSO) Alliance. CA, USA, San Jose 80. Winter T et al (2013) RPL: IPv6 routing protocol for low-power and lossy networks, Internet Eng. Task Force (IETF), Fremont, CA, USA, Request for Comments: 6550 81. Clausen T et al (2011) A critical evaluation of the IPv6 routing protocol for low power and lossy networks (RPL). In: Proceeding of IEEE 7th international conference WiMob, pp 365–372 82. Palattella MR et al(2013)., Standardized protocol stack for the Internet of (important) things, IEEE Commun. Surveys Tuts 15(3):1389–1406 83. Ko et al J (2011) Connecting low-power and lossy networks to the Internet. IEEE Commun Mag 49(4(Apr.)):96–101 84. Hui JW, Culler DE (2008) Extending IP to low-power, wireless personal area networks, IEEE Internet Comput 12(4(Jul./Aug.)):37–45

294

4 The Communication and Security Technology of IoT

85. Frank R et al (2014) Bluetooth low energy: an alternative technology for VANET applications. In: Proceedings of. 11th Annual Conference WONS, pp 104–107 86. Siekkinen M, et al (2012) How low energy is Bluetooth low energy? Comparative measurements with ZigBee/802.15.4. In: Proceeding of IEEE WCNCW, pp 232–237 87. Jones EC, Chung CA (2011) RFID and Auto-ID in planning and logistics: a practical guide for military UID applications, Boca Raton, FL, USA: CRC Press 88. Minoli D (2013) Building the Internet of Things With IPv6 and MIPv6: the evolving world of M2M communications, New York, NY, USA: Wiley 89. Bagci I et al (2012) Codo: confidential data storage for wireless sensor networks. Proceeding IEEE 9th International Conference MASS, pp 1–6 90. Raza S et al (2012) Secure communication for the Internet of Things—A comparison of link-layer security and IPsec for 6LoWPAN. Security Commun Netw 7(12(Dec.)):2654–2668 91. Raza S et al (2011) Securing communication in 6LoWPAN with compressed IPsec. In: Proceedings of International Conference DCOSS, pp 1–8 92. Srivatsa M, Liu L (2005) Securing publish-subscribe overlay services with EventGuard. In: Proceeding of 12th ACM conference on computer and communications security, pp 289–298 93. Corman AB et al (2007) QUIP: a protocol for securing content in peer-to-peer publish/ subscribe overlay networks. In: Proceedings of 13th Australasian Conference Computer Science vol 62, pp 35–40 94. Raza S et al (2013) Lithe: lightweight secure CoAP for the internet of things. IEEE Sens J 13(10):3711–3720 95. MQTT NIST Cyber Security Framework. https://www.oasis-open.org/committees/download. php/52641/mqtt-nist-cybersecurity-v1.0-wd02.doc 96. IEEE standard for a convergent digital home network for heterogeneous technologies. IEEE Std. 1905. 1–2013, pp 1–93 (2013) 97. Ammar M, Russello G, Crispo B (2018) Internet of things: a survey on the security of IoT frameworks. J Inf Sec Appl 38:8–27. https://doi.org/10.1016/j.jisa.2017.11.002 98. Yang Y, Wu L, Yin G et al (2017) A survey on security and privacy issues in internet-of-things. IEEE Internet Things J 99. Kumar JS, Patel DR (2014) A survey on internet of things: security and privacy issues. Int J Comput Appl 90(11) 100. Vikas B (2015) Internet of things (iot): a survey on privacy issues and security 101. Borgohain T, Kumar U, Sanyal S (2015) Survey of security and privacy issues of internet of things. ArXiv: 150102211 102. Bouij-Pasquier I, El Kalam AA, Ouahman AA et al (2015) A security framework for internet of things, International conference on cryptology and network security. Springer, pp 19–31 103. Fremantle P, Scott P (2017) A survey of secure middleware for the internet of things. Peer J Comput Sci 3:e114 104. Amazon, Aws iot framework. https://aws.amazon.com/iot 105. Amazon, Amazon dynamodb. https://aws.amazon.com/dynamodb 106. Amazon, Amazon s3. https://aws.amazon.com/s3 107. Amazon, Amazon machine learning. https://aws.amazon.com/machine-learning 108. Amazon, Components of aws iot framework. https://aws.amazon.com/iot/how-it-works/ 109. Amazon, Amazon iot protocols. http://docs.aws.amazon.com/iot/latest/developerguide/pro tocols.html 110. Amazon, Amazon lambda. https://aws.amazon.com/lambda 111. ARM, Arm mbed iot device platform. http://www.arm.com/products/iot-solutions/mbed-iotdevice-platform 112. ARM, mbed os. https://www.mbed.com/en/platform/mbed-os/ 113. mbed A, mbed cloud. https://cloud.mbed.com/ 114. Microsoft, Tap into the internet of your things with azure iot suite. https://www.microsoft. com/en-us/cloud-platform/internet-of-things-azure-iot-suite 115. Azure M Microsoft azure iot reference architecture. https://azure.microsoft.com/en-us/upd ates/microsoft-azure-iot-reference-architecture-available/

References

295

116. Azure M Azure iot hub. https://azure.microsoft.com/en-us/services/iot-hub/ 117. Azure M, Communication protocols. https://azure.microsoft.com/en-us/documentation/art icles/iot-hub-devguide-messaging/#communication-protocols 118. Azure M, Azure iot protocol gateway. https://azure.microsoft.com/en-us/documentation/art icles/iot-hub-protocol-gateway/ 119. Azure M, Azure products. https://azure.microsoft.com/services/ 120. Microsoft, Power bi. https://powerbi.microsoft.com 121. Google, Brillo. https://developers.google.com/brillo/ 122. Google, Weave. https://developers.google.com/weave/ 123. Gargenta A (2012) Deep dive into android ipc/binder framework, AnDevCon: the android developer conference 124. Google, Ota updates. https://source.android.com/devices/tech/ota/ 125. Ericsson, Open source release of iot app environment calvin. https://www.ericsson.com/res earch-blog/cloud/open-source-calvin/ 126. Ericsson, A closer look at calvin. https://www.ericsson.com/research-blog/cloud/closer-lookcalvin/ 127. Apple, The smart home just got smarter. http://www.apple.com/ios/home/ 128. Apple., About bonjour. https://developer.apple.com/library/content/documentation/Cocoa/ Conceptual/NetServices/Introduction.html 129. Apple, Icloud. http://www.apple.com/lae/icloud/ 130. Apple, Tvos. http://www.apple.com/tvos/ 131. Alliance O, Osgi architecture. https://www.osgi.org/developer/architecture/ 132. Organization E, Kura framework. http://www.eclipse.org/kura/ 133. Organization E, Kura-osgi-based application framework for m2m service gateways. http:// www.eclipse.org/proposals/technology.kura/ 134. Organization E, Kura - a gateway for the internet of things. http://www.eclipse.org/commun ity/eclipse_newsletter/2014/february/article3.php 135. Organization E, Mqtt and coap, iot protocols. http://www.eclipse.org/community/eclipse_n ewsletter/2014/february/article2.php 136. SmartThings, Smartthings documentation. http://docs.smartthings.com/en/latest/ 137. SmartThings, Cloud and lan-connected devices. http://docs.smartthings.com/en/latest/cloudand-lan-connected-device-types-developers-guide/ 138. SmartThings, Smartthings architecture. http://docs.smartthings.com/en/latest/architecture/ index.html 139. Cooper D, Internet x.509 public key infrastructure certificate and certificate revocation list (crl) profile. https://tools.ietf.org/html/rfc5280 140. Amazon, Iam users, groups, and roles, http://docs.aws.amazon.com/iot/latest/developerguide/ iam-users-groups-roles.html 141. Amazon, Amazon cognito identities. http://docs.aws.amazon.com/iot/latest/developerguide/ cognito-identities.html 142. Amazon, Aws identity and access management (iam). https://aws.amazon.com/iam/ 143. Amazon, Amazon cognito. https://aws.amazon.com/cognito/ 144. Amazon, Signature version 4 signing process. http://docs.aws.amazon.com/general/latest/gr/ signature-version-4.html 145. Amazon, Aws authorization, http://docs.aws.amazon.com/iot/latest/developerguide/authoriza tion.html 146. mbed A, mbed security. https://www.mbed.com/en/technologies/security/ 147. mbed A, mbed uvisor. https://www.mbed.com/en/technologies/security/uvisor/ 148. mbed A, mbed tls. https://tls.mbed.org/core-features 149. Microsoft, Security development lifecycle. https://www.microsoft.com/en-us/sdl/default. aspx 150. Microsoft, Operational security assurance. https://www.microsoft.com/en-us/SDL/Operation alSecurityAssurance

296

4 The Communication and Security Technology of IoT

151. Azure M, What is azure active directory. https://azure.microsoft.com/en-us/documentation/ articles/active-directory-whatis/ 152. Azure M, Documentdb. https://azure.microsoft.com/en-us/services/documentdb/ 153. Android, Hardware-backed keystore. https://source.android.com/security/keystore 154. Ericsson, Security in calvin. https://github.com/EricssonResearch/calvin-base/wiki/Security/ 155. Apple, ios security. http://www.apple.com/business/docs/iOS_Security_Guide.pdf 156. Bernstein DJ, Duif N, Lange T, Schwabe P, Yang B-Y (2012) High-speed high-security signatures. J Cryptograph Eng 2(2):77–89 157. Bernstein D, A state-of-the-art diffie-hellman function https://cr.yp.to/ecdh.html 158. Snow KZ, Monrose F, Davi L et al (2013) Just-in–time code reuse: on the effectiveness of fine-grained address space layout randomization. Security and privacy (SP), 2013 IEEE symposium on IEEE. pp 574–88 159. Barker E, Kelsey J Recommendation for random number generation using deterministic random bit generators. https://doi.org/10.6028/NIST.SP.800-90Ar1. 160. Eurotech, Eurotech. https://www.eurotech.com/en/about+eurotech/ 161. Lawton G How to put configurable security in effect for an iot gateway, http://www.theserver side.com/tip/How-to-put-configurable-security-in-effect-for-an-IoT-gateway 162. Kawaguchi K, Groovy sandbox. http://groovy-sandbox.kohsuke.org/ 163. Li S, Xu LD, Zhao S (2018) 5G internet of things: a survey. J. Ind. Inf. Integrat 10:1–9. https:// doi.org/10.1016/j.jii.2018.01.005 164. Jaiswal N, et al 5g: continuous evolution leads to quantum shift, https://www.telecomasia. net/content/5gcontinuous-evolution-leads-quantum-shift 165. Bridgera 5g promises new horizons for iot solutions, ttps://bridgera.com/5g-promises-new -horizons-for-iot/ 166. Akpakwu GA, Silva BJ, Hancke GP et al (2017) A survey on 5g networks for the internet of things: communication technologies and challenges. IEEE Access PP (99) 167. Egham (2016) Gartner says 8.4 billion connected “things” will be in use in 2017, up 31 percent from 2016. https://www.gartner.com/newsroom/id/3598917 168. The Internet of all things, Nokia networks to power internet of things with 5g connectivity, https://theinternetofallthings.com/nokia-networks-to-power-internet-of-thingswith-5g-connectivity-2015-02-19/ 169. GSA, The road to 5g: Drivers, applications, requirements and technical development, 2015. Arxiv: 1512.03452 170. Akyildiz IF, Lee A, Wang P et al (2014) A roadmap for traffic engineering in sdn-openflow networks. Comput Netw J 71:1–30 171. Akyildiz IF, Wang P, Lin SC (2015) Softair: a software defined networking architecture for 5g wireless systems. Comput Netw 85(C):1–18 172. Wu J, Zhang Z, Hong Y et al (2015) Cloud radio access network (c-RAN): a primer. IEEE Netw 29(1):35–41 173. Project CONTENT FP, 2012–2015, http://cordis.europa.eu/fp7/ict/future-networks/ 174. Akyildiz IF, Nie S, Lin S-C et al (2016) 5G roadmap: 10 key enabling technologies. Comput Netw 106:17–48 175. Blyler J Top 5 RF technologies for 5g in the iot. http://www.mwrf.com/systems/top-5-rf-tec hnologies-5g-iot 176. SDX Central, How 5g NFV will enable the 5g future. https://www.sdxcentral.com/5g/defini tions/5g-nfv/ 177. Hasan M, Hossain E (2013) Random access for machine-to-machine communication in LTEadvanced networks: issues and approaches. IEEE Commun Mag 51:86–93 178. Ge X, Cheng H, Guizani M et al (2014) 5G wireless backhaul networks: challenges and research advances. IEEE Netw 28(6):6–11 179. Pereira C, Aguiar A (2014) Towards efficient mobile m2m communications: survey and open challenges. Sensors 14(10):19582–19608 180. Biral A, Centenaro M, Zanella A et al (2015) The challenges of m2m massive access in wireless cellular networks. Digital Commun. Networks 1(1):1–19

References

297

181. Mach P, Becvar Z, Vanek T (2015) In-band device-to-device communication in OFDMA cellular networks: a survey and challenges. IEEE Commun Surv Tut 17(4):1885–1922 182. Talwar S, Choudhu D, Dimou K et al Enabling technologies and architectures for 5G wireless kenneth stewart intel corporation, Santa Clara, CA 183. Ndiaye M, Hancke GP et al (2017) Software defined networking for improved wireless sensor network management: a survey. Sensors 17(5):1–32 184. Modieginyane KM, Letswamotse BB, Malekian R et al (2017) Software defined wireless sensor networks application opportunities for efficient network management: a survey. Comput Electr Eng 1–14 185. Elkhodr M, Shahrestani S, Cheung H (2016) The internet of things: new interoperability, management and security challenges. Arxiv:1604.04824 186. Ishaq I et al (2013) IETF Standardization in the field of the internet of things (iot): a survey. J Sensor Actuator Netw 2(2):235–287 187. Gudipati A, Perry D, Li LE, Katti S (2013) SoftRAN: software defined radio access network. In: Proceedings of the second ACM SIGCOMM workshop on hot topics in software defined networking, ACM, pp 25–30 188. Zhao S, Yu L, Cheng B (2016) An event-driven service provisioning mechanism for iot (internet of things) system interaction. IEEE Access 4(2):5038–5051 189. Girson A Iot has a security problem – will 5g solve it? https://www.wirelessweek.com/article/ 2017/03/iot-has-security-problemwill-5g-solve-it 190. Lei K, Zhong S, Zhu F et al (2017) A NDN iot content distribution model with network coding enhanced forwarding strategy for 5g. IEEE Trans Ind Inf PP (99):1–1 191. Morgado A, Huq KMS S, Mumtaz et al (2017) A survey of 5g technologies: regulatory, standardization and industrial perspectives. Digital Commun Netw 192. Ejaz W, Ibnkahla M (2017) Multi-band spectrum sensing and resource allocation for iot in cognitive 5g networks. IEEE Internet Things J PP(99):1–1 193. Fadel E et al (2015) A survey on wireless sensor networks for smart grid. Comput Commun 71:22–33 194. Yigit M et al (2014) Power line communication technologies for smart grid applications: a review of advances and challenges. Comput Netw 70:366–383. https://doi.org/10.1016/j.com net.2014.06.005 195. Sauter T, Lobashov M (2011) End-to-end communication architecture for smart grids, IEEE Trans Ind Electron 58(4):1218–1228 196. Goldfisher S, Tanabe S (2010) IEEE 1901 access system: an overview of its uniqueness and motivation. IEEE Commun Mag 48(10):150–157 197. Yang Q et al (2011) Communication infrastructures for distributed control of power distribution networks. IEEE Trans Ind Inform 7(2):316–327 198. Khan RH, Khan JY (2013) A comprehensive review of the application characteristics and traffic requirements of a smart grid communications network. Comput Netw 57:825–845 199. Wang W, Xu Y, Khanna M (2011) A survey on the communication architectures in smart grid. Comput Netw 55:3604–3629 200. Kuzlu M et al (2014) Communication network requirements for major smart grid applications in HAN. NAN and WAN, Comput Netw 67:74–88 201. Liu S et al (2014) Modeling and distributed gain scheduling strategy for load frequency control in smart grids with communication topology changes. ISA Trans 53:454–461 202. Ancillotti E, Bruno R, Conti M (2013) The role of communication systems in smart grids: architectures, technical solutions and research challenges. Comput Commun 36:1665–1697 203. Aputro N, Akkaya K, Uludag S (2012) A survey of routing protocols for smart grid communications. Comput Netw 56:2742–71 204. Rahman MM et al (2011) Medium access control for power line communications: an overview of the IEEE 1901 and ITU-TG. Hnstandards, IEEE Commun Mag 49:183–191 205. Brown J, Khan JY (2013) Key performance aspects of an LTE FDD based smart grid communications network. Comput Commun 36:551–561

298

4 The Communication and Security Technology of IoT

206. Xu Y, Wang W (2013) Wireless mesh network in smart grid: modeling and analysis for time critical communications. IEEE Trans Wirel Commun 12:3360–3371 207. Wang H et al (2013) Multimedia communications over cognitive radio networks for smart grid applications. IEEE Wirel Commun 20:125–132 208. Kulkarni P et al (2012) A mesh-radio-based solution for smart metering networks. IEEE Commun Mag 50(7):86–95 209. Abdulrahaman OO, Mohd WM, Raja ML (2017) Smart grids security challenges: Classification by sources of threats. J Electr Syst Inform Technol. https://doi.org/10.1016/j.jesit.2018. 01.001 210. Amin M (2000) Toward self-healing infrastructure systems. Computer 33(8):44–53 211. Lund H et al (2012) From electricity smart grids to smart energy systems–a market operation based approach and understanding. Energy 42(1):96–102 212. Amin SM, Wollenberg BF (2005) Toward a smart grid: power delivery for the 21st century. IEEE Power Energy Mag. 3(5):34–41 213. El-Hawary ME (2014) The smart grid–state-of-the-art and future trends. Electr. Power Compon. Syst. 42(3–4):239–250 214. Gao J et al (2012) A survey of communication/networking in Smart Grids. Future Gener. Comput. Syst. 28(2):391–404 215. Khan F et al (2016) A survey of communication technologies for smart grid connectivity. In: The 2016 international conference on computing, electronic and electrical engineering (ICECube), pp 256–261 216. Khurana H et al (2010) Smart-grid security issues. IEEE Secur Priv 8(1):81–85 217. Komninos N et al (2014) Survey in smart grid and smart home security: issues, challenges and countermeasures. IEEE Commun. Surv. Tutor. 16(4):1933–1954 218. Lee A, Brewer T (2009) Smart grid cyber security strategy and requirements, In: Draft interagency report NISTIR 7628 219. Sanjab A et al (2016) Smart grid security: threats, challenges, and solutions. arXiv:1606. 06992 220. Yu X, Xue Y (2016) Smart grids: a cyber-physical systems perspective. Proc IEEE 104(5):1058–1070 221. Ghansah I (2009) Smart grid cyber security potential threats, vulnerabilities and risks. In: California energy commission, PIER energy-related environmental research program, CEC500-2012-047 222. Kuzlu M et al (2017) A comprehensive review of smart grid related standards and protocols. In: The 5th international istanbul smart grid and cities congress and fair (ICSG), pp 12–16 223. Metke AR, Ekl RL (2010) Security technology for smart grid networks. IEEE Trans. Smart Grid 1(1):99–107 224. Calderaro V et al (2011) Failure identification in smart grids based on Petri net modeling. IEEE Trans Ind Electron 58(10):4613–4623 225. De Santis E et al (2017) A learning intelligent system for classification and characterization of localized faults in smart grids. In: The 2017 IEEE congress on evolutionary computation (CEC), pp 2669–2676 226. Scala A et al (2015) Cascade failures and distributed generation in power grids. Int J Crit Infrastruct 11(1):27–35 227. Rahnamay-Naeini M (2016) Designing cascade-resilient interdependent networks by optimum allocation of interdependencies. In: The 2016 international conference on computing, networking and communications (ICNC), 1–7 228. Shahidehpour M et al (2016) Microgrids for enhancing the power grid resilience in extreme conditions. IEEE Trans Smart Grid 99:1. https://doi.org/10.1109/TSG.2016.2579999 229. Panteli M et al (2016) Boosting the power grid resilience to extreme weather events using defensive islanding, IEEE Trans. Smart Grid, PP(99):1–10. https://doi.org/10.1109/TSG. 2016.2535228 230. Farhangi H (2010) The path of the smart grid. IEEE Power Energy Mag 8(1):18–28

References

299

231. Sha K et al (2016) A secure and efficient framework to read isolated smart grid devices, IEEE Trans Smart Grid PP(99):1–13. https://doi.org/10.1109/TSG.2016.2526045 232. Anderson M (2010) WiMax for smart grids. IEEE Spectr 47(7):14 233. Xu Y et al (2016) Microgrids for service restoration to critical load in a resilient distribution system. IEEE Trans Smart Grid PP(99), 1. https://doi.org/10.1109/TSG.2016.2591531 234. Dong X et al (2015) Software-defined networking for smart grid resilience: opportunities and challenges. In: The Proceedings of the 1st ACM workshop on cyber-physical system security, pp 61–68 235. Yip S-C et al (2014) A privacy-preserving and cheat-resilient electricity consumption reporting scheme for smart grids. Paper presented at the international conference on computer, information and telecommunication systems (CITS), pp 1–5 236. Staff C (2016) Future cyber defenses will defeat cyber attacks on PCs. Commun ACM 59(8):8– 9 237. Goel S, Hong Y (2015) Security challenges in smart grid implementation smart grid security, Springer 238. Mendel J (2017) Smart grid cyber security challenges: overview and classification, e-mentor1 (68):55–66 239. Chakraborty AK, Shaniia N (2016) Advanced metering infrastructure: technology and challenges. In: The 2016 IEEE/PES transmission and distribution conference and exposition (T&D), pp 1–5. 240. Greer C et al (2014) NIST framework and roadmap for smart grid interoperability standards. Release 3.0, Tech. Rep. NISTSP-1108r3. The National Institute of Standards and Technology 241. Delgado-Gomes V et al (2015) Smart grid security issues. In: The 9th international conference on compatibility and power electronics (CPE), pp 534–538 242. Dennis D, Keogh M () The smart grid: an annotated bibliography of essential resources for state commissions. National Association of Regulatory Utility Commissioners, https://www. smartgrid.gov/files/TheSmartGridAnnotatedBibliographyEssentialResourcesfo200909.pdf 243. Dennis D, Keogh M (2009) The smart grid: an annotated bibliography of essential resources for state commissions. national association of regulatory utility commissioners, https://www.smartgrid.gov/files/TheSmartGridAnnotatedBibliographyEssentialRes ourcesfo200909.pdf. Accessed on 25 July 16 244. McDaniel P, McLaughlin S (2009) Security and privacy challenges in the smart grid. IEEE Secur Priv 7(3):75–77 245. Rahnamay-Naeini M (2016) Designing cascade-resilient interdependent networks by optimum allocation of interdependencies. In: The 2016 international conference on computing, networking and communications (ICNC), pp 1–7 246. Daneshgar S, Dasgupta K, Thakkar C et al (2018) A 27.8gb/s 11.5pj/b 60ghz transceiver in 28 nm cmos with polarization mimo. In: 2018 IEEE international solid-state circuits conference-(ISSCC), IEEE, pp 166–168 247. Niyato D, Wang P (2012) Cooperative transmission for meter data collection in smart grid. IEEE Commun Mag 50(4):90–97

Chapter 5

Big Data in Smart Grid and Edge Computing of the IoT

The smart grid surpasses the traditional grid in terms of the type, scale, and speed of data generated during the transition. In addition to monitoring grid operations, the smart grid also focuses on gathering power consumption data from various user appliances. This necessitates the implementation of big data technology to efficiently manage, analyze, and even schedule grid operations. By doing so, the smart grid can operate with enhanced precision and efficiency while swiftly responding to user demands. Over the years, data storage, computing, and network management have been predominantly centralized. However, with the advent of the Internet of Things (IoT), data has experienced explosive growth, exhibiting the characteristics of big data. Consequently, big data technology becomes essential for supporting the development and application of the IoT. Nonetheless, centralized cloud processing, which currently handles big data storage and processing, may not meet the demands of latency-sensitive applications. This is due to the fluctuating bandwidth of transmission networks, which is not consistently stable at high speeds. Furthermore, many applications only require specific regional or local data rather than global data. Therefore, these applications need to be executed at the edge. This chapter will first delve into the applications of big data in the context of the smart grid. It will then discuss fog/edge computing in relation to the IoT.

5.1 Smart Grid and Big Data Big data has gained significant attention and is increasingly recognized as the “oil” of knowledge that drives the progress of modern society and the economy. In the field of information science, big data refers to vast and intricate datasets that pose challenges in terms of storage, processing, and analysis for conventional tools [1, 2]. In the energy sector, a revolutionary shift is occurring as the traditional one-way

© Chemical Industry Press 2023 X. Zeng and S. Bao, Key Technologies of Internet of Things and Smart Grid, Advanced and Intelligent Manufacturing in China, https://doi.org/10.1007/978-981-99-7603-4_5

301

302

5 Big Data in Smart Grid and Edge Computing of the IoT

grid gives way to the smart grid, often referred to as the “next generation grid”. The smart grid offers a myriad of advantages over its traditional counterpart, including self-healing and restoration capabilities, enhanced integration of renewable energy sources, situational awareness, and improved transient stability, all made possible by the utilization of smart meters and the application of big data analytics [3].

5.1.1 Big Data Sources of Smart Grid and the Benefits of Big Data Analysis (1) Sources of Big Data in the Smart Grid Big data in the context of the smart grid is derived from various sources, including: (i) SCADA The SCADA system (Supervisory Control and Data Acquisition) is extensively employed in generation, transmission, distribution, and substation protection. It samples grid operating parameters at intervals of 2–4 s, accumulating a substantial volume of data over the years. This data plays a crucial role in grid dispatching operations. (ii) PMU To capture transient stability and oscillations that the SCADA system’s sampling rate cannot capture, phasor measurement units (PMUs) are utilized. PMUs offer a faster sampling rate, acquiring 30–60 samples per second and directly providing voltage/ current amplitude and phase with time stamps [4]. PMUs have been widely deployed, with over 1380 units installed in the United States by the end of 2015, covering nearly 100% of the transmission system. Furthermore, State Grid and China Southern Power Grid had installed 1717 PMUs in China by the end of 2013 [5, 6]. With 100 PMUs sampled at a frequency of 60 Hz and conducting 20 measurements per day, the generated data exceeds 100 GB per day [7]. (iii) AMR In addition to PMUs, Advanced Meter Reading (AMR) systems are deployed to replace traditional power meters that collect consumption data monthly. AMR systems read power consumption data every 15 min, resulting in a massive amount of data from each meter. For a large-scale deployment of AMRs, daily and monthly power consumption data becomes substantial. In addition to the PMU, an AMR (advanced meter read) that reads power consumption data every 15 min is also deployed to replace the traditional power meter that reads power consumption data once a month. This means that each meter, even if it reads 96 data per day, then reads 2880 power consumption data per month. For AMRs deployed with large numbers, the daily and monthly power consumption data will be massive.

5.1 Smart Grid and Big Data

303

(iv) Other Smart Devices Intelligent Electronic Devices (IEDs), Digital Fault Recorders (DFRs), Event Sequence Recorders (SERs), and other advanced measurement devices [8, 9] contribute a substantial volume of data to the power system. This data can be utilized for storage, planning, mining, sharing, and visualization. The global installation of smart meters is projected to increase from 10.3 million in 2011 to 29.9 million by the end of 2017, as highlighted in [10]. The combination of data from SCADA, PMUs, AMRs, and other smart devices presents an extensive pool of data that facilitates the efficient management, analysis, and optimization of the smart grid. (2) The Benefits of Big Data Analysis Big data has brought numerous benefits to power companies and power users, which include: (i) Improving the Stability and Reliability of the Power System Ensuring the stability and reliability of the power system is of paramount importance. Big data analytics enables a deeper analysis of stability and reliability through oscillation detection, voltage stability assessment, event detection and recovery, islanding detection and recovery, and post-event analysis [11]. Big data analytics introduces new approaches that enhance traditional monitoring and analysis methods. For instance, PMUs can detect wind farm oscillations, which are not observable using traditional SCADA systems. This exemplifies the advantages of big data analytics facilitated by the high-density data obtained in the smart grid [12]. (ii) Improving Asset Utilization and Efficiency Big data analytics plays a crucial role in enhancing asset utilization and efficiency. It enables a better understanding of asset operating characteristics, equipment limitations, and device constraints. Models can be validated and calibrated more effectively, and the integration of renewable energy resources can be optimized using big data tools. For instance, in [13], smart meters and geographic information system (GIS) data are employed to measure voltage and conduct transformer fatigue analysis. This allows operators to proactively check or replace transformers. Additionally, research works such as [14–16] explore the usage of big data for model validation and calibration. (iii) Enhancing Customer Experience and Satisfaction The widespread deployment of smart meters [17, 18] has contributed to a better customer experience and increased satisfaction. Smart meters facilitate applications such as easy billing, fraud detection, early outage warnings, smart real-time pricing schemes, demand response, and efficient energy utilization. However, these applications rely on high-speed sampling rate instrumentation, advanced data analysis, and robust information communication infrastructure, all enabled by the analysis of acquired big data. As a result, customers enjoy an enhanced experience, and power companies achieve higher satisfaction levels.

304

5 Big Data in Smart Grid and Edge Computing of the IoT

5.1.2 Big Data Application in Smart Grid (1) Wide Area Situational Awareness (WASA) The concept of situational awareness (SA) originated within the aviation industry and has since found application in various fields. SA involves three essential steps: perception, understanding, and mapping. Perception, the initial step, entails collecting diverse data from sources like traditional SCADA systems, as well as newly installed IEDs or PMUs. The second step involves comprehending the interrelationships among the collected data, particularly in relation to system oscillations or instabilities. This step demands significant resources and relies on quality information to extract meaningful insights. Lastly, mapping refers to forecasting the system’s future behavior based on the knowledge gained from the previous two steps. Through continuous and accurate mapping, control operators gain ample time to respond to events, thus averting potential cascading disasters. In real-world wide-area situational awareness scenarios, two primary challenges need to be addressed: the constraints imposed by the limited number of PMUs and the delays caused by decision-making algorithms. Due to the cost and complexity associated with deploying PMUs throughout the grid, the placement of synchrophasor sensors is restricted and requires careful optimization. To address this issue, researchers have proposed several optimal PMU placement (OPP) methods, such as mixed-integer programming [19], modelbased OPP [20], zero injection reduction method [21], and general algorithms [22, 23]. Sodhi et al. [24] introduced an enhanced OPP framework that encompasses five applications, including improved state estimation, evaluation of voltage/angle stability, monitoring tie-line oscillations, and assessing communication infrastructure availability to identify the most effective PMU placement strategy. For transient faults, where response time typically falls within 100 ms, automated protection devices take immediate action when no human presence is required. In the case of frequent failures, control operators have ample time to assess the situation through simulations or their expertise, enabling them to implement appropriate countermeasures. Regardless of the scenario, operators must make prompt decisions. Although batch AI methods can assist in the decision-making process, the computational delays they entail are intolerable. Decision trees show promise for medium-sized data processing, while stream mining is suitable for making decisions based on large volumes of data. Domingos et al. [25] introduced the Hoeffding binding and proposed an algorithm for constructing decision trees from data streams. This approach incorporates both a primary tree classifier and a cache-based classifier capable of handling high-speed data streams, thus enhancing the performance of intelligent decision-making. Stream mining techniques do not rely on model information but can provide online state estimates with reasonable accuracy, processing time, and computational resources.

5.1 Smart Grid and Big Data

305

WASA has found practical application in various contexts. For instance, the SMDA context system (version 5.0) facilitates wide-area monitoring and event detection [26]. NYISO employs real-time and offline data to present relevant information on dashboards, alerting operators to abnormal conditions such as voltage drops, transient oscillations, and line trips [4]. Peppanen et al. [27] developed a distributed system state estimation (DSSE) and situational awareness system for monitoring and controlling distribution systems. The system employs a three-dimensional graphical user interface to enhance situational awareness. (2) State Estimation Currently, power system state estimation (PSSE) holds significant importance in power system automation. However, traditional state estimation approaches face challenges due to the nonlinear measurements obtained from the SCADA system. These methods rely on iterative algorithms, which are inefficient and sensitive to “bad data” anomalies. Driven by the emergence of big data and smart grids, novel algorithms and techniques have been proposed and implemented to improve state estimation. For instance, [28] introduces a linearly decoupled estimation method that utilizes timing data acquired by a PMU. This approach breaks down the estimation problem into two independent smaller problems, resulting in accelerated estimation processes. Another example is the PMU-based robust state estimation method (PRSEM) [29], which employs a weight assignment function to eliminate unnecessary interference data and enhance the algorithm’s robustness. In the PSSE, two main challenges exist: bad data filtering and the dimensionality reduction of large datasets. Multiple factors can contribute to “bad data” (BD) occurrences, such as failures in metering equipment or electromagnetic interference. Techniques for detecting BD can be classified into two categories: pre-estimation and post-estimation methods. Pre-estimation methods involve normalized residual testing and iteratively reestimating the state, incorporating BD detection as part of the iterative process. However, post-BD estimation methods offer greater reliability, speed, and noniterative characteristics, making them suitable for detecting and handling BD in PSSE applications. Dimensionality reduction plays a crucial role in managing the vast amounts of data in smart grid environments. One widely used approach for dimensionality reduction is principal component analysis (PCA). PCA demonstrates excellent performance in preserving the original data while offering fast computational capabilities [30]. By reducing the dimensionality of the data, PCA aids in optimizing the efficiency of state estimation processes. (3) Event Classification and Detection Power system disturbances encompass various types, including faults, line trips, load shedding, generation losses, and oscillations. Traditional event detection relies on post-mortem analysis based on system models and topologies. However, the abundance of data and information in smart grids enables the utilization of data-driven approaches for real-time event classification and detection.

306

5 Big Data in Smart Grid and Edge Computing of the IoT

Event classification serves as a preliminary step in event detection and localization. For instance, an event labeled as “voltage drop” indicates a voltage decrease exceeding 10% (up to 30%) of the nominal voltage for a duration ranging from 8 ms to 1 min. Such events are typically associated with disturbances. All voltage and frequency events can be classified as oscillatory or non-oscillatory. Nevertheless, it is important to acknowledge that this hierarchical classification only accounts for the most commonly observed events in power systems. The availability of big data provides ample information for accurate event classification and detection. Algorithms such as machine learning and data mining can be employed to achieve precise event classification and detection. As an example, a comprehensive unsupervised clustering method was proposed and utilized to classify 2226 disturbances recorded by the Public Service Company of New Mexico (PNM) between 2007 and 2010 [31]. This approach showcases the potential of leveraging advanced algorithms to effectively classify and detect events based on the available data. (4) The Verification and Calibration for Power Plant Model The validation and calibration process for power plant models has posed persistent challenges for utilities. Assessing the performance of a power system typically involves conducting staged tests, which often necessitate costly plant shutdowns. However, the advent of abundant measurement data acquired through devices like PMUs, IEDs, and FDRs opens up opportunities for developing new data-driven methodologies to verify power plant models. Simultaneously, the recorded data of disturbances can be utilized for comparative analysis with simulation results, thereby enhancing baseline tests and facilitating model adjustments. (5) Short-term Load Forecasting Short-term load forecasting has witnessed the emergence of big data-based methods in recent years [32–34]. These methods employ association and cluster analysis techniques to classify load patterns, utilizing data from smart meters as well as historical load data and environmental variables such as temperature, humidity, and rainfall. While traditional “abstract metrics” like mean absolute error (MAE) and root mean square error (RMSE) have been commonly used to evaluate the accuracy of load forecasting models, they may not be sufficient when dealing with fine spatial and temporal granularity data. To address this evaluation challenge, more advanced techniques such as regression tree learning [35] and artificial neural networks [36] can be employed, enabling a more precise assessment of the residuals between predicted and actual load values. (6) Distribution Gird Verification Regular verification of grid connections becomes necessary due to the potential inaccuracies in geographic data entered into the Geographic Information System (GIS). In the context of smart grids, big data analysis can play a significant role in verifying the topology of distribution networks, particularly for underground feeders

5.1 Smart Grid and Big Data

307

that are challenging to inspect visually. This scenario presents a typical use case for leveraging statistical algorithms to harness the power of big data in the field of electricity. Utilizing similar algorithms, various applications have been developed, such as quadratic modeling [37], transformer identification [38], and power theft detection [39]. These applications showcase the effectiveness and versatility of statistical algorithms in harnessing the potential of power big data for distribution grid verification. (7) Demand Response Driven by Big Data Demand response management is an effective strategy for alleviating load burden during peak periods. However, the traditional approach of curtailing scheduled loads lacks flexibility. In a study conducted by Kwac et al. [40], over 200,000 smart meters from the Pacific Gas and Electric Company (PG&E) collected a vast amount of load data, totaling 66,434,179 data points per 24 h. This rich dataset was leveraged to achieve customer demand response (DR) objectives. Demand response can be viewed as a stochastic knapsack problem (SKP), which can be addressed using heuristic and greedy algorithms within the realm of big data analytics. These algorithms enable efficient and effective solutions to the challenges posed by demand response, leveraging the power of big data to optimize load management during critical periods. (8) The Parameter Estimation of Distribution system Traditionally, automatic parameter estimation (PE) techniques have primarily been applicable to transmission systems rather than distribution systems. This is attributed to the complexities associated with the radial topology of distribution systems and the limited availability of measurements. However, the widespread deployment of sensors in smart grids has paved the way for new parameter estimation methods specifically designed for the secondary networks of distribution systems. The availability of big data, obtained from Advanced Metering Infrastructure (AMI) and other sensors, presents an opportunity to implement line impedance calibration for secondary systems. This approach has shown promising results in practical applications [41]. (9) System Security and Protection The interconnectivity of measurement and control equipment in the power grid exposes it to the risk of cyber-attacks, making cybersecurity a critical concern for smart grids. To counter these threats, an effective method is the deployment of an Intrusion Detection System (IDS). However, traditional IDS systems have limitations in scalability and flexibility as they are typically knowledge-intensive and host-based. To address these limitations, a promising solution is to develop a specificationbased hybrid IDS that incorporates multiple data sources. This approach allows for comprehensive system monitoring and protection [42]. By leveraging multiple data sources, such as network logs, system logs, and sensor data, a hybrid IDS can enhance the detection and response capabilities against cyber-attacks in a smart grid environment.

308

5 Big Data in Smart Grid and Edge Computing of the IoT

Big data analytics has also made significant contributions to security and privacy in various ways. Some notable achievements in big data security and privacy include cryptographic systems tailored for handling big data, anomaly detection algorithms designed for detecting unusual patterns in large datasets, and intelligent applications that leverage big data for enhanced security measures. In addition to cybersecurity, big data analysis has found practical applications in other aspects of power grid operations. These include islanding detection [43], oscillation detection [44], and real-time rotor angle monitoring [45]. These applications highlight the potential of big data analytics to empower various aspects of smart grid operations. Table 5.1 provides an overview of some implemented applications where big data has been successfully harnessed to enhance the capabilities of smart grids on a global scale. These applications demonstrate the transformative impact of big data analytics in improving the efficiency, reliability, and security of modern power systems.

5.2 Big Data Analysis Technology for Smart Grid 5.2.1 Big Data Analysis Platform The utilization of cloud platforms and cloud computing modules has gained significant attention in the application of smart grids, enabling efficient big data analysis and processing. Several enterprise-level cloud platforms have been employed in smart grid contexts, including Microsoft Azure, Google APP Engine, and Eucalyptus. These platforms offer flexible and interactive environments for hosting cloud applications and performing data processing tasks. Microsoft Azure, for example, has been recognized as a versatile platform for hosting cloud applications and conducting data processing in smart grids [53]. Holm is a private home energy management system that utilizes the Azure cloud platform [54]. Google’s PowerMeter is another application that tracks household energy consumption and is based on the Google APP Engine [55]. InterPSS, which stands for Open Source Power System Simulation Based on Internet Technology, aims to develop a cloud-based simulation platform for smart grid applications [55]. Smart-Frame, proposed in [56], is a cloud computing-based big data information management framework designed specifically for smart grids. In addition to these platforms, various cloud computing modules have been researched for their application in smart grids, including MapReduce, Chord, Dynamo, Zookeeper, Chubby, and others [57–60]. Among these, two of the most widely used platforms in smart grids are Hadoop MapReduce and Apache Spark. (1) Hadoop MapReduce Hadoop MapReduce, originally developed by Google, is a popular programming model for large-scale data processing. Hadoop, the most prominent implementation

5.2 Big Data Analysis Technology for Smart Grid

309

Table 5.1 Smart grid applications drived by big data Application

Name of Software

Developer

Description

Situational awareness

FNET/GridEye

Yilu Liu

Develop a variety of [46–48] applications including real-time event detection and position estimation, oscillation detection, and more

References

WASA

SMDA (ver5.0)

Hydro-Quebec

Real-time acquisition of wide-area phasor data and monitoring of inter-area oscillations, covering approximately 25% of 735 kV substations

Event detection and alarm management

e-terra3.0

Alstom

Present and visualize [50] disturbances and navigate to relevant diagnostic information

Validation for power plant model

CERTS

BPA 和CERTS

BPA engineers calibrate Columbia Generating Station (CGS) model without offline generators

[14]

Oscillation detection and mitigation

GRID-3P

Electric Power Group

Oscillations are not observable using SCADA techniques, but are observable with fine-grained PMU data

[4]

Renewable energy integration

DEMS

Siemens

Data-driven system to monitor, manage distributed generation and integration of renewable energy into bulk power systems

[51]

[49]

(continued)

310

5 Big Data in Smart Grid and Edge Computing of the IoT

Table 5.1 (continued) Application

Name of Software

Developer

Description

Transient stability and intrusion detection

WARMAP5000

NARI Technology

Combining real-time [52] monitoring data with simulated data for wide-area transient stability control and cyber-attack prevention

References

of MapReduce, is known for its scalability, fault tolerance, and automatic failure recovery techniques. It is extensively used by major companies such as Google, Yahoo, Facebook, IBM, and Microsoft. Hadoop MapReduce comprises two main components: the MapReduce processing model and the Hadoop Distributed File System (HDFS). Researchers have proposed enhancements to Hadoop MapReduce to cater to the unique characteristics of energy big data, such as i2MapReduce for incremental processing and Petuum for machine learning [61–63]. (2) Apache spark Apache Spark, developed at UC Berkeley’s AMPLab, is an open-source framework for big data computing. It offers capabilities for batch processing, stream processing, and iterative processing. Compared to Hadoop MapReduce, Spark is more suitable for real-time and streaming data analysis. Spark is known for its speed, with the ability to run computations up to 100 times faster than Hadoop MapReduce in memory or 10 times faster on disk. The Spark framework includes various libraries, such as SQL, MLlib for machine learning, GraphX, and Spark Streaming. Spark has been implemented in smart grid applications, such as FNET/GridEye, which deploys frequency disturbance recorders for event discovery in power systems [48, 64, 65]. Overall, cloud platforms and cloud computing modules, including Microsoft Azure, Google APP Engine, Hadoop MapReduce, and Apache Spark, have proven instrumental in enabling efficient big data analysis and processing in the context of smart grids. These platforms and technologies offer scalability, fault tolerance, and real-time capabilities, supporting various applications and improving the performance of smart grid systems.

5.2.2 Big Data Analysis of Power and Its Key Technologies (1) Big Data Analysis of Power Data mining plays a crucial role in extracting valuable information and patterns from large datasets in the field of power systems. However, traditional data mining methods based on SQL databases or spreadsheets are insufficient to handle the massive and

5.2 Big Data Analysis Technology for Smart Grid

311

heterogeneous data streams generated by smart grids. Therefore, there is a need for new and efficient algorithms and tools to address these challenges. In the context of smart grids, multi-source mining mechanisms and dynamic data mining methods have been proposed to overcome the limitations of traditional approaches [66]. Wu et al. introduced the concept of local pattern analysis, which laid the foundation for multi-source mining and the divide-and-conquer approach in data mining [67]. Distributed computing has also been explored as an alternative to traditional centralized data processing, offering more efficient and cost-effective solutions for mining large-scale multi-source datasets in smart grids [68]. Several studies have investigated edge data processing methods for distributed data analysis in the context of smart grids [48, 69]. With the increasing computing power and decreasing hardware costs, new information extraction methods have emerged, with machine learning being a prominent technique. Machine learning algorithms, such as k-means, linear support vector machines (LSVM), logistic regression (LR), locally weighted linear regression (LWLR), Gaussian discriminant analysis (GDA), back-propagation neural network (BPNN), expectation maximization (EM), naive Bayes (NB), and independent variable analysis (IVA), are commonly used in smart grid applications [70, 71]. Each algorithm has its own characteristics and can be applied in different scenarios. For example, in [70, 71], a hybrid approach combining k-means clustering with principal component analysis was used for data dimensionality reduction and estimation mapping to estimate solar generation at unseen sites. In [72], the authors proposed an additional quantile regression method for making probabilistic predictions based on smart meter readings. This approach, applied to 3639 household meters installed by the Irish Energy Regulatory Commission (CER), enabled individual smart meter data estimation, going beyond traditional total load forecasting. Table 5.2 provides an overview of proposed or implemented algorithms for various applications in smart grids, showcasing the diversity of approaches employed in data mining for extracting insights and enhancing the performance of smart grid systems. (2) Key Technologies for the analysis of smart grid big data The analysis of smart grid big data has the potential to uncover valuable insights and knowledge about the smart grid system, its operations, and power users. This knowledge can then be used to make informed decisions and improve the efficiency of the smart grid to meet the power demand of users effectively. However, the application of big data technology in power systems is still at an early stage, and there are several key technologies needed for smart grid big data analysis. These technologies include: (i) Multi-source data integration and storage Traditional data analysis typically focuses on datasets from a single domain, necessitating the development of fusion methods to handle multi-source datasets that exhibit variations in modalities, formats, and representations. When it comes to storing big data, existing systems like the Hadoop Distributed File System (HDFS) may appear viable; however, customization and modification are still required to effectively accommodate grid-scale big data.

312

5 Big Data in Smart Grid and Edge Computing of the IoT

Table 5.2 Algorithms for data analysis in smart grid Algorithm

Application

References

Principal component analysis (PCA)

Dimensionality reduction

[73, 74]

ANNs, K-means, fuzzy c-means, Load classification eXtended classifier system for clustering (XCSc)

[75–78]

Artificial neural networks, empirical mode decomposition, extended kalman filter, extreme learning with kernels, decision trees

Short-term load forecasting (STLF)

[72, 79–81]

Statistical relational learning (SRL)

Knowledge graph

[82, 83]

Random matrix theory

Abnormal detection

[84, 85]

Deep neural networks, multi-view learning, matrix factorization

Cross-domain data fusion

[86, 87]

Lyapunov index

Rotor angle monitoring

[45]

Playback process, sensitivity analysis

Generation unit model validation

[88]

Decision tree (DT), radom forest (RF)

Fault detection and classification

[89]

Additive quantile regression

Personal smart meter data estimation

[72]

K-means clustering and principal component analysis

Estimating implicit solar farms

[70, 71]

(ii) Real-time data processing technology In certain critical applications like fault detection and transient oscillation detection, the response time required is on the order of milliseconds. While cloud systems offer rapid computing capabilities, delays can still occur due to network congestion, intricate algorithms, and the sheer volume of data involved. To address this challenge, an in-memory database presents itself as a viable solution. SAP’s HANA, an inmemory database, has been developed specifically for processing vast quantities of power meter data to enhance power flow distribution [90]. (iii) Data compression Data compression technology plays a vital role in the effectiveness of a Wide Area Monitoring System (WAMS). It is crucial for this technology to possess distinctive characteristics that align with high-fidelity (hi-fi) requirements. Additionally, when aiming to detect transient disturbances while maintaining a high compression ratio (CR), specific compression methods become necessary. (iv) Big data visualization technology Visual graphs and charts play a crucial role in offering operators detailed and explicit information about voltage and frequency changes. Nevertheless, the effective identification and representation of correlations or trends among multi-source data remains a pressing challenge that requires urgent attention. This entails the development of

5.3 Edge/Fog Computing in IoT

313

key technologies such as advanced visualization algorithms, information extraction and presentation techniques, and image synthesis methods. By addressing these technological advancements, operators can gain valuable insights and a comprehensive understanding of the relationships and patterns within multi-source data. Furthermore, these advancements enhance the visualization capabilities of the system, enabling operators to make informed decisions and take proactive measures based on the visual representations [91]. (v) Data Privacy and Security The coexistence of traditional SCADA systems with new AMI and IT systems is a reality in the evolving landscape of power grids. However, it is important to note that traditional SCADA system designs typically do not incorporate robust measures to prevent cyber attacks. The reliance on legacy systems and interoperability through APIs can expose the grid to potentially dangerous scenarios, including metadata spoofing, wrapping, and phishing attacks [92]. On the customer side, the proliferation of household power smart meters leads to the generation of vast amounts of personal information [93]. The sharing of data among different entities poses the risk of private data leaks, which can have catastrophic consequences and trigger a chain of cascading problems.

5.3 Edge/Fog Computing in IoT The IoT revolutionizes connectivity by enabling the interconnection of various devices and offering people a wide range of information services. The IoT is typically structured into three layers: the perception (sensing) control layer, the network transmission layer, and the comprehensive service layer. The comprehensive service layer serves as the hub for information collection, processing, and application within the IoT. Given the massive amount of data generated by the IoT, the storage and processing of this data necessitate large-scale or ultra-large-scale data centers, with cloud computing emerging as the preferred technology for building such facilities. Recent research has underscored the significance of service-based edge/fog computing in the IoT landscape. This computing paradigm extends the capabilities of cloud computing by introducing intermediate services at the network’s edge. By adopting an edge/fog computing-based distributed architecture, the IoT can enhance the performance of Cloud-to-Things services, making it suitable for mission-critical applications. Moreover, edge/fog devices are situated in close proximity to the data they generate, enabling them to deliver exceptional performance in terms of resource allocation, service delivery, and privacy protection. From a business standpoint, the IoT built on edge/fog computing will drive the rapid development of small and medium-sized enterprises (SMEs), enabling them to reduce information service costs and enhance their competitiveness. This paradigm shift empowers SMEs to leverage the benefits of IoT technologies, opening up

314

5 Big Data in Smart Grid and Edge Computing of the IoT

new opportunities for growth and innovation. In this section, we will delve into the concept, architecture, and application of edge/fog computing in the IoT across various industries, exploring its potential impact and benefits.

5.3.1 Basic Concepts In this subsection, we will provide an overview of some fundamental concepts related to cloud computing, fog computing, and edge computing. Additionally, we will compare the similarities and differences between these three computing paradigms. (1) Cloud Computing Prominent cloud computing providers like Amazon, Microsoft Azure, Google Cloud Platform, and IBM Cloud follow a centralized model, where big data analysis, decision-making, and computing are concentrated in remote cloud data centers. However, this centralized model is associated with several disadvantages, as outlined in [94]. The increase in machine-type communication (MTC) within the IoT results in a significant influx of data traffic, making it challenging to manage network congestion and traffic using the cloud computing model. Moreover, due to the considerable distance between IoT terminal devices and cloud data centers, latency-sensitive data and applications experience significant delays. Another drawback of the centralized cloud computing model is the high cost of building and maintaining cloud infrastructure. This cost can be prohibitively expensive for small and medium-sized enterprises (SMEs) [95]. To address these challenges, the emergence of Fog/Edge Computing (FEC) offers SMEs low-cost services, lower latency, and higher bandwidth. However, it’s important to note that the cloud still plays a crucial role in FEC deployments. Nevertheless, FEC is susceptible to dynamic changes within the network, including constraints on processing and storage capacity, bandwidth limitations, security threats, link downtime, and cost considerations [95]. These factors can impact the performance and reliability of FEC systems. In Fig. 5.1, the cloud computing model is depicted, along with three general services it offers. These services are described as follows: (i) Platform-as-a-Service (PaaS) PaaS is a customer-centric cloud computing service that empowers customers to develop, run, and manage web-based applications. It provides customers with the flexibility to build and maintain applications without the complexities associated with infrastructure development and deployment. PaaS supports the entire lifecycle management of cloud applications, including coding, testing, deployment, and maintenance [96]. An example of a PaaS provider is Apprenda, which offers private cloud PaaS for.NET and Java.

5.3 Edge/Fog Computing in IoT

315

Fig. 5.1 Cloud computing model

(ii) Infrastructure-as-a-Service (IaaS) IaaS, also known as Hardware-as-a-Service (HaaS), is a cloud computing service model that offers computing infrastructure to users. It manages computing resources, storage, and network services, providing fundamental resource services directly to PaaS or end-users [96]. Typically, IaaS offers hardware (including software), storage, servers, data center space, and network services. Examples of IaaS providers include Amazon Web Services (AWS), Cisco Metacloud6 (formerly Metapod), Microsoft Azure, Google Compute Engine (GCE), and Joyent. (iii) Software-as-a-Service (SaaS) SaaS is a software distribution model that enables clients to access applications hosted by third-party providers over the internet [97]. Notable examples of SaaS applications include Twitter, Instagram, Facebook, and Google’s suite of productivity applications (formerly known as Google Apps). These three services—PaaS, IaaS, and SaaS—represent different layers of cloud computing, offering various levels of abstraction and management for customers. PaaS provides a development and runtime environment, IaaS offers fundamental computing resources, and SaaS delivers complete software applications accessible over the internet. (2) Fog Computing (FC) The concept of fog computing (FC) was introduced by Bonomi et al. in 2012 [98]. FC aims to bring intelligence to the local area network (LAN) level and process data at the IoT gateway. Its primary objective is to extend the services and functions provided by the cloud to the network edge. These functions include storage, processing, database operations, integration, security, and management of IoT end-devices, leveraging their capabilities close to the edge. By doing so, FC offers several advantages such as minimizing network congestion, reducing end-to-end latency, addressing connection bottlenecks, enhancing security and privacy, and improving scalability. The industry believes that the emergence of FC can bring significant business opportunities as it meets the demands for local content, resource pools, and real-time processing by

316

5 Big Data in Smart Grid and Edge Computing of the IoT

efficiently distributing cloud-to-thing continuous and unified computing, storage, network, and management services [99]. Consequently, FC has garnered interest from both academia and industry. It is important to note that FC is not meant to replace cloud computing but rather complement it by offloading data or service requests that can be processed locally [100]. FC acts as an intermediary, deploying existing computing infrastructure and bridging the gap between the cloud and edge devices. It plays a crucial role in the success of various technologies, including smart grid, smart home, smart city, wireless sensor networks, mobile healthcare, manufacturing, and in-vehicle networks. The key functions and features of FC include: • • • • •

Geographic dispersion to support distributed systems. Support for large-scale sensor networks and IoT terminals. Enhanced real-time response compared to traditional cloud computing models. Support for heterogeneity and interactivity among different devices and systems. Online analysis and interaction with the cloud for more comprehensive data processing and utilization.

These capabilities make FC an essential component in enabling efficient and effective deployments of existing and emerging technologies in various domains. (3) Edge Computing (EC) Edge computing (EC) refers to computing that is performed at the edge of the network, closer to the data source or end-users. EC aims to overcome the limitations of cloud-based models by acting as an intermediary between end-users/devices and the cloud. It provides processing and storage capabilities for a multitude of IoT devices, reducing the computing load on distant data centers and improving realtime response and latency [101]. Additionally, EC offers advantages in terms of distributed nature and support for device mobility within heterogeneous networks. Notably, research in Mobile Edge Computing (MEC) is actively being conducted [101]. According to [101], the edge layer can be implemented in three modes: MEC, Fog Computing (FC), and Cloudlet Computing (CC). MEC involves deploying intermediate nodes within a base station (BS) of a cellular mobile network to enable cloud computing capabilities within a wireless area network (RAN). Cloudlets, on the other hand, are smaller versions of the cloud that provide cloud-like capabilities to specialized appliances. An example cited in [102] demonstrated that moving facial recognition computation from the cloud to the edge reduced response time from 900 to 169 ms. EC offers various functions and features, including: • Geographical dispersion, enabling computing resources closer to the data source or end-users. • Increased security as encrypted data remains closer to the core network. • Improved real-time response compared to cloud computing. • Enhanced scalability through virtualization. • Mitigation of potential communication bottlenecks.

5.3 Edge/Fog Computing in IoT

317

These capabilities make EC an attractive solution for addressing the challenges associated with cloud-centric models, enabling more efficient and responsive computing at the network edge. (4) Fog/Edge Computing (FEC) Fog/Edge Computing (FEC) is a paradigm where devices are located close to the edge of the network, although not necessarily at the exact edge. In contrast, edge devices are typically situated at the network’s edge and serve as the primary contact point for IoT end devices. While both FEC and EC devices are in proximity to IoT end devices, EC devices are generally even closer. In some systems, fog computing and edge computing are used interchangeably, with FC considered as part of the EC and Micro Data Center (MDC) paradigms for IoT [103]. Both FC and EC aim to provide services near the end user, but EC specifically resides in edge devices, whereas FC is typically located one or more network hops away from the edge. EC platforms are often constrained in terms of energy and storage capacity, classifying them as constrained devices. As the number of IoT applications increases, resource contention and additional latency may become more prevalent [104]. EC generally experiences higher resource contention than FC due to its close proximity to IoT end devices. Moreover, EC primarily focuses on the “thing” domain, while fog computing concentrates more on the infrastructure domain. FEC inherits certain support and characteristics from both FC and EC, including security, scalability, openness, autonomy, reliability, agility, hierarchical organization, and programmability. Therefore, the integration of FC and EC is motivated by the specific advantages and characteristics each brings to the table.

5.3.2 The Architecture of FEC (Fog/Edge Computing) The motivation behind “FECIoT (Fog/Edge Computing-based IoT)” is to emphasize the significant potential that arises from integrating the fog/edge computing paradigm into IoT architectures. This subsection will explore the architectural framework of FECIoT. By connecting fog/edge devices in a mesh network, various benefits can be achieved, including cloud-to-IoT load balancing, resiliency, fault tolerance, data sharing, and communication load reduction [105]. Architecturally, this necessitates fog/edge devices to possess the capability to vertically and horizontally communicate within the IoT ecosystem. FECIoT builds upon the foundational architecture of IoT and leverages the distributed FEC paradigm to fulfill all IoT requirements in a more efficient manner. It is important to note that, similar to IoT, there is no universally accepted architecture for FEC. Therefore, this subsection will discuss three different architectures.

318

5 Big Data in Smart Grid and Edge Computing of the IoT

Application layer

Application layer

Business Layer

Network layer

Service layer

Application layer

Sensing/control layer

Network layer

Service layer

Sensing/control layer

Network layer Sensing/control layer

(a) Three-layer

(b) Four-layer

(c) Five-layer

Fig. 5.2 FEC Architecture for IoT

(1) Three-Layer Architecture As illustrated in Fig. 5.2a, the FECIoT architecture consists of three layers: the sensing/control layer, the network layer, and the application layer. (i) Sensing/Control Layer This layer is responsible for data acquisition and sensing. Various devices such as RFID, sensors, Wireless Identification and Sensing Platform (WISP), and actuators are employed to gather data from the environment. (ii) Network Layer The network layer facilitates data transfer across different networks. It receives information from the sensing/control layer and routes it over the Internet to IoT hubs and other devices. This layer supports various computing platforms, including cloud computing platforms, Internet gateways, mobile communications, and switching and routing equipment. It also leverages advanced communication technologies such as 5G/LTE, Bluetooth, and WiFi. Gateways are used to transmit data across heterogeneous networks and multiple protocols and technologies. (iii) Application Layer The application layer provides services based on the data received from the network layer. It encompasses storage, processing, and analysis functionalities. This layer hosts multiple IoT applications with diverse requirements and is deployed alongside middleware functions. With the advent of fog/edge deployments, it is essential

5.3 Edge/Fog Computing in IoT

319

for multi-vendor ecosystem applications to migrate and operate across the heterogeneous system. These applications should be able to span all levels of deployment to maximize their value. Although the three-layer architecture appears straightforward, a closer examination reveals the complexity of integrating data services such as data aggregation, data mining, and analytics. To address this complexity, a new layer called the service layer is introduced. The introduction of the service layer accommodates the intricate requirements of data services within the FECIoT architecture. This layer enables seamless integration and operation of applications across the entire architecture, allowing for efficient data aggregation, mining, and analytics. (2) Four-Layer Architecture This architecture is commonly referred to as service-oriented architecture (SoA), an application framework that empowers enterprises to construct, deploy, and seamlessly integrate services independent of the underlying technical systems. Positioned between the Application layer and Network layer, the Service layer plays a crucial role in enhancing data services for the IoT. The primary focus of this service-oriented architecture lies in crafting workflows that effectively coordinate services and enable hardware/software reuse. Its remarkable capability to facilitate the design, deployment, and integration of technology-independent services sets it apart [106]. To visualize this four-layer architecture, Fig. 5.2b depicts the following layers: (i) sensing/ control layer, (ii) network layer, (iii) service layer, and (iv) application layer. Now, let’s delve into a brief discussion of the service layer, as highlighted below. The service layer, as the name implies, serves as the gateway to a wide array of services. It is also known as the interface or middleware layer and can be further broken down into four distinct components [107, 108], which are as follows: (i) Service Discovery One of the vital components of the service layer, plays a crucial role in facilitating the identification and acquisition of necessary service requests. An insightful publication [109] presents a comprehensive global service discovery framework, enabling users to effortlessly register their sensors into public infrastructures. Through the use of mobile devices, this framework empowers users to explore and discover an extensive range of available services. (ii) Service Composition Another significant aspect of the service layer, offers functionality for combining specific services to construct network objects tailored to specific applications. Within the service composition, Service Webs hold immense importance as they facilitate the precise definition of interface object functionality and enable seamless interaction with them [110]. Efficient interaction with networked objects is achieved through the management of service requests using workflows. These workflows can be nested and represented as a series of coordinated actions executed by a single component, enhancing the overall efficiency and effectiveness of the system.

320

5 Big Data in Smart Grid and Edge Computing of the IoT

(iii) Service Management An integral aspect of the service layer, encompasses the provision of essential functional requirements and management capabilities for each object. These functions span various areas, ranging from cross QoS management to lock management and semantics. Additionally, the service management component facilitates the deployment of updated services in real-time to cater to evolving application requirements. To effectively identify service object pairs within the Internet of Things (IoT), the Service Repository is implemented within this sublayer [111]. In a comparative study conducted by Rykowski et al. [111], two alternative architectures for service management, namely the Open Service Gateway initiative (OSGi) and the Representational State Transfer (REST), were examined. It was observed that OSGi offers a simpler framework that is well-suited for homogeneous sensor networks. On the other hand, REST was found to be more complex but highly suitable for heterogeneous and widely distributed IoT devices and services. (iv) Service Interface The Service Interface, a pivotal component within the service layer, serves as a vital bridge connecting all the available services. Its primary purpose is to streamline business processes and alleviate the complexity associated with them. By providing a unified interface, the Service Interface enables access to the diverse range of services offered, simplifying the overall integration and utilization of these services within an enterprise. Situated atop this architecture, the application layer assumes a critical role in providing extensive support to end users, catering to their specific requirements and leveraging the capabilities of the system. Diverging from the conventional threelayer architecture, the application layer is not confined to the middleware domain. Instead, it encompasses the service/middleware layer, thereby enabling interactive interfaces with standard Web service protocols and service composition technologies that seamlessly connect heterogeneous distributed systems and applications [112]. Various domains benefit from this versatile layer, including smart home, smart transportation, smart industry, smart healthcare, and many more, each demonstrating the transformative potential of this architecture in enhancing daily life and industry operations. (3) Five-Layer Architecture The five-layer architecture, designed with a comprehensive business perspective, goes beyond the traditional application layer to deliver more intricate and sophisticated services [113]. This architecture comprises five key layers: (i) sensing/control layer, (ii) network layer, (iii) service layer, (iv) application layer, and (v) business layer. In this discussion, our focus lies on the business layer, which plays a pivotal role in the overall framework.

5.3 Edge/Fog Computing in IoT

321

The primary function of the business layer is to meticulously record and analyze all IoT operations that occur within heterogeneous systems. Operating on a petabytescale data analytics infrastructure, the business layer adheres to compliance regulations and record retention policies. To further enhance operational optimization, insight mining, and business planning, machine learning models are commonly deployed within this layer. Additionally, the business layer encompasses the management of metadata, reference data, business rules, and operational health across the lower layers. The scope of the business layer encompasses the entire IoT system, including applications, business models, and confidential user information. Figure 5.2c visually depicts the business layer as an additional function within the service-oriented architecture (SoA). The SoA framework facilitates the development of systems that enable the derivation of independent business solutions, irrespective of technical constraints. The FEC (Fog-Edge-Cloud) architecture is poised to play a significant role in reshaping the networking, server, and software industries. It achieves this by converging routers, switches, storage, and application servers into FEC devices. Furthermore, the distributed FEC architecture provides support for the emerging Fog-as-a-Service (FaaS) paradigm. This opens up opportunities for small businesses to participate in offering private and public services of varying scales to end users, thereby democratizing access to advanced services and expanding the service ecosystem.

5.3.3 Services Provided by Fog/Edge Computing The FEC (Fog-Edge-Cloud) architecture serves as a complementary and mutually beneficial framework to cloud computing, offering interdependent services that integrate storage, computation, control, and communication capabilities. By enabling these services, the FEC architecture effectively reduces latency and bandwidth requirements [114]. One of the key advantages of the FEC paradigm is its ability to ensure service continuity. Higher-performance devices can locally provide computation, control, and even data analysis to lower-performance devices within IoT systems. To illustrate this concept, consider the scenario where sensors are worn on the human body to monitor physiological parameters of various organs. By acquiring and analyzing the sensor data, the wearable smart device can transform into an FEC device. Similarly, when a person is driving, the vehicle itself can act as an FEC device by providing functions such as alarm display, user interface, and situation updates to the worn smart device. Furthermore, the roadside traffic control unit can also serve as an FEC device for the mobile vehicle, facilitating the transmission of sensed physiological information to a medical center through this series of FECs. It is worth noting that cloud services can be deployed to manage the fog, allowing the fog to remotely provide cloud services to end users. IoT management systems

322

5 Big Data in Smart Grid and Edge Computing of the IoT

should be designed to empower end users to invoke the most appropriate services. In the realm of offloading computation (OC), a new service called Offload as-aService (OaaS) has been introduced [115]. Although OC research has been ongoing for some time, focusing on scaling mobile resource constraints in terms of CPU, GPU, memory, storage, and battery life, it is widely recognized that IoT terminals are resource-constrained devices. Therefore, it is crucial to synergize offload computing technology with fog/edge computing. Offloading involves transferring data and services to other devices, effectively migrating computing tasks to more powerful and resourceful devices. The integration of OaaS within the FECIoT (Fog-Edge-Cloud Internet of Things) environment offers numerous benefits to mobile devices, each possessing some level of computing power. These devices often have underutilized resources, including extra storage capacity, idle CPU cycles, and available memory. A typical FEC environment comprises various FEC devices such as PC terminals, Internet gateways, servers, smartphones, and sensors, all contributing to the overall the architecture. Figure 5.3 provides an overview of the service logic underlying the FEC framework. The specific configuration and functionality of fog/edge applications may vary depending on deployment, use case, and resource availability. Within the FEC application, a series of microservices play crucial roles, including: (i) Fog Connector Service This service encompasses a set of APIs designed for selected edge communication protocols, enabling higher-level fog services to communicate seamlessly with devices, sensors, actuators, and other platforms. The fog connector service transforms the generated data from these devices and processes the incoming data into a unified format, subsequently passing it to the core service. (ii) Core Services These services are responsible for acquiring data from the edge and relaying it to higher-level services and systems. Core services facilitate the routing of requests from higher systems to edge devices [116]. Additionally, they can handle the reception and translation of commands from the cloud to edge devices for execution. Fig. 5.3 FEC application service

User Interface Service

Integration Services

Analysis Service Support Services Core Services Fog Connector Service

5.4 Protocol and Enabling Technology in Fog/Edge Computing of IoT

323

(iii) Support Services As the name implies, support services encompass a wide range of microservices, including logging, scheduling, service registration, and data cleansing. For instance, based on a security report, logging can trigger scheduling processes or even erase received data. (iv) Analysis Services This category of services includes both reactive and predictive functions. Reactive functions typically manifest in fog nodes located near the network edge, while higher-level fog nodes with greater processing power exhibit enhanced cognitive and predictive capabilities, often employing machine learning techniques. (v) Integration Services These services facilitate the registration and determination of data delivery specifications, including the where, when, how, and in what format data is delivered. For example, through a REST request, a taxi driver can obtain real-time traffic information on a specific road and transmit it to a specific customer in an encrypted and XML format. (v) User Interface Services The primary objective of user interface services is to display acquired and managed data, service status and operations, analysis results, and system management on FEC devices. Given the increasing reliance on web and mobile applications, prioritizing the user interface becomes crucial in enhancing the overall user experience.

5.4 Protocol and Enabling Technology in Fog/Edge Computing of IoT Given that IoT fog/edge computing operates within a distributed architecture framework, addressing issues such as scalability, mobility, and compatibility becomes crucial for facilitating device-to-device communication across diverse IoT systems. The heterogeneous nature of these systems necessitates the consideration of technologies and protocols that can support resource-constrained devices, including those with limited energy and bandwidth. Recognizing this need, the Internet Engineering Task Force (IETF) has taken the lead in standardizing protocols specifically tailored for communication in resourceconstrained IoT fog/edge computing environments. Two notable examples are the Routing Protocol for Low Power and Lossy Networks (RPL) and the Constrained Application Protocol (CoAP) [117]. These protocols enable efficient communication and data exchange among constrained devices.

324

5 Big Data in Smart Grid and Edge Computing of the IoT

Furthermore, other organizations such as the Institute of Electrical and Electronics Engineers (IEEE) and the International Telecommunication Union (ITU) have also proposed several IoT protocols over the past decade to address the unique requirements of IoT systems. These protocols aim to enhance interoperability, scalability, and efficiency in resource-constrained environments. To meet the stringent energy demands of constrained devices, the design of resource allocation protocols is actively being investigated. These protocols aim to increase the resilience and robustness of communication while minimizing energy consumption. By optimizing resource utilization, they contribute to extending the operational lifetime of constrained devices [118]. Figure 5.4 illustrates the range of protocols that can be employed within the IoT fog/edge computing domain, encompassing various layers such as the Sensing/Data Link Layer, Network Layer, Transport Layer, Service Layer, Business/Management Layer, and Application Layer. While certain traditional Internet protocols can be utilized in the IoT context, it is important to define protocols and standards that are specifically tailored to the limited processing power and communication capabilities of IoT devices. Additionally, Fig. 5.5 showcases several enabling technologies that offer unique capabilities within the IoT fog/edge computing domain. Here is a brief overview of these technologies. Fig. 5.4 Protocols within the FECIOT domain

Business/Management layer IEEE 1905, IEEE 145,1etc. DTLS, TLS, etc.

Security layer

Network layer RPL, 6LowPAN, IPv4/6, etc.

WSDL, UDDI, DDS, etc.

Transport layer UDP, TCP, DCCP, etc.

Service layer

Application layer CoAP, XMPP, MQTT, AMOP, etc.

Sensing/Data Link Layer 4G/5G, IEEE802 , Wi-Fi

Fig. 5.5 Some enabling technologies in FECIoT

Data acquisition, perception, execution and communication technology, etc.

ANT RFID WISP NFC 6LowPAN UWB ZigBee

Z-Wava

WiMAX

Dash-7

WiFi

Weightless

3G/4G/5G Bluetooth

5.4 Protocol and Enabling Technology in Fog/Edge Computing of IoT

325

5.4.1 Technical Protocols in Fog/Edge Computing of IoT Domain (1) Physical Layer and Data Link Layer The enabling technologies and data link layer primarily encompass a range of innovative solutions. These include Radio Frequency Identification (RFID), the Wireless Identification and Sensing Platform (WISP), Wireless Sensor Networks (WSN), barcodes, Bluetooth Low Energy (BLE), Near Field Communication (NFC), IEEE 802.15.4, IEEE 802.11AH, Z-Wave, and the advanced 4G/5G mobile communication networks. Among these technologies, WISP stands out as a wireless identification and sensing platform, offering remarkable capabilities in this domain. (2) Network Layer The network layer encompasses a variety of enabling technologies and standards that facilitate efficient communication. Notable among these are LoRaWAN, IPv6, 6LoWPAN, RPL, and Sigfox, which have been extensively discussed in Chap. 4. Additionally, next provides a brief introduction to CORPL, CARP, E-CARP, and other related technologies. (i) CORPL (Cognitive RPL) CORPL, an extension of the RPL protocol, is specifically tailored to cognitive networks, showcasing its adaptability and intelligence. Similar to RPL, CORPL adopts a Directed Acyclic Graph (DAG) topology and leverages opportunistic forwarding techniques to efficiently route packets across nodes [118]. In CORPL, each node, including the parent node, actively maintains a forwarding list, which is regularly updated. With the aid of updated information, CORPL employs a dynamic prioritization mechanism that utilizes a cost function approach to determine the optimal nodes within the forwarder set [119]. This intelligent approach enables CORPL to optimize packet forwarding and improve overall network performance in cognitive network environments. (ii) CARP (Channel-Aware Routing Protocol) CARP is a distributed routing protocol specifically crafted for wireless sensor networks, showcasing its suitability for this context. One of CARP’s distinctive features is its cross-layer approach, where it leverages link quality information to identify a relay node. By analyzing the success of a node’s previous transmissions with neighboring nodes, CARP intelligently selects it as a relay node [120]. Moreover, the protocol incorporates a power control mechanism to further enhance the selection of robust links. This ensures that the network maintains reliable connections. Another notable advantage of CARP is its ability to support lightweight packets, making it particularly well-suited for deployment within the Internet of Things (IoT) edge/fog computing domain [118].

326

5 Big Data in Smart Grid and Edge Computing of the IoT

(iii) E-CARP (Enhanced Channel-Aware Routing Protocol) E-CARP, an enhanced version of CARP developed by Basagni et al. [120], introduces notable improvements to the original protocol. It focuses on achieving a locationfree and greedy hop-by-hop packet forwarding strategy, enhancing the efficiency of communication within wireless sensor networks. By adopting this strategy, ECARP effectively reduces communication overhead, making it well-suited for deployment in IoT edge/fog computing applications. Additionally, the forwarding strategy employed by E-CARP is particularly advantageous in scenarios where the environment being monitored remains relatively stable, as it helps to minimize energy consumption [121]. (3) Business/Management Layer In order to enhance interoperability across various applications, network topologies, and technologies, the development of well-designed business/management layer standards is essential. Several typical standards that play a crucial role in achieving this goal are briefly described below. (i) IEEE Std 1905.1a™-2014 To address the complexity associated with applications, network topologies, and technology interfaces in the realm of the IoT, effective deployment of management standards becomes imperative. IEEE Std 1905.1a is a significant standard that offers a solution by defining an abstraction layer capable of supporting the deployment of common interfaces for multiple home networks [122]. When packets arrive from any interface or application, the IEEE Std 1905.1a abstraction layer handles connection selection. Positioned between layer 2 and layer 3, this abstraction layer abstracts each interface individually, enabling seamless integration of diverse networks. Additionally, the abstraction layer ensures end-to-end Quality of Service (QoS) and provides a platform for enhancing network coverage, establishing secure connections, and performing various network management functions like discovery, path selection, auto-configuration, and QoS negotiation. The flexibility of IEEE Std 1905.1a allows for its easy deployment in IoT edge/ fog computing environments, where it exhibits features such as self-installation, aggregated throughput, load balancing, and support for multiple simultaneous streams. (ii) IEEE 1451 The IEEE 1451 set of standards was developed to address the challenges arising from the heterogeneity of device standards. These standards serve the purpose of integrating different protocols and standards by offering a unified protocol that facilitates interoperability between sensor/actuator networks and buses [123]. A notable feature of these standards is the inclusion of Transducer Electronic Data Sheets (TEDS) for each sensor. These TEDS are delivered in a standardized format over the Internet, irrespective of the specific physical layer device being used. This uniform approach ensures consistent and seamless communication across devices.

5.4 Protocol and Enabling Technology in Fog/Edge Computing of IoT

327

The hardware interface supported by IEEE 1451 includes popular open standard interfaces such as RS-232/USB, CAN, IEEE 802.11, Bluetooth, and ZigBee (802.15.4). By encompassing these interfaces, the standards effectively manage a wide range of smart sensors. This enables interoperability and inclusivity within the network and enables seamless integration with the IoT edge/fog computing framework. (4) Service (i) WSDL (Web Service Description Language) The WSDL (Web Services Description Language) standard leverages the Extensible Markup Language (XML) syntax to define the functions and calling mechanisms of web services. In essence, a web service encompasses comprehensive information about a physical object, encompassing both its functional and non-functional components. WSDL serves as a means to describe web services based on an abstract model of service provisioning. One of the key strengths of WSDL is its extensibility, allowing for the description of endpoints and their messages regardless of the communication protocol’s format or type. This flexibility ensures that WSDL can accommodate various protocols used in communication, facilitating interoperability between different systems. Typically, WSDL is deployed in conjunction with SOAP (Simple Object Access Protocol) and XML Schema to enable the provision of web services over the Internet. SOAP provides a standardized protocol for exchanging structured information in web service communication, while XML Schema defines the structure and validation rules for the XML documents exchanged between service providers and consumers. (ii) SOAP The Simple Object Access Protocol (SOAP) protocol specification defines a welldefined XML message format for encapsulating service requests, responses, and potential errors in a standardized manner. SOAP provides a transport-independent messaging system that relies on the XML Schema standard, as defined by the World Wide Web Consortium (W3C), to describe the structure and data types of message payloads. SOAP messages can be transmitted using various transport mechanisms such as HTTP, HTTPS, or other protocols. In the context of resource-constrained IoT environments, a strategy proposed by Moritz et al. [124] involves binding SOAP to the Constrained Application Protocol (CoAP). This approach aims to create a lightweight protocol that is wellsuited for deployment within IoT edge/fog computing frameworks. While HTTP and UDP protocol bindings may not deliver the desired network performance in highly resource-constrained situations, binding SOAP to CoAP provides a more efficient alternative. CoAP is designed to operate in constrained networks with limited resources, making it a suitable choice for resource-constrained IoT deployments.

328

5 Big Data in Smart Grid and Edge Computing of the IoT

(5) Security (i) DTLS DTLS (Datagram Transport Layer Security) is specifically designed to support the utilization of secure communication in resource-constrained devices and networks, taking into consideration limitations such as memory constraints and limited support for security algorithms. In the context of IoT environments, which are highly vulnerable, DTLS provides a robust solution. It introduces a DTLS record layer that enables the secure transmission of multicast messages by utilizing session keys. Furthermore, DTLS incorporates mechanisms to address challenges such as message fragmentation, retransmission, and reordering, which can occur in constrained networks. The DTLS handshake mechanism can be effectively utilized to minimize complexity and optimize the secure transmission of messages in such scenarios. In the context of CoAP, DTLS serves as a binding to ensure secure communication. It enables the secure transmission of CoAP messages, thus safeguarding the integrity and confidentiality of IoT data. To mitigate the possibility of denial-of-service (DoS) attacks in resourceconstrained devices, an architecture described in [125] employs DTLS policy processing. This architecture leverages an IoT Security Support Provider (IoTSSP) device and incorporates two key mechanisms: optional handshake authorization and a new extension of DTLS called session transfer. These mechanisms contribute to reducing the potential impact of DoS attacks and enhancing the security posture of constrained devices within the IoT. (ii) TLS TLS (Transport Layer Security) is a widely deployed security protocol that operates between the transport layer and the application layer. It is designed to provide secure communication over a reliable transport, typically TCP. However, the lightweight nature of UDP makes it more suitable for many IoT applications compared to TCP. Using TLS over UDP may introduce additional overhead in the network. Similar to DTLS, TLS offers flexibility in terms of choosing credentials and encryption algorithms. However, due to bandwidth constraints in IoT scenarios, TLS is often used primarily for secure key exchange and establishing secure data connections. This allows for efficient utilization of network resources while still ensuring the necessary security measures are in place. A recent introduction to the TLS landscape is GUARD TLS, also known as MatrixSSL. GUARD TLS is a modular implementation of TLS and DTLS specifically designed for IoT use cases. It features a minimal memory footprint and efficient RAM utilization, making it well-suited for resource-constrained IoT devices. It provides C language source code that is compact and well-documented, facilitating easy integration into IoT systems. This makes it an ideal choice for the proposed IoT edge/fog computing framework, where efficiency and compactness are crucial considerations.

5.4 Protocol and Enabling Technology in Fog/Edge Computing of IoT

329

(6) Application Layer Application layer protocols play a crucial role in facilitating communication and data exchange between applications in the IoT. Some notable protocols in this layer include MQTT and CoAP, which were discussed in Chapter 3, along with XMPP and AMQP. (i) XMPP XMPP (Extensible Messaging and Presence Protocol), commonly known as Jabber, is a standardized protocol by the IETF that offers several advantages for the edge/fog computing domain of IoT, including enhanced security and interoperability. While the protocol exhibits high scalability, it does consume bandwidth and processing power without providing guaranteed Quality of Service (QoS) [126]. One of the strengths of XMPP lies in its extensive support for open-source software, including servers, clients, and libraries across various operating systems. This broad ecosystem enables flexibility and platform independence, leading to potential cost savings and reduced complexity in IoT deployments. By facilitating technologyagnostic and protocol-independent data transmission over both wired and wireless networks, including the Internet, XMPP establishes a foundation for communication and integration. The XMPP protocol offers two key communication models: request/response and publish/subscribe. The request/response model enables bidirectional communication, allowing for efficient exchange of messages between entities. On the other hand, the publish/subscribe model supports multidirectional communication, enabling the dissemination of information to multiple subscribers. This flexibility is depicted in Fig. 5.6a, showcasing the versatility of XMPP in accommodating different communication scenarios. (ii) AMQP AMQP (Advanced Message Queuing Protocol) is an open-source standard that facilitates communication between diverse applications across heterogeneous devices and networks. Initially designed for efficient M2M communication, AMQP offers comprehensive support for interoperability between standards-compliant clients and messaging middleware servers, commonly referred to as “brokers.” This functionality is illustrated in Fig. 5.6b. AMQP boasts several key features that contribute to its versatility and effectiveness. It is a multi-channel protocol that allows for negotiation, asynchronous communication, security, portability, neutrality, and efficiency. These attributes enable interaction between applications, irrespective of the underlying technologies and platforms. The protocol can be conceptually divided into two layers: the functional layer and the transport layer. The functional layer defines a set of commands that perform specific tasks on behalf of applications, while the transport layer determines the mechanism for transmitting these commands between the application and the server. In practice, an AMQP server operates similarly to an email server. Each exchange acts as a message transfer agent, facilitating the routing of messages, while each

330

5 Big Data in Smart Grid and Edge Computing of the IoT

Fig. 5.6 a XMPP architecture model b AMQP architecture model

message queue serves as a mailbox for storing messages. This architectural design enables efficient and reliable message exchange. AMQP finds practical application in various contexts, including the Windows Azure Service Bus, where it is leveraged to develop robust applications. By utilizing AMQP 1.0, Service Bus enables portable data representation, allowing messages sent from .NET programs to be easily consumed by applications developed in other programming languages such as Java, Python, Ruby, and more.

5.4.2 Simulation Technology in FECIoT Domain (1) Simulation Technology Simulators play a crucial role in the development of systems by providing researchers and developers with a powerful tool for experimentation. Simulation-based methods

5.4 Protocol and Enabling Technology in Fog/Edge Computing of IoT

331

offer a cost-effective platform to gather data and replicate experiments, enabling the validation of hypotheses and analytical results. One of the key advantages of simulation is its flexibility, allowing for parameter adjustments to simulate different network scenarios. For the specific context of fog/edge computing in the IoT, a simulator must fulfill certain requirements to provide comprehensive support. This includes high-precision computing, scalability support, mobility support, and the ability to model complex networks in diverse heterogeneous scenarios. Researchers in the IoT field can select an appropriate simulator based on their specific requirements to effectively achieve their research goals. To address the complex needs of IoT architecture, the use of a multilevel simulator is recommended [127]. Multilevel simulations involve the integration of multiple simulation models, with each model performing a specific task. This approach serves as the fundamental framework for supporting large-scale IoT network simulations while preserving critical details. By incorporating various simulation models at different levels, the multilevel simulator can effectively capture the intricacies of the IoT system and provide a more comprehensive analysis. (2) Main Simulator When it comes to simulating IoT environments, adaptive simulators that incorporate multi-level simulation, agent-based parallelism, and distributed simulation techniques can be highly effective. Several simulation tools are available for simulating the FECIOT (Fog/Edge Computing in the Internet of Things) architecture, including Network Simulator (NS-3) [128], Cooja [129], NetLogo [130], IoTSim [131], iFogSim [132], CupCarbon [133], OMNET++ [134], and QualNet [135], among others. Let’s provide brief introductions for a few of them: (i) NetLogo NetLogo, developed in 1999, is a powerful modeling and simulation tool that is widely used to study complex systems with dynamic behavior. It operates through an emulator that runs on a Java virtual machine, making it compatible with major operating systems like Windows, Linux, and macOS. NetLogo belongs to a family of advanced multi-agent modeling languages, providing researchers with a versatile platform for studying various phenomena. One of the key strengths of NetLogo is its programmability. Users can leverage its user-friendly syntax to develop customized models and simulations. NetLogo supports variants of the Agent’s Logo language, which is known for its simplicity and ease of use. The language allows researchers to define agents, which can represent various entities in the system, and to program their behavior and interactions. NetLogo’s capabilities extend beyond individual agents. It also supports the creation of mobile agents and the establishment of links between agents, enabling the modeling of network structures and graph-like connections. This network/graph representation is valuable for simulating systems where entities interact with one another and exchange information.

332

5 Big Data in Smart Grid and Edge Computing of the IoT

Overall, NetLogo offers a comprehensive and flexible environment for modeling and simulating complex systems. Its user-friendly syntax, support for multi-agent modeling, mobile agents, and network/graph structures make it a popular choice among researchers exploring various phenomena and dynamics. (ii) Cooja Cooja is an emulator that operates on the Contiki1 operating system, which is specifically designed for the Internet of Things (IoT). Contiki OS is tailored for low-power microcontrollers, enabling the development of efficient applications while providing standardized support for low-power wireless communication across various hardware platforms. One of the notable features of Contiki OS is its comprehensive support for both IPv6 and IPv4 standards. This allows devices running Contiki to seamlessly integrate into both modern and legacy networks. Additionally, Contiki OS incorporates lowpower wireless standards such as 6LowPAN, RPL, and CoAP, enabling energyefficient communication in IoT deployments. Contiki applications are developed using the widely adopted C programming language, providing a familiar and accessible environment for developers. By leveraging C, developers can write code that efficiently utilizes hardware resources and interacts with the underlying system. Cooja, the emulator associated with Contiki, is a flexible tool built in Java. It enables the simulation of networks consisting of motes or nodes (depending on capabilities) running Contiki OS. Cooja supports multi-level simulation, allowing users to analyze and evaluate the behavior of their IoT networks at different levels of abstraction. It enables simultaneous simulation at the network level, operating system level, and even machine code instruction set level, providing a comprehensive view of the system’s behavior. The combination of Contiki OS and the Cooja emulator offers developers a powerful platform for designing, testing, and analyzing IoT applications. The low-power capabilities, support for wireless standards, and multi-level simulation provided by Contiki and Cooja make them valuable tools for IoT researchers and developers. (iii) CupCarbon CupCarbon is an advanced simulator specifically designed for wireless sensor networks (WSNs), smart cities, and the IoT. It utilizes a combination of multi-agent and discrete event simulation techniques to provide an accurate representation of these complex systems. One of the distinguishing features of CupCarbon is its use of geographic location to simulate sensor networks. It leverages the digital representation of real-world environments provided by “OpenStreetMap” to create a realistic simulation environment. This enables researchers to emulate the behavior of sensor networks in specific geographic locations, taking into account factors such as terrain and obstacles. 1

http://www.contiki-os.org/.

5.4 Protocol and Enabling Technology in Fog/Edge Computing of IoT

333

The simulator takes advantage of parallelism in both agent and event processing, allowing for efficient and optimized simulations. The multi-agent simulation environment provided by CupCarbon enables the execution of simulations and monitoring of various events over time. Additionally, it includes a mobile simulation component that facilitates the coordination of agent mobility, allowing for the study of dynamic scenarios. CupCarbon also incorporates a WSN/IoT simulator that relies on its core capabilities to simulate sensor events. This feature enables researchers to model and evaluate the behavior of sensor nodes and their interactions within the simulated environment. The simulator’s 3D environment is particularly noteworthy, as it enables precise deployment and positioning of nodes, accounting for elevation and other spatial dimensions. Given its support for multi-agent simulation and discrete-event simulation, CupCarbon is well-suited for the FECIOT architecture. It enables the parallelization of IoT end-devices, allowing researchers to study the behavior and interactions of individual devices within the larger network. Additionally, the discrete-event simulation capabilities of CupCarbon facilitate the analysis of complex interactions and communication patterns among IoT devices. CupCarbon offers a comprehensive simulation platform for WSNs, smart cities, and IoT applications. Its utilization of geographic data, parallel processing, and 3D environment make it a valuable tool for researchers and developers working on IoT projects. (3) Mathematical Method for Simulating Network Communication in FECIoT Fog/Edge devices have gained recognition for their ability to support mobility and perform real-time processing of data and service requests originating from various IoT end-devices. This versatility allows devices like smartphones, smartwatches, and industrial robots to assume the role of fog devices, enabling local control and application data analysis for any IoT end-device. Additionally, these Fog/Edge Computing (FEC) devices can act as relays within IoT ecosystems, enhancing network resilience. Within a diverse and multi-layered architecture based on fog computing, different vendors can provide a range of services across various layers. It’s worth noting that in certain cases, the destination for specific data and service requests may be located far from the source. As a result, the communication link connecting the source and destination via the fog relay requires careful consideration. Given that the majority of IoT terminal devices rely on wireless communication, which can be hindered by sparse deployment and non-line-of-sight obstacles, the deployment of FEC devices as relays becomes essential to effectively mitigate communication interruptions. Consequently, employing a single relay scenario allows for the simulation of outage probability from the source (IoT end device) to the destination (static fog node).

334

5 Big Data in Smart Grid and Edge Computing of the IoT

5.4.3 Security and Privacy Ensuring the security of connected IoT devices is of utmost importance to guarantee their safe and reliable operation. With the widespread deployment and diverse application of heterogeneous terminals, safeguarding data confidentiality, integrity, privacy, and verifying the identity of data sources becomes crucial. Similar to the Internet, the IoT ecosystem is susceptible to various forms of attacks, including eavesdropping, denial of service, man-in-the-middle attacks, data and identity theft, and more. Traditional Internet security measures, such as encryption and authentication at different layers, have been utilized to mitigate these attacks. However, due to the constrained processing power of IoT devices, implementing a comprehensive security suite is nearly impossible. FECIoT presents a solution that addresses some of the challenges encountered in deploying existing cloud-based IoT architectures. When it comes to security, FEC devices can be deployed as proxies for IoT end devices, acting as intermediaries responsible for managing and updating the security credentials of these devices. By offloading security functions to FEC nodes, the limited resources and computational capabilities of IoT terminals are alleviated. These FEC nodes can perform crucial security tasks, including malware scanning, monitoring the security status of distributed systems, and enabling real-time threat detection without compromising overall system performance. Despite the benefits offered by FECIoT, there are still certain security issues associated with this architecture. In the following discussion, we will delve into the security features of FECIoT and explore potential security attacks that may arise within this framework. (1) Security Functions in FECIoT Security requirements play a crucial role in the different layers of a system, depending on the specific security needs [136]. In FECIoT, several important security functions can be identified: (i) Trust Trust is essential for establishing secure communication between IoT devices, FEC devices, and the cloud infrastructure. Building trust within the FECIoT architecture requires secure and trusted elements. Trusted devices form the foundation of a secure FECIoT ecosystem. Trust extends beyond device-to-device communication and encompasses relationships between different IoT layers and applications. Since IoT devices are vulnerable to malicious attacks, trust is often established based on previous interactions. For instance, an FEC device providing services to an IoT end device should be able to verify the authenticity of the requested service based on previous experience, known as reputation. Similarly, IoT end-devices transmitting data or requests should be able to verify the trustworthiness of the intended FEC device. Implementing trust in the FECIoT architecture requires a robust trust model that ensures reliability and security. While trust management models like artificial

5.4 Protocol and Enabling Technology in Fog/Edge Computing of IoT

335

intelligence, fuzzy methods, game theory, and Bayesian estimation have been applied in cloud computing [137, 138], lightweight trust evaluation models are needed for practical implementation in FECIoT. (ii) Authentication Authentication involves the identification of entities. Before becoming part of an IoT network, a device needs to undergo authentication. However, the limited resources of IoT devices pose challenges in terms of the complexity of registration and reauthentication phases. Traditional authentication mechanisms utilizing certificates and Public-Key Infrastructure (PKI) are not suitable for IoT devices due to their constrained nature. With the distributed nature and proximity of FEC devices, leveraging FEC-based authentication servers instead of centralized cloud authentication servers is a more appropriate choice. Mobility within the FECIoT architecture introduces authentication issues for IoT end-devices, particularly when transitioning between previously connected FEC nodes. Therefore, designing a robust authentication mechanism is crucial in FECIoT. (iii) Integrity Integrity ensures that data or service requests remain unaltered during transmission. Data integrity is vital, as corrupted data can cause severe disruptions in the network and impair the functionality of IoT applications. One proposed solution for ensuring integrity in FECIoT is a sampling and signature scheme, where a local aggregator acts as a coordinator, periodically sending sampled packets for global traffic analysis [139]. This scheme can be modified to fit the FECIoT architecture. Another approach, employing game theory, explores optimal strategies for undermining the integrity of IoT networks [140]. This game-theoretic approach aids in designing effective defenses for FECIoT. (iv) Confidentiality Confidentiality guarantees that only authorized users/devices can access and modify information, preventing unauthorized interference with data and services. In the FECIoT architecture, data flows from physical devices (sensors, actuators) to FEC devices and then to/from higher layers, increasing the risk of malicious devices accessing this data. Addressing access control mechanisms and device authentication processes are crucial for ensuring confidentiality in FECIoT. However, scalability issues make it challenging to ensure confidentiality, and controlling access to heterogeneous data and services is complex due to real-time responses within the system. (v) Privacy Privacy ensures that data is only accessible to corresponding entities/devices within the network. It is essential to ensure that other users/devices have limited control over received data and cannot infer additional valuable information. Given the vast number of IoT end-devices and the massive data flow within the FECIoT ecosystem, ensuring

336

5 Big Data in Smart Grid and Edge Computing of the IoT

privacy is challenging. Therefore, personal data of IoT end-users should be kept confidential and efficiently stored to ensure strict access control only by authorized users/devices. Implementing security mechanisms such as reducing data acquisition and knowledge collection in FEC devices and discarding raw data effectively after deriving auxiliary context can help minimize privacy threats in FECIoT. However, such mechanisms may introduce trade-offs in system performance. (vi) Availability Availability is a vital security function in FECIoT, ensuring that authorized users/ devices can access data and system resources. Since most IoT applications are latency-sensitive, any system downtime can have negative implications for endusers. Distributed denial of service (DDoS) attacks, which make data and services unavailable to legitimate users/devices, pose a significant disruption to web services. (vii) Access Control Access control determines whether a user/device can access system resources, including data or services. The process involves granting or revoking access, especially to unauthorized users/devices. Robust access control techniques are essential for secure interoperability among heterogeneous devices and applications within the FECIoT ecosystem. Several access control mechanisms have been proposed using the Cloud-Things model. Many developers utilize a combination of encryption schemes to create effective data access control mechanisms. For example, an access control system has been developed that offloads complex access control decisions to thirdparty trusted parties [141]. This design, based on a simple communication protocol, minimizes overhead and is suitable for FECIoT applications. (2) Common Security Attacks in FECIoT The FECIoT architecture is susceptible to various security attacks, including the following: (i) Distributed Denial of Service (DDoS) DDoS attacks pose a significant threat to FECIoT. Malicious clients and “botnets” can join forces to initiate DDoS attacks. These attacks may originate from compromised IoT terminal devices. For instance, multiple malicious IoT devices can simultaneously generate a large number of virtual service requests, overwhelming FEC devices with their limited processing capabilities. As a result, FEC devices become overwhelmed with handling these malicious requests, rendering them unable to process legitimate service requests. It’s important to note that these malicious requests can even come from compromised legitimate IoT end devices. Given the massive deployment of IoT devices, authenticating all end devices becomes challenging. Relying on a trusted third party, such as a certificate authority that issues credentials between communicating parties, can help mitigate DDoS attacks. However, filtering service requests or spoofing incoming IP packets adds complexity due to the large scale of IoT. Additionally, FEC devices themselves can be exploited to launch DDoS attacks, given the increasing shift of computing and processing to the network’s edge.

5.5 Summary

337

(ii) Man-in-the-Middle Attack Man-in-the-middle attacks pose a serious threat to FECIoT, particularly in terms of privacy. These attacks exploit vulnerabilities in the platform to intercept and disclose sensitive information, such as the location and identity of FEC devices. The resource constraints of devices within FECIoT often limit their ability to perform secure communication protocols, making them susceptible to such attacks. Although existing methods can mitigate man-in-the-middle attacks, addressing this challenge remains a significant concern in FECIoT. (iii) Physical Attack Physical attacks involve targeting the hardware components of the FECIoT ecosystem. These components can include RFID tags, sensor devices, FEC devices, or even more centralized infrastructure. Physical attacks pose a direct threat to the integrity and availability of the system, making it crucial to implement appropriate security measures to safeguard these hardware components. It is important to address these security threats and implement robust security measures to ensure the safe and reliable operation of the FECIoT architecture.

5.5 Summary In the context of the smart grid, big data is gaining significant attention as it is considered the valuable resource of contemporary information technology. Big data refers to large and complex data sets that traditional tools find challenging to store, process, and analyze. In the smart grid domain, big data offers numerous advantages by extracting new rules, knowledge, and value from the vast amount of data generated. This enables better power demand management, improved response to the demand side, and enhanced grid scheduling and management. The sources of big data in the smart grid are diverse and include SCADA systems from power plants and transmission and distribution grids, meter reading data from power consumption management, data from power grid operations and management, and various user-related data from household appliances. The generated big data from smart grids holds immense potential for various applications, such as wide-area situational awareness, state estimation, event classification and detection, power plant model validation and calibration, short-term load forecasting, demand response, distribution system parameter estimation, and system security and protection. Currently, many enterprise-level cloud platforms are employed for big data analysis in smart grids. Key technologies for power big data analysis encompass multisource data integration and storage, real-time data processing, data compression, visualization, data privacy, and security. Machine learning and data mining algorithms are commonly utilized for big data analysis in the smart grid domain. These include algorithms such as k-means,

338

5 Big Data in Smart Grid and Edge Computing of the IoT

support vector machine, logistic regression, linear regression, Gaussian discriminant analysis, BPNN, and naive Bayesian, among others. Edge/fog computing plays a crucial role in the IoT and serves as a complement and extension to cloud computing. Edge/fog devices are located in close proximity to the data generation source, allowing for efficient resource allocation, service delivery, and privacy. This proximity reduces information service costs for enterprises and enhances the competitiveness of small and medium-sized businesses. The motivation behind FECIoT is to emphasize the significant potential that arises when integrating the fog/edge computing paradigm into IoT architectures. Connected fog/edge devices form a mesh network, enabling cloud-to-IoT load balancing, resiliency, fault tolerance, data sharing, and communication load reduction. FECIoT, built upon the foundation of the basic IoT architecture, efficiently fulfills all IoT requirements by leveraging the distributed FEC paradigm. FECIoT plays a vital role in latency-sensitive IoT applications and finds applications in various domains such as smart transportation, smart grid, smart healthcare, smart home, smart environment, and smart cities, among others.

References 1. Oussous A et al (2017) Big data technologies: a survey. J King Saud Univ Comput Inf Sci https://doi.org/10.1016/j.jksuci.2017.06.001 2. Chunming T, Xi H, Zhikang S et al (2017) Big data issues in smart grid—a review. Renew Sustain Energ Rev 79:1099–1107. https://doi.org/10.1016/j.rser.2017.05.134 3. Farhangi H (2010) The path of the smart grid. Power Energy Mag IEEE 8(1):18–28 4. DOE., Advancement of synchrophasor technology in ARRA Projects. https://www.smartg rid.gov/recovery_act/program_publications.html 5. DOE., Summary of the North American synchrophasor initiative (NASPI) activity area. https://www.naspi.org/documents 6. Yuan J, Shen J, Pan L et al (2014) Smart grids in China. Renew Sustain Energy Rev 37(3):896– 906 7. Klump R, Agarwal P, Tate JE et al (2010) Lossless compression of synchronized phasor measurements. In: Proceedings of the IEEE power and energy society general meeting. Minneapolis, MN, USA, pp 1–7 8. Depuru SSSR, Wang L, Devabhaktuni V (2011) Smart meters for power grid: challenges, issues, advantages and status. Renew Sustain Energy Rev 15(6):2736–2742 9. Kabalci Y (2016) A survey on smart metering and smart grid communication. Renew Sustain Energy Rev 57:302–318 10. Alahakoon D, Yu X (2016) Smart electricity meter data intelligence for future energy systems: a survey. IEEE Trans Ind Inf 12(1):425–436 11. Shuai Z, Sun Y, Shen Z et al (2016) Microgrid stability: classification and a review. Renew Sustain Energy Rev 58:167–179 12. Electric Power Group, Wind farm oscillation detection and mitigation. https://www.smartg rid.gov/recovery_act/project_information.htm 13. Short TA (2013) Advanced metering for phase identification, transformer identification, and secondary modeling. IEEE Trans Smart Grid 4(2):651–658 14. Overholt P, Kosterev D, Eto J et al (2014) Improving reliability through better models: using synchrophasor data to validate power plant models. IEEE Power Energy Mag 12(3):44–51

References

339

15. Luan W, Peng J, Maras M et al (2015) Smart meter data analytics for distribution network connectivity verification. IEEE Trans Smart Grid 6(4):1–1 16. Hajnoroozi AA, Aminifar F, Ayoubzadeh H (2015) Generating unit model validation and calibration through synchrophasor measurements. IEEE Trans Smart Grid 6(1):441–449 17. Erlinghagen S, Lichtensteiger B, Markard J (2015) Smart meter communication standards in Europe—a comparison. Renew Sustain Energy Rev 43:1249–1262 18. Zhou K, Yang S (2016) Understanding household energy consumption behavior: the contribution of energy big data analytics. Renew Sustain Energy Rev 56:810–819 19. Aminifar F, Fotuhi-Firuzabad M, Shahidehpour et al (2011) A probabilistic multistage PMU placement in electric power systems. IEEE Trans Power Deliv 26(2):841–849 20. Gopakumar P, Chandra G, Reddy M et al (2013) Optimal placement of PMUs for the smart grid implementation in Indian power grid—a case study. Front Energy 7(3):358–372 21. Xu B, Abur A (2004) Observability analysis and measurement placement for systems with PMUs. In: Proceedings of the IEEE PES power systems conference and exposition, vol 2, no 10, pp 943–946 22. Huang J, Wu N, Ruschmann M (2014) Data-availability-constrained placement of PMUs and communication links in a power system. IEEE Syst J 8(2):483–492 23. Li Q, Cui T, Weng Y, Negi R et al (2013) An information-theoretic approach to PMU placement in electric power systems. IEEE Trans Smart Grid 4(1):446–456 24. Sodhi R, Srivastava SC, Singh SN (2011) Multi-criteria decision-making approach for multistage optimal placement of phasor measurement units. IET Gener Transm Distrib 5(2):181–190 25. Domingos P, Hulten G (2000) Mining high-speed data streams. In: Proceedings of the sixth ACM SIGKDD international conference on knowledge discovery and data mining, pp 71–80 26. Basu C, Agrawal A, Hazra J et al (2014) Understanding events for wide-area situational awareness. In: Proceedings of the innovative smart grid technologies conference. IEEE, pp 1–5 27. Peppanen J, Reno MJ, Thakkar M et al (2015) Leveraging AMI data for distribution system model calibration and situational awareness. IEEE Trans Smart Grid 6(4):2050–2059 28. Gol M, Abur A (2015) A fast decoupled state estimator for systems measured by PMUs. IEEE Trans Power Syst 30(5):2766–2771 29. Zhao J, Zhang G, Das K et al (2015) Power system real-time monitoring by using PMU-based robust state estimation method. IEEE Trans Smart Grid 7(1):1 30. Xie L, Chen Y, Kumar PR (2014) Dimensionality reduction of synchrophasor data for early event detection: linearized analysis. IEEE Trans Power Syst 29(6):2784–2794 31. Dahal OP, Brahma SM, Cao H (2014) Comprehensive clustering of disturbance events recorded by phasor measurement units. IEEE Trans Power Deliv 29(3):1390–1397 32. Khan AR, Mahmood A, Safdar A et al (2016) Load forecasting, dynamic pricing and DSM in smart grid: a review. Renew Sustain Energy Rev 54:1311–1322 33. Selakov A, Cvijetinovi´c D, Milovi´c L et al (2014) Hybrid PSO–SVM method for short-term load forecasting during periods with significant temperature variations in city of Burbank. Appl Soft Comput 16(3):80–88 34. Deihimi A, Orang O, Showkati H (2013) Short-term electric load and temperature forecasting using wavelet echo state networks with neural reconstruction. Energy 57(3):382–401 35. Zhu L, Wu QH, Li MS et al (2013) Support vector regression-based short-term wind power prediction with false neighbors filtered. In: Proceedings of the international conference on renewable energy research and applications. IEEE, pp 740–744 36. Rocio Cogollo M, Velasquez JD (2014) Methodological advances in artificial neural networks for time series forecasting. IEEE Lat Am Trans 12(4):764–771 37. Huang SC, Lu CN, Lo YL (2015) Evaluation of AMI and SCADA data synergy for distribution feeder modeling. IEEE Trans Smart Grid 6(4):1–1 38. Berrisford AJ (2013) A tale of two transformers: an algorithm for estimating distribution secondary electric parameters using smart meter data, pp 1–6

340

5 Big Data in Smart Grid and Edge Computing of the IoT

39. Jokar P, Arianpoo N, Leung VCM (2016) Electricity theft detection in AMI using customers’ consumption patterns. IEEE Trans Smart Grid 7(1):216–26 40. Kwac J, Rajagopal R (2016) Data-driven targeting of customers for demand response. IEEE Trans Smart Grid 7(5):2199–2207 41. Peppanen J, Reno MJ, Broderick RJ et al (2016) Distribution system model calibration with big data from AMI and PV inverters. IEEE Trans Smart Grid 7(5):2497–2506 42. Pan S, Morris T, Adhikari U (2015) Developing a hybrid intrusion detection system using data mining for power systems. IEEE Trans Smart Grid 6(6):134–143 43. Bayrak G, Kabalci E (2016) Implementation of a new remote islanding detection method for wind–solar hybrid power plants. Renew Sustain Energy Rev 2016(58):1–15 44. Li C, Higuma K, Watanabe M et al (2008) Monitoring and estimation of interarea power oscillation mode based on application of CampusWAMS. Turk J Agric Food Sci Technol 3(1) 45. Dasgupta S, Paramasivam M, Vaidya U et al (2015) PMU-based model-free approach for real-time rotor angle monitoring. IEEE Trans Power Syst 30(5):2818–2819 46. Liu Y, Zhan L, Zhang Y et al (2015) Wide-area measurement system development at the distribution level: an FNET/GridEye example. IEEE Trans Power Deliv 31(2):1–1 47. Chai J, Liu Y, Guo J et al (2016) Wide-area measurement data analytics using FNET/GridEye: a review. In: Proceedings of the power systems computation conference, pp 1–6 48. Zhou D, Guo J, Zhang Y et al (2016) Distributed data analytics platform for wide-area synchrophasor measurement systems. IEEE Trans Smart Grid 7(5):2397–2405 49. Kamwa J, Beland G, Trudel et al (2006) Wide-area monitoring and control at hydroquébec: past, present and future. In: Proceedings of the power engineering society general meeting 50. e-terraphasorpoint 3.0. http://www.alstom.com/Global/Grid/Resources/Documents/Automa tion/NMS/e-terraphasorpoint%203.0.pdf 51. Siemens (2015) http://www.engerati.com/resources/efficient-network-integrationrenewableenergy-resources-distribution-level 52. NARI (2015) http://www.naritech.cn/html/jie166.shtm 53. Singh M, Ali A (2016) Big data analytics with Microsoft HDInsight in 24h. Sams Teach Yourself: Big Data, Hadoop, and Microsoft Azure for better business intelligence 54. Rusitschka S, Eger K, Gerdes C (2010) Smart grid data cloud: a model for utilizing cloud computing in the smart grid domain. In: Proceedings of the IEEE international conference on smart grid communications, pp, 483–488 55. Birman KP, Ganesh L, Renesse RV et al (2012) Running smart grid control software on cloud computing architectures. In: Proceedings of the workshop on computational needs for the next generation electric grid 56. Baek J, Vu QH, Liu JK et al (2015) A secure cloud computing based framework for big data information management of smart grid. IEEE Trans Cloud Comput. 3(2):233–244 57. Simmhan Y, Prasanna V, Aman S et al (2013) Cloud-based software platform for big data analytics in smart grids. Comput Sci Eng 15(4):38–47 58. Khan M (2015) Hadoop performance modeling and job optimization for big data analytics. Brunel University, London 59. Markovic DS, Zivkovic D, Branovic I et al (2013) Smart power grid and cloud computing. Renew Sustain Energy Rev 24(1):566–577 60. Fang B, Yin X, Tan Y et al (2016) The contributions of cloud technologies to smart grid. Renew Sustain Energy Rev 59:1326–1331 61. Apache Hadoop. http://hadoop.apache.org/ 62. Zhang Y, Chen S, Wang Q et al (2015) I2MapReduce: incremental MapReduce for mining evolving big data. IEEE Trans Knowl Data Eng 27(7):1 63. Xing EP, Ho Q, Dai W et al (2015) Petuum: a new platform for distributed machine learning on big data. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 1–1 64. Shyam R, Bharathi GHB, Sachin KS et al (2015) Apache spark a big data analytics platform for smart grid. 21:171–178 65. Apache Spark. http://spark.apache.org/

References

341

66. Li D (2014) Machine learning aided decision making and adaptive stochastic control in a hierarchical interactive smart grid. Dissertations and theses—grad-works 67. Wu X, Zhu X, Wu GQ et al (2014) Data mining with big data. IEEE Trans Knowl Data Eng 26(1):97–107 68. Al-Jarrah OY, Yoo PD, Muhaidat S et al (2015) Efficient machine learning for big data: a review. Big Data Res 2(3):87–93 69. Khazaei J, Fan L, Jiang W et al (2016) Distributed prony analysis for real-world PMU data. Electr Power Syst Res 133:113–120 70. Shaker H, Zareipour H, Wood D (2016) A data-driven approach for estimating the power generation of invisible solar sites. IEEE Trans Smart Grid 7(5):2466–2476 71. Shaker H, Zareipour H, Wood D (2016) Estimating power generation of invisible solar sites using publicly available data. IEEE Trans Smart Grid 7(5):2456–2465 72. Taieb SB, Huser R, Hyndman RJ et al (2016) Forecasting uncertainty in electricity smart meter data by boosting additive quantile regression. IEEE Trans Smart Grid 7(5):2448–2455 73. Partridge M, Calvo RA (1998) Fast dimensionality reduction and simple PCA. Intell Data Anal 2(1–4):203–214 74. Poekaew P, Champrasert P (2015) Adaptive-PCA: an event-based data aggregation using principal component analysis for WSNs. In: Proceedings of the international conference on smart sensors and application. IEEE 75. Nuchprayoon S (2014) Electricity load classification using K-means clustering algorithm, Proceedings of the Brunei international conference on engineering and technology. IET, pp 1–5 76. Yang H, Zhang J, Qiu J et al (2016) A practical pricing approach to smart grid demand response based on load classification. IEEE Trans Smart Grid 1–1 77. Mu FL, Li HY (2014) Power load classification based on spectral clustering of dual-scale. In: Proceedings of the IEEE international conference on control science and systems engineering. IEEE 78. Ye M, Liu Y, Xiong W et al (2014) Voltage stability research of receiving-end network based on real-time classification load model. In: Proceedings of the IEEE international conference on mechatronics and automation, pp 1866–1870 79. Che JX, Wang JZ (2014) Short-term load forecasting using a kernel-based support vector regression combination model. Appl Energy 132(11):602–609 80. Liu N, Tang Q, Zhang J et al (2014) A hybrid forecasting model with parameter optimization for short-term load forecasting of microgrids. Appl Energy 129:336–345 81. Hernández L, Baladrón C, Aguiar JM et al (2014) Artificial neural networks for short-term load forecasting in microgrids environment. Energy 75(C):252–64 82. Nickel M, Murphy K, Tresp V et al (2015) A review of relational machine learning for knowledge graphs. Proc IEEE 104(1):11–33 83. Drumond LR, Diaz-Aviles E, Schmidt-Thieme L et al (2014) Optimizing multi-relational factorization models for multiple target relations. In: Proceedings of the ACM international conference, pp 191–200 84. Schweppe F, Wildes J (1970) Power system static-state estimation, part I, II, III. IEEE Trans Power Appl Syst PAS-89(1):120–35 85. Xu X, He X, Ai Q et al (2015) A correlation analysis method for power systems based on random matrix theory. IEEE Trans Smart Grid 86. Yang Q (2016) Cross-domain data fusion. Computer 49(4):18–18 87. Diou C, Stephanopoulos G, Panagiotopoulos P et al (2011) Large-scale concept detection in multimedia data using small training sets and cross-domain concept fusion. IEEE Trans Circuits Syst Video Technol 20(12):1808–1821 88. Tsai CC, Chang-Chien LR, Chen IJ et al (2016) Practical considerations to calibrate generator model parameters using phasor measurements. IEEE Trans Smart Grid 1–11 89. Mishra DP, Samantaray SR, Joos G (2016) A combined wavelet and data-mining based intelligent protection scheme for microgrid. IEEE Trans Smart Grid 7(5):2295–2304 90. Hana S (2012) SAP AG, in-memory database, computer appliance. Vertpress

342

5 Big Data in Smart Grid and Edge Computing of the IoT

91. Kennedy SJ (2012) MCP. M.C.P, Massachusetts institute of technology transforming big data into knowledge: experimental techniques in dynamic visualization. Massachusetts Inst Technol 92. Cuzzocrea A (2014) Privacy and security of big data: current challenges and future research perspectives. In: Proceedings of the international workshop on privacy and security of big data. ACM, pp 45–47 93. Bertino E (2015) Big data—security and privacy. In: Proceedings of the IEEE international congress on big data. IEEE 94. Chiang, M, Zhang T (2016) Fog and IoT: an overview of research opportunities. IEEE Internet of Things 3(6):854–864 95. Pan J, McElhannon J (2018) Future edge cloud and edge computing for internet of things applications. IEEE Internet of Things J 5(1):439–449 96. Chen A et al (2017) HCOS: a unified model and architecture for cloud operating system. ZTE Commun Mag 15(4):23–29 97. Shi W et al (2016) Edge computing: vision and challenges. IEEE Internet of Things J 3(5(Oct.)):637–646 98. Pan J, McElhannon J (2018) Future edge cloud and edge computing for internet of things applications. IEEE Internet Things J 5(1):439–449 99. Chen S et al (2017) Fog computing. IEEE Internet Comput 21(2):4–6 100. Bonomi F, Milito R, Natarajan P et al (2014) Fog computing: a platform for internet of things and analytics. In: Bessis N, Dobre C (eds) Big data and internet of things: a roadmap for smart environments, studies in computational intelligence, vol 546. Springer, Cham 101. Dolui K, Datta SK (2017) Comparison of edge computing implementations: fog computing, cloudlet and mobile edge computing. In: IEEE Global Internet of Things summit (GIoTS), Geneva, 1–6 102. Shi W et al (2016) Edge computing: vision and challenges. IEEE Internet Things J 3(5):637– 646 103. Aazam M, Huh EN (2016) Fog computing: the cloud-IOT/IoE middleware paradigm. IEEE Potentials 35(3):40–44 104. Hu P et al (2017) Survey on fog computing: architecture, key technologies, applications and open issues. J Netw Comput Appl (Nov.):27–42 105. Open Fog Consortium, OpenFog reference architecture for fog computing. https://www. openfogconsortium.org/wp-content/ploads/OpenFog_Reference_Architecture_2_09_17FINAL-1.pdf 106. Swiatek P et al (2012) Service composition in knowledge-based SOA systems. N Gener Comput 30(2):165–188 107. Gubbi J et al (2013) Internet of Things (IoT): a vision, architectural elements, and future directions. Future Gener Comput. Syst 29(7(Sept.)):1645–1660 108. Silva DM et al (2016) Villalba. Wirel Pers Commun 91, 1711 109. Jara AJ et al (2013) Mobile discovery: a global service discovery for the internet of things. In: 27th IEEE international conference on advanced information networking and applications workshops, Barcelona, pp 1325–1330 110. Stelmach P (2013) Service composition scenarios in the internet of things paradigm, DoCEIS 2013. IFIP Adv Inf Commun Technol 394 111. Rykowski J et al (2014) Comparison of architectures for service management in IoT and sensor networks by means of OSGi and REST services. In: 2014 Federated conference on computer science and information systems, Warsaw, pp 1207–1214 112. Lin N, Shi W (2014) The research on Internet of things application architecture based on web. In: 2014 IEEE workshop on advanced research and technology in industry applications (WARTIA), Ottawa, ON, pp 184–187 113. Freeman H, Zhang T (2016) The emerging era of fog computing and networking. IEEE Commun Mag 54(6):4–5 114. Yang Y (2017) FA2ST: fog as a service technology. In: 2017 IEEE 41st annual computer software and applications conference (COMPSAC), Turin, pp 708–708

References

343

115. Tran DH et al (2017) OaaS: offload as a service in fog networks. Computing 99:1081 116. Shi Y et al (2015) The fog computing service for healthcare. In: 2015 2nd IEEE international symposium on future information and communication technologies for ubiquitous healthcare (Ubi-HealthTech), Beijing, pp 1–5 117. Sheng Z et al (2013) A survey on the ietf protocol suite for the internet of things: standards, challenges, and opportunities. IEEE Wirel Commun 20(6):91–98 118. Salman T, Jain R (2015) Networking protocols and standards for internet of things, internet of things and data analytics handbook. Wiley 119. Aijaz A et al (2015) CORPL: a routing protocol for cognitive radio enabled AMI networks. IEEE Trans Smart Grid 6(1(Jan.)):477–485 120. Basagni S et al (2012) Channel-aware routing for underwater wireless networks. In: 2012 Oceans—Yeosu, Yeosu, pp 1–9 121. Zhou Z et al (2016) E-CARP: an energy efficient routing protocol for UWSNS in the internet of underwater things. IEEE Sens J 16(11):4072–4082 122. IEEE Standard for a Convergent Digital Home Network for Heterogeneous Technologies Amendment 1: Support of New MAC/PHYs and Enhancements, IEEE Std 1905.1a-2014, pp 1–52 123. Wobschall D (2007) IEEE 1451-a universal transducer protocol standard. In: 2007 IEEE autotestcon, Baltimore, MD, pp 359–363 124. Moritz G et al (2011) A lightweight SOAP over CoAP transport binding for resource constraint networks. In: 2011 IEEE eighth international conference on mobile ad-hoc and sensor systems, Valencia, pp 861–866 125. Santos GLD et al (2015) A DTLS-based security architecture for the Internet of Things. In: 2015 IEEE symposium on computers and communication (ISCC), Larnaca, pp 809–815 126. Yassein MB et al (2016) Application layer protocols for the Internet of Things: a survey. In: 2016 International conference on engineering & MIS (ICEMIS), Agadir, pp 1–4 127. D’Angelo G et al (2016) Simulation of the Internet of Things. In: 2016 International conference on high performance computing and simulation (HPCS), Innsbruck, pp 1–8 128. Zhao Y et al (2015) NS3-based simulation system in heterogeneous wireless network. In: 11th International conference on wireless communications, networking and mobile computing (WiCOM 2015), Shanghai, pp, 1–6 129. Osterlind F et al (2006) Cross-Level sensor network simulation with COOJA. In: 31st IEEE conference on local computer networks, Tampa, FL, pp 641–648 130. Muscalagiu I et al (2013) Large scale multi-agent-based simulation using NetLogo for the multi-robot exploration problem. In: 2013 11th IEEE international conference on industrial informatics (INDIN), Bochum, pp 325–330 131. Zeng X et al (2017) IOTSim: a simulator for analysing IoT applications. J Syst Architect 72(issue C):93–107 132. Gupta H et al (2016) ifogsim: a toolkit for modeling and simulation of resource management techniques in internet of things, edge and fog computing environments, tech. report CLOUDSTR-2016-2. Cloud Computing and Distributed Systems Laboratory, University of Melbourne 133. Mehdi K et al (2014) CupCarbon: a multi-agent and discrete event wireless sensor network design and simulation tool. In: Proceedings of the 7th international ICST conference on simulation tools and techniques (SIMUTools ’14), Brussels, Belgium, Belgium, pp 126–131 134. Xian X et al (2008) Comparison of OMNeT++ and other simulator for WSN simulation. In: IEEE conference on industrial electronics and applications (ICIEA’08), pp 1439–1443 135. Ghayvat H et al (2015) Simulation and evaluation of ZigBee based smart home using Qualnet simulator. In: 2015 9th International conference on sensing technology (ICST), Auckland, pp 536–542 136. Burg A et al (2018) Wireless communication and security issues for cyber-physical systems and the internet-of-things. Proc IEEE 106(1):38–60 137. Suryani V, Selo, Widyawan (2016) A survey on trust in internet of things. In: 2016 8th International conference on information technology and electrical engineering (ICITEE), Yogyakarta, pp 1–6

344

5 Big Data in Smart Grid and Edge Computing of the IoT

138. Kim H, Lee EA (2017) Authentication and authorization for the internet of things. IT Prof 19(5):27–33 139. Li X et al (2017) An IoT data communication framework for authenticity and integrity. In: 2017 IEEE/ACM second international conference on internet-of-things design and implementation (IoTDI), Pittsburgh, PA, pp, 159–170 140. Margelis G et al (2016) Smart attacks on the integrity of the internet of things: avoiding detection by employing game theory. In: 2016 IEEE global communications conference (GLOBECOM), Washington, DC, pp, 1–6 141. Fotiou N et al (2016) Access control for the internet of things. In: 2016 International workshop on secure internet of things (SIoT), Heraklion, pp 29–38 142. Mrityunjaya DH et al (2017) Smart transportation. In: 2017 International conference on ISMAC (IoT in social, mobile, analytics and cloud) (I-SMAC), Palladam, pp 1–5

Chapter 6

AMI and DR, the Enabling Technologies for Information Processing in the Smart Grid

In addition to the conventional transmission and distribution automation and automatic meter reading, the secondary systems of measurement and control in smart grids necessitate enhanced functionality. This entails upgrading the secondary system of the traditional power grid, reinforcing the intelligence of secondary equipment, and facilitating user terminal participation as well as the integration of renewable energy sources. To achieve this purpose, two notable advancements have emerged: Advanced Metering Infrastructure (AMI) and Demand Response (DR), both of which bolster user-side engagement. Furthermore, in the era of smart grids, the computation, control, and monitoring within the traditional power grid operate under a centralized information processing mode that encompasses the generation, transmission, and distribution network. However, with the escalating penetration of renewable energy into the power system, the conventional centralized information processing paradigm struggles to meet the demands of smart grids. Consequently, it becomes imperative to engage in comprehensive discussions and research regarding a new information processing mode for smart grids, both in theory and practice. This will enable us to address the challenges that smart grids pose to the traditional information processing mode. Therefore, a thorough examination of the information processing mode of smart grids is essential, encompassing distributed computing, self-organizing sensor networks, active control, and an overarching computing framework. This chapter will first delve into AMI as a user-centric infrastructure, facilitating direct participation. Subsequently, it will explore the intricacies of demand response, building upon the foundation laid by AMI. Finally, the chapter will scrutinize enabling technologies for information processing in smart grids.

© Chemical Industry Press 2023 X. Zeng and S. Bao, Key Technologies of Internet of Things and Smart Grid, Advanced and Intelligent Manufacturing in China, https://doi.org/10.1007/978-981-99-7603-4_6

345

346

6 AMI and DR, the Enabling Technologies for Information Processing …

6.1 AMI 6.1.1 Advanced Metering Infrastructure (AMI) and Smart Grid The vision of the smart grid is to deliver electric power to users in a controllable and intelligent manner, while also empowering users to actively participate in the consumption and production of energy. Unlike the traditional power system, which solely supplies electricity from power plants to users through transmission and distribution grids, the smart grid transforms users from passive consumers to active participants. Users can dynamically modify their power consumption patterns and behaviors based on received information, incentives, and inhibiting factors. Moreover, they can also act as distributed energy providers, feeding electric energy back into the grid [1–3]. The advantages of the smart grid largely stem from its ability to enhance power supply reliability, responsiveness of consumers, and to encourage efficient decisionmaking by both consumers and utilities. As a result, demand-side management (DSM), encompassing various functions executed on the demand side, plays a vital role in the smart grid [4, 5]. A comprehensive DSM approach necessitates the integration of communication systems, sensors, automatic metering, smart devices, and information processing systems. DSM typically refers to an information system deployed by the power supply company to manage and measure power consumption at the user end. This system benefits both the power supply company and the user, facilitating more efficient operations within the power market [6], reducing peak demand, and mitigating fluctuations in spot prices [7]. In practice, the evolution from the traditional grid to the smart grid involves the extensive adoption of information and communication technologies, as well as IoT technologies. This transition empowers the grid with enhanced operational effectiveness, agile monitoring and control capabilities, and the ability to promptly respond to user needs, power generation cost fluctuations, and variations in renewable energy sources [8–10]. Consequently, the smart grid can dynamically track changes in the supply–demand balance in real time. The smart grid comprises a series of interconnected subsystems [11]. Each subsystem fulfills specific functions that contribute to the overall performance of the smart grid. If we envision each subsystem as a layer within the smart grid, the output of each layer serves as the input for the subsequent layer. Figure 6.1 illustrates this relationship, highlighting the role of each subsystem within the comprehensive framework of the smart grid [12]. Advanced Metering Infrastructure (AMI) is not a standalone technology; rather, it represents a configurable infrastructure that integrates multiple technologies to achieve its objectives. This infrastructure encompasses smart meters, various layers of communication networks, a Meter Data Management System (MDMS), and an application platform and interface that assimilates acquired data into the software

6.1 AMI

347

Fig. 6.1 Smart grid-related subsystem framework [12]

Advanced Asset Management (AAM)

Advanced Transmission Operation (ATO)

Advanced Distribution Operations (ADO)

Advanced Metering Infrastructure (AMI)

system. Figure 6.2 provides a basic representation of AMI’s composition. At the user end, advanced smart meters are installed to gather real-time data. These smart meters transmit the collected data via commonly used communication networks, which can include power line broadband (or narrowband) communication networks, public fixed or mobile communication networks, and more. The AMI host system receives the measured power usage data, which is then forwarded to the MDMS for data storage, analysis, and presentation in the desired format to utility providers. Thanks to the bidirectional communication enabled by AMI, commands or price signals from the power supply company can elicit two-way responses from the meters or load control equipment [13]. Below is a brief description of the smart grid subsystem framework depicted in Fig. 6.1: Fig. 6.2 The diagram of AMI three-layer structure [11]

Data acquisition, analysis, storage, and system management system

Communications network smart meter

smart meter

smart meter

348

6 AMI and DR, the Enabling Technologies for Information Processing …

• Advanced Metering Infrastructure (AMI): Establishes a connection with the load (user) and provides time-stamped system information. • Advanced Distribution Operation (ADO): Utilizes AMI to collect distribution information and leverages this information to improve distribution operations. • Advanced Transmission Operation (ATO): Applies ADO information to enhance transmission capacity, improve voltage quality, and enables users to access the power market through AMI. • Advanced Asset Management (AAM): Utilizes AMI, ADO, and ATO information to send control signals, thereby enhancing operational efficiency and maximizing grid asset utilization.

6.1.2 Subsystems of AMI AMI finds applications not only in power systems but also in other utility systems such as natural gas, water supply, and heating. While the system structures of various metering systems share similarities, they also possess distinct characteristics. Figure 6.2 depicts the AMI composition, comprising three key subsystems: data (information) management subsystem, communication network subsystem, and smart devices. One example of a smart device is a smart meter, which is primarily responsible for gathering data related to power feeders. For specific details on the collected information, refer to Table 3.5. (1) (1) Smart Devices Smart devices, such as smart meters, offer the capability to acquire data or measurements at specific time intervals, which are then time-stamped. These devices communicate with telematics management systems or data centers, transmitting the collected information to the relevant subsystems at predetermined intervals. Given the bidirectional communication of AMI, smart devices, including load control devices, can receive remote commands and execute corresponding actions. From the user’s perspective, a smart device serves as an instrument that provides power consumption data to both users and power supply companies. The display on the smart device allows users to view their power consumption information, enabling them to understand their energy usage better. Furthermore, pricing information for power, gas, and water allows load control devices (e.g., smart thermostats) to adjust usage based on pre-defined user metrics and commands. When distributed or stored energy sources are available, smart devices can intelligently allocate these energy sources according to demand. In terms of measurement phenomena, smart meters can be categorized into three types: electric, fluid, and thermal. Additionally, smart meters can be equipped with various sensors to measure parameters such as humidity, temperature, and light, which can impact electricity consumption. The inclusion of sensors can be expanded based on user or system designer requirements and preferences, taking into consideration cost and functionality.

6.1 AMI

349

A smart meter performs two key functions: measurement and communication. Consequently, each meter comprises two subsystems: metering and communication. The metering subsystem is influenced by factors such as area, measurement phenomena, required accuracy, data security level, and specific applications. Security considerations are crucial, and appropriate security measures should be implemented for communication. Regardless of the measurement type or quantity, a smart meter should possess the following functions [14]: • Accurate Measurement: It should accurately measure object parameters using various physical, chemical, statistical principles, theorems, or rules. • Control and Calibration: The meter should compensate for minor system changes, with specific requirements varying depending on the meter type. • Communication: It should be able to transmit stored data, receive operational commands, and support firmware upgrades. • Power Management: In case of primary power source failure, the meter system should maintain functionality. • Display: Users should have access to meter information, as it serves as the basis for billing. Monitors are essential, and without real-time knowledge of power usage, demand management at the consumer end would not be feasible. • Synchronization: Accurate timing synchronization is critical for reliable data transmission to the concentrator for analysis and billing, especially in wireless communication scenarios. Based on the above, the main features of smart meters can be summarized as follows: • • • • • • •

Time-based pricing. Provision of power consumption data to users and power supply companies. Net metering. Fault and outage notifications. Remote command (on/off) operation. Load limitation based on demand response objectives. Power quality monitoring, including phase, voltage, current, active and reactive power, and power factor. • Power theft detection. • Communicate with other smart devices. (2) Communication Network The smart meter plays a crucial role in transmitting the collected information to the information management system or information center, while also receiving operation commands from the system. Therefore, the communication network is an integral part of AMI. With a large volume of data to transmit, it is essential to have a highly reliable communication network. Designing and selecting an appropriate communication network requires careful consideration of key factors such as massive data, data access restrictions, confidentiality of sensitive data, expressing complete power

350

6 AMI and DR, the Enabling Technologies for Information Processing …

consumption information of users, grid status, data authenticity, accurate communication with target devices, cost-effectiveness, support for advanced features beyond AMI requirements, and future scalability [15]. Various communication technologies can be employed to establish an AMI communication network, including Power Line Carrier (PLC), Broadband over Power Lines (BPL), copper wire or fiber optic, cellular mobile communication, WiMax, Bluetooth, General Packet Radio Service (GPRS), Internet, satellite communications, NB-IoT, and Zigbee. Within the AMI framework, devices within a household communicate with each other through smart meters and establish communication with the power supply company’s network. This network is often referred to as the Home Area Network (HAN). Additionally, the HAN interacts with utilities, forming another network known as the utility network. The HAN connects smart meters, smart devices within the home, energy storage systems, renewable energy generation sources (e.g., solar, wind), electric vehicles, and more. Since data flow within HANs is typically instantaneous rather than continuous, the bandwidth requirement typically ranges from 10 to 100 Kbps, depending on the specific task. Given the short distances between HAN nodes, low-power wireless technologies are preferred for HAN implementations. These technologies may include 2.4 GHz WiFi, the 802.11 wireless network protocol, Zigbee, and HomePlug [16]. (3) Information (data) Management System On the utility side, the system is needed to store, analyze, and manage data for various purposes such as billing, demand response (DR), real-time grid monitoring, and emergency response. This system typically consists of several modules, each serving a specific function. The main modules of the utility management system include: • Meter Data Management System (MDMS) • Consumer Information System (CIS), Billing system and Powering corporate Website • Outage Management System (OMS) • Enterprise Resource Planning (ERP), Power Quality Management and Load Forecasting System • Mobile Workforce Management (MWM) • Geographic Information System (GIS) • Transformer Load Management (TLM). MDMS, the central module of the management system, serves as the pivotal component encompassing essential analysis tools to facilitate communication with other modules. Its crucial role extends to the validation, compilation, and estimation of AMI data, guaranteeing the integrity and completeness of information flow between users and management modules in the event of lower layer malfunctions. Within the current framework of AMI, operating on a 15-min data acquisition

6.1 AMI

351

interval, the volume of acquired data reaches a colossal scale, measured in terabytes, thus qualifying as “big data” [17]. The management and analysis of such vast datasets necessitate specialized tools and techniques. The sources contributing to the generation of smart grid big data, while not exclusively limited to the grid itself, consist of the following components: • AMI (Smart Meter): Acquires power usage data at a given frequency. • Distribution Automation System: Acquires real-time control data for the system, often involving up to 30 samples per second per sensor [18]. • Third-party systems interconnected with the grid, including energy storage, distributed energy resources, and electric vehicles. • Asset Management: Facilitates communication between the central control room and smart components within the grid, encompassing tasks such as firmware updates. It is important to note that the definition and design of MDMS may vary among different power supply companies, as they tailor their systems to align with their specific requirements and concepts. Consequently, the number and nature of additional features or applications will differ between providers. However, all MDMS solutions must fulfill three fundamental criteria: enhancing and optimizing grid operations, improving utility management processes, and enabling active customer participation. Data analysis has emerged as a focal point within smart grid research, aiming to harness the entirety of available data from both internal and external grid sources. By leveraging advanced data analysis and mining techniques, valuable insights can be extracted to inform decision-making processes. Given that the collected data contains sensitive personal and business information, it is imperative to ensure secure and resilient storage facilities, incorporating robust backup mechanisms and contingency plans to mitigate various scenarios. However, it is worth noting that the associated costs for implementing such measures can be substantial. Virtualization presents an opportunity to maximize resource utilization and return on investment by consolidating available resources. Nevertheless, it entails additional technological complexity. Alternatively, cloud computing enables access to virtual resources located in diverse locations, yet raises significant concerns surrounding data security.

6.1.3 Security of AMIs As the proliferation of smart meters continues at an exponential rate, the security considerations surrounding smart grids and AMIs become increasingly paramount. The intricate details encompassing a customer’s power usage hold significant value as they can inadvertently disclose information about their lifestyle and habits. Consequently, the transmission and storage processes involved in handling this data pose

352

6 AMI and DR, the Enabling Technologies for Information Processing …

potential network security vulnerabilities. Furthermore, the price signals and execution orders received by clients are susceptible to diverse attacks, thereby jeopardizing the rights and interests of users. In addition to these concerns, physical damage to power infrastructure and instances of power theft pose significant financial losses for power companies. Hence, this subsection delves into security issues from three distinct perspectives: safeguarding the privacy of consumer information, fortifying the system against cyber-attacks, and combating power theft. (1) End User Privacy The analysis of smart meter data enables accurate insights into energy consumption, revealing information such as the number of occupants in a household, check-in times, and types of home appliances used. Remarkably, even without employing complex algorithms or computer-aided tools, it is possible to analyze residents’ behavior. Murrill et al. [19] have demonstrated that by examining just 15 min of cumulative power usage data, it is feasible to identify the usage patterns of major appliances within a residence. Similarly, Molina-Markham et al. [20] have showcased that using current statistical schemes, patterns of power usage can be discerned from AMI data, even in cases where the available power usage data is insufficient. While capturing detailed information stands as a primary objective of the smart grid, the collection and utilization of such granular data without user consent pose potential risks to user privacy. In certain countries, legal discussions surrounding AMI and data acquisition for smart grids are underway. For instance, the Information and Privacy Commissioner of Ontario, Canada, has emphasized the importance of privacy protection within smart meter data management systems. The commissioner seeks to address information privacy across three domains related to the smart grid and AMI: information technology, business practices, and network infrastructure. It is worth noting that a one-size-fits-all formulation does not exist to cover security requirements within these domains. Each domain entails its own unique requirements, measures, and considerations [21]. To ensure the preservation of individual freedom and personal control over personal information, the concept of “Privacy by Design” (PbD) is introduced, employing the following seven fundamental principles: • Proactive non-reaction: PbD adopts a proactive approach, focusing on preventive measures to forestall privacy breaches before they occur. • Privacy as default: Privacy is embedded as the default setting in the system, relieving users from the need to activate privacy features as they are already enabled by default. • Embedding privacy into the design: Privacy is integrated into the system’s design and architecture, becoming an integral part of its functionality without compromising its overall operation. • All positive-sum functions: PbD aims to address all legitimate interests and goals in a mutually beneficial manner, avoiding unnecessary trade-offs associated with outdated zero-sum approaches.

6.1 AMI

353

• End-to-end security protection over the life cycle: PbD is implemented from the outset, ensuring data security from the moment the first piece of information is collected and continuing throughout the entire lifecycle of the data. This approach guarantees secure data retention and the option for safe data destruction if desired at the conclusion of the process. • Maintaining open visibility and transparency: System components and operations remain visible and transparent to both users and providers. • Respecting user privacy and user-centricity: PbD necessitates that designers and operators prioritize customer satisfaction by providing robust privacy defaults, appropriate notifications, and user-friendly options. (2) Security Against External Cyber or Physical attacks In an Advanced Metering Infrastructure (AMI), numerous security requirements align with those of conventional IT networks. However, there are distinct security considerations that warrant attention, as outlined below: (i) Confidentiality Confidentiality pertains to safeguarding the privacy of consumer power usage patterns. It encompasses the protection of metering and power usage information, ensuring it remains private. On the AMI side, user information must be kept confidential, with access restricted to authorized systems for specific datasets. (ii) Integrity While the AMI resides in a physically secure environment within the power enterprise, its multiple interfaces to various systems expose vulnerabilities. Integrity in the AMI applies to both data transmission from the meter to the utility and control commands from the utility to the meter. Upholding integrity entails preventing unauthorized changes to data received from the meter and commands issued to the meter. Hackers may attempt to compromise the system’s integrity by masquerading as authorized entities and issuing malicious commands. Smart meters, compared to their electro-mechanical counterparts, offer greater resilience against physical or cyberattacks. They should also possess the ability to detect cyber-attacks and disregard unauthorized control commands to uphold system integrity. (iii) Availability Availability concerns vary depending on the type of information transmission within the system. Some data may not require real-time acquisition and can be obtained at larger intervals, with estimates serving as substitutes for actual values. However, in certain scenarios, acquiring actual values at very short intervals, such as every minute, becomes crucial. Component failures, resulting from physical damage, software issues, or tampering, can lead to data unavailability. Communication failures, caused by factors like interference, cable cuts, aging lines, bandwidth loss, or network congestion, can also contribute to unavailability.

354

6 AMI and DR, the Enabling Technologies for Information Processing …

(iv) Accountability Accountability, also known as non-repudiation, ensures that the receiving entity cannot deny receiving the data, and conversely, if an entity has not received the data, they cannot claim otherwise. This is particularly significant from both a financial perspective, concerning AMI billing, and the accuracy of metered data and responses to control signals. Accountability requirements become complex due to the involvement of different components within the AMI system, often manufactured by different suppliers and owned by various entities (e.g., users, service providers). Ensuring accurate information timestamping and time synchronization across the AMI network is vital for accountability. Audit logs serve as a common method to ensure accountability, but they are inherently vulnerable. In smart meters, all changes in metered values, parameters, and tariffs should be accountable since they form the basis for billing. The study of attacks on AMIs should also consider the attackers and their motivations, providing valuable insights for designing effective security countermeasures. It is evident that a single solution is inadequate for grid protection. Cleveland [22] examines threats to system security and suggests techniques and strategies to enhance system security. These include asset security risk assessments, security compliance reporting, and security attack litigation, among others. Additional security technologies encompass Intrusion Detection Systems (IDS), firewalls with Access Control Lists (ACLs), Network and System Management (NSM), and public key infrastructure (PKI). In summary, several security constraints within the AMI are highlighted [22]: • Smart meters should undergo annual rating certification. • The installation of smart meters in unsecured locations poses challenges for achieving physical security. • Some parts of the AMI network rely on low-bandwidth technologies like Zigbee, WiFi, or Power Line Communication (PLC), which can hinder security measures due to limited throughput for transmitting large certificates to meters with high communication frequencies. • Certain AMI networks utilize common communication services like cellular networks, which offer limited security compared to purpose-built networks. • The functionality of the overall system necessitates numerous other systems accessing AMI data on the utility side. Ensuring unified security across the network requires coordinated security policies and techniques. However, achieving such coordination can be challenging since different systems are often owned and operated by different entities. (3) Power Theft From a technical standpoint, power theft can have detrimental effects, including the potential overloading of generators, which in turn can result in overvoltage. This occurs because the power supply company lacks an accurate estimate of the actual

6.1 AMI

355

power consumption due to the theft. As a consequence, the generator set can experience tripping, leading to power cuts. Moreover, maintaining a good power factor and stable voltage on the feeder necessitates an adequate supply of reactive power. However, power theft disrupts the calculation of total load flow and complicates the compensation of reactive power. Traditionally, electromechanical meters lack safety features and are easily manipulated, making them vulnerable to power theft. There are several methods used to achieve power theft in electromechanical meters [23]. These methods include direct connection to distribution lines, grounding the neutral wire, attaching magnets to the meter, obstructing the spinning coil to stop its rotation, damaging the spinning coil, and reversing the input and output connections. The implementation of smart meters offers a solution to mitigate these issues. However, more sophisticated techniques can be employed to bypass smart meters, and one such technique involves tampering with current transformers (CT). CTs are commonly used to match the grid current rating to the meter rating of the load. By manipulating the CT’s ratio, the meter can be made to register lower or even zero current. Some power theft techniques used in electromechanical meters can also be applied to smart meters and advanced metering infrastructure (AMI) systems. Intervened data can occur in three distinct phases: the data acquisition phase, the data storage phase within the meter, and the data transmission phase over the network. Both traditional and smart meters can be vulnerable to data manipulation during the data collection process. However, data manipulation in the storage and transmission phases is only possible with smart meters. Mclaughlin et al. [23] have depicted various ways of power theft in an “attack tree.“ These different methods of power theft can lead to the falsification or manipulation of demand data. AMI systems employ data loggers that make tampering with meters more challenging compared to traditional systems. The data loggers can detect power outages or reverse flow within the meter. Attackers attempting inversion or disconnection techniques would also need to erase the logged events stored in the meter. Therefore, erasing logged events falls under the second category of tampering with the data stored in the meter. If an attacker gains access to the stored data of a smart meter, they can assume full control over the meter, including usage time tariffs, executed commands, event logs, consumption and timestamp data, and firmware. Typically, in cases of power theft, the firmware and entire stored data in the meter are not of interest to attackers. Instead, manipulating the total demand and audit logs stored in the meter is sufficient, which requires knowledge of the meter’s password. In another scenario, data can be altered while in transit over the network, which involves injecting erroneous data or intercepting communications within the infrastructure. This type of attack can occur at any node within the infrastructure. If the attack takes place at the aggregation point or the backhaul link, the data of a group of power meters or users can be compromised. To execute such an attack, the perpetrator would need to introduce a backhaul link or gain access to a communication channel to modify or inject faulty data between the meter and the utility. Since AMIs utilize encryption and authentication for communication, an attacker would need to obtain the encryption key stored in the meter. If the authentication and encryption processes

356

6 AMI and DR, the Enabling Technologies for Information Processing …

or the integrity protocol between the meter and the utility are flawed, attackers can employ spoofing techniques to send fake demand values or event logs to the utility. In cases where the authentication process is faulty but encrypted communication exists between the meter and the utility, the attacker would need to impersonate the utility company’s meter at a node between the meter and the utility on the backhaul, and vice versa, to obtain the encryption key. This type of attack is commonly referred to as a man-in-the-middle attack [23]. Various techniques and technologies have been developed to estimate and detect power theft, some of which rely on smart meters while others operate independently. These technologies include the genetic algorithm-support vector machine algorithm, power line impedance technology, and harmonic generator technology [24]. Additionally, several mathematical methods have been introduced to detect power theft, such as support vector machine linear, support vector machine-radial basis function, artificial neural network-multilayer perceptron, and optimal path forest classifier [25].

6.1.4 The Standards and Protocols Related AIM In order to facilitate communication within the grid, it is essential to establish a common language and adhere to associated standards. Various communication protocols are commonly used for automatic meter reading (AMR), including ZigBee, Modbus, M-Bus, DLMS/IEC62056, IEC61107, and ANSI C.12.18. DLMS/COSEM serves as the prevailing language in AMR/AMI systems. DLMS, also known as Device Language Message Specification, provides a conceptual framework for modeling communicating entities. On the other hand, COSEM, which stands for Companion Energy Metering specification, establishes the guidelines for exchanging data with power meters using existing standards. The significance and functionality of DLMS/COSEM can be defined as follows: • It offers an object model that enables the examination of a meter’s functionality, including its interface. • It serves as an identification system for all metering data. • It utilizes a messaging method to communicate with the model and convert the data into a series of bytes. • It provides a transmission method for transferring information from a metering device to a data acquisition system. DLMS is developed and maintained by the DLMS User Association, which has collaborated with IEC TC13 WG14 to create an international version of DLMS known as the IEC 62,056 series of standards. In this collaborative effort, the DLMS Users Association takes responsibility for maintenance, registration, and conformance testing services for this new international standard. Meanwhile, COSEM encompasses a set of specifications that define the transport and application layers of the DLMS protocol [26].

6.2 Demand Response (DR)

357

DLMS has published four sets of specifications:: • Green Paper serves as an introduction to the architecture and protocols. • Yellow Paper addresses all matters related to conformance testing. • Blue Paper describes the COSEM instrumentation object model and object recognition system. • White Paper contains a glossary. A product that conforms to the DLMS Yellow Paper indicates compliance with the IEC62056 standard. IEC TC13 WG14 has categorized the DLMS specification under the heading “Meter - Meter Reading, Data Exchange for Tariff and Load Control.“ Furthermore, IEC 62056–21, known as the Direct Local Data Exchange (version 3d of IEC 61107), provides guidance on utilizing COSEM on local ports, whether they are optical or current loop connections. Additionally: • IEC 62056–42: Physical layer services and procedures for connection-oriented asynchronous data exchange. • IEC 62056–46: Data link layer using HDLC protocol. • IEC 62056–47: COSEM transport layer for IPv4 networks. • IEC 62056–53: COSEM application layer. • IEC 62056–61: Object identification system (OBIS). • IEC 62056–62: Interface class.

6.2 Demand Response (DR) 6.2.1 Basic Concepts and Benefits of DR (1) Fundamental Concept of Demand Response (DR) Demand Response (DR) refers to the practice of adjusting end-user power consumption in response to time-varying power prices, high wholesale power prices, or when system reliability is at risk, with the aim of incentivizing users to consume less power [27, 28]. DR has both short-term and long-term impacts on the power market. In the short term, it encourages user interaction and response, leading to economic benefits for users and power supply enterprises. In the long run, DR improves power system reliability, reduces peak demand, lowers overall power plant costs and investment expenses, and delays the need for grid upgrades [29]. DR programs are designed to coordinate the relationship between power consumption and power system operation by facilitating the response to power consumption. These programs employ various types of DR resources, including distributed generation, dispatchable loads, energy storage, and other resources that assist in regulating the main supply grid. Inducement mechanisms are typically used in DR programs to reduce consumer demand, limit peak demand, and support increased demand during periods of high generation and low demand. It’s important to note that implementing

358

6 AMI and DR, the Enabling Technologies for Information Processing …

DR may cause temporary discomfort for users as it can reduce the comfort level of power consumers. However, automation, monitoring, and control technologies serve as the foundation for managing energy usage and implementing DR, ensuring that the inconvenience to users is minimized. When customers participate in DR, there are three potential ways their power consumption can be altered [29]: • Load shedding strategies can be employed to reduce power consumption. • Energy consumption can be shifted to different time periods. • Power generated by on-site devices can be utilized to reduce dependence on the main grid. For instance, load reduction strategies can involve dimming lighting levels and adjusting air conditioner temperatures. On the other hand, load shifting for commercial or residential customers can be achieved by pre-cooling buildings during lowercost hours to shift the load of air conditioning operation from higher-cost hours. Industrial facilities can also benefit from utilizing low-cost off-peak energy by employing storage technology to postpone certain production operations to overnight shifts or by transferring their production to other industrial facilities in different service areas. Thanks to advancements in smart grid technology [29], coordination among users can be automated through two-way digital communication, providing a robust foundation for implementing DR procedures. In a study proposed by [9], an incentivebased smart grid power dispatching scheme is introduced, aiming to minimize the energy cost of the system. The scheme analyzes a scenario where energy is shared among different customers, each equipped with an automatic power scheduler embedded in the smart meter, connected to the communication network via the power line. The smart meters interact automatically, running a distributed algorithm to determine the optimal power usage plan for each customer. Incentives are provided to users to encourage cooperation, utilizing simple pricing mechanisms based on game theory that contribute to overall system performance improvements. By considering pricing schemes, optimal solutions to system-wide optimization problems are achieved. Furthermore, the DR algorithm can be implemented as an integrated function within a distribution management system (DMS) at the grid control center level. (2) Advantages of Demand Response (DR) Depending on various factors such as goals, design, performance, and more, DR offers a range of benefits in terms of system operation, market efficiency, and system scalability [27, 30]. (i) System Operation By implementing DR plans, users can respond to price signals, effectively reflecting the actual operating costs of power generation and grids, and in turn, obtain cost reductions in system operations. For example, during periods of high generation costs, a portion of demand can be shifted to lower generation cost periods, thereby

6.2 Demand Response (DR)

359

optimizing energy usage. Grid operators and distribution companies benefit from DR implementation by securing discounts on generation, transmission, and distribution costs. In the event of generation or distribution interruptions, DR allows grid operators to quickly reduce power demand during critical times, aiding in the recovery of the power system to pre-emergency levels. DR plays a crucial role in mitigating the challenges posed by the variable and uncertain output of intermittent renewable energy sources by facilitating real-time supply and demand balance [31]. Moreover, in situations where power outages occur, DR contributes to real-time balancing and provides supplementary power, thereby enhancing short-term power supply reliability and reducing the need for excess capacity [32]. Additionally, DR can help reduce line losses [33], which alleviate grid constraints and mitigate the risk of power interruptions during unforeseen circumstances [34]. DR plans can also offer ancillary services to grid system operators, including voltage support, active and reactive power balancing, frequency regulation, and power factor correction [35]. (ii) Market Efficiency Active participation in the market on the demand side is believed to bring significant benefits, particularly in terms of market efficiency [36–41]: • Consumers can reduce power costs by shifting their load from periods of higher prices to periods of lower prices. • Changes in demand flatten the overall load curve, thereby reducing the overall cost of generation. • Transforming the cost reduction into a price reduction prevents changes in demand due to price fluctuations, safeguarding the interests of consumers. • Market power exercised by generating companies is alleviated. Market-driven DR schemes, typically implemented through time-varying tariffs, enable the demand side to actively engage in the market, resulting in increased market efficiency. In fact, DR has the potential to lower the wholesale market price for all traded energy, thereby mitigating the impact of high wholesale and retail prices as well as extreme system events [42, 43]. By adjusting their demand based on price signals, users can optimize their utility by proportionally consuming power at specific times [42]. The benefits of lower wholesale prices are contingent on the total amount of power traded in the market. Short-term benefits also include shielding customers from variable supply costs in vertically integrated utility companies. Flattening demand conditions lead to lower prices, effectively redistributing wealth from generation to consumers. Enhanced demand elasticity helps limit the magnitude and frequency of price surges and reduces the ability of generators to exercise market power in wholesale electricity markets [44]. In organized markets, during periods of high demand and insufficient supply, more expensive electricity is generated, resulting in higher market-clearing prices. The price-response mechanism of DR allows demand to decrease when market-clearing prices rise, preventing suppliers from exerting market power, increasing the number of suppliers in the market, reducing market concentration, and making collusion more difficult. DR also

360

6 AMI and DR, the Enabling Technologies for Information Processing …

enables utilities, retailers, and users to mitigate price fluctuations and system emergencies. Conversely, more elastic demand mostly reduces the profits of generating companies [45, 46]. (iii) System Scalation As mentioned previously, DR has the potential to alter customer load patterns, which can lead to changes in the mix of peak and base load capacity. By reducing local and system peaks in specific areas, DR eliminates the need for additional generation, transmission, or distribution infrastructure. Local grids are typically designed to handle peak expected demand, and reducing local peaks allows for the avoidance of grid upgrades at a specified reliability level or defers investment in capacity due to improved grid reliability in the long run [47]. Overall, DR offers benefits in terms of optimizing system operation, enhancing market efficiency, and enabling scalability in power systems. By leveraging the flexibility of energy consumption, DR contributes to a more sustainable and cost-effective energy ecosystem.

6.2.2 User Classification and User Model in DR (1) User Classification According to the smart grid conceptual model of the National Institute of Standards and Technology (NIST), there are two types of entities that interact with utilities or independent system operators (ISOs) to facilitate DR: users and demand response providers (DRPs) [48]. Users are entities that consume electrical energy, and their participation in DR programs can be voluntary or mandatory. DRPs act as intermediaries between utilities/ISOs and customers, offering a range of services related to DR. In many cases, users may require technical and financial support from utility companies to install automated equipment for DR that can respond to signals from the utility [49]. Users can be classified into the following categories based on their power consumption profiles: • • • • •

Large industrial and commercial enterprises (C&I). Small industrial and commercial enterprises (C&I). Residential. A single plug-in electric vehicle (PEV), and PEV fleet.

Large industrial and commercial customers are typically equipped with state-ofthe-art technology to effectively control the loads within their facilities, especially in manufacturing and process control settings. This technological advantage enables them to actively participate in both wholesale and retail power markets. On the

6.2 Demand Response (DR)

361

other hand, within commercial facilities, the primary loads primarily revolve around managing the facility itself, such as HVAC systems and lighting. For industrial users and certain large commercial customers that have on-site generation capabilities for emergency backup or auxiliary power, this self-generated energy can be utilized for demand response (DR) purposes. Additionally, some industrial facilities have autonomous and independent production processes that can be flexibly shifted to different times of the day or even different days when required. Residential customers, in contrast, have relatively smaller and more limited types of loads, which often discourage significant investments in managing their power usage. Their participation in the power market is typically confined to retail power schemes, mainly through direct load control programs. The emergence of new standards and technologies, such as Advanced Metering Infrastructure (AMI), enables cost-effective devices to join the market. Furthermore, advancements in building automation systems’ standards and technologies pave the way for smart homes to contribute to smart grids. Small industrial and commercial customers exhibit a diverse range of characteristics, sometimes resembling residential customers and at other times resembling large business customers. The introduction of Plug-in Electric Vehicles (PEVs) presents a substantial new load for existing distribution systems, and their dispersed nature lends support to load shifting. However, it is crucial to properly reinforce distribution systems to prevent voltage fluctuations during DR implementations, as power quality degradation can lead to damage to utilities and consumer equipment. (2) Conceptual Model of User Domain The NIST Interim Roadmap for Smart Grid Interoperability Standards [48] introduced a conceptual model of the smart grid, as depicted in Fig. 6.3. This conceptual model serves as a valuable tool for regulators at all levels to evaluate optimal strategies for achieving public policy and commercial objectives. It aims to promote investment in the development of the nation’s power system and the establishment of a clean energy economy. NIST recommends this model as a framework for understanding the various roles involved in the smart grid. It provides a reference for configuring different components of the electrical system in efforts to standardize the smart grid. The conceptual model divides the smart grid into seven distinct domains, each comprising specific subdomains that encompass smart grid roles and applications. Within these domains, actors, which can be devices, systems, or programs, determine actions and exchange necessary information to implement specific applications. Meanwhile, applications represent the tasks that actors within the domain need to accomplish. For example, actors such as smart meters, solar generators, and control systems can be found in the model, with corresponding applications like home automation, solar power generation, energy storage, and energy management. Table 6.1 presents an updated view of the smart grid domain, focusing on fully supporting the demand response (DR) business model. Participants in the user domain, including clients, have the ability to effectively manage their energy usage

362

6 AMI and DR, the Enabling Technologies for Information Processing …

Fig. 6.3 Smart grid domains [48]

and generation [2]. Certain actors also facilitate control and information flow between users and other domains. Typically, utility company meters and communication gateways like energy management systems (EMS) serve as the boundaries of the customer domain. The user domain is typically divided into subdomains, such as home, business/ building, and industry, each with its specific energy demand threshold. Domestic demand is usually set below 20 kW, commercial/building demand ranges from 20 to 200 kW, and industrial demand exceeds 200 kW. Each subdomain comprises various actors and applications, with power meters and EMS consistently included across all subdomains. EMS functions as the primary service interface within the user domain and can be located within power meters or as standalone gateways. It enables the consumer domain to connect with the distribution, generation, operations, markets, and service provider domains. Communication systems like Advanced Metering Infrastructure (AMI) or the Internet facilitate EMS’s interaction with other domains. EMS utilizes home area networks or local area networks to communicate with equipment within customer premises. Multiple EMSs may be present per customer, resulting in multiple communication paths. EMS serves as the entry point for applications such as remote load control, home displays for customer use, non-energy meter readings, monitoring and control of distributed generation, integration with building management systems

6.2 Demand Response (DR)

363

Table 6.1 Smart grid domains that fully support the DR business model Domain name

Description for the domain

Customer

Any entity that consumes gas and/or electricity services. Consumers of power. Customers range from small to large industrial, commercial and residential Customer

Market

A power market is a system that enables the purchase and sale of power, using supply and demand to set prices

Service Provider Entities that provide power services to retail or end users Operations

Manage generation, markets, transmission, distribution and consumption of power

Generation

Produce high-capacity power for industrial, residential and rural use. It also includes power storage and distributed energy

Transmission

Power transmission is the mass transfer of electrical energy, the process of delivering power to consumers

Distribution

Distribution is the final phase in delivering power to end users. The grid of distribution systems that transmit power from the transmission system and deliver it to consumers

Microgrid

Local grid for distributed energy management and delivery

and enterprises, as well as providing auditing and logging for cybersecurity purposes. Certain actors also play a role in controlling and facilitating information flow between users and other domains, as illustrated in Fig. 6.4.

6.2.3 Demand Response Plan To motivate users, a DR plan should highlight the benefits of participation and enable customers to use control technologies like smart thermostats and power information systems. Customers are encouraged to participate in DR plans to save money, avoid outages, and contribute to a sense of responsibility. Power supply companies should provide coordination services and support customers in adopting technologies to enhance participation at different stages of the plan. (1) Classification of Demand Response Plan DR plans can be classified based on various criteria, as summarized in Table 6.2 according to [50]. Furthermore, DR classifications are also presented in [51]. However, in terms of the side of demand reduction, DR plans can be broadly categorized into three main categories, as illustrated in Table 6.2. (i) Rate- Based or Price-Based DR Plan In the Rate-Based or Price-Based DR Plan, DR implementation relies on approved utility tariffs or contractual agreements within deregulated markets. These arrangements lead to varying power prices over time, which serve as incentives for customers

364

6 AMI and DR, the Enabling Technologies for Information Processing …

Fig. 6.4 The overview of User domain [2]

to modify their consumption behaviors. The prices can fluctuate for a predetermined period or dynamically, taking into account factors like the day, week, year, and existing reservation margins. During peak hours, customers may encounter higher prices, while off-peak hours offer lower prices. The determination of prices can occur daily, hourly, or even a day in advance in real-time, necessitating users to react to fluctuations in electricity prices. Table 6.3 provides an illustrative example of this type of plan. While numerous utility companies offer price-based DR tariffs, they represent only a small fraction of the total range of existing DR plans. (ii) Incentive or Event-based DR Plans In Incentive or Event-based DR Plans, customers are incentivized or rewarded for reducing their power loads when requested, or they grant plan administrators a certain level of control over their power equipment. The utility or DR service provider

6.2 Demand Response (DR)

365

Table 6.2 Classification of demand response plan Criteria

Dualities

Aim

Reliability

Economy

Trigger factor

Emergency-based

Price-based

Signal origin

System leading

Market leading

Signal type

Load response

Price response

Motivation method

Motivational

Time-based rate

Control

Directly load control

Passive load control

System/market Structure

Vertically integrated regulatory Free market system

Promotions and financing

Regulatory authority

Marketing agent

Target customers

High voltage (large industrial and commercial enterprises)

Low voltage (small commercial and domestic)

Automatic response

Manual response (without enabling technology)

Automatic response (using AMI and/or other smart devices)

Table 6.3 Common types of demand response plans Price option

Incentive or event-based options

TOU (time-of-use rate): Fixed Directly load control: price block rates that vary over customers can obtain time incentives that allow the utility to perform some degree of control over certain equipment CPP (critical peak pricing): Contains pre-specified supercharged rates triggered by utility companies and in effect for a limited time

Emergency Demand Response Plan: Customers are incentivized to shed loads when needed to ensure reliability

RTP (real-time pricing): A rate that changes continuously (usually hourly) in response to wholesale market prices

Capacity Marketplace Plan: Customers accept incentives for load shedding as alternative capacity for the system Interruptible/Available: Customers can agree to a discounted rate for load reduction upon request Ancillary Services Marketplace Plan: Customers receive payment from the grid operator to shed load when needed to support grid operation (i.e. ancillary services)

Bid to reduce demand Demand bid/buyback Plans: When wholesale market prices are high, customers place bids to reduce load

366

6 AMI and DR, the Enabling Technologies for Information Processing …

(aggregator) sends a series of demand reduction signals to participating customers in the form of voluntary requests or mandatory orders. These signals prompt customers to adjust their electricity consumption accordingly. Incentive or event-driven DR can be activated in response to various trigger conditions. (iii) Bids that Reduce demand In Bids that Reduce Demand plans, customers actively participate by initiating and submitting demand reduction bids to utility companies or aggregators. These bids typically outline the available capacity for demand reduction and specify the desired price. This plan primarily serves as an incentive for larger customers to offer load reductions at a price they find acceptable, or to indicate the amount of load reduction they are willing to pay for at a predetermined price set by the utility company or aggregator. DR plans can also be classified as market DR and physical DR. Market DR focuses on economic factors and includes strategies like real-time pricing, price signaling, and incentives. It aims to reduce system costs through price-based approaches and market-led initiatives. On the other hand, physical DR prioritizes grid management and emergency signaling to enhance system reliability. It involves emergency-based actions, system-guided strategies, and load-responsive measures. This distinction allows for different approaches to be employed based on the objectives of cost reduction or system reliability.

6.2.4 Enabling Smart Technology for Demand Response (1) Enabling Smart technology for Demand Response. Incorporating cutting-edge technologies and advancements can yield significant economic and social advantages for utilities while elevating the attainable objectives of existing demand response (DR) plans [52]. Notably, the progress in integrated electronic circuits, control systems, and information and communication technologies has revolutionized the capabilities of metering and DR solutions. The confluence and interplay of pivotal elements determine the efficiency of power consumption and the DR potential of user facilities. Particularly, the integration of innovative enabling technologies assumes a pivotal role in fostering grid efficiency enhancements and facilitating seamless DR coordination. The array of enabling technologies encompasses various advancements, including but not limited to: • Optimization strategies for demand reduction: These strategies address multiple objective functions concerning energy prices and emergency situations. • Two-way communication and time period metering: This enables power supply companies to display real-time power consumption patterns to users. • Communication equipment: This facilitates informing users about load reduction actions.

6.2 Demand Response (DR)

367

• Energy information tools: These tools provide near real-time access to load data, enable post-load shedding performance assessments, and alert operators to potential load shedding requirements. • Energy management control system: This system incorporates DR optimization load controllers and building energy management control systems. • On-site generation equipment for emergency backup or meeting primary facility power needs. Several innovative smart technologies, such as smart meters, are essential components of most DR plans. Automatic response technology, which enables remote control over power consumption and peak load, serves as the fundamental basis for implementing DR. This technology can be categorized into control equipment, monitoring systems, and communication systems. The subsequent focus will be directed towards the control equipment and monitoring system aspects of DR. (2) Control Equipment for DR Load control devices, such as load control switches and smart thermostats, can be deployed as standalone units or integrated into larger Energy Management Systems (EMS) within facilities. Load control switches enable remote control of specific enduser loads like compressors or motors, establishing a connection with utilities via the communication system. On the other hand, smart thermostats can be remotely controlled by both the utility company and the customer, providing the flexibility to set temperature change points through more adaptable controls rather than rigid settings. (i) Smart Technologies for Building and Home Energy Management Active DR systems located within homes facilitate interactions between customers and the distribution grid. These systems comprise simple devices and local EMS that enable energy management and establish two-way communication with energy distributors and retailers. By receiving market and system signals, the EMS can effectively manage loads, heating, ventilation, and air conditioning (HVAC) systems, as well as energy storage and local generator sets based on user preferences. The advancement of smart home technology allows appliances like washing machines, water heaters, dryers, dishwashers, and refrigerators to autonomously respond to price signals, enhance reliability, and adapt to DR event notifications. (ii) Backup Generators and Energy Storage for Industrial and Commercial Users Industrial and commercial customers can employ backup generators as a practical and convenient solution to alleviate grid load and subsequently reduce their power expenses. Moreover, they can derive additional economic value from these generators by keeping them operational. However, it’s important to note that the emission of pollutants becomes a significant concern when backup generators are utilized for DR purposes. Consequently, due to limitations imposed by emissions permits, backup generators are primarily used for emergency situations, operating for a restricted number of hours annually.

368

6 AMI and DR, the Enabling Technologies for Information Processing …

Integrating energy storage units into home and building energy management systems enhances load management capabilities and ensures safety for critical applications. Additionally, surplus power generated locally can be utilized to charge Plugin Electric Vehicles (PEVs) or be deployed during emergency situations or high-price periods. (3) Monitoring System The monitoring system encompasses various components such as smart meters, Advanced Metering Infrastructure (AMI), Energy Management Systems (EMS), and Energy Information Systems (EIS). While smart meters and AMI have been discussed in detail in previous chapters, EMS and EIS are briefly described below: (i) Energy Management System EMS, also known as Energy Management System, enables the monitoring, analysis, and control of building systems and equipment through a combination of sensors, switches, controls, and algorithms. Monitoring individual loads and devices is essential for tasks like developing and updating load control strategies, validating control responses, and creating load models. Certain load control strategies, particularly those designed for residential customers, may require dedicated monitoring capabilities. In some cases, specific infrastructure needs to be deployed within the end user’s facility to facilitate information transfer from individual devices to the control center. While the primary objective of EMS is to enhance building energy performance by conserving energy and reducing peak demand, it can also perform automated DR functions. (ii) Energy Information System An Energy Information System (EIS) serves as a gateway for two-way communication between utilities and the existing EMS or operates independently of the EMS. Similar to EMS, EIS installations primarily focus on energy information and load management rather than their role in DR. These systems are primarily used to collect data and provide actionable information regarding system performance to end users and utilities. However, they can also incorporate automatic response features to notify users. EIS functions revolve around monitoring and recording real-time energy usage data for billing analysis and reporting, enabling users to receive alerts about DR events, and offering notification and analysis capabilities. Additionally, EIS can facilitate automated responses to incidents or support requests from utilities, assisting in error detection, impact analysis of operational changes made in response to incidents, and decision-making processes.

6.3 Enabling Technology of Information Processing in Smart Grid

369

6.3 Enabling Technology of Information Processing in Smart Grid 6.3.1 Problems and Challenges Faced by Information Processing Technology in Smart Grid (1) Challenges Encountered in the Optimization of Distribution Grid Assets In the traditional approach to planning and operating distribution grids, the fundamental assumptions revolve around the concept of “passivity,” which implies that power flow from the transmission grid to the distribution grid is unidirectional. Consequently, significant investments in the grid primarily focus on enhancing the interconnection of the transmission grid and increasing the transmission capacity of its components, ensuring the reliable and efficient transmission of power over long distances. Unfortunately, the distribution system is often overlooked as a strategic asset within the power system infrastructure, and its planning and operational standards have remained inadequately adapted over time [53, 54]. In the present day, numerous challenges arise within distribution systems, particularly concerning the imperative need for optimal coordination of distributed energy resources and the pursuit of extensive expansion in renewable energy generation. The optimization of grid assets becomes essential to meet the growing demand while striking a balance between resource adequacy, reliability, and the formulation of appropriate planning and operational strategies that align with the principles of economic viability and environmental sustainability [55]. The advent of the smart grid represents a highly promising avenue to address these intricate challenges mentioned earlier [56], as it empowers the distribution grid to evolve into a resilient, self-organizing, and self-healing [51, 57]. (2) The Challenge of Heterogeneity of Data The fundamental strength of the smart grid lies in its capacity to enable ubiquitous computing and foster collaboration among heterogeneous entities, including software, remote processing units, and smart sensors. These entities seamlessly share and exchange data, operating within strict time constraints to generate actionable information that can be applied to specific application areas [58]. In this regard, the modernization of existing energy and power distribution management systems poses a significant technical challenge for future smart grids. These systems often rely on computing paradigms with limited scalability, featuring restricted interoperable interfaces, a wide range of heterogeneous information technologies, and traditional proprietary platforms [59]. To tackle this issue head-on, High-Performance Computing (HPC) systems must reevaluate their architectural requirements, design criteria, and assumptions pertaining to scalability, adaptability, flexibility, and technological evolution [60, 61]. The heterogeneity of data emerges as a critical concern in the operation of conventional distribution systems, as the

370

6 AMI and DR, the Enabling Technologies for Information Processing …

deployment of measurement systems is unlikely to adhere to a consistent hardware and software architecture over time [62]. (3) Challenges for Big Data The effective management of large-scale data presents yet another critical challenge that must be confronted, particularly considering the anticipated exponential growth in the number of field sensors within smart grids [63]. This growth necessitates the timely processing of corresponding data streams to extract actionable information [64]. In order to tackle this complex issue, smart grid operators must accurately represent and manage the inherent uncertainties that impact measurement data, allowing for a comprehensive understanding of the information environment and the ability to assess confidence in the derived content [65]. Moreover, even with the availability of sophisticated mathematical models for analyzing data flows, several challenges need to be addressed. These challenges encompass communication network congestion, the escalating complexity of optimization problems, the management of uncertainty in vast datasets, and the vulnerability of centralized computing systems. Overcoming these obstacles requires the conceptualization of decentralized, self-organizing, active, and holistic computing frameworks that can serve as decision support systems within data-rich but information-limited domains. This research endeavor stands as one of the most significant challenges, as these paradigms enable the enhancement of grid operations through knowledge discovery and the provision of information services derived from data mining, moving beyond the confines of predefined power system states to deliver the most valuable insights to operators within a useful timeframe [66]. The deployment of such information services holds immense potential for various crucial smart grid applications, including online grid optimization, voltage control, security analysis, synchronized wide-area measurements, pervasive grid monitoring, real-time information sharing, energy price forecasting, and renewable energy power forecasting.

6.3.2 The Optimization Model for Smart Grid (1) The Optimization Model for Smart Grid Smart grid optimization endeavors to identify the most favorable operating state of the grid, considering economic and technical criteria while ensuring compliance with system and component operating limitations. This process is not confined to any specific application domain and can be effectively addressed by solving the following constrained nonlinear multi-objective programming problem: ⎧ ⎪ ⎨

min f i (x, u) i = 1, · · · , p x,u

s.t. g j (x, u) = 0 j = 1, · · · , n ⎪ ⎩ h k (x, u) ≤ 0k = 1, · · · , m

(6.1)

6.3 Enabling Technology of Information Processing in Smart Grid

371

where, x is the dependent variable vector, u is the decision variable vector, f i is the i th scalar objective function, g j is j th equality constraints, h k is k th inequality constraints. Decision variables depend on the specific smart grid application, including the real power produced by the programmable generator, the set point of the grid controller, and the state of the controllable load. Dependent variables include the voltage magnitude and angle on the load bus, the voltage angle and reactive power produced on the generating bus, and the active and reactive power produced on the slack bus. Inequality constraints include the maximum transmission capacity of each power line, allowable bounds for each decision and dependent variable, while equality constraints represent the load flow equation. Objective functions can quantify to technical and economic principles, including generation costs, power system losses, regulatory costs, etc. Since these functions often describe competing objectives, an appropriate trade-off between objective functions should be made [67]. (2) Overview of the Solution of the Smart Grid Optimization Model To address optimization challenges in power systems, various algorithmic approaches have been proposed, including nonlinear programming, quadratic programming [68, 69], and linear programming. These algorithms formalize the optimality conditions, such as the Karush–Kuhn–Tucker condition, and employ the Newton iterative algorithm to solve the corresponding system of nonlinear equations. However, these algorithmic approaches have notable limitations when applied to smart grid optimization [70, 71]. They struggle with large-scale problem solving, managing multiple and diverse constraints, achieving globally optimal solutions, and handling complex and pathological problem instances. In response to these limitations, metaheuristic techniques have emerged as a promising direction for research [72, 73]. These techniques encompass genetic algorithms, evolutionary algorithms, and biologically-inspired approaches. The application of these computational paradigms allows smart grid operators to explore solution spaces more comprehensively and reduce the likelihood of converging to local optima. However, further research is needed to rigorously analyze the convergence and efficiency of metaheuristic algorithms [74]. For instance, in solving reactive power scheduling problems, particle swarm optimization (PSO) can be employed. However, the mathematical formulation of particle motion in PSO introduces challenges as specific values of internal parameters may lead to convergence or divergence. All the aforementioned solution technologies for smart grid optimization can be classified as “centralized solutions” since they rely on a central facility to collect and process grid data. Recent discussions have raised concerns regarding the suitability of this hierarchical solution paradigm for future distribution networks. Literature speculates that this computing paradigm may not be adequate for solving optimization problems in

372

6 AMI and DR, the Enabling Technologies for Information Processing …

smart grids due to the demand for real-time grid optimization and the substantial uncertainties impacting power system operations. As a result, there is a need for more resilient and flexible computing paradigms [75]. The adoption of a distributed, cooperative, and self-organizing multi-agent optimization paradigm has emerged as a promising research direction [76]. Numerous studies have highlighted the crucial role played by multi-agent and cooperative paradigms in solving key smart grid optimization problems, such as optimal management of distributed energy resources [77], economic dispatch [78], and demandside management [79]. These studies demonstrate that a decentralized and selforganizing computing framework can significantly enhance smart grid performance by mitigating the impact of unexpected events and improving the grid’s resilience in the face of external disturbances or component failures [80]. Moreover, these computing frameworks exhibit stability and self-healing capabilities when appropriately designed and deployed.

6.3.3 Overview of Online Voltage Control The proliferation of distributed and renewable energy resources in existing distribution grids poses a significant challenge in smart grids: the need to address the problem of online voltage control. This issue arises due to the impact of these resources on grid voltage and the resulting increase in complexity associated with maintaining grid stability. To tackle this challenge, the design of an online control system becomes crucial, guided by the principles of the “passivity hypothesis” [81]. The injection of real power by regenerative generators, operating at a fixed power factor, can have a significant impact on the voltage amplitude at the injection point, leading to fluctuations in the generation curve. If left unaddressed, this phenomenon could impose limitations on the maximum power that renewable generators can inject into the grid, thereby hindering their full utilization [82]. Moreover, effective coordination between renewable generators, distributed energy sources equipped with power electronic interfaces (acting as controllable var generators), and other voltage controllers is essential to enhance overall grid voltage quality in smart grids. To achieve this, smart grid voltage control necessitates solving a multi-objective mathematical programming problem periodically, which can be viewed as a specific instance of the general problem formulated in Eq. (6.1). In this context, the decision variables encompass the set point of the voltage controller, the state of the on-load tap-changer of the power transformer, and the reactive power generated by the allocated energy resource. The objective function commonly considered for solving this problem incorporates technical and economic criteria, such as average voltage deviation, network loss, and regulation cost. However, since these objectives often conflict with each other, determining an appropriate trade-off becomes crucial. Traditionally, the online voltage regulation problem is addressed using a nonlinear programming approach,

6.3 Enabling Technology of Information Processing in Smart Grid

373

leveraging the availability of a centralized computing facility to identify the optimal settings for the voltage controller that minimize a scalar cost function obtained by appropriately combining all control objectives. The primary drawbacks of deploying these solution paradigms in smart grid voltage control arise from the inability of scalar cost functions to adequately capture the inherent multi-criteria nature of the voltage control problem and the exhaustive search for solutions in the solution space, known as convex sextic problem optimization [83]. To address these critical issues, the smart grid literature has proposed more efficient formal solutions to the voltage control problem [84]. These improved schemes capture the multi-objective nature of the voltage control problem by first approximating the set of non-dominated solutions and then selecting the final solution based on a predefined selection criterion. Standard nonlinear optimization techniques, such as goal attainment, or more advanced heuristic approaches like evolutionary algorithms and fuzzy-based evolutionary algorithms [85], are employed to achieve this. Optimal voltage regulation can be achieved through both decentralized and centralized approaches. It is important to note that while centralized methods may be more efficient in terms of minimizing the objective function, they require a detailed grid model and assume the availability of all relevant information. While multi-objective-based solution paradigms effectively address the effectiveness problem of smart grid voltage control, their deployment in real-time environments may introduce computational challenges. This motivates the exploration of more efficient online smart grid voltage control solution paradigms that enhance solution process efficiency by reducing the need for complex, computationally intensive algorithms [86, 87]. In tackling this challenge, the fusion of case-based reasoning and machine learning is considered a highly effective solution strategy. The rationale behind this approach is that, in real operating scenarios, algorithms are often employed to solve power system state multi-objective problems that closely resemble previously solved problems. Therefore, computational intelligence-based algorithms can “learn” how to solve control problems based on historical information. Recently, several studies have proposed novel advanced decentralized computing frameworks based on highly pervasive computing and intelligent cooperative voltage controllers [83, 88]. The adoption of cooperative controllers in online smart grid voltage control is expected to improve task distribution among distributed processing units, thereby significantly reducing processing resources and enhancing voltage efficiency and reliability, simplifying the control process.

6.3.4 Overview of Online Security Analysis Online security assessment is indeed a crucial aspect of smart grids, as an unreliable distribution system can have widespread implications for users and grid operators who share grid resources.

374

6 AMI and DR, the Enabling Technologies for Information Processing …

The process of online security assessment involves regularly solving static and dynamic power system state equations to evaluate the potential consequences of unforeseen events. The results of these complex calculations must be obtained within strict time constraints, enabling smart grid operators to plan and implement preventive and corrective actions to mitigate the impact of critical contingencies. The requirement for time-constrained analysis drives research efforts towards identifying the most effective and reliable approaches to handle emergencies, thereby reducing the computational time required for emergency analysis [89]. For grid state stability problems, various contingency screening and ranking methods based on soft computing techniques have been explored [90]. These methods leverage machine learning-based paradigms to identify the most probable contingencies once the current power system operating point is known. In terms of complexity reduction in emergency analysis, some studies have proposed the use of neural network-based computational paradigms to streamline the assessment process [91]. As power systems become increasingly complex, computing performance may be impacted, necessitating continuous upgrades to hardware resources. In such complex scenarios, the computational burden of online smart grid emergency analysis can dynamically increase, requiring the deployment of more scalable computing paradigms. Grid computing has been proposed as a state-of-the-art solution to address this challenge. This approach involves leveraging a network of interconnected and dynamically reconfigurable collaborative resources over the Internet to support intensive power system computing [92]. The concept of “Ubiquitous Computing Grid” envisions a future computing paradigm for smart grid computing that interacts and collaborates with heterogeneous computing resources across various networks, ranging from supercomputers to pervasive sensor networks.

6.3.5 Wide-Area Monitoring Protection and Control System (WAMPAC) Wide-area monitoring protection and control systems (WAMPAC) rely on timesynchronized sensor networks, such as phasor measurement units (PMUs), strategically placed within the smart grid to collect phasor and frequency information [93, 94]. A typical WAMPAC architecture consists of several interconnected components, including PMUs, data concentrators, application tools, and wide-area communication networks. WAMPAC applications encompass various functionalities, such as topology and state estimation, optimal distributed resource management, intelligent recovery techniques, and proactive warning services [95].

6.3 Enabling Technology of Information Processing in Smart Grid

375

On-site experiences have demonstrated that the widespread deployment of WAMPAC in smart grids can significantly reduce the occurrence of large-scale disturbances by enabling smart grid operators to implement advanced protection schemes and adaptive control strategies. However, despite its potential benefits, the development of WAMPACs in distribution systems is still in its early stages, and several open issues need to be addressed. One particular challenge is the design of a comprehensive and highly flexible WAMPAC architecture capable of withstanding internal and external disturbances that could jeopardize its operation. The conventional hierarchical WAMPAC architecture has certain drawbacks that may hinder its deployment in future smart grids. Specifically, the expected significant increase in grid data collection and exchange, estimated to be around four orders of magnitude, could lead to rapid saturation of the centralized WAMPAC architecture [96]. Moreover, the use of weak GPS signals for time synchronization in PMUs renders WAMPAC systems highly susceptible to radio interference. To tackle these challenges, distributed computing architectures can be employed by implementing pervasive and self-organizing computing frameworks that enable PMUs to collaborate and function collectively with all power components within a substation [97, 98].

6.3.6 Power Market Forecast Power price forecasting plays a crucial role in the smart grid, as market dynamics significantly impact the behavior of various stakeholders, including generation companies, traders, and load-service entities, especially in highly volatile power markets. For instance, power generation companies can utilize price forecasting models to optimize their profits and manage market risks by improving bidding strategies and pricing forward derivative contracts [99]. Load-service entities can leverage this information to manage price volatility risk by making decisions on service loads, such as opting for short-term or long-term contracts or purchasing power from the spot market. Smart grid operators can utilize forecast data to anticipate price changes that could impact generator dispatch and the power demand in the distribution grid. Large customers can use this data to quantify market volatility and manage associated risks through long-term bilateral contracts. Traditionally, forecasting algorithms based on production cost models or statistical methods are employed in this domain. Production cost models analyze past system operations by processing historical data, including the electrical characteristics of generating units and grid information. These forecasting methods, based on econometric models, aim to predict consumer behavior by discovering historical relationships and extrapolating them. However, these algorithms require detailed information, such as market bids, generation data, grid data, and fuel prices, which can limit their applicability. To address these limitations, statistical algorithms based

376

6 AMI and DR, the Enabling Technologies for Information Processing …

on the Autoregressive Integrated Moving Average (ARIMA) have been widely used for short-term forecasting scenarios [100]. ARIMA is relatively easy to implement and exhibits acceptable performance. However, as power prices are non-stationary, especially with fluctuating prices characterized by non-constant means, variances, and significant outliers, their performance tends to decline over longer forecast horizons due to the complex bidding behavior influenced by various factors. To overcome this limitation, nonlinear learning techniques have been proposed, including feed-forward neural networks, neuro-fuzzy networks, recurrent neural networks, and hybrid approaches [101–103]. These techniques combine different predictive techniques, such as ARIMA and Artificial Neural Network (ANN) hybrid models. Predictive models are developed based on complementary features of statistical and nonlinear learning models, resulting in an adaptive architecture that outperforms the predictive accuracy of individual components used alone [103]. By merging supervised learning techniques with statistically based predictors, these hybrid models can provide improved forecasting results.

6.3.7 Adaptive Wind Power Forecasting Supporting the large-scale integration of wind turbines into distribution grids while mitigating their negative impacts on system operation and control is a significant challenge in smart grids. The integration of intermittent and non-programmable wind generators can affect line current and bus voltage magnitude, resulting in various side effects [104]. Efficient prediction of the injected wind power profile is crucial in addressing this challenge as it provides strategic information for smart grid operators regarding power market dynamics and enables efficient prediction-based maintenance programs. Wind forecasts can also help minimize the occurrence and duration of power outages [105]. Wind forecasting is commonly approached using Numerical Weather Prediction (NWP) models [106]. These models predict climate variables’ profiles over large areas by solving dynamic atmospheric equations on a fixed spatial grid. However, the spatial resolution of NWP models, typically several km2 , may not accurately capture local wind forces in complex regions. Moreover, NWP models require substantial computing resources and time-consuming solution algorithms, making them impractical for real-time grid operation scenarios. Consequently, much research has focused on developing prediction algorithms that process local measurement data using statistical black-box models, offering higher spatial resolution and lower computational burden. Several learning techniques, including ARIMA, have been proposed for short-term wind forecasting (1–3 h ahead) with acceptable performance [107]. However, their performance may vary in medium-term forecast horizons due to the non-stationarity, high instability, and non-constant means, variances, and outliers in wind profiles. To overcome these limitations, there has been a shift towards the application of nonlinear learning techniques such as feedforward neural networks and neuro-fuzzy networks [108].

6.4 Summary

377

More recently, advanced techniques that integrate physical modeling and nonlinear learning techniques, known as hardware-in-the-loop modeling algorithms, have been proposed for wind forecasting [109, 110]. These techniques combine domain experts’ physical knowledge with empirical evidence from measurements to enhance the performance of climate models and black-box modeling techniques. By integrating the strengths of both approaches, more accurate and reliable wind forecasts can be obtained.

6.4 Summary The secondary system of the smart grid requires an upgrade from the traditional grid to accommodate the increased participation of users and the integration of renewable energy sources. Advanced Metering Infrastructures (AMIs) play a crucial role in enabling user participation. The information processing paradigms of the traditional grid are inadequate for the requirements of the smart grid, necessitating the exploration of new modes of information processing. This includes distributed computing, self-organizing sensor networks, active control, and an overall computing framework. AMI serves as a configurable infrastructure that incorporates various technologies, such as smart meters, communication networks, Meter Data Management Systems (MDMS), and application platforms/interfaces. The MDMS manages data storage and analysis, providing information to power supply companies as needed. However, the widespread use of smart meters also introduces security issues in smart grids and AMIs. The transmission and storage of AMI information can pose network security risks, and users may be vulnerable to attacks that compromise their rights and interests. Additionally, physical damage to power infrastructure and power theft can result in significant losses for power companies. Demand Response (DR) plays a significant role in smart grids, referring to the variation of end-user power consumption in response to time-varying power prices or system reliability conditions. DR promotes user interaction and response, leading to economic benefits for users and power supply enterprises. In the long run, DR helps improve power system reliability, reduce peak demand, and delay the need for grid upgrades. To encourage user participation in DR, it is important to increase customer awareness of its benefits and enhance their ability to utilize control techniques for participation. Motivations for customers to participate in DR include cost savings, outage avoidance, and a sense of responsibility. Power supply companies should also provide comprehensive coordination services to facilitate customer participation at different planning stages and with different technologies. The computing paradigm of the smart grid is shifting from traditional centralized computing to a decentralized, self-organized, active, and holistic computing paradigm. Information services based on knowledge discovery and data mining play a crucial role in enhancing smart grid operations. Various smart grid applications, such as grid optimization, voltage control, security analysis, monitoring, real-time

378

6 AMI and DR, the Enabling Technologies for Information Processing …

information sharing, energy price forecasting, and renewable energy forecasting, can benefit from the deployment of these information services.

References 1. Siano P (2014) Demand response and smart grids—A survey. Renew Sustaina Energy Rev 30:461–478. https://doi.org/10.1016/j.rser.2013.10.022 2. The Modern Grid Initiative (2007) Version 2.0, conducted by the National Energy Technology Laboratory for the US Department of Energy Office of Electricity Delivery and Energy Reliability ,January 2007. http://www.netl.doe.gov/moderngrid/resources.html 3. EPRI’s IntelliGridSM Initiative. http://intelligrid.epri.com 4. Zhong J, Kang C, Liu K (2010) Demand side management in China. In: Proceedings of the 2010 IEEE power and energy society general meeting. Institute of Electrical and Electronics Engineers, Minneapolis, MN 5. Saffre F, Gedgein R (2010) Demand-side management for the smart grid. In: Proceedings of the 2010 IEEEIFIP network operations and management symposium workshops, pp. 300–303 6. Hyung Seon O, Thomas RJ (2008) Demand-side bidding agents: modeling and simulation. IEEE Trans Power Syst 23(3):1050–1056 7. Nguyen DT (2010) Demand response for domestic and small business consumers: a new challenge. In: Proceedings of the 2010 IEEE transmission and distribution conference and exposition. Institute of Electrical and Electronics Engineers, New Orleans, LA 8. Hamed MRA, Wong Vincent WS et al (2010) Autonomous demand-side management based on game-theoretic energy consumption scheduling for the future smart grid. IEEE Trans Smart Grid 1(3):320–331 9. Ramanathan B, Vittal V (2008) A framework for evaluation of advanced direct load control with minimum disruption. IEEE Trans Power Syst 23(4):1681–1688 10. Masters GM (2004) Renewable and efficient electric power systems, Hoboken. Wiley, NJ 11. Rashed Mohassel R, Fung A, Mohammadi F et al (2014) A survey on advanced metering infrastructure. Electr Power Energy Syst 63:473–484. http://creativecommons.org/licenses/ by-nc-nd/3.0/ 12. National Energy Technology Laboratory for the U.S. Department of Energy. Advanced metering infrastructure, NETL modern grid strategy (2008) 13. Electric Power Research Institute (EPRI) (2007) Advanced metering infrastructure (AMI) 14. Silicon Laboratories, Inc. Smart metering brings intelligence and connectivity to utilities, green energy and natural resource management. Rev.1.0. http://www.silabs.com/Support% 20Documents/TechnicalDocs/Designing-Low-Power-Metering-Applications.pdf 15. Depuru SSSR, Wang L, Devabhaktuni V (2011) Smart meters for power grid: challenges, issues, advantages and status, Renewable and sustainable energy reviews, pp 2736–2742 16. US Department of Energy (2010) Communications requirements of smart grid technologies 17. Deign J, Salazar CM (2013) Data management and analytics for utilities. FC Business Intelligence Ltd. 18. Anderson D, Zhao C, Hauser C et al (2011) A virtual smart grid. IEEE Power Energ Mag 13(11):49–56 19. Murrill BJ, Liu EC, Thompson II, RM (2012) Smart meter data: privacy and cyber security, congressional research service 20. Molina-Markham A, Shenoy P, Fu K et al (2010) Private memoirs of a smart meter 21. Information and Privacy Commissioner of Ontario, Canada. Building Privacy into Ontario’s Smart Meter Data Management System: A Control Framework 22. Cleveland FM (2008) Cyber security issues for advanced metering infrastructure (AMI). In: IEEE power and energy society general meeting: conversion and delivery of electrical energy in the 21st century, pp 1–6

References

379

23. McLaughlin S, Podkuiko D, McDaniel P (2010) Energy theft in the advanced metering infrastructure, critical information infrastructures security. Lect Notes Comput Sci 6027:176–187 24. Bandim CJ, Alves JER, Pinto AV et al (2003) Identification of energy theft and tampered meters using a central observer meter: a mathematical approach. In: Proceedings of the IEEE PES transmission and distribution conference and exposition, Rio de Janeiro, Brazil; September, pp 163–168 25. Anas M, Javaid N, Mahmood A et al (2012) Minimizing electricity theft using smart meters in AMI 26. DLMS User Association. http://www.dlms.com 27. DOE Report (2006) Benefits of demand response in electricity markets and recommendations for achieving them. http://westvirginia.sierraclub.org/ 28. Chiu A, Ipakchi A, Chuang A et al (2009) Framework for integrated demand response (DR) and distributed energy resources (DER) models. http://www.neopanora.com/ 29. Vojdani A (2008) Smart integration. IEEE Power Energy Mag 6(6):72–79 30. Adela C, Pedro L (2012) The economic impact of demand-response programs on power systems, A survey of the state of the art, Handbook of networks in power systems I energy systems, vol 2012, pp 281–301 31. Zibelman A, Krapels EN (2008) Deployment of demand response as a real-time resource in organized markets. Electr J 21(5):51–56 32. Earle R, Kahn EP, Macan E (2009) Measuring the capacity impacts of demand response. Electr J 22(6):47–58 33. Shaw R, Attree M, Jackson T et al (2009) The value of reducing distribution losses by domestic load-shifting: a network perspective. Energy Policy 37:3159–3167 34. Affonso CM, da Silva LCP, Freitas W (2006) Demand-side management to improve power security. In: Proceedings of the transmission and distribution conference and exhibition, 2005/ 2006 IEEE PES 35. Crossley D (2008) Assessment and development of network-driven demand-side management measures, IEA Demand Side Management Programme, Task XV, Research Report No. 2. Energy Futures Australia Pty Ltd., NSW, Australia 36. Gyuk I, Kulkarni P, Sayer J et al (2005) The united states of storage electric energy storage. IEEE Power Energy Mag 3(2):31–39 37. Chua-Liang S, Daniel K (2009) Quantifying the effect of demand response on electricity markets. IEEE Trans Power Syst 24(3):1199–11207 38. Goldman C et al (2005) Customer strategies for responding to day-ahead market hourly electricity pricing. Lawrence Berkeley National Laboratory, Berkeley, CA 39. Kirschen DS (2003) Demand-side view of electricity markets. IEEE Trans Power Syst 18(2):520–527 40. Rassenti S, Smith V, Wilson B (2003) Controlling market power and prices pikes in electricity networks: demand-side bidding. Proc Natl Acad Sci 100(5):2998–3003 41. Borenstein S, Bushnell J, Wolak F (2002) Measuring market in efficiencies in California’s restructured whole sale electricity market. Am Econ Rev 92(5):1376–1405 42. PLMA (2002) Demand response: principles for regulatory guidance, Peak Load Management Alliance 43. Violette D, Freeman R, Neil C (2006) Valuation and market analyses. Volume I: overview. Prepared for International Energy Agency, Demand Side Programme 44. Kirschen D (2008) Demand-side view of electricity markets. In: Proceedings of the IEEE transactions on power systems, LBNL-60132.18 May, 2008, vol 2, pp. 520–527 45. Stoft S (2002) Power system economics: designing markets for electricity. Wiley-Interscience, New York 46. Su C-L (2007) Optimal demand-side participation in day-ahead electricity markets. University of Manchester, Manchester, UK 47. Zhang Q, Li J (2012) Demand response in electricity markets: a review. In: Proceedings of the 9th international conference on European energy market (EEM), pp 1–8

380

6 AMI and DR, the Enabling Technologies for Information Processing …

48. National Institute of Standards and Technology (NIST) Framework and roadmap for smart grid interoperability standards, release1.0; January 2010, http://www.nist.gov/publicaffairs/ releases/upload/smartgridinteroperabilityfinal.pdf 49. Assessment of Demand Response and Advanced Metering Staff Report Docket AD06-2-000; August 2006 (Revised December 2008) 50. Adela C, Pedro L (2012) The economic impact of demand-response programs on power systems, A survey of the state of the art, Handbook of networks in power systems I energy systems, pp 281–301 51. Palensky P, Dietrich D (2011) Demand side management: demand response, intelligent energy systems, and smart loads. IEEE Trans Ind Inf 7(3):381–388 52. Jamshid A, Mohammad-Iman A (2013) Demand response in smart electricity grids equipped with renewable energy sources: a review. Renew Sust Energy Rev 18:64–72 53. Vaccaro A, Pisica I, Lai LL et al (2019) A review of enabling methodologies for information processing in smart grids. Electr Power Energy Syst 107:516–522. https://doi.org/10.1016/j. ijepes.2018.11.034 54. Madani V, King RL (2008) Strategies to meet grid challenges for safety and reliability. Int J Reliab Saf 2:1–2 55. Li F, Qiao W, Sun H et al (2010) Smart transmission grid: vision and framework. IEEE Trans Smart Grid 1(2):168–177 56. Yang Q, Barria JA, Green TC (2011) Communication infrastructures for distributed control of power distribution networks. IEEE Trans Ind Inf 7(2):316–327 57. Qiang Y, Green TC, Barria JA (2011) Communication infrastructures for distributed control of power distribution networks. IEEE Trans Ind Inf 7(2):316–327 58. Gungor VC, Lu B, Hancke GP (2010) Opportunities and challenges of wireless sensor networks in smart grid. IEEE Trans Ind Electron 57(10):3557–3564 59. Sabbah AI, El-Mougy A, Ibnkahla M (2014) A survey of networking challenges and routing protocols in smart grids. IEEE Trans Ind Inf 10(1):210–221 60. Albano M, Ferreira LL, Pinho LM (2015) Convergence of smart grid ICT architectures for the last mile. IEEE Trans Ind Inf 11(1):187–197 61. Weilin L, Ferdowsi M, Stevic M et al (2014) Cosimulation for smart grid communications, IEEE Trans Ind Inf 10(4):2374–2384 62. Anderson K, Du J, Narayan A et al (2014) GridSpice: a distributed simulation platform for the smart grid. IEEE Trans Ind Inf 10(4):2354–2363 63. Loia V, Terzija V, Vaccaro A et al (2015) An affine arithmetic based consensus protocol for smart grids computing in the presence of data uncertainties. IEEE Trans Ind Electron 62(5):2973–2982 64. Chun-I F, Shi-Yuan H, Yih-Loong L (2014) Privacy-enhanced data aggregation scheme against internal attackers in smart grid. IEEE Trans Ind Inf 10(1):666–675 65. Vaccaro A, Loia V, Formato G et al (2015) A self organizing architecture for decentralized smart microgrids synchronization, control and monitoring. IEEE Trans Ind Inf 11(1):289–298 66. Khan M, Ashton PM, Li Maozhen et al (2015) Parallel detrended fluctuation analysis for fast event detection on massive PMU data. Smart Grid, IEEE Trans 6(1):360–368 67. Torelli F, Vaccaro A, Xie N (2013) A novel optimal power flow formulation based on the Lyapunov theory. IEEE Trans Power Syst 28(4):4405–4415 68. Xu Y, Zhang W, Liu W (2015) Distributed dynamic programming-based approach for economic dispatch in smart grids. IEEE Trans Ind Inf 11(1):166–175 69. De Angelis F, Boaro M, Fuselli D et al (2013) Optimal home energy management under dynamic electrical and thermal constraints. IEEE Trans Ind Inf. 9(3):518–527 70. Frank S, Rebennack S (2012) Optimal power flow: a bibliographic survey, part i formulations and deterministic methods. Energy Syst 3(3):221–258 71. Loia V, Vaccaro A, Vaisakh K (2013) A self-organizing architecture based on cooperative fuzzy agents for smart grid voltage control. IEEE Trans Ind Inf 9(3):1415–1422 72. Siano P, Cecati C, Hao Yu, Kolbusz J (2012) Real time operation of smart grids via FCN networks and optimal power flow. IEEE Trans Ind Inf 8(4):944–952

References

381

73. Di Silvestre ML, Graditi G, Sanseverino ER (2013) A generalized framework for optimal sizing of distributed energy resources in micro-grids using an indicator-based swarm approach. IEEE Trans Ind Inf. 10(1):152–162 74. Zhao J, Wen F, Dong ZY et al (2012) Optimal dispatch of electric vehicles and wind power using enhanced particle swarm optimization. IEEE Trans Ind Inf 8(4):889–899 75. Mudumbai R, Dasgupta S, Cho BB (2011) Distributed control for optimal economic dispatch of power generators: the heterogenous case. In: 2011 50th IEEE conference on decision and control and european control conference (CDC-ECC), Orlando, FL, USA, pp 12–15 76. Lai LL, Chan SW, Lee PK et al (2011) Challenges to implementing distributed generation in area electric power system. In: Proceeding of the 2011 IEEE international conference on systems, man, and cybernetics (SMC), October 9–11, Anchorage, Alaska, pp 797–801 77. Yongcan C, Wenwu Y, Wei R et al (2012) An overview of recent progress in the study of distributed multi-agent coordination. IEEE Trans Ind Inf 9(1):427–438 78. Zhang W, Xu Y, Liu W et al (2015) Distributed online optimal energy management for smart grids. IEEE Trans Ind Inf 11(3):717–727 79. Safdarian A, Fotuhi-Firuzabad M, Lehtonen M (2014) A distributed algorithm for managing residential demand response in smart grids. IEEE Trans Ind Inf 10(4):1551–1563 80. Rahbari-Asr N, Mo-Yuen C (2014) Cooperative distributed demand management for community charging of PHEV/PEVs based on KKT conditions and consensus networks, IEEE Trans Ind Inf, 10(3):1907–1916 81. Etherden N, Vyatkin V, Bollen M (2015) Virtual power plant for grid services using IEC 61850, IEEE Trans Ind Inf 82. Rostami M-A, Kavousi-Fard A, Niknam T (2015) Expected cost minimization of smart grids with plug-in hybrid electric vehicles using optimal distribution feeder reconfiguration. IEEE Trans Ind Inf 11(2):388–397 83. Haomin M, Wing CK, Mingbo L (2013) An intelligent control scheme to support voltage of smart power systems. IEEE Trans Ind Inf 9(3):1405–1414 84. Vaccaro A, Zobaa AF (2013) Voltage regulation in active power networks by distributed and cooperative meta-heuristic optimizers. Electric Power Syst Res 99:9–17 85. Mendes A, Boland N, Guiney P et al (2013) Switch and tap-changer reconfiguration of distribution networks using evolutionary algorithms. IEEE Trans Power Syst 28(1):85–92 86. Farag HE, El-Saadany EF (2011) Voltage regulation in distribution feeders with high DG penetration: from traditional to smart. In: Proceeding of 2011 IEEE power and energy society general meeting, 24–29 July 2011, San Diego, CA; pp 1–8 87. Shenga G, Jianga X, Duana D et al (2009) Framework and implementation of secondary voltage regulation strategy based on multi-agent technology. Int J Electr Power Energy Syst 31(1):67–77 88. Bidram A, Davoudi A, Lewis FL (2014) A multiobjective distributed control framework for islanded AC microgrids. IEEE Trans Ind Inf 10(3):1785–1798 89. Navarro J, Zaballos A et al (2013) The information system of INTEGRIS: INTelligent electrical GRId Sensor communications. IEEE Trans Ind Inf. 9(3):1548–1560 90. Xu Y, Zhao Yang D, Xu Z et al (2012) An intelligent dynamic security assessment framework for power systems with wind power. IEEE Trans Ind 8(4):995–1003 91. Kucuktezan CF, Genc VMI (2010) Dynamic security assessment of a power system based on probabilistic neural networks. In: 2010 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe), pp 1–6 92. Erol-Kantarci M, Mouftah HT (2015) Energy-efficient information and communication infrastructures in the smart grid: a survey on interactions and open issues. Commun Surv Tutorials 17(1):179–197 93. Das S, Singh ST (2014) Application of compressive sampling in synchrophasor data communication in WAMS. IEEE Trans Ind Inf. 10(1):450–460 94. Ghosh D, Ghose T, Mohanta DK (2013) Communication feasibility analysis for smart grid with phasor measurement units. IEEE Trans Ind Inf 9(3):1486–1496

382

6 AMI and DR, the Enabling Technologies for Information Processing …

95. Terzija V, Valverde G, Cai Deyu et al (2011) Wide area monitoring, protection and control of future electric power networks. Proc IEEE 99(1) 96. Terzija V, Cai D, Valverde G et al (2010) Flexible wide area monitoring, protection and control applications in future power. In: The 10th institution of engineering and technology conference on developments in power system protection DPSP 2010 29 March–1 April 2010, Manchester, UK 97. Moreno-Munoz A, Pallares-Lopez V, Gonzalez de la Rosa JJ et al (2013) Embedding synchronized measurement technology for smart grid development. IEEE Trans Ind Inf 9(1):52–61 98. Formato G, Loia V, Paciello V, Vaccaro A (2013) A decentralized and self organizing architecture for wide area synchronized monitoring of smart grids. J High Speed Netw–IOS Press 19(3):165–179 99. Zareipour H, Canizares CA, Bhattacharya K et al (2006) Application of public-domain market information to forecast ontario’s wholesale electricity prices. IEEE Trans Power Syst 21(4):1707–1717 100. Areekul P et al (2010) A hybrid ARIMA and neural network model for short-term price forecasting in deregulated market. IEEE Trans Power Syst 25(1):524–530 101. Hong YY, Lee CF (2005) A neuro-fuzzy price forecasting approach in deregulated electricity markets. Electr Power Syst Res. 73(2):151–157 102. Aggarwal SK, Saini LM, Kumar A (2008) Electricity price forecasting in Ontario electricity market using wavelet transform in artificial neural network based model. Int J Control Autom Syst 6(5):639–650 103. Mandal P, Senjyu T, Urasaki N et al (2007) Price forecasting for day-ahead electricity market using recursive neural network. Proc IEEE power engineering society general meeting, Tampa, FL, pp 1–8 104. Lerner J, Grundmeyer M, Garvert M (2009) The role of wind forecasting in the successful integration and management of an intermittent energy source. Energy Central Topic Centers Wind Power 3(8):1–6 105. Guannan Q, Jie M, Dawei H (2013) Short-term wind power forecasting based on numerical weather prediction adjustment. In: 2013 11th IEEE international conference on industrial informatics, pp 453–457 106. Terciyanli E, Demirci T, Kucuk D et al (2014) Enhanced nationwide wind-electric power monitoring and forecast system. IEEE Trans Ind Inf. 10(2):1171–1184 107. Palomares-Salas JC, de la Rosa JJG, Ramiro JG et al (2009) Comparison of models for wind speed forecasting, International conference on computational science—ICCS 2009 May 25-–7, Baton Rouge, Louisiana (USA) 108. Katsigiannis YA, Tsikalakis AG, Georgilakis PS et al (2006) Improved wind power forecasting using a combined neuro-fuzzy and artificial neural network model. Lecture Notes in Computer Science, Adv in Art Intell. Springer, Berlin/Heidelberg Publisher, Heraklion 109. Vaccaro A, Bontempi G, Ben Taieb S et al (2012) Adaptive local learning techniques for multiple-step-ahead wind energy forecasting. Electric Power Syst Res. 83(2):129–135 110. Ozkan MB, Karagoz P (2015) A novel wind power forecast model: statistical hybrid wind power forecast technique (SHWIP). IEEE Trans Ind Inf 11(2):375–387

Chapter 7

Renewable Energy and Microgrid

With the rapid pace of economic development and escalating energy consumption, countries and regions worldwide, including China, find themselves grappling with the intertwined challenges of meeting increasing energy demand while ensuring environmental sustainability. Recent studies have revealed that between 2015 and 2040, energy consumption is projected to surge from 663 trillion BUT to a staggering 736 trillion BUT [1], accompanied by an alarming rise in annual carbon dioxide emissions from 3.12 billion tons to a staggering 45.5 billion tons. During the period spanning from 1978 to 2014, China witnessed an extraordinary surge in total energy production, soaring from 627.7 million tons of standard coal to an impressive 3.6 billion tons of standard coal. This remarkable growth showcased an annual growth rate of 4.83%. Simultaneously, energy consumption experienced a slightly higher growth rate of 5.58%, culminating in reaching 4.26 billion tons of standard coal in 2014—a staggering increase of 7.45 times [2]. By the end of 2014, China had accounted for 23% of global energy consumption and an astounding 61% of net energy consumption growth. Consequently, China has established its status as the world’s largest energy consumer and carbon dioxide emitter. In light of these circumstances, China has committed to a target of reducing carbon dioxide emissions per unit of GDP by 40–45% compared to 2005 levels by the year 2020 [3]. The daunting pressure to curtail CO2 emissions presents unprecedented challenges for China [4]. Given the substantial strain placed on energy supply and demand and the environment, the adoption of new technologies, harnessing the full potential of renewable energy sources, and transforming the existing energy structure have become urgent imperatives. The advent of smart grids, particularly the emergence of microgrids as their pivotal component, and the proliferation of new power sources predominantly reliant on renewable energy within microgrids, hold the promise of alleviating this dual pressure.

© Chemical Industry Press 2023 X. Zeng and S. Bao, Key Technologies of Internet of Things and Smart Grid, Advanced and Intelligent Manufacturing in China, https://doi.org/10.1007/978-981-99-7603-4_7

383

384

7 Renewable Energy and Microgrid

This chapter aims to provide an overview of the current state and future prospects of renewable energy in China. Subsequently, it delves into the intricacies of AD/CD microgrids, followed by a thorough exploration of optimal operation strategies.

7.1 Development Trend of Renewable Energy in China Given the escalating and substantial energy demand, the Chinese government faces mounting pressure to address energy shortages and combat environmental degradation resulting from the overreliance on fossil fuels. Currently, coal reigns supreme in China’s energy landscape, accounting for approximately 70% of the total energy supply and maintaining a pivotal role in the nation’s economic progress [5, 6]. However, coal-centric energy production and consumption systems encounter numerous critical challenges, including resource scarcity, low energy efficiency, excessive emissions leading to environmental damage, and inadequate system management. In light of China’s prevailing energy situation, it is imperative to rectify the existing energy consumption structure. China possesses abundant reserves of renewable energy, which remain largely untapped, presenting significant opportunities for renewable energy development [7, 8]. Despite substantial efforts and notable advancements in wind and solar energy sectors, the share of renewable energy in China’s overall energy structure lags far behind the global average [9]. In September 2007, the Chinese government unveiled its plans to raise the proportion of renewable energy within the overall energy composition from 8% in 2006 to 15% by the year 2020 [10]. Given the energy dilemma, China embarks on a lengthy journey to optimize its energy consumption structure and propel the adoption of renewable energy sources to meet the imperatives of sustainable development.

7.1.1 Renewable Energy and Emission Reduction in China Renewable energy is progressively establishing itself as a pivotal component of the global energy landscape, particularly within the power sector. The World Energy Outlook 2015, published by the International Energy Agency (IEA), highlights that renewable energy accounted for 22% of the global power generation in 2015 and is projected to rise to 31% by 2035 [11]. China’s medium and long-term plan for renewable energy development from 2010 to 2020 identifies key focus areas such as hydroelectric power, bioenergy, wind energy, solar energy, as well as other renewable energy sources encompassing geothermal energy and ocean energy [12]. The utilization of renewable energy has garnered significant attention in China in recent years, with a notable increase of 15.1% compared to 2014. China currently contributes 16.7% to the global total of renewable energy, indicating a 1.2% increase over the past decade [13]. According

7.1 Development Trend of Renewable Energy in China

385

to the 13th Five-Year Plan (2015–2020), non-fossil fuel energy is targeted to account for 15% of the total primary energy consumption in China by 2020. As of 2014, the cumulative installed capacity for grid-connected wind power, solar power, and hydropower in China stood at 96.57, 24.96, and 304.86 million kW, respectively. China has experienced remarkable growth in renewable energy power generation in recent years, with the total generation in 2013 nearly tripling that of 2005. While renewable energy resources are known to be abundant, their utilization presents specific technical, economic, and environmental challenges. The subsequent sections will provide a concise overview of the current status of renewable energy development in China.

7.1.2 Hydropower Generation China boasts the most abundant hydropower resources globally, with a theoretical total potential of 694 GW. The completion of the fourth national survey of hydrological resources in November 2005 unveiled compelling figures. The survey estimated the technically exploitable installed capacity to be around 542 GW, with an annual average power generation potential of 247 billion kWh. Additionally, the economically viable installed capacity was calculated to be 402 GW, with a corresponding annual power generation potential of 1750 TW hours [14, 15]. Over the past six decades, China’s hydropower sector has witnessed remarkable growth [16]. By 2014, the installed hydropower capacity in China had soared to 304.86 million kilowatts, with an annual power generation reaching 1,370.18 million kilowatt-hours, constituting a substantial 22.25% share of the country’s total power generation. It is worth noting that the Three Gorges Dam alone contributed an impressive power output of 84.37 billion kWh. The proportion of hydropower in the overall installed capacity surged from 8.8% in 1949 to 22.24% in 2014, with China’s installed capacity accounting for a quarter of the world’s hydropower capacity. With future plans in place, the installed hydropower capacity is expected to reach 350 million kilowatts, underscoring the vast potential for hydropower development in China [17]. China occupies the top spot globally in terms of hydropower installation and power generation capacity. The hydropower generated in China translates into significant energy savings, amounting to 313 million tons of standard gas, and a notable reduction of 600 million tons of carbon dioxide emissions.

386

7 Renewable Energy and Microgrid

7.1.3 Wind Power China possesses substantial wind energy potential, as indicated by the third national survey of wind energy resources conducted by the China Meteorological Administration. Onshore wind energy potential ranges from 600 to 1000 GW, while offshore potential ranges from 400 to 500 GW. The wind energy industry in China is rich in resources and has undergone rapid growth in the past decade, as illustrated in Fig. 7.1 [18]. By the end of 2015, the cumulative installed capacity of wind energy had reached an impressive 180.4 GW. In 2015 alone, new installations accounted for 30.5 GW, representing approximately 48.4% of the world’s new wind power capacity, thereby securing China’s position as the global leader in wind energy. Despite the significant growth of wind power in China, the distribution of installations is uneven and does not align with the country’s economic development. More than 28% of the cumulative installed capacity is concentrated in Inner Mongolia and Gansu Province, even though these regions account for only 6.78% of China’s total electricity consumption. On the other hand, provinces in southeastern China, such as Zhejiang, Fujian, and Guangdong, boast developed economies and dense populations, yet their cumulative installed capacity for wind energy amounts to only 4.7%, while their electricity consumption represents 20.5% of the national total.

Fig. 7.1 The growth of wind power in China over the past ten years (source CWEA)

7.1 Development Trend of Renewable Energy in China

387

7.1.4 Solar Energy Situated in the northeast of East Asia, China spans a vast area of 9.6 million square kilometers, encompassing latitudes ranging from 4° to 53° north and longitudes spanning 73°–135° east [19]. The country benefits from an abundance of solar energy resources, with more than 67% of its territory classified as areas with ample solar potential. The annual solar radiation surpasses 5000 MJ/m2 , accompanied by sunshine durations exceeding 2200 h [20]. Several regions in China, including Tibet, Xinjiang, Qinghai, Gansu, Ningxia, and Inner Mongolia, boast significant solar power generation capabilities, with annual solar radiation surpassing 1750 kWh/m2 [21]. Harnessing the wealth of solar energy resources, China’s solar photovoltaic (PV) industry has experienced rapid growth since 2004, with an average annual growth rate surpassing 100%. Since 2007, China has consistently maintained its position as the world’s leading producer of PV cells. Starting from March 2009, and particularly during the period between 2011 and 2015, the Chinese government implemented a series of incentives to drive the solar PV sector. These initiatives included direct subsidies for PV installations and the introduction of the national Feed-in Tariff (FIT) program [22]. In response to these incentives, China’s domestic PV market witnessed steady expansion, with cumulative installed capacity surging from 300 MW in 2009 to 800 MW in 2010, and reaching an impressive 4380 MW by the end of 2015 [23]. By the conclusion of 2015, China’s cumulative installed capacity of solar PV had reached 43.18 gigawatts (GW). Of this, fixed photovoltaic power generation accounted for 37.12 GW, while distributed photovoltaic power generation represented 6.06 GW. This marks a staggering 48-fold increase from 0.9 GW. In 2015 alone, newly installed solar capacity reached 15.13 GW, constituting over a quarter of the world’s total. In 2009, China’s photovoltaic installed capacity only accounted for 1.24% of the global total, but it subsequently experienced nearly 12-fold growth. By 2014, China’s share had increased to approximately 14.9% of the global installed capacity [23].

7.1.5 Bioenergy Biomass, as a versatile feedstock, can undergo chemical and biological processes to produce solid, liquid, and gaseous fuels [24]. Projections indicate that biofuels could account for 15–50% of the world’s primary energy consumption by 2050 [25]. Sustainable biomass resources suitable for energy production can be categorized into five main types: agricultural residues, forest residues, biomass cultivation on degraded lands, organic waste, and other sources [26]. China, being a large agricultural nation, possesses abundant biomass resources. The annual availability of agricultural residues alone is equivalent to 440 million tons of standard coal [27]. Forest residues account for an annual availability equivalent to

388

7 Renewable Energy and Microgrid

350 million tons of standard coal, while dung resources amount to approximately 28 million tons of standard coal. Additionally, China’s municipal solid waste can yield 12 million tons of standard coal each year. Despite this significant biomass potential, the installed biomass capacity in China stood at only 14.23 million kilowatts by the end of 2014, with an average utilization rate of 9.49 million kilowatts. However, the rapid development of China’s biomass power generation industry is evident due to the abundant biomass resources [27]. China’s biomass energy sector holds enormous potential, buoyed by favorable natural conditions and government policies. Over the long term, from 2010 to 2050, China’s biomass energy potential is poised for substantial growth and significant utilization [28].

7.1.6 Other Renewable Energy Sources While marine renewable energy sources, including tidal energy, ocean current energy, wave energy, ocean thermal energy, and salinity gradient energy, are the subject of ongoing research, they are seldom utilized for commercial power generation due to various challenges such as high costs, low efficiency, poor reliability, limited stability, and small-scale applications [29, 30]. However, it is worth noting that the total reserves of China’s available ocean energy resources are estimated to reach 1000 GW, indicating significant potential for future development. China boasts abundant and widely distributed geothermal resources, accounting for 7.9% of the world’s theoretical total energy, amounting to 11 × 106 EJ.a−1 [31]. By the end of 2010, the geothermal heating area in China had already surpassed 140 million square meters, and geothermal power generation was experiencing rapid growth. Among the notable geothermal power stations in China is the Yangbajing facility in Tibet, which boasts an annual production capacity of 25 MW and generates approximately 100 million kWh of electricity annually [31].

7.1.7 Prospects for China’s Renewable Energy Development In September 2007, China’s National Development and Reform Commission (NDRC) issued a medium and long-term plan outlining the development of renewable energy, aiming to increase the share of renewable energy to 10% and significantly contribute to carbon dioxide emissions reduction by saving approximately 5–6 billion tons by 2020. According to this plan, clean coal technology will play a dominant role in coal consumption, while the total installed capacity of hydropower and wind power will reach 300 and 150 GW, respectively. The development of solar power is also on a rapid trajectory, with an installed capacity of 500 megawatts in 2010, projected to exceed 20 GW by 2020. By 2020, the consumption of renewable energy sources

7.2 Integration of Smart Grid and Microgrid

389

such as hydropower, wind power, biomass energy, solar thermal, and solar energy is expected to approach 500 million tons of standard coal. With clean coal technology at the forefront, coal consumption is projected to stabilize after 2030. The combined share of nuclear and renewable energy is expected to reach 15% in 2020, 19% in 2030, and 29% of primary energy consumption by 2050. The growth of renewable energy, particularly wind and solar power, is anticipated to outpace other sources. Wind power is predicted to experience continuous rapid growth, surpassing 150 GW of installed capacity by 2020 and reaching 300 GW by 2030. Additionally, solar photovoltaic power generation is expected to gain widespread adoption in China as technology advances and becomes more costeffective. By 2030, the installed capacity of solar photovoltaics is estimated to reach 200 GW. These developments signify a dramatic transformation in China’s energy landscape towards a low-carbon and sustainable system. To address the conflicting demands of rapid economic growth and high carbon dioxide emissions, China recognizes the imperative of transitioning to a low-carbon and sustainable energy system, with renewable energy taking center stage. China possesses significant untapped potential in renewable energy, and integrating renewables into the future energy system is of utmost importance. It is anticipated that China’s renewable energy sector will continue to flourish in the future, making substantial contributions to the low-carbon economy.

7.2 Integration of Smart Grid and Microgrid Renewable energy has gained widespread acceptance as a viable alternative to traditional energy sources, with the aim of meeting electricity demand while ensuring grid stability and accommodating high levels of renewable energy penetration. The integration and coordination between renewable energy sources and the power system can be enhanced through the use of energy storage systems, which store excess renewable energy and provide ancillary services such as peak shaving. This integration not only improves the reliability, security, and resilience of microgrid applications but also yields significant socioeconomic and environmental benefits by reducing greenhouse gas emissions from conventional power plants [32]. However, the inherent randomness and intermittency of renewable energy, particularly in photovoltaic power generation, pose significant challenges to the power grid, leading to unstable power supply [33]. The intermittent nature of renewable energy sources means they cannot guarantee continuous and reliable power output. In addition to the aforementioned challenges related to integration, there are significant operational challenges for the power system itself [34]. One such challenge is reverse power flow, which occurs when local renewable energy generation exceeds local load demand. This reverse power flow can cause voltage rise in the distribution grid, compromising the quality of power supply. Thus, the power grid must be capable of responding to this integration by adjusting generation, managing power consumption, or utilizing storage systems.

390

7 Renewable Energy and Microgrid

Furthermore, the integration of renewable energy generators as new distributed generators, spanning large-scale installations at the transmission level, medium-scale installations at the distribution level, and small-scale installations at the commercial or residential building level, presents significant challenges in terms of dispatchability, control of capacity, and power system operation [35]. The addition of these distributed generators poses complexities that require careful management and planning.

7.2.1 Microgrid and Hybrid AC/DC Microgrid The earliest power grids were isolated DC microgrids consisting of DC generators. However, the present power grids have evolved due to challenges related to generating the required voltage levels and transmission losses. Centralized control has been a common method for operating present power grids, but it has significant drawbacks. It requires substantial investments in generation and transmission infrastructure to accommodate load growth, and there is often a lack of government investment in these areas. Moreover, centralized control has become less popular as industries strive to improve efficiency and not all industries keep pace with technological advancements. To address these challenges and reduce operation and maintenance costs, microgrids are gaining importance and finding their place within the grid. Different types of microgrid configurations have been proposed for various applications [36, 37], and they can be broadly classified into three categories: (1) DC (Direct Current) Microgrid A DC microgrid primarily consists of DC loads and power sources. It offers advantages such as energy storage system integration, higher overall efficiency due to fewer AC-DC-AC conversions, and no need for distributed generator (DG) synchronization. Although it lost popularity due to limitations in transmitting DC power over long distances, the increasing use of DC-powered household appliances and advancements in DC power sources, such as photovoltaic (PV) and fuel cells, are revitalizing DC microgrids. (2) AC Microgrid AC microgrids have dominated the power systems for many years. They enable easy voltage level changes through low-frequency transformers and facilitate fault handling and protection. AC power is also easier to transmit, and most industrial equipment requires AC power. In recent years, AC renewable energy sources like wind turbines, tidal energy, biogas, and wave turbines have been integrated into AC microgrids. However, AC microgrid control faces challenges related to DG synchronization and reactive power control, which can lead to increased transmission system losses. Frequency control is another challenging task for microgrids incorporating AC renewable energy due to climate and geographical variations.

7.2 Integration of Smart Grid and Microgrid

391

(3) Hybrid AC/DC Microgrid This configuration combines the advantages of both AC and DC microgrids, allowing the integration of AC and DC loads with relevant power sources. Hybrid AC/DC microgrids are suitable for smart grids and the present grid, offering benefits such as voltage conversion, economic feasibility, and harmonic control. Although they have some disadvantages, such as protection issues and complex coordination among units, these challenges can be addressed through optimized operation techniques. Hybrid AC/DC microgrids serve as appropriate case studies for operational issues and challenges due to their overall advantages over other types of microgrids. The hybrid AC/DC microgrid operates in two main modes: (i) Grid-connected Mode In this mode, the microgrid is connected to the main grid, and all generators (gensets) operate at their maximum operating point. There are two types of grid connection modes. In the first mode, the grid-connected system prioritizes meeting local energy demands. Any surplus energy generated can be injected into the microgrid, while any shortage can be supplemented by the main grid. In the second grid-connected mode, the microgrid’s primary function is to aggregate the generated power and supply it to the main grid. In this mode, the grid acts as a large battery for the microgrid, ensuring coverage for all seasonal load variations. However, the overall cost in this mode is higher due to the interface required to connect the microgrid to the main grid. The system can also operate in island mode during failures or based on operational priorities. (ii) Island Mode In island mode, the microgrid is disconnected from the main grid, and the energy storage system plays a significant role. This mode incurs additional operating costs as excess energy cannot be stored. It is more suitable for remote areas, particularly for seasonal purposes, where local loads take priority. PV systems are often the primary renewable energy source in AC island microgrids. The converter in an AC island microgrid is responsible for multiple AC-DC-AC conversions and serves as a frequency and voltage reference. In the next subsection, we will further discuss the hybrid AC/DC microgrid.

7.2.2 Components and Models of Hybrid AC/DC A hybrid AC/DC microgrid consists of the following main components shown as Fig. 7.2: (1) Load Generally, the loads that can be fed by a hybrid AC/DC microgrid divided into two broad categories: thermal loads and electrical loads.

392

7 Renewable Energy and Microgrid

Fig. 7.2 General diagram of a hybrid AC/DC microgrid

Generally, there is to be required to provide a combination of thermal and electrical loads in residential installations. but there is no limit to the use of hybrid AC/DC microgrids. Depending on its high integration capabilities, microgrids can also be used in commercial, institutional, industrial, rural, remote, and military applications. As described in [38], two types of loads should be studied. The first type is identified by multiple measurements of load, and the second models the load in terms of its constituent parts. These two types are also known as static and dynamic modeling. So, the characteristics of the load such as current, impedance and power are fixed values. Additionally, loads are modeled based on one of the following types (Please refer to the appendix for the meaning of the symbols in the model): Constant power (most common): Pspsc + j Q Spec

(7.1)

Constant current: (

Pspsc + j Q Spec |V |

) (7.2)

Constant impedance: (

Pspsc + j Q Spec |V |2

) (7.3)

7.2 Integration of Smart Grid and Microgrid

393

Fig. 7.3 The main schematic diagram of the PV model

The modeling for control, implementation, utilization and sensitivity analysis of microgrids, which are constrained by constant power loads, have been studied in [39– 43]. Constant current, constant impedance, constant Impedance and combinations of constant Impedance, Current, and power, known as the ZIP model [44, 45]. (2) Renewable Energy (i) Photovoltaic (PV) The feature of photovoltaic panels is based on the doping of atoms in the PN junction layers of semiconductors, which form the panels exposed to solar irradiance. Photovoltaic cells are mainly divided into three categories [46]: monocrystalline silicon; polycrystalline silicon; amorphous. To simulate the influence of the behavior of solar cells in different models, using physical models, according to Fig. 7.3, four main types of PV models can be deduced: • • • •

Ideal model [47]: exist D1 , IL Simple mode [48]: exist D1 , IL , Rs Standard Model [49]: exist D1 , IL , Rs , RSH Standard version with two diodes [50]: exist D1 , D2 , IL , Rs , RSH

The mathematical modeling of the photovoltaic array is shown in formula (7.4). The output energy of PV depends on its area, and also depend on the material and the efficiency of the panel of PV. Additionally, annual solar radiation and performance ratios must be considered while calculating PV output energy. Using (7.4) the output power can be calculated at any desired time period. E P V = A P V × r P V × HP V × P R

(7.4)

Table 7.1 is a comparison of MPPT tracking methods for photovoltaic systems, comparing two main algorithms. (ii) Wind Turbine (WT) According to the environmental and economic benefits of WT, they are considered as one of the reliable alternatives to traditional power resources. The main generator types used in wind turbine systems are compared in Table 7.2 [51]. There are two main control methods for wind turbines, as shown in Table 7.3 [52].

394

7 Renewable Energy and Microgrid

Table 7.1 Comparison of the two most common MPPT tracking methods [46] No.

The description of the approach

Advantage

Disadvantage

1

Perturb and observe algorithm (P&O): compare power and voltage and their deviation and vary voltage to get maximum power

Simple Popular

Inaccurate at very high/very low V, slower than ICM, no global optimum

2

Incremental method (ICM): The speed is higher than the compare voltage and current and P&O method, the accuracy is vary voltage to maximum power higher, and the oscillation is less, Fast track voltage transitions

High complexity High cost Cannot find the global optimum

Table 7.2 Comparison of wind turbines No.

Type

Era

Rated power (MW)

Characteristical

1

Constant speed squirrel cage induction generator (CSSCIG)

1998

< 1.5

Inexpensive, rugged, self-starting

2

Doubly-fed induction generator (DFIG)

1996–2000

~ 1.5

Flexibility, power quality assurance, relatively cheap

3

Brushless generator

Start 2005

> 1.5

Variable speed, enhanced fault tolerance

4

Gearless generator

Start 1991

> 1.5

No gearbox trouble, low speed and high torque, expensive and heavy

Table 7.3 Comparison of two control methods for wind farms No.

Method description

Advantage

Disadvantage

1

Centralized (centralized converter) Two-level hierarchical controller Local: check reference supply signal Control: control power production

Wind turbines are separated from the grid

Loss of multi-shift mode

2

Decentralized (individual Every wind turbine is at its control) optimum speed

Coordination and frequency change issues

• Centralized: In this method, there is one main center that controls all references, speeds and currents of the wind farm [53]. • Decentralized: Each wind turbine acts as an independent unit with its own converter [54].

7.2 Integration of Smart Grid and Microgrid

395

Fig. 7.4 General configuration of a wind turbine

Several parameters must also be controlled and considered during the implementation of a wind turbine, such as voltage and frequency control, active power control, protection and communication, etc. The optimal allocation of wind turbines in distribution grid and microgrids is generally performed using meta-heuristic optimal allocation algorithms [55, 56]. Modeling becomes a difficulty task when considering the dynamics of a wind turbine. Dynamic modeling is also required to check the stability of the system and its controllability. The general configuration of a wind turbine is shown in Fig. 7.4. The output power of the WT is a function of the coefficient of performance of the turbine, the air density, the swept area of the turbine, and of course the wind speed. The formula governing the law of the WT is shown in Eq. (7.5) [57]. 3 Pm = c p (λ, β)(ρ A/2)Vwind .

(7.5)

Each of the values in (7.5) can be seen in (7.6), which use the values in (7.7) and (7.8) to provide their coefficients. The c p − λ features for different β values are provided in [57]. 3 Pm_ pu = k p c p_ pu Vwind_ pu

(7.6)

c p (λ, β) = c1 (c2 /λb − c3 β − c4 )e−c5 /λb + c6 λ

(7.7)

1/λb = 1/λ + 0.08β − 0.035/β 3 + 1

(7.8)

(iii) Energy Storage System (ESS) Due to the rising trend of renewable energy (RES) utilization and distributed generation capacity, ESS has become an integral part of hybrid AC/DC microgrids. Although the penetration of ESSs in hybrid AC/DC microgrids is increasing, it can be inferred that advances in battery technology will lead to lower costs and higher efficiencies in 2020. The mathematical modeling of the three most common battery charging functions is shown in (7.9)–(7.11) [58–60]: Lead-acid battery: ( ) f it, i ∗ , i bat , E x p = E 0 − K (Q/Q − it) × i ∗ − K (Q/Q − it) × it

396

7 Renewable Energy and Microgrid

+ Laplace−1 (E x p(s)/sel(s) × 1/s)

(7.9)

Lithium Ion battery: ( ) f it, i ∗ , i bat , E x p = E 0 − K (Q/Q − it) × i ∗ − K (Q/Q − it) × it + A A × E x p(−B × it) (7.10) Nickel–cadmium battery: ( ) f it, i ∗ , i bat , E x p = E 0 − K (Q/|it| − 0.1Q) × i ∗ − K (Q/Q − it) × it + Laplace−1 (E x p(s)/sel(s) × 1/s)

(7.11)

But there are some other commonly used energy storage components that can be integrated into a hybrid AC/DC microgrid, in which, the two main important components are the supercapacitor and the flywheel [61]. In contrast to batteries, supercapacitors store energy in the outer layers of the electrodes (rather than in an electrochemical solution), which results in faster charge/discharge, longer lifetime and higher power density. In addition, flywheels are likely one of the oldest methods of energy storage in history, converting kinetic energy into rotational energy in a flywheel with variable speed, power density, and long life. Table 7.4 lists the main characteristics of ESS components [62]: (iv) Converter In hybrid AC/DC microgrids, the converters play a crucial role in enabling the connection and control of different power sources and loads. These converters facilitate the conversion between AC and DC power and ensure stable voltage, frequency, and power quality in the system. The main converter in a hybrid AC/DC microgrid serves as an interconnection unit and is responsible for AC-DC or DC-AC power conversion. It plays a vital role in maintaining voltage and frequency stability, particularly during the transition from Table 7.4 Main characteristics of ESS components Power density (W kg−1 )

Energy Total cost density per unit of (Wh kg−1 ) rated power (EUR kW −1 )

Charge Life cycle and Year Cycles discharge time

Lead acid

75–300

30–50

3254

s/h

5–15

2000–4500

Lithium

50–2000

150–350

2746

m/h

5–15

1500–4500

247

ms/m

5–8

50,000

1446

ms/m

15–20 20,000–100,000

Type

Battery

Supercapacitor

800–1200 1–5

Flywheel

1000

5–100

7.2 Integration of Smart Grid and Microgrid

397

grid-connected to islanded mode. The control of power sharing between AC and DC subgrids is typically achieved through vertical control methods [63]. Fully controlled three-phase rectifiers are often employed to connect AC and DC microgrids, with pulse width modulation (PWM) control being implemented to regulate the voltage level [64]. Apart from the main converter, other converters are utilized in a hybrid AC/DC microgrid to support various functions. One such converter is a boost converter used in photovoltaic systems [65]. Since the output power of the hybrid AC/DC microgrid depends on factors like irradiance and temperature, implementing a maximum power point tracking (MPPT) system is essential. The integration of a boost converter helps regulate the output voltage to optimize power generation. Another converter present in the energy storage system is the bidirectional DC/ DC converter used for battery sets. This converter, controlled using pulse width modulation, is connected to the main DC bus and the batteries. It enables control of charge current, depth of discharge (DoD), state of charge tracking, and other battery-related parameters. Additionally, a back-to-back AC/DC/AC converter is employed in hybrid AC/DC microgrids in conjunction with the doubly-fed induction generator (DFIG) of wind turbines. This converter has two primary control objectives: i) controlling the active and reactive power on the stator, and ii) stabilizing the DC link voltage. These converters, in combination, enable effective power conversion, control, and integration of different energy sources and loads within a hybrid AC/DC microgrid. (v) Micro Turbine (MT) Micro Turbines (MTs) are utilized in microgrids to meet the demands of large loads while providing a reliable and environmentally friendly renewable energy solution. These units consist of gas turbines, permanent magnet synchronous motors (PMSMs), inverters, and rectifiers. MTs can be used in both grid-connected and island modes, offering the flexibility to switch between these modes as required [66]. MTs have gained popularity in recent years due to their output range of 25– 500 kW and relatively compact size. They can be classified into single-axis group and double-axis group configurations based on the layout of their main components. The single-axis group configuration is more common, as it offers higher rotational speeds and easier implementation. The key advantages of MTs include their reliability, particularly during failures, the integration of heat and power, and their ease of implementation [66]. (vi) Fuel Cell (FC) Fuel cells (FCs) are efficient and environmentally friendly components in microgrids. They generate low DC voltages through chemical reactions and consist of air flow, hydrogen flow, cooling, and humidification systems. FCs are classified based on the type of electrolyte used, such as Polymer Electrolyte Membrane Fuel Cells (PEMFC), Phosphoric Acid Fuel Cells (PAFC), Molten Carbonate Fuel Cells (MCFC), and Solid Oxide Fuel Cells (SOFC). Each type has its own advantages

398

7 Renewable Energy and Microgrid

and considerations regarding temperature, efficiency, durability, and cost. FCs offer high energy conversion efficiency, low emissions, and the ability to use various fuel sources. (vii) Traditional Energy Traditional energy sources encompass oil, natural gas, and coal, which are considered non-renewable due to their finite nature. Regrettably, despite extensive endeavors to enhance the efficiency of internal combustion engines and various machinery, these valuable resources continue to inflict greater harm on the environment. Nonetheless, it remains imperative to integrate hybrid AC/DC microgrids with traditional energy sources. These microgrids serve as a crucial backup for systems that heavily rely on capricious geographical phenomena, ensuring their reliability and resilience even in unpredictable conditions.

7.3 Optimal Operation of Microgrid In this section, we will discuss the optimization problem of microgrids. We will focus specifically on the hybrid AC/DC structure, which is the predominant mode in existing microgrids. Firstly, we will examine the optimal operation model for the microgrid. Following that, we will provide a brief introduction to the approach used to solve this optimal model.

7.3.1 Optimal Operation Model of Microgrid The operation of a hybrid AC/DC microgrid encompasses three key categories of objectives: environmental, economic, and technical. These microgrids play a crucial role in integrating both the demand and supply sides, necessitating the analysis of environmental, economic, and technical aspects from both perspectives. The ultimate goal of a successful hybrid AC/DC microgrid operation is to enhance “social well-being.” However, since this concept lacks a standardized measurement, optimizing costs and emissions is considered a proxy for achieving “social wellbeing.” To gain a deeper understanding of these issues, an economic perspective is essential. This perspective encompasses distribution systems, micro power sources, and end users, taking into account greenhouse gas emissions such as carbon dioxide, sulfur dioxide, and nitrogen oxides. These considerations are incorporated as constraints in a model, as outlined in reference [67]. (1) Technical Indicators Before delving into the formalization and operational approaches of the problem, it is crucial to comprehend several standards. These include SAIFI, SAIDI, ENS, voltage deviation, and loss values.

7.3 Optimal Operation of Microgrid

399

SAIFI represents the average frequency of system interruptions, while SAIDI reflects the average duration of system interruptions. These indicators were studied by the authors of [68, 69] to assess the reliability of the proposed system. Another reliability criterion for microgrids is the ENS index, which signifies the unsupplied energy value. It can be used as a threshold for microgrid design and operation, particularly when considering peak loads and distributed generators [70]. Voltage deviation is another important indicator [71, 72]. It is widely used in closed-loop control of active distribution grids, with or without microgrids. Besides serving as an indicator, voltage deviation is employed to improve unit service life, reduce maintenance costs, and enhance power quality. (2) Economic Indicators This section primarily focuses on economic indicators. In addition to analyzing the cost function defined by each microgrid unit, cost definition can be approached from two perspectives: a cost-based approach and a price-based approach. The cost-based approach involves nonlinear terms in the cost function of scheduling units to achieve controlled independent performance and simplicity for each unit. The price-based approach, described in [73], incorporates dynamic pricing that varies based on retailers, distribution companies (Dis.Cos), retailer’s grid requirements, time, load, and the significant role played by generation companies (Gen. Cos). The objective functions of cost and revenue are derived from microgrid operations, with physical constraints on both sides and power balance constraints serving as the main constraints. (3) Environmental Factor From a technical standpoint, the objective function primarily focuses on minimizing losses and costs while considering all economic constraints except grid voltage and load. Conversely, from an environmental perspective, the objective function emphasizes cost and emissions reduction, with the constraints remaining the same as those in the economic zone. As mentioned earlier, the operation of a hybrid AC/DC microgrid can be modeled as a single or multi-objective optimization problem. This problem typically involves minimizing the operation and maintenance costs of renewable energy sources (RES) and distributed generation, while considering technical, environmental, and demand constraints. These problems can also be combined, taking into account grid and micropower constraints [74]. The model of the decision variable vector X of the cost and emission minimization objective function is shown in formulas (7.12)–(7.21): ] [ X = Pg , Ug

(7.12)

Pg = [PGrid , PBat , PFC , PM T ]

(7.13)

400

7 Renewable Energy and Microgrid

Note that the power of wind turbines and photovoltaics are omitted from the decision variable vector because energy policy dictates that we get the maximum power from renewable sources. Where, Ug ∈ {0, 1} represents the running/stop status of the FC/MT unit. Pg represents the generating power (KW) of the generator set. Similarly, below PX represents the generating power of X . Ug = [U FC , U M T ] [ Ug = u 1FC1 , u 2FC1 , . . . , u TFC1 , . . . , u 1FCi , u 2FCi , . . . , u TFCi , u 1M T1 , u 2M T1 , . . . , u TM T1 , . . . , u 1M T j , u 2M T j , . . . , u TM T j

(7.14)

] (7.15)

g = {1, . . . , N W T }, h = {1, . . . , N P V }, i = {1, . . . , N FC }, j = {1, . . . , N M T } as well as T = 24 ] [ 1 2 T PGrid = PGrid , PGrid , . . . , PGrid

(7.16)

] [ PFC = PFC 1 , PFC 2 , . . . , PFC i

(7.17)

] [ 1 2 T PFC i = PFC , PFC , . . . , PFC i i i

(7.18)

[ ] PM T = PM T 1 , PM T 2 , . . . , PM T j

(7.19)

] [ PM T j = PM1 T j , PM2 T j , . . . , PMT T j

(7.20)

] [ PBat = PBat1 , PBat2 , . . . , PBatk k = [1, . . . , N Bat ]

(7.21)

] [ 1 2 T PBat k = PBat , PBat , . . . , PBat k k k

(7.22)

The cost minimization objective function can be defined as (7.23): f 1 (x) =

T ∑

Cost i

(i=1)

= min

T ∑ ( ) i i i i i i CostGrid + CostW T + Cost P V + Cost Bat + Cost FC + Cost M T i=1

(7.23)

7.3 Optimal Operation of Microgrid

401

The emission minimization objective function is defined as (7.24). Although RES does not add emissions to the environment, emissions generated during WT and PV manufacturing are pollutants and must also be considered. f 2 (x) =

T ∑ t=1

Emission t ⎡⎛

= min⎣⎝

NW T ∑

⎞ PWt Tg Emission tW T ⎠

+

g=1

+

(N FC ∑

T ∑ t=1

⎛ +⎝

)

u tFCi

×

t PFC i

×

Emission tFC ⎞

u tM T j × PMt T j × Emission tM T ⎠

j=1

+

(N Bat ∑

) PPt Vh Emission tP V

h=1

i=1

NMT ∑

(N PV ∑

) t PBat k

×

Emission tBat

] +

t PGrid

×

Emission tGrid

(7.24)

k=1

Equations (7.25)–(7.30) describe the pollutant emissions per kg MW−1 and are the sum of CO2 , SO2 and NOx emissions. Note that emissions from WT and PV divices are emissions generated during the production of these divices. Therefore, the value used in (7.24) is the average time-weighted value. Emission tGrid = C O t2Grid + S O t2Grid + N O tx Grid

(7.25)

Emission tW T = C O t2W T + S O t2W T + N O tx W T

(7.26)

Emission tP V = C O t2P V + S O t2P V + N O tx P V

(7.27)

Emission tBat = C O t2Bat + S O t2Bat + N O tx Bat

(7.28)

Emission tFC = C O t2FC + S O t2FC + N O tx FC

(7.29)

Emission tM T = C O t2M T + S O t2M T + N O tx M T

(7.30)

402

7 Renewable Energy and Microgrid

The main constraints are shown in formulas (7.31)–(7.49): • Load Balancing Constraints NW G ∑

PWt Tg +

g=1

NPV ∑ h=1

+

N Bat ∑ k=1

PPt Vh +

N FC ∑

t u lt PFC + l

l=1 t t PBat + PGrid = k

NMT ∑

u tj PMt T j

j=1 Nl ∑

Plt

(7.31)

l=1

where, l = {1, . . . , Nl }。 • Real Power Constraints t t t PGrid ≤ PGrid ≤ PGrid min max

(7.32)

t t t PBat ≤ PBat ≤ PBat min max

(7.33)

t t t u it PFC ≤ PFC ≤ u it PFC min max

(7.34)

u tj PMt T min ≤ PMt T ≤ u tj PMt T max

(7.35)

PtM T max /FC max = min{PM T max /FC max , PMt−1 T j /FC i + (U p Ramp Rate) j/l }

(7.36)

PtM T mmi /FC min = min{PM T min /FC min , PMt−1 T j /FC i + (Down Ramp Rate) j/l }

(7.37)

• Battery Energy Balance E tBat = E initial Bat +

T ∑ ( t ) t t t Ucharge × PC_Bat × ηc − Udischarge × PDch_Bat × ηd

(7.38)

t=1

• Grid t Cost tGrid = C Grid × PGrid

(7.39)

Cost tW T g = ai + bi + PWt T g

(7.40)

• Wind Power [75]

7.3 Optimal Operation of Microgrid

403

where, ai and bi are ai =

CC × Cap × A R B Li f etime × 365 × 24 × L F bi = R E f uel + O M Cost tW T =

NW T ∑

Cost tW T g

(7.41) (7.42)

(7.43)

g=1

Alternately, as described in the Grid Costs section, it can be calculated by multiplying the unit cost per unit of power by Eq. (7.43): Cost tW T = C W T × PWt T g

(7.44)

Cost tP V h = ai + bi × PPt V h

(7.45)

• Photovoltaic [76]

Above ai and bi are as same as (7.40) and (7.41). Cost tP V h = C P V × PPt V h

(7.46)

t Cost tBat k = ai + bi × PBat + Cost tBat deg k

(7.47)

• Battary

Cost tBat =

N Bat ∑

Cost tBat k

(7.48)

t Cost tBat k = C Bat × PBat k

(7.49)

k=1

There are other cost constraint functions, the form of which is the same as above, and will not be repeated here. Considering the fact that the operation of a hybrid AC/DC microgrid can be modeled by a single/multi-objective optimization problem, the optimal operation of a microgrid can be analyzed from various viewpoints.

404

7 Renewable Energy and Microgrid

7.3.2 Overview of Microgrid Optimal Operation Model Solution Solving the optimization model for microgrid operation established in Sect. 7.3.1 proves challenging, as obtaining an analytical solution is difficult, and numerical solutions are typically pursued. Heuristics and metaheuristics are commonly employed to find numerical solutions, and this subsection reviews some of the most effective and commonly used variants of these approaches. In [77], a hybrid strategy for AC/DC microgrid bidding optimization is proposed, based on a stochastic/robust approach. The approach aims to optimize the battery charging/discharging status, power procurement and sales costs, and scheduling in response to load and schedulable resources. This approach formulates a 3-stage mixed integer linear programming (MILP) problem. Compared to stochastic solutions, the proposed approach exhibits robust performance against uncertainty. Nonlinear objective functions can also be transformed into a mixed-integer linear form. Consequently, implementing this approach in the WT/PV/FC/MT DG/BESS/responsive load system significantly enhances the operational status of the hybrid microgrid. In [78], the power management optimization of WT/BESS systems is discussed. The system incorporates wind and load profiles, and a dynamic programming approach is employed to establish the optimal system operation. The forecasting is performed in two distinct time domains. Macro dynamic programming is utilized for long-term forecasting based on hourly wind speeds and market prices. The resulting schedules are subsequently refined using microscale dynamic programming. Najafi-Ravadanegh et al. [79] proposes a system comprising WT/FC/P/MT/ thermal units, power loads, and resources. Optimal runs are obtained using a combination of the Imperialist Competition (IC) and Monte Carlo (MC) algorithms. The problem is modeled as a nonlinear system, considering technical, economic, and environmental constraints. The IC algorithm consists of two main groups: the colonial group and the imperialist group. The primary objective is to minimize the sum of emissions costs, operation and maintenance (O&M) costs, unit installation costs, and power interactions. Microgrids are subject to various uncertainties stemming from environmental and economic factors. Environmental uncertainties arise from weather conditions and microgrid geography, influenced by solar and wind patterns. Economic uncertainty mainly stems from fluctuations in fuel prices and loads. To improve uncertainty modeling accuracy, various methods have been developed. For instance, in [80], the authors utilize the MILP method to address the optimal operation problem of microgrids, considering the impact of uncertainties. Uncertain parameters are modeled using a Latin hypercube sampling method, which generates discrete scenarios reduced to a finite number. In [81], Analytical Hierarchy Process (AHP) is employed to study the optimal operation of microgrids. This approach is primarily used for multi-objective optimization problems to classify different possibilities in microgrid operations. AHP

7.4 Summary

405

assigns different standard values to real-world data, allowing for the classification of environmental, economic, and financial perspectives in microgrid operation decisions. Khayatian et al. [82] analyzes the implementation of stochastic programming approaches in the optimal operation of AC/DC microgrids, with a specific focus on cost-efficiency and safe operation. The author decomposes the problem into a main problem and sub-problems. The main problem aims to minimize costs, while the sub-problems focus on short-term operating cost minimization and system resilience improvement. Furthermore, in [83], a novel approach for real-time market participation of microgrids is demonstrated under uncertainty. The authors emphasize that real-time market prices are the main uncertainties in microgrid planning problems. The main problem addresses the primary optimization investment problem, while the subproblems analyze operational aspects. In the subproblems, worst-case optimal runs are examined to account for uncertainty, and if the solution is infeasible within the domain, optimality cutting is employed to reduce the problem space and increase the likelihood of convergence. Various approaches are well-known for modeling uncertainties in microgrids. These include the Monte Carlo (MC) approach, point estimate methods (POEM), scenario-based approach, chance-constrained methods, and unscented transformation approach (UT). The differences between these methods lie in the generated sampling points, precision, and running time. The MC method is known for its accuracy, as it generates a large number of sampling points for uncertain input parameters, resulting in a more precise output distribution.

7.4 Summary The rapid economic development has created a substantial demand for energy, while simultaneously leading to significant environmental challenges. Countries, including China, are facing the dual pressures of increasing energy supply and demand and environmental protection. To address these challenges, it is crucial to adopt new technologies, maximize the utilization of renewable energy, and transform the existing energy structure. The emergence of smart grids, particularly microgrids as their key component, along with the growing prominence of renewable energy sources within microgrids, offers a potential solution to alleviate these dual pressures. It is anticipated that the share of renewable energy consumption will progressively increase in the coming decade, reaching 19% by 2030 and 29% by 2050. The growth of renewable energy, especially wind and solar power, is outpacing expectations. For China, the transition to a low-carbon and sustainable energy system, primarily through the development of renewable energy, is the only way to address the conflict between rapid economic growth and high carbon dioxide emissions. China possesses significant renewable energy potential, and the integration of renewable energy into its future energy system is crucial. China’s renewable energy sector is expected

406

7 Renewable Energy and Microgrid

to further expand in the future, making significant contributions to the low-carbon economy. One of the key distinctions between smart grids and traditional grids is the introduction of microgrids, which are centered around renewable energy sources, into the power system. The goal is to meet power demand while ensuring grid stability and achieving high penetration of renewable energy. By integrating and coordinating renewable energy with the power system, energy storage systems can enhance the reliability, security, and resilience of microgrid applications by providing ancillary services such as peak shaving. Integrating renewable energy into the power system offers substantial socioeconomic and environmental benefits, while also minimizing greenhouse gas emissions from conventional power plants. However, the inherent randomness and intermittency of renewable energy, particularly photovoltaic generation, pose challenges to grid stability, leading to unstable power supply. Furthermore, the integration of renewable energy presents operational challenges for the power system, such as voltage rise in the distribution network due to reverse power flow, resulting in a decline in power supply quality. Therefore, the grid must be equipped to respond to this integration by adjusting generation, managing power consumption, or utilizing storage systems. This chapter provides an overview of the current status and development trends of renewable energy in China. It further discusses the significance of AC/DC microgrids and addresses the optimization problem associated with their operation.

Appendix A

Turbine swept area (m2 )

APV

Total solar panel area (m2 )

AA

Exponential voltage of the battery (V)

ARB

Annual rate of benefit ($/year)

B

Exponential capacity of the battery (Ah)−1

B Err

Difference of calculated and actual battery energy (kWh)

c1 to c5

Coefficients modelling c p (−)

C Bat

Battery incremental cost ($/kWh)

C FC

Fuel cell incremental cost ($/kWh)

C Grid

Grid incremental cost ($/kWh)

CMT

Microturbine incremental cost ($/kWh)

cp

Performance coefficient of the wind turbine (−)

CPV

PV incremental cost ($/kWh)

CW T

Wind turbine’s incremental cost ($/kWh) (continued)

Appendix

407

(continued) Cap

Capacity (kW)

CC

Capital cost ($/kW)

CC Bat

Battery capital cost ($)

Cost Bat

The total cost of battery operation ($)

Cost FC

The total cost of fuel cell operation ($)

CostGrid

The total cost of grid operation ($)

Cost M T

The total cost of microturbine operation ($)

Cost P V

The total cost of PV operation ($)

CostW T

The total cost of wind turbine operation ($)

DC t

Discharge capacity at hour “t”(Ah)

E0

Constant voltage of the battery model (V)

EB

Battery energy (kWh)

E tBat

Battery energy at hour “t”(kWh)

E initial Bat

Battery initial energy at hour “t”(kWh)

E tD,v t PDch_Bat t PFC t t PFC min , PFC max t PGrid t t PGrid , PGrid max min Plt

Drive energy of the vehicle at hour “t”(kWh)

The load’s production at hour “t”(kW)

Pm

Mechanical output power of the WT unit (MW)

Battery discharging power at hour “t”(kW) The fuel cell’s production at hour “t”(kW) Minimum and maximum grid power value at hour “t”(kW) The grid’s production at hour “t”(kW) Minimum and maximum grid power value at hour “t”(kW)

Pm_ pu

Per united Pm (−)

t PM T

The microturbine’s production at hour “t”(kW)

t t PM T min ,PM T max

Minimum and maximum micro turbine power value at hour“t”(kW)

PPt V

The photovoltaics’ production at hour “t”(kW)

Pspec

active power (W)

t PW T

The wind turbine’s production at hour “t”(kW)

PR

Performance ratio of solar panel (∈ [0.5, 0.9])

Q

Maximum battery capacity (Ah)

Q spec

Reactive power (Var)

Ra

Armature resistance (Ω),fuel cost ($/kWh)

rPV

Solar panel efficiency (%)

EPV

PV output Energy (kWh)

Emission tBat

The total emission of the batteries at hour “t”(kg MW−1 )

Emission tFC Emission tGrid

The total emission of the fuel cell at hour “t”(kg MW−1 ) The total emission of the grid at hour “t”(kg MW−1 ) (continued)

408

7 Renewable Energy and Microgrid

(continued) Emission tM T

The total emission of the micro turbine at hour “t”(kg MW−1 )

Emission tP V Emission tW T

The total emission of the photovoltaics at hour “t”(kg MW−1 ) The total emission of the wind turbine at hour “t”(kg MW−1 )

E x p(s)

Representing the exponential zone dynamics of the battery (V)

HP V

Annual average solar irradiation on tilted panels (kWh/m2 )

i∗

Representing the frequency current dynamics (A)

i bat

Battery current (A)

Id

Armature current (A)

it

Extracted capacity of the battery (Ah)

kP

Proportional gain of PI controller (−)

kI

Integral gain of PI controller (−)

km

Motor wiring constant (−)

kp

Power gain (−)

K

Polarization resistance (Ω)

LH

Inductor value at high side (H)

LF

Load factor (−)

LT

Lifetime (year)

m

Number of discharging cycles (−)

N W T /M T /FC/P V

Number of units (−)

OM

Operation and maintenance cost ($/kWh)

p, K

Battery coefficients (−)

Pg

Generation of power units (kW)

t PBat t PBat min , t PC_Bat

The battery’s production at hour “t”(kW) t PBat max

SL K

Minimum and maximum battery power value at hour “t”(kW) Battery charging power at hour “t”(kW) Complex power (VA)

sel(s)

Battery charging mode (∈ {−1, 0, 1})

t Ucharge

The states of charge of the batteries (−)

t Udischarge

The states of discharge of the batteries (−)

Uvt

Vehicle’s charge/discharge state at hour “t”(−)

Ug

The on/off state of FC/MT units (∈ {0, 1})

V

Voltage (V)

Vd

Motor voltage (V)

VH

Terminal voltage (V)

VL

Voltage at low side (V)

Vwind

Wind speed (m/s)

X

Decision variable vector (−) (continued)

References

409

(continued) β

Blade pitch angle (deg)

η

Electrical efficiency of the fuel cell (%)

ηc ,ηd

Charge, discharge efficiency (%)

λ

Tip speed ratio of the rotor blade tip speed to wind speed (−)

λb

The base λ value derived from c p − λ characteristic (rpm)

ρ

Air density (kg m−3 )

ωd

Armature speed (Rad/s)

References 1. U.S. Energy Information Administration (2017) International Energy Outlook 2017, www.eia. gov/forecasts/ieo/pdf/0484(2016).pdf 2. National Bureau of Statistics of China (2015) China energy statistical yearbook-2015. China Statistic Press, Beijing 3. China’s targets and achievements in emission reduction. http://www.china.org.cn. 4. Zeng N, Ding Y, Pan J et al (2008) Climate change—the Chinese challenge. Science 319:730– 731 5. Zhang D, Wang J, Lin Y et al (2017) Present situation and future prospect of renewable energy in China. Renew Sustain Energy Rev 76:865–871. https://doi.org/10.1016/j.rser.2017.03.023 6. Chai Q, Zhang X (2010) Technologies and policies for the transition to a sustainable energy system in China. Energy 2010(35):3995–4002 7. Mathiesen BV, Lund H, Karlsson K (2011) 100% renewable energy systems, climate mitigation and economic growth. Appl Energy 88:488–501 8. Liu W, Lund H, Mathiesen BV, Zhang X (2011) Potential of renewable energy systems in China. Appl Energy 88:518–525 9. Ma H, Oxley L, Gibson J, Li W (2010) A survey of China’s renewable energy economy. Renew Sustain Energy Rev 14:438–445 10. NDRC (2007) Medium and long-term development plan for renewable energy 2007 11. IEA (2013) World energy outlook 12. Wang S, Yuan P, Li D, Jiao Y (2011) An overview of ocean renewable energy in China. Renew Sustain Energy Rev 15:91–111 13. BP (2016) BP statistical review of China. BP, London 14. Huang H, Yan Z (2009) Present situation and future prospect of hydropower in China. Renew Sustain Energy Rev 13:1652–1656 15. Chang X, Liu X, Zhou W (2010) Hydropower in China at present and its further development. Energy 35:4400–4406 16. Ministry of Water Resources (2014) Statistic Bulletin on China Water Activities 2015 17. Niu X (2015) The practice and challenges of Chinese hydropower. Renming Changjiang 18. China Wind Energy Association (2009) 19. Liu L-q, Wang Z-x, Zhang H-q, Xue Y-c (2010) Solar energy development in China: a review. Renew Sustain Energy Rev 14:301–311 20. National Development and Reform Commission (NDRC) (2006) China renewable energy development report 21. Li JF, Wang SC, Zhang MJ, Ma LJ, China solar PV report 2007 22. Zhang S, He Y (2013) Analysis on the development and policy of solar PV power in China. Renew Sustain Energy Rev 21:393–401

410

7 Renewable Energy and Microgrid

23. National Energy Administration (2016) Statistical Data of PV Generation in 2015 24. Zhao Z-y, Yan H (2012) Assessment of the biomass power generation industry in China. Renew Energy 37:53–60 25. Kumar A, Kumar K, Kaushik N, Sharma S, Mishra S (2010) Renewable energy in India: current status and future potentials. Renew Sustain Energy Rev 14:2434–2442 26. Zhou X, Wang F, Hu H, Yang L, Guo P, Xiao B (2011) Assessment of sustainable biomass resource for energy use in China. Biomass- Bioenergy 35:1–11 27. China National Renewable Energy Center. China renewable energy industry development report 2015 28. Report 2009-Series of Renewable Energy (2010) China Economic Information Network 29. Zhang D, Li W, Lin Y (2009) Wave energy in China: current status and perspectives. Renew Energy 34:2089–2092 30. Liu T, Xu G, Cai P, Tian L, Huang Q (2011) Development forecast of renewable energy power generation in China and its influence on the GHG control strategy of the country. Renew Energy 2011(36):1284–1292 31. Ling-zhen K (2006) Development trend of geothermal energy resource in China. Coal Technol 7:067 32. Benedek J, Sebestyén TT, Bartók B (2018) Evaluation of renewable energy sources in peripheral areas and renewable energy-based rural development. Renew Sustain Energy Rev 90:516–535 33. Notton G, Nivet ML, Voyant C et al (2018) Intermittent and stochastic character of renewable energy sources: consequences, cost of intermittence and benefit of forecasting. Renew Sustain Energy Rev 87:96–105 34. Bouhouras AS, Sgouras KI, Gkaidatzis PA et al (2016) Optimal active and reactive nodal power requirements towards loss minimization under reverse power flow constraint defining DG type. Int J Electr Power Energy Syst 78:445–454 35. Edvard, Smart grid deployment, what we’ve done so far 2012. https://electricalengineering-portal.com/smart-gr. https://electrical-engineeringportal.com/smart-grid-deploy ment-what-weve-done-so-far. 36. Elsayed Ahmed T, Mohamed Ahmed A, Mohammed OA (2015) DC microgrids and distribution systems: an overview. Electr Power Syst Res 119:407–417 37. Planas E, Andreu J, Garate JI et al (2015) AC and DC technology in microgrids: a review. Renew Sustain Energy Rev 43:726–749 38. Shang X, Li Z, Ji T et al (2017) Online area load modeling in power systems using enhanced reinforcement learning. Energies 10:1852, 2017. https://doi.org/10.3390/en10111852 39. Acevedo SS, Molinas M (2012) Assessing the validity of a propose stability analysis method in a three phase system with constant power load. In: 2012 3rd IEEE international symposium on power electronics for distributed generation systems. IEEE 2012, pp 41–5 40. Xu Q, Hu X, Wang P, Xiao J et al (2016) Design and stability analysis for an autonomous DC microgrid with constant power load. In: 2016 IEEE appl power electrons conference and exposition 2016, pp 3409–3415. https://doi.org/10.1109/APEC.2016.7468357 41. Cupelli M, Moghimi M, Riccobono A et al (2015) A comparison between synergetic control and feedback linearization for stabilizing MVDC microgrids with constant power load. In: IEEE PES innovative smart grid technologies conference Europe. https://doi.org/10.1109/ISG TEurope.2014.7028870 42. Grainger BM et al (2016) Modern controller approaches for stabilizing constant power loads within a DC microgrid while considering system delays. In: 2016 IEEE 7th international symposium on power electronics for distributed generation systems. PEDG 2016. https://doi. org/10.1109/PEDG.2016.7527001 43. Cupelli M et al (2016) Case study of voltage control for MVDC microgrids with constant power loads-comparison between centralized and decentralized control strategies. In: Proceedings of 18th MELECON 2016. https://doi.org/10.1109/MELCON.2016.7495331 44. Hossan MS, Maruf HMM, Chowdhury B (2018) Comparison of the ZIP load model and the exponential load model for CVR factor evaluation. IEEE Power & Energy Society General Meeting, pp 1–5. https://doi.org/10.1109/PESGM.2017.8274490

References

411

45. Hatipoglu K et al (2012) Investigating effect of voltage changes on static ZIP load model in a microgrid environment. In: 2012 North American power symposium. IEEE, pp 1–5. https:// doi.org/10.1109/NAPS.2012.6336407 46. Bayeh C, Moubayed N (2015) A general review on photovoltaic, modeling, simulation and economic study to build 100 MW power plant in Lebanon. Br J Appl Sci Technol 11:1–21 47. Mahmoud Y, El-Saadany E (2015) Accuracy improvement of the ideal PV model. IEEE Trans Sustain Energy 6:909–911. https://doi.org/10.1109/TSTE.2015.2412694 48. Jazayeri M, Uysal S, Jazayeri K (2013) A simple MATLAB/Simulink simulation for PV modules based on one-diode model. In: 2013 High capacit opt networks emerging/enabling technol IEEE, pp 44–50. https://doi.org/10.1109/HONET.2013.6729755 49. Abdulkadir M, Samosir AS, Yatim AHM (2012) Modeling and simulation based approach of photovoltaic system in Simulink model. ARPN J Eng Appl Sci 7:616–23 50. Alrahim Shannan NMA et al (2013) Single-diode model and two-diode model of PV modules: a comparison. In: Proceedings-2013 IEEE international conference on control system, computing and engineering. ICCSCE 2013 IEEE, pp 210–214. https://doi.org/10.1109/ICCSCE.2013.671 9960 51. Polinder H (2011) Overview of and trends in wind turbine generator systems. IEEE Power Energy Soc Gen Meet IEEE 1–8. https://doi.org/10.1109/PES.2011.6039342 52. Martinez J (2007). Modelling and control of wind turbines. https://doi.org/10.1007/978-3-64241080 53. Hang Y et al (2016) Study of the collector-line-current-protection setting in centralized accessed double-fed wind farms. In: 2016 IEEE power & energy society general meeting. IEEE, pp 1–5. https://doi.org/10.1109/PESGM.2016.7741664 54. Prieto-Araujo E et al (2015) Decentralized control of a nine-phase permanent magnet generator for offshore wind turbines. IEEE Trans Energy Convers 30:1103–1112 55. Siano P, Mokryani G (2015) Evaluating the benefits of optimal allocation of wind turbines for distribution network operators. IEEE Syst J 9:629–638 56. Mokryani G, Siano P, Piccolo A (2013) Optimal allocation of wind turbines in microgrids by using genetic algorithm. J Ambient Intell Humaniz Comput 4:613–619 57. Heier S (2014). Grid integration of wind energy. https://doi.org/10.1002/9781118703274 58. Tremblay O (2015) Experimental validation of a battery dynamic model for EV applications experimental validation of a battery dynamic model for EV applications. World Electr Veh J 3:289–298 59. Zhu C, Li X, Song L, Xiang L (2013) Development of a theoretically based thermal model for lithium ion battery pack. J Power Sources 223:155–164 60. Saw LH, Somasundaram K, Ye Y, Tay AAO (2014) Electro-thermal analysis of Lithium Iron Phosphate battery for electric vehicles. J Power Sources 249:231–238 61. Mousavi GSM et al (2017) A comprehensive review of Flywheel Energy Storage System technology. Renew Sustain Energy Rev 67:477–490 62. Fraleoni-Morgera A, Lughi V (2015) Overview of small scale electric energy storage systems suitable for dedicated coupling with renewable micro sources. In: 2015 International conference on renewable energy research and applications. ICRERA 2015, pp 1481-1485. https://doi.org/ 10.1109/ICRERA.2015.7418653 63. Loh PC, Li D, Chai YK et al (2013) Autonomous control of interlinking converter with energy storage in hybrid AC-DC microgrid. IEEE Trans Ind Appl 49:1374–1382 64. Mohamed A et al (2011) Bi-directional AC-DC, DC-AC converter for power sharing of hybrid AC, DC systems. IEEE Power Energy Soc Gen Meet IEEE 2011:1–8 65. Zeng H, Zhao H, Yang Q (2014) Coordinated energy management in autonomous hybrid AC/ DC microgrids. In: POWERCON 2014 International conference on smart cities and green ICT systems, pp 3186–3193. https://doi.org/10.1109/POWERCON.2014.6993710 66. Bracco S, Delfino F (2017) A mathematical model for the dynamic simulation of low size cogeneration gas turbines within smart microgrids. Energy 119:710–723 67. Mousavi GSM, Faraji F, Majazi A et al (2016) A comprehensive review of flywheel energy storage system technology. Renew Sustain Energy Rev. https://doi.org/10.1016/j.rser.2016. 09.060

412

7 Renewable Energy and Microgrid

68. In-Su B, Jin-O K (2008) Reliability evaluation of customers in a microgrid. IEEE Trans Power Syst 23:1416–1422 69. Buque C, Chowdhury S (2016) Distributed generation and microgrids for improving electrical grid resilience: review of the Mozambican scenario. IEEE Power Energy Soc Gen Meet. https:// doi.org/10.1109/PESGM.2016.7741488 70. Tautiva C, Cadena A, Rodriguez F (2009) Optimal placement of distributed generation on distribution networks. In: Proceedings of 44th International Universities Power Engineering Conference (UPEC), pp 1–5. https://doi.org/10.1109/TDC-LA.2008.4641762 71. De Brabandere K et al (2007) Control of microgrids. 2007 IEEE Power Eng Soc Gen Meet IEEE 1–7. https://doi.org/10.1109/PES.2007.386042 72. Shafiee Q et al (2014) Distributed secondary control for islanded microgrids: a novel approach. Power Electron IEEE Trans 29:1018–1031 73. Vivekananthan C et al (2015) Real-time price based home energy management scheduler. IEEE Trans Power Syst 30:2149–2159 74. Kim B-G et al (2016) Dynamic pricing and energy consumption scheduling with reinforcement learning. IEEE Trans Smart Grid 7:2187–2198. https://doi.org/10.1109/TSG.2015.2495145 75. Khooban MH, Kavousi-Fard A, Niknam T (2014) Intelligent stochastic framework to solve the reconfiguration problem from the reliability view. IET Sci Meas Technol 8:245–59. https:// doi.org/10.1049/iet-smt.2013.0106 76. Koutroulis E et al (2006) Methodology for optimal sizing of stand-alone photovoltaic/windgenerator systems using genetic algorithms. Sol Energy 80:1072–1088 77. Liu G, Xu Y, Tomsovic K (2016) Bidding strategy for microgrid in day-ahead market based on hybrid stochastic/robust optimization. IEEE Trans Smart Grid 7:227–237 78. Zhang L, Li Y (2011) Optimal energy management of hybrid power system with two-scale dynamic programming. IEEE/PES Power Syst Conf Expo 2011:1–8 79. Najafi-Ravadanegh S, Nikmehr N (2015) Optimal operation of distributed generations in microgrids under uncertainties in load and renewable power generation using heuristic algorithm. IET Renew Power Gener 9:982–990 80. Moshi GG, Bovo C, Berizzi A, Taccari L (2016) Optimization of integrated design and operation of microgrids under uncertainty. Power Syst Comput Conf 1–7 81. Mousavi-Seyedi SS et al (2013) AHP-based prioritization of microgrid generation plans considering resource uncertainties, Smart Grid Conf. SGC 2013:63–68. https://doi.org/10.1109/SGC. 2013.6733800 82. Khayatian A, Barati M, Lim GJ (2016) Market-based and resilient coordinated Microgrid planning under uncertainty. Proc IEEE Power Eng Soc Transm Distrib Conf. https://doi.org/ 10.1109/TDC.2016.7520030 83. Khodaei A, Bahramirad S, Shahidehpour M (2015) Microgrid planning under uncertainty. IEEE Trans Power Syst 30:2417–2425. https://doi.org/10.1109/TPWRS.2014.2361094