AI Enabled IoT for Electrification and Connected Transportation (Transactions on Computer Systems and Networks) 9789811921834, 9789811921841, 9811921830

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Transactions on Computer Systems and Networks

Naveenkumar Marati Akash Kumar Bhoi Victor Hugo C. De Albuquerque Akhtar Kalam   Editors

AI Enabled IoT for Electrification and Connected Transportation

Transactions on Computer Systems and Networks Series Editor Amlan Chakrabarti, Director and Professor, A. K. Choudhury School of Information Technology, Kolkata, West Bengal, India

Transactions on Computer Systems and Networks is a unique series that aims to capture advances in evolution of computer hardware and software systems and progress in computer networks. Computing Systems in present world span from miniature IoT nodes and embedded computing systems to large-scale cloud infrastructures, which necessitates developing systems architecture, storage infrastructure and process management to work at various scales. Present day networking technologies provide pervasive global coverage on a scale and enable multitude of transformative technologies. The new landscape of computing comprises of self-aware autonomous systems, which are built upon a software-hardware collaborative framework. These systems are designed to execute critical and non-critical tasks involving a variety of processing resources like multi-core CPUs, reconfigurable hardware, GPUs and TPUs which are managed through virtualisation, real-time process management and fault-tolerance. While AI, Machine Learning and Deep Learning tasks are predominantly increasing in the application space the computing system research aim towards efficient means of data processing, memory management, real-time task scheduling, scalable, secured and energy aware computing. The paradigm of computer networks also extends it support to this evolving application scenario through various advanced protocols, architectures and services. This series aims to present leading works on advances in theory, design, behaviour and applications in computing systems and networks. The Series accepts research monographs, introductory and advanced textbooks, professional books, reference works, and select conference proceedings.

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Naveenkumar Marati · Akash Kumar Bhoi · Victor Hugo C. De Albuquerque · Akhtar Kalam Editors

AI Enabled IoT for Electrification and Connected Transportation

Editors Naveenkumar Marati Wipro Limited Bengaluru, Karnataka, India Victor Hugo C. De Albuquerque Department of Teleinformatics Engineering Federal University of Ceará Fortaleza, Brazil

Akash Kumar Bhoi KIET Group of Institutions, Delhi-NCR, Ghaziabad, India Directorate of Research Sikkim Manipal University Gangtok, Sikkim, Australia Akhtar Kalam College of Engineering and Science Victoria University Melbourne, VIC, Australia

ISSN 2730-7484 ISSN 2730-7492 (electronic) Transactions on Computer Systems and Networks ISBN 978-981-19-2183-4 ISBN 978-981-19-2184-1 (eBook) https://doi.org/10.1007/978-981-19-2184-1 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 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 translation, 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 publisher, 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 publisher 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 publisher remains 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

Preface

The day-to-day advances in the computational intelligence, data analytics, smart sensors, and electrifications enable the paradigm shift in the automotive sector. These improvements in the electrification and connected transportation play a key role in the opportunities and challenges in the automotive sector. This book mainly focuses on the new techniques of AI and IoT in electrification and connected transportation. The advances in the intelligent techniques, sensor technology, security, and electrification converter topologies allow enormous opportunities in the automotive sector. These advances also help in the smart ecosystem applications in the different directions from the intelligent transportation system to smart grid to the smart city applications. The AI and IoT not only play a key role in the autonomous or connected vehicle applications but also the electrification segment, especially in the areas like prognostic diagnosis, charging station location, vehicle to grid (V2G), grid to vehicle (G2V), and demand side management, etc. The increase in the demand for the reduction of CO2 emissions in the automotive sector across the globe also enables the different improvements in the automotive power train systems from internal combustion engine (ICE) to hybrid electric vehicle (HEV), electric vehicle (EV), and fuel cell EV (FCEV), etc. These improvements in the power train architectures involve improvement in battery management or energy storage management systems, which again enables the control algorithm development, where these prominent control techniques also depend on the computational intelligence, data analytics such as part of artificial intelligence techniques. The demand of AI and IoT also enables the importance in the development of the smart and accurate sensing systems/sensors. This book covers different aspects of the IoT and artificial intelligence use cases in the electrification and connected transportation applications. Anupam Baliyan et al. in their manuscript entitled “Role of AI and IoT Techniques in Autonomous Transport Vehicles” has explored problems and opportunities with the recent AI and IoT techniques which are prominent in the autonomous vehicle (AV) and its applications. Anupam Baliyan et al. also explained the cloud and edge computing IoT network shown to be able to exploit large volumes of data created by connected and networked devices. Akhtar Kalam and Pejman Peidaee discuss the prospect of near future of the railway operation within the context of IoT platform v

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and highlight requirements for further preparation and promotion of the research area relevant to the smart railway operation in their manuscript “IoT-Enabled Railway System and Power System”, and it also demonstrated an integrated ecosystem of cutting-edge digital technologies and advanced data analysis framework which are adopted to demonstrate a responsive and agile operating environment for operators in railway system. Jyothirmoy dutta and Ribu Mathew detail on the different categories sensors required for the applications of intelligent transportation systems in the applications of traffic management, vehicle diagnostics, driver behaviour, environmental conditions, and vehicle safety, respectively, in their manuscript titled “An Overview of Sensors in Intelligent Transportation Systems and Electric Vehicles”. Shola Usha Rani and Karmel A. discuss the challenges and IoT sensor communication technologies for the smart parking system in their manuscript titled “Smart Parking System and Its Applications”, which also details the smart parking system architecture and also develops a use case for a practical smart parking application through mobile platform. S. Rajarajeswari and N. Hema explore in the advancements in the security and door locking system in the automotive applications such as a mix of wireless technologies such as Bluetooth and WiFi which are used to control smart lock using smart lock application from a place wherever Internet access is available in their manuscript titled “Smart Door Locking System”. Farroque Azam et al. in their chapter titled “Privacy and Authentication Schemes in VANETS Using Blockchain: A Review and a Framework to Mitigate Security and Privacy Issues”, discusses the various research works related to privacy and authentication schemes in VANETS using Blockchain has been made and the framework based on consensus algorithm have been proposed for secured dissemination of messages. Biranchi Narayan Kar et al. explain in their manuscript titled “Photovoltaic Array-Fed Indirect Vector-Controlled Induction Motor Drive for EV Transportation System Using Brain Emotional Learning-Based Intelligent Controller” about the single-stage indirect vector control of induction motor for PV-fed EV transportation system using brain emotional learning-based intelligent control (BELBIC). In the proposed system, they have considered a solar photovoltaic (PV) array, a threephase voltage source inverter, and a motor-with EV chassis system to demonstrate the proposed algorithm. The BELBIC operates effectively for motor-drive systems with changes in system parameters, load disturbances, and a sudden shift in reference speed. Merugu Kavitha et al. explains the configuration of wireless power transfer (WPT) technology at the system level and Governing International standards of WPT powered EVs are summarized along with the different coil architectures and circuit configurations utilizing for achieving improved system’s efficiency are discussed in the manuscript titled “Electrical Vehicles (EVs)—An Application of Wireless Power Transfer (WPT) System”. D. Suresh et al. discuss the topology of an isolated diodebridge rectifier-based battery charger for the electric vehicle applications in their manuscript entitled “Twelve Pulse-Based Battery Charger with PV Power Integration”, which also covers the IRPT-based scheme which is combined with maximum power point tracking (MPPT) technique for estimation of maximum power from modules of the photovoltaic array.

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V. Kumar et al. in their chapter titled “Design and Development of Brushless DC Motor Drive for Electrical Vehicle Application” discuss the overview of electric vehicle technology and speed control techniques of a brushless DC motor in EV applications. As well as, the performance of the BLDC motor is investigated under steady, dynamic state speed, and torque conditions. Khammampati R Sreejyothi et al. discuss on the phase-shift DC–DC converter for the applications of the battery charging. The authors have discussed different modes of operations of the converter with the closedloop control in their chapter titled “Zero Voltage Switching (ZVS)-Based DC–DC Converter for Battery Input Application”. Kalagotla Chenchireddy and V. Jegathesan in their chapter titled “Three-Leg Voltage Source Converter-Based D-STATCOM for Power Quality Improvement in Electrical Vehicle Charging Station” explained grid to vehicle technology for the battery charging station and also implemented two topologies; one is AC–DC converter for converting AC supply to DC supply, and the second one is DC–DC converter for battery charging. The editors would like to thank all the authors for submitting their manuscripts in this book on: AI Enabled IoT in Electrification and Connected Transportation. We also acknowledge the reviewers for their contributions in reviewing the papers and providing constructive and valuable comments to the authors. We would also like to thank Springer for providing the opportunity. Bengaluru, India Gangtok, India Fortaleza, Brazil Melbourne, Australia

Naveenkumar Marati Akash Kumar Bhoi Victor Hugo C. De Albuquerque Akhtar Kalam

Contents

Role of AI and IoT Techniques in Autonomous Transport Vehicles . . . . . Anupam Baliyan, Jagjit Singh Dhatterwal, Kuldeep Singh Kaswan, and Vishal Jain

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IoT Enabled Railway System and Power System . . . . . . . . . . . . . . . . . . . . . . Akhtar Kalam and Pejman Peidaee

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An Overview of Sensors in Intelligent Transportation Systems and Electric Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Jyotirmoy Dutta and Ribu Mathew Smart Parking System and Its Applications . . . . . . . . . . . . . . . . . . . . . . . . . . Shola Usharani and A. Karmel

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Smart Door Locking System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101 S. Rajarajeswari and N. Hema Privacy and Authentication Schemes in VANETS Using Blockchain: A Review and a Framework to Mitigate Security and Privacy Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127 Farooque Azam, Sunil Kumar, and Neeraj Priyadarshi Photovoltaic Array Fed Indirect Vector-Controlled Induction Motor Drive for EV Transportation System Using Brain Emotional Learning-Based Intelligent Controller . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 Biranchi Narayan Kar, Paulson Samuel, and Bandi Mallikarjuna Reddy Electrical Vehicles (EVs)—An Application of Wireless Power Transfer (WPT) System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165 Merugu Kavitha, D. Mohan Reddy, and N. S. Kalyan Chakravarthy Twelve Pulse-Based Battery Charger with PV Power Integration . . . . . . . 191 D. Suresh, V. Kumar, and Mote Mahesh

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Design and Development of Brushless DC Motor Drive for Electrical Vehicle Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201 V. Kumar, Kalagotla Chenchireddy, Khammampati R Sreejyothi, and G. Sujatha Zero Voltage Switching (ZVS)-Based DC–DC Converter for Battery Input Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 219 Khammampati R Sreejyothi, V. Kumar, Kalagotla Chenchireddy, and P. Tejaswi Three-Leg Voltage Source Converter-Based D-STATCOM for Power Quality Improvement in Electrical Vehicle Charging Station . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235 Kalagotla Chenchireddy and V. Jegathesan

Editors and Contributors

About the Editors Naveenkumar Marati was with Wipro Limited, Bengaluru, India, as Architect for Electrification in EV COE from Jan 2021 to Jan 2022. Earlier, he was working with Valeo India Private Limited, Chennai, India, as Senior Engineer, Power electronics (R&D). Before joining industry, he was associated with the Department of Electrical Engineering, Shiv Nadar University, Greater Noida, India, as a research scholar from 2013 to 2017. He obtained his M.Tech. from Rajiv Gandhi University of Knowledge Technologies (IIIT Nuzvid), Andhra Pradesh, in 2013. He obtained his B.Tech. from CVR College of Engineering, JNTU Hyderabad, in 2009. He has published several papers in journal and reputed conference. He has also published few book chapters in the area electric vehicle. He is an editorial board member of the Journal of Electrical and Electronic Engineering (JEEE). He is an active volunteer in IEEE, currently serving as Secretary PES/PELS/IAS joint chapter IEEE Hyderabad section for the year 2019 and 2020. His interest areas are power electronics, digital control of power electronics, electrification in automotive, HEV, EV, IoT in automotive, and charging stations. e-mail: [email protected] Akash Kumar Bhoi currently associated with KIET Group of Institutions, India as Adjunct Faculty and Directorate of Research, Sikkim Manipal University as Adjunct Research Faculty. He is appointed as the honorary title of “Adjunct Fellow” Institute for Sustainable Industries & Liveable Cities (ISILC), Victoria University, Melbourne, Australia for the period from 1 August 2021 to 31 July 2022. He is also working as a Research Associate at Wireless Networks (WN) Research Laboratory, Institute of Information Science and Technologies, National Research Council (ISTI-CRN) Pisa, Italy. He was the University Ph.D. Course Coordinator for “Research & Publication Ethics (RPE) at SMU.” He is the former Assistant Professor (SG) of Sikkim Manipal Institute of Technology and served about 10 years. He is a member of IEEE, ISEIS, and IAENG, an associate member of IEI,

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UACEE, and an editorial board member reviewer of Indian and International journals. He is also a regular reviewer of reputed journals, namely IEEE, Springer, Elsevier, Taylor and Francis, Inderscience, etc. His research areas are Biomedical Technologies, the Internet of Things, Computational Intelligence, Antenna, Renewable Energy. He has published several papers in national and international journals and conferences. He has 130+ documents registered in the Scopus database by the year 2021. He has also served on numerous organizing panels for international conferences and workshops. He is currently editing several books with Springer Nature, Elsevier, and Routledge & CRC Press. He is also serving as Guest editor for special issues of the journal like Springer Nature and Inderscience. e-mail: [email protected] Victor Hugo C. De Albuquerque [M’17, SM’19] is Professor and Senior Researcher at the Department of Teleinformatics Engineering (DETI)/Graduate Program in Teleinformatics Engineering (PPGETI) at the Federal University of Ceará (UFC), Brazil. He earned a Ph.D. in Mechanical Engineering from the Federal University of Paraíba (UFPB, 2010), a M.Sc. in Teleinformatics Engineering from the PPGETI/UFC (UFC, 2007). He completed a BSE in Mechatronics Engineering at the Federal Center of Technological Education of Ceará (CEFETCE, 2006). He specializes in image data science, IoT, machine/deep learning, pattern recognition, automation and control, and robotics. e-mail: [email protected] Akhtar Kalam has been at Victoria University, Melbourne since 1985 and is currently Head of Engineering and Chair of the Academic Board and lectures in the Masters by coursework program at Engineering Institute of Technology, Perth, Australia. Further, he has a distinguished professorship position at the University of New South Wales, Sydney, and 5 Malaysian Universities. He received his B.Sc. and B.Sc. engineering from Calcutta University and Aligarh Muslim University, India. He completed his M.S. and Ph.D. at the University of Oklahoma, USA, and the University of Bath, UK. His areas of interest are power system analysis, communication, control, protection, renewable energy, smart grid, IEC61850 implementation, and cogeneration systems. He has conducted research, provided industrial consultancy, and published over five hundred publications on his area of expertise and written over 29 books in the area. e-mail: [email protected]

Contributors Farooque Azam Department of Computer Science and Engineering, Sangam University, Bhilwara, Rajasthan, India Anupam Baliyan Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India

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Kalagotla Chenchireddy Department of EEE, Teegala Krishna Reddy Engineering College, Hyderabad, India Kalagotla Chenchireddy Department of EEE, Teegala Krishna Reddy Engineering College, Hyderabad, Telangana, India; Department of EEE, Karunya Institute of Technology and Sciences, Coimbatore, India Jagjit Singh Dhatterwal Department of Artificial Intelligence & Data Science, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India Jyotirmoy Dutta India Urban Data Exchange, Bangalore, India; Centre for Digital Innovation, CHRIST (Deemed to be University), Bangalore, India N. Hema Vellore Institute of Technology, Chennai, India Vishal Jain School of Engineering and Technology, Sharda University, Greater Noida, India V. Jegathesan Department of EEE, Karunya Institute of Technology and Sciences, Coimbatore, India Akhtar Kalam College of Engineering and Science, Victoria University, Melbourne, Australia N. S. Kalyan Chakravarthy QIS College of Engineering and Technology, Vengamukkapalem, Ongole, Prakasam District, Andhra Pradesh, India Biranchi Narayan Kar Electrical Department, MNNIT Allahabad, Prayagraj, U. P., India A. Karmel Vellore Institute of Technology, Chennai, India Kuldeep Singh Kaswan Department of Computer Science and Engineering, School of Computing Science and Engineering, Galgotias University, Greater Noida, India Merugu Kavitha QIS College of Engineering and Technology, Vengamukkapalem, Ongole, Prakasam District, Andhra Pradesh, India Sunil Kumar Department of Computer Science and Engineering, Sangam University, Bhilwara, Rajasthan, India V. Kumar Department of Electrical and Electronics Engineering, Teegala Krishna Reddy Engineering College, Hyderabad, Telangana, India Mote Mahesh Department of Electrical and Electronics Engineering, Vignan Institute of Technology and Science, Deshmukh, India Ribu Mathew School of Electrical and Electronics Engineering (SEEE), VIT Bhopal University, Bhopal, India

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D. Mohan Reddy QIS College of Engineering and Technology, Vengamukkapalem, Ongole, Prakasam District, Andhra Pradesh, India Pejman Peidaee College of Engineering and Science, Victoria University, Melbourne, Australia Neeraj Priyadarshi Department of Business Development and Technology, CTiF Global Capsule, Aarhus University, Herning, Denmark S. Rajarajeswari Vellore Institute of Technology, Chennai, India Bandi Mallikarjuna Reddy Hardware Design, TransDigm India Private Limited, Bangalore, Karnataka, India Paulson Samuel Electrical Department, MNNIT Allahabad, Prayagraj, U. P., India Khammampati R Sreejyothi Department of EEE, Teegala Krishna Reddy Engineering College, Hyderabad, Telangana, India G. Sujatha Department of EEE, G Narayanamma Institute of Technology and Science (for Women), Hyderabad, India D. Suresh Department of Electrical Engineering, National Institute of Technology Raipur, Raipur, India P. Tejaswi Department of EEE, G. Narayanamma Institute of Technology and Science (for Women), Hyderabad, Telangana, India Shola Usharani Vellore Institute of Technology, Chennai, India

Role of AI and IoT Techniques in Autonomous Transport Vehicles Anupam Baliyan, Jagjit Singh Dhatterwal, Kuldeep Singh Kaswan, and Vishal Jain

Abstract Artificial Intelligence (AI) is computer technology with enormous possibilities for smart industrial transformation. The Internet of Things (IoT) is the 4:0 revolution industrial idea that comprises a worldwide knowledge gathering and direct maintenance for storage, transmission, sensing, enhanced services, and technology development. The combination of high-speed, durable, low-latency intercoms and AI and IoT contributing to significant the move toward a full-smart independent (AV) transport electrifier that demonstrates how physical and virtual transport information complement one another. This chapter discusses how the recent AI and IoT techniques may help the quest for autonomous transport vehicles. It was demonstrated that 90% of automobile crises are caused by human mistakes, and 10 times the median driver is safer. Autonomous vehicle safety is important and users need an acceptable level of risk 1000 times lower. AVs have some unbelievable benefits such as (i) enhancing the safety of vehicles, (ii) cutting back on accidents, (iii) reducing fuel usage, (iv) opening up drivers’ time and commercial prospects, (v) AVs must, however, utilize vast knowledge from their wearable sensing. Keywords Artificial intelligence · Internet of things · Autonomous vehicle · Advanced driver assistance systems · Machine learning

A. Baliyan (B) Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India e-mail: [email protected] J. S. Dhatterwal Department of Artificial Intelligence & Data Science, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India K. S. Kaswan Department of Computer Science and Engineering, School of Computing Science and Engineering, Galgotias University, Greater Noida, India V. Jain School of Engineering and Technology, Sharda University, Greater Noida, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 N. Marati et al. (eds.), AI Enabled IoT for Electrification and Connected Transportation, Transactions on Computer Systems and Networks, https://doi.org/10.1007/978-981-19-2184-1_1

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1 Introduction Artificial Intelligence (AI) provides a huge range of smart industrial revolutions tool for machine intelligence. It enables the collection, identification of alternatives, choice of alternatives, action, decision-making, evaluation of decision-making, and intelligent prediction. In turn, the Internet of Things (IoT) becomes the centerpiece of the transportation sector transformation, incorporating a worldwide infrastructure for storage, control, sensing, enhanced services, and wireless communications for data collection and processing. The combination of elevated, reliable, low bandwidth connection and AI and IoT technology into full-smart autonomous vehicles is made feasible in addition to real-based digital knowledge in the transport sector (AV). This section aims to examine how the present approaches of AI and IoT may assist in searching for AVs. The source of 90% of automobile crashes was human error, whereas the healthiest cars going 10 times above their average [1]. The independent safety of the automobile is crucial and users require an appropriate 1000-fold lower incidence. The huge advantages of AVs include: (i) improving vehicle safety; (ii) reducing fuel usage; (iii) releasing drivers time and opportunities; (iv) new market possibilities. However, large-scale data/info from their sensors and actuators have to be used by AVs. Advanced driver support (ADAS) and infotainment use AV documents increasingly sophisticated. The processing is 1 GB per second. Hardware and software need using sensors, actuators, and applications are therefore required to compete with functions comparable to the AI-directed superhuman brain. AV devices and sensors inform decisions like time, date, surveillance systems, navigation, energy speed accelerate, natural language processing, engine recommendation systems, surveillance of the eyes and the driver, picture identification, feeling analyzes, speech, and gestures, etc. The total annual data for 100,000 automobiles is above 100 GB [2, 3]. These data are expected to expand further as connected vehicles are increasingly being adopted (CVs). The rise of the AV gives industrial manufacturers and distributors new options, which enables firms to employ AI to improve their customers’ value. When it comes to the processing of the data by AI, Machine Learning (ML) algorithms are the most effective technique. The ML algorithms assist to develop behavioral patterns of some driver profiles and also provide car owners with the right application for what they want in both the vehicle and their mobile phones. You do this by memorizing your behavior and by analyzing your driving history and road conditions. Although AI can handle massive AV data, certain more data needs are necessary, the different IoT network functions are to be used by traffic, foot-and-mouth, and exchanges, such as the Local Area Network (LAN), WAN, the Wireless Sensor Network, and Personal Area Networks (PAN). In certain ways, they can collect and transmit the data needed for this huge number of people such as embedded electrical equipment, sensing, vehicles, buildings, software, and connections. Through different embedded systems, these IoT-enabled AVs provide several benefits in realtime, such as increased safety, fuel economy, and safety regulations. Both IoT and the

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automobile industry 4.0 will be amended to provide considerable stimulus through reduced machine breakdown, improved quality assurance, increased production, and at the same time cost reduction. IoT technology’s potential and predictions are incredible. A Morgan Stanley Research report [4] suggests that, with a range of advanced technology, main characteristics, and services, at least nine industrial producers will gain from AVs: (i) OEM (ii) Auto Dealers (iii) Self-employment, (iv) Electrical Engineering, (v) Semi conducers, (vi) IT hardware, (vii) Electronics and Communications, and (viii) Beverage and Restaurant Manufacturing. The technological trend towards AV is seen in this chapter with discussions on important difficulties for the automobile industry and Sect. 2 details the area of AI with its methods. The AV has problems and opportunities to be explained in Sect. 3. In Sect. 4, the cloud and edge computing IoT network is shown to be able to exploit large volumes of data created by connected and networked devices. Finally, it concludes the integration of IoT and AI methods.

2 Artificial Intelligence (AI) Approaches AI is an informatics and technological area utilized to build intelligent devices for many advanced technologies. AI operates and behaves intelligently and autonomously as humans by acquiring knowledge and adapting to new interventions.

2.1 Revolution in Artificial Intelligence Figure 1 illustrates the development of industrialization such as technology, automation, and data sharing. Current businesses have new competitive and market demanding difficulties and need to undergo dramatic developments in Industry 4.0. Artificial Intelligence (AI) provides superior dynamically for industry 4.0 and improves decision accuracy, leading to greater corporate strategy, reduced machine

Fig. 1 The fourth industrial revolution

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failure, higher quality control, increased performance, and cost reduction. AI offers several advantages, including (i) data used for automated learning, (ii) intelligence capacity improvement in the existing products, (iii) smart learning algorithms responding to data presentations (iv) logical data analysis, and (v) data accuracy improvement [5]. While AI is probably changing the world today, it has its restrictions. AI’s main problems include learning from experience, and it is not possible to incorporate the information in the learning process. In addition, any errors in the information are highly tough and evident in the outcomes.

2.2 Brief History Artificial Intelligence AI is built on massive data combinations. With a focus on the improvement, it analyzes the information very quickly using clever algorithms, allowing the program to learn from data characteristics or trends. AI is increasingly prevalent since the eyesight error (less than 5%) was considerably reduced recently compared to human vision error [6, 7]. AI’s history began in the ancient era, but John McCarthy established it in the 1950s. Three key areas were invented for AI: (1) Schematic development is a brief development of AI [8]. (2) machine learning (ML), which from the 1980s to the 2010s was popular with AI methods, and (3) deep learning (DL), which is now driving the gains, are the key motivators in this process. Figure 2 illustrates our schematic design of AI methods. As is generally apparent, AI may be split into three basic areas: symbolic, statistical, and master learning. These are the following briefly: • Symbolic Learning: Symbol main structure of the readable human Symbolic of human communication logic, difficulties, search and symbolism basic competencies. Symbol combinations with their relationships are known as reasoning. In the building of a symbolic argument, people begin to get to know the laws of the phenomena and then convert the coding of the social phenomenon into a programmer. Computer technology and vision can be classified as symbolic learning. • Statistical Learning: Statistical learning involves heavy mathematics that addresses the challenge of creating a data-based prediction function. Before we create a model, it entails formulating a hypothesis. Probabilistic learning is based on regulatory programming and is officially established as a relation of variables. Statistical education also uses a smaller dataset that includes certain features, depends on such normality, no multi-collinearity, and heteroscedasticity requirements. • Machine Learning: To increase system behavior in a specific activity, Machine Learning (ML) builds and executes observational tools and theories. ML employs

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Fig. 2 Artificial intelligence approaches/apparatuses

heuristic techniques, operations, and statistical research to find meaningful information within knowledge without specifically planning where to investigate or execute. Uncontrolled, monitored, and enhancement education are the main ML subfields, described in the following way: • Unsupervised Learning: Uncontrolled learning is a collection of data that is dependent only on data intake. A collection of data points across a series of databases is one of the unattended training approaches. Each collection of data points may be classified into a grouping of the clustering method. • Supervised Learning: Supervised Learning (SL) creates an input- and outputbased predicting model. Divided into: SL reaches (i) Regression which is a way of finding a link between variables. This is often used in machine education to anticipate the result of an occurrence based on the connection between data set variables. (ii) a classification that seeks to determine which of the new development groupings is properly classified and which attempts to anticipate the target category for each type of data. • Reinforcement Learning: Reinforcement Learning (RL) is a novel decisionmaking AI technique, which helps AI make a major contribution to the field of realworld machinery learning. Table 1 provides a quick comparative of unmonitored, supervised, and strengthen learning.

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Table 1 Difference between unsupervised, supervised, and reinforcement Unsupervised machine learning

Supervised machine learning

Reinforcement machine learning

Structural scope

Modeling does not impact the data entered

Modeling does not The agent may impact the data entered influence his remarks

Framework of learning

Learning the clustering algorithm underpinning

Learn about reference responses

Optimal technique learning through testing and error

Output

Feedback is not necessary

You need the right answers

Feedback on the own activities of the agent

Fig. 3 Framework of reinforcement learning

RL employs an agent who develops from its responses to situations interacting with the world. Figure 3 displays a straightforward (RL) system that solves decisions by interactively measuring information effectiveness using desired information problems [9]. RL usually seeks to find a reasonable map that specifies the notion that the decision-maker cooperates with an environment can take any action to solve circumstances. RL is a strong way to accelerate initial acquisition with amazing results [10, 11] that is widespread within the IT, automated, control, and systems engineering sector and has been frequently utilized in recent years to tackle multi-agent challenges in particular. This RL may be stated theoretically. The atmosphere is the present condition s of a set S and the action of the agents is an action set A, in which S: S × A → S and R: S × A → R the state S(s), a–R: (s, a). The objective is to develop a policy function p: S → A, via the selection of acts to maximize future benefits. In general, the discounting factor is used to calculate future γ ∈ [0, 1) incentives in the overall value of a strategy: The following is utilized: V p(s) =

+∞ 

γ k−1r k = r1 + γ r2 + γ 2 r3 + · · ·

(1)

k=1

while r k is the reward earned after k steps, from state s and subsequently from policy p.

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2.3 Artificial Intelligence State of Art Approaches Deep Learning is one of the latest strong and adaptable machinery that describes the environment with more abstract concepts of hierarchies of repercussions (DL): • AI-based Deep Learning: The relevant features must be established by a certain domain for many machine learning techniques so that data dimensionality is minimized and patterns may be accessed by classification methods. The fundamental advantage of DL algorithms is to gradually learn from data at high-level characteristics [12]. DL’s design, perceptions, and decision-making process are the basis. It utilizes enormous artificial neural layers by utilizing numerous processing units which have advancements in improving training approaches to understanding complicated patterns in a large amount of data. Common complicated engineering implementations include the identification of activity, video marking, picture, voice recognition, object recognition, and many sorts of applications. DL also transmits important inputs to other perceptual domains, including audio, language, and the development of natural language. • Deep Reinforcement Learning: While AI has numerous effective techniques, deep learning and strengthening (RL) are the fundamental technology in AI that produces amazing learning outcomes. The deep RL method enhances the learning by the use of deeper networks of neuronal and state construction free of explanations [13, 14]. Deep RL relates to methodologies that are targeted at enable numerous software innovations in domains like architecture and more.

3 What is Autonomous Vehicle? An autonomous car (AV) is not a human-operated vehicle, but a vehicle that can navigate itself. The AV is a drivers’ unrealized vehicle, an art of driving in the future utilizing computers. AVs were targeted because (i) enhanced safety in automobiles, (ii) fewer incidents, (iii) fuel costs reduction, (iv) time and opportunities freed to the driver, (v) new market potential, and (vi) particulate matter and pollution emissions lowered. AVs have been attacked: About 10 million AVs are scheduled to be on the highways by 2020 and AVs are forecast to generate an annual turnover of about $7 trillion in 2050 [15].

3.1 History of Automated Vehicle For Autonomous Driving Support Systems (ADASs), cars have six tiers for autonomous vehicles. The automated path stage to completely self-sufficient vehicles is Null level—no automation and humans’ function to accelerate or stop completely all the dynamic traffic activities such as steering, braking, etc.

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Level 1: driver support via acceleration/deceleration system or steering utilizing driving circumstances knowledge. Level 2: partial automotive mechanization, combining both propelling and controlling functions. Level 3: dependent automation of operating mode with an autonomous vehicle system that meets the requirement with correct performance. The fourth level—the cars are equipped to complete all driving responsibilities under specified conditions even if the driverless car does not respond to the demand. The vehicle performed for all driving employment opportunities under all situations is level five—full automation. Levels 4 and 5 enable AVs to conduct all driving duties together with various devices and technologies to manage a driver-free automobile. The six autonomous vehicle level (described in the part called “AV Objective Sensors”) comprises the technology of ADAS, sensors, and actuators built till now [16]. Autonomous vehicles (AVs) began in the 1930s with science fiction authors seeing and innovating the automobile industry as a different experience. Shortly, AV is going to achieve excellent human performances using sensing techniques for required driving competencies. Smart sensing is close to human activities such as identification, location, tracking of paths, and AV tracks. The study provides for the broad adoption of AVs by 2020 and does not confine the adoption of competencies in AV to individual transport [17]. Since 2016, some nations, including the USA (the States of Nevada, Florida, California, Michigan), Canada, France, the UK, and Switzerland, have authorized certain public road-testing rules and regulations.

3.2 Autonomous Vehicle Problems and Complexities Autonomous mobile navigation is often needed to (i) locate, (ii) create the map, (iii) design the route, and (iv) track. Furthermore, detection and classification need the avoidance of AV obstacles. AVs have several main mobility issues: • the accurate software and non-proof software are necessary to ensure no problems occur. • The comprehensiveness and appropriateness of the map are enhanced by the map, including certain other details, such as the identification of the textured surfaces and the creation of some virtual maps which help the AVs to see energetic hurdles. • Signal processing and calibrated estimates need to be able to determine various unpredicted calibration circumstances from highly hazardous scenarios. Due to task difficulties, the sensors, sensing area, infrastructure, and software of AVs become more complicated. Current sensors cannot discriminate between hazardous scenarios within the above-mentioned AVs problem. To keep the vehicle on track and avoid impediments, a multitude of sensors and gadgets are necessary. The immense information generates a consciousness of the condition of the vehicle and its environment and, while driving, makes appropriate judgments. The combination of sensors with varied awareness, error, and reaction in real-time illustrates

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the complexity of AVs that a complete software is required. The logical evolution of actions is one method to reducing the complexity of AVs. Another method is to limit the quantity of state information and the retention time. A restricted data entry into the AV system makes its behavior. The primary issue in reducing the data, however, is that the vehicle has limited navigation and maneuverability. There are numerous AV issues to be taken into consideration and may be handled by the AV fashion technology and application development.

3.2.1

Objectives AV Sensors

The AV objective sensors are: • Ultrasonic: The sensors employ high-frequency sound energy that rebound to estimate a vehicle’s subjective proximity. The sound waves are released (50 kHz) and the bounty listens. Then it estimates the time-off light-based range. • Cameras helpful moving obstacles: Special images are a real-time barrier for tracking knowledge and tracking (similar to road signs). A camera picture contains a large array of individual pixel values; these numbers are virtually useless. The image has to be understood by applying computational methods to translate low info voters into high-level visual information. Views in computers include: (a) heat sensor analysis, (b) webcams, (c) laser scope detectors, (d) X-ray detectors, etc. Three components of image recognition: (1) Segmentation—where the physical objects are, (2) Classification—what these objects are, and (3) 3D reconstruction—estimating ranges from 2D pictures. • Radar used in Vehicle Position: The sensor generates radio waves that measure the deepness of the short and long-range. The location of the cars is monitored by from around car, radar systems points. The communication techniques a (green) signal which is scattered in all directions (blue). The time of transfer t delivers the information to the sensor and sets the given distance. • LiDAR set Boundary: This sensor detects the range by pulse-lens luminousness and measures impulses with detectors to generate a map of a 3D environment. LiDAR sensors assist to detect road borders and recognize lane markers using dynamic light pulses from the surroundings of the car. • DSRC lase AV: Specifically developed for usage on an automobile and standardized set of regulations and protocols, the DSRC is a one or two-way, short to medium, modern communications channel. DSRC is a simple term communication system. DSRC can use 4G, Wi-Fi, smartphones, etc. for automobile connectivity. Vehicle—GPS: Triangular location for a moving destination; the vehicle’s satellites’ alignment with latitude, length, altitude, and speed, and speed of movement may also be computed. Curring to specified distances, current Arduino controller [18]. • Wheel Odometry in AV: It also converts the directional angle and velocity into cinematic equations (x, y, θ ) and calculates 2-dimensional changes (x, y, θ ) (from

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the steeper angle of the sensor and velocity of shaft microcontrollers or motion detector). • Accelerometers in AV: A variation of location (x, y, z) and force F for measurement. • Gyroscopes in AV: Specifies one, two, or three free rotational measures. It calculates (´c, μl, μl) by adding up revolutions of the accelerometer. The most important difficulty of AV is sensor fusion, as AV needs many heterogeneous catalysts sensors to detect and identify AV targets that are obligatory in the development of AVs [19]. • Dynamics of Vehicle: While posture sensors are solely designed based on vehicle motion cinematics, a dynamical model for engine performance validation is necessary. The lateral mechanics of the automobile are characterized by the one-way or bike model with a single position of lateral speeds, yield, and rpm. The model is a typical approximation in grounded driver behavior, which has been commonly used in [20, 21]. Although several textbooks [22, 23] include comprehensive derivation and explanations, the fundamental assumptions are that the inner and outside wheels have nearly identical slip angles and the influence of the vehicle rollable is negligible.

3.2.2

Data Fusion in Autonomous Vehicle

The contextual knowledge of Autonomous Vehicle (AV) depends, as stated in the section above, mostly on collecting information from different sensors (e.g. camera, LiDAR, radar) (e.g. lighting, range, power). The question of how to merge locally sensory information to serve a particular decision job such as detecting vehicles is a clear challenge for reconstruction and comprehension of AV’s environment [24]. Signal processing is a multi-sensor data combination program to determine system performance improvement. Calculation of the precision independent location of the vehicle and the orientation of the combined data from the different sensors [25]. The strategies for data fusion can be classified as follows: • Approximation: the three-act the evaluation task in the best possible fashion, using a clearly defined statistical framework such as [14] • Categorization: can be used to tackle classification problems. The problems are divided into multifunctional features, each with the following categories: Density Assessments (DE), Vector Machinery Support (SVM) Trees Decision (DT) [26]. • Interpretation: Another group of probabilistic theory-based fusion methods is included in this methodology, such as Naive Bayesian Inference (NBI). • Artificial intelligence: such techniques are heuristic, as in (1) Fuzzy Logic (FL) [27];

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3.3 Modern Developments Autonomous Vehicle Actual cars employ a broad range of sensory skills. Moderately, the car now includes 70 sensors comprising lighting conditions U.S. monitors, electric motors, proximity sensors, and moisture sensors [28]. Before 2000, automotive detectors were developed and are not new aspects; vehicle sensors such as its wheel’s location, accelerator, and velocity were utilized for the internal condition of the vehicle. Vehicles have already had a series of functions, including Anti-lock Braking Systems (ABS), Airbag Control (AC), Traction Control Systems (TCS), and Electronic Stability Control (ESC). Table 2 lists the recent marketing of automated competencies. • The automatic features aid motorists or take completely specified measures to enhance convenience and protection. Current cars can carry out adjustable cruise controls on motorways, park autonomously, inform blind spot drivers of objects and steer at traffic stops and stops. Vision and radar technologies are utilized Table 2 Merits and demerits of ML algorithm Algorithm

Merits

Demerits

K-nearest neighbor (KNN) Significant results in classification No (re)workout stage measurements of distance The chance of mistake is limited

Consumption of time for grading Use of memory No online learning to fmd optimum

Mahalanobis distance classifier (MDC)

Significant results in classification instructional design approximately

Statistical estimate matrix ops complex

Linear discriminant analysis (LDA)

Borderline nonlinear selection Gaussian suppositions time of Quick classification workout Online education quick Matrix ops complex parameter estimate

Quadratic discriminant analysis (QDA)

The boundary for square decision quick categorization Estimation of quick parameters learning online

Gaussian suppositions time of workout Matrix ops complex

Naive Bayes classifiers (NBC)

Fast performance Quick categorization learning Online

Gaussian generalizations

Artificial neural networks (ANN)

Fast performance Quick classification The boundary of incorrect amount learning online

Heuristics training time

SVM

Fast performance quick classification learning online

Limited bounds of decision

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to automatically prevent a collision when an accident risk for cars is recognized. Vehicles can identify and distinguish sound items in their surroundings by applying deep learning [28]. In recent years, the automobile industry has been working on the ongoing development of autonomous vehicles (AV) [29]. Approximately 46 private enterprises are working in the automobile industry (AV). A study by Gartner reveals that about 250 million cars are projected to be connected, by 2020, to any vehicle, V2X, or V2I systems [29]. Thus, not only vehicle locations but also traffic circumstances (e.g., weather conditions, congested roads and accidents, traffic characteristics, wind, etc.) in real-time is gathered and transmitted completely. Although the AVs contain camera systems, the AVs create huge amounts of statistical information via communication. The following are briefly provided for some recent truck hardware and software developments [30]. In cooperation with Las Vegas City, Keolis and NAVYA (2017) inaugurated the first autonomous completely electric shuttle on the US public highway (2017). The “e-palette car” idea, which may be modified for apps such as distribution of the product (Pizza Hut), commute or storefronts, a partnership, is announced in Toyota (2018) (Amazon). The first transportation in San Mateo by a self-driving car was performed by Udelv (2018), the Bay Area technology firm. Hyundai (2018) has promised a complete automated journey from Seoul to Pyeongchang from a flotilla of its electric carriages. For the first time in the history of the fuel cell, electric automobiles a Level 4 car has been run.

3.4 Artificial Intelligence Equipped Autonomous Vehicle The Autonomous Vehicle (AV) Artificial Intelligence (AI) model has three steps: (i) gathering of data (ii) path scheduling, (iii) behave as shown in Fig. 4. • Data Gathering: AVs are fitted to create large data from their vehicle and surroundings using multi-sensors and equipment such as Radar, Cams, and Communications. These AV data include roads, transport networks, other cars,

Fig. 4 An artificial intelligence model equipped autonomous vehicle including the gathering of data, path scheduling, and behave:

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and all other objects on/off the street, parking, congestion, transit, and meteorological parameters identical to a driver of humans. This information is then provided as AV-updated metadata to be evaluated. This is the first AV communication with particular vehicle circumstances and situations. • Path Scheduling: The large AV system data is stored and added in a giant database named Big Data containing every life’s journey past driving trials. An AI agent also operates on information accumulation by regulating strategy in order important judgments. The path optimization control technique for AVs permits the personality vehicle, using its experience in the future, to find A-safest, B’s most pleasant, and fiscally profitable paths. The pathways to be identified and bypassed are complicated in all stationary and movable obstacles. Circulation technique includes looking for a geometrical route from an originating setup to a certain setup to make every setup and condition on the path workable (if time is considered). Road management strategy is the result of the route management plan that takes care of the location defined by route planning and trajectory planning that is to plan the movement of a vehicle in real-time between a viable state and another, fulfilling the cinematically limits of the vehicle based on the mechanics of its vehicle and being limited. In this road simulator and/or driving circumstance the AV understands just what to do. • Behave: The AV can identify traffic items, maneuver around traffic, car park, barriers, amusement, traffic signals, bicycles, pedestrians, work zones, weather, and other vehicles without interruption by human drivers, and may travel securely to its destination based on judgments taken by the AI agent. AVs have AI-based electronic systems and contractile functions, including steering control, pedal acceleration, speech and voice commands, rear brake control, image recognition, alarm systems, administration checks, low fuel, and other features. The AV activity loop will be conducted repeatedly involving data collecting, route planning, and action. The more data loops occur, the smarter the AI agent grows and the more accurate judgments are to be taken, particularly while driving in complicated scenarios.

3.5 Challenges of AI-Based Vehicle Today, however, after many years of intensive research, automated driving (AVs) has become an actuality, but there are still mountain range obstacles to fully develop an autopilot vehicle for AVs, for example, engineering technologies, regulation, the lack of manufacturing technology, and tools, and consumer confidence and acknowledgment. The challenges become more difficult at every social democratic amount of independence. But, in the context of perception, location, planning, monitoring, and prediction (PLPPT), information still has to be enhanced, notably in advanced materials, for the following conditions/areas [31]: • Highway Conditions: traffic conditions are highly variable from point to point and fluctuate unpredictably. Smooth and well-signposted roadways exist in several

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locations. However, road conditions are deteriorating greatly in several other locations with no lane markings. Routes are poorly specified and the exterior signs for orientation are not very obvious and similar. Mountaineering and tunneling roads are not very obvious. Climate: Another spoilsport is played by weather. Sunny, clear, or wet, stormy weather might occur. In all kinds of weather situations, AVs should work. No-fault or interruption is available at all. Traffic Regulations: AVs would have to drive along a road in all kinds of circumstances. They had to drive a car with other AVs when there were also many people. There are a lot of feelings whenever parties are interested. Transportation might be quite modest and autonomous. But often people may violate the laws of traffic. In unforeseen circumstances, an object can turn up. Even movements of a few centimeters per minute are important in the event of congested traffic. It is not possible to wait for congestion to spontaneously clear and to be moved. If more people on the roads intend to clean up congestion, they might eventually lead to a traffic impasse. Accident liability: responsibility for mishaps is the main feature of AVs. Who is responsible for AV accidents? In the instance of AVs, the software is the major driving element of the car and all essential choices are taken. While the original designs feature a human behind the control wheel, new Google concepts have no cockpit and no stepper motor. In such designs, when a steering wheel, a stop pedal, or an automatic transmission have no controls. In the event of an unforeseen occurrence, how should the vehicle person handle the car? Furthermore, because of their nature, the participants are generally in a relaxed mood and cannot pay special attention to the circumstances of traffic. It might be too late to prevent the situation in circumstances where their concentration is needed when they need to act. Navigation Radar: Laser beams and infrared are used to navigate AVs. The lasers are mounted on the ceiling and the sensors are mounted on the steering column. The sonar concept works by gathering reflections of radio waves from surrounding objects. On the highway, a vehicle continually generates waves of radio frequency which vehicles and other objects reflect on the wayside. The time required for reflections is measurements to calculate the distances here between the automobile and the target. Appropriate action is then formulated based on the sonar findings. If the technology is useful, a car can distinguish between its own (transmitted) signal and that (embodied or broadcasted) message from some other automobile. Although multiple frequency bands are offered for radar, the resonant frequency for all vehicles produced is unlikely to be sufficient. Big Data Analytics: Classification model and decision-making process of AV data quantities in real time must both be implemented. Innovation can be slowed considerably without effective data management in the pure resources consumed by the process. Examine four data aspects inside the AV: (i) collection of data, (ii) storage of data, (iii) managing data, and (iv) labeling of data. The study and deliberate decision-making of the data collecting process early on will assist to guarantee that the implementation plan is both executive and expendable, for

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individuals who are more insecure in collecting data. A thorough update that exploits the existing method can lead to a safer, more transparent, and more ‘s contribution to the organization. • This work presents and discusses a survey of the six intelligent approaches [32]. These include representations schooling, deep learning, decentralized and simultaneous learning, transfers, and active learning for use in the use of extensible Big Data learning technologies and kernel-based learning. • Communication with the vehicle: AVs must have a network platform via Information dissemination with a large amount of information for the insertion and deployment of PLPCP to resolve: (1) Car side: data for vehicle diagnostic systems, location of the vehicle in real-time, accelerating, speed, energy and environmental usage; real-time understanding of traffic signals and indicators, danger notices, eco-routes, limits on eco-speed, parking statistics and so forth.

4 Internet of Things In 1999, Kevin Ashton suggested the Internet of Things as a technical phrase (IoT). The concept of “things” modified when development progresses in the last decade, yet the primary aim of a digital device without human participation can be meaningful. The Internet has improved communication amazingly. The next generation of connectivity connects objects substantially faster and creates a smart environment. There is already 9 billion linking equipment that exceeds the global population and is predicted to reach 77 million by 2020. A huge number of linked devices have the major advantage of having access to large datasets which may be used in intelligent systems [33]. This technology has already been implemented in several industries like farming, mining, manufacture, and the car industry to enhance the efficiency and supervision of their operations. In this part, we will present the usage of IoT technology primarily for autonomous cars in the automotive sector. Automated Vehicles must have a variety of capacities, such as road congestion and accidents, to generate, collect, analyze, automobile data representation and storage from many sources. IoT is a revolutionary innovation with a network of independent things including the built environment, the vegetables, and hardware-embedded apps, sensors, and other items. Communication protocols that make data are collected and shared without human interaction [34]. The Internet of Things is an innovation that has emerged. The IoT idea evolves from the Machine-to-Machine connection (M2M). With no or little human interaction, M2M links discrete sensor systems to servers, whereas IoT connects equipment to machines, incorporates web applications, and connects to cloud supercomputers. Implementing IoT in cornering speeds automobiles gives numerous advantages in terms of technology along with the ability to track vehicles to improve excellent connectivity, safety, limit the impact of cars, utilize the vehicles and provide excellent customer services.

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4.1 Internet of Things Based Electrifying Autonomous Vehicle Driving An interconnected system that supports millions of contemporaneous connectivity options to create a great deal of data to be transported and processed into cloud technology is a typical IoT infrastructure. In the standard IoT platform as shown in Fig. 5, there are four basic elements. The IoT-AV platform has three principles: (1) First elements are sensing and equipment that is fundamental and gathers different data kinds from the external surroundings; (3G, 4G, 5G), (2) The next ingredient is a communications protocol that is typically based on the cellular innovations such as Wi-Fi or mobile technology. (3) Big data representing the amount, speed, and diversity of data created is a final element; this data should be transported, stored, and translated; (4) Cloud, where the data are kept and analyzed as fog computing includes numerous computing, analytical and preservation capabilities, is the fourth element of the platforms. Sensor networks have always been hosted in the cloud and may give hardware components input and choices. Cloud is a centralized administration system for AVs that implements all software applications and penetration testing. IoT transforms the transportation infrastructure into a fragmented wide community for

Fig. 5 IoT platform

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independent mobility. IoT provides many benefits to cut insurance prices and minimize congestion problems and possible accidents, include interactive technology solutions, intelligent vehicle controls, and apps. We have to expand the platforms in Fig. 5 to establish a new ecosystem that incorporates the major parts and interconnections and then add a network architecture comprising of the in-vehicle model and the ecological model to develop an IoT platform for vehicles. As previously noted, in the standard network infrastructure there are several sensing and processor architectures. There are two unique personal information collection elements in this platform, i.e.: (i) use of data from own sensing, transfer information with neighborhood users, and (ii) IoT as gathering channel for large volumes of data from various gateways through devices integrated (car park, travel information, transportation, environmental parameters) (parking spot, train, entertainment, traffic lights, bicycle, pedestrians, working areas, weather conditions, other vehicles). This provides a more extensive intelligence for VA’s by highlighting the power of multiple data sources and big data analysis.

4.2 Internet of Things Based Vehicle Environment A vehicle environment based on IoT consists of six elements interacting with each other, which include: (i) a vehicle; (ii) a person; (iii) a personal instrument; (iv) an information system; every vehicle in the vicinity can build a communication connection to provide relevant information in the environment, including road and traffic conditions, alarms and other physical characteristics. People who want or have access to the IoT ecosystem service are included. Personal equipment belongs to and utilizes and/or offers a service to anybody in the environment person (e.g. driver, customer, biker). Internet infrastructure relates to all communications system equipment utilized for ecosystem transmission. Sensors and devices might be the sensors that gather data on the measurements, health levels of the individual, and environmental factors of the vehicle. For example, tire pressure, fuel use, vehicle temperatures for automobiles and cholesterol levels, the person’s heart rate and the pollution, the degree of noise, and climate conditions might be included. Finally, the transport surroundings are the roadside equipment, such as traffic signals, information displays, or radars capable of disseminating vital road traffic conditions intelligence, accidents, or probable diversion. The fundamental component of this IoT-based ecosystem is to create multi-level data interchange through the interaction of all IoT parts. This connection, known as Device-to-Device interaction (D2D), might involve multiple gadgets (both within and without a car), capable of communicating, collecting, storing, and processing information or making choices without or without human participation. Six kinds of D2D interactions are discovered, as proposed in [35]. The interaction includes vehicle-to-vehicle (V2V), vehicle and staff (V&P), vehicle and route (V&R), V&S, roadside and personnel (R&P) devices. The interaction includes vehicles and sensors (V&I), vehicles, and infrastructures (V&I). There are also two interactions within

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R2R and Sensor and Actuator, Roadside-to-Roadside (S&A). The sensor and some of the smart devices are in the AV and are regarded as training to meet and the other connections are more environmental and contextual information.

4.3 Transportation Edge Computing of Independent Vehicles Current AV IoT solutions do not permit low power consumption and in real-time data processing and must be transferred into the cloud, as Fig. 6 shows. The cloud offers hardware and communications access and facilitates the building and operation of applications and related data. While the public cloud enhances resource usage, it is unable to provide an effective solution to host smart apps in AVs. These lead to various barriers and challenges that prohibit IoT powered AV technologies from being adopted, particularly regarding: • A huge number of data may be sent through the cloud network with substantial overhead in time, flow, energy usage, and expenses. • Real-time processing of huge amounts of IoT data, which does not help apps and consumers, increases the strain of providers and cloud data centers. • Heterogeneity of IoT sensors and devices in hardware and software components. • Not always elegantly fit together.

Fig. 6 Transportation edge computing of independent vehicles

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At the edges of networks, information technology may be done near these problems by producing data that will reduce overall data and transmission [36]. The concept is the phrase edge computer (or Fog computing). This technological innovation claims to offer highly reactive IT, environmentalism and confidentiality, and the capacity to mask transitional cloud faults [37]. To obtain real-time data analytics in the necessary applications, AVs are connected to peripheral devices utilizing a wireless communications system. Edge sensors are integrated with other neighboring borders to build a local peer-to-peer network under the cloud. Data analysis is on the edge of the natural universe in which the IoT and the data in the cloud are connected to the intermediary layer. In the analysis of premise data and the capacity of IoT devices to interact and coincide in a distributor ecosystem and cloud, the cloud services and edge computing are compared [38]. The expansion of earlier technologies like peer-to-peer connectivity, dispersed data, self-healing communication, and distant cloud services may be called edging computing. It has several advantages compared to typical centered cloud designs such as maximizing the use of resources in a cloud computing environment and decreasing network traffic, thereby lowering the danger of bottlenecks. Information technology also enhances safety and privacy, ensuring data is more near the core network and protects personal data from common cloud settings. Compare the rim with cloud technology to have a good picture. The number of data transferred is significantly lower than data gathered from IoT devices as they are pre-processed, filtered, and cleaned in the edge before cloud reload. Furthermore, the insights on the edge are in real-time, whereas the cloud analysis is online. Edge has restricted calculation storage and processing in general, but cloud processing results in a greater delay. The rim provides a high level of defect tolerance as the duties in the area can be transferred to the other rim in event of error whereas dependability is one of the major needs is an essential aspect for AVs. Edge may utilize several equipment types like a board, multi-threading, FPGA, or GPU against such a cluster of homogenous cloud nodes [39]. Epis can leverage multimedia, fine-grained CPU. For every edge device, the hardware that the application contains is permanent and consumer and most assigned resources are hidden and exclude the cloud permission. The advantage of edges is that the movable IoT nodes may integrate. A subsystem is incorporated into this paradigm that allows edge heat to be communicated and exchanged in the proximity of several sensor nodes. Cloud has proved to be a cost-as-you-go economic model, whereas edge is a useful asset. Rechargeable batteries edge devices must be energy-efficient while the cloud has a steady energy supply supplied with the development of energy-efficient resources that is feasible. To the car (V2V) and the car-to-all X (V2X), the appropriate device must be latency-low.

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5 Assimilation of Edge Transportation Artificial Intelligence Computing For AI for AVs to use artificial intelligence, the conventional cloud-based approach, where all storage space and analysis is conducted in the cloud, needs to be changed, where control methods are being applied in the cloud, and database system. To enhance this approach, the process and planning area must be divided into two modules that can be cooperatively managed by edge and cloud. Figure 7 illustrates an AI-based AV that uses edge computing to transmit the data acquired from AV to a pre-processing and decision-making edge node. The IoT sensor data will be immediately processed at the edge while edge node data is gathered and transferred in the cloud for worldwide offline analysis and time-sensitive judgment. Thus, timesensitive choices like identification of obstacles or crash prevention are taken at the edge node in far quicker time. Data are processed in the cloud on roads, traffic, and driving patterns to increase pedestrian safety and driver skill [40]. The AI model at the edge node may be dynamically installed and modified based on ideology and regulatory standards and needs of the client. The volume of sent data is lesser than that produced by IoT sensors in AVs as the data is pre-processed, screened, and cleansed on the edge node before the cloud is unloaded. The capacity and costs can be saved considerably.

Fig. 7 Assimilation of edge transportation artificial intelligence computing

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6 Conclusion It increases in the productivity and effectiveness of automated decision-making in recent times has created a new trend to embrace numerous intelligent technologies and approaches. The combination of the AI and the IoT for the AV achieves outstanding microcontrollers to be used in the environment and provide for more flexible, resilient systems of control. While the major software and hardware of AVs are often hosted by cloud computing environments, the latency, bandwidth, and security problems are addressed by the emerging edge computing approach. This chapter presents the idea structure of a new AI-based AV employing artificial intelligence and may be viewed as a key architecture in the additional investigation.

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40. A. Starzacher, Multi-sensor Real-Time Data Fusion on Embedded Computing Platforms (Alpen-Adria-Universitat Klagenfurt Fakultat fur TechnischeWissenschaften, Klagenfurt, 2010)

IoT Enabled Railway System and Power System Akhtar Kalam and Pejman Peidaee

Abstract The fourth industrial revolution, Industry 4.0, has incited new ways for Enterprise Management System (EMS) and innovations on supply and demand side addressing wide range of use cases. With the continuing acceleration in implementing use cases of Industry 4.0, digitalization of railways and transportation industry have been introduced through IoT framework and its potential advantages for smart operation of railways. Although, challenges in modern rail operation such as operational efficiency, maximized availability and maintenance of the physical assets still have been remained to be addressed in benefits for serving customers and railway operators. In this chapter an integrated ecosystem of cutting-edge digital technologies and advanced data analysis framework are adopted to demonstrate a responsive and agile operating environment for operators in railway system. The emphasis is on building blocks of IoT based solutions within the railway industry including interconnectivity between different layers of IoT platform and railway Operation Control Centre (OCC). Moreover, relevant issues such as interoperability, data integrity and cybersecurity issues are highlighted as prospective challenges to deployment of the IOTenabled railway systems. Further to that both technical and methodological aspects corresponding to the research studies and validations of the results in the field of IoT systems have been addressed through IoT use cases in Smart Grid. In summary, this chapter envisages the prospect of near future of the railway operation within the context of IoT platform and highlights requirements for further preparations and promotions of the research area relevant to the smart railway operation. Keywords IoT · Railway systems · Interoperability · Data integrity · Hardware in the loop · 5G · Industry 4.0

A. Kalam (B) · P. Peidaee College of Engineering and Science, Victoria University, Melbourne, Australia e-mail: [email protected] P. Peidaee e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 N. Marati et al. (eds.), AI Enabled IoT for Electrification and Connected Transportation, Transactions on Computer Systems and Networks, https://doi.org/10.1007/978-981-19-2184-1_2

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Abbreviations AEP AR/VR COTS EMS FIS GOOSE HIL HMI IDC IED IETF IoT ITU KPI LPWAN LTE M2M MAS MBSE OASIS OCC OT/IT PIS PLM RAMI 4.0 ROI RTDS SCADA SDO SE SME SOS TRL

Application Enabled Platform Augmented/Virtual Reality Commercial Off The Shelves Enterprise Management Systems Freight Information System Generic Object Oriented Substation Event Hardware In Loop Human Machine Interface International Data Corporation Intelligent Electronic Device Internet Engineering Task Force Internet of Things International Telecommunication Union Key Performance Indexes Low-Power Wide Area Network Long-Term Evolution Machine to Machine Multi Agent System Model-Based System Engineering Organization for the Advancement of Structured Information Standard Operation Control Centre Operation Technology/Information Technology Passenger Information System Product Life-cycle Management Reference Architecture Model for Industries 4.0 Return of Investment Real-Time Digital Simulation Supervisory Control and Data Acquisition Standard Development Organizations System Engineering Small and Medium-size Enterprise System Of System Technologies Readiness Level

1 Introduction Global population of the world will be around 8.5 billion and it is projected to 9.7 billion by 2050 as many cities experience exponential demand on using services and infrastructures essential for modern human societies. Consequently, futuristic visions on operation of modern cities are reliant on efficient and reliable performance of the

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infrastructures which are stretched to their limits in terms of scalability, environment and security. Along with the requirements to deal with cities’ growth, technological advancements are crucial for addressing the productivity (increasing operational efficiencies) and reducing management costs within modern infrastructures. In fact, the objectives of integrating modern concepts into the architecture of cities take into consideration long-term scalability and sustainable development for human societies within crowded and congested of the future cities. Recent years have been identified as a challenging time for governmental planners and policy makers to adopt suitable development roadmaps consistent with future cities demographic, economic, social and environmental conditions. As a matter of fact, the response to modern smart urbanism is a complicated and multidisciplinary issue which requires knowledge about integrated business model, Enterprise Management Systems (EMS), Information and Communication Technologies (ICT), communication networks and System Engineering (SE). While there are many important domains, including energy, healthcare, transportation, emergency response and manufacturing system which relies on the delivery of services by systems composed of largely independent subsystems, railway infrastructure and relevant services for introducing a smart railway infrastructure constitute the focus in this chapter. This chapter has been developed and prepared to address some of the key technical aspects in establishment of the smart infrastructures for railway system based on Internet of Things (IoT) technology. From the technical point of view, the advent of digital ecosystem and potential connectivity among various systems have introduced IoT technologies as an enabler for efficient operation of future smart cities. Under current circumstances many researchers and entrepreneurs have adopted the IoT as a dominant solution to large-scale smart infrastructures which deliver wide ranges of services from utilities to healthcare systems. For example, the successful treatment of a patient in emergency results from the interaction of several separately owned and managed systems including telephony, ambulance assignment, information sharing, communications, and hospital management. These constituent systems may have existed before requirements (for example, to meet maximum response times or to guarantee the confidentiality of patient data) were imposed on their collective behavior. But with the advances in network and communications technology it is possible to conceive of deliberately engineering and maintaining such “Systems of Systems” (SOS). Therefore, IoT considers a pervasive presence in the environment of the variety of the things where wireless or wired connections can be utilized for coordinating and addressing unique solutions among different services to reach to common goals. For enabling railway system as modern smart infrastructure, technical requirements are discussed and explained by considering elements of the IoT technologies including hardware, software, communication protocols, standardizations, potential services and simulation platform for IoT-based research studies. The structure of this chapter first starts with an introduction on IoT and the context of emerging themes within the human societies such as smart cities and sustainability in management of the resources. In the second part some of the main challenges for deployment of IoT as an emerging concept in dealing with large scale, multifaceted and multidisciplinary nature engineering projects (SOS engineering) are emphasized. The prospect of IoT application with respect to value creation and

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key enabling technologies such as 5G for developing IoT systems are explained. Standardization and functionalities cycles within IoT framework have been demonstrated in Sects. 4 and 5 respectively. Following that, typical architectural layers and services corresponding to IoT framework integrated into railway systems have been covered in Sects. 6 and 7. System integration and issues related to interoperability within EMS including IoT platform, middleware and reference architecture on standard harmonization in industry 4.0 are discussed. In Sect. 9 cyber security issues and relevant IoT security practices have been highlighted. A brief overview on two common IoT use cases in industrial automation (industry 4.0) and power system (Smart Grid) have been provided in Sect. 10. Sections 11 and 12 address the critical role of the simulation platform and methodologies to be adopted for verification of the results in large-scale IoT-based enterprise (such as power system) as a holistic engineering system with interactions between multi-domain physical systems. Finally, conclusion on this chapter is provided in Sect. 13.

2 System of Systems Engineering and IoT Ecosystem In general application of the IoT for operating large-scale interdependent infrastructures is a special case of System of Systems (SOS) engineering which focuses on broader view of interactive infrastructure networks with several functional, operational and management layers. Thus, in dealing with technical requirements and modelling of the IoT-based infrastructure understanding about the methodologies and parameters involved in SOS plays a critical role in developing IoT research studies. The term “System of Systems” (SOS) has been used since 1950s to describe systems that are composed of independent constituent systems act jointly toward a common goal through the synergism between them. Examples of SOS arise in areas such as power grid technology, transport, production and military enterprises which are highlighted by boundaries of the overall SOS and their independent constituents. A review of existing attempts to define and classify SOS has been conducted in [1] to identify several dimensions that characterize applications, modelling and challenges of the SOS within the context of infrastructures in modern societies. The SOS field is exemplified by a series of representative systems which are siloed operation through multitude of different departments. Therefore, a general approach for management of SOS is simply relied on independent operations of each departments based on defined tasks and functionalities [2]. In essence silo-based design and planning are developed according to what is most convenient to the organization without sharing information with other departments or any customer centric values [3]. In Fig. 1, organizational architecture for short-term sectors or silo-based planning of infrastructures has been illustrated as separate business units without any cross functional links (the name silo-based comes from the way of storing grains that ensures separation of different grains and often uses top-down distribution). While silo-based planning for infrastructures is currently practiced in the developing countries there are certain drawbacks related to this approach failing in providing robust, resilient

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Fig. 1 Silo-based planning of infrastructure within the context of SOS engineering

and sustainable solutions [4]. The core problem to the silo-based planning is rooted to the fact that cross-functional capabilities are not defined due to lack of information sharing as a result a single risk can affect many different parts of the organization. Currently, fundamental challenges surround foreign provision of infrastructure systems in the 21st century as socio-economic development; population growth and climate change are rapidly changing the world. As these changes significantly affect long-term demand and provision of infrastructure there are vast uncertainties concerned with the performance and reliability of the essential services delivered by infrastructures. To maintain the efficiency and high reliability, the need for resiliency and quick response to disruptions are critical for infrastructure systems while at the same time comprising the sustainability has to be avoided. For infrastructures to be able to meet future demands, long-term planning and sustainable solutions must be considered throughout the life span of the infrastructure systems. Technically, the paradigm of the smart cities or smart infrastructure has been defined, according to Anthony Townsend in his book Smart Cities [5], as “places where information technology is combined with infrastructure, architecture, everyday objects, and our bodies to address social, economic, and environmental problems”. Therefore a smart city is a city seeking to address public issues via ICT-based solutions on the basis of a multi-stakeholder and municipality-based partnership [6]. According to the definitions for smart cities, it is obvious that ICT plays a pivotal role in developing infrastructure systems that can adapt to the needs of its citizens. While smart cities promise multiple benefits for Safety and Security, Environment and Transportation, Home Energy Management, Educational facilities, Tourism and Citizens’ health,

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IoT technology is emerging as potential solution to deal with the complexities in management of the infrastructure systems. Thus, by considering cities infrastructures as complex interdependent systems, technical issues relevant to SOS engineering is overlapped with the modern management strategies based on IoT. In Fig. 2 one of the most widely adapted and adopted models for representing the smart cities elements have been illustrated. As it is seen in Fig. 2, the notion of smart cities is rooted on complex systems called SOS and integration of the IoT technologies which consists of six components such as governance, people, environment, living, mobility and economy. The basic underpinning of a smart city entails IoT and its elements of modern ICT infrastructure to implement and deploy effective solutions for future infrastructure systems. Although, designing and deploying smart cities need experts from multiple fields, including economics, sociology, engineering, ICT and policy and regulation but requirement for information exchanges between heterogeneous systems is critical within IoT framework. One of the main IoT goals is to make the Internet more immersive and pervasive. As a network of highly connected devices, IoT technology works for a range of heterogeneous devices (such as sensors, RFID tags, and smartphones). Multiple forms of communications are possible among such “things” and devices. IoTs must be designed to support a smart city’s vision in terms of size, capability, and functionality, including noise monitoring, traffic congestion, city energy

Fig. 2 Smart cities as complex systems based on SOS and IOT

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consumption, smart parking meters and regulations, smart lighting, automation and public buildings. Moreover, IoTs should exploit the most advanced communication technologies, supporting added-value services for a city’s administration and citizens [6]. Generally, transportation and railway systems constitute the most important infrastructures which are critical to economy and logistics industry. Railways have long been desperate to identify, locate and track the condition of all of their complicated, expensive and dangerous physical assets. Although the prehistory of IoT is full of clever ways, managers who have tried to understand the assets which they are responsible for but transforming of the railways is a dominant trend to address overwhelming demand for services from transportation infrastructures. With the leverage of IoT technologies railway infrastructure are supposed to bring together two main families of technologies enterprise Information Technology (IT) and Operation Technologies (OT), which enforce rail companies to be viable stakeholders and innovative contributors for digital future of the railway transports. Moreover, having the second oldest industry workforce profile the risk of skills shortages in transport sectors can be offset by technologies such as IoT which reduce the number of staff required [7]. In the following some important perspectives regarding the integration of IoT system within enterprise business systems are discussed.

3 Prospect of IOT In a nutshell, future technologies are expected to enable digital infrastructures with features which efficiently meet societal needs in terms of resource utilization, collaborations and competence transfer. Therefore, potentials of IoT technologies in improving the performance of large infrastructures and business enterprises are becoming limitless to leverage automated interactions between physical world consisting of humans, physical objects and industrial processes. Aside from interconnectivity among heterogeneous devices in a networked architecture, it is the huge volumes of actionable data which can be used to overcome challenges for sustainable management of the resources and infrastructures [8]. With the waves of exciting IoT applications through intuitive humane to machine interaction and advanced ICT integration, there are two main factors which highlight future capacities and opportunities for IoT framework. In the following value creation and 5G communication technology are explained as critical elements in projecting the effective integration of the IoT for sustainable solutions of the modern societies.

3.1 Value Driven (Value Creation) With the introduction of IoT variety of expectations are assumed to improve global market competitions by adopting different solutions and services for companies or businesses [9]. However, these expectations are dependent on how IoT devices are

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connected from architectural point of view or what are the services and values that will be created to increase a company’s market share, etc. Technically, the rapid growth in expansion of the IoT applications is attributable to the creation of new services and innovations which IoT can introduce to industry, governments, products and services. Concerning the evolution created by IoT device market, Gartner predicts that, by 2020, the number of connected things will reach 25 billion and the service market will grow to USD $300 billion by the same year [5]. In Directions 2016 [6], the International Data Corporation (IDC) forecasted that the number of terminals connected to the Internet will reach approximately 80 billion units in 2025. In addition, Cisco expects that, by 2030, there will be over 37 billion Internet units, the number of IoT devices will reach 50 billion, and the IoT will develop into the Internet of Everything (IoE) [7]. It has been also realized that China, North America and Western Europe will be most active in adopting IoT devices, which account for 67% of all Internet devices in 2017 [1, 9]. Therefore, under current circumstances, the growth of the service market for companies and businesses will comprise a major portion of the IoT market which can directly impact the value chain of the companies. In Fig. 3 the elements constituting the value chain in a typical Small and Mediumsize Enterprise (SME) have been represented. Although not only the value chains of companies in different industries differ, but also different value chains are created by each company operating in the same industry. Consequently, the prospect of adopting

Fig. 3 Potential applications of the IoT within value chain of the companies

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IoT applications within the value chain structure depends on the company’s strategy, its strategy implementation, and corporate traditions as various services and solutions can be embedded into life cycle of the value chain. In fact, the value that a chain generates is the amount that the product (service) worth for the buyer and this price must go far beyond cost as the basis for every company to survive. So, considering and serving the value-based approach, i.e., customers’ needs, as the foundation of corporate strategy introducing IoT as an important factor integrated into management of the future businesses and infrastructures is essential. While advantages of IoT for different operational approaches are becoming critical for many SMEs, the need for managing wide range of solutions within the context of complex SOS is critical for coordinating the activities among different subsystems. Therefore, a unified approach to provide solutions which are transferable and specific to individual topology is preferable in order to evaluate respective costs, Technology Readiness Levels (TRL) and proper engineering within IoT framework. For instance, different reference architecture models have been introduced in different domains such as Smart Grid for power system engineering and Smart Manufacturing for company’s value chain which provide structured basis for design, development and validation of new solutions and technologies based on IoT provisions [10]. In Fig. 4 Reference Architecture Model for Industry 4.0 (RAMI 4.0) has been illustrated to represent a sophisticated model for Smart Manufacturing in the framework of IoT. Industry 4.0 organizes suppliers, manufacturers and customers in a virtual, vertically and horizontally integrated, value chain, so manufacturers (enterprise management) can introduce appropriate technologies to avoid losing their position and to fully

Fig. 4 Proposed RAMI 4.0 for smart manufacturing based on IoT solution

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integrate into the customer’s network [11]. Overall, the significant amount of data generated through digitization affects all areas of the company’s business. Thus, improving transparency, integration and designability are reliant on providing much more information on customer needs and the individual tasks needed to fulfil them. Industry 4.0 creates completely new value-creating business areas; for example, product design and development, and data security, will become much more important in the future. The RAMI 4.0 in the corporate value chain can be seen as a tool which relates most of the technologies span over functional boundaries and entire value creating process throughout the enterprise [11].

3.2 The 5G Factor In IoT technologies telecommunication plays a critical role for interconnectivity and deployment of different devices range from small heart rate-monitors to autonomous vehicles, smart home appliances, intelligent factories, and much more. However, a primary driver of 5G is not merely ever-growing consumer demand for faster Internet but it is the proliferation of connected devices in industrial settings, from agriculture to pharmaceuticals and automotive manufacturing. Generally, 5G is a set of emerging global telecommunications standards, using high-frequency spectrum, to offer network connectivity with reduced latency and greater speed and capacity relative to its predecessors, most recently 4G LTE (Long-Term Evolution). According to IDC, by 2021 5G’s broad enablement of IoT use cases will drive 70% of G2000 companies to spend US$1.2 billion on connectivity management solutions. Though 5G is still in a preliminary stage, its technical capabilities offer plenty of reasons for excitement. Apart from addressing connectivity issues and providing increased network coverage, 5G will allow technological innovations in network slicing, edge computing, AI, and machine learning processes delivered to the end-consumer. Additionally, edge computing will be possible due to ultra-low latency via Multi-Access Edge Compute (MEC), which moves workload processing to the edge. This will further drive major revenue from enterprise use cases [12]. In Fig. 5 advantages related to using 5G technologies within IoT framework have been illustrated. In fact, 5G is expected to help businesses more effectively manage the ever-increasing quantities of information produced by the IoT, as well as improve the near-instantaneous communication necessary for mission critical services like robotics-assisted surgery or autonomous driving. To shed light on the many IoT-related activities 5G will help enable, there are four key markets that can be identified which are likely to be disrupted by the hyperconnected future [13]: • Augmented and Virtual Reality (AR/VR): Increasing use of AR/VR technology heralds the creation of completely simulated digital environments, as well as the overlay of digital tools in everyday environments. Consumer gaming, industrial manufacturing, and medical services are just a few of the early use cases for which

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Fig. 5 Advantages of the 5G technology for IoT solutions

AR/VR is gaining traction. 5G is expected to be a critical enabler of reduced latency and enhanced speed that should make these bandwidth-heavy services possible [14]. • Autonomous vehicles and smart infrastructure: Autonomous vehicles, at their peak level of self-direction, are expected to require a level of IoT maturity powered by 5G. Indeed, to achieve real-time awareness and safety, autonomous vehicles need sufficient network speed and capacity, as well as near-instantaneous latency. And while the path to full self-driving (level 5 autonomy) is still a work in progress, vehicle connectivity continues to reach all-time highs. In 2017, estimates suggest between 60 to 80% of cars sold globally contained installed telematics, and by 2020, 90% of new cars will have Internet connectivity. Additionally, smart highways, grids, properties, and other infrastructure investments would require sensor technology to support the development of not only autonomous vehicle ecosystems but also smart cities generally. • Healthcare: From wearables for physical health monitoring to high-tech diagnostic equipment, the evolution of sensor technology should offer the healthcare industry an unprecedented opportunity to generate actionable insights from patient data. Other types of connected medical equipment, such as mobile robots, surgical-assistants and even exoskeletons may help improve both health services efficiency and patient outcomes. The medical robotics market alone is expected to reach $17 billion by 2023, up from $6.5 billion in 2018, for a compound annual growth rate (CAGR) of 21% [15]. • Low-power devices: Not all devices connected to 5G networks will require ultra-fast speeds; in fact, many low-power devices will instead rely on 5G for its increased capacity. From crop monitors gauging water levels in agricultural environments to power-management systems in residential properties, low-power devices are likely to be among the early yet frequently adopted use-cases for IoT [13].

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4 IoT Standardizations and Communication Protocols Essentially, network communication constitutes the backbone for emerging IoT technologies by allowing people and things virtually integrated in form of information systems. However, most of the existing Internet standards are not sufficient to support IoT, both from technical and economical point of views [9]. To maintain seamless operation of the IoT, it is essential that the “things” or devices follow a common standard with well-defined protocols and interoperable interfaces [14]. Thus, in the era of digital evolution where many vendors supply different IoT-based products, researchers, entrepreneurs and government agencies are trying hard to develop a solution to agree to a common standard [9]. Standardization for IoT framework is necessary to ensure (i) interoperability across products, applications, and services that preclude vendor lock-in; (ii) economy of scale (advantages of cost reduction in large scale and efficient production of goods), where the three sections of the society—developer (researcher), government (regulator) and the user—get benefited; (iii) security and privacy of the data and the users; (iv) promoting innovations and integrations in researches relevant to IoT; and (v) interoperation across physical communication systems, protocol syntax, data semantics, and domain information [9]. From technical perspective, moving from a vertical approach to a horizontally integrated communication between different devices and applications within IoT framework requires standardization on protocol layers underlying communication technologies in OSI communication model. However, it is unrealistic to assume that a single standard on lower protocol layers can be defined covering a large variety of different communication standard or standards which will exist in future. As a matter of fact, the interconnecting mechanism for future horizontally integrated IoT platforms must be a common cross vertical service which provides general services to the applications (layer) over the gateways in network domain. In Fig. 6 the heterogeneous environment for standardization in IoT frameworks has been represented which highlights set of commonly required services to a broad range of applications with the underlying communication technologies [9, 14]. As shown in Fig. 6 the standardization activities for integrating IoT framework is reliant on cross vertical Machine to Machine (M2M) service layer which include different communication standards and interoperability methods [14]. There are several ongoing efforts in different Standard Development Organizations (SDOs) across the world to build standard platforms, protocols, and technologies to ensure seamless operation of these devices. From technological perspective, different SDOs can be broadly categorized into two classes: (i) generic and (ii) application specific. In the first category, SDOs such as ITU, IEEE, IETF, 3GPP, and one M2M, have traditionally performed a pivotal role in defining technology standards to cover the overall problem space. They have specified either policies or generic reference architectures or have offered a standard protocol to carry out the communication. These SDOs also specify technology domain. In general, the SDOs are open in a sense that anyone can go through the specifications from these SDOs without being a member of the same. However, to contribute one needs to be a member. IETF is an exception since it is

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Fig. 6 Heterogeneous standards environment in IoT

indeed open in true sense, any individual can contribute to IETF standardization, but the contribution is valued depending on the significance and expertise of the individuals (meritocratic manner). On the other hand, there are SDOs, or alliances created in the interest of standardizing technologies for some specific domain of applications. These SDOs fundamentally use the existing architectures and protocol offerings with generic approach to create the communication model. They create specific standards for specific exchange models to fill up typical gaps in the available standard offerings. There have been some standardization efforts for overall IoT network stack which are listed as follows: • • • • •

IoT standardization with International Telecommunication Union (ITU) IoT standardization with IEEE IoT standardization with 3GPP IoT standardization with Internet Engineering Task Force (IETF) IoT standardization with Organization for the Advancement of Structured Information Standard (OASIS) • IoT standardization with one M2M. In the next section of this chapter functionalities addressed through the life cycle of IoT framework and its application for developing IoT-based projects are explained.

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5 IoT Functionality Cycle Many companies are currently embracing IoT paradigm to develop their products and services where collection of sensors data can be used to improve operations, design new product variations, and even discover new business models [16]. This concept is called closed-loop Product Life-cycle Management (PLM) as utilization of smart analytics turns the sensors data into information which drive efficacy of the existing products and services [17]. From technical point of view, architecting [18] IoT system is critical to address analysis, design, implementation, and deployment issues which involve interactions between different elements and functionalities delivered within IoT integrated infrastructures. As a matter of fact, within a typical IoT-based application there are certain functional requirements which have been composed to provide effective solution to the problem via collaboration between different elements of the system. Figure 8 represents basic functionalities existing in IoT system such as monitoring, measuring, controlling, automating, optimizing, and learning process. The cycle of activities shown in Fig. 7 identifies various tasks and their sequences in developing IoT applications. In the following some of the characteristics and tasks associated to these elements are explained which can provide better insight in developing software applications and services that is essential to any IoT system. • Monitoring enables IoT applications to capture and diagnose network requests, errors and events for applications running on devices that impact business objectives [19]. Fig. 7 Functionality cycle in IoT framework applications

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Fig. 8 Architectural view of the IoT technologies within modern railway infrastructure

• Measuring plays a critical role in providing information about the environment and changes in measure and where the IoT application is developed to analyze these measurements. • Controlling devices and actuators are part of the task through interaction with the environment. This part mainly constitutes the Operation Technology (OT) for the IoT system. • Automating processes can utilize actionable data from OT to coordinate or manage workflows and remove inefficiencies of human errors in repeated and sequential tasks. • Optimizing processes can involve computational and numerical methods which are aimed to predict, manage, or track certain performance of the system while using knowledge derived from the sensory data for running algorithms or fulfil decision makings. • Learning in IoT system can be defined as applying advanced AI approaches and machine learning methods to embed human mind capability in the process of manufacturing or management domain.

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6 IoT Framework for Railway System In Australia, rail freight dominates commodity exports and expected to jump by two-third by 2030 increasing pressure on railway system. Thus, within the railways system a period of transition has been introduced where significant challenges are emerging in the movement of goods and people. The responses to transforming infrastructure and transport sector can have a clear impact in long-term prosperity and national economic growth [7]. As a result, the attempts to incorporate IoT concept into railway industry are rooted from the innovative approaches and context solutions where transport industry can benefit by relying on sensors data and decision making derived from the sensory data context. Thus far railway operators have been desperate to identify, locate and track the condition of all their expensive and complicated physical assets. However, today with the confluence of technological advancements and availability of devices to deploy unexplored applications within railway systems IoT framework is getting momentum. The most important aspects of the IoT framework within railway applications are to acquire data, analyze and use it to understand business cases. Thus, depending on the settings of exact applications, technology drives for IoT framework in railway systems can be listed as [9]: • Connectivity in terms of data rate, availability, and cost • Network-based addressing mechanism for different assets/things—Internet Protocol (IP) • Availability of computational and communication devices along with miniaturized low-cost advantages • Cloud computing and availability of storage systems • Relevant data analytics and KPI for the application. In the following architectural arrangement and communication links between different layers for IoT framework in railway systems are discussed [20].

6.1 IoT Architecture A typical architectural arrangement for IoT is shown in Fig. 8, which represents interconnectivity between different levels (layers) of the EMS within the smart infrastructure for railway system. In such scenarios data is collected from various sensors and devices over a wide geographical area and evaluated to improve the performance and reliability of the railway infrastructure. Thus, considering heterogeneity of the devices and operation characteristics at different layers within railway systems, various complexities have to be addressed. For examples requirements for system integrity and its technical aspects related to software, hardware and communication engineering are crucial for successful development of EMS. In addition to that, simulation and verification of the performance for such a large-scale and interdependent system using general software simulation packages cannot be a viable solution for

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testing and analysis of the interactions between different levels of the IoT system. Also in IoT architecture, due to critical role of communication links between different elements of the IoT subsystems the need for real-time simulation of the network system and data traffic to maintain accuracy and reliability of the simulation results is of crucial importance. As shown in Fig. 8, data from existing equipment will be incorporated into data flow where existing communication devices or low-bandwidth radio installations will continue to provide connectivity, in remote locations. Consequently, flow of information from various equipment can be utilized to deploy a digitalized railway system where applications and services are developed to meet the expectations of the business enterprise of the railway industry.

7 Railway Digitalization Digitization can enhance the railway infrastructure, maximize availability, and improve the operational efficiency of all assets. From the manufacture of rolling stock to how rail operators serve their customers, new technologies will lead to entirely new ways of doing business. Despite steady progress, the rail industry needs to continue to pursue these objectives. By embracing an integrated rail ecosystem and new cognitive technologies to acquire, associate and apply information, railways can become more efficient and effective, and can create a more responsive and agile operating environment. In Fig. 9 [21] an overview of the railway system enabled with

Fig. 9 Railway services in digital ecosystem enabled with IoT technologies

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services using IoT framework has been illustrated. Further to that in the following section some the main applications relying on IoT framework within railway digital ecosystem are explained.

7.1 Smart Infrastructure In general, the concept of the smart infrastructure is defined to enhance reliability and cost efficiency to any enterprise infrastructure as explained in Sect. 4 for potential applications for IoT (Fig. 3). One of the essential and critical factors in improvement of the efficiency in railway services including minimization of the delays on metros is timely prediction of the defective assets which are the major cause of hold-ups on metro networks. Therefore, a new service known as predictive maintenance has been designed for metro operators where the in-service asset failures are prevented by predicting the potential points of failure within the metro train systems where critical front-line equipment, such as point machines, axle counters, track and signalling systems, are all monitored as potential points of operational interruption. The implementation of the predictive maintenance service is address through IoT framework as data received from different assets (potential points of interruption) are collected in a cloud service and bench marked against optimal performance profiles of the equipment. With this approach (using cloud services in devising predictive maintenance) not only the system can detect any changes in the performance of the equipment it will be capable to improve the accuracy of predictive maintenance by using accumulated data (in different operating conditions) and self-learning utilizing machine learning techniques. Consequently, the problems/interruption within the railway systems can be detected and corrected before any failure takes place within days or weeks. There are three main advantages by adopting/integrating predictive maintenance into railway system which are listed as follows: • Enhanced reliability: predictive maintenance combats delays by minimizing interruptions caused by equipment failure. It also helps to tackle reactionary delays—cascading holds-ups triggered by a single incident. Reactionary delays can paralyze a line or even a network. Rising congestion means they are a growing problem. • Improved asset performance: predictive maintenance protects critical assets. Early intervention offers the chance to fix before failure. This helps to extend the useful life of assets and improve ROI by eliminating the need to replace otherwise serviceable equipment. • Cost-effective maintenance: smarter maintenance allows operators to make the transition from reactive to predictive maintenance regimes. This has the potential to reduce costs and improve the availability of networks.

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Thales’s [22] predictive maintenance service works by tapping into data generated by assets. This is monitored live and constantly benchmarked against optimal performance profiles. When data received from an asset—such as a point machine—deviates from the norm, alarms and recommended actions are generated automatically. The system can detect tiny changes in performance—tell-tale signs of problems to come.

7.2 Digital Services In relation to modern transportation systems there are many factors which are driving the transformation of the transport industry. For example, issues such as congestions, changing customer expectations, climate change and recently COVID-19 are just few challenges that can be considered to affect the prospect of the future transport industry. Thus, the need for reliable and effective solutions to challenges can be realized through IoT framework and digitalization of transport system. In fact, the next generation of transport industry is reliant on ICT services and innovative technologies which are critical to address the requirement of sustainability and viability within the operation of the transportation systems. A digitalized transportation infrastructure is conducive to create more efficient and safe operation of the transport systems where services such as Safety assurance and signalling system, Passenger Information System (PIS), Freight Information System (FIS) and other KPIs are integrated/implemented based on digital platform supporting IoT framework [23]. In brief, digital transportation services are improving operational efficiency, transforming passenger experience and opening up business opportunities to the benefit of both operators and passengers. Potential advantages to integrate digital services into railway system are multiples listed as follows: • • • • •

Maximize revenue and cost-efficiency Improve efficiency and reliability Hone safety and security within transport networks Provide a better passenger experience Increase sustainability.

7.3 Data Driven OCC The scope of operation in modern railway networks is varied from everything related to communications and security to power supplies and passenger information which ultimately aimed to ensure safety and comfort of the passengers. Operation Control Centre (OCC) is the nerve center of the modern railway infrastructures where railway operators coordinate and manage the enterprise by utilizing operational information from sensors and systems. However, knowing where when and how the railway network is being used will enable the operators at the OCC to deliver right decision

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at the right time. Thus, in data driven OCC big data analysis tools such as machine learning and data analytics are utilized to provide new functions and assistance for improving system performance. There are key benefits such as elimination of operational silos (discussed in Sect. 3-Fig. 1)—allowing different functions communicating with each other on the platform—and provision of decision support based on real-time data-enabling rapid reaction in crisis. However, in deploying data driven OCC railway operators are still facing major challenges to operate a modern railway network. Some of these challenges can be highlighted as listed as follows: • Lack of a common interface: Transportation control systems have different hierarchical task analysis where various tools and techniques are applied to encourage deliberate actions from the operator avoiding inadvertent activations in automation levels and alarm managements. This in fact leads to various ergonomics as operators need to intervene during any abnormal situation when work load can vary from passive mode to other extreme, active mode, stressful task in a short time which can be difficult to do (less likely to be able to react appropriately) [24]. In contrast to conventional transportation control system a Data-driven OCC utilizes a secure digital platform (IoT framework) where new functions are defined to unify the user experience with common Human Machine Interface (HMI) assisting operators and improve system performance during emergency conditions. This approach makes life much easier for operators and also reduces the need for training. • Functional silos: There are various digital services with different functions such as traffic management video surveillance, FIS and PIS which operate separately, without cross-collaboration. However, to improve incident management by integrating communication links between all the operational systems (IOT framework) data driven OCC becomes important as it eliminates functional silos allowing rapid reactions during abnormal condition and managing several functions at the same time. • Information overload: In general, the OCCs deal with raw data from thousands of sensors. To accelerate decision making, operators need easy ways to visualize the data that matters. Therefore, the data-driven OCC provides solutions to all of these problems by analyzing real-time data (IoT Framework) and combining data analytics for improvement of the decision support.

8 System Integration Aside from the interconnectivity between different layers of the IoT architecture which enable access to information covering broad scope of the railway system operation, one important factor for stable and safe operation of the railway system is the interoperability between the elements of IoT within the EMS. In simple term

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Fig. 10 Layers of interoperability in IoT-based systems

interoperability within IoT framework is mainly concerns with how to get the information or how to exchange the information so each device or element can understand it and process it. In fact, to reach for full functionality of the IoT technologies different dimensions to interoperability have to be addressed which further complicates development of the IoT-enabled railway infrastructure under real-world scenario. In Fig. 10 dimensions of interoperability to meet seamless connectivity within the IoT system has been represented [19]. Also, the concept of interoperability for developing the simulation platform is critical due to desired scalability for deploying large-scale infrastructures such as railway system where frequently there are developments according to the system capacity for transportation of the passengers and freights.

8.1 Interoperability First, there is need to understand interoperability. The main objective of this section is not to produce a new definition on interoperability but explore the different roles and functionality interoperability plays in the Internet of Things today. In this sense there are many definitions of interoperability but for instance in the context of the 3rd Generation Partnership Project, 3GPP, interoperability is: “the ability of two or more systems or components to exchange data and use information.” This definition is interesting as it provides many challenges on how to: • Get the information, • Exchange data, and • Use the information in understanding it and being able to process it. A simple representation of interoperability can be seen as follows (Fig. 10): In white papers on interoperability in IoT [25, 26], the following definition(s) has been established:

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• Technical Interoperability is usually associated with hardware/software components, systems and platforms that enable machine-to-machine communication to take place. This kind of interoperability is often centred on (communication) protocols, and the infrastructure needed for those protocols to operate. • Syntactical Interoperability is usually associated with data formats. Certainly, the messages transferred by communication protocols need to have a well-defined syntax and encoding, even if it is only in the form of bit-tables. However, many protocols carry data or content, and this can be represented using high-level transfer syntaxes such as HTML, XML or ASN.1 • Semantic Interoperability is usually associated with the meaning of content and concerns the human rather than machine interpretation of the content. Thus, interoperability on this level means that there is a common understanding between people of the meaning of the content (information) being exchanged. • Organizational Interoperability, as the name implies, is the ability of organizations to effectively communicate and transfer (meaningful) data (information) even though they may be using a variety of different information systems over widely different infrastructures, possibly across different geographic regions and cultures. Organizational interoperability depends on successful technical, syntactical and semantic interoperability.

8.2 IoT Platforms-Middleware In the context of IoT ecosystem, requirement for a platform which aggregates critical components such as sensors (device layer), IoT gateways and applications software is of significance importance. From technical point of view IoT platforms are called Application Enabled Platform (AEP) providing essential capabilities to deploy a unified interoperable environment/infrastructure consisting of diverse types and large number of devices interconnected within enterprise applications [15]. As form of middleware, IoT platform is responsible to provide accessibility/connectivity to data from devices, assets and environmental parameters. Therefore, the use of middleware within IoT framework is considered in being an ideal fit with IoT application development, as it simplifies the establishment of new applications and services in complex IoT distributed infrastructures with numerous heterogeneous devices [27]. Generally, there are two widely used approaches in developing or architecting middleware within IoT framework. One is service-based and the other one is cloud-based middleware. From architectural point of view the service-based IoT middleware deployment of the devices are defined as services within building automation, healthcare, and agriculture scenarios. For the cloud-based IoT middleware, the users can be enabled to easily interpret data collected (produced by physical devices) with the ability to integrate useful data from different systems. The architecture of the cloud can be split into several layers including datacentre (hardware), infrastructure, platform, and applications where each layer provides services for the upper layer. Cloud services can be grouped into three main categories: Software as a Service (SaaS), Platform

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Fig. 11 Functionalities of cloud-based middleware in IoT framework

as a Service (PaaS), and Infrastructure as a Service (IaaS) [19]. In Fig. 11 the cloudbased IoT middleware and its functionalities for addressing IoT platform has been represented. In the following section some of the main functionalities provided by the cloud-based architecture in IoT framework have been listed [28]. • Device Management is a suite of software components that enable remote data collection and control over connected devices through a secure connectivity between devices and backend system (application stations). Some of the common functionalities provided by IoT platforms include Over the Air (OTA) device configuration/firmware updates. • Data Security and Control constitutes an integral part of any IoT platforms providing features and solutions for secure environment in data transfer and storage. Thus, enterprise or industrial grade security systems are becoming essential requirement that secure data directly from devices to the backend cloud. • Interoperability promotes proper standards on information modelling, communication technologies and protocols which enable application programming and

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connect with different business applications that is easy for application programmers. For example types of programming language, information model or data format can identify IoT system with open IoT platform. • Scalability represents the capability of the IoT platform to support deployment of more devices considering new opportunities, possibilities and innovations which have not been planned in an initial project. This can be implications on many different parameters including security, bandwidth, latency and even protocols which provide additional solutions to IoT system. In the following various standards related to Industry 4.0, as one of the important use cases of IoT frameworks within manufacturing industry has been explained.

8.3 Standards in Industrial IOT-Industry 4.0 From the very beginning interweaving of Industry 4.0 and IoT world has been the focus for many SDOs and working groups which develop technical principles and documents on this complex topic. As a matter of fact, the fourth revolution of the industry is envisioned through IoT framework which involves Internet of Services and cyber-physical systems established as Industry 4.0. Given the fact that both IoT and Industry 4.0 aim to link independent devices and systems into an integrated interdependent system, adopting relevant standards is important for realizing of the future vision in industrial IoT or Industry 4.0. To simplify and reproduce the essential and often complex structures (or functions), modelling a reference architecture is a key to effectively systemize IoT frameworks within industrial manufacturing. Technically, reference architecture models provide a logical framework and the necessary mechanism/tools to support the development/integration/modification of a new or existing technical system throughout its life cycle [29]. However, the main objective is to develop a strategy to harmonize current standards for reference architectures in order to achieve a common understanding of the characteristics of reference architecture models and related standards in Industry 4.0. Thus, Reference Architecture Model for Industry 4.0 (RAMI4.0) is developed to support harmonization activities at the international level for addressing standard mappings in industrial IoT. The mapping is intended to provide an overview of the current standards landscape and uncover possible standardization gaps in the area of industrial IoT. To give an overview of the Industry 4.0 standards Fig. 12 represents RAMI 4.0 according to standards within manufacturing industry standards [30]. In Fig. 12 it is obvious that relevant IoT standards are divided into the corresponding RAMI 4.0-layers and other relevant areas in the context of Industry 4.0. For example IEC standards covering different functional and operational aspects of the industrial automation have been used to identify standards for RAMI 4.0. Consequently, it is recommended that research projects, both national and international, actively apply the current standards to enable faster industrial implementation

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Fig. 12 IEC standards for RAMI 4.0 envisioned through IoT applications

and to identify possible standardization gaps. After discussing about mapping standards relevant to industrial IoT or Industry 4.0, in the next section cybersecurity challenges and cybersecurity preparedness associated to IoT framework and its critical role to maintain Confidentiality, Integrity and Availability (CIA triad) of IT frameworks are explained.

9 Cybersecurity Preparedness With the influx of new technologies and convergence of Information Technology (IT) and Operation Technologies (OT), cybersecurity issues are becoming most important challenges in deployment of IoT-based systems. In the world of IoT framework main concerns relevant to IT security are Confidentiality, Integrity, and Availability of the information (aka CIA triad) as information is exchanged between different devices or applications. On the other hand, in relation to OT environment concerns are mainly corresponded to stability, safety and reliability of the equipment or devices. Consequently, the possible security risks for IoT systems are of various levels and types which of course necessitate solutions to be devised from the concept phase and integrated at the hardware, firmware, software and service levels [19]. Therefore, in cybersecurity for IoT it is important to drive security, privacy, data protection and trust across the whole IoT ecosystem. As it has been shown in Fig. 13, IoT security

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Fig. 13 Cybersecurity challenges with IoT frame considering elements of IT and OT system

challenges are shared with the convergence of IT and OT world which represents wide range of possible cybersecurity threats by considering both the IT and OT aspects of the IoT system. For example, the more devices that connected to the IoT infrastructure more difficult is to administer OS/firmware updates for maintaining the security at high levels. Consequently, a holistic approach including various types of devices connected to IoT network, data network and application layer have to be considered for cybersecurity protection within IoT systems. The cybersecurity policies and procedures for risk management in IoT platforms can be reflected through existing cybersecurity frameworks [31]. To describe the scenario of the IoT cybersecurity issues in a simple form, IoT systems can be attacked often by obtaining access to IoT applications (mobile/pc apps, firmware or open IoT platforms) then start monitoring, controlling and tampering the IoT devices. Thus, all these applications are required to be protected to avoid any undesired effect on the IoT device operation. There are certain security measures which have been highlighted as a point of study in the domain of IoT cybersecurity to provide guidelines and recommendation for users and operators within IoT ecosystem paradigm. There are three main domains where cybersecurity measures can be addressed policies, organizational practices and technical practices. The following section discusses some of the cybersecurity practices in IoT systems.

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9.1 IoT Security Practices While IoT security measures can be approached in different categorization, requirement to address technical practices as the last piece of the puzzle for ensuring cybersecurity of the IoT system is essential. In fact due to substantial amount of technologies offering services in terms of software, hardware and networks technical capabilities of the IoT devices can be utilized to improve their level of security within the environment where they are deployed [32]. In recent years many enterprises have started to extend the risk management for their growing networks of IoT devices into technical levels where vulnerabilities of the IoT devices are becoming attractive targets for cybercriminals. Thus, in response to this challenging scenario there are certain cybersecurity practices which are adopted to ensure successful cybersecurity measures at the device levels. Figure 14 identifies some of the technical measures that are practices within the enterprises transformed with the IoT devices. Some of the best practices adopted within IoT environment have been explained as follows: • IOT Authentication is a model for building trust in the identity of IoT machines and devices to protect data and control access when information travels via an unsecured network such as the Internet [33]. • Encryption The purpose of data encryption is to protect digital data confidentiality as it is stored on computer systems and transmitted using the Internet or other computer networks [34]. • Division of Network/Segmentation The security goal of network segmentation is to reduce the attack surface. Network segmentation divides a network into two Fig. 14 Cybersecurity practices adopted in response to IoT transformed enterprises

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or more subsections to enable granular control over lateral movement of traffic between devices and workloads. • Artificial Intelligence It is analyzing data from millions of cyber incidents, and using it to identify potential threats within IoT system [35]. In the following some of the use cases of IoT ecosystem within the field of manufacturing and electric power supply industry are explained.

10 Other Use Cases Although there are many IoT pilot projects which are deployed in various field of applications, IoT use cases are known based on categories of the applications which are possible to be leveraged by utilizing IoT technologies. For example applications such as predictive maintenance or asset tracking depend on the context in which the solutions are defined whether it is in manufacturing or building management [36]. From technical point of view IoT use cases are not universally agreed set of applications which are limited to specific domain or industry, but they are mainly expressed in the terms of industry specific or cross-industry IoT use case. Therefore, the actual usage perspectives for IoT use cases are concerned with a bigger picture which includes challenges and goals that might be addressed through IoT solutions. For instance the International Data Corporation (IDC) [36] which is a premier global provider of market intelligence and advisory services have forecasted on the evolution of IoT by using two essential types of use cases. One is the industrial-specific use case which is related to manufacturing operations and the other one is energy-specific use case dealing with power system operations. In many of the aforementioned areas there are many types of potential IoT applications which are fully operational and standardized for realizations of the IoT use cases. Industry 4.0 and Smart Grid are two highlighted use cases which meet the essence of scalable IoT deployments from the business and end goals perspectives. In the following two of the main IoT use cases within the modern manufacturing and power supply industry are briefly explained.

10.1 Industry 4.0 In the field of manufacturing industry networking of machines and processes are becoming dominant trend to address issues such as cost efficiency and sustainability within value chain of industrial manufacturing. Therefore, Industry 4.0 has been introduced as fourth industrial revolution where industrial automation (third industrial revolution) is shifted from a central control system toward a more distributed and information-intensive transformation connected to big data, services, people and IoT enabled assets/devices. Consequently, Industry 4.0 is identified as one of the important IoT use cases in industrial domain which identifies a broad vision about

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the modern future manufacturing processes and provides a framework and reference architecture for smart manufacturing. Indeed, IoT plays a key role in the scope of Industry 4.0 in which it includes many IoT stack components from IoT platforms, IoT gateways and many more hardware and software elements enabling new ways of production [37].

10.2 Smart Grid Today’s technologies in IoT enabled energy grid have introduced a new paradigm on smart operation of the electric power systems where sophisticated connectivity and communication businesses, services and private citizens are all benefited from a reliable and cost-effective electric power. With utilization of data communication infrastructures and intelligent/smart meters power companies are enabled to assess and monitor the health of their power distribution networks with granularity and accuracy which were not possible with the older technologies. As a matter of fact, the possibility of using IoT devices for monitoring real-time power demands and automatically redirect power during grid contingencies can add resiliency to power grids to avoid blackouts or outages for customers. There are numerous benefits to update power grid for smart grid which can be highlighted with increase of renewable energy generation, better prediction and billing for customers and businesses. Finally, in smart grid IoT technology and sustainability will go hand in hand to support and deliver electric power suitable for future modern society [38].

11 Designing, Simulation and Validation As explained earlier in Sect. 3, application of IoT use cases deal with multifaceted, large-scale and interdependent subsystems in which classical systems evolve into complex SOS including both technological and social context [39]. Therefore, the process of designing, simulation and verification for IoT-based systems are reliant on set of requirements such as real-time capability, adaptability, expandability and interoperability. In dealing with such complex scenarios the process of validating IoT system performances are critical to be tested against simulation tools and multiple test categories [40]. Although the emergence of the computers and software tools in many different domains have strongly contributed in good system design but within System Engineering (SE/SOS) field Model-Based System Engineering (MBSE) is described as formalized application modelling where principles, method, languages and tools are developed for entire life cycle of large complex system [39]. In brief, for successful simulation and validation of the IoT-based use cases development of a test bed addressing holistic IoT solutions including Operating System (OS), architecture, third-party hardware, connectivity, processing power, communication bandwidth and etc. are of significant importance.

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12 IoT-Based Smart Grid Application Despite the advancement in developing complex scenarios for intelligent and efficient operation of the IoT integrated system, validation of the operational and conceptual approaches for the proposed scenarios have not been addressed properly. Due to multi-domain simulation environment and highlight the effective approach for devising practical test bed and incorporating the requirements specific to IoT use cases a simulation scenario within Smart Grid paradigm has been described in the following. Generally, in planning and study of power system protection, modelling and validation of the research outcomes are fundamental to ensure accuracy and consistency of the results in comparison to real-world process. However, considering state of the art computing, sensing, and communication technologies embedded in operation of the modern power system, the need for up to date and reliable simulation techniques is becoming inevitable. In fact, the paradigm of Smart Grid architecture and interactions between subsystems of multi-domain physical environment has introduced various intricacies, where utilizing conventional simulation tools can compromise the reliability and consistency of the simulation results. Thus, this chapter focuses on protection system and its operational performance, where there are certain aspects related to modelling and validation of the proposed MAPS, which are critical to replicate system integrity including hardware interaction for producing accuracy required for development of the protection system. To deal with the complexities, a testbed setup constituting different simulation tools and equipment has been developed in which, hybrid simulation techniques such as real-time co-simulation and HIL are adopted for implementing MAPS. In Fig. 15, architectural layers constituting the simulation platform for implementing MAPS have been illustrated where protection agents, power system real-time simulation and HIL layer are interfaced via communication services of TCP/IP and IEC61850 GOOSE messaging. The two-sided arrows on the left in Fig. 15 illustrate two duplex communication paths for data exchange between different layers of the MAPS sub-domains. As shown in Fig. 15, real-time measurements and protection settings derived through heuristic decision-making process between protections agents are exchanged between the two top layers of the MAPS architecture. Similarly, a separate connection path has been established between power system and HIL layers to exchange GOOSE messages for selecting desired protection settings in protection IEDs while trip signals published by protection IED are subscribed simultaneously open CBs within power system simulation layer [41]. Finally, with respect to the architectural integration, for incorporating ICT elements into power system operation, there are different hardware and software tools, which must be coordinated through different simulation techniques. In the next section [41, 42], some of the technical details corresponding to each simulation techniques are discussed, and the final experimental setup developed in Victoria University Zone Substation laboratory has been demonstrated.

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Fig. 15 Architectural integration for MAPS testbed

12.1 Real-Time Digital Co-simulation As discussed earlier, one of the challenging aspects in verifying the functionality and performance of the MAPS under real-world operation scenario is the inclusion of interaction between different domain subsystems where separate simulation tools and solver engines are adopted. Although there are simulation tools for specific simulation domains, which has been optimized and improved through years of development, their simulation results are bounded to constrain on modelling and conditions of the aspect of study. Similarly, having introduced new equipment and improved functionalities for secondary system in power system operation such as CTs, VTs and protection IEDs, realistic modelling and simulation of the modern power is reliant on multiple simulation environments, where there are already wellestablished tools for modelling desired domain of study. From technical point of view, integration of several simulation software tools into one single software tool, which can take into account all the details and intricacies of the existing subsystem domains of study is not an easy task, if not possible at all [43]. However, as an alternative way for simulation of multi-domain heterogeneous physical systems, a hybrid approach based on simultaneously interfacing different simulation software tools and running the simulation process in real-time has been proposed, which is called cosimulation. Through literature [43–45], application of co-simulation has been highlighted as a cost effective and reliable method for research studies on modern power systems. Thus, in the current research study co-simulation approach for interfacing between continuous simulation domain of the power system and discrete event-based communication messaging services among protection agents is imperative. However,

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Fig. 16 Block diagram representation of co-simulation between power system and MAPS

the requirement of precise timing for interaction between two heterogeneous physical environments is reliant on synchronization method, where real-time data from power system elements can be exchanged with the agent development environment as decision-making process takes place. Although, for conventional software tools timing of the simulation process is not critical as solver engines are bounded to solve specific physical phenomena, but for co-simulation between different software tools running in parallel, real-time synchronization of the processes have to be addressed using specific equipment rather than PC desktops. Figure 16 represents block diagram for co-simulation between power system and MAPS within smart grid simulation framework.

12.2 Hardware in the Loop (HIL) Generally, the state of the art of communication technologies/capabilities embedded into protection IEDs have been devised to introduce more functionalities and flexibilities to meet the requirement for system protection in modern power systems where applications such as self-healing and adaptive intelligent protection schemes are implemented. However, given the heterogeneity within hardware and software design of each specific IEDs complexity for detailed simulation and interaction between the protection devices cannot be a cost-effective solution if not possible. Therefore, as a relevant approach to deal with the aforementioned drawbacks where communication and data exchange plays a critical role the application of HIL is integrated with co-simulation of the MAPS to establish a laboratory-based simulation platform for studying and analysis of the advanced protection schemes such as MAPS. In fact, application of the HIL is necessary for assessing the performance of the protection system as there are other factors such as communication latency (timescale for protection IEDs), cybersecurity and interoperability between the protection IEDs which

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may affect the overall performance of the protection scheme under real-word operation scenario [9, 42]. In Fig. 17 the proposed HIL-based architecture to establish a simulation platform for MAPS has been illustrated consisting of the primary power system distribution network and the ICT infrastructure which operate in parallel within different simulation domain/environment. As shown in Fig. 17 OPAL-RT (OP5600) is utilized for real-time simulation of the power system network while there are 6 protection IEDs from ABB (REF615) which constitute (for) the HIL subsystem and configured to interact with MAPS using GOOSE messaging defined in IEC61850 communication standard. The GOOSE messages subscribed by protection IEDs are pre-configured to activate desirable TOC curve within the protection IED depending on the output of the decision-making process from MAPS. In addition to that/furthermore, each protection IED configured to send/publish trip signals back to the simulator box (OP5600) to open corresponding CBs upon detection of faults in the distribution network. Thus it is important to note that the real-time interaction between protection IEDs as HIL and the power system components, in this case CBs, take place through communication network which has been represented with red dashed line is Fig. 17. For implementation of the real-world scenario for the proposed simulation platform there are some details related to testbed components and accessories such as power amplifier unit, Ethernet switch and real-time simulation of the power system which explained in next section [41, 42].

Fig. 17 Proposed HIL architecture for MAPS

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13 Conclusion Rail industry is expected to be decentralized and integrated with advanced technologies enabling real-time interactions within different levels of EMS. As a result, railway operators are entering into competitions for developing interconnected rail ecosystem and services which aim for delivering consumers with more efficient and effective transportation performance. Therefore, the role of IoT and its functionalities to meet existing bottlenecks within railway systems is critical. In this chapter it has been tried to highlight some of the technical aspects related to IoT framework for addressing its feasibilities and potentials within railway industries. There are four main factors that are imminent for successful transitioning from traditional operation of the railway system into a modern smart infrastructure. In the following key areas for research and development studies within the smart railway industry are explained. • Connectivity demand for new services in railway systems is reliant on seamless integration of communication protocols and semantic data modelling (ontology engineering) to exchange information at different operational levels in timely and secure way. Therefore, a unified framework based on IoT technologies (software, hardware and etc.) and taking into account the domain of application are required to comply with certain standards within IoT railways. • Cloud Services and its infrastructures are key to utilize databases and pool resources of the IoT system for data analysis in relation to operational management and decision making. Therefore, in terms of Return on Investment (ROI), the capital cost and investment on integrating ICT technologies including cloud data services, software and hardware can reduce the operational expenditures for railway operators through improvement traditional silo-base operation of the multiple railway lines. • Artificial Intelligence (AI) are becoming imminent elements of the management system within large-scale enterprise such as railway system. As a matter of fact, the use cases of AI are becoming widespread and integrated into rail automation, incident and disruption simulation, customer data engine and etc. In addition to that, the immediate future application for AI within railway industry is related to predictive maintenances of the assets which are critical to prevent failures in the system. Consequently, the use of AI can be understood as an important factor to improve the reliability and efficiency in operation of the future railway systems. • Verification To ensure a unified infrastructure providing a platform for testing and setup of different operation scenario with railway industry is critical. This part of the research should entail different aspects of the IoT platform including all the above-mentioned components of the IoT and also perform functionalities which are defined for railway operation industry. As a matter of fact simulation and development of a suitable testbed is a challenging task since IoT framework is inherently heterogeneous in which different software tools and simulation domains have to be integrated to capture real-world operation of the IoT framework in real-time scales.

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An Overview of Sensors in Intelligent Transportation Systems and Electric Vehicles Jyotirmoy Dutta and Ribu Mathew

Abstract With the advent of machine learning, data analytics, cloud and edge computing, and rapid increase in the computational capabilities and progress in wireless communication, the traditional transport systems is fast transforming into an Smart or Intelligent Transport Systems (ITS). An efficient transport system is one of the cornerstones of smart cities and leads to the attainment of sustainable development goals. Along with the integration of electric vehicles and autonomous vehicles, the vision for an ITS is to provide a safe and comfortable travel which makes the optimum use of available resources along with reducing the carbon footprint. To achieve this vision, sensors play an important role. Thousands of in-vehicle and outof-vehicle sensors are collecting vast amount of data and exchanging it through cloud computing to make the transportation system efficient. This also brings forth various challenges like the sensor fusion, data privacy, and the performance of sensors over a period of time under external conditions. This chapter provides an overview of various categories of sensors used in transportation system and electric vehicles. It also presents various impending challenges in sensor deployment. Keywords Electric vehicles · Intelligent transport systems · Sensors · Sustainable development · Smart cities · Environment

1 Introduction For any civilization to progress and prosper, an efficient transport system is a basic need. The Roman Empire, the Incas, and the ancient Indian civilizations had a good network of roads and a well-developed transport system as per those times Present Address: J. Dutta (B) India Urban Data Exchange, Bangalore, India e-mail: [email protected] Centre for Digital Innovation, CHRIST (Deemed to be University), Bangalore, India R. Mathew School of Electrical and Electronics Engineering (SEEE), VIT Bhopal University, Bhopal, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 N. Marati et al. (eds.), AI Enabled IoT for Electrification and Connected Transportation, Transactions on Computer Systems and Networks, https://doi.org/10.1007/978-981-19-2184-1_3

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[1]. In modern times, transport systems have evolved significantly, from the first three-color traffic signal deployed in 1914 to the Global Positioning System (GPS) using a network of satellites for navigation [2]. Since the mid-1990s, technological innovations have transformed the transportation systems into what is known as an “Intelligent Transport System” (ITS). The creation of melodic roads, vehicle-to-vehicle communication, vehicle-toinfrastructure communication, vehicular ad hoc networks, electrified roads, energy harvesting from roads, self-weighing roads, smart streetlights, smart parking, and recording of driving behavior are some of the significant milestones in the evolution of ITS [3]. Figure 1 sums up some of these milestones. These technology innovations were created to improve mobility, safety, and transportation productivity while conserving energy and lowering carbon emissions. Information and Communications Technology (ICT), the Internet of Things (IoT), mobile applications, cloud computing, GPS, digital radio, Radio Frequency Identification (RFID) are the tools that are being used to achieve these goals [4]. These technologies working in tandem leads to the end user getting better travel information, making travel more safe and secure and more coordinated, and eventually leads to more intelligent use of the existing transport infrastructure. Today’s automobiles are getting more powerful and intelligent and have many AI-based features with many sensors. With the advent of autonomous vehicles (AV) and electric vehicles (EV), the demand to transform the traditional ITS is growing. The traditional ITS will see a shift with the inclusion of edge computing, machine learning (ML), and data analytics in the coming years. The future of ITS lies in the seamless integration of EVs with the existing infrastructure and resources.

Fig. 1 A timeline of technology trends in the last two and a half decades showing the significant milestones in the evolution of ITS [3]

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Although EVs have caught the general public’s attention recently, the idea of building an EV coincided with the invention of electric motors. Between 1897 and 1900, electric vehicles accounted for roughly 28% of all vehicles, and they were once preferred over Internal Combustion Engine (ICE) vehicles. But due to low oil prices, ICE cars soon became the ideal mode of transportation, and with a greater emphasis on research and development, ICEs quickly became the market leader. Around 1935, EVs almost disappeared from the markets. In 1996, General Motors’ EV1 reintroduced the electric vehicle, shortly followed by the Toyota Prius, which many suggest is the turning point in the history of modern EVs. Other major automobile manufacturers have followed suit, and customers now have a range of EV options [5]. A brief timeline of the development and evolution of EVs is shown in Fig. 2. One of the key elements bringing the EV concept back as a commercially viable and a product which attracts customers is an urgent need to take proactive steps to control climate change and an increased public awareness about EVs. The world emits around 50 billion t of greenhouse gases each year, and transportation contributes to 16.2% of it, and this includes direct emissions from burning fossil fuels to run automobiles and some indirect emissions [6]. Another reason is that EVs are contributing significantly to the advancement and deployment of autonomous vehicles (AV). Autonomous features are easier to install on electric vehicles with fewer moving parts [8]. The platform utilized to access, collect, and interpret reliable data from the environment through sensors is critical to the effectiveness of ITS. In general, there are two types of sensing platforms. The intra-vehicular sensing platform is the first category, collecting data on the vehicle’s conditions and the driver’s behavior. The second type is used to collect data on traffic conditions [9]. This sensor data is then used for traffic management.

Fig. 2 A brief timeline showing the major milestones in the evolution of EVs [7]

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For a typical ITS, sensors like the inductive loop detector for vehicle detection, presence and count, magnetic sensors to detect the presence of vehicles, piezoelectric sensors for measuring vehicle weight and vehicle speed, road surface condition sensors, acoustic array sensors, ultrasonic sensors, etc., to measure vehicle presence, speed, etc., are used [9]. A high-end electric vehicle may have more than a hundred individual sensors [10]. Temperature sensors, gas detection and pressure sensors, inductive position sensors, and battery coolant ultrasonic level sensors are some of the sensors used to monitor the battery pack and to avoid thermal runaway, high voltage charger connector, cell connection system, etc. [11]. The chapter provides a brief overview of the different sensors used in EVs and ITS and discusses the possible challenges.

2 Sensors for EVs and Transportation Sustainable ITS requires the integration of sensors with vehicles and other transport infrastructure. Typically, sensors convert a physical, chemical, or biological parameter into an equivalent electrical signal. This section discusses various sensor modules that are an integral part of EVs and transportation systems for vehicle functionality, performance, safety, entertainment, and traffic management system. Some mandatory sensors are related to safety, whereas others are installed by the manufacturers/users for performance improvement, entertainment, and environmental conservation, to cite a few. In EV’s like battery EV (BEV), hybrid EV (HEV), plug-in hybrid EV (PHEV), and fuel cell EV (FCEV), various sensor modules govern their operation [12]. An EV may require 2 to 3 types of sensors, whereas a hybrid EV requires more than ten sensor types for efficient and reliable operation [12]. In a typical EV, there are mainly three types of sensors which are (i) temperature sensors, (ii) voltage sensors, and (iii) current sensors. Sensors integrated into EV’s are typically realized with micro/nanoelectromechanical system (M(N)EMS) technology for measuring various physical [13–17], chemical [18–21], and biological [22–25] parameters. A typical range of sensors integrated with a smart vehicle is shown in Fig. 3. Presently, MEMS-based sensors have been used for vehicle stability control systems: accelerometer gyroscope, airbag release, temperature measurement, flow measurement, pressure measurement, etc., along with efficient algorithms for improving the performance and control of vehicles. For an ITS, sensor classification is typically done either on the sensor placement on the vehicle or based on the sensor application. Here, we have considered the latter approach in which we have elucidated the sensor based on its application in EVs and transportation system. A list of categories of sensors and their functionality is summarized in Table 1.

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Fig. 3 MEMS-based sensor in a typical EV and HEV [26]

Table 1 Class of sensors in a typical ITS Category

Type of sensors

Traffic management

Ultrasonic sensor, proximity sensor, camera, radars, inductive loop detectors, Piezoelectric sensors, magnetic sensors

Vehicle diagnostic

Temperature sensor, pressure sensor, chemical sensor, gas sensor

Environment conditions

Pressure sensor, distance sensor, temperature sensor, moisture sensor

Driver behavior

ECG sensor, EEG sensor, sleep sensor, camera, level sensor, IR sensor, microphone (acoustic sensor), GPS

Vehicle safety

Speed sensor, inertia sensor, ultrasonic sensor, camera

2.1 Traffic Management Category The traffic management system includes mainly the surveillance on the road with the help of sensors and detecting units installed in the roadways (fixed surveillance units) and the vehicles (mobile units) for effective traffic monitoring. The intensity of traffic is typically governed by traffic management systems that include installed cameras, IR sensors, integrated image processing, and predicting algorithms. Integration of such sensors helps control traffic on roads, avoids congestion, and avoids high traffic intensity in a particular route in case of emergency. The traffic management system helps monitor vehicles’ velocity, direction, and intensity, thereby helping in better traffic management. An example of a sensor used in traffic management is the inductive loop detector. It is used for collecting information on the

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traffic flow, vehicle’s occupancy, length, and speed. It comprises a long wire coiled into a loop installed into or beneath the road surface and measures the change in the electrical characteristics of the circuit when a vehicle crosses over it [9]. Magnetic sensors and piezoelectric sensors are also used for similar applications. Laser sensors are typically installed in vehicles to scan the road identifying any obstruction continuously. Such sensors coupled with a feedback mechanism can notify the driver and adjust vehicle speed avoiding collision or fatal accidents. LIght Detection and Ranging (LIDAR) technology uses a laser to calibrate distance in 360°, thereby providing information regarding any obstruction while driving an autonomous vehicle. RFID sensors are used for intelligent parking.

2.2 Vehicle Diagnostic Category The main functionality of diagnostic sensors is to detect any failure, thereby improving vehicle reliability early. Such sensors are critical as electrical/mechanical faults may result in vehicle breakdown or malfunction, resulting in fatal accidents. A few important sensors in the diagnostic category are as follows: (i) temperature sensors for monitoring vehicle fluid temperature, (ii) pressure sensors for detecting vehicle tire pressure with Tire Pressure Monitoring System (TPMS), (iii) level sensors for monitoring fuel level, (iv) gas sensors for exhaust gas monitoring, and (v) electrical fault diagnosis sensors, to mention a few. The temperature and fluid monitoring sensors play a critical role, especially in monitoring engine health and preventing engine seizing. Speed sensors that monitor the wheel speed also play a crucial role in traction tracking systems and antilock brake systems (ABS).

2.3 Environment Category In this category, the sensors typically collect data related to the external environment in which the vehicle is maneuvering. For instance, road temperature, external atmospheric pressure, and air composition are collected through these sensors. Details such as road temperature, road surface roughness, and water/moisture are detected with the help of IR sensors, moisture sensors, GPS, gas sensors, pressure sensors, etc. Such sensors are critical in continuous real-time monitoring of external conditions and may also provide warning signals to the driver upon an alarming change in external parameters compared to safe limits. For instance, pressure and temperature sensors that measure the ambient pressure and temperature can be integrated with the vehicle acting as a mobile station that provides ambient pressure details to the driver and weather stations or third parties. Cameras with efficient image processing algorithms are used to determine the road/track condition when there is rain or snow on the track.

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2.4 User Category The main objective of sensors in this category is to monitor the driver for improving safety. As most accidents occur due to drowsiness/falling asleep of drivers or driving under alcohol influence, such sensors detect the driver’s characteristics while driving. Typically, air quality measurements and temperature are installed in the car cabin to maintain the ideal ambience for the driver and co-passengers. Camera and head and eye motion sensors are used to detect the driver’s alertness level while driving periodically. Upon detecting abnormalities, specific actions like steering vibrations or automatic slowdown/braking with parking assistance which avoid fatal accidents are taken. Steering angle sensors are also used for detecting the level of driver’s drowsiness [27]. The anomalies in the driving pattern can also be understood by periodically analyzing the brain signal with electroencephalogram (EEG) sensors coupled with AI algorithms for analyzing, predicting, and decision making. Such sensors coupled with ECG sensors and temperature sensors can also monitor the driver’s health condition. This may be used to alarm the driver or co-passengers/authorities/relatives during abnormalities. This requires hardware (sensor) and software (AI-based efficient algorithm and cloud) codesign. Driving assistance through an electric power steering system that continuously monitors steering wheel torque with the help of a torque sensor and reduces the driver’s effort through steering assistance with feedback mechanism is also an integral part of smart vehicles.

2.5 Vehicle Safety Category The primary use of sensors under this category is to improve the safety of the driver and the co-passengers, thereby preventing accidents and vehicle damage. Safety sensors combined with their associated software enhance the driver’s safety when parking the vehicle in times of a collision and navigating through traffic. An essential class of sensor in this category is the collision avoidance and detection sensors that not only helps in avoiding collision of vehicles while parking or navigating through traffic and detect the intensity collision and take preventive measures in case of a collision. Sensors like IR sensors, proximity sensors, cameras, accelerometer, etc., help in preventing fatal accidents. For improved navigation through traffic, a lane management system that includes rear mirror mounted cameras help to keep the vehicle within the selected lane. IR sensors and proximity sensors, along with cameras, are used for safe parking providing parking assistance. There is long-range distance (up to 120 m) measurement sensors like LIDAR and RADAR that improves vehicle maneuvering by mapping the vehicle proximity. Such sensors are an integral part of adaptive cruise control (ACC) system that automatically changes vehicle speed depending on the traffic, thereby avoiding collisions and fatal accidents. In addition, when combined with data extraction and processing

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algorithms, short range distance (up to 30 m) measurement sensors like proximity IR sensors, cameras, ultrasonic sensors, etc., can improve safety. Night vision cameras also enhance the safety of vehicles, especially on challenging roads with low visibility range. These are mainly found with near IR system (active night vision) and far IR system (passive night vision). A near IR system encompasses a non-visible IR light source and a detector to capture the reflected light, whereas in a far IR system thermographic cameras capture the thermal radiations emitted by objects in the proximity. Electronic stability control (ESC) system that integrates wheel speed sensors and steering wheel angle sensors can maintain car stability under extreme braking conditions and high speeds. Further, the hardware–software codesign that includes integrating sensors with neuro-fuzzy and genetic algorithms helps predict the route with low traffic intensity.

3 Communication Protocols The communication protocols are mainly two which are (i) intra-vehicle communication protocol and (ii) inter-vehicle communication protocol.

3.1 Intra-vehicle Communication Protocol Modern vehicles that integrate electronics like processors, electromechanical units like sensors, actuators, and feedback mechanisms form a closed-loop system. The intra-vehicle communication systems constitute mainly of the following: (i) controller—the electronic control unit (ECU) that consists of processing unit (processor), storage unit (memory), and communication units, (ii) sensor units that convert a physical entity into an equivalent electrical signal, and (iii) actuator units that change the environment depending on the control signal. For intra-vehicle communication, smart vehicles use the following communication protocols [28]: (i) local interconnect network (LIN), (ii) media-oriented system transport (MOST), (iii) control area network (CAN), and (iv) flex ray.

3.2 Inter-Vehicle Communication Protocol Inter-vehicle communication protocols transfer data from the vehicle to other data processing/transfer stations. Various inter-vehicle communication protocols include Dedicated Short-Range Communication (DSRC), Communication Access for Land Mobiles (CALM), Visible Light Communication (VLC), ZigBee technology, Wireless Access for Vehicular Environment (WAVE), Time-Triggered Light Weight

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protocol (TTP), Bluetooth, Wi-Fi, and ultra-wideband, to mention a few [29]. These communication protocols have different data transfer speeds, and the choice of protocol depends on the application.

4 Challenges and Way Forward The evolving industry of EV and AV demands safety and reliability. A Tesla driver seated in the car in an autopilot mode was recently killed in a crash while because the car’s sensor system failed to detect a truck and trailer crossing the highway [30]. This incident posed a serious question on the safety of AVs and particularly on the reliability of sensor systems. Researchers are looking at sensor fusions to make the sensor systems more reliable, robust, and free of errors. Every sensor has its strengths and weaknesses, but when they are combined, data obtained provide valuable insights into the performance or behavior of the vehicle. This helps in optimizing the system’s performance and reducing error [31]. An example of sensor fusion is the traction control system. When a vehicle’s wheel begins to slip, sensors, one on each wheel, share the information on the state of revolution of wheels in terms of speed and inclination. If needed, this information can be processed and can adjust the engine power or even apply breaks [32]. Figure 4 below shows the block diagram of a sensor fusion system. It shows how data from various sensors from the V2X, infotainment, GPS, ultrasonic processing, LIDAR, radar, and image processing subsystems combine together by the sensor fusion/AI platform.

Fig. 4 Sensor in a typical EV [33]

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The size of the sensor fusion market for automotive industry is expected to increase at a CAGR of 25.4 percent from USD 2.9 billion in 2021 to USD 22.2 billion in 2030 [34]. Sensor fusion also makes the sensor system complex, and it needs extensive processing, which further adds to the cost. In addition to that, there are no standards in place for hardware systems for sensor fusion. When it comes to the software part, all the market leaders develop their software to attract customers. The lack of standardization will also become a hindrance to the development of Sensor fusion technology. Information from various sensors and devices are gathered and analyzed to gain further insight and to take correct decisions. If, for example, the data from the traction system is combined with the ambient air temperature and rain sensing systems, it would then be possible to assess the situation in which the wheel slip occurred. If there is a cloud-based analytics system receiving real-time telemetry from the vehicle, there is an improved capability for evaluating and predicting, for example, ice in the area. This information could then be broadcasted to other drivers so that they avoid the road or are more careful. This kind of sensor data would be generated by hundreds of vehicles and need a huge amount of data processing [32]. As per an estimate, around 1 GB/s of data speed is needed to make safe decisions. Some autonomous driving applications may require more complex data-processing capabilities and greater storage capacity. Conventional vehicular networks face considerable challenges to meet these requirements [35]. Edge computing limits the amount of data pushed out intelligently, reducing data transmission costs and the amount of sensitive data leaving the vehicle. It also helps in bringing machine learning models trained in the cloud to the device. But there is a flip side to it. The energy consumed by Edge computing reduces the vehicle’s mileage by 30% [36]. ITS needs thousands of sensors, leading to a challenge of “too many” sensors [37]. As per an estimate, in 2020, around 110 million vehicles were expected to be delivered with 200 sensors per vehicle, which equates to 22 billion sensors in automobiles alone, consuming an estimated 22,000 megawatt-hours. This power is equivalent to 17.8 million energy customers just to power sensors in automobiles. In addition to that, there are sensors used for smart parking, road condition monitoring, etc. This is a genuine concern and has to be addressed. Intelligent network selection, intelligent sleeping scheduling for sensors, and energy-efficient routing for networks are among the solutions that have been studied to reduce power consumption IoT devices. Then, there is a major concern of security. In the last few years, there have been reported incidents where hackers have attacked the transit systems. In some case, these attacks have rendering ticketing machines inoperable [38]. Security researchers have also demonstrated a security breach by remotely taking control of a Tesla model [39]. Only authorized ITS users should have access to data, and unauthorized users should not be able to snoop on or interfere with the data. The ITS should also have data integrity, i.e., an assurance that the data is delivered precisely. Integrity is essential because wrong or messages can misguide authorized ITS users. The security feature of “availability” guarantees that the servers and data are always available to

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authorize an ITS users. The feature of identification ensures that unauthorized users or vehicle cannot be linked in ITS. To avoid the tracing of an authorized ITS user, there is a need to design and develop robust security and privacy mechanisms [40]. Another pertinent question is how the sensors and components will perform after two to three years on the road. For the safety of AVs, the sensors should not degrade with time. It has to be still seen how the sensors will react to changes in temperature, salt, water, and other external factors. This is going to be a challenge the manufacturers are going to face in coming years [41] For the ITS industry and the EV technology to be truly useful, these challenges concerning the sensors need to be addressed right from the beginning to make them truly sustainable. Summary The last two decades have witnessed the transformation of traditional transportation systems to an intelligent transportation system. Electric vehicles’ market has also seen an unprecedented rise. Sensors have an important part to play in these developments. In-vehicle sensors and sensors placed outside on roads, traffic junctions, and other places collect the data and process it at the edge or in the cloud. This has also helped in bringing machine learning algorithms in the picture making the systems more efficient. Sensors for traffic management to sense the environmental conditions, to help the drivers drive safe, and to optimize the available resources have been extensively deployed. This also brings its own set of challenges like sensor fusion, having “too many sensors,” power consumed by the sensors, how they perform over a period of time under harsh conditions, and most importantly the security and privacy issues related to exchange of this communication. The future design and development in sensor technology will have to address these concerns.

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Smart Parking System and Its Applications Shola Usharani and A. Karmel

Abstract The need of parking space came into the world because of growth in urbanization and car ownership. According to the statistics of World Bank, the mobility of people moving to city will increase to 60% of current population. The challenge here is, need to accommodate the infrastructure, transportation of network, and demand in raising the comfortable parking. Also, there was 35% of considerable loss in productive time in searching the parking spot. The statistics of USA announce that this waste time cost for fuel driver is $345. The demands of parking demands for autonomous vehicles need to be analyzed. As many of the automatic vehicles are based on electric, it tolerates the rules of parking-based system. These need to include the electric-based charging stations in the parking system. Keywords Smart transport systems · Smart parking · IoT using smart parking · Smart parking applications etc.

1 Introduction Visualize the smart home of the future. It is acting by itself and notifies you about abnormalities. It understands when to turn on the lights, ovens, and heater while you are on the way to home from work and it switches on your favorite music station to may be even turns on your favorite streaming music station to welcome you when you are near to your doorsteps. It suggests you when is the best times of in your day are to start the washer and clean the dishes for kitchen; it knows to turn off the lights when you vacate a room and lock the doors when you move away. Imagine the fitness machines at your local gym programming notify your personal workout when you leave the locker room.

S. Usharani (B) · A. Karmel Vellore Institute of Technology, Chennai, India e-mail: [email protected] A. Karmel e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 N. Marati et al. (eds.), AI Enabled IoT for Electrification and Connected Transportation, Transactions on Computer Systems and Networks, https://doi.org/10.1007/978-981-19-2184-1_4

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Imagine a factory where every machine in every room feeds back information to help the production line run more efficiently. Imagine an entire city that manages its public lighting, utility delivery, and road repairs automatically, based on real-time conditions and needs. All these are possible through Internet of Things. What is an Internet of Things (IoT)? It is a network of connecting millions of computers globally, smartphone, and tablets, enabling electronic communication between those computing devices. When connecting all of these computing devices, the IoT connects all those computers. It is about sharing the information and messages among people using the global network. The IoT is not only creating the network for the people and even it connects lot of objects through network. Once connected all these, everything can interconnect with every other thing for a useful purposes. In a more technically way, the IoT is the communication of distinctively identifiable embedded computing things. Which means any computing device could be connected through various types of sensors and actuators or monitors. The interconnection of all these will take place through the available Internet infrastructure. In another ways, it is an Internet of people. Connecting the people through the smart gadgets like smartphones, iPad, and tablets, etc., all these devices are connected for the purpose of people to complete their tasks like searching for, reading the data, sending and receiving the emails, instant messages, and downloading music and videos through the Internet. So the devices are connected to serve the cause for human users. Instead of accessing the data and communicating physically with one another, the IoT enables things the possibility of accessing the data and communicating with one another. What happens when we connect to the IoT network? Tons of data will be generated from the things. Whether a thing is connected is a refrigerator, TV setup box, heart monitor, automobiles, and thermistors? Each of these devices will generate a mass of vast amount of information from the things it obtained and the things will start interacting with each other by using its environment. All this data can be transmitted to new objects, things and is used to systematize through additional actions by those and other devices. The IoT can connect things that contain a wireless transmitter that uses Wi-FI, Bluetooth, or the one that employing the wireless protocol. The IoT can connect: • • • • • •

The devices from the home like smart TV, streaming audio, or video devices Medical equipment like pacemakers and health monitoring devices. Home appliances like refrigerators, ovens, and laundry. Automobile devices like cars and self-driving cars. Home automation sensors like thermostats, smoke detectors, and alarm systems. Homes, townships, cities, and countries that can be observed and controlled.

If everyone, every home and every business is connected via the IoT why not the entire neighborhood not connected? And even the whole city? The connected devices will reduce the crowding on the resident roadways, alerting the fire department during emergency cases, and even it signal the need for additional police patrols and road maintenance.

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1.1 What is Smart Transport System? In the art of our inherent lives, people used to move from one place to another, not now but throughout the history, from chariots, horses to carriages, automobiles, steam trains, and spacecraft. Civilization [1] has come a long way from riding horses and camels to get from place to place. With the emergence of intelligent transportation systems and the Internet of Things (IoT), the world is entering the next stage of movement—smart transportation. Smart transportation shown in Fig. 1 with smart city traffic management is transforming how cities manage mobility methods and emergency response, while dropping congestion on city streets. It is also termed as “Intelligent Transportation Systems (ITS) apply a variety of technologies to monitor, evaluate, and manage transportation systems to enhance efficiency and safety.” Technologies that cover the smart transport system are IoT devices along with 5G technology. The former one is making use of low-cost sensors and controllers that can be surrounded into nearly any physical machine that could be controlled and managed from distinct places. The latter one provides the high speed transportations needed for handling and monitoring transportation systems in real time with minimal delay. Smart transportation contains the practice of numerous technologies, like navigation of car; signal control systems for traffic; container management systems; AIbased plate recognition with high speed cameras during monitoring applications, and

Fig. 1 Smart transportation with smart traffic system (source from: [2])

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extending their services to more progressive applications that will interconnect the live data and to get response from multiple external sources. The goals of the smart city are to utilize the public resources better, improve the quality of services provided to citizens, and reduce the functioning costs of the public administrations. To incorporate all these goals, the smart city must include the infrastructure that provides simple and reasonable access to all public services including transportation, parking, lightening utilities, surveillance, and maintenance of public areas and many more few if all is not possible.

1.2 The Need for Smart Transportation System The success and failures of the recovery systems in new places show how the smart transportation is being executed in many cities today. There are numerous benefits of smart technology that bring to transportation within a smart city. • Safety with Smart Transportation: Reduction of human involvement during accidents by adding the advanced IoT, and machine learning with 5G in autonomous transportation systems vehicles and in immobile infrastructure will improve the safety. • Management of Smart Transportation: The data collection is a primary key to account for public management of infrastructure in smart transportation. It provides administrators to monitor the operations efficiently with every aspect of data point related to transportation. Even, it identifies the key resources for track maintenance to be fixed. • Efficient Smart Transportation: The efficiency of the smart system is improved with better management of quality data; this even improves the schedule adjustments of train journeys and bus routes, etc. • Smart Transportation is cost effective: Proactive maintenance, less energy consumption, and less usage of resources used in the direction of accident management, and use of improved resources in smart transportation will cut down the costs. The benefits are gained by the drivers when the private vehicle ownership is less expensive or inexpensive than the public transportation. • Smart Transportation provides rapid perceptions: The city traffic management centers (TMCs) throughout the city have the visibility of trouble spots about the city and even can get notifications on affected congestion city streets and issues in city. Other important services include safety in public, emergency intervention systems, and responding or communicating efficiently with other activities and emergency responders. • Security: When the vehicles are integrated through the infrastructure that includes services like public transportation and vehicle autonomous systems, they will easily solve the problems like criminals making physical threats and stealing the cars and vehicles using weapons by terrorists. But we need to aware the possibility of cyber-attack vulnerability to the critical infrastructure like power

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grids and banking systems through smart transportation. Suitable novel tools are the considerable good solution to attack those cybercrimes. • Environmental Considerations: The history of vehicles transportation from condensed based vehicles run by burned coal and wood reaches to combustion engines run by gasoline. All these use our planet resources and atmosphere. While making use of scientific advances and smart transportation, applications allows the urban places to use their available resources more efficiently and even provides the other sources of energy to power transportation as alternative benefits. • Supply Chain Resiliency: The coronavirus pandemic global disaster disrupts the worldwide supply chain process. The solution of smart transportation with supply chain goods transportation system becomes a literal lifesaver. For example, the system like Wyoming’s [1] has smart system for transportation and logistics that connects the supply chain process via AI-connected vehicle system is supported by smart system, city-to-city transport, and logistics systems. It mobilizes the important and very urgent processes like food and emergency medicine supplies without the involvement of human riders even they are risk. It is good that this expert system will create more jobs, programmers, analyst, technicians, and administrator assistance to improve the investments to market and to manage them.

1.3 The Transport System Without Smart Technology The important point of smart transportation system is helping the critical conditions in a very simple manner. For example, the critical situations of natural disaster events like earthquakes, floods, hurricanes, and man-made attacks and toxic waste spills are some that get advanced notification systems. Some of these critical conditions affect the damage of existing telecommunication infrastructure system. These scenarios can be handled by the smart transport system as rescue operations. To do all this, development of telecommunication infrastructure is very essential with various software solutions like network re-configurability, open-source soft wares, and interoperable with scalability solutions. And smart network is a key important factor to all of these.

2 Applications in Smart City Management 2.1 Smart Traffic Management To avoid traffic jams and minimize the number of red lights, you have to stop through the way. By constantly monitoring traffic flow with the road sensors, the IoT system manipulates the traffic signals and even provides the availability of the lane that make the highest number of drivers to get to their end points with less number of pauses. The sensors that involve for these processes are traffic detectors, camera monitors,

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traffic lights, etc.; all these sensors are embedded in roadways or installed street side in traffic signals or light poles. All these sensors will monitor different things like vehicle traffic data, air quality data, noise levels of the vehicles, and so on. All these data are coordinated from driver’s smartphones and smart cars. These sensors will in turn communicate each other through signal to signal communications. This will result in reduction in the time spent at traffic lights and reduction in the percent of travel time. The data of all these in turn will cause the drivers to go faster, lowering the accidents rate, road deterioration, and the quality of travel times.

2.2 Smart Road Lights The smarter traffic management becomes good when the roads become smarter. Lighted roads are safer than dark stretches of highway, and it costs high to power all those lights. The smart roadway lighting uses motion sensor technology to inform when the car is arriving and then the lights of that section of road highway turn on. The lights grow brighter when a car reaches closer and slowly dim when it passes. As it is economically not feasible where required, the smarter lightening even can be implemented by wind-powered lighting or by smart roadways.

2.3 Smart Public Lighting Like the smart lightening for highways, the options of smart lightening will come for all public spaces, indoor and outdoor. It is basically making the availability of lightening when needed and saving the energy cost when no one is needed it around. Public smart lightning is a big expense for most cities; this may save up to 20% of overall electricity consumption.

3 Smart Transport System with Smart Parking System Consider if we live in a city of any descent size, you know how hard it is to find a parking place, especially during popular events. It would be great if we know exactly where the nearest open space was, so you would not spend half your time driving around looking for it. Smart parking technology is the solution for the above problem. Street line is a company that makes special parking sensors that cities are embedding in on-street parking spaces. These sensors detect if a car is parked there or not and send that data to a central service. The drivers need to use the company’s mobile app to find the nearest unoccupied slots for the parking. Even this facility when it is incorporated

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into cars the driver will navigate to the best available slots for the parking when he presses a button or through a voice command.

3.1 The Future of Smart Parking System Smart parking future is discussed with the following example; consider you are planning for shopping in a big shopping mall, then dinner in one of favorite restaurant, and then followed by movie. You planned it to be done in an optimized scheduled time manner. So you opted to travel through your smart car. And before you are head to your evening pleasure shopping, you well organized the car parking slot earlier. You entered all details in an APP either through laptop/smartphone/tablet to get the nearest car parking place that will charge less price (may be you are comprised with a bit of waling to get the offer). Your vehicle is not an electric vehicle, so you will not prefer the option of vehicle charging in your parking place. You do not even mind if the parking slot is on-street, off-street, covered, or uncovered. The APP produces the three price options like the probability of (likely underpriced) on-street spots or two more expensive but reservable off-street spots. So, finally the spot is reserved in the garage and even the APP recommends you the tea shop that you always used to have in the retail shop space. Then, you will pay the amount for the parking and tea payment at nearby shop through and advance payment in online by credit card-based system and finally you will receive the bill as a SMS or email. Finally, you started your journey by settling in the car by switching ON your GPS system. You fed the information in the APP. It even notifies the accident information of your normal trip route, about the construction information. Then, the GPS reroutes the trip automatically with alternative route. You reached the place in the required time and the garage of parking spot recognizes your car, and the smart parking system inside the garage guides you to the reserved parking spot.

3.2 Smart Parking System That’s how the smart parking is a policy parking that integrates technology and human modernization to effectively using the possible few resources like time, fuel, and space to attain faster, easily parking of vehicles in majority of time instead of keeping the place remain idle. Smart parking and the sibling method like Intelligent Transportation are obtained from the important natural principle that allows us to be connected. The transportation with smart parking is the association of goods with people. Even though the vision of smart parking and Intelligent Transportation system is same, both uses the technologies that are overlapping but are steadily merging through one integrated stream.

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3.3 Need of Smart Parking in Urban Areas Consider an example [3]; you have a multistory building with commercial space for running various businesses. As the business is expanding and you experienced problems to provide parking to the vehicles that are owned by your employees and clients, the office staff members also feel it difficult with the available limited narrow parking space to park their vehicles around the building. To expand and use the limited space more efficiently, the space solution is recommended, that is, smart parking. Car owners can have various options to park their vehicles with the smart parking systems. This helps [3] in managing the car space efficiently. The available sensor technology in smart parking places will let you know about the variable road sign messages, payment system flexibility, and smart navigation system and help the drivers with parking space option awareness. This will reduce the amount of time in parking their cars and searching for the slot. It even improves the revenue for parking managers and commercial peoples. The unoccupied places can be utilized in future coming cars in better way. Motorist looking for parking places sometimes may not find and will keep their vehicle on street roads. This may not be observed by other drivers during busy or peak timings and may lead to road hazards. So, the probability of reducing the accidents and traffic congestion will happen with smart parking system. The drivers should spend more than 20 min to find the parking space. This will increase the emission level and fuel usage. The smart parking system even reduces the demand of parking space allotment in the city during congestion times.

4 Challenges and Important IoT Sensor Communication Technologies for Smart Parking System The new advances in manufacturing of low-power and low-cost, embedded systems are helping creators to invent new products for Internet of Things as shown in Fig. 2. Then, with more improvements in sensor technology, many cities are currently installing several IoT-based arrangements for the determination of monitoring. These enhanced technology need well-organized sensors to be placed in the parking areas for checking the occupancy and easy data processing units from various data collected sources to gain more practical visions. Cloud computing and IoT play very important roles in smart parking system to be witnessed a large evolution in the system. The smart parking system even can include the features of IoT and cloud structure. The purpose of these two technologies will include. Storage capacity: IoT consists of a huge amount of information sources (things) from sensors and actuators that produce large amounts of data either in nonstructured or structured or semi-structured data. So the IoT things need to have to analyze such huge amount of data to process, visualize, collect, and share. The cost-effective

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Fig. 2 Smart parking system (source from: [4])

solution for IoT data is provided by Cloud platform. It has storage capacity in ondemand, unlimited service, low-cost [5] solution to deal with data created by various technologies used by IoT. The data maintained from the IoT visualized and accessed from Cloud [6] is accessed from everywhere through standard interfaces like APIs. Computation power: The IoT system being developed with processing abilities is very limited. Data obtained from different objects and sensors is normally moved to powerful nodes that are used for processing and aggregating the data. As discussed, the features [7] of processing unlimited capabilities and on-demand cloud model will address the issues of computation power. With the cloud computing usage, most approachable domain-based and real time-based applications are possible with these IoT systems. Communication resources: The cloud computing facilitates effective track of connecting, tracking, and managing the things as smarter and is also cheap for remote access. As the basic working of IoT is an IP-enabled device to transfer with one another through faithful hardware setup. Built-in applications of IoT systems can monitor in real time and control these things that are not accessible locally. Scalability: IoT provides solution to the real-world applications as “anywhere and any place.” So the devices are going to be added or subtracted from it. Thus, it allows allocating and deallocating the resources in a dynamic fashion. Cloud provides such interaction model to IoT through its scalability features. The cloud even assigns the resources to the things and applications of IoT. Availability: Cloud integration supports the resources to be accessed very easily as anytime and anywhere. Most of the cloud service suppliers ensure the availability

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by continuously running up the applications and services that are being delivering to the end operators. Interoperability: The devices used for IoT is heterogeneous, because the objects or devices are made by hardware vendors and software is different. So these produce configuration issues during data communication. Interoperability between these devices ensures compatibility issues avoidance, the cloud provides one such interoperable common interface platform where all devices can communicate and connect each other. In this platform, the devices are used to exchange data in a format that is suitable among them. The working of smart parking is based on the IoT technology of sensor, actuators, and services included in it. It uses sensing objects like cameras, vehicle counting equipment, the sensors installed in streets, etc., which will identify the availability of the parking slots. As shown in Fig. 3, the vehicles occupancy in the pavement of the parking space is identified by the embedded sensors included with the system. The information of these sensors is given to the gateways, and the result data is sent to the cloud for further processing. The cloud will present the accessibility of the parking slot through remote applications to the drivers. The applications accessed from the smartphone will help the customers in obtaining the instantaneous availability of the places and slots. Inclusion of very strong sensing systems is being assembled to analyze and forward the data to the databases in real time. Smart parking applications are used in hotels, private parking lots, hospitals, offices, shopping malls, public parking garages, etc., to create their vacant parking commercial slots to be available to the drivers. These intelligent parking systems

Fig. 3 Smart parking system (source from: [5])

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will let the drivers of automobiles to reserve the parking spots earlier and also get real-time vacant parking spaces from their mobile devices. Based on Fig. 3, the primary players of working methodology of the parking system are discussed here. Parking Sensors: The parking system has sensors like ultrasonic sensors, passive infrared (PIR), and infrared. These sensors will sense the status of the sensors and inform whether a slot is free or not. The ultrasonic sensor will sense the existence of a car. These sensors are associated with Raspberry Pi using wireless communication via ESP8266 Wi-Fi chip. The ESP8266 Wi-Fi chip is an independent SOC [8] that has TCP/IP protocol stack integrated that will let the access to a Wi-Fi network. The sensors are connected to Raspberry Pi either through a 5 V supply or through an external source. Processing Unit: Here, it is Raspberry Pi. It is a mediator between the cloud and sensing devices. Each Raspberry Pi unit consists of 26 GPIO pins with 26 other sensors that can be associated with it. These pins can be extended by connecting it with a multiplexer (MUX). The ground pins of Pi and the sensors should be connected together to transfer the information through the PIO pins. The python script programming written on it will check the status of these pins and sends it to cloud for remote access. Esp8266 chip will direct the data generated by sensors to Raspberry Pi. The information at Raspberry Pi is delivered to IBM MQTT Server using MQTT protocol. MQTT [9] (Message Queue Telemetry Transport) communication protocol is a publish–subscribe-based “light weight” messaging protocol that will run above the TCP/IP protocol. This will invoke the connection to remotely connected applications under low bandwidth limitation cases. Mobile application: The users will interact with the end users through mobile applications. The application is the interface which is developed in JAVASCRIPT programming language using Apache Cordova along with Angular Js framework. The applications created by Apache Cordova source code will work on both iOS and android platforms. Two factor authorizations are used to transfer information to IBM MQTT server through a secure channel. The applications of these include parking spaces and permitting the end customer rider to reserve a slot accordingly. The data is transferred between mobile applications, and IBM MQTT will be in JSON format. The communication can be confirmed among all these by subscribing the mobile application and Raspberry Pi with IBM MQTT server. The Cloud resources: The IBM MQTT server is deployed on to the cloud. Cloud database storage has information linked to parking areas and end customers those are connected to the system. It helps to maintain every user information like user connected, time of car parking duration, amount paid by the end user, and payment mode. Any number of users can be added to this system due to its cloud-based infrastructure during the entire day. To make sure for easy access and quick recovery, regular backup of data is performed. This will avoid the cases of problems for system failures. The empty slots are indicated by green light and occupied slots as red light. From the Fig. 3, if we noticed that empty spots in the parking are specified by red

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light (shown in Lane A) and other spots as green light (shown in Lane B). Since the red light for empty slot in Lane A indicates that the slot is reserved by some user. If the parking slot in Lane B is indicated as green, it is neither booked nor parked.

5 Use Case Development of Smart Parking System Taxonomy is the defined as the abstract level for the collection of various domains in IoT. The reference model will cover the concepts, relationships, and axioms among all the entities of one particular context. This reference model should be expanded with many different stages that will lead to abstraction level of the system. By defining high level of independent implementations, standards, and technologies to the abstraction stage of taxonomy, the architecture levels will be derived. This abstract level will be used to obtain the frameworks and reference architectures by defining their building components and other important blocks required in the application design choice. Generally, the abstract levels are defined as interfaces to the systems. The required components include performance, security, functionality, and deployment procedures. The sample diagram for taxonomy of IoT is listed in Fig. 4. From the figure, the IoT system is represented with perception or sensor information, where this part dealt with all sensor-related information. For example, we may have medical sensors like Brain signal band, Fitbit machines, wearable tattoos, etc. Likewise, we have neural sensors, environmental sensors for measuring weather information, chemical, or biosensors, infrared sensors, and RFID technologies, etc., after obtaining the data from these sensors the data will be pre-processed, aggregated, and filtered, finally delivered to communication layer. Fig. 4 Taxonomy of IoT system (source from: [10])

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5.1 Architecture of Smart Parking System The first communication layer of the system will have low power technologies through Bluetooth, ZigBee, and low-power Wi-Fi and RFID [11] as wireless sensor network integration technologies. Finally, the data collected is utilized by the end user through various applications and via middle ware technologies. The middle ware will convert the data from the network into the required format of the end user applications using service-oriented architectures, event-based services, semantic-based models, etc., the applications that will interact the user as smart agriculture, energy conservation, smart transport, and home automation using different user interactions like web pages and visualization tools. Requirements of IoT Architectures. • The system should include RFID through various technologies. • Miniaturized objects as sensors technology support to make the system to behave as smart. • The solution of IoT like communication and tagging is well developed for manufacturing and logistics. • Business benefits for tracking asset and supply chain management. • Same solutions or technologies may not be applicable to all other solutions. • Need to consider Interoperable, application area. • Specify a single design pattern or taxonomy that satisfies the domains of all applications. • Common ground or an abstract level pattern that combines many applications need to consider. Based on the above discussion, the architecture of IoT is represented with four layer format as shown in Fig. 5. The functionality of each layer is discussed as below:

Fig. 5 Architecture diagram of IoT (source from: [9])

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IoT Sensor Layer: This one is made up of smart devices and sensors. These devices are used to gather the instantaneous information and to process it. The sensor objects need low-power, data rate connectivity to provide energy-efficient solutions to IoT application. All these sensors form a network with communicating device and connect it wirelessly and form a wireless sensor network. These sensors are connected according to their data types and data. Sensors Aggregation and Gateways: These are the collecting devices’ data from the objects through communication networks. The local area network is used when the communicating devices are within a range of 20–50 m distance. The wired Ethernet and Wi-Fi connections like WLAN and Wi-Fi are the examples of these LAN aggregators. If the communication range of devices is within 20 m range, Wireless Personal Area network is used. Bluetooth, ZigBee, and 6LowPAN are the examples for it. But in some scenarios if there is no provision of Bluetooth or LANs, the smart devices will connect to the Internet directly to the internet through a WAN (wide area network) interface. The gateways involved to collect all the information to these networks are carried by microcontrollers and processors that have support to the network with various Wi-Fi modules like radio communication module and access point. Along with these, the gateways need the support of embedded/OS support, encryption of data, etc. Management Service layer: It is responsible for information analytics, security control, modeling the process, and management of devices. This layer even supports the periodic IoT sensor data filtering which means if the data is obtained for every regular [12] intervals of time, required information from the sensors is to be obtained by applying the filtering process. Similarly, the aperiodic events are also processed and serviced by these layers. IoT application layer: This layer is responsible to enhance various applications from industry sectors like environmental, energy, transportation, health care, fleet management, asset management, etc., as shown in Fig. 6. This layer is classified by considering the business model, type of networks used, and availability of resources, coverage, size, and real-time and non–real-time requirements. Based on the previous section, the detailed layered structural design of smart parking system is shown in Fig. 7. The architecture is designed with four layer, namely application layer, network layer, transport layer, and physical layer. Fig. 6 Classification of application sectors based on size, connectivity, and bandwidth of network (source from: [9])

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Fig. 7 The detailed layered architecture of smart parking system (source from: [9])

• Application Layer It provides the interaction of user with the system either through web-based (PC or computer) or mobile-based application (IOS or Android). The participants will reserve the preferred parking locations by searching from the applications. Similarly, the service provides will allot the parking slot via the integrated platform. • Network Layer It enables the communication sharing among the parking centers, integrated system, and users. It supports various communication technologies like LAN and WAN that would be utilized by users, parking centers, and IoT devices like parking sensors and security cameras. It fetches circulated public ledger and content services flawlessly to the concerned stakeholder doorstep as part of the standard wireless communication technologies (e.g., Lora, Bluetooth, Wi-Fi) and supports other available GSM, LTEbased technologies, such as 4G and 5G. It provides the scalability to the integrated system by dynamically adding and deleting the stakeholders whenever required. It even ensures the security of physical layer. • Transaction Layer The data transaction between the nodes is ensured here. All the stakeholders can exchange the data here in a secure manor by making use of secure protocols and block chain technologies. It validates new transactions. Other features include transparency in transaction and secure data transmission are handled independently. • Physical Layer

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This layer ensures the combination, connection, and communication of various types of IoT devices to communicate. All these devices are connected through a common p2p network protocol. Various categories of things like sensors and actuators are the main components of this layer. Along with these, the embedded integrating technologies are Raspberry Pi and Arduino, and some WSN devices are included here. The IoT device’s data from physical layer is transmitted to the parking center server through the transaction layer. The p2P network will connect all these parking centers and updated the information to the public ledger. It also enables the traceability of sensor, actuators traceability, and responsibility of data over the p2p network. A particular parking slot in the system is recognized by the IoT device sensor of the physical layer. From the above discussion, the smart parking system based on block chain technology is shown in Fig. 8. There are three stakeholders are shown in the proposed system: block chain network, parking service provider, and user. The parking service provider updates the info to the integrated system. It includes the information like parking space updates and offers information, etc., the block chain network is formed with a public ledger. It will update the public ledger with the legal transactions only. The transactions verified by consensus mechanism. Each participant will communicate about the parking system slot through a separate application interface that was initiated by the integrated parking system individually. Each participant can be communicated with the integrated smart parking system through an individual application interfaces.

Fig. 8 Integrated smart parking system using block chain technology (source from: [13])

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From the diagram, the system is considered, and the smart parking system to a city that consists of space where multiple cars can be parked. The system used block chain with a secure-immutable storage. It allows for the IoT devices to transmit the data safely and securely. The user initiation is verified through cryptography technology by the integrated system, and vacant parking information is informed to the public ledger. The cryptography certification method done at the transaction layer will be handled by the smart contract. The user request at the application layer is processed through the network layer. From each individual parking space, the data will be updated to the distributed ledger through consensus mechanism as shown in the figure. As shown here, each parking space is connected with one parking service provider and is integrated as smart contract based on block chain technology. Every parking has local ledger which will maintain two types of transactions. The first one is about parking sensor [14] data information (assume a parking sensor is attached at each parking space and is equipped with an IoT device). This data will indicate the parking space availability as a transaction. For every change in the occupancy, “occupied” or “vacant” is indicated as a transaction, which will be communicated to the local block. This will be sent to block chain network for verification by the local block. The second data is related to the parking prices. Consider these prices are sent by the parking service providers based on the time allotted for parking. A separate smart contract is generated and is transferred to the block chain network. Whenever there is a change in the price of parking slot, a new contract will be generated and each new one is verified by the block chain network. The transaction is validated by the block chain network through consensus mechanisms. If the transaction is a valid one, it will be updated and stored at the public ledger and all other local blocks are updated similarly.

5.2 Requirements for Smart Parking • The system needs to include cloud-based IoT management system to support the parking lot that is located near to any commercial shopping complex or departmental store that can manage nearly 400 parking spaces. • The system needs to provide high-quality user experience. • It needs to support various kinds of payment methods: third-party payment, credit card, easy card, and cash. • The system should reduce the costs of management and labor. To incorporate the above requirements, the main objectives of the parking system that is developed is given below: • Implementation of a toll collection management system, a parking guidance and car-searching system, and a smart lighting control system.

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• It is needed to install a smart car-searching kiosk, a license plate recognition gateway machine, parking guidance signs, and wireless sensing devices for each parking lot. • Inclusion of device to cloud-based architecture to connect the smart parking service with the cloud for remote management and monitoring. • The system needs to support the configuration adjustments and new interface design.

5.3 Connected Smart Parking System with PAAS Cloud Platform The smart parking service connected system is divided into 4 parts: the low level layer is for sensor part with the smart edge application, the data collection gateway system handles the vehicle access and leaving process. The next layer is for controlling various functions like automatic [15] lighting control, parking grid sensing, and providing the parking entrance guidance to the parking slots. The third layer fills a connection between devices and applications and is implemented here with the WISE-PaaS cloud platform; and the final layer was for responsible for various applications developed at the system integrator. The smart integration parking system shown in Fig. 9 is a solution from edge-tocloud, with the bottom layer implementation of three industrial workstations ITA1711, MIC-7700, and PPC-3150. These devices are small in size and utilize less space and are good in heat dissipation. The second layer with parking spots consists of Wizard sensor node and the gateway, WISE-6610. To save the cost, these gateways are configured with wireless. The third layer is SaaS applications through the database, WISE-PaaS/EnSaaS database. It allows the programmers to easily develop their applications, dashboard features, and IoT hub. With the assistance of the cloud message, the WISEPass/EdgeSense remote control feature help in handling the bottom layer devices and users. It even supports to restore the advanced hardware products distantly without visiting the site place. This even reduces the costs and system maintenance more quickly and convenient. The operating procedure of the system was as follows: from the bottom layer the devices collect the data and transferred it to cloud platform through Ethernet and that information was forwarded to three different SaaS applications at the upper layer. When a driver is entered into the parking lot, gates will be opened and the signs of LED in the parking lot will guide the car driver toward unoccupied parking spots. The location of parking slot is initiated by the facilities of lighting that result from the sensing system. The abnormality information is received by real-time field devices for example when an irregularity occurred (such as the gate is blocked), the control room is alerted immediately and the concerned staff will resolve the problem. The integrated system fills the requirement of sensing the vehicle access, collecting toll, identifying the parking space, cloud platforms connectivity and human to

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Fig. 9 Advantech’s SRP for this project Integrated smart parking (source from: [16])

machine interaction, and other interfaces. For example, the MIC-7700, is included with four PCI expansion slots. These are allowed as gateway to license plate recognition and to retrieve eTag data from RFID signals. The ITA-1711 connected up to 14 COM ports so that other automated applications like machine paying bills, credit cards, or coins. Advanced wireless technology like LoRa is used for more options. The other third-party features like enquiry about parking space can be obtained from the public, private cloud platform architecture like WISE-PaaS. Thus, the developed cloud-based Smart IoT devices for interested parking system will increase the revenue and reduces the management costs and labor costs for parking users. The customer fulfillment can be achieved by multi-payment mechanisms and convenience in allotting the empty parking slots very quickly.

6 Smart Parking Systems Applications As discussed before, the automatic parking systems will reduce the usage of land and improve the parking space usage. These systems are completely automated with restricted access, so parking vehicle through this system is high secure than the traditional systems. This even reduced the engine emissions and accumulates the driving time during peak hours.

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6.1 Smart Sensor System for Car Tracking The advent of IoT technology and GPS or OBD sensors helps in tracking the vehicles. These sensors will collect the data from a vehicle of fleet to identify the occupied places in the parking places. The information is transferred to cloud through the network server. This data is used by the drivers of corporate organizers of fleet management system remotely. This technology can be extended to car drivers with the upcoming advance technologies like 5G.

6.2 Counter Systems for Smart Car The smart IoT-connected car system can sense entrance of a vehicle and exit of it. This let the parking facility with counting system for vehicles. Facility managers can train their counter systems to efficiently raise the parking facility during the important or peak times. It determines the patterns and trends about flow of customer and able to guess the vehicle rushes in future times.

6.3 Sponsored Meter Time Extension Notification to the user about the parking space expiry is indicated to the drivers with the connected platforms by using the parking meter. The automated parking meter will extend the services by reducing the traffic violations and increase the income through smart parking. It even extends the parking service if the driver likes to extend their parking slot time.

6.4 Identify the Safety of Parking Spots Parking violation is main concern in urban life and this leads to many accidents too. The smart parking platform will identify the driver about red zones of bus stop, passenger loading, unloading spots, and parking spots to handicapped people. These alerts will reduce the number of parking violations too. If the owner of a vehicle still parked his vehicle in a no-parking zone, the integrated interface will immediately report to the concerned department about the driver of that vehicle and it reduces the process of violation by adding penalties and let the parking process happen smoothly.

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7 Case Study on IoT Parking System Through Mobile Application In the previous segment, we covered about the architecture and technical stack of smart parking system. This section will deliver the development and working of it through the following case study. A parking IoT management system in parking can be a one-stop shop that connects the drivers, parking facility managers, law reinforcement organs, and other investors through a single network. To achieve this, an IoT-based integrated parking supervision system has to facilitate the very recent technology offers to smart parking. The following flowchart Fig. 10 shows the complete process of booking a parking slot for vacancy and occupied. The system will check the accessibility of the parking space to a car at each stage. From the flowchart, the driver will open the application and book for the slot. He pays the charge based on this parking time in the area. The application provides the identification, parking time, and payment details as an e-copy. All these details are updated into its cloud database. When the driver reaches to the parking slot, the driver parks the car in the allotted slot. During his entire parking period, once parking time over notifications will be informed to the owner of the vehicle as an attachment. The driver even can extend his parking time if he needs to, otherwise he will leave the car from the parking slot. Then, the empty slot information will be updated into the parking software system.

Fig. 10 Flowchart of the system smart parking system (source from: [5])

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This scenario implementation is done at shopping mall. To book a slot to this, the driver needs to use the parking application system. The details of the driver app are explained by the following diagrams. Booking the parking slots is explained with the above three figures. The presence or vacancy of the slot is indicated by booking a parking slot screen as shown in Fig. 11. Here, the screen is shown as A1, A3 are vacant and A2 is occupied. The driver opted for A1 slot and then the next screen of selecting the duration is opened as shown in Fig. 12. The driver will choose the duration; finally, the confirmation screen is opened as shown Fig. 13. If the driver fails to indicate his confirmation in next 30 s, an alarm will be generated. Similarly if the owner parked his vehicle in wrong place, it is also indicated to the concern authorities. In case the driver extended the parking time, an alert would be send to him and to the parking authority. The driver is provided the option to extend his time with his extra payment details as an alert. If the driver not adheres to this option, the parking Fig. 11 Booking a parking slot (source from: [5])

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Fig. 12 Selecting the amount of time (source from: [5])

authority will charge extra money as a fine. This fine will be collected from the driver when he is leaving the space.

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Fig. 13 Occupancy confirmation check (source from: [5])

References 1. Steve Mazur,https://www.digi.com/blog/post/introduction-to-smart-transportation-benefits. Business Development Director, Government, 09 Dec 2020 2. https://www.nec.co.nz/market-leadership/publications-media/what-is-smart-transportation/ 3. https://www.softwebsolutions.com/resources/smart-parking-iot-solution.html 4. https://www.plasmacomp.com/blogs/benefits-of-smart-parking-solution/ 5. A. Khanna, R. Anand, IoT based smart parking system, in 2016 International Conference on Internet of Things and Applications (IOTA). Maharashtra Institute of Technology, Pune, India, 22–24 Jan 2016 6. https://www.sciencedirect.com/science/article/pii/B9780128034545000146 7. https://parksmart.gbci.org/what-smart-parking 8. https://www.digiteum.com/iot-smart-parking-solutions/ 9. J.-M. Chung, IoT (Internet of Things) Wireless & Cloud Computing Emerging Technologies Course (School of Electrical & Electronic Engineering Yonsei University, Coursera). Link to access https://www.coursera.org/learn/iot-wireless-cloud-computing 10. V. Neelanarayanan, R. Gayathri, Cloud computing, in Cloud Infrastructure and IoT by Dr Shola Usharani (CBA Publishers, 2021)

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11. M. Chandran, N.F. Mahrom, T. Sabapathy, M. Jusoh, M.N. Osman, M. Najib Yasin, N.A.M. Hambali, R. Jamaluddin, N. Ali, Y. Abdul Wahab, An IoT based smart parking system, in International Conference Computer Science and Engineering, 2019 12. T. Giuffrèa, S. Marco Siniscalchia, G. Tesoriere, A novel architecture of parking management for Smart Cities, in SIIV—5th International Congress—Sustainability of Road Infrastructures (2012) 13. S. Ahmed, Soaibuzzaman, M.S. Rahmany, M.S. Rahaman, A Blockchain-Based Architecture for Integrated Smart Parking Systems, 20 Nov 2019 14. S. Rupani, N. Doshi, A review of smart parking using internet of things, in 3rd International Workshop on Recent Advances on Internet of Things: Tecchnology and Application Approaches, November 2019 15. J. Guerrero-Ibáñez, S. Zeadally, J. Contreras-Castillo, Sensor technologies for intelligent transportation systems. Sensors 18, 1212 (2018). https://doi.org/10.3390/s18041212 16. https://www.advantech.com/resources/case-study/building-a-cloud-connected-smart-parkinglot-with-advantech-solution-ready-packages-for-a-boutique-department-store-in-taipei%E2% 80%99s-xinyi-district

Smart Door Locking System S. Rajarajeswari and N. Hema

Abstract A well-made traditional front door lock will be enough when it comes to securing the front entrance. Its inner mechanisms have been developed for nearly two centuries and are tried and true. However, considering this: A “smart door lock” can offer totally new dimensions to the ease, utility, and security of a lock thanks to technological advancements. Some of the benefits of having smart door lock are as follows: considering the following scenario: As a person approaches the front entrance with arms full of shopping, the door lock identifies his/her smartphone and unlocks instantly. When a person is not at home, he might want to give access to relatives, friends, guests, or service providers, so he can simply text them a code. When a person is away from home and wants to know who opens the door or be notified when someone does, a smart door lock can do all of these advanced features. A mix of wireless technologies such as Bluetooth and Wi-Fi is used to control smart lock using Smart Lock Application from a place wherever Internet access is available. In this chapter, various smart lock systems using Bluetooth, Android, and Arduino are analyzed. Keywords Smart door lock · Arduino · ZigBee · Android · Microcontroller · IoT

1 IOT-Based Digitized Smart Door Lock System The goal of this work is to study and assess the appropriate set for developing a smart door lock that provides high security, quick access, and control. The privacy and security of IoT systems is a significant factor in this smart door lock project. However, this work will give a thorough research into the privacy and security of IoT systems, with the goal of refining the door lock mechanism by linking this to the Internet, thereby building it further strong, productive, and innovative. S. Rajarajeswari (B) · N. Hema Vellore Institute of Technology, Chennai, India e-mail: [email protected] N. Hema e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 N. Marati et al. (eds.), AI Enabled IoT for Electrification and Connected Transportation, Transactions on Computer Systems and Networks, https://doi.org/10.1007/978-981-19-2184-1_5

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The purpose of the work is to build an IoT system that incorporates the Smart Door Lock application. The system would be safe and simple to use. The subsequent sub-goals are assigned to the primary goal: 1. 2. 3. 4.

Designing a security and functionality-focused architecture. Using Bluetooth, establish a reliable method for determining if a person is physically close to the door lock. Establishing an appropriate policy for authenticating users attempting to gain access to the door. Developing an Android application as a user interface.

1.1 Test Environment Because the smart lock is exposed to considerable traffic during the day, testing it with a partially functional lock is not ideal and will yield inconsistent results. Instead, it was decided to construct a test environment that mimics a real-world setting and allows the product to be thoroughly tested during the project’s development phase.

1.1.1

Choice of Microcontroller Unit

There are a variety of microcontrollers (MCUs) on the marketplace which will meet the hardware requirements. There is a need for secure and reliable MCU that can stay connected to the Internet. The Arduino hardware is a well-known option with a wealth of technical details and a simple Internet setup. The Arduino, in contrast, is aimed more at the hobbyist and leaves a lot to be desired in terms of security. Instead, the Particle Photon gadget is selected for the smart door lock system. The Particle Photon is a tiny but dominant Internet-of-Things device that can communicate over Wi-Fi and Bluetooth. Whereas the Photon has many I/O pins as well as a processor capable of handling the logic required for this project. The Photon comes with a wellbuilt and powerful SDK that includes several tools for quick device configuration. With secure HTTPS transmissions [1] and token handling, all communication to and from the device will occur through the Spark cloud. Using Photon would increase the prototype’s security while also saving a large amount of money that would otherwise be spent on constructing a similar solution.

1.1.2

Choice of Bluetooth Beacons

The hardware structure of a Bluetooth beacon is relatively straightforward. The beacon’s sole purpose is to broadcast a Bluetooth signal to the environment. The beacon have to be adjustable to the extent where an administrator can change the beacon’s broadcasting data, such as the beacon’s transmitting power and ID tag.

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The Bluetooth beacons meet the requirements and provide all of the capabilities required for the design in progress. The Beacons come with a dependable setup interface and also a well-documented SDK for a variety of systems. Third-party Bluetooth transmission protocols are supported by the beacons, giving developers more options.

1.2 Experimental Design This part presents a test environment for evaluating the system’s development and enhancement.

1.2.1

Controlling a Smart Door Circuit with a Relay

A relay consists of two circuits: one for control and one for load. Once the control circuit is getting activated, current passes through a coil, which forms a magnetic field. This magnetic field attracts the framework and then closes the load circuit. A relay may control multiple circuits with a single signal. Relays are active when a low power circuit wants to manage a high voltage or high power circuit [1]. Like Microprocessors and any low power devices can activate relays to manage electrical loads in addition to the direct drive.

1.2.2

Test of MCU

The Photon MCU was used to test the design’s basic functioning. On the microcontroller itself, a test program was created to operate an LED. Then, using a relay to manage the high voltage flowing from the source, a test environment was built up on a breadboard to regulate an external LED. The signal would be interpreted by the microcontroller to decide whether the LED should be turned on or off (Fig. 1).

1.2.3

Schematic Representation of the Smart Door Lock

See Fig. 1.

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Fig. 1 Schematic representation of the relay [2]

2 Security in Smart Door System Using Arduino and Bluetooth Recently, the quality of home security systems has been deplorable. These research projects include a smart door locking system, which is an excellent way to improve house door safety management. The smartphone, the microcontroller, and the door lock were connected using Arduino IDE software and a Bluetooth module hc-05 to allow authorized people simple access. Installing the required application with an open/close button allows the person with the authorization to open the door to have access at their fingertips. The hc-05 not only acts as a receiver and transmitter, but also connects with the microcontroller, which acts as a processing unit in this project, determining whether the user’s password is correct or incorrect and then sending commands to the servo motor to open/close the respective door. The user can gain entry to the door if the password is correct, but if the password is wrong, the user will have no access at all. Main entrance doors are being made safer and more secure.

2.1 Methodology The model was created in such a way that it can be maintained in a secure location within the house. This is accomplished in the simplest and most cost-effective manner

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Fig. 2 Block diagram [3]

feasible. The system, in contrast, is adaptable and may be altered for future improvements. Changing the configuration of one of the components must be compatible with the appropriate software. Every component in this system was individually programmed and tested for safety and compatibility with the correct driver. Each component was written using a distinct Arduino IDE and an Arduino UNO. They were also run on many machines. All of these were eventually integrated into a single Arduino IDE. The system IDs represented in Fig. 2.

2.1.1

Input Unit

This is the unit where a command is delivered to begin the execution of a program, and in this case, the smartphone acts as the device for providing commands as input. When a connection is made between the device’s Bluetooth and the Bluetooth module, the mobile smartphone transmits a signal (HC-05). The smartphone delivers the input command using a built application that includes a lock and unlock slider, allowing the user to either lock (close) or unlock (open) the relevant door. When a Bluetooth connection is made between the device and the module, and the device is operated within the Bluetooth range limit, the input command can be executed.

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Receiver Unit

The receipt of command signals provided from the input is the primary function of this unit. A Bluetooth module receives the command issued by the smartphone and transmits it to this equipment (HC-05). The module can also be used to connect a mobile device to an Arduino microcontroller.

2.1.3

Processing Unit

This unit determines the project’s output, as well as what command to run, how to run it, when to run it, and where to run it. The Arduino microcontroller [4] is responsible for processing the project’s commands. The Arduino gets a command from the mobile smartphone via the Bluetooth module, and the microcontroller determines what function to perform and how to perform the task, as well as providing the appropriate output for the task. The hardware (which is the Arduino board) and the IDE (integrated development environment), which is a software application that runs the entire operation and how the circuit works, make up the Arduino, which serves as the main processing unit. Smartphone Application In this study, Android app is used to control all of the household appliances. From an Android phone, any household appliance can be selected from the app’s settings and then choose whether to open or close it. The app cannot be used outside the house unless it is within the Bluetooth range of the Bluetooth module. It enables Bluetooth support devices to establish point-to-point connections. Android support for the Bluetooth network stack, which allows data to be exchanged wirelessly, is known as this technology. The Android Software Development Kit (SDK) includes all of the tools needed to create Android apps (API). This application is written in Java. The application is installed on Android using an .apk file. Android Studio is used to write the code. The code is written to match the appearance of the phone’s options. To open device lists; Important android widget button; Important android widget List View; To create variables for Bluetooth: Private Bluetooth Adapter my Bluetooth = null; Private Set paired Device; After initialization, The code starts with initializing characters as “String”. 2.1.4

Output Unit

On receiving the command from the microcontroller, this is the final stage of the approach. The task at hand could be to either open or close the door. The equipment

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that performs the work in this unit is a servomotor. When the servomotor receives an open instruction from the microcontroller, it moves to ninety degrees, which raises and opens the slider, and when the command is to shut, the servomotor moves to one hundred and eighty degrees, which lowers and locks the slider, closing the door. All of this action is carried out and recorded in the Arduino-integrated development environment, as well as how the task will be carried out. The process flow is represented in Fig. 3. The total power requirement of this project is 5 V. The power supply unit consists of a rectifiers, 240 v/12 v step down transformer, voltage regulator, and filters (Figs. 4 and 5). Transformer Section Using a transformer, the 240 v ac is stepped down to 12 v ac. The resultant output is given by Transformer rectifier filter regulator 240 V 50 Hz input

Fig. 3 Power supply unit [3]

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Fig. 4 Supply block diagram [3]

Fig. 5 Power supply circuit [3]

12 V/5 V DC. Rectifier Section A full wave bridge rectifier of 5 A is selected for the capacity to a load up to 2 A with the help of IN4001 diodes. The DC value of the rectified voltage is given by Vdc = (2/π ) × V p The maximum load current is given by Hence, the Average load current can be obtained from; Hence, due to standard and transformer size, the final transformer specification chosen was transformer. Filter Section The ripple voltage is represented by the equation below. Therefore, the filtering capacitor is calculated as shown below, a peak-to-peak ripple of is chosen, i.e., 0.01 is approximated. Hence, the ripple factor is 0.01 The shunt capacitor filter is obtained from;

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Fig. 6 Voltage regulator [3]

Therefore; where I dc = current taking by the load (mA); f = frequency of supply (Hz); C = shunt filtering capacitor (microfarads); and = rms value of the ac component ripple voltage and, I dc = 449.72 mA, r= 0.01, F = 50 Hz, and, Hence, due to standard and capacitor size, the final capacitor specification chosen is. Voltage Regulation Section 7805 IC Rating • Input is voltage range (7–35 V) • Recent rating • Output will be the voltage range of V min = 4.8 and V max = 5.2 V (Fig. 6). 2.1.5

Soldering

Soldering is the process of connecting specific metals using a soft solder to create a strong electrical and mechanical connection. This is a lead and tin alloy with a low melting point. A soldering iron is used to heat the joint to the proper temperature. Miniature mains-powered soldering irons are utilized for the majority of electronic work. These have a handle on which the heating element is attached. The “bit” is located at the end of the heating element and is responsible for heating the joint. At roughly 190 °C, solder melts, and the bit reaches temperatures of above 250 °C. Because this temperature is enough to cause a terrible burn, caution is advised. Soldering is a skill that may be learned via practice. The most crucial factor to remember during soldering, both parts of the junction must be at the same temperature. Only when both parts of the joint are at the same high temperature, the solder will flow uniformly and produce a satisfactory mechanical and electrical joint.

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2.2 Result The program for the Arduino Uno microcontroller was written in C and then compiled with the Arduino IDE into an executable file. The executable file was then imported into the Proteus Construct Suite, which was used to design and simulate the hardware circuit illustrated. The Proteus simulation of the door security system outcomes for each process of entering the correct and incorrect passwords is shown in the figure. After the software simulation was completed successfully, the system’s hardware was built on a bread board and the Arduino microcontroller was programmed using the Arduino IDE. The hardware configuration, which includes 29 connections, as well as the system’s numerous activities, is depicted in the diagram below. When interacting with the Arduino board, the security door’s hardware responds.

3 Smart Digital Door Lock System Using ZigBee A digital smart door lock system replaces the traditional key system with technology that includes digital information like a secret code, semi-conductors, fingerprints, and smart cards for authentication. In this suggested system, a ZigBee component is placed inside a digital smart door lock, whereas the door lock behaves as the total home automation system’s principal controller. This system is a network which consists of sensor nodes and sufficient actuators in addition to a digital door lock serving as the base station. The RFID reader is used for authentication of user. motor module and a touch LCD are used to open and close the door, sensor modules are used for sensing the context with in the home, control module and communication module are used for controlling, and further modules are all part of the door lock system proposed here. Sensor nodes for environmental sensing are placed in strategic locations throughout the house. The digital door lock, the centralized controller, can monitor and control the status of each ZigBee module. Because the door lock could be the first thing and last thing which people see when entering and departing the house, the automation feature in a digital door lock system allows users to observe and regulate the whole home environment at one time before entering or departing. It allows users to check the status of the house remotely via the Internet/any public network. The most significant benefit of this system above existing systems is that this system can be quickly and simply built whenever and wherever it is needed, without the need for any infrastructure or careful design.

3.1 Proposed System The process of the ZigBee module, the sensor module, and the digital door lock is followed by a brief overview of the proposed system. In this system, the ZigBee

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module as the communication module and the sensor node acts as the integrated node that includes the sensors, actuators, ZigBee module, and other auxiliary circuits.

3.1.1

Overview of the System

A digital door lock system allows you to keep track of and operate numerous gadgets in a home environment. Wireless sensor network is used to control digital door lock system. As shown in Fig. 1, it comprises sensor nodes associated with a digital door lock as the sink node. The motor module, the control module, the sensor module, the I/O module, and the communication module are the important five components of the digital door lock system. The control module is made up of a microcontroller (MCU) placed in the digital door lock that serves as the system’s brain. The motor module is in charge of the locking mechanism. The communication module allows devices and the control module to communicate with one another. Through the I/O module, the user can have right to use the door lock system. The I/O module features an RFID reader and digital dial pad for authentication, as well as a TFT Touch LCD is used to control and display the information. The user will monitor and operate the household applications from the centralized control panel once the system has verified their identity. The door lock has a microphone, camera module, and speaker to engage with the guest. On both sides of the door, there is a touch LCD. Through these devices, the user can simply observe and communicate with visitors on the next side of the door.

3.1.2

ZigBee Component

The RF communication module in the ZigBee module is employed in smart door locks and sensor nodes. The structure of a ZigBee module linked to a home appliance is shown in Fig. 7. ZigBee transceiver and MCU are the two primary components of a ZigBee module. The ZigBee transceiver uses a commercial RFchip with a modem for implementing the IEEE802.15.4 MAC (Medium Access Control) and PHY (physical layers at 2.4 GHz). MCU-microcontroller unit is a controller that can control a ZigBee transceiver and run applications [5]. A program memory for developing the MAC, an application layer and a network layer, is included in ZigBee. The ZigBee stack architecture’s PHY and MAC layers follow IEEE802.15.4 standards; however, the application layer’s interface is designed by the ZigBee Alliance.

3.1.3

Digital Door Lock

Inside the digital door lock is the control module, motor module, and I/O module. Figure 8 depicts the structure of the digital door lock as well as the components’ connections. A core CPU, a ZigBee module, a CDMA module, a door lock controller, a camera module, a microphone, a card reader, and a speaker make up the digital

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Fig. 7 ZigBee module with home appliances [5]

Fig. 8 Structure diagram of smart digital door lock [5]

door lock. The control module is called as system’s brain. The control module serves two primary purposes. To begin with, it is in charge of the door lock. Second, it manages and supervises the whole network. The open or close button on the door lock controller opens and closes a digital door lock. The motor drive runs the motor as an actuator and is controlled by the control module. Authentication using cards and RFID tags is done with the help of a card reader. The touch LCD is used to

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input and change the password for authentication, change sensor node settings, and display pertinent details on the screen. In a digital door lock, the ZigBee module serves as a link between the control module and sensor nodes. The ZigBee module is used to transmit data between the control module and sensor nodes. SMS (Short Message Service) and MMS (multimedia messaging service) are utilized by the CDMA module to warn users of critical situations. Finally, before opening the door, a microphone, speaker, and camera module are employed to facilitate interaction between the guest and the user.

3.1.4

Sensor Module

Two major duties have been assigned to sensor nodes. The first task is to keep an eye on the surroundings of the house, and the next one is to change the power condition of household gadgets. Sensors are attached to the ZigBee module to monitor environmental conditions such as gas leakage, temperature, fire, burglary, and so on. As shown in Fig. 7, the ZigBee component is supported by a ZigBee relay unit in the sensor node for devices whose power status need to be managed. The ZigBee relay module is used to turn on and off household machines. The sensor nodes send their current condition and related data to the smart digital door on a regular basis. Sensor nodes also provide response messages that include operation outcomes, similar to how digital door locks communicate commands.

3.2 Functions of Smart Digital Door Lock System 3.2.1

Communication

The digital door lock system has two ways of communication. One is centralized, and other one is emergency. In centralized mode of communication, the digital door assumes command of the network’s whole communication, and sensor nodes respond as directed by the door lock. This form of communication is most commonly used in everyday situations while all is well. This type of communication style saves energy by reducing unnecessary communication between the central controller and sensor nodes. In case of an emergency scenario, like a burglary/a fire, communication is switched to emergency mode. When the sensor node detects an emergency mode, the appropriate action is done, such as spraying water in the case of a fire or turning on the buzzer in the case of a burglary, and the event is immediately relayed to the door lock without doing any action from the door lock. The door lock then sends an SMS or MMS to the end user to notify them of the event.

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Fig. 9 Activities in smart digital door lock system [5]

3.2.2

Smart Digital Door Lock System

The door lock will be unlocked and the LCD will display the condition of various equipment in the home once the user has been authenticated through RFID tag or password. The user has the option of changing the existing status of the appliances or leaving them alone. This system can function in two different modes either in manual or in automatic, for the simplicity of the end user. As shown in Fig. 9, a digital door lock system will detect three events: a person arriving into the house, a person is parting the house, and an emergency circumstance. On the basis of these events, both operational modes will be discussed.

3.2.3

Modes of Operation

Outgoing Event The flowchart for an outgoing event, such as a person leaving the house, is shown in Fig. 10. Because the digital door lock is the final thing a user sees before parting the home, when the user hits the door lock switch, the door lock asks all sensors to communicate their fresh state, which is displayed on the touch LCD. At first, the system operates in manual mode. Whereas in manual modes, customers can select

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Fig. 10 Flowchart for outgoing event [5]

which home appliance they want to switch on or off manually from a menu. In manual mode, consumers are no longer need to manually check the status of specific home equipment. He can leave his room in its current state and then choose which devices to turn off or on from the door. If the user notices that the television is turned on, he can switch it off from the door itself. The device goes into automated mode if the user does not touch the LCD screen for a particular time after locking the door. To make automated mode operate, we must first adjust the device’s priority setting. In the lack of a user, the priority setting determines which device should be turned on or off. Priority 1 indicates that the device should remain on, whereas priority 0 indicates that it should be switched off. As a result, the user selects which devices should be

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Fig. 11 Flowchart incoming event [5]

turned on and off while he is away using the touch LCD panel. This is a one-time procedure that can be repeated whenever it is deemed necessary. As a result, when the system arrives automated mode, it switches off all devices with a priority of 0 if they are still turned on. With the deployment of this mode, users will no longer have to be concerned about the power state of the devices in their rooms when they leave the house. When he leaves the house, he can leave the lights and television on. They are finally turned off by our system.

Incoming Event The flowchart for an incoming event, such as a person is entering the house, is shown in Fig. 11. Incoming events can be controlled manually or automatically. The mechanism unlocks the door once the user has been verified. The system then seeks the most recent conditions of all devices and checks for an emergency. The corresponding emergency message like burglary, fire, and numerous alert messages is displayed on LCD if there has been an emergency scenario. In any scenario, the

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user may observe the room’s state on the touch LCD. The present state of the home’s environment can be simply viewed on the LCD. In case of manual mode, the user can use the touch LCD to turn on and off individual devices. If he notices that the room is heated, he can use the LCD to turn on the air-conditioner. This system goes into automated mode if the user does not touch the LCD screen for a particular amount of time once the door is unlocked. There is a need to assign the priority of devices for automatic mode to work. The priority is not the same as it was in the last outgoing event. As a result, the system retains two levels of priority information for each device. Priority 1 indicates that the device should be turned on, whereas priority 0 indicates that it should be turned off. This is a one-time process that can be repeated whenever it is deemed necessary. As a result, when the system go into automated mode, it switches on all devices with a priority of 1 if they are still turned off. The priority of gadgets such as the air-conditioner and television can be set to 1 so that they turn on as soon as the user enters the house.

Emergency Event The system may be confronted with an emergency circumstance, such as a break-in or a fire. Figure 12 depicts the emergency flowchart for digital doors and sensor nodes. Figure 12a depicts a sensor node flowchart, where Fig. 12b depicts a sensor node flowchart. The digital door lock is referred to as Fig. 12b. When a sensor node detects an emergency scenario, the door lock receives the necessary information right away. Meanwhile, the sensor node activates the actuators to deal with the present emergency circumstance. After receiving notification of the emergency condition, the door lock will send an SMS to the user informing them of the situation. The alarm is also triggered by the system. When a sensor node detects a gas leak, it broadcasts the current status via a door lock and reduces the electrical power of appliances in house attached to the nodes by transmitting a signal.

Relay Node The home automation will be fulfilled with the smart digital home server. However, RF signal power attenuation is still a problem that occurs regularly in inside environments such as the house or workplace. This inconsistent RF transmission would be even worse at home, where identical frequency ranges are used by microwave ovens and consumer devices. ZigBee RF repeaters has been built in the entrances of each room to support consistent RF signal transmission, and these nodes can also be used to lock/unlock each room’s door.

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Fig. 12 Flowchart for emergency event [5]

3.3 Conclusion This work introduced a revolutionary ZigBee-based home automation system that merges home safety with the home automation in this study. Over the digital door lock, the suggested solution fully utilizes the complete capacity of ZigBee for observing and operating the house environment and status. Because the suggested system is based on a wireless sensor network, it is a low-cost, flexible, and simple-to-install system that does not require meticulous planning, wiring, or construction.

4 Android-Based Smart Door Locking System 4.1 Introduction Smart door locking systems have become increasingly advanced as technology has progressed. The smart door lock system based on Android is primarily considered

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for standard and multimode operations. In a bank or a business organization, such a system is essential. The system also includes features for general users, such as the ability to operate the lock by a single user. The system’s cost-effective implementation, combined with comprehensive capabilities and an intuitive user interface, makes it extremely helpful. Unauthorized access, trespassing, and intrusion are all prevented by an Android-based smart door locking system. Authorized access, trespassing, and intrusion are regular objectives at banks, corporate offices, financial institutions, jewelry stores, and government institutions. Typically, the goal of such acts is to steal jewels, money, or vital papers for personal advantage. The goal of the smart door locking system based on Android is to offer a wise result to these problems that is also practical. This approach is based on the concept of a predetermined password [6]. It raises the security level to prevent an adversary from unlocking the device without permission. In the event that a user forgets their passwords, the system allows them to modify or reset them. This automatic password-based lock solution allows users to lock and unlock the machine in a more secure manner.

4.2 Proposed System The suggested system’s key feature is its multimode functionality. The suggested system has two modes of operation which is given below: A.

Normal Mode

A single user can log in and make the system in this mode. It has the subsequent features: • The user can log in using the password that was previously set up. • The password allows the user to lock or unlock the door. • If he/she forgets his/her password, he/she can recover it using the e-mail address which is registered. • If the user believes the password is insecure, he can reset it. B.

Multiuser Mode

When two or more user is permitted to work the lock, this mode is useful. It has the following characteristics. • Individual users can log in using the password they created. • Each user can log in using his or her own unique ID and password. • If he forgets his password, he can recover it by sending an e-mail to the e-mail address which is registered. • If a user believes their password is insecure, they can change it.

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Assumption

Users are expected to control the system utilizing Android phones with Bluetooth capabilities and the Bluetooth HC-05 module placed in the system, whereas the app was created for Android smartphones and will not run on any further platform. The application can, however, be adapted for use on other platforms. It is assumed that the user will utilize it within the 10–100 m Bluetooth availability range. The system in Fig. 13 is developed using below mentioned hardware modules: A.

Arduino Uno Board

The control board in Fig. 14 connects with the Android apps and activates the door strike in response to the commands coming from the Android apps. B.

GSM Module

The SIM808 module in Fig. 15 is a comprehensive Quad-Band GSM/GPRS module [8] that also includes GPS for satellite navigation. Consumers can save money and time by implementing GPS-assisted applications since to the small design that combines GPS and GPRS in a single SMT module. With GPS capability and an Fig. 13 Proposed system [7]

Fig. 14 Arduino Uno Board [7]

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Fig. 15 GSM module [7]

industry standard interface, it permits variable resources to be tracked in real time at any place with signal coverage. C.

Electric Door Strike

An electronic lock (also known as an electric lock) is a locking device that uses electricity to operate Fig. 16. In this paper, an Electric Door Strike is employed as a locking device that operates using instructions from an Arduino Uno Board. D.

Bluetooth HC-05

The HC-05 module is a Bluetooth Serial Port Protocol (SPP) unit [9] that permits to build a clear wireless serial connection. This module is used to communicate among the Arduino Board and the Android apps that will be used to observe the lock. Fig. 16 Electric door strike [7]

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4.3 Conclusion As a result, the “Android-Based Smart Door Locking System” is a modern replacement for traditional door locks. This proposed system is incredibly cost effective and simple to mount, and it is constructed to work in a variety of modes, making it quite useful.

5 Smart Door Lock System Based on IoT and Mobile App In this study, the proposed approach leverages the application of smartphone technology an IoT technology to a conventional door lock device either to close or open a door remotely via authentication. This study, in specific, provides a security upgrade plan for smart door lock systems.

5.1 Proposed Method Traditional physical key security has problems with abuse caused by key damage, theft, and duplicating, and it is difficult to regulate who opens or closes the door. Using mobile app and security validation, the managerial and technical security of autonomous automated systems and equipment can be improved. This study seeks to present a security system that combines information security and physical security, which does not have physical key and opens/closes a door using security verification (Authentication), and also controls information (Logging). By establishing a connection among a smartphone and a security authentication server in a mobile communication network, the authentication-based centralized control system will control a door lock as an object in both wired and also wireless communication (Bluetooth, USB, and audio channel) based on IoT technology. Figure 17 depicts the system’s architecture.

5.2 Implementation of Authentication Server for Door Lock Security A relevant door lock have to be recognized by the security authentication server, which should then transmits a signal when the device release request came. Then, it releases the device. To perform this, the server must verify the door lock’s and a relay device’s authentication, as well as the adequacy of the release request as well as the signal of the device. For authenticating the door lock, the server generates an authentication key, which the door lock then encrypts using its own method. If the

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Fig. 17 System diagram [10]

server asks for authentication, then the door lock will send authentication key to the server. Then, the server compares the key values and then authenticates the user. In case of relay device authentication, the device will be registered in advance with the server and will receive an authentication number from the server, which is checked at the time of access request. The server employs encryption communication with the relay to safeguard information sent at the time of authentication and locking device information. To unlock the door, the smartphone must be connected to the door lock via a wired or wireless network, and the app must be engaged to communicate with the door lock. To identify the device, the mobile phone receives a device’s exclusive number from the door lock (Firmware) and sends mobile phone, device, and user details to the server for authentication process. The server records the lock release state and stops the communication when the locking device confirms the authentication which results to release the door lock.

5.3 Implementation of the Mobile Application for Controlling Door Lock The mobile phone is used to connect the door lock to the security authentication server via USB, voice, and Bluetooth connections. HTTP communication connects the smartphone to the server. The communication connection among the security authentication server and the door lock is handled by the mobile app. Following the

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connection of a mobile app to a door lock, the application will receive the door lock’s authentication information, will encrypt, and send it to the authentication server, which confirms the door lock’s authentication and provides back its authentication information to the mobile application. The server authentication information is sent by the mobile app to the door lock, which then transmits the information about the door lock to the mobile application and then sends the door lock information to the server. To ensure that the information processed by the door lock is correct, the server provides authentication. If the server’s authentication information is correct, the mobile app sends a lock release signal to the locked device.

5.4 Implementation of the Communication Unit Between Smartphone and Door Lock USB, audio signal, and Bluetooth communication are the three methods by which the smartphone and locking device communicate with each other.

5.4.1

USB Connections

When a smartphone is linked to a door lock using USB, smartphone acts as a USBhost, while the door lock acts as a target device. The component is used by the device to transform USBcommunication to a common communication so as to deliver signals to the smartphone, and it analyzes USBsignals received from the OS to retrieve device signals.

5.4.2

Audio Communication

When a digital signal comes from a smartphone is converted into an analogue sound signal and will be sent to a door lock, the door lock gets the analogue sound signal and then demodulates it to recover the initial digital signal. While the digital signal from the door lock is transferred to the smartphone, it is translated into an analogue sound signal and relayed via audio cable to the Android phone. The smartphone’s locking device release program then demodulates the analogue sound signal to extract data.

5.4.3

Bluetooth Communication

The smartphone’s Bluetooth unit allows it to communicate with the locking device using Bluetooth. The Bluetooth module is fitted in the door lock for pairing with a smartphone and transmitting signals with the smartphone.

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5.5 Implementation of the Embedded Board for Smart Door Lock Control In most cases, a solenoid is used to open or close a door. General door mechatronics is only engaged in the power supply to the solenoid. This study’s method involves the ability to interact between a door lock and a smartphone via wired and wireless connection. As a result, the interface element of the door lock control board must be developed, as well as the OTP algorithm, in order to meet the system performance requirements for driving the encryption method for data protection. The DC geared motor, the CPU board, motor control circuit, and sensor circuit make up the door lock control board. The board also has an external connection module that allows it to connect to a smartphone via cables (audio channel and USB) or wireless communication so as to communicate with an authentication server, and it receives authentication from the server in order to regulate the motor and to open or close a door. In addition, the input terminal’s ON/OFF function is utilized to switch on/off + 5 V output via the smartphone connection cable’s Plug On/Off function and also a circuit which controls CPU Wake-Up in the CPU board’s low power consumption mode.

5.6 Comparison Analysis In terms of security, convenience, and also scalability, Table 1 compares the smart door lock system in the aspect of security enhancement, the physical key system, and the digital door lock system. Based on the comparison analysis, the physical key system requires a master key and also all related personnel to have keys; the digital door lock system does not require a key; the smart door lock system needs a personal smartphone. As an outcome, the convenience of the digital door lock system and also the smart door lock system has increased. When it comes to scalability, the smart door lock system supports Bluetooth, USB, Audio Jack, and OPT. In terms of security, the physical key system allows for key duplication; the digital door lock Table 1 Comparison analysis [10] Category

Physical key system

Digital door lock system

Smart door lock system

Convenience

Master key, all related people are essential to have keys

Not necessary to have key

Usage of personal smartphone

Scalability

None

None

USB, Audio Jack, Bluetooth, OTP

Security

Key copying

Password leakage

Minimized risk through security authentication

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system allows for password leakage; and the smart door lock system, which is based on security authentication, can reduce security risk.

6 Summary and Conclusion Now that India is developing toward a digital world, an application called “smart digital door lock system” can let working people keep control of their homes from outside at any time. The IoT-based central control system connects a smartphone to a door lock via wired and wireless connection methods such as USB, Bluetooth, and audio channel and permits the smartphone to access a secure authentication server via the mobile network either to open or close any door. The smart door lock system comprises a security authentication server, a communication module for smartphone, a mobile app for door lock control, and firmware and embedded panel for door lock control. It is a door lock system based on IoT that uses a smartphone instead of physical key to enhance security and increase convenience over secured authentication. Here, various smart door locking system with different technologies is discussed.

References 1. K. Sha, R. Errabelly, W. Wei, T. Andrew Yang, Z. Wang. Edgesec: design of an edge layer security service to enhance iot security, in 2017 IEEE 1st International Conference on Fog and Edge Computing (ICFEC), pp. 81–88. IEEE (2017) 2. IoT Security Applied on a Smart Door Lock Application Kristoffer Djupsjö Masar Almosawi, Examensarbete Inom Teknik, Grundnivå, 15 Hp Stockholm, Sverige 2018 3. U. Muhammad, Design and Construction of Smartdoor Security System Using Arduino and Bluetooth Application. Halliru 4. M.A.E. Mowad, A. Fathy, A. Hafez, Smart home automated control system using android application and microcontroller. Int. J. Sci. Eng. Res. 5(5), 935–939 (2014) 5. Y.T. Park, P. Sthapit, J.-Y. Pyun, Smart digital door lock for the home automation, in Published 2009 Engineering TENCON 2009–2009 IEEE Region 10 Conference 6. M. Sahani, C. Nanda, A.K. Sahu, B. Pattnaik, Web-based online embedded door access control and home security system based on face recognition, in 2015 International Conference on Circuits, Power and Computing Technologies 7. A.V. Patil, S. Prakash, S. Akshay, Mahadevaswamy, C. Patgar, A.J. Sharath Kumar, Android based smart door locking system. Int J Eng Res Technol (IJERT) NCESC–2018 6(13) (2018) 8. B. Pandurang, J. Dhanesh, S. Pede, G. Akshay, G. Rahul, Smart lock: a locking system using bluetooth technology & camera verification. Int. J. Tech. Res. (2013) 9. R.D.H. Arifin, R. Sarno, Door automation system based on speech command and pin using android smartphone, in 2018 International Conference on Information and Communications Technology (ICOIACT), pp. 667–672 (2018) 10. J. Jeong, A study on the iot based smart door lock system, in Information Science and Applications (ICISA) 2016. Lecture Notes in Electrical Engineering, vol 376, ed. by K. Kim, N. Joukov. Springer, Singapore (2016). https://doi.org/10.1007/978-981-10-0557-2_123

Privacy and Authentication Schemes in VANETS Using Blockchain: A Review and a Framework to Mitigate Security and Privacy Issues Farooque Azam, Sunil Kumar, and Neeraj Priyadarshi

Abstract Technology advances through time. Telecommunications and wireless technology are pioneers among the emerging technologies. Vehicular Ad hoc Network is the most progressive and foreseen research field under wireless communications as they are able to provide a large variety of ubiquitous services. They are a growing technology which provides a vast range of safety applications for the vehicle passengers. With an increase in such services, there will be an increase in the vulnerabilities which could be compromise the VANET communication. Successfully defending against such VANET’s attacks is continuously under research and growth. Blockchain offers decentralized, distributed, collective maintenance to counter malicious attacks. In view of the aforesaid issues, in this paper a dedicated discussion of various research works related to privacy and authentication schemes in VANETS using Blockchain has been made. At last, a framework based on consensus algorithm have been proposed for secured dissemination of messages. Keywords Blockchain · Security · Privacy · VANET

1 Introduction VANET is a sub-implementation of Mobile Ad hoc Network (MANET) which does not rely on a pre-existing infrastructure for communication. VANET resembles MANET but with some changes. Vehicles moving on the road and communicating with each other build a network known as Vehicular Ad hoc Networks (VANET). The main components of a VANET are (i) vehicles which are embedded F. Azam (B) · S. Kumar Department of Computer Science and Engineering, Sangam University, Bhilwara, Rajasthan, India e-mail: [email protected] S. Kumar e-mail: [email protected] N. Priyadarshi Department of Business Development and Technology, CTiF Global Capsule, Aarhus University, 7400 Herning, Denmark © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 N. Marati et al. (eds.), AI Enabled IoT for Electrification and Connected Transportation, Transactions on Computer Systems and Networks, https://doi.org/10.1007/978-981-19-2184-1_6

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with onboard unit consisting of various sensors, tamper-proof device (TPD), etc., (ii) the road side unit (RSU) that are deployed on the intersection of the street/road as the mode of communication between vehicle for exchange of messages, and (iii) trusted authority which supervise the overall communication and ensure security, privacy, and trust. Communication between vehicles is V2V communication while communication between vehicle and RSU is V2I communication. This communication takes place by using DSRC (Dedicated short range communication) and WAVE (Wireless Access for Vehicular Environment) standards. The DSRC signal allocated by Federal communications commission (FCC) is 75 MHz spectrum in 5.9 GHz band for ITS (Intelligent transportation systems). VANET is a progressive field under Intelligent Transport Systems. VANET offers a plethora of applications ranging from safety, traffic-related information to the paying of toll fees, information regarding EV charging when the battery drains, weather forecasting, nearby restaurant, movie theater, and many more. Usually, the communication between V2V and vehicle to RSU takes place using DSRC signal while RSU communicated using 4G/5G technology [1–3]. Various benefits as well as the growth of autonomous intelligent vehicle attracted both academia and industry for its successful deployment. In V2V communication, if vehicle “X” wants to send a message to vehicle “Y” to alert it for bad weather condition, then there must be a mechanism to prove the message from “X” is legitimate and communication is secure. Also, authenticity of both vehicles must be checked prior to communication. Privacy is a personal choice of revealing to someone. Thus, privacy preservation is also a major goal of VANET design. Various authentication schemes have been developed by researchers for mitigating the security and privacy attack. In [1], authors have discussed various authentication schemes with its merits and demerits.

Fig. 1 Demonstration of a PKI-based system [1]

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Numerous public key infrastructure-based protocol have been proposed. Figure 1 depicts the working of a PKI-employed system. Public key infrastructure is a type of framework which associates public keys to the identities of node. This is done through a certificate authority issuing certificates to the legitimate vehicle/RSU during the time of registration along with public/private key pairs at the time of registration request with the registration authority (RA). Private keys are employed for signing the message while the public key is used for the verification purpose. Blockchain can be an influential aspect in solving the security problem faced by VANETS. Blockchain is a type of distributed technology which consists of cryptographic and hash functions to store data in a chain to ensure tamper resistant data, and it also offers a decentralized, immutable, and transparent database that store transactions and records in a highly trusted and secured way. It functions as a digital ledger and a mechanism enabling transfer of messages without an intermediary. Regardless of the type of protocol that is deployed, Blockchain holds a promising value in paving way for great levels of security in VANET.

1.1 Objective of the Manuscript In this paper, following details has been highlighted: • Some of the recent works carried out using Blockchain in vehicular networks. • Characteristics of Blockchain that makes it suitable for Vehicular Ad hoc Networks. • Some privacy and authentication-related schemes in VANET using Blockchain. • A framework to have secured communication using Blockchain has been proposed.

1.2 Organization of This Paper The paper is summarized as follows: In Sect. 2, some of the recent works carried out using Blockchain in VANET. Section 3 gives basic architecture of VANET. Section 4 briefs concept of Blockchain in VANET. Section 5 discusses Blockchain-integrated VANET and some of the most recent works carried out in the field of privacy and authentication of VANET using Blockchain technology and future uses. Section 4.5 presents the framework based on Consensus algorithm for secured dissemination of messages. Finally, Sect. 7 concludes the work.

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2 Related Work In this section, an overview of some of the recent works and research carried out in Vehicular Ad hoc Network based on Blockchain technology (Table 1). In [4], a Blockchain-based Incentive Announcement system is proposed where two issues are given importance. Firstly, maintaining the user’s privacy and secondly the lack of user’s enthusiasm to respond back to a message. In order to solve these two challenges, the model suggests that the user who comes across an accident and intends to pass on the message should be paid as an incentive. But there needs to be another witness to confirm the information. Here, they are using Blockchain to establish a privacy-preserved incentive mechanism. Though the system manages to show efficiency in computation cost, there needs to be an improvement in the flexibility of announcing messages. In [5], in order to overcome the security challenges in VANET and the scalabilityrelated issues in traditional Blockchain, a DAG (Directed Acyclic Graph) employed vehicular network is used based on advanced Blockchain which gives stronger security and data immutability. The communication between requesting vehicles and Road Side Units have been modeled by using an auction-based game-theoretic smart contract deployed on the Blockchain for V2R cost bargaining. The simulation results have shown that the effectiveness of the model thus enhances the Quality of Experience for vehicles in VANET. In [6], the authors have implemented an anonymous data sharing protocol for VANET using Consortium Blockchain and ZKP (Zero Knowledge Proof). This helps in ensuring the security and privacy of the users. It also gives a provision for the government to access the data stored to the Blockchain, and the animosity is maintained by ZKP. This model proves to increase the security and privacy of authentication. In [7], a reputation-based traffic event validation and vehicle authentication model is presented. The proposed scheme aims to create a trust mechanism and also provides fast and efficient dissemination of messages. It employs a threshold centric system for traffic event validation using Blockchain and Mutual Authentication which facilitate the reliability of messages sent by RSU. In [8], the researchers have aimed at reducing the dependency on Certificate Authority (CA) by introducing a shared ledger where every nodes and blocks are of Table 1 Review of some of the recent works on Blockchain Paper ID Domain reviewed

Year

[4]

Blockchain-based incentive announcement system

2019

[5]

Framework using advanced blockchain for VANET

2020

[6]

Data sharing scheme for vehicular networks using blockchain

2020

[7]

Reputation-based traffic event validation and vehicle authentication

2020

[8]

Identity authentication and expeditious revocation framework using blockchain 2018

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are encrypted. The CA is used only while registering and all the other tasks authentication, revocation, and verification are done by the Road Side Units employing the Blockchain ledger. The security requirements are achieved by the ledger ensuring a highly secured communication. Though it reduces the authentication steps, the paper does not give a clear idea of decentralizing the VANET environment.

3 Understanding the VANET 3.1 Basic VANET Architecture The basic VANET infrastructure consists of vehicles (v) equipped with an Onboard Unit (OBU) and Road Side Unit (RSU) comprising of street lights, traffic signals, electric poles, and others located aside the road which serve as a gateway between OBUs and communicating objects and a Trusted Authority (TA) which can authenticate the devices and enable wireless communication between them. Along with these, the vehicles involved are equipped with a GPS tracking system and Electronic License Plate (ELP) for identification use. These vehicles are also equipped with high battery power. The communication between all the devices involved can be Vehicle to Vehicle (V2V), Vehicle to Infrastructure (V2I), or Vehicle to Everything (V2X). The OBUs and RSUs can communicate through a DSRC communication Protocol where as RSUs and TA communicate over a fixed network technology, viz. 4G and 5G. Figure 2 shows the VANET architecture.

3.2 Some Common Attacks in VANET In order to make sure these messages are not altered by any kind of malicious attackers, VANET network must be in a position to identify whether any changes are made. These attacks can be internal or external. Internal attacks are generally from sources that have an access to the network, whereas external is from unauthorized individuals or community. These attacks can be active or passive depending on whether the third party wants to just access the information or alter the messages completely. Some of the most common attacks are: Sybil attack: It is kind of attack where the vehicle tries to take over the network by forging the identity of other vehicles in the network to disturb the normal functioning of the network. Denial of Service (DoS): It is a cyber-attack where the network resource is made temporarily unavailable to disrupt the services of the network.

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Fig. 2 Architecture diagram of a typical VANET

Replay attack: In this, the valid data is maliciously sent on repeatedly to bring delay in the network. Modification attack: In this, the attacker replaces the original message with fraud ones. Location tracking: Here, the attacker tries to access the vehicle’s location by tracking it down.

3.3 Authentication Schemes in VANET Farooque et al. [1] have discussed a taxonomy of authentication schemes in a very clear manner. • Public Key Infrastructure-based Authentication: In this type of authentication, a large anonymous pool of certificates with their associated private keys is preloaded. These certificates are authorized by the TA, and they do not contain any information about the identity of the vehicles which makes

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these certificates truly anonymous. To verify the signature of the vehicle using these certificates, the verifying vehicle gets the signer’s public key from the TA, which stores all certificates issued in the beginning to the vehicles [9]. Therefore, only the TA can determine the identity of the users. The drawback of this scheme is the revocation process. The Certificate Revocation List (CRL) maintains all the revoked public keys. The more the users, the bigger the CRL and thus increases verification time. • Group Signature-Based Authentication: The group members can anonymously do signature on behalf of the entire group. Thus, the signer’s identity is not disclosed. The group manager has the authority to reveal the identity in matters of dispute. But with increased group size, the time taken to check the signature increases and it is also impossible for the vehicles to change their private keys. This is because the vehicle should register with TA and receive the private key and thus increases the probability of attack. • Identity-Based Authentication: In order to overcome the problems mentioned above, this method is implemented where the identity information of the user used as public key and the private key issued by the Trusted Authority is distributed using the identity information. The receiving vehicle verifies the message with the sender’s public key and then signs it with the sender’s private key. Since the user’s identification is known already, the computational process of verification is reduced. In [9], authors have proposed a novel RSU-based authentication scheme using hash chain approach.

4 Understanding the Blockchain 4.1 Basics of Blockchain Nakamoto [10] first introduced Blockchain which have attractive features as discussed in the introduction section. Figure 3 depicts the general structure of Blockchain, in which every block comprises two hash values. The current and previous hash values are used to build the Blockchain. As shown, Block2 comprises two hash value, namely hash value of its own and the previous hash values of Block1. Similarly, Block3 comprises two hash values, namely hash value of its own and the previous hash value of Block2 and so on.

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Fig. 3 General structure of a Blockchain [1]

4.2 Block Header Format and Structure The block header of a block consists of the following fields as depicted in Fig. 4. Each block comprises block header and block body. The fields in block header are previous hash value, nonce, hash value, timestamp, and the Merkle root. However, the body comprises transactions along with other information as per requirement of the Blockchain. However, the body comprises transactions along with other information as per requirement of the Blockchain. The size of each block is 80 bytes as depicted in Fig. 4. Genesis block is the first block, and it is the origin of all the blocks in the Blockchain. It consists of hashes of all records and each block has the knowledge of previous block’s hash to form the chain as depicted in Fig. 5.

Fig. 4 Block header of a block in Blockchain

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Fig. 5 Structure of a Blockchain

4.3 Characteristics of Blockchain The characteristics of Blockchain that makes it the future scope of VANET are: • Decentralization: Decentralization means that there is no one central organization or authority. Rather there exists a mutual trust relationship between all the nodes in the network, thus reducing the dependence on Trust Authority (TA). • Transparency: The data stored in the Blockchain can be accessed by any node in the network. The fault-tolerant mechanism (Consensus Mechanism) is used to achieve an agreement over a single state or value in a network of distributed processes. • Autonomy: This feature makes it possible for anyone to join the Blockchain network and also get an access to the entire copy of the database. Therefore, the data can be exchanged freely and securely where no human intervention is welcomed. • Tamper Resistance: Any modification or change in the database by a node will not affect the database of other nodes as Blockchain employs a hash function and an asymmetric encryption mechanism to ensure that there is no tampering with the information in Blockchain.

4.4 Types of Blockchain Figure 6 shows the different types of Blockchain. • Public: Public Blockchain does not require permission which is also called as distributed ledger system without restrictions. To sign into a public Blockchain platform, one just needs to have access to the Internet and become an authorized node. Thus, anyone have free access to all records from current to the old records, e.g., Ethereum and Bitcoin.

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Fig. 6 Types of Blockchain

• Private: It is permission-oriented restrictive Blockchain operating in a closed network. Private Blockchains are generally used by an enterprise or organization with selected number of people. The security, authorization, and access are all in the control of that particular organization or enterprise, e.g., Corda, Fabric, and Sawtooth. • Consortium: Consortium Blockchain is similar to private Blockchain except that they are semi-decentralized with more than one organization managing the Blockchain network for information exchange or mining. They are usually used by banks and government organizations, e.g., Energy Web Foundation and R3. • Hybrid: This is a combination of private and public Blockchain. They can have either permission-oriented or permission less network based on the needs of the users. Here, only restricted amount of data is in the hands of the public users. This type of Blockchain is more flexible compared to public or private. It also enhances the transparency and security of the network, e.g., Dragonchain.

4.5 Consensus Algorithm Consensus algorithm accomplishes required agreement between nodes in a distributed network. Consensus provides the monopoly and sets rules between the communicating nodes. The database is shared publicly and hence requires an efficient reliable and real-time approach to guarantee transactions in the network are trustworthy, and communicating nodes must adapt the particular consensus algorithm. Based on the type of Blockchain, i.e., public Blockchain consensus algorithm are of different types. Figure 7 shows different consensus protocol. For public Blockchain, Proof of work (PoW) and Proof of Stake (PoS) are commonly used. • PoW: In this, a miner is responsible to compute the previous hash value of the block header and the Merkel root chooses different nonce value until the resulting hash become less than the difficulty target [10]. This algorithm resembles a cryptographic puzzle which is tough to solve but it is easy to verify once all inputs are known.

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Fig. 7 Consensus protocol in Blockchain

• PoS: A miner is chosen based on its wealth or stake [11]. Here, miner stake their claim in terms of currency or coins and verify without requiring the owner to prove its authenticity for each transaction. It is cheaper and greener distributed consensus algorithm. Authors from [12–20] discuss other categories of consensus algorithm like Byzantine Fault Tolerant-based Proof of Stake, Practical Byzantine Fault Tolerance (PBFT), Casper the Friendly Ghost, Casper the Friendly Finality Gadget, Delegated Proof of Stake (DPoS), Federated Byzantine Agreement (FBA), PoET, RAFT, Ripple protocol, etc.

5 Blockchain-Integrated Vanet Figure 8 shows Blockchain-integrated VANET architecture [21]. The vehicular network with the vehicles, On Board Unit, Road Side Units, and Trusted Authority (TA) forms the Perceptual layer.

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Fig. 8 Blockchain-integrated VANET

The edge computing layer is implemented using a new technology called Mobile Edge Computing which enables mobile devices to access the cloud services to perform computational tasks using mobiles which optimizes processing. The service layer comprises both Blockchain and cloud services. Storing all the enormous data coming from VANET in Blockchain is not suitable since every block of Blockchain maintains all the data which leads to lot of resource utilization. So, only the important data like traffic violations, accidents, etc., is stored in Blockchain and the remaining data is stored by cloud service.

5.1 Privacy and Authentication Schemes Using Blockchain Various authentication schemes have been proposed based on the different types of Blockchain mentioned above. Some of them are:

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• Blockchain-based Anonymous Reputation System (BARS) Even though public key infrastructure-based Authentication deals with several security-related issues of VANET, its privacy and trust are still open issues. In [22], the author proposes a model which aims at breaking the link between the public key and original identity to protect the privacy of the vehicle while providing a trustbased communication environment. A reputation evaluation scheme is implemented for establishing trust in the communication in order to prevent the broadcasting of manipulated and other malicious messages. The results obtained after analyzing the security and validity showed that BARS efficiently improves the trustworthiness of messages that has been broadcasted and protects privacy of the vehicle with high efficiency. • A Fine-Grained Access Control Scheme for VANET Data Based on Blockchain (FADB) Hui et al. [23] have proposed an Access Control Scheme based on Blockchain to secure the data. The model uses cipher text policy attribute-based encryption (CP-ABE) mechanism. In CP-ABE, a data owner that can encrypt its data under a specified access policy using a set of attributes. It decrypts data only when the attributes of data visitor fulfill the policy requirements. It does not need any information about the person accessing the data. Based on CP-ABE algorithm, they have presented a more refined security mechanism called HECP-ABE which uses Ethereum Blockchain technology to obtain encryption and decryption process step by step. Since new components like Blockchain and HEC-ABE is used, unknown performance evaluation cost must be considered. • Blockchain-Based Unlinkable Authentication (BUA) Privacy and security are the biggest concerns of a VANET Network. In [24], the authors have proposed a scheme to implement unlinkability in authentication in order to prevent various attackers from accessing the secured data by linking multiple messages. The model consists of a Service Manager (SM) which acts as a Blockchain node that stores registration data of each vehicle. Every SM has its own logical coverage area known as security domain. An Audit Department (AD) is a trusted third party which authenticates a newly registered Service Manager (SM). The Blockchain Network comprises Service Managers of each domain. Lastly, the vehicles equipped with an OBU along with the RSU form the Vehicular Ad hoc Network. The Blockchain-based Unlinkable Authentication Scheme uses consortium Blockchain, where SM of each domain act as nodes of consortium Blockchain and maintains registered information of the vehicles in the network. It is a mutually trusted environment where the vehicles themselves generate different pseudonyms to prevent retraceability and the SM verifies the validity of these pseudonyms. Because of the uniqueness and confidentiality of original identity, only a true owner can generate true authentication messages. The performance results show that this scheme provides better security with less computational overhead. However, it fails to address collision attack between the Service managers.

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5.2 Future Scope and Application of Blockchain in Vehicular Networks and Beyond Based on the study [25–33], Blockchain opens new avenues for the researchers because of its highly efficient application in the following domain. • Cyber Security: The vehicular network is prone to many security-related challenges because of its dynamic topology and unevenly distributed nodes. Protecting the network from attackers and cybercrimes has become the biggest concern. The Blockchain technology with its features like immutability, decentralization, autonomy, and transparency is proving to be a valid solution to all the security issues of vehicular networks. Cyber security is the protection of systems and networks from malicious activities like theft or damage of data, both hardware and software, misguiding of services and so on. This field is gaining significance in the recent years due to increased technology and reliance on computers and wireless networks [25]. With increased obsession for Artificial Intelligence (AI), it has become easy for hackers to have access to most of the IoT-based home automation devices. Therefore, Blockchain with its decentralization property can make things harder for the cyber criminals, since the data itself is no longer in a single location. Since the vehicular communication involves all kinds of mobile devices and computers, the abovementioned challenges are applicable to vehicular networks as well. Thus, Blockchain with its applications in cybersecurity is the future of vehicular networks. • 5G Technology: The integration of 5G technology and Blockchain in vehicular networks opens new opportunities in the field of vehicular communication. The low latency communication provided by the 5G technology and the distributed ledger services provided by Blockchain help in building a trusted environment by enhancing data reliability and security. 5G is the next generation of mobile networks. • With emerging technologies like Cloud Computing and Mobile Edge Computing (MEC), Vehicular Network systems require highly secure transactions, proper data storage, and less interference in the network. • Features like tamper-proof and security make it one of the most suitable options for 5G vehicular network. • Blockchain also finds application in solving payment options in case of electric vehicle charging. • Blockchain also finds its application on power grids, power distribution, etc.

6 A Framework for Secured Dissemination of Messages in Internet of Vehicle Using Blockchain Approach A Blockchain-based scheme that stores the trust value of vehicles also called nodes in public Blockchain in order to provide transparent and secure exchange of messages

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Fig. 9 Architecture of proposed Blockchain-based VANET system

among them. This approach uses message as transaction in similarity to bitcoin to create new block and link the hashes of consecutive blocks to build the block chain. The approach is constrained to create and share blocks limited to a geographical area as it is meaningless to share traffic information of one country with other. Figure 9 shows the proposed system architecture consisting of RSU, vehicles’ OBU, VANET message, Blockchain network (BN), and blocks which are described as follows: RSU: RSU are deployed across the city and have high computational power and storage capability. RSU is an important component which is responsible for calculation and updation of the trust value of a legitimate vehicle on to the public Blockchain and hence enhances the trustworthiness of the vehicle. The RSU generates genesis block based on an event. OBU: OBU are present in each vehicle and consist of cryptographic information and sensors for collecting the data. Also, OBU is responsible for communication with other vehicle’s OBU and the RSU. Apart from this, it generates messages and mine the current blocks and update its trust value through the RSU onto the Blockchain to prove its trustworthiness. Vehicles are allotted trust value ranging from 0.0 to 1.0 in the beginning based on their presence and behavior during the communication. VANET messages: We have categorized messages which are periodically broadcasted to inform about traffic status to the communicating vehicle in the RSU range

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as beacon and the message to intimate about a road accident and catastrophe as critical safety messages. All these trigger an event message. Beacons are periodically broadcasted by an RSU at regular intervals. BN: The Blockchain network is built by the RSU, and it is a P2P network. RSU runs the mining function and sends transactions. All miners have to solve proof of work (PoW) for creating new blocks. Block: Each block consists of a header and a body as discussed above in Sect. 4.2. Here, the framework uses public Blockchain-based consensus approach where an RSU calculates the trust value of a node and regularly update it to the Blockchain to avoid misbehaving node who are trustworthy in beginning but later become malicious. RSU is responsible for ensuring both entity trust and message trust, and thus, the scheme provides highly secured communication. Algorithm 1: Generating Location Certificate Input: V pub , Curr loc , S I D Output: Loc_Cer t Begin 1: Vehicle sends a request by sharing its public key and current location (V pub , Curr loc ) to the RSU 2: RSU selects a random number which acts as session_ID (S I D) and sends it the vehicle 3: Vehicle sign the S I D with its private key and send to RSU as: Sig_ V pr k (S I D) 4: RSU check the legitimacy of the signed S I D using vehicles public key (V pub ) and checks the elapsed time between exchange of S I D 5: if time difference is less than few milliseconds then, RSU publishes the location certificate which acts as a proof of location as: Loc_Cer t := Sig_RSU pr k (T s, V pub , Curr loc ) 6: else RSU doesn’t issue certificate 7: end if 8: End Location_Certificate: Location certificates are provided by the RSU to the vehicle. The process to generate the location certificate is as shown in Algorithm 1. Following assumptions have been made to conduct this proposed research.

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• All vehicles have their public/private key pair during the offline registration with the Transport Authority of the country. • Each RSU in a region is self-capable and have high computational power. Also, each RSU are highly trusted and cannot be compromised in any circumstances. • RSU are connected to the Blockchain through the RSU server. • Vehicle can become the part of the VANET only when it is trustworthy. The trustworthiness of each vehicle is improved based on its trust value which is periodically updated by the RSU. RSU calculates its public, private, and secret key using the following equations. RSUprk := key1(RIDRSU , Ts)

(1)

RSUpubk := key2(RSUprk , Ts)

(2)

where, Vpub : Currloc : Loc_Cert: SID: Sig_ Vprk : Sig_RSUprk :

Vehicle public key Current location of Vehicle Location Certificate Session ID generated by RSU Signature using Vehicle’s private key Signature using RSU private key

7 Conclusion In this paper, an easy understanding of VANET characteristic and threat have been explained with its existing solution. Then, an understanding of Blockchain have been presented with research works on privacy and authentication techniques in Vehicular Ad hoc Network (VANET). Comparison of some of the recent works carried out in Blockchain have been presented, highlighting the useful applications of Blockchain in the area of vehicular networks, wireless network, 5G technology, cyber security, IoT, cryptocurrency exchange, smart grid, power distribution, etc. Finally, a Blockchain based on consensus protocol have been presented for secured dissemination of messages in Internet of Vehicle (IoV). Future work will be focused on simulating the proposed idea to prove its effectiveness in comparison with exiting work.

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Photovoltaic Array Fed Indirect Vector-Controlled Induction Motor Drive for EV Transportation System Using Brain Emotional Learning-Based Intelligent Controller Biranchi Narayan Kar, Paulson Samuel, and Bandi Mallikarjuna Reddy Abstract This article deals with single-stage indirect vector control of induction motor for PV-fed EV transportation system using brain emotional learning-based intelligent control (BELBIC). A solar photovoltaic (PV) array, a three-phase voltage source inverter, and a motor with EV chassis system are all part of the proposed system. The BELBIC operates effectively for motor drive systems with changes in various operating conditions. The use of a power feed forward term improves the system’s dynamic response. This accomplishes the goal of effective and efficient EV transportation system. The system is simulated in the MATLAB/Simulink platform and is validated on a real time using OPAL-RT. Keywords Solar photovoltaic (SPV) · OPAL-RT · Electric vehicle (EV) transportation system · Brain emotional learning-based intelligent control (BELBIC) · MATLAB/Simulink

1 Introduction Due to the fast depletion of nonrenewable resources, numerous governments are promoting renewable energy alternatives to meet the growing demand for electrical energy [1, 2]. The solar PV used DC motor for different applications [3]. However, because of the durability, low cost, lower maintenance, and higher efficiency associated with induction motors, they have largely supplanted DC motors [4, 5]. A induction motor drive fed by a PV array with indirect vector control is used in this work.

B. N. Kar (B) · P. Samuel Electrical Department, MNNIT Allahabad, Prayagraj, U. P., India e-mail: [email protected] P. Samuel e-mail: [email protected] B. M. Reddy Hardware Design, TransDigm India Private Limited, Bangalore, Karnataka, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 N. Marati et al. (eds.), AI Enabled IoT for Electrification and Connected Transportation, Transactions on Computer Systems and Networks, https://doi.org/10.1007/978-981-19-2184-1_7

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Solar PV output is dependent on solar insolation and temperature, with a single power peak in the PV module’s characteristic. The goal of a photovoltaic system is to get the highest amount of power. As a result, a variety of maximum power point tracking (MPPT) systems have been implemented. A MPPT technique, i.e., incremental conductance (InC) technique, is utilized to track MPPT [6–10]. To solve some of the problems in the perturb and observe (P&O) algorithm, this technique was devised. In slow-changing environmental circumstances, it has a better option over the P&O approach, which increases losses by fluctuating around the maximum power point (MPP) [8, 11]. A voltage source inverter is utilized in this work to change DC power directly to AC power, eliminating the requirement for an intermediate DC–DC converter and its related cost. Because there is no inductor, the overall system size is minimized [12]. In terms of response time and accuracy, the vector control technique outperforms scalar control, as demonstrated in [13–15]. Induction machines (drives) with indirect field-oriented control (IFOC) have been employed in a wide range of industries. However, one of the key problems of IFOC induction motor drives is induction motor parameter change, which impacts the decoupling properties of vector control of induction motors. Because of their simplicity, traditional PI controllers have been routinely employed to provide variable speed operation. This method controls the torque and flux-producing components of stator current separately. It enables the induction machine to control torque in a manner similar to that of a separately stimulated D.C. machine. Because of the induction motor’s nonlinear features, traditional controllers such as the PI controller fail to provide optimal performance under varied operating situations such as parameter changes. To solve these problems, advanced controller like intelligent controller is utilized. Intelligent controllers can handle any nonlinearity of any complexity without requiring an exact mathematical model of the system to be designed. Fuzzy logic, neural networks, and evolutionary algorithms are some of the artificial intelligence techniques that have recently become prominent in motor drives. Artificial intelligence’s goal is to make a machine think like a human by simulating human or natural intellect. The BELBIC is a novel artificial intelligence controller relying on a computer model of the brain’s emotion processing system.

2 Configuration of the System The SPV-based EV transportation system configuration is presented in Fig. 1.

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Fig. 1 The configuration of SPV-based EV transportation system

3 Design of the System 3.1 Design of Solar PV Array In order to account for the loss, a 2.4 kW photovoltaic array is designed for a rating of 2.2 kW induction motor. For the PV array of needed capacity, a PV module with MPP voltage and current of 26.3 V and 7.6 A is chosen. At MPP, the voltage rating is set to 315 V. Accordingly, the other parameters are defined [16]. Impp = i pv =

Pmpp 2400 = 7.6 A = Vmpp 315

(1)

where Pmpp = Ppv is the photovoltaic power at MP. Number of series connected modules Ns =

Vmpp 315 = 11.99 ≈ 12 = Vm 26.3

Similarly, parallel connected modules

(2)

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Np =

7.6 =1 7.6

(3)

3.2 Design of Capacitor (DC Link) The fundamental frequency component is used to estimate the value of the DC-link capacitor in this way: ωrated = 2 × π × f rated = 2 × π × 50 = 314 rad/s

(4)

  1 2 × Cdc × Vdc2 − Vdc1 = 3αV I t 2

(5)

  1 × Cdc × 4002 − 3752 = 3 × 1.2 × 133 × 8.2 × 0.005 2

(6)

Cdc = 2023 µF

(7)

3.3 Design of Water Pump The torque–speed characteristics of a pump are used to select and design it [17]. Therefore, TL = K 1 ω2

(8)

4 Control Scheme 4.1 Maximum Power Point Tracking The MPP tracking approach is often utilized to maximize solar array output power [8]. This work employs an INC approach of MPP tracking because of its capacity to provide highly accurate tracking even under rapidly changing insolation conditions. The following equation can be used to determine INC’s operating principle. Ppv = Vpv × Ipv

(9)

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dP d(I V ) dI I = = I +V = I +V dV dV dV V At maximum power point,

dP =0 dV

151

(10) (11)

Hence, I I =− V V

at Maximum power point

(12)

I I >− V V

at left of Maximum power point

(13)

I I 20 kW

DC Level 1

200–450 V DC, up to 36 kW @ 80 A

DC Level 2

200–450 V DC, up to 90 kW @ 200 A

DC Level 3

200–600 V DC (proposed) up to 240 kW @ 400 A

For wireless charger

For wired charger SAE J1772 [20, 22, 23]

Power levels

120 V, 1.9 kW @ 16 A

standards are now available. Table 1 lists some standards for wired and wireless chargers. The international special committee on radio frequency interference “CISPR (Comité International Spécial des Perturbations Radioélectriques),” which is a sub-committee of International Electrotechnical Commission (IEC) has prescribed certain frequency bands for wireless power transfer to electric vehicles. The frequency band recommended for light duty vehicle which requires less than 3.7 kW charging power is 81.38–90.00 kHz. Similarly, the frequency band recommended for passenger vehicles (with typical wireless power transfer in the range of 3.7–20 kW) is 79–90.00 kHz. For heavy vehicles like trucks and buses (power transfer >20 kW), three different frequency bands have been recommended and are 19–21, 36–40, and 55–65 kHz. It may be noted that as power level increases, the frequency of power transfer comes down.

4 Broad Classification of WPT System for EV Application Wireless power transfer system for electric vehicle may be classified into three modes based on the way of charging. These classifications are shown in Fig. 2.

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Fig. 2 Classification of WPT system

4.1 Static Wireless Power Transfer (Static WPT) System Static WPT can be the alternative for Plug-In chargers of EVs. This eliminates safetyrelated issues such as electric shocks due to wear and tear of cable [24]. It avoids messy wiring and cables and provides user-friendly environment requiring less effort from driver. Static WPT wireless charging happens when EV is stationary. Figure 3 shows the typical arrangement. Static WPT charging pad is installed at convenient locations like shopping centers, home garages, office parking slots, apartment parking areas, etc. [25]. Some kind of wireless communication system will be necessary to communicate between vehicle and charging station. The state-of-the-art communication system helps the driver in vehicle alignment and also communicates the charging parameters like start and end of charging, charging power, state of charge of battery, etc. If any fault occurs, that is also communicated. Generally, the state-ofthe-art wireless communication system is implemented via ZigBee. Use of Bluetooth technology and Wi-Fi are other possible options. The charging time of WPT system depends on the source (transmitter) power level, receiver side arrangement, and the battery capacity.

Fig. 3 A typical static WPT system for EV (image: https://www.motorbeam.com/volvo-developswireless-charging-technology-for-electric-ehicles/)

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Table 2 Developments in static WPT system for EVs University/Industry

Operating frequency (kHz)

Power level (kW)

Efficiency (%)

Coil to coil distance (mm)

References

Korea Advanced Institute of Science and Technology (KAIST) University

20

15

74–83

120–200

[26, 27]

Wuhan University

100

6–16

≈ 81

300

[28]

University of Auckland

10–40

2–5

>85

100–300

[29, 30]

Oak Ridge National Laboratory (ORNL)

>20

3.3, 5–6.6 and 10.2

≈ 90

100–160

[31, 32]

Michigan State University

20

1

83

200

[33]

WiTricity Corporation

85

3.6–11

>90

100–250

[34]

Qualcomm Halo

85

3.6 and 7

>90

160–220

[35]

Energy Dynamics Laboratory and Utah State University

20

5

>90

175–265

[36]

• Current status of development in static WPT system Many research groups across the world have reported development of static WPT system with good efficiency and varying amount of output power. Table 2 shows the summary of such developments.

4.2 Quasi-dynamic Wireless Power Transfer (Quasi-dynamic WPT) System In quasi-dynamic WPT system, EV could be charged in stop-and-go mode [37, 38], where vehicle stops for short periods of time on conveniently located charging pads. The battery capacity is generally low so that they get charged during this short period. Such charging pads may be installed at traffic signal points or pick-up points for city cabs/buses, shopping malls, etc. During the course of travel, especially in city area, the vehicle is expected to stop almost regularly at several charging pads. The EV’s battery capacity needs to be just enough to travel to the next charging pad. The charging time and the cost of batteries can thus be reduced. It may be noted that batteries occupy significant space in the EV and is a major component of cost. With quasi-dynamic wireless charging, the requirement of energy storage battery in EV reduces. With single charging, the driving range of vehicle is less but before battery gets completely drained a new charging pad should be available.

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4.3 Dynamic WPT System Dynamic wireless charging (DWC) or dynamic WPT system (also known as inmotion wireless charging system) is the latest technology being developed for charging of electric vehicle [39]. In DWC system, the entire stretch of road network needs to have the charging pad. EV could be charged while vehicle is in motion. The charging pads are laid continuously over long stretches of road, and these pads are energized when a vehicle approaches. The vehicle is supposed to be moving slowly during charging. The DWC system may be suitable for city travel with speed restrictions. Under this arrangement, the size and weight of the EV’s battery reduce significantly [8]. Figure 4 shows a schematic representation of the dynamic wireless charging for electric vehicle. The driver assistance system shown in Fig. 4 will guide the vehicle’s path. As shown in Fig. 4, DWC system consists of a sequence of transmitter coils deployed just below the road’s surface and a receiver placed at the base of the vehicle [40, 41]. Here, instead of segmented (discrete) transmitter modules a single loop, long track transmitter coupler is used [42]. These two types (single loop type and segmented type) of DWC systems are shown in Fig. 5. As shown in Fig. 5a, single loop track has a simple structure and easy to power the whole track. The whole track is permanently connected to a single power converter and makes the whole track always active (even in the absence of vehicle on the track). This causes the electromagnetic field (EMF) emissions and also huge power losses in the power circuitry. These issues can be avoided using the segmented transmitter track method [43, 44]. The basic representation of segmented tracks is shown in Fig. 5b.

Fig. 4 Schematic representation of a dynamic wireless charging system for EV

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Fig. 5 Schematic representations of different transmitter tracks. a Basic representation of a single loop track. b Basic representation of segmented transmitter tracks

It has a string of symmetrical transmitter coils placed in a sequence and evenly separated by a fixed distance. Each transmitter coil is connected to an individual power converter or else same converter may be switched to different transmitter coils. In such structure, the nearest transmitter coil gets powered up. Information about approaching vehicle’s position is available for control of transmitter coils [45]. Table 3 shows few of such developments.

5 Modes of Wireless Charging for EVs Wireless power transfer system is mainly categorized into two regions such as (i) Far-field (Radiative) and (ii) Near-field (non-radiative) techniques. These regions are generally classified based on the electromagnetic field produced by the transmitter coil and mode of charging. In electric vehicle application, near-field mode of charging seems more obvious because of its numerous benefits such as user-friendly, convenient, and good power transfer efficiency. Also, the distance between transmitter coil and receiver coil is typically varies from few millimeters to several centimeters. The further classification of near-field technique is shown in Fig. 6.

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Table 3 Developments in dynamic WPT for EVs University/Research Laboratory

Operating frequency (kHz)

Power transfer level (kW)

Efficiency (%)

Coil to coil distance (mm)

References

University of Auckland

12.9

20–30

–

50

[46]

20

2

85

220

[29]

Oak Ridge National Laboratory (ORNL)

22–23

20

90

125–175

[47]

EV System Lab and Nissan Research Center

90

1

>90

100

[48]

KAIST University

20

>100

>80

170

[49]

Fig. 6 Types of wireless power transfer techniques for EVs

5.1 Capacitive Coupling Technique or Capacitive Power Transfer Technique Capacitive coupling technique is an electrically coupled network, which utilizes high-frequency electric fields to realize the concept of wireless power transmission. This phenomenon is considered to be an “electrical resonance” [5, 50]. In the literature, various circuit configurations [51, 52] were proposed to realize short- and long-distance capacitive coupling techniques. This technique does not involve any ferrite cores in its architecture for magnetic shielding, and thereby, it enables the reduced size and cost. Due to the limited area available at the vehicle’s base, the design of capacitive coupling systems is one major challenge. Also, large value of ground clearance provides a small coupling capacitance. Thus, it is necessary to operate the capacitive coupling system at higher frequencies which may provide low

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Fig. 7 Basic block diagram of a capacitive power transfer technique

capacitive reactance and further it gets efficiently compensated. However, the presence of parasitic capacitances between the vehicle chassis, capacitor plates, and the road may degrade the transfer of power. Thus, the system requires a high-frequency operation to efficiently transfer power in the presence of parasitic effect. Figure 7 shows the schematic representation of capacitive power transfer system for EVs. As shown in Fig. 7, the capacitive power transfer is achieved using a pair of capacitors (each capacitor consists of two parallel plates separated by a definite distance—capacitors C 1 and C 2 ). First plates of two capacitors are connected to the transmitter, and the second plates are connected to the receiver. A regulated DC voltage is fed to the inverter which converts the constant DC voltage into a highfrequency AC voltage. The high-frequency AC voltage may further amplified in the matching network and fed to the first set of conductive plates. This in turn generates the varying electric filed across the two plates of conductive plates. As per Maxwell’s equations, time-varying electric field produces the displacement current (which may be proportional to the rate of change of the electric field). The displacement current enables the transfer of energy between the two capacitor plates over its medium. Consequently, an electric current flows through the receiver and further fed the load via a high-frequency rectifier.

5.2 Inductive Coupling Technique or Inductive Power Transfer (IPT) Technique In the near-field wireless power transfer systems, inductive power transfer technique is the popular technique in transferring power over an airgap. It is a simple and elegant method. Figure 8 shows a basic block of an inductively coupled wireless power transfer system. In these systems, transmitter and receiver coils are the coupled coils which are placed in a close proximity [53]. In inductively coupled system, efficiency of the system is mainly depended on structure and geometry of the coupled coils. The coupled coils are electrically isolated and magnetically coupled. This enables the safe and convenient charging for a consumer device.

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Fig. 8 Basic block diagram of an inductive power transfer technique

As shown in Fig. 8, a high-frequency output AC voltage of the DC to AC converter is applied to the transmitter coil of the WPT system and produces the alternating highfrequency magnetic fields. The magnetic fields travel through the airgap and induces electromotive force (EMF) across the receiver coil. The received high-frequency AC voltage is fed to load (RL ) via a high-frequency AC to DC converter [54]. Due to the limited power transfer distance of an inductively coupled wireless transfer system, it is applicable for low power applications (like mobiles, laptops, and bio-medical implants, etc.) but not feasible for high power applications (like electric vehicle).

5.3 Magnetic Resonance Coupling (MRC) Technique In the inductively coupled system, leakage inductance is highly dominant over mutual inductance, which results in low coupling factor between transmitter and receiver coils. For achieving enhanced efficiency of the system, “Magnetic Resonance Coupling” technique was introduced in the literature [8]. Figure 9 shows the block diagram of magnetic resonance coupling technique. In this technique, compensation capacitors are placed on transmitter and receiver sides of the WPT system. Compensation capacitors are used to cancel the leakage inductance present in the system and tune for impedance matching of the circuit. At a particular operating frequency, transmitter and receiver coils attain the magnetic resonance and amplifies voltages/currents. Similar to IPT system, magnetic resonance coupling technique has some common components such as high-frequency DC/AC converter, coupled coils, high-frequency rectifier, and the load. An amplified high-frequency alternating magnetic fields travel through the airgap and induce AC voltages across the receiver coil. The received voltages are further rectified and

Fig. 9 Basic block diagram of magnetic resonance coupling technique

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Table 4 Comparisons of IPT and MRC techniques Attribute

Wireless charging techniques Inductive coupling technique

Magnetic resonance coupling technique

Field type

Near-field

Near-field

Power range

Low and medium

High

Frequency range

High

Medium

Charging distance range

Low (typically varies from few millimeters to few centimeters)

Medium (typically varies from few centimeters to few meters)

Efficiency

Medium

High

Flux leakage

More

Less

Benefit

Simple and safe technique

Safe technique, no tight alignment is required between transmitter and receiver coils

Drawback

It requires tight alignment between transmitter and receiver coils

Complex implementation

Comment

Not efficient for EV charging

It is efficient for EV charging

utilized for charging of the vehicle’s battery. The details of compensation topologies presented in the literature are discussed in the later section of this chapter. Table 4 shows the basic differences between IPT and MRC techniques.

6 State-of-the-Art Research Development of WPT System The research in WPT systems is mostly intense on three areas: (6.1) coil design, (6.2) compensation topologies, and (6.3) power electronic converters.

6.1 Coil Design The coil (or WPT coil) is the utmost important part in a WPT system, which converts energy from electric form to magnetic form and making wireless power transfer possible, while it is also responsible for amount of power transferred and efficiency of the system. These WPT coils (transmitter and receiver coils) need to transmit the required charging power to the electric vehicle. Since these coils are loosely coupled, the effect of leakage inductances is nullified by compensation circuits. After compensation, the voltage and current through the coils may be significantly high. The coils need to be designed to meet the insulation and current requirements. Shielding against stray high-frequency electromagnetic radiation will be necessary to

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meet the international standards. Both transmitter and receiver coils are wound using required gauge of Litz wire. Litz wires are suitable for high-frequency applications. The size and shape of the coils need to be carefully designed. Coil design has been a major research area in the field of wireless charging. Several different types of coil geometry and their interconnections have been reported in [55–58]. The transmitter and receiver coils of the WPT system need not be identical. The size and shape of the WPT coils need to be chosen for required power handling capacity, separation between transmitter and receiver coils, and the operating frequency. The weight, cost, coupling factor, and the degree of misalignment tolerance are other considerations. Electromagnetic shielding of the coils is also important. Generally, the coils used in WPT system are planar with spiral type windings. The overall shape of the coil may be circular or square or rectangular [59–61]. Figure 10 shows general structure of circular spiral and square spiral coils. Square and circular coils are non-polarized because orientation of the coil in the coil’s plane may not affect the coupling between transmitter and receiver coils. Many other types of coils have also been proposed in the literature, for example double-D (DD) coil, double-D coil with additional quadrature winding (DDQ coil), and double-D coil with two additional quadrature windings (DD2Q), have been reported [62–64]. Some of these coils have limited tolerance against misalignment between transmitter and receiver coils. Some of these coils are also polarized which means that the coupling factor between WPT coils changes with orientation. Figure 10 also illustrates some of these coil structures.

6.2 Compensation Networks A WPT system is a loosely coupled transformer. Due to the loose coupling between WPT coils, majority of magnetic flux produced by transmitter coil will not link receiver coil. Mutual flux between them is only a small fraction of total flux. The leakage flux dominates, which leads to low coupling coefficient (k) between coils. Also, power factor of the uncompensated WPT system is poor. A WPT system with large leakage inductances requires the use of compensation networks. Usually, the compensation networks are put both on transmitter and receiver sides. On the transmitter side, the compensation network is employed to decrease the VA rating of the power supply. On the receiver side, the compensation network offers impedance matching with transmitter side circuit to maximize the power transfer between coils. For improved power transfer, the predominantly large leakage inductances need to be fully compensated using external compensation capacitors. The exact compensation should also consider the load on the receiver side. There are four basic compensation networks, namely (i) series–series (SS), (ii) series–parallel (SP), (iii) parallel–series (PS), and (iv) parallel–parallel (PP) present in the literature [65]. The terminology of these networks (or topologies) is based on the way the compensation capacitors are connected to transmitter and receiver coils. “S” and “P” stand for series and parallel

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Fig. 10 Different coil structures. a Circular coil. b Square coil. c DD-coil (double-D coil). d Coil design with integrated compensated coil (unipolar coil) into main coil structure (bipolar coil). e DDQ coil

connection of compensation capacitors to the WPT coils. The circuit representations of these four basic compensation networks are shown in Fig. 11. The choice of transmitter (TX ) side compensation network should depend on the available high-frequency source vis-à-vis the coil parameter. The voltage and current rating of the source should match with the load impedance. The compensated transmitter coil’s input impedance (seen by the source) is low in case of series compensation while for parallel compensation the transmitter coil’s input impedance is high. After compensation, the WPT system should ideally appear as a unity power factor load across the high-frequency source. In series compensation, the coil’s voltage is

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Fig. 11 Basic compensation networks for WPT coils. a Series–series compensation network. b Series–parallel compensation network. c Parallel–series compensation network. d Parallel–parallel compensation network

amplified, compared to input voltage but the current through the coil remains same as input current. As against series compensation, in parallel compensation the coil’s current is amplified but the voltage remains same as source voltage. In WPT system, due to large gap between the coils, the leakage inductance of the coil is nearly same as its self-inductance. As a firsthand approximation the magnitude of compensation capacitor may be chosen based on self-inductance value and the operating frequency. Thus, for series compensation of transmitter coil the magnitude  of compensation capacitor (CP ) should be close to 1/ ω2 L P , where L P is transmitter side self-inductance and “ω” is the operating frequency. It may be added here that the series compensation capacitor (CP ) should not only be compensating the transmitter coil’s leakage inductance but also be compensating for non-unity power factor of reflected load that appears in parallel with the magnetizing inductance. On the receiver side, the compensating capacitor helps maximize the output power. If no compensation is done, the receiver coil’s leakage inductance will limit output current. In both series–series (SS) and series–parallel (SP) compensation, the receiver  side compensation capacitor (CS ) should be close to 1/ ω2 L S , where L S is selfinductance of receiver coil. By putting compensating capacitor in series with receiver coil, the load current magnitude is maximized, whereas by putting compensating capacitor in parallel with receiver coil the load voltage is amplified but load current is less than receiver coil’s current. The choice of series or parallel compensation topology on receiver side should keep the load voltage and current requirement in mind. Generally, power electronic converters are used to produce high-frequency AC voltage source for the WPT systems. In order to reduce switching losses (associated with high-frequency switching), several other compensation networks, like LCL-S,

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Fig. 12 Some soft switching compensation networks. a LCL-S compensation network. b DoubleLCL (LCL-LCL) compensation network. c SP-S compensation network. d LCC-S compensation network. e Double-LCC (LCC-LCC) compensation network

double-LCL (LCL-LCL), SP-S, LCC-S, and double-LCC (LCC-LCC), have been proposed in literature [66–69]. Figure 12 shows these topologies. These provide advantages of soft switching. In spite of several different compensation topologies, basic SS and PP compensation topologies are quite popular.

6.3 Power Electronic Converters Power electronic converters are required to obtain high-frequency AC source for WPT systems. The converter should be energy efficient for the required high-frequency output. The design of suitable power electronic converter for generating such highfrequency voltages has always been a challenge. Generally, H-bridge inverter is used to get square output of desired high frequency. The switching losses become dominant in conventional H-bridge kind of inverter. Use of new devices like GaN (Gallium Nitride) FETs may become more popular as these can be switched faster and the switching losses are significantly low. To produce high-frequency ac, current several DC to AC converters with soft switching characteristics have been proposed in the literature. Table 5 briefly describes some such converters used in WPT applications.

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Table 5 Popular DC/AC converters used in WPT applications Converter

Description

Figure 13a

• Full-bridge or H-bridge inverter is the most popularly used DC to AC converter [70] for WPT applications. It generates square wave output voltage. The compensated transmitter coil is like an L-C tank circuit with resonant frequency nearly equal to frequency of square wave output. The resultant current in the transmitter coil is nearly sinusoidal • To achieve soft switching small magnitude of capacitor has been put in parallel with each switch. For this, the tank circuit consisting of transmitter coil and compensation capacitors is operated in slightly inductive power factor mode such that the output current from the inverter lags output (square wave) voltage. Suitable compensation circuit should use a series compensating capacitor. Figure 13a depicts one such configuration • A half-bridge version of this converter which uses only one leg (pole) of switches may also be used. Here, the WPT load is connected between mid-point of DC supply (E) and output point of pole voltage

Figure 13b, c • Class-E converter shown in Fig. 13b has extensively been reported in connection with resonant DC link inverters [71]. Only few researchers have reported use of this basic converter (with some modifications) for WPT applications. The circuit topology is simple as it requires only one switch and is capable of operating at high frequencies. The switching loss is also low • It uses resonant operation to enable zero-voltage switching (ZVS) • The peak voltage stress across the switch may need to be limited by adding extra circuit as shown in Fig. 13c–e. Figure 13c uses extra switch whereas the other two topologies use passive elements for voltage clamping. The active clamping circuit of Fig. 13c has once again been widely proposed by researchers working in the area of resonant DC link inverter Figure 13d

• The inverter shown in Fig. 13d is similar to class-E inverter. Some researchers have named it as class-F2 inverter [72]. This circuit also uses only one switch • The extra series L-C branch helps to reduce voltage stress across the switch • To avoid switching losses, class-F2 inverter resonant elements are tuned to operate at zero-voltage switching • In class-F2 inverter, L MR and C MR are responsible to shape the output voltage of the inverter

Figure 13e

• The inverter shown in Fig. 13e is similar to class-F2 inverter but with some difference in resonant frequency of series L-C branch across switch (Q1 ). Both aims to reduce the voltage stress across switch. Such a series resonant branch is also used in class-F inverter [73], which is connected in parallel to the load • Consequently, the combination of class-F inverter and class-E inverter form a class-EFn inverter, where “n” indicates the ratio of series L-C branch’s resonant frequency to inverter’s switching frequency (f S ) • This inverter uses a single switch. The series L-C branch of this inverter is tuned to second harmonic of the switching frequency (2f S ), where “n (= 2f S /f S )” equals to an integer 2, due to which the inverter was named as class-EF2 inverter (continued)

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Table 5 (continued) Converter

Description

Figure 13f

• The push–pull class-E inverter shown in Fig. 13f is reported by some researchers [74], which is used exclusively for WPT applications • The push–pull class-E inverter has two typical class-E inverters, followed by WPT load • The power switches (S 1 and S 2 ) of push–pull class-E inverter are driven inversely to each other • The push–pull class-E inverter achieves low harmonic distortions due to its differential mode of operation • The two class-E inverters of push–pull class-E inverter is operated under zero-voltage switching (ZVS) conditions to maintain low switching losses

(a)

(c)

(e)

(b)

(d)

(f)

Fig. 13 a H-bridge inverter with zero-voltage switching. b Class-E inverter. c The actively clamped class-E converter under no-load. d Class-F2 inverter. e Class-EF2 inverter. f Push–pull class-E inverter

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7 Major Challenges Arises in Application of Wireless Power Transfer (WPT) System In WPT system, misalignment between transmitter and receiver coils is a major issue which may reduce the amount of power transfer and efficiency. A good design should tolerate permissible levels of misalignment. Recently, several coil designs have been proposed [75, 76] which allow certain degree of misalignment without causing significant loss in power transfer. Design of power electronic converters with low losses for these high-frequency applications is another challenge. Also, the compensation circuits for the coils may be tuned dynamically to offset the effect of misalignment. In loosely coupled WPT systems, misalignment between transmitter and receiver coils is quite common. Misalignments can be either lateral or vertical. The coil parameters may vary due to misalignment, thereby changing the compensated WPT coil’s resonant frequency appreciably. The change in these parameters leads to increased leakage inductance over mutual inductance (M). The analysis on effects of misalignments is established in three different modes such as (1)

(2)

(3)

Ground clearance (D) is kept constant and lateral distance is varied from 0 to 24 cm, which is repeated for different D values. In Fig. 14a, M starts dropping with increase in lateral misalignment and D values. Initially, WPT coils are kept under perfectly aligned condition and the ground clearance is varied from 10 to 18 cm. The same was repeated with lateral misalignment distance of 8 cm. The plot of mutual inductance against ground clearance (vertical separation between WPT coils) is shown in Fig. 14b. The effect of lateral misalignments is also analyzed through the distribution of magnetic flux over the WPT coils. The magnetic field plots under perfectly aligned condition and lateral misalignment of 15 cm are shown in Fig. 15a, b. In the color bars (Shaded Plots), shaded colors indicate the intensity of magnetic flux density (B) over WPT coils. The red color emphasizes the maximum value of |B|, green color signifies the average intensity, and dark blue implies the least intensity. In Fig. 15a, under perfectly aligned condition, a part of the receiver coil is covered by red, yellow, and green colors. But in Fig. 15b, at misalignment distance of 15 cm the center area is filled with green and light blue colors. This implies that the change in flux distribution is large for varying misalignment values. The low flux density leads to less coupling factor between the WPT coils.

8 Conclusions The system level configuration of wireless power transfer (WPT) technology is presented in this chapter. EVs wireless charging governing international standards are summarized. The investigations on broad classification of WPT system for EVs

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Fig. 14 a Effect of lateral misalignment on mutual inductance. b Effect of vertical separation on mutual inductance

Fig. 15 a Filed plot under perfectly aligned condition. b Filed plot under lateral misalignment of 15 cm

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are performed, and significant details are documented. Possible modes of wireless charging for EVs are reviewed and discussed. Several novel coil architectures and circuit configurations presented in the literature are picked, and the state-of-the-art research development is emphasized. Rising potentials and challenges in the application of the WPT system for EVs are identified and elaborated here.

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Twelve Pulse-Based Battery Charger with PV Power Integration D. Suresh, V. Kumar, and Mote Mahesh

Abstract In this paper, the isolated diode rectifier-based battery charger is proposed for the electric vehicle. The proposed battery charger operates at line frequency. The diode rectifier-based battery charger is cost-effective solution for electric vehicles (EV). The diode rectifier-based battery charger introduces the harmonics in the supply. The harmonics does not contributes to useful power and need to be eliminated. The active power filter is well-established technique, and it is integrated with photo-voltaic modules for harmonics elimination, and photo-voltaic power generated from cell injected into the utility grid. The reference current estimated with instantaneous reactive power theory. The IRPT-based scheme is combined with maximum power point tracking (MPPT) technique for estimation of maximum power from modules of the photo-voltaic array. The adopted method simultaneously eliminates the harmonics and inject active power from the PV-modules without requirement of the separate inverter. The computer simulation study is carried out to establish efficacy of control scheme and topology for simultaneous compensation of harmonics and photo-voltaic also known as active power fed to the grid from PV-modules. Keywords APF · PV cell · Harmonics · Active power

1 Introduction Industrial revolution has made electric motors as fundamental needs of human life. The total around 3.6 million electric motor are used in India. By the end of the year 2021, the expected electric market around the world will across $170 billion. The D. Suresh (B) Department of Electrical Engineering, National Institute of Technology Raipur, Raipur, India e-mail: [email protected] V. Kumar Department of Electrical and Electronics Engineering, TKREC, Medbowli, Meerpet, India M. Mahesh Department of Electrical and Electronics Engineering, Vignan Institute of Technology and Science, Deshmukh, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 N. Marati et al. (eds.), AI Enabled IoT for Electrification and Connected Transportation, Transactions on Computer Systems and Networks, https://doi.org/10.1007/978-981-19-2184-1_9

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technological advancement of semi-conductor and micro-electronics has allowed simple, flexible speed, and torque control of AC motors. The robust control scheme has allowed in speed regulation and precise position control [1–3]. The adjustable speed drives offer several benefits such as ability to control starting current, reduced power usages, wide range of speed control, four-quadrant operations, energy saving, smoother operation, and improved system reliability. Medium power variable speed drives schemes offer substantial in process control, pumping system, and fan with improved efficiency. However, the medium voltage adjustable system continuously growing rapid rate in various applications. The commercial variable speed drives comprise of a supply frequency transformer, uncontrolled bridge rectifier as front converter followed the multilevel inverter. The topology makes the system bulky and induces the harmonics in the supply system. The harmonics in supply system causes adverse impact on the system and does not develop useful power and need to be compensated. The quality of power supply depends on the loads and their sensitivity to variations in currents. As the loads become more and more sophisticated, current disturbances become very costly for manufacturers in terms of loss of production, labor costs, loss of raw materials, and damage to equipment. This article is dedicated to the detailed study of with voltage structure connected in shunt active power filter with isolated line frequency battery charger. Firstly, this paper describes the operating principle of the active power filter followed by control strategies allowing the determination of reference current and harmonics mitigation. And also, presents the configurations of active power filter can be used for injection of active power filter to the grid. The grid is relieved the excessive power demand from the load [4–9].

2 Configuration of Diode Battery Charger with APF Topology The battery charger with isolation transformer is depicted in Figs. 1 and 2, separately. The battery charger is realized with three single-phase transformers with primary and two secondary, twelve diodes, inductive filter, and DC link capacitor. Three singlephase transformers are connected in star-star-delta configuration, which will allow the connection of twelve pulse bridge rectifier. It is twelve pulse converter and the output frequency is twelve times the input frequency. Twelve pulse converter increases the pulse number which results in reduction harmonics in the output of charger. In addition to this, twelve pulse converter reduces the requirement of filter. The disadvantage of line frequency battery is pollution of supply harmonics. Historically, harmonics current is mitigated with passive filters. However, passive filter has several issues such as resonance, bulky, and fixed compensation. The shunt active filter also known as APF is used to protect sensitive loads against current disturbances in the electrical network. It is inserted in parallel with the disturbed network and the load to be protected by means of an interfacing inductor. The power circuit of an APF is given

Twelve Pulse-Based Battery Charger with PV Power Integration

Fig. 1 Twelve pulse rectifier-based charger for electric vehicle Fig. 2 Topology of electric vehicle battery charger

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Fig. 3 EV-charger with photo-voltaic-based APF

in Fig. 3; it is mainly composed of two power blocks, one for control. The power pack most often consists of a three-phase two-level, a DC power supply system, an output filter and three interfacing inductors. The control unit consists of the identification of the disturbing currents and of the control of the currents injected into the network.

3 Dynamic Model of Photo-Voltaic Cell Figure 4 shows the dynamics model of PV cell. The voltage and current rating of the PV-modules are increased by means of the series and parallel combination of PV cells. The array can be formed with interconnections of the PV-modules. The modules, which are used to meet the required voltage level or current level. The arrays are connected at the input of the active power filter (APF). The APF act as interface between the photo-voltaic array and load. The photo-voltaic cell can be replaced as equivalent constant current source. The PV cell as equivalent constant current source (I–V characteristics) is mathematically represented as follows Fig. 4 Equivalent circuit of photo-voltaic cell

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Fig. 5 IRPT theory-based control scheme

  q Voci Vouti + RSi Ii Ii = ILi − Idi − Ishi = ILi − IDi e −1 − Ci ki Ti Rshi

(1)

  where IDi the saturation is current, qi is the electron charge 1.6 × 10−19 C , Ci is the  diode emission factor, ki is used denote the Boltzmann constant used to represent 1.38 × 10−23 J/K , and Ti is used to denote the temperature [10].

4 Control Scheme of APF Reference current estimation based on IRPT [5] is widely reported time-domainbased approach for computation of reference currents for active power filter. The IRPT-based scheme involves transformation of the 3-F quantities into the two-phase quantities, and consequently, estimation of the active power and reactive power is used for reference current estimation. These computed active power and reactive power used for subsequent computation of the bi-phase current. The bi-phase currents again transformed back to three quantities and are used as reference current for APF to obtain the desired sinusoidal signal at the source side of the APF power circuit. The control strategy of APF established on IRPT is presented in Fig. 5.

5 MPPT-Based DC Voltage Regulator The MPPT algorithm with PI controller is shown in Fig. 6. The reference voltage of APF voltage regulator is used to compute the desired voltage to the summation block of the PI regulator. The desired level of DC side voltage of capacitor is used to decide the active power extracted from the PV-array. Consequently, the active power

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Ip

Vp

Maximum power point tracking algorithm

_

+

Voltage regulator

is computed and added with the harmonics power of the control structure of the APF. The active power from PV-modules injected to the grid with APF. The computed active power should have negative sign, and then only active power fed from active power filter DC side to the grid will be possible.

6 Simulation Results and Discussion The output current of isolated line battery charger with PV-active power filter is presented in Fig. 7. The current (I La ) waveforms with stepped in shape are used to represent the current immediately after the uncontrolled bridge rectifier. The uncontrolled rectifier current is highly distorted and contain harmonics, and its value found to be 11.5%. The waveforms V sa and I sa are used to represent the source voltage and source current after elimination of the harmonics in source current. Figure 7b shows the twelve pulse converter AC side waveform and it is in stepped wave shape. The DC link voltage and current waveform of battery charger are depicted in Fig. 8. The battery charger voltage contains less ripple as compared to six pulse converter. The DC link current waveform is also shown in Fig. 8.

Fig. 7 Battery charger output current waveform

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Fig. 8 Simulated waveform of twelve pulse charger

The overall power system of APF consists of the power source, APF and uncontrolled twelve pulse bridge rectifier as nonlinear load. The active power filter as photo-voltaic power injector as well as compensator simulated response is depicted in Fig. 9a, b. The performance simulation study of the APF is carried out with PV-modules are connected on DC side of the APF. The active power filter inject compensating current from t = 0 to t = 0.5 s. to triplen harmonics components at the point of common coupling (PCC) and mitigates the harmonics current caused by the battery charger. Figure 9 shows after compensation with APF,

Fig. 9 APF simulated waveform with MPPT

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where the switch is connected at instant t = 0 s. The APF reduces the harmonics of source current from 11.5 to 2.8% and line current lean toward to sinusoidal waveform and total harmonics distortion is reduced to 2.8%. Figure 10 shows the simulation study of the photo-voltaic-based APF. It can be observed from Fig. 7, the simulated response of the APF is same as that of the active

Fig. 10 Characteristics of APF with power variation

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power filter. Before APF operation, source phase current is same as that of load current. After APF switched-on, source current follows source voltage and leads to sinusoidal as it can be shown in Fig. 10. When PV-modules are coupled on the DC side of the APF, the MPPT algorithm estimates the photo-voltaic power also known as real power from PV-modules of PV-array. The estimated active power altered the DC bus voltage reference voltage to injected active power from PV panels to the grid. As this can be shown in Fig. 10, the active power caused by PV-modules flow from APF to source is in reverse to the source current. At the same time, variation in currents waveforms is also observed, this variation is due to the variation in solar insolation. Figure 10a shows APF characteristics with power variation. The different waveform from the spectrum is recognized as photo-voltaic panel voltage, source voltage and current, photo-voltaic panel current and active power generated from PVpanel. The photo-voltaic voltage is same as that of the DC link voltage of capacitor. The DC side voltage is used for regulating the active power injected into the grid. As the solar irradiation is constant, then the active power generated from the PVpanel is constant. When the solar insolation increases corresponding increase in the generated active power is witnessed from Fig. 10. The increase in the active power generated on DC side of the APF inverter, then the corresponding increase in the source current is witnessed from Fig. 10. The peak value of the source phase current increases progressively with injected compensating current. As the solar irradiance increases, then the corresponding sources current in opposite direction increases, this results in relieving the loading on the source.

7 Conclusion This article projected a line frequency transformer built 12-pulse uncontrolled bridge rectifiers as charger or front AC-DC converter for adjustable speed drives. The topology consists of line frequency transfer, diode bridge rectifier, and the APF with photo-voltaic cell on its DC side. The rectifier is used as charger for the battery. The harmonics induced by the isolated line frequency battery charger is eliminated with active power filter. The APF also inject the real power from the PV-modules. The active power filter does not demand the separate inverter for the active power from the photo-voltaic. The control scheme exhibits the improved performance characteristics and inject the active power to the grid and relieve excessive stress on the supply system. The proposed system based the line frequency battery charger with photovoltaic active power filter can efficiently implemented with MATLAB/Simulink library Simpower system block sets.

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References 1. S. Inoue, H. Akagi, A bidirectional isolated DC–DC converter as a core circuit of the nextgeneration medium voltage power conversion system. IEEE Trans. Power Electron. 22(2), 535–542 (2007) 2. Toshiba MV Drives. Available: http://www.emainc.net/downloads/ToshibaMTX.pdf. 17 Nov 2014 3. TM-GE MV Drives. Available: http://www.wmea.net/Technical%20Papers/GE%20Medium% 20Voltage%20Drives.pdf. 17 Nov 2014 4. L. Chun-Kit, S. Dutta, B. Seunghun, S. Bhattacharya, Design considerations of high voltage and high frequency three phase transformer for solid state transformer application, in 2010 IEEE Energy Conversion Congress and Exposition (ECCE), 12–16 Sept 2010, pp. 1551–1558 5. H. Akagi, Y. Kanazawa, A. Nabae, Generalized theory of the instantaneous reactive power in three-phase circuits, in Proceedings of IEEE JIEE IPEC (1983), pp. 821–827 6. G. Gong, U. Drofenik, J.W. Kolar, 12-pulse rectifier for more electric aircraft applications, in 2003 IEEE International Conference on Industrial Technology, vol. 2, 10–12 Dec 2003, pp. 1096, 1101 7. D. Suresh, K. Venkateshwarlu, S.P. Singh, T2FLC based CHBMLI APF for power quality improvement, in IEEE Conference, ICCCI-2018, Jan 2018 8. D. Suresh, K. Venkateshwarlu, S.P. Singh, Adaptive control of three level active power filter, in IEEE Conference, ICCCI-2018, Jan 2018 9. D. Suresh, K. Venkateshwarlu, S.P. Singh, SIFLC based control implementation of APF, in IEEE Conference, ICCCI-2018, Jan 2018 10. M.G. Villalva, J.R. Gazoli, E.R. Filho, Comprehensive approach to modeling and simulation of photovoltaic arrays. IEEE Trans. Power Electron. 24(5), 1198–1208 (2009)

Design and Development of Brushless DC Motor Drive for Electrical Vehicle Application V. Kumar, Kalagotla Chenchireddy, Khammampati R Sreejyothi, and G. Sujatha

Abstract Nowadays, the usage of conventional vehicles’ importance is decreased due to increase in fuel cost and very high levels of air pollution, and also decreased the greatest negatives to human beings in petroleum resources: to produce safe, clean, and high-efficiency transportation implemented. Future driving technology will include electric, hybrid electric, and fuel cell-driven vehicles. This paper presents an overview of electric vehicle technology and implemented Speed Control of a Brushless DC motor for Electrical Vehicle applications. The performance of the BLDC motor is investigated under steady, dynamic state speed, and torque conditions. In the above two conditions, the actual value reached the reference value. The test results are verified in MATLAB/SIMULINK. Keywords BLDC motor · Electric vehicle · Speed · Torque

1 Introduction Field programmable gate array (FPGA) is implemented for BLDC motor having a digital controller. The BLDC system had two levels of operation: low duty level (DL) and high duty level (DH). The implemented controller utilizing the PID control reduced the cost of the system and also provided ease of operation that is capable of regulating the speeds without an observer. Regenerative braking improved the efficiency and extended the driving distance of electric vehicles [1]. Because of its great torque and efficiency, the BLDC motor is commonly utilized in electric vehicles. A dynamic load system cannot be controlled by the traditional PID controller found in BLDC motors. A PID-fuzzy controller was used to solve this flaw. In a variable speed and dynamic load situations, the PID-fuzzy controller maintained the steady-state V. Kumar (B) · K. Chenchireddy · K. R. Sreejyothi Department of EEE, Teegala Krishna Reddy Engineering College, Hyderabad, India e-mail: [email protected] G. Sujatha Department of EEE, G Narayanamma Institute of Technology and Science (for Women), Hyderabad, India © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2022 N. Marati et al. (eds.), AI Enabled IoT for Electrification and Connected Transportation, Transactions on Computer Systems and Networks, https://doi.org/10.1007/978-981-19-2184-1_10

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condition of the BLDC motor speed. As a result, the BLDC motor’s performance has improved [2]. In BLDC, the flux-weakening control and energy regeneration braking control were investigated. The controller described has high efficiency and improved the system’s dynamic performance under load-changing settings. In the system, the proportional–integral–differential control approach was presented. The problem of correctly regulating the speed over a vast range is solved with an arithmetic variable velocity [3]. This research [4] showed a BLDC braking system with an electrical braking system motor-based electrical vehicle using single, two, and three switching topologies as well as plugging as braking methods. The maximum voltage conversion, the boost ratio, and the braking torque ratios were employed for each braking method. The applied technique considers stopping the vehicle at any speed while recharging its batteries. Surface Brushless DC Motor (BLDC) and Mounded Permanent Magnet Synchronous Motor (SPMSM) were compared. In designing point of view, implemented an identical design of criteria such as the outermost diameter of the core, current density, and magnetic flux density. The results are compared to BLDC, the output power density of SPMSM is increased by 12.8% due to reduction in stack size, which reduced the torque ripple by 78% [5]. The hybrid energy storage system [HESS] was desirable for the interaction of the battery and super capacitor. The proposed HESS was composed of a super capacitor module, buck converter, battery pack, and a diode. The regenerative braking system improved the system efficiency by about 20%. The proposed scheme can capture the braking energy with suitable efficiency and also ensure the safe declaration of the electric vehicle [6]. A permanent magnet brushless DC (BLDC) motor is most commonly utilized for cost-cutting applications. A low-cost application was supplied by the fault operation in-wheel EV application [7]. Switched reluctance motor (SRM) had a simple and durable construction, and the fault operation in-wheel EV application provided a low-cost application. With the use of electro-mechanical braking (EMD) system, this paper proposed [8] fuzzy sliding mode control (FSMC) in a BLDC motor. Motor torque distributions were examined, and a motor driver was used to rotate the motor. When compared to PID and SMC, the suggested control technique has greater control performance, stronger resilience, and better adaptability to diverse loads. To predict the reference voltage applied by the inverter, a model of predictive control with a finite control set was given. In a voltage source inverter (VSI), the short circuit fault detection (FD) approach can detect open switch, open phase, and short circuit faults. It can also detect open phase and single switch problems [9]. In hybrid electric vehicles, the axial-flux permanent magnet BLDC is utilized to power traction motors for vehicles with a high constant-power speed ratio [10, 11]. The key benefits of a high pole count motor drive include increased kilovolt-ampere, efficiency, gradability, acceleration, and maximum speed. Using the baseline mot, the maximum gradability may be calculated. This paper presents a total of five sections. Section 1 presents the introduction, Sect. 2 presents Overview of Electrical vehicle, Sect. 3 presents BLDC motor, Sect. 4 presents Control Techniques, and Sect. 5 presents Simulation results.

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2 Electrical Vehicle Overview Electric motor propulsion, energy supply, and auxiliary are the three key subsystems that make up the drive train. The electric propulsion subsystem consists of a vehicle controller, power electronic converter, electric motor, mechanical transmission, and driving wheels. The energy base subsystem includes the energy source, energy management unit, and energy replenishment unit. The power steering unit, the hotel climate control unit, and the auxiliary supply unit make up the auxiliary subsystem [12] (Fig. 1). The vehicle controller generates proper control signals to the electronic power converter, which regulates and controls based on the control inputs from the accelerator and brake pedals and the power flow between the electric motor and the energy source. Regenerative braking in an electric vehicle enables the power flow to reverse, allowing the regenerated energy to be returned towards the energy source. The ability to receive regenerated energy is simply acquired by most EV batteries, ultra-capacitors, and flywheels. The vehicle controller and the energy management unit work together to keep regenerative braking and energy recovery within control. It also helps with energy recharging unit to regulate and execute the refilling unit, as well as to monitor the energy source’s usability. All of the EV auxiliaries, particularly the hotel temperature control and power steering units, receive power from the auxiliary power supply at different voltage levels. The electric motor’s speed control is comparable to that of the wheel speed; therefore, the vehicle speed is likewise controlled. However, to start and accelerate the car, this block necessitates a larger torque ratio from the electric motor. A permanent magnet synchronous motor with variable magnetic reluctance field modification was described in Ref. [13]. The force on the permanent magnet is used in a flux-weakening control approach. The PMSM has the advantage of being able to implement varied loads with variable magnetic flux.

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Fig. 1 Block diagram of general EV configuration

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3 BLDC Motor Electric cars (EVs) and hybrid electric vehicles (HEVs) rely on electric propulsion technologies (HEVs). Electric motors, power converters, and electronic controllers are all part of them. Electric energy is converted into mechanical energy by the electric motor. The BLDC motor drive is presented in this study for use in electric vehicles. The block diagram of the torque control of a BLDC motor is shown in Fig. 2. The BLDC motor drives the accelerator and brake pedals in vehicle traction applications by following the desired torque. As a result, torque control is a must. The desired current I* is calculated using a torque controller and the commanded torque T *. The current controller and commutation sequencer get the desired current I* position data from the position sensors, as well as current feedback from current transducers, and generate gating signals. The three-phase inverter (power converter) receives these gating signals and produces the phase current that the BLDC machine prefers. The ‘speed controller’ blocks are related to traditional controllers like a PI controller or advanced controllers like artificial intelligence controls, for example. The ‘current controller and commutation sequencer’ provides appropriately sequenced gating signals to the ‘three-phase inverter’ while trying to compare measured currents to a comparison to ensure constant peak current control via hysteresis (current chopping) or voltage source (PWM)-type current control. Tian and colleagues [14] presented a sensor-free control scheme for a low-speed five-phase permanent magnet brushless DC (58-BLDC) motor. When using high frequency (HF) injection-based sensorless control, the system delay and stator resistance may degrade sensorless control performance. This paper primarily establishes the proposed HF model of 58 BLDCs in the frequency domain. To optimize the power density, the phase current provided to the BLDC motor is also quasi-rectangular in shape, principally consisting of the fundamental and third harmonics. When a DC Supply

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Fig. 2 Block diagram of the torque control of BLDC motor

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square-wave voltage injection is employed, the offered method’s execution time in a DSP does not increase, saving more resources than the typical sine/cosine function. The performance of BLDC motor is given by Vt (s) = E s (s) + (Rs + s L s )Is (s)

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kT Rs + s L s Vt (s) − TL (s) (Rs + s L s )(s J + B) + kT kE (Rs + s L s )(s J + B) + kT kE (5)

In this paper [15], for radiated emissions from printed circuits, principal component analysis (PCA) and independent component analysis (ICA) are recommended. Two different methodologies compared, exhibiting similarities and differences in the retrieved components. Due to the measured circuit’s continuous wave (CW) excitation, this research focussed on space-frequency analysis. A comparison of PCA and ICA in the space–time and space–frequency domains has been seen for timevarying emissions from a complicated circuit. A five-phase brushless permanent magnet (PM) motor was proposed in Ref. [16]. The suggested motor has concentrated windings, resulting in a nearly trapezoidal back EMF. The motor is powered by a combination of sinusoidal and third harmonic currents. An equivalent PM brushless DC motor produced the same average torque as a BLDC motor (BLDC). The proposed five-phase motor is tested alongside its three- and five-phase PMSM and BLDC counterparts. The flux density is studied, and the produced static torque is calculated using the finite element approach. In some circumstances, this strategy has proven to be effective. In the flux-weakening area, however, quantitative speed or torque control is more difficult than with a PMSM motor (Fig. 3). If high-energy permanent magnets are employed as the field excitation mechanism, a permanent magnet motor drive with high power density, high speed, and high operation efficiency can be constructed. These benefits make electric and hybrid electric vehicle installation more appealing. Brushless DC (BLDC) motor drives, a type of permanent magnet motor, are the most promising candidate for EV and HEV applications. High efficiency, compactness, ease of control, ease of cooling, low maintenance, tremendous longevity, and reliability are all advantages of BLDC motors.

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The BLDC machine has a three-phase armature winding in the stator and permanent magnets in the rotor. BLDC motors are electronically controlled and do not require a commutator or brushes, resulting in improved overall performance and efficiency. It will save money, and the smaller size will decrease sparks. In comparison with DC motors, BLDC motors operate at higher speeds and torque. Brushless DC machines and power electronics-based power converters make up a BLDC motor. The rotor will be sensed by the sensor points H1, H2, and H3. By turning off the machine’s stator pole windings, the rotor position is fed to the hall sensors, which provide gating signals to the power converter. The motor torque and speed are controlled in this method. Because of their excellent efficiency and power density, PM brushless motors have become the preferred choice for EV propulsion. EVs have been classified using the classification of electrical machinery for EVs. EV machines can be divided into two categories: commutator and commutatorless. The former indicates that they have a commutator and carbon brushes, while the latter indicates that they do not have either. The line current pulses should be coordinated with the line-neutral back-EMF voltages of the particular phase to drive the motor with maximal and constant torque/ampere.

4 Control Techniques The active torque ripple correction technology suggested by de Castro and colleagues [17] is based on direct power regulation of permanent magnet brushless DC (BLDC) motor drives. The torque computations are induced by the non-sinusoidal back-EMF waveforms of the stator current. The solution to this problem is a straightforward and comprehensive strategy based on direct power control. Instead of employing classic field situated control (FOC) or direct force control (DTC) schemes, which are commonly used in AC machines with sinusoidal transition propagation, we adopted

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a new method. An alternative method of controlling shared force creation is introduced through a functioning and receptive rotor power control circle. As a result, the proposed method provides a fundamental way for reducing the force wave of an incredibly durable magnet coordinated engine with non-sinusoidal back EMF that does not require rotor direction or a back-EMF consonant substance assessment procedure [18]. For an electro hydrostatic actuation system in an aircraft application, this research suggested a brushless DC (BLDC) drive with a single-sided matrix converter (SSMC). The combination of an SSMC and a BLDC engine is innovative, and it is used to do tasks without the use of a chip. To control engine force, a basic hysteresis current control approach is used. With the punishment of more powerful devices, the multiphase SSMC provides high steady quality and adaptation to internal failure. The components of a five-phase SSMC model are put together. The findings of the examination are presented to confirm the drive’s execution. The torque, speed, and power of an EHA system’s electrical drive define the system’s performance. In the typical procedures for the control of a synchronous permanent magnet motor, Refs. [19, 20] provide a vector control (PMSM). The external speed of a PMSM drive with vector control has a considerable impact on the drive’s performance. Half breed fluffy PI regulators, in which the units are switched according to the pace of independent loads, are used to combine the advantages of corresponding in addition to vital (PI) and fluffy regulators. The controller gains are changed with the input error signal in a basic gain planned PI speed controller suggested. The stator currents are divided into torque and flux-producing components and controlled individually using coordinate transformations. This work [21] carefully researched brushless DC motors with direct torque and indirect flux control without the use of a location sensor (BLDC) motors with no sinusoidal back EMF. This review introduces a fresh and basic method for achieving low-recurrence force swell-free direct force control (DTC) with the most extreme ability based on the DQ reference outline. The suggested sensorless technique closely resembles the standard DTC scheme used for sinusoidal AC engines in that it uses d-pivot current to regulate the force directly and stator transition abundance in a roundabout fashion. This method allows for the regulation of fluctuating signals without the need for pulse width modulation or proportional plus integral regulators (Figs. 4 and 5). Three-phase stator armature stator currents are Ia, Ib, and Ic. Back EMFs are Ea, Eb, and Ec. Three hall sensors are Ha, Hb, and Hc (Fig. 6). When the north pole of a magnet is close to the sensor, the output is high, and when the south pole of a magnet is close to the sensor, the output is low. The supply delivered to the stator in a BLDC motor is determined by the rotor position, which is sensed by the hall sensor, whose output voltage is proportional to the strength of the magnetic field (Fig. 7). We generate six waveforms for the three-phase inverter circuit in 120° of operation using logic gates (output is six-stepped waveforms). When the rotor position controller only delivers two pulses, it signifies that one pulse is turned off while the other is turned on.

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5 Simulation Results A three-phase motor rated at 1 kW, 500 Vdc, and 3000 rpm is driven by a six-step voltage inverter. A MOSFET bridge serves as the inverter. The DC bus voltage is controlled by a speed regulator. The Hall Effect signals from the motor are decoded to create the inverter gates signals. The inverter’s three-phase outputs are applied to the stator windings of the PMSM block. The torque is applied to the machine’s shaft

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