Green Engineering and Technology: Innovations, Design, and Architectural Implementation 2020057631, 2020057632, 9780367758059, 9781003176275, 9781032008936

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Green Engineering and Technology

Green Engineering and Technology: Concepts and Applications Series Editors: Brojo Kishore Mishra, GIET University, India and Raghvendra Kumar, LNCT College, India Environmental degradation is an important issue these days for the whole world. Different strategies and technologies are used to save the environment. Technology is the application of knowledge to practical requirements. Green technologies encompass various aspects of technology that help us reduce the human impact on the environment and create ways of sustainable development. This book series will enlighten the green technology in different ways, aspects, and methods. This technology helps people to understand the use of different resources to fulfill their needs and demands. Some points will be discussed as the combination of involuntary approaches and government incentives, a comprehensive regulatory framework will encourage the diffusion of green technology, and least developed countries and developing states of small islands require unique support and measures to promote the green technologies.

Green Internet of Things for Smart Cities Concepts, Implications, and Challenges Edited by Surjeet Dalal, Vivek Jaglan, and Dac-Nhuong Le

Green Materials and Advanced Manufacturing Technology Concepts and Applications Edited by C. Samson Jerold Samuel, M. Suresh, Arunseeralan Balakrishnan, and S. Gnansekaran

Cognitive Computing Using Green Technologies Modeling Techniques and Applications Edited by Asis Kumar Tripathy, Chiranji Lal Chowdhary, Mahasweta Sarkar, and Sanjaya Kumar Panda

Handbook of Green Engineering Technologies for Sustainable Smart Cities Edited by Saravanan Krishnan and G. Sakthinathan

Green Engineering and Technology Innovations, Design, and Architectural Implementation Edited by Om Prakash Jena, Alok Ranjan Tripathy, and Zdzislaw Polkowski For more information about this series, please visit: https://www.routledge.com/GreenEngineering-and-Technology-Concepts-and-Applications/book-series/CRCGETCA

Green Engineering and Technology

Innovations, Design, and Architectural Implementation

Edited by

Om Prakash Jena Alok Ranjan Tripathy Zdzislaw Polkowski

MATLAB® is a trademark of The MathWorks, Inc. and is used with permission. The MathWorks does not warrant the accuracy of the text or exercises in this book. This book’s use or discussion of MATLAB® software or related products does not constitute endorsement or sponsorship by The MathWorks of a particular pedagogical approach or a particular use of the MATLAB® software. First edition published 2021 by CRC Press 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742 and by CRC Press 2 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN © 2021 selection and editorial matter, Om Prakash Jena, Alok Ranjan Tripathy, Zdzislaw Polkowski; individual chapters, the contributors CRC Press is an imprint of Taylor & Francis Group, LLC The right of Om Prakash Jena, Alok Ranjan Tripathy, and Zdzislaw Polkowski to be identified as the authors of the editorial material, and of the authors for their individual chapters, has been asserted in accordance with sections 77 and 78 of the Copyright, Designs, and Patents Act 1988. Reasonable efforts have been made to publish reliable data and information, but the author and publisher cannot assume responsibility for the validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to publish in this form has not been obtained. If any copyright material has not been acknowledged, please write and let us know so we may rectify in any future reprint. Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted, or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, including photocopying, microfilming, and recording, or in any information storage or retrieval system, without written permission from the publishers. For permission to photocopy or use material electronically from this work, access www.copyright. com or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers, MA 01923, 978-750-8400. For works that are not available on CCC, please contact mpkbookspermissions@tandf. co.uk. Trademark notice: Product or corporate names may be trademarks or registered trademarks and are used only for identification and explanation without intent to infringe. Library of Congress Cataloging‑in‑Publication Data Names: Jena, Om Prakash, editor. | Tripathy, Alok Ranjan, editor. | Polkowski, Zdzislaw, editor. Title: Green engineering and technology : innovations, design, and architectural implementation / edited by Om Prakash Jena, Alok Ranjan Tripathy, Zdzislaw Polkowski. Description: First edition. | Boca Raton : CRC Press, 2021. | Series: Green engineering and technology : concepts and applications | Includes bibliographical references and index. Identifiers: LCCN 2020057631 (print) | LCCN 2020057632 (ebook) | ISBN 9780367758059 (hbk) | ISBN 9781003176275 (ebk) Subjects: LCSH: Sustainable engineering. Classification: LCC TA163 .G44 2021 (print) | LCC TA163 (ebook) | DDC 628—dc23 LC record available at https://lccn.loc.gov/2020057631 LC ebook record available at https://lccn.loc.gov/2020057632 ISBN: 978-0-367-75805-9 (hbk) ISBN: 978-1-032-00893-6 (pbk) ISBN: 978-1-003-17627-5 (ebk) Typeset in Times by codeMantra

Contents Preface.......................................................................................................................ix Editors..................................................................................................................... xiii Contributors.............................................................................................................. xv Chapter 1 Cloud and Green IoT-based Technology for Sustainable Smart Cities........................................................................................... 1 Parthasarathi Pattnayak, Om Prakash Jena, and Saundarya Sinha Chapter 2 Dynamic Models for Enhancing Sustainability in Automotive Component Manufacturing Systems................................................... 21 Jangam Ramesh, M. Mohan Ram, and Y.S. Varadarajan Chapter 3 Internet of Agriculture Things (IoAT): A Novel Architecture Design Approach for Open Research Issues....................................... 35 K. Lova Raju and V. Vijayaraghavan Chapter 4 E-Navigation: An Indoor System for Green City Sustainable Development Using a UGU Engine Architecture............................... 57 Ajay B. Gadicha, Vijay B. Gadicha, and Om Prakash Jena Chapter 5 Biomass Waste-derived Electrode Material and Bio-based Solid Electrolyte for Sustainable Energy Systems....................................... 71 Sudhakar Y. N. and Anitha Varghese Chapter 6 RF Energy Harvesting for WSNs: Overview, Design Challenges, and Techniques................................................................ 85 J. John Paul and A. Shobha Rekh Chapter 7 Sustainable and Renewable Isolated Microhydropower Generation Using a Variable Asynchronous Generator Controlled by a Fuzzy PI AC–DC–AC Converter and D-STATCOM.................................................................................... 103 P. Devachandra Singh and Sarsing Gao

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Chapter 8 Phytoconstituents of Common Weeds of Uttarakhand Proposed as Bio-pesticides or Green Pesticides with the Use of In-Silico and In-Vitro Techniques.................................................................... 121 Somya Sinha, Kumud Pant, Manoj Pal, Devvret Verma, and Ashutosh Mishra Chapter 9 On Energy Harvesting in Green Cognitive Radio Networks............ 137 Avik Banerjee and Santi P. Maity Chapter 10 Mitigation on the Impact of Electric Vehicle Charging Stations by Splitting the Capacity and Optimally Locating on a Reconfigured RDS............................................................................ 151 M. Satish Kumar Reddy and K. Selvajyothi Chapter 11 Parameter Estimation of a Single Diode PV Cell Using an Intelligent Computing Technique................................................. 177 Shilpy Goyal, Parag Nijhawan, and Souvik Ganguli Chapter 12 On the Dynamics of Cellular Automata-based Green Modeling toward Job Processing with Group-based Industrial Wireless Sensor Networks in Industry 4.0.......................................................207 Arnab Mitra and Avishek Banerjee Chapter 13 Green Cloud Computing: An Emerging Trend of GIT in Cloud Computing.......................................................................... 225 Satya Sobhan Panigrahi, Bibhuprasad Sahu, Amrutanshu Panigrahi, and Sachi Nandan Mohanty Chapter 14 Internet of Things for Green Technology.......................................... 243 Saurabh Bhattacharya and Manju Pandey Chapter 15 Green Health: Making Green Healthcare Using Reinforcement Learning in Fog-assisted Cloud Environment.................................. 259 Saeed A. L. Amodi, Sudhansu Shekhar Patra, Om Prakash Jena, Suman Bhattacharya, Nitin S. Goje, and Rabindra Kumar Barik

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Chapter 16 Smart Agricultural Robot.................................................................. 273 Vimal Kumar M. N., Aakash Ram S., and Bennet Niffin N. Chapter 17 A Survey of Lightweight Cryptography for Power-constrained IoT Devices: Security Challenges and Issues.................................... 293 Sunil Kumar and Dilip Kumar Chapter 18 Nanogenerator-based Sensors for Human Pulse Measurement........ 315 Ammu Anna Mathew, S. Vivekanandan, and Arunkumar Chandrasekhar Chapter 19 Future Challenges and Applications in Green Technology.............. 327 Kali CharanRath, Ravindra N. Bulakhe, and Anuradha B. Bhalerao Chapter 20 Implementation and Use of Green Computing in Polish Companies versus Implementation of Features Characteristic of Teal Organizations........................................................................ 343 Agnieszka Rzepka, Maria Kocot, and Elżbieta Jędrych Chapter 21 Design of a Pentagon Slot-based Multi-band Linear Antenna Array for Energy-efficient Communication: Future Challenges and Applications in Green Technologies.......................................... 357 Satheesh Kumar P. and Balakumaran T. Index....................................................................................................................... 369

Preface Without compromising on economic feasibility and productivity, green engineering comprises the design, marketing, and use of processes and goods in a way that eliminates waste, promotes sustainability, and minimizes risk to human health and the environment. Green engineering promotes the idea to protect human health and the environment, in the design and developmental phase of a process or product, when applied early and may have the greatest impact and cost-effectiveness. Green computing develops engineering strategies and applies them while becoming mindful of local geography, aspirations, and cultures. It creates engineering strategies beyond existing or dominant technologies, to achieve sustainability, enhance, innovate, and invent (technologies). Instead of being circumstantial, designers must aim to ensure that all inherently non-hazardous materials and energy inputs and outputs are as practicable. The concepts of Green engineering are inherent. Waste reduction is better than handling or cleaning up waste after it has been made. Separation and purification activities should be planned to minimize the consumption of energy and the use of materials. This maximizes the efficiency of products, processes, and procedures. Green engineering approaches the design of goods and processes to achieve one or more of the goals by applying financially and technologically feasible principles as follows: (1) reducing the amount of pollution generated by the construction or operation of a facility, (2) decreasing the human population’s exposure to potential hazards (including reducing toxicity), and (3) decreasing the volume of pollution created by the construction or operation of facility performance and viability. Green architecture is active in many fields of engineering. This includes sustainable design, life cycle analysis (LCA), avoidance of waste, environmental design (DfE), disassembly design (DfD), and recycling design (DfR). As such, green engineering is a branch of sustainable engineering. Green engineering requires four fundamental approaches to improving processes and goods from an environmental point of view in order to make them more effective. From a holistic perspective that incorporates various technical disciplines, Green engineering covers land use planning, architecture, landscape architecture, and other design areas, as well as social sciences (e.g. to decide how different types of people use goods and services), in addition to all engineering disciplines. Green engineers are concerned with space, the sense of place, seeing the site map as a collection of boundary-wide fluxes, and considering these structures’ combinations across larger regions, such as urban areas. Life cycle analysis is an important method for Green engineering that provides a holistic view of the entirety of a product, process, or service, including raw materials, manufacturing, transport, supply, use, repair, recycling, and final disposal. By determining its life cycle, a full image of the product should be given. The  first step in a life cycle assessment is to gather data on the movement of a

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product into a recognizable community. Once the amounts of various components of such a flow are known, the important functions and impacts of each step in the processing, manufacture, use, and recovery/disposal are estimated. In sustainable design, engineers have to optimize performance for variables that offer the best in temporal frames. This book provides a comprehensive study of the use of Green computing in the modern world by embedding it with IoT. It also presents several studies based on Green IoT-based technology that will be helpful for readers of all levels. It consists of a wide range of topics containing the use of Green computing in IoT. Chapter 1 provides an overview of cloud and Green IoT-based technology for sustainable smart cities. This chapter discusses the basic applications and services of green IoT features in smart cities. Chapter 2 gives a methodical treatment of dynamic models for enhancing sustainability in automotive component manufacturing systems and a case study. Chapter 3 presents the Internet of Agriculture Things (IoAT): a novel architecture design approach for open research issues. It explains the functional blocks, characteristics, and applications of IoT. Chapter 4 explains e-navigation: an indoor system for Green city sustainable development using a UGU engine architecture. Chapter 5 explores the biomass waste-derived electrode material and bio-based solid electrolyte for sustainable energy systems. RF energy harvesting for WSNs is discussed in Chapter 6. Chapter 7 presents an efficient and robust renewable small hydropower generation scheme for supplying remote areas. The proposed model is mostly suitable for supplying household loads in remote areas located far away from the grid. CAG and Hydro Turbine models controlled by a fuzzy PI-based AC-DC-AC converter and a PI-based D-STATCOM are discussed. Chapter 8 discusses the in-silico analysis, molecular interaction, and ADMET studies of the phyto-compounds of Parthenium hysterophorus, Alternanthera ­sessilis, and Lantana camara weeds with the essential proteins of beetles and aphids to estimate the binding affinity or interaction of phytoconstituents with proteins of the pests. Chapter 9 suggests a simple energy harvesting EH-CRN model operated by a typical frame structure. The frame structure consists of two non-overlapping time slots, one for the spectrum sensing and the other slot performs energy harvesting or secondary data transmission depending on the transmission or non-transmission state of the PU, respectively. Chapter 10 explores the mitigation on the impact of electric vehicle charging stations (EVCS) by splitting the capacity and optimally locating on a reconfigured RDS. The basic objective is to reduce the losses and to place EVCS at various locations. Chapter 11 discusses the parameter estimation of single diode PV cells using an intelligent computing technique. The aim is to identify the five different parameters of a single diode (SD) model depending on the parameters obtained from the commercial PV modules in the datasheet.

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Chapter 12 is titled On the Dynamics of Cellular Automata-based Green Modeling toward Job Processing with Group-based Industrial Wireless Sensor Networks in Industry 4.0. It provides energy-efficient scheduling and deployment with Industrial Wireless Sensor Networks (WSNs). It discusses the power consumption with the present CA-based approach. Chapter 13 is about Green cloud computing: an emerging trend of GIT in cloud computing. Green cloud computing has efficient energy consumption as compared to the traditional cloud architecture and is highly environmentally sustainable. It basically proposed a load-balancing algorithm using the basic rules of the genetic algorithm. Chapter 14 reviews the Internet of Things for Green technology. IoT helps mankind and at the same time, it generates e-toxic waste, a large amount of heat. It can be minimized by combining IoT with Green technology. This chapter discusses IoT, steps to achieve Green IoT, different issues, and challenges to attain Green IoT. Chapter 15 discusses Green health: making Green healthcare using reinforcement learning in fog-assisted cloud environment. It uses the VM consolidation technique using reinforcement learning to optimize the energy consumption in the fog-assisted cloud data centers. Automation of farm activities can lead to a transformation of the agricultural domain from static to intelligent, which leads to higher production. The manual labor on the field for irrigation can be reduced by using a smart agricultural robot, minimizing the time, cost, and power, as discussed in Chapter 16. Due to the increase in IoT, data security becomes a primary concern in IoT devices. Chapter 17 focuses on a survey of lightweight cryptographic algorithms that includes lightweight block ciphers, hash functions, and stream ciphers for power-constrained IoT devices. The cryptographic algorithms are evaluated based on the key size, block size, round size, and structure. Chapter 18, Nanogenerator-based Sensors for Human Pulse Measurement, focuses on nanogenerators in the biomedical field that combine medical principles with nanogenerators. Nanogenerators serve as an alternative supplier of power in healthcare electronics. The future challenges and applications in Green technology discussed in Chapter 19 state the green innovation without harming the assets of the world. Green development includes energy effectiveness, reusability, security and well-being concerns, and sustainable assets. Chapter 20 discusses the implementation and use of Green computing in Polish companies versus implementation of feature characteristics of Teal organizations. It  analyzes the phenomenon of Green computing in companies that develop characteristics of Teal organizations. The aim is to examine the characteristic features of Teal organizations. Chapter 21, Design of a Pentagon Slot-based Multi-band Linear Antenna Array for Energy-efficient Communication: Future Challenges and Applications in Green Technology explores a patch array antenna that provides different applications such as mobile data communications to vehicles, machines, smart objects, and sensors and handles a huge number of users to manage the future growth of traffic.

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MATLAB® is a registered trademark of The MathWorks, Inc. For product information, please contact: The MathWorks, Inc. 3 Apple Hill Drive Natick, MA 01760-2098 USA Tel: 508-647-7000 Fax: 508-647-7001 E-mail: [email protected] Web: www.mathworks.com

Editors Dr. Om Prakash Jena is an Assistant Professor in the Department of Computer Science, Ravenshaw University, Cuttack, Odisha. He has 10 years of teaching and research experience in undergraduate and postgraduate levels. He has published several technical papers in international journals/ conferences and edited book chapters in reputed publications. Dr. Jena is a member of IEEE, IETA, IAAC, IRED, IAENG, and WACAMLDS. His current research interest includes Database, Pattern Recognition, Cryptography, Network Security, Artificial Intelligence, Machine Learning, Soft Computing, Natural Language Processing, Data Science, Compiler Design, Data Analytics, and Machine Automation. He has guided many projects and thesis at the UG and PG levels. He has many edited books, published by Wiley, Bentham Science Publication, and CRC Press to his credit, and he is also the author of two textbooks under Kalyani Publisher. He is an editorial board member and reviewer of several international journals. Dr. Alok Ranjan Tripathy is an Assistant Professor in the Department of Computer Science, Ravenshaw University, Cuttack, Odisha. He has more than 15 years of teaching experience in undergraduate and postgraduate levels. He earned his MTech degree and PhD in Computer Science from Utkal University. He has published several technical papers in international journals/conferences and edited book chapters in reputed publications. He is a life member of ISTE, CSI, and Odisha Information Technology Society (OITS). His current research interests include Algorithms, Pattern Recognition, Cloud Computing, Network Security, Artificial Intelligence, Machine Learning, Quantum Computing, and Wireless Sensor Networks.

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Dr. Zdzislaw Polkowski is a Professor of UJW at the Faculty of Technical Sciences and Rector’s Representative for International Cooperation and Erasmus+ Program at the Jan Wyzykowski University Polkowice. He earned a PhD in Computer Science and Management from the Wroclaw University of Technology. He has published more than 75 papers in journals, 25 conference proceedings, including more than 20 papers in journals indexed in the Web of Science, Scopus, IEEE. Dr. Polkowski served as a member of the Technical Program Committee at many international conferences in Poland, India, China, Iran, Romania, and Bulgaria. To date, he has delivered 24 invited talks at different international conferences across various countries. His areas of interest include IT in Business, IoT in Business, and Education Technology. He has successfully completed a research project on developing the innovative methodology of teaching Business Informatics funded by the European Commission. He also owns an IT SME consultancy company in Polkowice and Lubin, Poland.

Contributors Saeed A. L. Amodi School of Computer Engineering KIIT Deemed to be University Bhubaneswar, Odisha, India

Suman Bhattacharya CAAS KIIT Deemed to be University Bhubaneswar, Odisha, India

Balakumaran T. Department of Electronics and Communication Engineering Coimbatore Institute of Technology Coimbatore, Tamil Nadu, India

Ravindra N. Bulakhe Korea National University of Transportation Chungju, South Korea

Avik Banerjee Department of Electronics and Communication Engineering Madanapalle Institute of Technology and Science Angallu, Andhra Pradesh, India

Arunkumar Chandrasekhar Department of Sensors and Biomedical Technology Vellore Institute of Technology Vellore, Tamil Nadu, India

Avishek Banerjee Department of Information Technology Asansol Engineering College Asansol, West Bengal, India

Ajay B. Gadicha Department of Computer Science and Engineering P.R. Pote College of Engineering and Management Amravati, Maharashtra, India

Rabindra Kumar Barik School of Computer Applications KIIT Deemed to be University Bhubaneswar, Odisha, India Anuradha B. Bhalerao Applied Science Department K.K. Wagh Institute of Engineering Education & Research Nasik, Maharashtra, India Saurabh Bhattacharya National Institute of Technology Raipur, Chhattisgarh, India

Vijay B. Gadicha Department of Computer Science and Engineering G. H. Raisoni University Amaravati, Maharashtra, India Souvik Ganguli Department of Electrical & Instrumentation Engineering Thapar Institute of Engineering & Technology Patiala, Punjab, India

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Sarsing Gao Department of Electrical Engineering North Eastern Regional Institute of Science and Technology Nirjuli, Arunachal Pradesh, India

Satheesh Kumar P. Department of Electronics and Communication Engineering Coimbatore Institute of Technology Coimbatore, Tamil Nadu, India

Nitin S. Goje Faculty of Science, IT Department Tishk International University Erbil, Kurdistan Region, Iraq

Sunil Kumar Department of Computer Science & Engineering National Institute of Technology Jamshedpur, Jharkhand, India

Shilpy Goyal Department of Electrical & Instrumentation Engineering Thapar Institute of Engineering & Technology Patiala, Punjab, India Elżbieta Jędrych Faculty of Business and International Relations Management Institute Academy of Finance and Business Vistula Warsaw, Poland Om Prakash Jena Department of Computer Science Ravenshaw University Bhubaneswar, Odisha, India Maria Kocot Department of Economic Informatics University of Economics in Katowice Katowice, Poland Dilip Kumar Department of Computer Science & Engineering National Institute of Technology Jamshedpur, Jharkhand, India

Vimal Kumar M. N. Department of Electronics and Communication Engineering R.M.D. Engineering College Kavaraipettai, Tamil Nadu, India Santi P. Maity Department of Information Technology Indian Institute of Engineering Science and Technology Shibpur, Howrah, West Bengal, India Ammu Anna Mathew School of Electrical Engineering Vellore Institute of Technology Vellore, Tamil Nadu, India Ashutosh Mishra Department of Research Ethics Uttarakhand Council of Science and Technology Dehradun, Uttarakhand, India Arnab Mitra Department of Computer Science & Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar, Odisha, India

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Sachi Nandan Mohanty Department of Computer Science & Engineering ICFAI Tech Hyderabad, Telangana, India Bennet Niffin N. SciComm India Greater Noida, Uttar Pradesh, India Parag Nijhawan Department of Electrical & Instrumentation Engineering Thapar Institute of Engineering & Technology Patiala, Punjab, India Manoj Pal Department of Life Sciences Graphic Era (Deemed to be) University Dehradun, Uttarakhand, India Manju Pandey National Institute of Technology Raipur, Chhattisgarh, India Amrutanshu Panigrahi Department of Computer Science & Engineering SOA University Bhubaneswar, Odisha, India Satya Sobhan Panigrahi Department of Computer Science & Engineering BPUT Bhubaneswar, Odisha, India Kumud Pant Department of Biotechnology Graphic Era (Deemed to be) University Dehradun, Uttarakhand, India

Sudhansu Shekhar Patra School of Computer Applications KIIT Deemed to be University Bhubaneswar, Odisha, India Parthasarathi Pattnayak School of Computer Applications KIIT Deemed to be University Bhubaneswar, Odisha, India J. John Paul Karunya Institute of Technology and Sciences Karunya University Coimbatore, Tamil Nadu, India K. Lova Raju Electronics and Communication Engineering Vignan’s Foundation for Science, Technology & Research (VFSTR University) Guntur, Andhra Pradesh, India Aakash Ram S. KPIT Technologies Limited Pune, Maharashtra, India M. Mohan Ram Department of Industrial and Production Engineering The National Institute of Engineering Mysuru, Karnataka, India Jangam Ramesh Department of Industrial and Production Engineering The National Institute of Engineering Mysuru, Karnataka, India Kali Charan Rath Department of Mechanical Engineering GIET University Gunupur, Odisha, India

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M. Satish Kumar Reddy Department of ECE IIITDM Kancheepuram, Chennai, Tamil Nadu, India A. Shobha Rekh Karunya Institute of Technology and Sciences Karunya University Coimbatore, Tamil Nadu, India Agnieszka Rzepka Department of Economics and Economic Management Lublin University of Technology Lublin, Poland Bibhuprasad Sahu Department of Computer Science & Engineering BPUT Bhubaneswar, Odisha, India K. Selvajyothi Department of ECE IIITDM Kancheepuram, Chennai, Tamil Nadu, India P. Devachandra Singh Department of Electrical Engineering North Eastern Regional Institute of Science and Technology Nirjuli, Arunachal Pradesh, India Saundarya Sinha School of Computer Applications KIIT Deemed to be University Bhubaneswar, Odisha, India

Contributors

Somya Sinha Department of Biotechnology Graphic Era (Deemed to be) University Dehradun, Uttarakhand, India Sudhakar Y. N. Department of Chemistry CHRIST (Deemed to be University) Bengaluru, Karnataka, India Y. S. Varadarajan Department of Industrial and Production Engineering The National Institute of Engineering Mysuru, Karnataka, India Anitha Varghese Department of Chemistry CHRIST (Deemed to be University) Bengaluru, Karnataka, India Devvret Verma Department of Biotechnology Graphic Era (Deemed to be) University Dehradun, Uttarakhand, India V. Vijayaraghavan Electronics and Communication Engineering Vignan’s Foundation for Science, Technology & Research (VFSTR University) Guntur, Andhra Pradesh, India S. Vivekanandan Department of Instrumentation, School of Electrical Engineering Vellore Institute of Technology Vellore, Tamil Nadu, India

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Cloud and Green IoT-based Technology for Sustainable Smart Cities Parthasarathi Pattnayak KIIT Deemed to be University

Om Prakash Jena Ravenshaw University

Saundarya Sinha KIIT Deemed to be University

CONTENTS 1.1 Introduction ...................................................................................................... 2 1.2 Smart City Applications and Services .............................................................. 3 1.2.1 Smart Waste Management .................................................................... 3 1.2.2 Smart Energy ........................................................................................ 3 1.2.3 Smart Transportation ............................................................................ 3 1.2.4 Smart Water Management .................................................................... 4 1.2.5 Smart Health Care ................................................................................ 4 1.2.6 Smart Buildings and Lighting .............................................................. 4 1.2.7 Smart Public Safety .............................................................................. 4 1.2.8 Smart Education ................................................................................... 4 1.3 G-IoT Features in Smart Cities ......................................................................... 5 1.3.1 Green Smart Homes ............................................................................. 5 1.3.2 Green Smart Offices ............................................................................. 5 1.3.3 Green Smart Healthcare System........................................................... 6 1.3.4 Green Smart Transport System ............................................................ 7 1.3.5 Green Smart Environment .................................................................... 7 1.3.6 Green Waste Management .................................................................... 8 1.4 Use of Algorithm and Software in G-IoT Smart Cities .................................... 8 1.4.1 Green Computing Eco-Friendly Technology ....................................... 9 1.4.2 Design Green Data Center .................................................................... 9 1.4.3 Virtualization for Going Green ............................................................ 9 1.4.4 Green Power Management.................................................................... 9

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1.5 1.6 1.7

Big Data and IoT Utilizations: Smart Sustainable Cities versus Smart Cities .... 9 Smart Cities Green Index Indicators .............................................................. 10 Cloud-based G-IoT Architecture .................................................................... 12 1.7.1 Sensor Layer and Smart City Infrastructure....................................... 12 1.7.2 Network Layer .................................................................................... 13 1.7.3 Analytic Big Data Layer ..................................................................... 13 1.7.4 Application Layer ............................................................................... 14 1.7.5 Presentation Layer .............................................................................. 14 1.8 Analytical Framework .................................................................................... 15 1.8.1 Domains and Systems of Urban Areas ............................................... 15 1.8.2 Data Categories, Big Data Sources, and Storage Facilities in Urban Areas ................................................................ 16 1.8.3 Cloud Computing or Fog/Edge Computing........................................ 16 1.8.4 Big Data Applications ......................................................................... 17 1.9 Conclusion ...................................................................................................... 17 References ................................................................................................................ 18

1.1

INTRODUCTION

The Internet of Things (IoT) has become one of the most common technologies that we use in our day-to-day lives. From marketplaces to communication between robots, it is everywhere and it shows its vast range of services by improving its quality of service. This technology uses myriad sensors and sends data over networks, which consumes a great deal of energy. The increasing energy utilization causes emission of a vast amount of carbon dioxide (CO2) in the environment leading to the increase in global warming [1]. It is already known that electronic devices emit the gases that are harmful to the environment, and these increasing trends of digitization and technological development will lead to more emission of harmful gases leading to an increase in global warming. This problem demands introducing more energy-efficient and environmentally friendly devices, which provide the required technological facilities but also do not affect the already compromised environment. This is Green technology. Green IoT (G-IoT) is a term used to describe IoT-based devices that are more energy-efficient and environmentally friendly. G-IoT has various applications in industrial automation, improvement of health and living, habitat monitoring, smart cities, energy, transportation, etc. This chapter presents a study that suggests building IoT devices with more efficiency and effectiveness but with less impact on the environment. G-IoT for smart cities allows providing various services such as smart buildings, smart street lights, smart waste management, smart water management, and more. With increasing urbanization, the smart utilization of limited natural resources such as water is vitally important and here, G-IoT can be very helpful for continuous and precise monitoring of the resources to minimize their wastage. G-IoT, like other IoT devices, utilizes sensors, the Internet, tags, etc., for providing services. The main aim of G-IoT-enabled cities is to minimize the

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excessive harmful effects that these devices or their resources can impart to the environment. This chapter provides a brief overview of enabling G-IoT-based smart cities. We describe the various characteristics of urban smart cities and how G-IoT can be enabled in these cities to provide numerous technological functionalities. Here, we have used a cloud-based framework for minimizing the use of hardware.

1.2 SMART CITY APPLICATIONS AND SERVICES Smart cities rely on technological solutions such as IoT sensors, networks, and various applications to improve the services pertaining to energy usage, air quality, and traffic congestion in the cities, thereby enriching the quality of living of the residents. The smart city market is growing at a rapid pace. In the year 2020, it is estimated to be hundreds of billions of dollars. It is expected that the smart city market has huge potential to grow in the future as well. There are various types of services and applications such as transportation, public utilities, education, health care, and public safety. Applications related to disaster management, logistics, and smart buildings are also important for smart cities. Following are some of the important applications and services worth mentioning here.

1.2.1 Smart Waste Management The smart waste management mechanism includes activities such as waste sensing and collection, sorting, recycling, and disposal. These sensors attached to waste bins have the capability of notifying the status of the waste levels and upload data to the Smart City Cloud. The official in-charge can access the data from the cloud. The data thus accessed by the management official help redefine the schedule to maximize waste ­collection. It also helps locating vehicles and waste bins and ­redefines the shortest routes for each waste-collection truck to become more fuel-efficient. The entire process can be centrally regulated and provides quality services to residents of the smart city.

1.2.2 Smart Energy Urban IoT will provide means to conserve and enhance energy efficiency. The s­olution may provide a detailed account of the energy requirements of the city. For example, management would be able to get a clear-cut idea about the different sources such as transport, public lighting, traffic lights, and controlled cameras installed at various important locations in the city. IoT can be used to create a smart grid system, which will comprise a smart meter that will control the flow of electricity to meet the demands of the citizens.

1.2.3 Smart Transportation IoT can help cities use technology to make commuting easy and hassle-free. The smart features include parking, traffic regulation, and vehicle tracking. Passengers

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will be provided with real-time information through a passenger information system. For example, the expected arrival time is displayed on electronic signboards located at railway stations, bus stations, and airports. Information related to the availability of parking spaces can be provided through electronic signboards by real-time parking management systems.

1.2.4

Smart Water management

Water management is the most critical aspect of a city. It broadly includes storage, conservation, and efficient distribution. Smart water management by using IoT provides insights from data that help authorities regulate waste water treatment and flood control measures and enable optimized water consumption. The IoT-based water management solution can help transform the agricultural sector by using inputs obtained from sensors for alerting farmers regarding various emergency situations such as bad weather conditions and enables them to save their crops.

1.2.5

Smart HealtH Care

Smart health care is an integral part of smart cities. It comprises patients, doctors, hospitals, and various research organizations. Smart health care includes disease diagnosis, monitoring, prevention, hospital management, and medical research. Information and communication technology (ICT) plays a very important role in this regard. For instance, IoT, cloud computing, mobile Internet, and artificial intelligence (AI) form the core of smart health care that is required for smart cities.

1.2.6

Smart BuildingS and ligHting

Smart buildings constitute an important aspect in smart cities. They use cloud-based computing. It combines and coordinates all aspects of the building and provides a smart living experience. The important features include security and surveillance, heating, ventilation and cooling, and lighting management. They can be combined together and coordinated on the cloud through sensors and other IoT devices for better and complete control on a single dashboard.

1.2.7 Smart puBliC Safety Public safety in terms of minimum accidents, few traffic deaths, and reducing and tracking crimes is the key feature of smart cities. IoT-enabled data-driven systems can achieve all these objectives. The data on these items can be compiled and captured through sensors and can be processed in real time. It will help detect criminal activities and enable timely policing.

1.2.8

Smart eduCation

Cloud can facilitate the process of eLearning. It will enable students to access all study materials at any point in time through the Internet using a computer and

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other electronic devices. It will be able to solve the problems of Distance Learning. The  Smart City Cloud computing platform will help teachers to identify problem areas of the students by examining students’ study records efficiently.

1.3

G-IOT FEATURES IN SMART CITIES

A smart city has various components that are equally responsible for maintaining fully equipped smart cities. This section discusses various components of smart cities such as green smart homes, green smart offices, smart health care, smart transportation, green smart environment, and waste management, and how G-IoT-based devices can be used in these for providing the required service.

1.3.1

green Smart HomeS

A green smart home should be maintained to be environmentally sustainable. Its  main focus is the efficient utilization of resources such as energy, water, and building materials. G-IoT devices having sensors, agents, motion detectors, etc. can be used for designing such houses. The green smart home should be designed to autonomously control appliances inside the house and also for utilizing energy resources efficiently. Smart homes can react to the internal or external environment without the need for any human interaction for providing comfort to the occupants. They improve performance for reducing consumption of energy. The data captured from the devices used in smart homes can be stored in the repositories to perform future energy analyses. To perform these functionalities, smart homes require high computational power. Cloud computing can be the best-suited option for providing a higher level of computing services with more reliability in smart homes. It is very useful in scenarios where dynamic resources are required and smart homes require many dynamic resources such as big data, distributed processing, web services databases, and storage to operate correctly. For smart homes, cloud computing provides a cost-effective and fault-tolerant environment for processing and storing data about resource usage at a centralized point of control.

1.3.2

green Smart offiCeS

Smart offices should have an intelligent, integrated, and context-sensitive environment. The environment’s intelligence is based upon the context data that are being collected from the connected systems such as sensors, microphones, cameras, etc. Actuators including automatic door openers, displays, or speakers are used for having an interactive environment with the users. Based on the collected and analyzed data, the environment can flexibly change the state of its integrated systems. Smart home and smart office scenarios both are made for indoor environments but unlike smart home scenarios, smart offices have a heterogeneous group of users and require a sophisticated user access rights and security. Cost optimization,

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time  optimization, process optimization, security enhancement, and increasing employee satisfaction are some of the main goals of a smart office [1]. Green smart offices provide an environment that is techno-savvy, adaptive, and interactive, and they are designed for multiple users with a high level of security. They  also aim for energy saving techniques with a smaller carbon footprint. Connected IoT devices and sensors can be used for enhancing the efficiency of offices and reducing carbon footprints. These devices can be used for controlling appliances, environment, and lighting inside the office building, so that energy is not expended when the space is not occupied. Renewable energy such as solar panels can also be a good choice for having an energy-efficient scenario. It can be integrated into the overall power management for an energy-efficient and environmentally friendly solution for the office environment.

1.3.3

green Smart HealtHCare SyStem

Smart healthcare facilities combine the key concepts of safe and green hospitals to make facilities to be climate-resilient and ensure health services to be provided at all times. Green and smart healthcare facilities aim to: • Ensure that healthcare facilities are environmentally friendly and disasterresilient. • Reduce the impact of climate changes. • Reduce operational cost. • Enhance user comfort and performance. • Empower decision-makers to select the most cost-effective green improvements to be undertaken. Increases in patient satisfaction, energy efficiency, cost optimization, security enhancements, and service optimization are the main goals of smart healthcare places [1]. Green smart healthcare systems use devices that are eco-friendly and have enhanced functionality. Accuracy is the main key concept that is required for any healthcare device. Therefore, smart healthcare systems should always give accurate results. It is one of the major features that cannot be compromised while designing a healthcare system. Collected data from IoT devices along with other connected devices can be stored and used for predicting the health status of patients. Since these systems also require a high level of computational power for performing analysis over the data stored during the interaction of devices with the healthcare environment, cloud computing can perform a very vital role in this scenario for giving a reliable and cost-effective platform for storing and processing data over the Internet, which can be accessed from anywhere giving convenience to both patients and doctors.

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1.3.4 Green Smart Transport System Transportation is a very important and most used service of any city. With ­growing urbanization and increasing population, the number of transports used by the ­people is also increasing, leading to an alarming situation where transportation is becoming a major environmental problem by increasing pollution caused by burning fuels. Green smart transport systems can use IoT devices for providing the users details of congestion in a particular route and suggesting an alternative route. A model ­proposed in a study [2] shows how an IoT-based intelligent transportation system can be used in smart cities. An intelligent transport system should be able to perform the following functionalities: • Traffic incidents are detected and responded to promptly. • Increase reliability and convenience of transportation services. • Navigation systems are used for finding the best route based on ­real-time conditions. • Monitor the structural integrity of the infrastructures such as bridges. • Coordinate speed limits and signal timing with real-time traffic situations. • Real-time traffic and weather reports provided to the traveler. • Inform drivers about potentially hazardous situations in time to avoid car crashes. • Manage fuel consumption.

1.3.5 Green Smart Environment With massive damage inflicted on the environment by humans throughout history, the earth’s ability to support sustainability has been devastated. A green smart environment focuses on the following points: • Cleaning up the damage to the environment. • Replacing the current way of using vital energy resources with new ­technologies to conserve vital resources. • Using various green technologies that ­restore and repair the ­environment in the most energy-efficient and sustainable ways. G-IoT can continuously monitor real-time environmental conditions. IoT-based meters can measure and store air quality index to predict future environmental ­conditions. Intelligent IoT devices should be established to measure the usage of nonrenewable resources to increase conservation and smart utilization. IoT devices are very helpful in monitoring real-time changes to analyze environmental conditions.

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Weather forecasting should also be performed using IoT devices by performing computations on previously recorded data regarding humidity, precipitation, wind speed, and so on.

1.3.6

green WaSte management

Waste is an inevitable byproduct of human life. Increasing population and urbanization are contributing to increased waste products. Waste management is an important aspect of any city’s management to keep the city clean and healthy. G-IoT can be very beneficial in waste management in smart cities. Following are the points that can provide benefits in waste management using an IoT-based system: • It should monitor the amount of waste generated in a particular amount of time. • Sensors can be attached to the public dustbins, which should alert nearby garbage collectors to empty the dustbins. • The data collected by the sensors should be analyzed to determine the time pattern for emptying the dustbins. • Efficient routes should be set for the garbage collecting vehicles based on the position and location of the dustbins so that they can collect more garbage in less time. There are many more applications, which can be performed by the IoT devices for maintaining well-designed waste management systems. For example, in a study [3], a smart waste management system is proposed using IoT devices for having an efficient system. It shows a model in which IoT devices are used for making smart waste collecting systems based on the level of waste in the wastebin. IoT sensing devices are very useful in these scenarios for sensing the conditions that require some action and providing an efficient, effective, cleaner, and healthier city.

1.4

USE OF ALGORITHM AND SOFTWARE IN G-IOT SMART CITIES

G-IoT is defined by Murugesan as “the study and practice of designing, using, manufacturing, and disposing of servers, computers, and associated subsystems such as monitors, storage devices, printers, and communication network systems efficiently and effectively with minimal or no impact on the environment.” G-IoT becomes more efficient by green ICT through minimizing energy, unsafe emissions, pollution, and consumption of resources. Minimizing the technology and conserving artificial resources have an impact on human health and environment and minimize the cost significantly. Consequently, G-IoT thus focuses on green activity, green disposal, green design, and manufacturing [4]. 1. Green use: minimizing computer’s power consumption and other information systems as well as using them in a sound manner. 2. Green disposal: reusing, refurbishing, and recycling old unwanted electronic equipment and computers.

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approach, are scarcely investigated to date. Thus, another examination wave has begun to zero in on the best way to upgrade shrewd city approaches too as economical city models by consolidating the two metropolitan improvement procedures trying to accomplish the required degree of natural supportability through improving metropolitan tasks, capacities, plans, and administrations utilizing progressed ICT [6]. This integrated metropolitan advancement approach stresses the utilization of large information examination as a lot of cutting-edge strategies, measures, stages, frameworks, and applications, notwithstanding other progressed types of ICT like setting mindful figuring. Specifically, the advancing information-driven methodology supposedly holds incredible potential to address the test of natural supportability under what is marked as “brilliant reasonable urban areas” of things to come [7]. The route forward for future urban areas to progress ecological supportability is through progressed ICT that guarantees the usage of enormous information examination [8].

1.6

SMART CITIES GREEN INDEX INDICATORS

The increasing global average temperature increases the need for having a low carbon economy with sustainable and energy-efficient technologies. Green technology helps in lowering the dependencies on fossil fuel energy resources and giving the environment protection for unnecessarily increased carbon emission in the environment. The G-IoT-based smart cities make use of natural environmental resources as the sources of energy. These cities feature technologies to prevent the wastage of non-renewable resources such as water, electricity, and so on. A smart city has various features such as smart buildings, smart street lights, smart waste management systems, and a smart environment. G-IoT helps in providing environmentally friendly solutions for providing these facilities. For enabling G-IoT, it is necessary to keep in mind the indicating factors that must be controlled for giving better green solutions. These include controlled CO2 emission, providing energy-efficient solutions, smart green transport for reducing the emission of harmful gases, and so on. A green smart city must have the following green indicator factors that must be followed strictly for maintaining the green index of the environment. A high amount of CO2 is the main cause of growing global warming. Various technological devices are equally responsible for adding the amount of CO2 into the environment. It is estimated that IT industries account for almost 3% of CO2 emitted into the environment that is nearly 80% more than it was in 2007 [9]. This increasing percentage renders the need for making devices with less CO2 emission and intensity that helps in the reduction of this harmful gas in nature. Since smart cities have various numbers of electronic devices that increase the risk of high emission of CO2, there is a need for having G-IoT devices that emit less amount of CO2 and maintain the sustainability of the ecological balance of nature. IoT devices are electronic devices and hence require energy (electricity) for working. A large number of IoT devices in smart cities requires high energy for working properly. Therefore, energy-efficient devices having high-energy intensities are needed for smart cities. Lighting and rooftop gardening are some of the practices that can be used for providing green and energy-efficient concepts for G-IoT [10].

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Smart buildings and smart offices are some of the components of smart cities where photosensors, occupancy sensors, and timers can be used for controlling the indoor lights so that it can be switched ON and OFF whenever needed. They help in minimizing the unnecessary utilization of power consumption. Motion detectors can be used for turning off the lights of areas when they are empty, and no motion is detected for a while. Dimmers can also be used for adjusting the intensity of the light as per the user’s need. There are also many IoT devices that can be used efficiently as a helpful source for proper utilization of energy resources. Energy-efficient buildings with proper environment-friendly occupancy of devices should be settled. Various energy-saving techniques as discussed above like rooftop gardening and lightning should be accepted for providing energy efficiency. The lights of the building can be controlled by AI devices such as sensors and motion detectors as per the need. Rooftop gardening [10] can help in reducing high exposure to sunlight. Widely adopted rooftop gardening can help in reducing the level of urban heat island, which can lead to a reduction of smog episodes and heat stress leading to lowering the energy consumption. Transportation is another main component of a city. It is also one of the causes that badly affect the environment. With the growing population, the means of transportation is also increasing, which is becoming the cause of growing pollution. Green transportation can be the solution for the transportation-related pollution and problems in the city. It refers to using those transportation services that do not diminish the natural resources such as fossil fuels. Electric bikes can be used as a great way for having green transportation. Only light pedaling is needed for riding the bike, and no harmful gas is emitted into the environment. Green vehicles that are powered by clean energy [11] rather than non-renewable fossil fuels can be used along with advanced vehicle technologies, resulting in less pressure of pollution into the environment as compared to the conventional internal combustion engine vehicles. Green trains are also coming into the picture and can be proved to be a great initiative for having environmentally friendly transportation services. Trains with hybrid locomotives having other green technologies are becoming part of a greener urbanization. Waste reduction policy and recycling are important aspects for providing a greener and clean city. Various waste management techniques can be used for empowering the waste management of the city. Dustbins can be set with the sensors powered by the solar energies, which can sense the level of waste inside the dustbin so that they can send the alert to the nearby waste-collecting dump-yard that the dustbin should be emptied before it overflows. Various graph algorithms can be used for waste-collecting vehicles for making an efficient route that can help them in collecting more garbage in less time. Agents can be used to determine the pattern of the level of wastage on weekdays and weekends to setting an accurate timing for waste collection. Recycling the recyclable waste is also encouraged for reducing environmental pollution. Water is a limited resource, and it needs to be conserved and used wisely. Water consumption is also one of the main features of a smart city. Various IoT devices can help in monitoring the level of water. Sensors can be used for monitoring the level of water, and an alert will be generated whenever a very large volume of water

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is being used in less time. Water recycling policy should be adapted for its proper use. Smart meters and monitoring systems can be used for measuring real-time water consumption and identifying excessive usage and waste points, which helps in making proper usage patterns and predicting future usage. These techniques support water consumption and water distribution mechanisms. For automated water distribution, environmental sensors and self-learning algorithms help in supplying water automatically to the end-users. Smart water management using G-IoT can be used for reducing water wastage, improving the quality of water, and enhancing the efficiency of water. IoT devices can be very useful in monitoring the air quality. Sensors and microcontrollers can be used for making monitoring systems to measure the air quality index. Wireless sensors can be used by placing those at a strategic location to sense the level of dust particles, nitrogen dioxide, sulfur, carbon dioxide, and carbon monoxide in the air. IoT-based air monitoring systems can be used for keeping these data over the remote server and keeping those updated using the Internet. Various predictive models can be used for predicting the future air quality and suggesting the required measures for controlling the pollution. IoT also has a broad application in environmental monitoring. The areas include extreme weather monitoring, water management, commercial farming, endangered species protection, and many more. Sensors can be used for monitoring the environmental changes. IoT can help in collecting various samples, which can be used in various predictive modeling for analyzing the patterns in the environment. The features discussed above are very important for establishing a smart city with fully equipped G-IoT devices. G-IoT in a smart city is a great initiative for having an advanced techno-savvy city with environmentally friendly equipment.

1.7

CLOUD-BASED G-IOT ARCHITECTURE

Here, the proposed G-IoT framework for smart cities will attend correspondence, normalization, and quality aspects. There are five layers in the proposed G-IoT, i.e., sensor layer and smart city infrastructure, network layer, analytic big data layer, application layer, and presentation layer, as shown in Figure 1.1. This framework characterizes the fundamental correspondence ideal models for the associating elements. It gives a reference correspondence stack alongside an understanding of the fundamental associations around the model. This depicts the approach of correspondence plans, which can be applied to various kinds of G-IoT organizations. It is significant that different networks of sensors in various sorts of networks can speak with one another.

1.7.1

SenSor layer and Smart City infraStruCture

In smart cities, different kinds of sensors installed are operating in different systems with minimal power consumption, which is supported by this layer. Sensor networks (WSN), crowdsourcing, and RFID are the sensing framework in this layer. The labeled articles can be identified through RFID (automatic identification technique). These inactive RFID labels are not battery-worked. The power can take from

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FIGURE 1.1

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Green index indicators.

the pursuer’s transmission sign to the RFID reader by imparting ID. WSN plays a crucial part in urban-sensing utilization. The remote sensors are smaller in size, less expensive, and widely used. The long-range interpersonal communication is blasting another sort of detection of worldview, for example, savvy telephone innovation has advanced by empowering the residents of the keen urban areas to contribute toward the brilliant city the executives. It assumes a significant function in the government– resident communication. Therefore‚ this layer must have the option to help a gigantic volume of IoT information created by remote sensors and brilliant gadgets.

1.7.2

netWork layer

To accept the ability across networks, the higher communication layer, the network, and WSN preferably use common protocols in the lower communication layers. The other correspondence advances such as Wireless Hart, Zig Bee, WIA-PA, and ISA.100.11 are relying on their separations to convey [12]. The proposed framework is masking extra recurrence groups, for example, television blank areas and territorial groups, which work at ultra-low energy for various utilizations. Bluetooth such as Bluetooth 4.0 is a low energy convention and a lightweight variation for low force applications. A fundamental prerequisite of these correspondence advances is force utilization and little computational impressions for remote sensor organization so the IP convention suite is the principal contender for these layers. Indeed, even the already explicit principles that characterize their own convention can be moved to IP. Therefore, the WSN and IoT IPv6 are the attainable answers for brilliant urban areas utilizations.

1.7.3

analytiC Big data layer

Periodic and aperiodic are the two types of data management and information flow layer [13]. In intermittent information, the executives' IoT sensor information requires sifting since the information is gathered intermittently and some

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information may not be required. Therefore, this information should be sifted through. The information is an occasion to set off IoT sensor data, which may require quick conveyance and reaction for model health-related crisis sensor information. In these proposed engineering methods large information power through ventures IoT and explanatory devices is explained. The G-IoT correspondence advances, organizations, and administration movements ought to have the option to help dynamic climate through web engineering advancement, conventions, and remote framework access models, and developed security protection. The G-IoT cloud stage and cloud measure the power efficiency. They enable improving the application layer. It controls the data investigation and security controlling measures and gadget control to the G-IoT cloud stage. It is likewise liable for operational help framework, security, business rule the board, and business measure the executives. It has to offer support examination stage, for example, measurable investigation, information mining and text mining, prescient examination, and so on.

1.7.4

appliCation layer

In G-IoT through different communication techniques, this layer sets the most noteworthy purpose among the batch and stack and is in charge of the transportation of various utilization to various clients. Through fuzzy recognition, cloud computing and other technologies analyze massive data and information. In this layer, all the natural climate correspondence are a part such as comprehensive monitoring of energy, water resources monitoring management, monitoring environment protection, smart air pollution observation, supply consumption monitoring, water quality diagnostics monitoring, key pollution source, and automobile exhaust. Based on these new services, there are increasing efficiencies of urban management, real-time physical world data, addressing environmental degradation, and improving infrastructure integrity.

1.7.5

preSentation layer

In this layer, data are obtained from the application layer. This layer follows data programming structure plans created for various dialects and gives the continuous grammar required for correspondence between two articles such as layers, frameworks, or organizations. The information organization ought to be satisfactory by the following layers; in any case, the introduction layer may not perform effectively. Different city frameworks such as water flexibly framework, power gracefully framework, contamination control framework, transport division, and so on can share their data by utilizing web-based interfaces, web, versatile uses that are based on this layer. Individuals and government departments could get particular information as per their requirements through this layer, which can be utilized in the services of the city.

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FIGURE 1.2

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G-IoT and cloud-based architecture.

1.8 ANALYTICAL FRAMEWORK 1.8.1

domainS and SyStemS of urBan areaS

These should work and be overseen utilizing ICT of inescapable registering, in particular, the IoT and its fundamental enormous information examination as a lot of trendsetting innovations together with their novel utilization. These ought to preferably be joined with the typologies furthermore, plan ideas of feasible metropolitan structures [13]. Typologies incorporate minimization, thickness, variety, and blended land use as typologies relate to the manageable vehicle, greening, and detached sunlight-based plan as plan ideas. These typologies and plan ideas establish key procedures to accomplish the necessary degree of supportability with regard to practical metropolitan structures. These metropolitan segments are to be upheld by elevated requirements of natural and metropolitan administration, the thought is that shrewd sustainable

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urban communities ought to be – as types of arranging standards and plan ideas of maintainability – checked, comprehended, dissected, and intended to improve their commitment to the objective of environmentally maintainable advancement based on profoundly intelligent and inventive arrangements. Metropolitan frameworks and areas establish the primary wellspring of metropolitan information, which are created by different metropolitan substances regarding the physical resources related to the IoT, including city specialists, metropolitan offices, metropolitan administrators, singular residents, and privately owned businesses. They provide heterogeneous and epic measures of information as contributions for enormous information applications empowered through the IoT. Metropolitan information in their assortment, scale, and speed are constantly labeled with spatial and transient names, generally spilled from different tactile sources and put away in information bases, created regularly and consequently, and incorporated, what's more, mixed in information distribution centers. Subsequently, this segment includes distinctive sectoral and cross-sectoral wellsprings of metropolitan information of changed kinds and sizes that are to be collected, put away, and recovered for later preparing, examination, visualization, sending, and sharing all through the instructive scene to help metropolitan activities, capacities, plans, what's more, administrations with regard to ecological manageability.

1.8.2

data CategorieS, Big data SourCeS, and Storage faCilitieS in urBan

These include metropolitan large information sources, storerooms, and information classes. This segment is dedicated to information assortment, stockpiling, and executives. It includes information storehouses, information stockrooms, and storehouses of public information. For example, warehousing as a major information investigation method utilized in the metropolitan area involves a combination of information from a few information bases, which are kept up by different metropolitan units alongside verifiable and outline data. Database administration systems are utilized to keep up metropolitan information of huge scope and various classifications. Likewise, cloudbased capacity can be completely virtualized – PC produced variant of the storeroom – and all gadgets are straightforward to the metropolitan components as clients of the cloud that can interface with the distributed storage through the organization. The additional benefit of joining cloud capacity with clever pressure strategies lies in, notwithstanding altogether decreasing capacity costs, giving the chance of effectively putting away a wide range of huge information having a place with the spaces of brilliant practical urban areas.

1.8.3 Cloud Computing or fog/edge Computing This segment is devoted to the cycle of information disclosure/information mining. The sub-measures identified with information disclosure encompass choice, prepossessing, change, mining, interpretation, and assessment [14]. As  to the

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information mining, the sub-measures included incorporate information understanding, information planning, demonstrating, assessment, and organization [15]. The discovered or separated information includes insight capacities and results from information handling, which the executives carry out by Hadoop MapReduce dependent on distributed computing. Such capacities are expected for dynamic, choice help, and choice robotization. Insight capacities are utilized for constant furthermore key choices, contingent upon the application area traffic frameworks versus energy frameworks, as far as control, automation, advancement, and the executives are concerned.

1.8.4

Big data appliCationS

This part involves the assorted information-centric applications empowered by the IoT related to ecological manageability comparable to assorted metropolitan spaces. One application typically includes a few arrangements related to various sub-domains of every area, contingent upon the kind of natural supportability issue that will be fathomed [16]. To put it in an unexpected way, informationdriven applications include framework conduct and administration conveyance with regard to this chapter. At the center of this part is the result of the execution of improvement procedures and activity taking cycles. Along these lines, it executes activities and offers types of assistance as per the sort of the choice taken dependent on the extricated helpful information from the IoT information. Figure 1.3 shows the work of a huge information investigation utilizing the center empowering innovations on the cloud base IoT in the setting of brilliant reasonable urban communities.

1.9 CONCLUSION The proposed cloud administrations, visual specialized instruments utilizing fast broadband correspondence networks in savvy urban communities, can improve business in corporate and government divisions. Moreover, in the interim, sensor networks using an assortment of remote advancements in green brilliant urban areas offer admittance to data on the progression of merchandise and the status of hardware and the climate. They additionally encourage the utilization of the controller. This makes conceivable the execution of brilliant urban areas presuming sheltered with environmentally cognizant. Collaboration between networks can be energized as sensors and actuators, correspondence innovations, and control frameworks are getting more crude and wise. Empowered by the IoT, as a type of inescapable registering, large information applications are progressively getting perpetually imperative to keen maintainable urban areas as for their operational working and wanting to improve their commitment to the objectives of the naturally maintainable turn of events.

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FIGURE 1.3 The IoT to advance environmental sustainability in the context of smart sustainable cities.

REFERENCES

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2

Dynamic Models for Enhancing Sustainability in Automotive Component Manufacturing Systems Jangam Ramesh, M. Mohan Ram, and Y.S. Varadarajan The National Institute of Engineering

CONTENTS 2.1 Introduction..................................................................................................... 22 2.2 Contextualization.............................................................................................24 2.2.1 Role of Energy in the Manufacturing Sector.......................................24 2.2.2 Role of Computational Tools in Enhancing Sustainability in a Manufacturing System..................................................................25 2.2.3 Strategies Suggested by Researchers for Sustainable Value Creation in the Manufacturing Sector.......................................26 2.3 Framework for the Optimization of Parameters for Achieving Sustainability...................................................................................................26 2.4 Methodology of the Study...............................................................................28 2.4.1 Outlining the Manufacturing System..................................................28 2.4.2 Modeling the Manufacturing System.................................................. 29 2.4.3 Data Acquisition.................................................................................. 29 2.4.4 Simulating Manufacturing System...................................................... 29 2.4.5 Optimization of the Process Parameters............................................. 29 2.4.6 Analyzing Results................................................................................ 30 2.5 Case Study....................................................................................................... 30 2.6 Conclusions...................................................................................................... 31 References................................................................................................................. 32

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2.1 INTRODUCTION The economic growth of the developed and developing countries relies on the manufacturing sector. The manufacturing system is a transformation process (Davim 2013); it converts valuable resources into usable goods. It is a value-added system with many factors that are directly and indirectly associated. It is a process of absorption of various inputs such as raw materials, man power, machine, money, land, and information and conversion of them into a finished product, waste, and emissions as outputs. Rapid growth in population and the resulting economic activities have posed a real challenge in dealing with finite resources in the manufacturing sector. This can be due to cut-throat market competition and a lack of better approaches for effective resource utilization in the manufacturing sector. There is a need to utilize resources optimally and reduce emission and waste, which is not occurring effectively today. To overcome it, the manufacturing sector needs to precisely monitor and control its production operations and the costs involved for raw materials, energy, labor, and equipment (Ghani, Monfared, and Harrison 2012). This forces the industries to scrutinize maintaining the same level of production with optimum resource utilization (Billing 2016). This objective will not only strengthen the promotion toward sustainable value creation but also create a positive approach for an ever-growing economy. Ever since the “Brundtland Report” of the World Commission on Environment and Development (1987) was published, a growing social consciousness of industrial production and its impact has been observable. From the sustainability perspective, “an organization’s prime focus of profit maximization without appreciating stakeholder concerns has become progressively less agreeable” (Kiel and Arnold 2017). Though the sustainability perspective has diverse dimensions, yet, it is still principally viewed only in ecological contexts. Sustainability is incorporated in three dimensions, namely, economic, ecological/environmental, and social aspects. To be competitive and for existence, every organization focuses on moving toward economic prosperity through increased productivity, attaining profits, and return to scale (Raluca Gh. Popescu and Popescu 2019). Hence, organizations make certain long-run economic survival plans for the future. Therefore, organizations show less attention to sustainable development compared to economic development. According to the organization’s view, sustainable prosperity is achieved only with resources that can be reproduced (Bakkari and Khatory 2017). These perspectives determine an organization's environmental and social performance. Hence, the triple bottom line (TBL) concept collaborates with organizational approaches to conserve resources for value creation in enterprises. One of the major factors that cover the three dimensions of sustainability is energy. Inefficient utilization of energy resources tends to increase the production cost, and the emission is harmful to the ecosystem (Linke et al. 2013). Computational tools such as simulation, modeling, algorithms, and optimization are the main drivers for enhancing sustainability in manufacturing systems. These techniques combine technical operations along with interdependencies between the entities in the manufacturing layout. These techniques analyze the huge data and enable improving design and control parameters for achieving targets with less

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investment. The simulation is used for getting an optimal solution that reduces the time for planning and maintenance in the manufacturing system. The simulation planning phase will give information about the capacity of the resource, the number of operators, buffer sizes, transport systems, and alternative planning scenarios to produce goods (Seow, Rahimifard, and Woolley 2013). While in the operation phase, it focuses on optimizing resource utilization, scheduling, analyzing failure and interruptions, and material flow throughout the system. For an existing system, a dynamic model is developed using the simulation tools for integrating the resource efficiency strategy for sustainable value creation. The dynamic model examines diverse virtual manufacturing aspects and assesses parameters with the existing system (Terkaj and Urgo 2015). There are two types of simulation techniques: continuous simulation and discreteevent simulation (DES) that are used for logistic and manufacturing operations. These techniques are used based on the distribution of the random parameters that are discrete, continuous, or both. DES is a technique of simulation of the behavior and performance of an existing system. It models the system as a series of “events” that occur over time. In DES, operations are modeled as independent units, each of which can be given associated attribute information. It includes variables such as specific energy consumption, setup time, processing time, and location of various stations in the manufacturing system. It is the imitation of a system with its dynamic mechanism in an experimental model to interpret the best ones compared to reality (Shao, Kibira, and Lyons 2016). There are many DES software packages available for modeling and simulation, for example, Witness, Promodel, SIMUL8, ARENA, and plant simulation Tecnomatix, a plant simulation package, developed by Siemens product lifecycle management (PLM) software, is used for developing discrete event dynamic models. It enables us to generate a hierarchical modeling approach and control strategies for optimal utilization of resources and detect bottlenecks in material handling systems. It is incorporated with key capabilities such as visualization of 2D and 3D process layouts, genetic algorithms, and neural networks (PLM2020). Focusing on the potential users of this technique, automotive component manufacturing systems (ACMS) are chosen for carrying out the case study. They are categorized as small and medium enterprises, whose operations are carried out predominantly with obsolete technologies. Figure 2.1 shows a pictorial overview of the manufacturing system, depicting inputs, transformation processes, outputs, and boundary interactions. There is a need to incorporate sustainability aspects and improve energy efficiency in manufacturing systems that are to be addressed through computational tools. In the present work, a dynamic model has been developed and described using the Tecnomatix plant simulation software. The motivation and principles related to the technique are presented. This article also gives insights into capturing the non-value-added energy consumption activities in the manufacturing processes. These procedures can be enforced on the entire manufacturing system or a unit of it.

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FIGURE 2.1

Pictorial overview of the manufacturing system.

2.2 CONTEXTUALIZATION 2.2.1

role of energy in tHe manufaCturing SeCtor

The industrial sector in Brazil accounts for 32.5% of the energy, which is needed for the manufacturing processes. In China, the industrial sector accounted for 68% of the total electricity consumption in 2019. In India, the industrial sector accounts for 41.16% of the energy utilization (IEA 2020). India is in the category of developing countries with a well-built manufacturing base, but it is not coping up with developed countries due to inefficient resource utilization. Energy is an essential factor for the growth of the economy in terms of improving living standards and industrial development. Manufacturing enterprises have to focus on efficient utilization of energy because energy costs have experienced a sharp rise, and reduction of energy consumption is the agenda. However, there is a need for enhancing energy efficiency due to the availability of fewer energy resources that cannot meet the overall demand in countries like India due to the huge population and diverse sectors. In the meantime, the higher price of energy has an impact on production costs. The energy utilization behavior of the system is a prerequisite for adopting the concept of enhancing sustainability. From a manufacturing perspective, the emphasis shall be on the energy analysis of equipment. Many factors such as processing time, setup time, maintenance status, idleness of machine, and production seasonality are to be considered in evaluating energy consumption. Both value-added and non-value-added consumption must be analyzed. From it, non-value-added activities are eliminated, and the revision of the manufacturing system is to be carried out. This results not only in the optimal usage of energy resources but also in reducing social and environmental impacts. A few researchers highlighted that energy

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consumption is not normally fixed over time, but rather dynamic in nature, depending on the manufacturing activities and the actual state of the machines.

2.2.2

role of Computational toolS in enHanCing SuStainaBility in a manufaCturing SyStem

Mass production dominated the global market in the last few decades, but the dynamic situations in the current scenario shifted drastically to customized production due to unprecedented resource cost, non-availability of resources, and regulatory pressures. These challenges are overwhelmed by the utilization of computational tools in the manufacturing sector for strategic benefits by leveraging the production capacity. The entire value chain process should be upgraded so that manufacturers realize twofold benefits as they strive to “build the right product and build the product right.” Manufacturing organizations should constantly acclimate and enhance their operation strategies to achieve more sustainable production and to hold competitive global markets. This forces organizations to shift toward the environment-friendly and quality products, at a faster production rate. Hence, optimal investments in innovating technologies along with plant and machinery installed become the core of the growth and prosperity of successful manufacturing organizations. Simulation helps in decision-making for maximizing the overall production efficiency and engaging the manufacturing operations toward enhancing sustainability through optimal resource utilization. It increases productivity, optimizes production capacity, and effectively leverages capital investments through innovation and better strategies. Modeling is an abstraction procedure where entities of a system and their behavior are examined. The entities and their interdependencies are represented by logical and mathematical relations. DES models have experimental entities that resemble the physical system, which is probabilistic. A dynamic model is built, which is associated with the underlying probabilistic mechanism and interdependencies within the system. While generating a dynamic model in DES, an input is considered based on the parameters of interest in a physical system. Input data collected from appropriate entities of the manufacturing system of interest are important for modeling and are broadly classified into two approaches in simulation. The classical approach includes data collection from a designed experiment, and it is better in terms of control. On the other hand, the exploratory approach includes problems that are answered through existing data; it is better in terms of cost compared to the classical approach. Entities in the simulation model require certain input parameters for assessing the overall performance of a system. This should be incorporated by setting resources such as the number of workers assigned to each work station. Each entity is assigned with a specific setup time, cycle time, processing time, and type of probability distribution involved in processing the operation. The reliability of each entity is also accounted for. Each production operation will highlight the status of resources such as idle, busy, or down. DES can be used to explore the current production scenarios to estimate the energy consumption based on entities modeled from the existing system.

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The simulation captures the non-value added activities such as idle/underutilized machinery in the layout, which shows inefficient energy utilization since they will be running without use. This allows us to assess new scenarios with modified data and come up with optimal scenarios that eliminate non-value-added activities. This  is achieved either through checking and deactivating the machines or through redesigning the manufacturing system layout/processes so as to use them efficiently, thereby increasing production. The Tecnomatix plant simulation software provides a platform to integrate various tools to improve productivity resources using resources efficiently. It associates optimization tools with DES to enhance production efficiency. The competence of a dynamic model is that it is very close to a physical system.

2.2.3

StrategieS SuggeSted By reSearCHerS for SuStainaBle value Creation in tHe manufaCturing SeCtor

Several works have been carried out for capturing the complementary areas of sustainability research in manufacturing systems. Jayal et al. (2010) provided insights on “modeling and assessment strategies mainly focusing on sustainable manufacturing.” Duflou et al. (2012) provided an exhaustive review of procedures to be adopted to reduce energy consumption in the discrete part of manufacturing through the integration of “unit process, manufacturing line, facility, manufacturing system, and global supply chain.” This integration helped in cutting down energy utilization up to 50% in the industrial sector. Garetti and Taisch (2012) outlined the challenges in improving sustainability in the manufacturing sector and the strategies required to enable sustainable manufacturing. The relationships among intellectual capital, corporate social responsibility, and performance are addressed by Gallardo-Vázquez et al. (2019). They gave valuable insights into the Romanian business environment. The importance of setting up metrics and designing a sustainability impact for monitoring projects was noted by Lesic et al. (2019). They carried out subjective interviews about sustainability and the impact of innovation in the process industries. Their work can be taken as a guideline for introducing new impact metrics or evaluating existing ones.

2.3

FRAMEWORK FOR THE OPTIMIZATION OF PARAMETERS FOR ACHIEVING SUSTAINABILITY

The total effort in manufacturing a product requires an assessment of the entire process chain from the acquisition of raw materials to the finished product. The sequence of individual manufacturing entities is arranged in the process chain to determine the total effort, besides considering the efficiency of an individual process. Focusing on sustainability aspects, there is a need to examine the optimization techniques through discrete event dynamic models that are practically applicable to overcome inefficient process chains in the manufacturing system. The simulation dynamic model allows the consideration of variations in different scenarios in the system. Based on these inputs, strategies for designing, operation,

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control, maintenance, and predictions on energy consumption can be developed in the manufacturing sector (Rodrigues et al. 2018). Developing a dynamic model and applying optimization techniques in simulation tend to reduce energy consumption and help in enhancing sustainability parameters in manufacturing layout considering machines, workers, raw materials, and energy utilization in direct and indirect activities in an organization (Seow, Rahimifard, and Woolley 2013). The proposed procedure provides insights into actions to be incorporated for improving sustainability in the manufacturing system. The existing system is improvised using a discrete event dynamic model and examines it in different scenarios through a simulation tool. A flow chart depicting these steps is shown in Figure 2.2. • Characterize the manufacturing system: The factors affecting sustainability in the manufacturing system are analyzed, such as entities and process parameters in the system. • Generating the dynamic model: A DES dynamic model of the manufacturing system is built. It includes the model abstraction and implementation of the proposed model in a Tecnomatix plant simulation software. • Data acquisition from the physical system: The data required are acquired from manufacturing histories and observation of the operations. • Simulate the dynamic model: Use the dynamic model to simulate the physical system. Different scenarios are evaluated to arrive at an optimal scenario close to reality. • Optimize the factors using algorithms: By using algorithms, the factors of the manufacturing operations are optimized to enhance sustainability. The results are assessed for objective functions to reduce energy consumption.

Characterize the manufacturing system Generating the dynamic model Data acquisition from the physical system Simulate the dynamic model Optimize the factors using algorithms Analyze the sustainable indicators Quantify the process parameters for enhancing sustainability

FIGURE 2.2

Steps for optimizing the parameters to enhance sustainability.

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• Analyze sustainability indicators: Sustainable parameters can be determined for the operation, in addition to evaluating optimum scenarios for minimal energy consumption. This is a relevant aspect of the system that can cause the simulation activities to be re-executed with modified goals to reach these metrics. • Quantify the process parameters for enhancing sustainability: Once the optimal parameters are found, it is important to check the feasibility of the current implementation scenario to reduce energy consumption and satisfy the sustainability aspects.

2.4

METHODOLOGY OF THE STUDY

In any system, execution is an important aspect; it involves connecting several activities to accomplish its objective. For every activity, many characteristics are involved in planning and execution.

2.4.1

outlining tHe manufaCturing SyStem

The most important factor in every manufacturing system is acquiring data from various sources for the decision-making purpose. Interaction with the decision-making teams is the initial point for system characterization. Details such as plant layout, equipment specification, raw material acquisition, energy consumption patterns, and registries help to accord an overview of the system in operation. The primary data are captured by the knowledge and expertise of the worker through a field study in the manufacturing operations. Documents of plant layout, maintenance, and production managers are the sources to determine an efficient manufacturing system. The dynamic models focus on a specific manufacturing unit; it reduces the computational demand for optimization and simulation. Yet, an entire system model can help to identify the resource utilization loss that managers have not detected in daily operations. This can be examined by labeling work shifts, work hours, duration of machining operation, and the number of workers. It will allow the determination of precise details of direct and indirect power utilization in the manufacturing system. The manager outlines the utilization of various resources, development/change of energy consumption patterns, and activities in the manufacturing system. The information provided by the supervisory system also includes electricity bills and alternative fuel consumption. One can evaluate the correlation among operation, consumption, and idleness of machinery and workers in a manufacturing system. To ensure the analysis is compatible with the process, it is suggested that a subsequent interaction is organized with decision-makers to verify and validate the model. Sustainability aspects are crucial to be observed in the TBL viewpoint, which captures the three variables in any dynamic model. The production cost, raw materials, production volume, labor costs, and electricity consumption are some of the factors that are considered in the economic dimension side. The social variables are incorporated in the dynamic model to assess the system performance; it includes labor pool, environmental conditions, risks, and turnover rate. Environmental aspects are

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examined by specific variables such as emission of pollutants, energy consumption, and water consumption in the manufacturing system (Miller, Pawloski, and Standridge 2010).

2.4.2

modeling tHe manufaCturing SyStem

In the abstraction phase, model integration has to be achieved. Physical plant activities are replicated in dynamic models through simulation techniques. Implementation of simulation relies on the complexity of the production process; however, dynamic models can be applied for a unit manufacturing process or for the whole manufacturing system by utilizing simulation techniques. However, the researcher has to keep his objective focused either on the complete production process or on a particular line in a system based on problem identification. Primarily, DES translates the abstraction model specifications into a graphic or textual language; 2D and 3D models can be built that illustrate the process entities in terms of function/operations, their inputs/outputs, and how they are correlated in the system. Yet, the exertion may not be reliable to the abstraction model due to the limitation of the computational tool.

2.4.3

data aCquiSition

Before initiating data collection, process characterization helps to determine the process parameters and the proven acceptable levels for efficient manufacturing. It reduces the failure risks in the manufacturing system. Dynamic models are generated through the data collected from the specification of equipment, plant operation routines, and manufacturing cell layout. Lateral to the dynamic model, there is a need to collect relevant data of the manufacturing systems such as electricity consumption, employment of human resources, throughput, raw material utilization, and waste production.

2.4.4

Simulating manufaCturing SyStem

One can accomplish simulations of the manufacturing systems through the DES modeling and data collected in the process. The first validation of the simulation model is significant by feeding the input data observed in the physical system. By comparing the real system and simulated results, one can validate the model ability and confirm any further modifications that are incorporated in the dynamic model to correlate it to the real system. After validating simulation results, different simulation observations can be analyzed. In each observation, the decision variable values can be distinct from the real process ones. To optimize the process, input parameters play a key role in determining decision variables.

2.4.5

optimization of tHe proCeSS parameterS

Different output parameters of each observation are compared for optimization purposes. However, there is a limitation in decision variables chosen due to operating

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restriction and the range of factors included in the activity. The TBL includes the factors of production such as raw material availability for manufacturing, electricity cost, and operation restrictions like shifts in a plant. Manual optimization techniques are time-consuming, whereas, in simulation, these tools are integrated for faster prediction of activities. For optimization purposes, computational tools are embedded with several algorithms such as genetic algorithm, artificial bee colony, and so on. Hence, sustainable indicators are positively affected by optimal scenarios.

2.4.6

analyzing reSultS

It is to be noted that economic parameters impact the production cost. A non-feasible solution should be carefully checked to find the reason for its occurrence. Based on the evaluated optimization solution, the procedure is re-executed from initial steps by modeling, process characterization, simulation, and optimization. If the existing manufacturing layout is utilizing minimum resources, then full modification is necessary.

2.5 CASE STUDY The methodology for enhancing sustainability aspects is validated by the proposed dynamic model of an ACMS, located in the Mysore region of Karnataka, India. A  flowshop is taken for the case study. A Tecnomatix plant simulation v15 software was used to develop the DES dynamic model of the flowshop as shown in Figure 2.3. In this case, the manufacturing process involves the movement of raw materials, workers, and semi-finished goods to the next machining process. Along with it, process time, setup time, recovery time, cycle time, assigning workers to the work station, and other such operations are involved in the manufacturing process. An examination has been carried out to find barriers to sustainable value creation in the existing system. This work is concerned with energy consumption, such as power utilized by machines and accessories for the smooth running of the manufacturing

FIGURE 2.3 Discrete-event 2D and 3D dynamic models of the automotive component manufacturing systems (ACMS).

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system. Along with data acquired from the shop floor, production volume per cycle and the duration of shifts have to be evaluated. The dynamic model is incorporated with optimal settings of each entity process parameters such as process time, setup time, cycle time, and idle time. The framework proposed a detailed procedure to acquire the data for these settings. By associating entities and their interdependencies, dynamic models quantify the throughput of the system at optimal settings. The main factor assessed in this dynamic model is throughput; it is the output goods created in a day (24 h). The optimal process parameters of all the entities are used to enhance the sustainability of the system. The pool of workers is another factor to be incorporated; it impacts productivity and cost. The basic assumption that the pool of workers have the same skill and experience is taken while performing the simulation of an existing system. There is a need to ensure the reliability of each entity of the dynamic model to capture the failure rates during the manufacturing operation. The simulation output is important to evaluate the minimum number of workers, energy consumption, raw materials, and capital without compromising throughput. Due to economical constraints for new investments and lack of available time to accomplish protracted breaks in the production system, there is no scope for physical modification in the existing plant layout. Hence, computational tools are the main drivers for enhancing sustainability in the existing manufacturing system. DES incorporates optimization tools to determine optimal scenarios by changing the variables of interest that are defined by the user.

2.6 CONCLUSIONS The objective of this research article was to develop a framework to enhance sustainability. This paper addresses a barrier to implanting sustainability in the manufacturing system. There is a need to simplify the interdependencies in a complex manufacturing system. The design of the manufacturing layout involves a complicated procedure. It is a significant activity encompassing designs for a long time horizon and a major assurance of financial stability. In a manufacturing system, modeling and simulation involve the assessment of consistent activities for both design and operational phases. This article gives insights into sustainable value creation in the manufacturing system through computational tools such as simulation and optimization. The primary aim of this article is to capture the inefficient utilization of resources in a manufacturing layout through different scenarios. It helps to identify the non-value-added energy consumption by considering the idleness, processing times, changing operation routines, varying the number of workers, and assigning work stations to workers. It also aimed at identifying optimal process parameter settings for an efficient manufacturing system. Once optimal parameters were found for all entity processes, a digital manufacturing system was modeled, where every activity operated at its optimal process parameters. This article throws light on the significance of computational tools to address the significance of the dynamic model to assess the performance of the manufacturing system.

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REFERENCES Bakkari, Mohammed, and AbdellahKhatory. 2017. “Industry 4.0: Strategy for More Sustainable Industrial Development in Smes.” In Proceedings of the International Conference on Industrial Engineering and Operations Management – IEOM 2017 at Rabat, Morocco. http://ieomsociety.org/ieom2017/papers/414.pdf. Accessed on 23 July 2020, 1693–1701. Billing, Miha Alvesson Due. 2016. “Small Scale Sustainability: A Qualitative Study of Corporate Sustainability in Swedish SMEs.”https://www.diva-portal.org/smash/get/ diva2:934969/FULLTEXT01.pdf. Davim, Paulo. 2013. Sustainable Manufacturing.John Wiley & Sons. doi: 10.1002/9781 118621653. Duflou, Joost R., John W. Sutherland, DavidDornfeld, ChristophHerrmann, JackJeswiet, SamiKara, MichaelHauschild, and KarelKellens. 2012. “Towards Energy and Resource Efficient Manufacturing: A Processes and Systems Approach.”CIRP Annals Manufacturing Technology61 (2): 587–609. doi: 10.1016/j.cirp.2012.05.002. Gallardo-Vázquez, Dolores, Luis EnriqueValdez-Juárez, and José LuisLizcano-álvarez. 2019. “Corporate Social Responsibility and Intellectual Capital: Sources of Competitiveness and Legitimacy in Organizations’ Management Practices.”Sustainability (Switzerland)11 (20). doi: 10.3390/su11205843. Garetti, Marco, and MarcoTaisch. 2012. Sustainable manufacturing: trends and research challenges”, Production Planning & Control, 23 (2–3), 83–104, doi: 10.1080/09537287. 2011.591619. Ghani, Usman, Radmehr P. Monfared, and RobertHarrison. 2012. “Energy Optimisation in Manufacturing Systems Using Virtual Engineering-Driven Discrete Event Simulation.”Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture226 (11): 1914–29. doi: 10.1177/0954405412458625. IEA. 2020. https://www.iea.org/. Jayal, A. D., F. Badurdeen, O. W. Dillon, and I. S. Jawahir. 2010. “Sustainable Manufacturing: Modeling and Optimization Challenges at the Product, Process and System Levels.”CIRP Journal of Manufacturing Science and Technology2 (3): 144–52. doi: 10.1016/j.cirpj.2010.03.006. Kiel, Daniel, and ChristianArnold. 2017. “Sustainable Industrial Value Creation : Benefits and Challenges of Industry 4. 0 Julian Müller Kai-Ingo Voigt.”The XXVIII ISPIM Innovation Conference – Composing the Innovation Symphony, Austria, Vienna, 18–21. Lesic, Vedran, Richard E. Hodgett, AlanPearman, and AmyPeace. 2019. “How to Improve Impact Reporting for Sustainability.”Sustainability (Switzerland)11 (6): 1–21. doi: 10.3390/su11061718. Linke, Barbara S., Gero J. Corman, David A. Dornfeld, and StefanTönissen. 2013. “Sustainability Indicators for Discrete Manufacturing Processes Applied to Grinding Technology.”Journal of Manufacturing Systems32 (4): 556–63. doi: 10.1016/j.jmsy. 2013.05.005. Miller, Geoff, JanicePawloski, and CharlesStandridge. 2010. “A Case Study of Lean, Sustainable Manufacturing.”Journal of Industrial Engineering and Management3 (1): 11–32. doi: 10.3926/jiem.2010.v3n1.p11-32. PLM. 2020. https://www.plm.automation.siemens.com/global/en/products/manufacturingplanning/plant-simulation-throughput-optimization.html. Popescu, Cristina Raluca Gh, and Gheorghe N.Popescu. 2019. “An Exploratory Study Based on a Questionnaire Concerning Green and Sustainable Finance, Corporate Social Responsibility, and Performance: Evidence from the Romanian Business Environment.”Journal of Risk and Financial Management12 (4): 162. doi: 10.3390/ jrfm12040162.

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Rodrigues, Gislene Salim, João Carlos EspíndolaFerreira, and Carlos RodriguesRocha.2018. “A Novel Method for Analysis and Optimization of Electric Energy Consumption in Manufacturing Processes.”Procedia Manufacturing17: 1073–81. doi:10.1016/j. promfg.2018.10.078. Seow, Yingying, ShahinRahimifard, and ElliotWoolley. 2013. “Simulation of Energy Consumption in the Manufacture of a Product.”International Journal of Computer Integrated Manufacturing26 (7): 663–80. doi: 10.1080/0951192X.2012.749533. Shao, Guodong, DeogratiasKibira, and KevinLyons. 2016. “A Virtual Machining Model for Sustainability Analysis.”International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. Terkaj, Walter, and MarcelloUrgo. 2015. “A Virtual Factory Data Model as a Support Tool for the Simulation of Manufacturing Systems.”Procedia CIRP28: 137–42. doi: 10.1016/j. procir.2015.04.023.

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Internet of Agriculture Things (IoAT) A Novel Architecture Design Approach for Open Research Issues K. Lova Raju and V. Vijayaraghavan VFSTR University

CONTENTS 3.1 3.2

Introduction .................................................................................................... 36 Functional Blocks of IoT................................................................................. 38 3.2.1 Device ................................................................................................. 39 3.2.2 Communication .................................................................................. 40 3.2.3 Services ............................................................................................... 40 3.2.4 Management ....................................................................................... 40 3.2.5 Security ............................................................................................... 40 3.2.6 Application.......................................................................................... 40 3.3 Characteristics of IoT...................................................................................... 40 3.3.1 Self-Adapting and Dynamic ............................................................... 40 3.3.2 Self-Configuration .............................................................................. 40 3.3.3 Interoperable Communication Protocols ............................................ 41 3.3.4 Unique Identity ................................................................................... 41 3.3.5 Integrated into the Information Network............................................ 41 3.3.6 Context-Awareness ............................................................................. 41 3.3.7 Intelligent Decision-Making Capability ............................................. 41 3.4 IoT Protocol Stack .......................................................................................... 42 3.5 IoT Protocols ................................................................................................... 42 3.5.1 MQTT ................................................................................................. 42 3.5.2 CoAP................................................................................................... 43 3.5.3 XMPP ................................................................................................. 43 3.5.4 AMQP ................................................................................................. 43 3.5.5 DDS .................................................................................................... 43

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3.5.6 REST HTTP ....................................................................................... 43 3.5.7 Web Sockets........................................................................................ 44 3.6 IoT Enabling Technologies ............................................................................. 44 3.7 IoT Applications.............................................................................................. 44 3.8 Existing Works ............................................................................................... 44 3.8.1 Internet of Things in Agriculture ....................................................... 46 3.9 IoAT Architecture ........................................................................................... 46 3.9.1 Sensors Used in the IoAT Architecture .............................................. 48 3.9.2 Wireless Technologies Used in the IoAT Architecture ...................... 48 3.9.3 Hardware Platforms Used in the IoAT Architecture .......................... 48 3.10 Data Analysis in the IoAT Architecture ......................................................... 48 3.10.1 Various IoT-Based Cloud Service Platforms ...................................... 48 3.10.2 Big Data .............................................................................................. 49 3.10.3 Machine Learning Techniques ........................................................... 49 3.10.4 Security Issues .................................................................................... 53 3.11 IoAT Applications........................................................................................... 53 3.12 Conclusion ...................................................................................................... 53 References ................................................................................................................ 54

3.1 INTRODUCTION The term Internet of Things (IoT) was coined by Kevin Ashton in 1999. It aims to connect anything at any time in any place (Harbi et al. 2019, Bharathi et al. 2019), as shown in Figures 3.1 and 3.2. IoT techniques are a platform that can access and

FIGURE 3.1

Connectivity of IoT device examples (Harbi 2019).

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FIGURE 3.2 IoT view. (Adapted from Bharathi et al. 2019.)

control the devices remotely (Sangeetha et al. 2018) at any time. Things or objects in the physical design of IoT refer to communicate with each other, for mutual data exchanging over the Internet (Yan, Zhang, and Vasilakos 2014) without human intervention. In the generation of IoT, any objects are embedded with sensors, actuators, microcontrollers, and communication devices that can work together and make a smatter world (Adelantado et al. 2017). Nearly 50 billion things will be connected using the Internet by 2020 (da Cruz et al. 2018). It will increase exponentially as time moves on. The transformation of the Internet to the IoT consists of four (Khanna and Kaur 2019) phases. The first phase was represented where communication is possible through the fixed telephone line and by the way of Short Message Service (SMS). The second phase was focused on sending large size messages like e-mails in terms of attachments, information, entertainment, and so on. The third phase was recommended for electronic applications such as e-productivity, e-commerce, and so on. The fourth phase was associated with social media like Facebook, YouTube, Skype, and so on. Finally, the ongoing emerging technology is IoT as shown in Figure 3.3. This chapter (Raju and Vijayaraghavan 2020) comprises five parts. Part 1 addresses the introduction to IoT and Part 2 consists of fundamental concepts of IoT such as IoT functional blocks, characteristics of IoT, IoT protocol stack, IoT enabling technologies, and applications of IoT. Part 3 consists of IoAT architecture, which consists of IoT in agriculture, sensors used in IoAT, wireless technologies in IoAT, and hardware platforms in IoT. Part 4 represents the data analysis in IoAT and it contains various cloud platforms, big data, machine learning, and security issues. Part 5 describes the IoAT applications and conclusion of the chapter as shown in Figure 3.4.

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FIGURE 3.3 Transformation of the Internet to IoT (Khanna and Kaur 2019).

FIGURE 3.4 Outline of the article. (Raju and Vijayaraghavan 2020.)

3.2 FUNCTIONAL BLOCKS OF IOT The IoT system consists of various (Ray 2017, Al-Fuqaha et al. 2015) functional blocks to simplify different characteristics of the system such as sensing, identification, actuation, communication, and management as shown in Figure 3.5. Figure 3.6 highlights the functional blocks and Figure 3.7 shows the block diagram of an IoT device.

Internet of Agriculture Things (IoAT)

FIGURE 3.5

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Elements in IoT (Al-Fuqaha 2015).

FIGURE 3.6 IoT functional blocks. (Adapted from Ray 2017.)

FIGURE 3.7

3.2.1

Block diagram of an IoT device (Ray 2017).

deviCe

An IoT system consists of interrelated computing devices that are capable of performing a full-duplex mode of communication for the transfer and exchange of data with the other devices over the Internet. The data are communicated to the centralized servers or applications that involve the cloud for processing using local networks. For communication purposes, IoT devices utilize both wired and wireless protocols that are responsible for controlling the performed actions locally or remotely.

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3.2.2 CommuniCation The communication block in the functional diagram of the IoT device is mainly pledged for transferring data from one device to another device. The communication layers of the Open Systems Interconnection model, i.e., application, presentation, and transport layers, are mainly responsible for maintaining protocols.

3.2.3 ServiCeS A variety of services such as device modeling, controlling, recovering, data analytics, and publishing of data are provided by the services block of an IoT device.

3.2.4

management

For the governance of an IoT device, a management block plays a vital role. It provides different functionalities for an IoT device for device governance.

3.2.5 SeCurity Providing security for an IoT device includes a huge task. To accomplish this task, the functionalities such as privacy, authentication, content integrity, authorization, data security, and message integrity are provided.

3.2.6

appliCation

The application layer is mainly responsible for data visualization and analyzing the system status.

3.3

CHARACTERISTICS OF IoT

IoT can be defined as a container of essential service components (Ray 2018), as shown in Figure 3.8.

3.3.1

Self-adapting and dynamiC

Sensors are equipped with special features called dynamic switching and self-adapting, which are able to take actions based on their operating conditions.

3.3.2

Self-Configuration

An additional feature is also equipped for IoT devices called self-configuration, which can work with more number of devices to achieve some functionality, for example, agriculture monitoring in fields.

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FIGURE 3.8 Characteristics of IoT. (Ray 2018.)

3.3.3

interoperaBle CommuniCation protoColS

To accomplish successful means of communication, an IoT device must be able to allow other interoperable devices, protocols, and their infrastructures.

3.3.4

unique identity

To distinguish the IoT devices, each device is assigned a unique identifier.

3.3.5

integrated into tHe information netWork

The IoT sensors are unified within the system by making necessary connections among them and allowing the other devices to communicate by exchanging data. These formed networks are dynamic.

3.3.6

Context-aWareneSS

The physical information is gathered by the sensor and the sensor node gains knowledge about the neighboring context.

3.3.7

intelligent deCiSion-making CapaBility

To achieve long-distance communication, networks are multi-hop. After obtaining the information from the sensor, they make intelligent decisions on their own and improve their efficiency.

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FIGURE 3.9 IoT Protocol Stack (Čolaković and Hadžialić 2018).

3.4 IoT PROTOCOL STACK The most common protocols (Čolaković and Hadžialić 2018) shown in Figure 3.9 are based on the reference model of TCP/IP. It is essential to use suitable communication system architecture with various IoT protocols when they are needed to interoperate. The latest developments are the only way to develop for the future development of IoT based on the standardized approach. Consider developing novel IoT protocols and architectures that play a pivotal role in the present years.

3.5 IoT PROTOCOLS IoT consists of significant protocols (Glaroudis, Iossifides, and Chatzimisios 2020), which are present in the application layer. In this section, a few IoT protocols are discussed.

3.5.1

mqtt

The Message Queue Telemetry Transport (MQTT) is a lightweight TCP/IP-based messaging and publisher–subscriber network protocol that provides us a bidirectional communication and lossless connections between the publisher and subscriber. MQTT defines three QoS levels: QoS 0, 1, and 2. To provide more security for the MQTT protocol, an additional feature S-MQTT (Secure MQTT) is introduced. It supports only lightweight data packets; to overcome this, it uses a UDP protocol for an effective way of transmission of higher data packets over a long distance.

Internet of Agriculture Things (IoAT)

3.5.2

43

Coap

The Constrained Application Protocol (CoAP) is a specialized Internet-based web transfer protocol that is used for the devices that use the same constrained network (i.e., low power and lossy networks) and is also used for different constrained networks that are linked to the Internet. As opposed to HTTP, CoAP relies on a non-connection-oriented transport protocol (UDP), and it supports unicast and multicast.

3.5.3

xmpp

The Extensible Messaging and Presence Protocol (XMPP) is a message-oriented communication protocol, which enables the exchange of real-time data between two or more network entities. It is used for chatting, video, and voice calls by supporting each one of these applications for providing authentication and encryption services. For text messaging services, it uses Extensible Markup Language (XML). To provide more security for our data, the XMPP has an inbuilt TLS mechanism to determine more accuracy in terms of data integrity.

3.5.4

amqp

The Advanced Message Queuing Protocol (AMQP) is an open standard application layer protocol that mainly defines for queuing, reliability, routing (including pointto-point and publish-and-subscribe), security, and message orientation. It has built two dissimilar versions like version 0.9.1 and version 1.0. The advanced AMQP version is not exclusionarily associated with the publisher/subscriber mechanism. The AMQP has been designed with reliability, security, and interoperability in mind by providing an industrial-grade, open-source solution that works even in low-latency environments.

3.5.5

ddS

The Data Distribution Service (DDS) for real-time systems are sophisticated by an Object Management Group (OMG) that aims to get high performance, real-time, enable dependable, interoperable, and scalable data exchanges using a publish– subscribe pattern. It uses the UDP protocol by delinquency, but it can support the TCP protocol also. It offers 23 wide ranges of QoS policies. However, the security issue for this is still a pending question. Overall, it is backing both powerful and low capacity devices that give solutions for a wide range of IoT applications.

3.5.6

reSt Http

The Representational State Transfer (REST) Hyper Text Transfer Protocol (HTTP) is a basic web server protocol that uses the elemental client/server model. The HTTP service is correlated with the architectural style recently to expedite to maintain the interaction between different entities of web services.

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3.5.7

Green Engineering and Technology

WeB SoCketS

The main usage of the web sockets protocol is to maintain a perfect interaction between the web browsers and the web server with the lowest alternatives. It is an independent single TCP-based full duplex computer communicating protocol. It does not work based on the publisher/subscriber model or the response/request model. They maintain an asynchronous connection. It is not suitable for devices with strong constraints, but it can offer us real-time communication by minimizing the overhead solutions for the applications of IoT by using a WAMP (Web socket-based Application Messaging Protocol) sub-protocol.

3.6 IoT-ENABLING TECHNOLOGIES Presently, IoT-enabling technologies play a prominent role in the IoT. Now, we will discuss some of these IoT-enabling technologies (Bahga and Madisetti 2014) as shown in Figure 3.10.

3.7 IoT APPLICATIONS Figure 3.11 illustrates the taxonomy of applications of IoT such as environmental, commercial, smart city, industrial, health care, general aspects, and so on (Asghari, Rahmani, and Javadi 2019).

3.8 EXISTING WORKS Nowadays, IoT is the emerging technology for providing better solutions to agricultural applications full of the automation system and also some other emerging technologies are used like cloud computing, machine learning, artificial intelligence, and so forth. Different architecture designs are implemented for a smart agriculture

FIGURE 3.10

IoT enabling technologies. (Bahga and Madisetti 2014.)

Internet of Agriculture Things (IoAT)

45

FIGURE 3.11 IoT applications (Asghari, Rahmani, and Javadi 2019).

system using these technologies. Regarding this, innovative research is being done on agricultural applications. Some of the existing works are discussed below. Ayaz et al. (2019) suggested smart agriculture toward making the fields talk using IoT. This article explained well the major role-play of the IoT and wireless sensors in the agricultural applications such as preparation of the soil, status of the yield, watering, insects, and detecting bugs. This review gives an insight into IoT-based architectures, platforms, wireless technologies, and current and future challenges in agriculture issues. But there is no explanation about the proper architecture regarding the smart agriculture system in this article. Muangprathub et al. (2019) explained the smart farm using IoT and data analysis. This paper mainly focused on the trio areas of farms and deploying the sensors in each farm for crop yield and water management in the way of real-time monitoring. By getting information regarding agricultural farms, it is sent to the former via through web and mobile-based applications by using NodeMCU. The designed structure is constituted by trio sensors such as soil moisture, DHT22, and ultrasonic sensors along with a lesser price. In this work, there is no discussion regarding the IoT-based architecture. Chen and Yang (2019) proposed an IoT-based architecture for intelligent agriculture and emerging technologies. This article mainly concentrated on intelligence added to the traditional agriculture methods by using the IoT. That term is called smart agriculture. In addition, two types of analysis (data visualization analysis and cluster analysis) are incorporated with the development of intelligent agriculture and also the development of the IoT in the field of agriculture in terms of technical functions like sensing, identification, transmission, and monitoring. This paper article consists of layered architecture only.

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Mekala and Viswanathan (2019) implemented a novel CMM measurement index for a smart agriculture system. In this article, the authors described the thermal comfort levels of crops or plants and also determined the temperature quotient that is based on temperature (P), humidity (H), and soil moisture. By using layer architecture only, that is used to send messages like E-mail alerts and SMS to the farmers regarding their agriculture field information. Here, the hardware section represents the DHT 11 sensor that is interfacing with Arduino Uno, and the moisture sensor is also used in it. Shi et al. (2019) presented the protected agriculture using IoT. Protected agriculture is the method for the efficient development of new agriculture used to change climate conditions such as temperature and humidity, and also it is suitable for the growth of plants and animals. This review gives an insight into the IoT in the field of protected agriculture. In this article, the author explained the IoT-based layered architecture only. Mekala and Viswanathan (2020) proposed a THAM index-based IoT system for smart agriculture decision-making using sensor stipulation, a novel THAM index for finding the comfort levels of the crops and plants. In this article, the authors mainly focused on the sensor selection system that enables a sensor to impose the process, and the entire agriculture field is covered by the optimal number of sensors. Moreover, the NPK fertilizer regulatory model is recommended for the proper nutrition rate present in the soil. But there is no proper architectural explanation regarding the agriculture monitoring in this article. Zamora-Izquierdo et al. (2019) developed a smart farming IoT platform based on edge and cloud computing. This article represents the advancement and design of a system architecture along with a prototype that is used in Precision Agriculture with automation. The IoT protocols like MQTT or CoAP are used to communicate with Cyber-Physical Systems (CPS), while Next-Generation Service Interface (NGSI) for northbound and southbound APIs (Application Programming Interfaces) gets access to the cloud platform.

3.8.1

internet of tHingS in agriCulture

In the field of agriculture area, to develop smart farming solutions using the IoT technology, an important amount of work has been done. In smart farming, IoT has transferred the enormous innovation in the agriculture environment by analyzing the multiple issues and challenges (Elijah et al. 2018, Ojha, Misra, and Raghuwanshi 2015) in smart agriculture. In this present scenario, the growth of IoT technologies provides the solutions for the problems faced by farmers such as water shortages, productivity problems, and cost-effective management. Advanced IoT technologies are identified and all these issues are provided with solutions to increase the productivity with cost-effectiveness. Wireless sensor networks enable the collection of data from sensor devices and are sent to the main servers or clouds.

3.9 IoAT ARCHITECTURE The proposed architecture of the Internet of Agriculture Things (IoAT) consists of two subsystems (Singh, Chana, and Buyya 2020), namely, a User Subsystem (US) and a Cloud Subsystem (CS), as shown in Figure 3.12.

Internet of Agriculture Things (IoAT)

47

FIGURE 3.12 Proposed Architecture of Internet of Agriculture Things (IoAT). (Singh, Chana, and Buyya 2020.)

The US is responsible for monitoring the agriculture field to get information (agricultural parameters) from environmental sensors and deployment sensors (agricultural sensors). Therefore, the physical layer comes into the picture and it senses all agricultural parameters through sensors such as temperature, humidity, moisture, and so on. It sends the digital signal to another above level. Data acquisition is a collection of data from sensors deployed in the agriculture field. The data are processed after the completion of the data collection process and this is called data processing, which comes under hardware-embedded platforms (IoT gateway) along with wireless communication technologies for IoT, based on the conceptual and communication layers. Through communication protocols like MQTT and CoAP, only the messages are transmitted from the client to the server based on a set of rules for data in formats such as XML, JSON, CSV, and so on. These things have occurred while the Internet is available at farmers' place, and it comes under the Internet layer. The CS is to take care of the agriculture field data that are stored in the IoT cloud repository using IoT securities like APIs. Therefore, the data can be accessed from

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the cloud repository through preconfigured devices such as mobile phones, laptops, and so forth. The accessing layer is responsible for accessing the data from the cloud through a farmer’s mobile phone regarding agriculture data. The application layer is designed to perform graphical visualization, real-time monitoring, and statistical analytics in a fully automated manner. IoT cloud itself is a data storage platform, and data analysis is carried out in it. Some of the machine learning algorithms are used in the IoAT applications for data analysis. Finally, IoT cloud platforms provide cloud services for data storage and analysis. These cloud platforms are associated with the architecture of IoAT for providing cloud services.

3.9.1 SenSorS uSed in tHe ioat arCHiteCture The IoAT architecture consists of two types of sensors: environmental sensors and deployment sensors. An environmental sensor describes the environmental parameters such as temperature (Raju et al. 2019) and humidity sensor (DHT11), lightdependent resistor (LDR) sensor, and so forth. At the same time, a deployment sensor describes the soil parameters like soil moisture, pH value, and so on. Table 3.1 shows the details about the sensors that are used in the IoAT architecture.

3.9.2

WireleSS teCHnologieS uSed in tHe ioat arCHiteCture

Wireless communication technologies play a very significant role in agricultural applications. Table 3.2 shows the classification of different communication technologies (Bhoyar et al. 2019).

3.9.3

HardWare platformS uSed in tHe ioat arCHiteCture

Various hardware platforms or boards (Tzounis et al. 2017) are used for agricultural applications, along with the parameters such as Operating Voltage (OV), Clock Speed (CS), System Memory (SM), Programming Language (PL), and Integrated Development Environment (IDE) as shown in Table 3.3 and that are equipped with sensors and from the resources, the information is collected.

3.10 DATA ANALYSIS IN THE IoAT ARCHITECTURE Data analysis is a key part of the IoAT architecture.

3.10.1

variouS iot-BaSed Cloud ServiCe platformS

IoT-based cloud service platforms (Ray 2016) are used for the development of advanced use cases with better solutions. Table 3.4 provides various kinds of cloud platforms of IoT along with IoT-based cloud service platforms, IoT cloud service type, application improvement, monitoring data, visualization, developer cost, and research purpose.

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TABLE 3.1 Different Sensors Related to Agriculture and Environmental Applications (Raju 2019) S. No.

Sensor Image

Sensor Name

Technical Details

Use Cases

1

DHT11: Temperature and Humidity

Input voltage: 3.3–5.5 V, range of humidity: 20%–90%, range of temperature 0°–50°, Accurateness ±2, persistence −1, and exchangeability

Environmental and agricultural

2

LDR: Light Dependent Resistor

Environmental and agricultural

3

FC-28:Soil moisture

Input voltage:3.3–5 V dc,operating current:15 mA, digital output:0–5 V, analog output:0–5 V based on light falling on LDR Operating voltage: 3.3–5 V, output voltage: 0–4.2 V, input current: 35 mA, output signal: both analog and digital

3.10.2

Smart gardening and agricultural

Big data

Big data shows the data properties (Wolfert et al. 2017) characterized by high volume, high velocity, and high variety that require specific technology and analytical methods for their transformation into values.

3.10.3

maCHine learning teCHniqueS

Machine learning techniques are classified into three types, namely, supervised learning, unsupervised learning, and reinforcement learning. Again, supervised learning is categorized into classification (SVM, KNN, NB, RF, and AR) and regression

Long Range

Global Mobile Communications/ General Packet Radio Service Extended coverageGSM Narrow Band Internet of Things Sigfox

Cellular

6

9

10

Wide Area Network Wide Area Network Low Power Wide Area Network Metropolitan Area Network

Local Area Network

Local Area Network

Source: Information from Bhoyaret al. (2019).

8

7

Personal Area Network Local Area Network

Proximity

Network Type

ZigBee/IPv6 over Low Power Wireless Personal Area Networks Wireless Fidelity Local Area Network

Radio Frequency Identification Bluetooth

Communication Technology

5

4

3

2

1

S. No.

Between 700 and 2500 M

Between 750 and 900 M Licensed LTE bands 200 k

Between 850 and 1900 M

Between 433 and 915 M

Between 868 and 915 M, 2.4 G Between 2.4 and 5 G

2.4 G

13 M

Frequency Operated (Hz)

Between 500 M and 1 G

Between 100 and 600

250 K

240 K

Between 80 and 384 K

100 K

Between 54 and 600 M

250 K

25 M

424 K

Data Rate (b/s)

TABLE 3.2 Taxonomy of Different Wireless Communication Technologies

Within the coverage area

Between 30 and 50 km

Extended distance Below 35 km

Between 5 and 30 km

Between 3 and 5 km

100 m

Between 10 and 50 m

Below 10 m

10 m

Distance of Communication

High

Low

Low

Low

High

Very Low

High

Low

Low

Very low

Power Utilization Cost

Medium

Low

High

Low

Low

High

High

Low

Low

Very low

Use Cases

Environmental and agricultural

Environmental and smart agricultural Smart irrigation and environmental Precision agricultural

Environmental, agricultural, and waste management Environmental, agricultural, and waste management Environmental, agricultural, and waste management

Environmental and agricultural Environmental and agricultural Environmental and agricultural

50 Green Engineering and Technology

Arduino Mega

Arduino Uno

ESP8266 Wi-Fi Module CC3200 SimpleLink Wi-Fi LaunchPad

Node MCU

Raspberry Pi 3

ARM Processor

2

3

4

6

7

8

Source: Data from Tzounis(2017)

5

Arduino Nano

Name of the Hardware Platform

1

S. No.

32-bit and 64-bit RISC multi-core processors

quad-core processor

ESP8266, LX106

ARM Cortex -M4 Core

RISC CPU

ATmega328P

ATmega2560

ATmega328P

Processor/ Controller Used

5V

5V

5V

3.3 V, 3.6 V 5V

5 V, 3 V

5V

5V

OV

Above 1 GHz

1.2 GHz

80 MHz

80 MHz

80 MHz

16MHz

16MHz

16MHz

CS

8KB

256 MB to 1 GB

128 KB

256KB

32KB

2KB

8KB

2KB

SM

UART

I2C, SPI, UART, GPIO I2C, SPI, SERIAL

SPI, UART, GPIO UART

I2C, SPI, UART

I2C, SPI, UART

I2C, SPI, UART

I/O Connectivity

TABLE 3.3 Classification of Hardware Platforms Used for Agricultural Applications

Wi-Fi, Zigbee, Bluetooth, Ethernet, Serial

Wi-Fi, Zigbee, Bluetooth, Ethernet, Serial

Wi-Fi, Bluetooth, Serial

Wi-Fi, Bluetooth, Ethernet, Serial

Wi-Fi, Zigbee, Bluetooth, Ethernet, Serial Wi-Fi, Zigbee, Bluetooth, Ethernet, Serial Wi-Fi, Zigbee, Bluetooth, Ethernet, Serial Wi-Fi, Serial

Communication Technologies Used PL

Python, C/C++, Java, Ruby C/C++

Wiring, C/C++

Wiring, C/C++ Wiring, C/C++

Wiring, C/C++

Wiring, C/C++

Wiring, C/C++

IDE

Keil MDK, Arm Online Compiler

NOOBS, Raspbian OS

Lua and AT commands Code Composer Studio™ Cloud Arduino

Arduino

Arduino

Arduino

Internet of Agriculture Things (IoAT) 51

AMEE Arkessa Axeda Carriots

Connecterra Exosite

GroveStreams

Nimbits Phytech Plotly Thethings.iO ThingsBoard ThingSpeak ThingWorx

Ubidots Xively Yaler

5 6

7

8 9 10 11 12 13 14

15 16 17

IoT Cloud Service Platforms

1 2 3 4

S. No.

Platform as a service (Hybrid) Software as a Service (Private) Public Public Public Public Infrastructure as a Service (Private) Public Software as a Service (Public) Infrastructure as a Service (Private)

Private

Private IoT Software as a Service

Private Platform as a Service (Private) Platform as a Service (Private) Platform as a Service (Private)

IoT Cloud Service Type Π Π Π Π Π Π Π

Π Π Π Π Π Π Π Π Π Π

Π Π Π

Π Π Π Π Π Π Π Π Π Π

Monitoring Data

Π Π Π Π

Application Improvement

Π Π Π

Π Π Π Π Π Π Π

Π

Π Π

Π Π Π Π

Visualization

Free Free Use through pay

Use through pay Use through pay Use through pay Free of cost up to 10 devices Pay per access Free of cost up to 2 devices Free of cost up to 20 stream, 5 SMS, 500 Email Free Use through pay Free Free Low Free Use through pay

Developer Cost

TABLE 3.4 Classification of Different IoT-Based Cloud Service Platforms Used in Agricultural Applications (Ray 2016)

Π Π Π

Π Π Π Π Π Π Π

Π

Π Π

Ο Ο Ο Π

Research Purpose

52 Green Engineering and Technology

Internet of Agriculture Things (IoAT)

53

(DT, NN, and ET), and k-mean clustering and principal component analysis (PCA) come under unsupervised learning. Machine learning techniques (Liakos et al. 2018) are used for various applications in the field of agriculture such as agriculture yield prediction, managing diseases, soil maintenance, detection of weed plants in the agriculture field, and managing the water requirement. These techniques are developed for the qualitative and quantitative analyses regarding the yield of agriculture production.

3.10.4

SeCurity iSSueS

IoT security is the main concern while preserving (HaddadPajouh et al. 2019) and observing things and environments. Environmental monitoring by IoT sensors or things needs a reliable security mechanism for stopping the data gap and maintaining data confidentiality on the devices.

3.11 IoAT APPLICATIONS IoT has many applications in the field of agriculture (Farooq et al. 2019), as shown in Figure 3.13.

3.12 CONCLUSION IoT looks into the propriety of the agriculture field to improve crop yields, improve quality, and reduce costs. IoT can provide better solutions in the agriculture sector by deploying the sensors and other devices that have been discussed. This chapter

FIGURE 3.13 IoAT applications. (Farooq 2019.)

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examines the above-mentioned aspects and recommends the role of different technologies, particularly IoT, to build a smarter and cost-effective IoAT to meet future anticipation. This is why smart sensors and cloud computing communication technologies are discussed systematically. Moreover, passionate insight into current research works is provided. In addition, different kinds of cloud platforms are provided for agricultural applications. We proposed that the architecture of IoAT supports the contemporary research developments in the field of agricultural applications.

REFERENCES Adelantado, Ferran, XavierVilajosana, PereTuset-Peiro, BorjaMartinez, JoanMeliaSegui, and ThomasWatteyne. 2017. “Understanding the Limits of LoRaWAN.”IEEE Communications Magazine55 (9). IEEE: 34–40. Al-Fuqaha, Ala I., MohsenGuizani, MehdiMohammadi, MohammedAledhari, MoussaAyyash. 2015. “Internet of Things: A Survey on Enabling Technologies, Protocols, and Applications.”IEEE Communications Surveys & Tutorials17 (4): 2347–76. doi:10.1109/ COMST.2015.2444095. Asghari, Parvaneh, Amir MasoudRahmani, and Hamid Haj SeyyedJavadi. 2019. “Internet of Things Applications: A Systematic Review.”Computer Networks148 (January): 241–61. doi:10.1016/j.comnet.2018.12.008. Ayaz, Muhammad, MohammadAmmad-Uddin, ZubairSharif, AliMansour, and El-Hadi M. Aggoune. 2019. “Internet-of-Things (IoT)-Based Smart Agriculture: Toward Making the Fields Talk.”IEEE Access7: 129551–83. doi:10.1109/ACCESS.2019.2932609. Bahga, Arshdeep, and VijayMadisetti. 2014. Internet of Things: A Hands-On Approach: Bharathi, Ayyasamy, Balusamy Balamurugan, Kothandapani Chokkanathan, Ragupathi Sathiyaraj, andAbhishekSingh. 2019. “Internet of Things Technologies.” In Internet of Things in Biomedical Engineering, pp. 291–322. Elsevier. Bhoyar, Prachin, Parul Sahare, Sanjay B. Dhok, and Raghavendra B. Deshmukh. 2019. “Communication Technologies and Security Challenges for Internet of Things: A Comprehensive Review.”AEU - International Journal of Electronics and Communications99 (February): 81–99. doi:10.1016/j.aeue.2018.11.031. Chen, Jinyu, and AoYang. 2019. “Intelligent Agriculture and Its Key Technologies Based on Internet of Things Architecture.”IEEE Access7: 77134–41. doi:10.1109/ ACCESS.2019.2921391. Čolaković, Alem, and MesudHadžialić. 2018. “Internet of Things (IoT): A Review of Enabling Technologies, Challenges, and Open Research Issues.”Computer Networks144 (October): 17–39. doi:10.1016/j.comnet.2018.07.017. Cruz, Mauro AA da, Joel JPCRodrigues, Arun KumarSangaiah, JalalAl-Muhtadi, and ValeryKorotaev. 2018. “Performance Evaluation of IoT Middleware.”Journal of Network and Computer Applications109. Elsevier: 53–65. Elijah, Olakunle, Tharek AbdulRahman, IgbafeOrikumhi, Chee YenLeow, and MHD NourHindia. 2018. “An Overview of Internet of Things (IoT) and Data Analytics in Agriculture: Benefits and Challenges.”IEEE Internet of Things Journal5 (5): 3758–73. doi:10.1109/JIOT.2018.2844296. Farooq, Muhammad Shoaib, ShamylaRiaz, AdnanAbid, KamranAbid, and Muhammad AzharNaeem. 2019. “A Survey on the Role of IoT in Agriculture for the Implementation of Smart Farming.”IEEE Access7: 156237–71. doi:10.1109/ACCESS.2019.2949703.

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Glaroudis, Dimitrios, AthanasiosIossifides, and PeriklisChatzimisios. 2020. “Survey, Comparison and Research Challenges of IoT Application Protocols for Smart Farming.”Computer Networks168 (February): 107037. doi:10.1016/j.comnet.2019.107037. HaddadPajouh, Hamed, AliDehghantanha, Reza M. Parizi, MohammedAledhari, and HadisKarimipour. 2019. “A Survey on Internet of Things Security: Requirements, Challenges, and Solutions.”Internet of Things: 100129. doi:10.1016/j.iot.2019.100129. Harbi, Yasmine, ZiboudaAliouat, SaadHarous, AbdelhakBentaleb, and AllaouaRefoufi. 2019. “A Review of Security in Internet of Things.”Wireless Personal Communications108 (1). Springer: 325–44. Khanna, Abhishek, and SanmeetKaur. 2019. “Evolution of Internet of Things (IoT) and Its Significant Impact in the Field of Precision Agriculture.”Computers and Electronics in Agriculture157. Elsevier: 218–31. Liakos, Konstantinos, PatriziaBusato, DimitriosMoshou, SimonPearson, and DionysisBochtis. 2018. “Machine Learning in Agriculture: A Review.”Sensors18 (August): 2674. doi:10.3390/s18082674. Mekala, Mahammad Shareef, and Perumal Viswanathan. 2019. “CLAY-MIST: IoT-Cloud Enabled CMM Index for Smart Agriculture Monitoring System.”Measurement134 (February): 236–44. doi:10.1016/j.measurement.2018.10.072. Mekala, Mahammad Shareef, and P.Viswanathan. 2020. “(T, n): Sensor Stipulation with THAM Index for Smart Agriculture Decision-Making IoT System.”Wireless Personal Communications111 (3): 1909–40. doi:10.1007/s11277-019-06964-0. Muangprathub, Jirapond, NathaphonBoonnam, SiriwanKajornkasirat, NarongsakLekbangpong, ApiratWanichsombat, and PichetwutNillaor. 2019. “IoT and Agriculture Data Analysis for Smart Farm.”Computers and Electronics in Agriculture156 (January): 467–74. doi:10.1016/j.compag.2018.12.011. Ojha, Tamoghna, SudipMisra, and Narendra SinghRaghuwanshi. 2015. “Wireless Sensor Networks for Agriculture: The State-of-the-Art in Practice and Future Challenges.”Computers and Electronics in Agriculture118 (October): 66–84. doi:10.1016/j.compag.2015.08.011. Raju, K. Lova, Sk MdKhasim, K. Yaswanth Pavankalyan, Avula Naveen, and Pemmasani Vikas. 2019. “The State of Art of Internet of Things for Smart City Research Issues.” In 2019 International Conference on Vision Towards Emerging Trends in Communication and Networking (ViTECoN), 1–6. doi:10.1109/ViTECoN.2019.8899388. Raju, K. Lova, and Veeramani Vijayaraghavan. 2020. “IoT Technologies in Agricultural Environment: A Survey.”Wireless Personal Communications. 113(4), 2415–2446. Ray, ParthaPratim. 2016. “A Survey of IoT Cloud Platforms.”Future Computing and Informatics Journal1 (1): 35–46. doi:10.1016/j.fcij.2017.02.001. Ray, ParthaPratim. 2017. “Internet of Things for Smart Agriculture: Technologies, Practices and Future Direction.”Journal of Ambient Intelligence and Smart Environments9 (4). IOS Press: 395–420. Ray, ParthaPratim. 2018. “A Survey on Internet of Things Architectures.”Journal of King Saud University - Computer and Information Sciences30 (3): 291–319. doi:10.1016/j. jksuci.2016.10.003. Sangeetha, A. Lakshmi, Nallaiyan Bharathi, A. BalajiGanesh, and T. K. Radhakrishnan. 2018. “Particle Swarm Optimization Tuned Cascade Control System in an Internet of Things (IoT) Environment.”Measurement117. Elsevier: 80–89. Shi, Xiaojie, XingshuangAn, QingxueZhao, HuiminLiu, LianmingXia, XiaSun, and YeminGuo. 2019. “State-of-the-Art Internet of Things in Protected Agriculture.” Sensors19 (April): 1833. doi:10.3390/s19081833.

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Singh, Sukhpal, InderveerChana, and RajkumarBuyya. 2020. “Agri-Info: Cloud Based Autonomic System for Delivering Agriculture as a Service.”Internet of Things9 (March): 100131. doi:10.1016/j.iot.2019.100131. Tzounis, Antonis, NikolaosKatsoulas, ThomasBartzanas, and ConstantinosKittas. 2017. “Internet of Things in Agriculture, Recent Advances and Future Challenges.”Biosystems Engineering164 (December): 31–48. doi:10.1016/j.biosystemseng.2017.09.007. Wolfert, Sjaak, LanGe, CorVerdouw, and Marc-JeroenBogaardt. 2017. “Big Data in Smart Farming – A Review.”Agricultural Systems153 (May): 69–80. doi:10.1016/j.agsy. 2017.01.023. Yan, Zheng, PengZhang, and Athanasios V. Vasilakos. 2014. “A Survey on Trust Management for Internet of Things.”Journal of Network and Computer Applications42. Elsevier: 120–34. Zamora-Izquierdo, Miguel A., JoséSanta, Juan A. Martínez, VicenteMartínez, and Antonio F.Skarmeta. 2019. “Smart Farming IoT Platform Based on Edge and Cloud Computing.”Intelligent Systems for Environmental Applications177 (January): 4–17. doi:10.1016/j.biosystemseng.2018.10.014.

4

E-Navigation: An Indoor System for Green City Sustainable Development Using a UGU Engine Architecture Ajay B. Gadicha P.R.Pote College of Engineering and Management

Vijay B. Gadicha G H Raisoni University

Om Prakash Jena Ravenshaw University

CONTENTS 4.1 4.2 4.3 4.3 4.4 4.5 4.6 4.7

Introduction .................................................................................................... 58 4.1.1 Problem Statement .............................................................................. 58 4.1.2 Purpose of the Study........................................................................... 58 Literature Survey ............................................................................................ 59 4.2.1 Tango .................................................................................................. 60 Methodology ................................................................................................... 60 4.3.1 Interior Modeling ................................................................................ 60 4.3.2 Navigation ........................................................................................... 61 System Architecture ....................................................................................... 62 Flow Chart ...................................................................................................... 62 Progression System of Execution in E-Navigation ......................................... 62 How the UGU Architecture is Useful in E-Navigation for Smart Cities ............................................................................................... 64 Proposed Algorithm ....................................................................................... 64 4.7.1 Player Controller Mechanism ............................................................. 64 4.7.2 Obstacle Animation ............................................................................ 65

57

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Green Engineering and Technology

4.7.3 Level Generator Algorithm................................................................. 65 4.7.4 E-Navigation Movement Algorithm ................................................... 65 4.8 Results and Discussion ................................................................................... 67 4.9 Conclusion ...................................................................................................... 68 4.10 Further Scope.................................................................................................. 69 References ................................................................................................................ 70

4.1 INTRODUCTION Today, in this evolving time-centric world, people are so much into themselves that they cannot even waste a single second to ask and navigate in a huge institute or building. Rather than just roaming and asking, they want something, something cool and self-efficient, which can not only guide them but also provide a better navigation experience. Due to such large construction, many consumers visiting an institute or mall or super-market for the first time just cannot find exactly what they are looking for and then many a time, customers/users leave unsatisfied leading to a reduction in the popularity and the mark that a user-friendly organization must leave. Enormous development in navigating systems, 3D technologies, and the leaning of the world toward augmented reality (AR) and virtual reality (VR) has made the development of such indoor navigation systems possible, in many developed countries. There are 3D navigable super-markets where you can navigate directly to the item you want to search. As a citizen of one of the most rapidly developing countries, why not we provide such indoor navigation capabilities to our users and consumers to help them in saving their crucial time. Hence, this is the prime time to hit the metal.

4.1.1

proBlem Statement

To develop an AR-based system for indoor navigation for the users to help them navigate in indoor conditions and find their path more accurately and quickly.

4.1.2

purpoSe of tHe Study

The purpose of the study is to keep the crucial element time at the main focus for users while navigating in a huge building and finding the path to where they need to be. In this project, we have researched about many hardware and software to provide such facility; we have analyzed the problem faced by the consumers and users and then designed a robust system successfully for the implementation of indoor navigation. We have created our own 3D model of a building with all the components to provide indoor navigation in it by using a first-person camera system. Due to the lack of reach to the buildings and permission of the organizations, we have developed the system only for our college, but in the near future, this can be extended to every building. The main focus of this device is to provide the indoor navigation capability. This will not only be in the first person but also in the third person where you can traverse a building without even getting there.

E-Navigation

59

FIGURE 4.1 Green city framework [2].

4.2 LITERATURE SURVEY This section discusses the brief concept about the present strategies or methods associated with harmony, Godot and unreal engine (UGU) mechanism and one-of-a-kind modeling correlated to a 3D view of any item and its significance in an inexperienced metropolis. The 20th century has become characterized by means of fast and frequently out of control urban increase essential to the emergence of large dispersed or decomposed towns no longer like the small usage town of the 19th century. The rapid industrialization, innovations that incorporate e-vehicles, and the ease of use of reasonably priced terra firma and less expensive fossil fuels represent a quantity of the use of forces for city enhancement [1]. In the 1980s, the assessment “The restrictions to development” introduced the concept of sustainable financial growth [3]; “Our general expectations” tested that economic boom, environmental safety, and social improvement could be reconciled [4]; and the New Urbanism lobby group endorsed strategies to limit the detached built-up boom of cities through the use of more environmentally friendly metropolis layout practices [5]. The concept of sustainability in the 19th century resigned community impartiality, financial expansion, and ecological protection with the upgrading of the conurbation [6, pp. 296–312] and opened the way for the improvement of different ideas alongside sustainable cities [7], inexperienced urbanism, habitable metropolis [8–11], and compact metropolis [12, 13], which is probably modern and architectural, among others. In the 2000s, the inclusion of climate trade issues in the global political timetable put power [14] and the useful resources of overall performance [15] at the heart of the Sustainable Development and City Sustainability Dialogue. Discussions on city

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paperwork including the strength, overall performance of assets, and usual overall environmental performance have become the number one element in the search for new ideas and techniques for defining and smart city sustainability. These contemporary-day inclinations delivered about the improvement of the term “inexperienced.” “Green” approaches numerous subjects to remarkable humans. The time period is nowadays extensively utilized by non-private and private agencies as an emblem for sustainability and eco-friendliness. “Greening” is over again duration associated with the term green. In this text, “inexperienced” and “greening” are used synonymously for sustainability and associated issues in which power and beneficial resource are of primary concern. As a give up cease end result of the progressed interest given to electricity, beneficial aid performance, and concrete shape close to weather change, questions already formulated earlier such as “Are superb metropolis office work and metropolis designs more sustainable than others in terms of pollution, environmental impact and strength use?”; “What techniques and movements can correctly make a contribution to make towns extra sustainable (greener)?”; and, extra nowadays, “How are we able to manipulate the modern city increase manner underneath the outcomes of climate change, and on the identical time make this manner greener?” have regained significance. Although actively being studied to date, there can be no vital consensus regarding the exceptional answers to those questions. Scholars [12, 13, 16–19] stated the compact town form as one that could strongly contribute to city sustainability, specifically nearly about the influences of the metropolis increase technique and the usage of strength, belongings performance, infrastructure, and environmental ordinary regular average overall performance-related problems.

4.2.1

tango

In recent architecture and planning phases of smart cities, the majority of programmers concentrate on the latest software, which not only helps to generate the 2D or 3D view of the object but also gives rise to the planning of the smart city. In this context, Tango software is widely used in an AR or VR to visualize the object in depth and find out various cons of the building plan to convert into a smart version. This tango device is capable of capturing the position without GPS and gives the experience to visualize the object using AR or VR.

4.3 METHODOLOGY Our proposed system consists of two modules.

4.3.1

interior modeling

Any interior model consists of several isolated objects such as rooms, doors, walls, pillars, and many more. To design these models, we are using Unity 3d. Unity 3d provides various 3D models, which can be used as it is, and some of the models are needed to be developed by own due to cost issues. We also need to provide an accurate structure to those models and place them accurately according to the structure

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FIGURE 4.2

Academic factors of sustainable development.

of the building. We have created the ground floor model of our college premises with more than eight rooms and have applied textures to each one of them. These textures need to be created and are useful to provide the feel of the actual aura. This enables the user to get the exact feel that he/she is traveling in the same building. We have provided a marker to every room so that our camera/3D model can move towards the marker following the shortest path provided by the NavMesh. Every room is having a door and the name on it so that the user can confirm that he/ she has reached the right location.

4.3.2

navigation

The most important part of the app is navigation on the shortest path. In the model, we have provided the user with a dropdown menu where he/she can select a destination and as soon as they click on submit, the NavMesh tool will find the shortest path to the destination and will guide the user to the location. The tool will take care of all the paths and will only guide on the shortest path. This will save the time of the user and increase the efficiency of the product. The navigation system allows you to create characters that can intelligently flow around the sports world, the use of navigation meshes, which might be created routinely from your scene geometry. Dynamic limitations can help you regulate the navigation of the characters at runtime, while off-mesh links permit you to construct precise moves like starting doors or jumping down from a ledge. This section describes Unity’s navigation and routes locating systems in the element. Navigation requires the use of a simplified geometrical aircraft, frequently known as a NavMesh. The NavMesh allows characters to plan a direction across the numerous complex items in a scene. In this video, we can observe the way to create a map using NavMesh (often known as “baking”) with the use of Unity’s Navigation view.

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FIGURE 4.3

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System architecture.

4.3 SYSTEM ARCHITECTURE The major focus of the proposed work is to provide accurate indoor navigation even in the low network area. For this purpose, we are using algorithms to find out the shortest distance and baked the path according to the structure to avoid any type of collision. Unity provided a tool NavMesh from which we can provide the shortest path and make the movement accordingly. To test this, we have deployed the program on a Pie-based Android device where we have done the third-person navigation. As the user selects the location, the NavMesh will guide the user to the destination through the shortest path and if the user wants to go to another location, then he/she can add another location.

4.4 FLOW CHART Here the user will first start the app and will see the dropdown menu from where he/she can select the destination. As soon as the user confirms the destination, the request will be given to the NavMesh tool and then the NavMesh tool will provide the shortest distance to that location. After the path is confirmed, the navigation will start, and the user will be guided to the end location.

4.5 PROGRESSION SYSTEM OF EXECUTION IN E-NAVIGATION Step I: Select destination As the start-up screen, the user will be provided with a pre-defined dropdown menu from where the user can select the destination where he/she wants to navigate. Step II: Send the shortest path As soon as the user selects and confirms the destination from the dropdown menu, the input of the destination will be given to the NavMesh agent and the shortest path of the destination will be searched, and as the path is confirmed, the path is sent back.

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FIGURE 4.4 Flow working of the proposed modeling.

FIGURE 4.5

Progression flow.

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Step III: Navigate to the destination As the shortest path is received, the navigation will start, and the user will be navigated to the destination. Step IV: End navigation After the navigation, if the user wants to update the destination, then he/she can select another location and the process will be repeated. If the user wants to end the navigation, then he/she can just close the app.

4.6

HOW THE UGU ARCHITECTURE IS USEFUL IN E-NAVIGATION FOR SMART CITIES

With the proliferation of technologies and the tremendous development, sensible navigation structures have emerged as one of the maximum promising solutions for developing better and extra sustainable towns, smart cities as they are normally called. The following article addresses six future dispositions in the utilization of Smart Navigation for smart cities. I. Smart Navigation Systems: How it is far green? The time period navigation system refers to a positive machine that assists in navigation and is placed generally on board on an automobile or a vessel, or somewhere else and talked through indicators, or even integrated all the above. Smart Navigation resulted from the evolution inside the technological abilities of navigation systems through the years, additionally known as clever navigation technologies. Depending on their use, navigation structures might also incorporate maps in a human-readable format, decide an automobile or tool’s area, provide guidelines to a human through textual content or speech, offer commands straight away to a self-enough tool, which encompasses a robot, share statistics on nearby motors, gadgets, or even on-site visitors conditions, and recommend alternative routes. II. Smart navigation for smart towns? Urbanization is thought of as one of the maximum tremendous demanding situations for current societies (Suzuki et al., 2010). The expectation is that 80% of the area’s population will live in urban environments through using 2050 (Ordnance Survey, 2015). Achieving sustainable urbanization is a destiny pursue through organizing smart towns. The time period smart cities describe a technique through the use of facts and communication era designed to address metropolis demanding situations consisting of overcrowding, transport, and power.

4.7 PROPOSED ALGORITHM 4.7.1 i. ii. iii.

player Controller meCHaniSm Declaration of Unity_Engine Declaration of UnityEngine.AI Declaration of Player_Controller class as MonoBehaviour

E-Navigation iv. v. vi. vii. viii. iX. X. Xi. Xii.

Declaration of Cam Declaration of NavMesh_Agent Call update() once per frame if mousebutton down then set mouse position Else set_destination

4.7.2

oBStaCle animation

65

i. Declaration of Unity_Engine ii. Declaration of UnityEngine.AI iii. Declaration of System.Collections; iv. Declaration of System.Collections.Generic; v. Declaration of Obstacle_Animation as MonoBehaviour vi. inialize drift pace =.2f vii. inialize drift power=9f viii. initialization of Random_Range to 0f and 2f ix. call update method x. check vecot3 position using remode function xi. use the formula Time.Time * pace + randomOffset) * power xii. calculate the transform.Role = pos;

4.7.3

level generator algoritHm

i. inialization of width = 10; ii. inialization of peak = 10; iii. Use this for initialization iv. void Start () v. GenerateLevel(); vi. floor.BuildNavMesh(); vii. For Loop over the grid (width and height) viii. assign x=0 ix. if xRF power The PCE depends on the following factors:

1. Uncertainty in the availability of the RF power, 2. Associated component losses, 3. Insensibility of the sensor circuit,

FIGURE 6.3 General block diagram of an RFEH system.

(6.1)

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4. Measuring distance between the transmitter and the receiver, 5. Loss due to impedance mismatch, and 6. Limitation of the radiated power.

This chapter is arranged to give the reader an insight view of various energy harvesting antenna designs and their related rectification and matching circuits. Section 6.2 presents an overview of various antennas and metamaterial-based absorber designs for energy harvesting applications. Section 6.3 discusses the rectifier and matching circuits for energy harvesting. Section 4 closes with the conclusion.

6.2 DESIGN CONSIDERATIONS FOR ANTENNA The soaring demand for handheld communicating devices with a small form factor urges the need for compact antennas whose size, weight, ease of assembling, and low maintenance are major constraints. A compact antenna is the prerequisite of wireless network interface controllers, Wi-Fi devices, and many other gadgets. The extensive use of PCB led to the idea of microstrip antennas.

6.2.1

antenna miniaturization

The microstrip technology has become renowned over the years, due to its ubiquitous accessibility, lightweight nature, ease of construction, low fabrication cost, and convenient mounting on the ground plane due to its thin layer of substrate profile. Figure 6.4 illustrates a three-layered microstrip architecture with the patch (top layer), ground (bottom layer), and dielectric substrate (middle layer) sandwiched between them. However, the elevation of substrate height introduces surface waves; therefore, the height of the copper patch is kept as small as possible, and it varies from 0.035 mm to 0.07 mm. The thin layer of substrate in the microstrip antenna paves for a high Q-factor (25–100) that conversely reduces the bandwidth, power, and efficiency.

FIGURE 6.4

Q=

fr BW

Geometry of a microstrip patch antenna.

(6.2)

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In Eq. (6.2), the value of Q is inversely proportional to the bandwidth, which results in the bandwidth reduction. For a miniaturized antenna with a minimum loss, Chu suggested Eq. (6.3) for Q- factor,

Q≥

1 1 + k 3a 3 ka

(6.3)

where k is 2π/λ and “a” represents the antenna space. Thick substrates of lower dielectric range (2.2 ≤ ∈r ≤ 7) are desirable due to their better efficiency and performance, but it increases the cost of the material used for radiation. In addition, size reduction should be obtained with thin substrates of higher dielectric constants (7 ≤ ∈r ≤ 12), which are desirable for microwave circuitry to minimize undesired radiation. Other methods of miniaturization include shorted posts, slots, meandered slits, defective ground structures (DGS), and metamaterials. A cross-shaped slot [23] is etched from a square patch antenna, which reduces the size to about 32.5% compared with the conventional approach. A square patch antenna [24] with ten meandered slits each on its periphery reduces the size to 48%. Slots and slits help in patch size reduction compensating the shift in the resonant frequency and impedance mismatch. Figure 6.5 illustrates a square patch with (a) a cross-shaped etch and (b) meandered slits. Therefore, additional matching circuits are needed, which makes the design more complex and increases the overall form factor. DGS [25] is an alternate method of miniaturization, modeled using an LC or RLC equivalent circuit. A defective ground plane increases the dielectric strength and hence decreases the size of the patch. The size reduction of about 42% is achieved through circular slots on the ground plane [26]. Using H-shaped slots at the ground plane, a size reduction of 43% was achieved [27]. H-shaped slots achieve size

FIGURE 6.5 Miniaturized patch antenna designs: (a) square patch with cross-shaped etched [23] and (b) square patch with meandered slits [24].

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FIGURE 6.6 Plus-shaped patch H-shaped defective ground structures at the ground plane.

reduction but with decreased efficiency due to back radiation. Figure 6.6 illustrates a plus-shaped patch with an H-shaped DGS structure at the ground plane. Metamaterials are artificially engineered structures suggested by V. G. Veselago [28] in 1968, who proposed that if a material has both permeability and permittivity values lesser than zero (ε < 0 & µ < 0), the refractive index of such materials would be negative (n < 0). Split-ring resonator (SRR) and complementary split-ring resonator (CSRR)-based metamaterials exhibit a negative refractive index, which improves the overall efficiency in the applications such as antenna [29], absorber [30], amplifier [31], and RF oscillator [32]. The advantage of SRR is its compact size, impedance matching, and advantage of sensing a wide frequency band. Almoneef [33] in his experiment with a 12 × 12 SRR array provided an output power of more than 60% per footprint with wide bandwidth compared to a conventional patch structure. Fowler [34] in his work proposed an SRRbased metamaterial structure with very high efficiencies of 230% and 130% at power densities of 10µWcm−2 and 1µWcm−2, respectively. Almutairi [35] proposed a compact CSRR-based structure with a unit cell size of 5.5 × 5.5 mm2 with an effective medium ratio of 8.0 for energy harvesting applications. Figure 6.7 shows that electromagnetic propagation can be controlled by a special class of metamaterials known as the electromagnetic band gap (EBG) [36]. A patch antenna [37] achieved a size reduction of 22.38% and a bandwidth improvement of 39.3% with a square-shaped EBG structure at the ground plane. Various antenna size reduction techniques are compared in Table 6.1.

6.2.2 antenna polarization Antenna polarization is an important parameter that influences the conversion efficiency of a harvesting system. Various polarization techniques include horizontal polarization, vertical polarization, slant polarization, and circular polarization.

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FIGURE 6.7 Metamaterial-based structures: (a) CSRR-based structure [35] and (b) periodic EBG structure [37].

Out of those, the circular polarization is considered the best type for the RFEH system since it does not result in energy loss like the other types of polarization. The “minimum loss” property of a circularly polarized antenna makes it the most efficient in the RFEH system [39]. The circular polarization can be achieved by truncating the corners, loading stubs, cross dipoles, slits, spur lines, and y-shaped slots in the patch [40]. The simplest of all to achieve circular polarization is to truncate the corners of the patch. The single feed circular polarized structures TABLE 6.1 Antenna Size Reduction Techniques

Antenna Patch antenna with a cross-shaped slot [23] Planar antenna with meandered slits [24] Patch with H-shaped split rings (SRR) [26] Patch antenna with DGS [27] Metamaterial-based CSRR structure [35] EBG-based printed patch antenna [38]

Substrate

Polarization

Frequency (GHz)

Size (mm)

Size Reduction (%)

Arlon A25N & Rohacell 51 foam Taconic(TLY5lamiate High dielectric material of 3.2 mm Argon material

Circular

2.45

34 × 34

32.5

Linear, orthogonal Circular

2.36

41 × 0.5

48

2.865

34 × 20

27

Linear

2.4–2.49

56 × 66

46

FR-4

Circular

4–8

5.5 × 5.5

50

RT Duroid 5880

Linear

10

25 × 25

89

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FIGURE 6.8 Single-feed CP microstrip patches: (a) square patch and (b) square patch with truncated corners.

are fed at 45° with respect to perturbation. Typical configurations [41] of a single point feed patch capable of producing linear and circular polarization are shown in Figure 6.8. The single feed configuration sets up asymmetrical current paths with two different resonant frequencies at a 90° phase difference between them. The disadvantages are reduced bandwidth and less efficiency. Limitations of bandwidth and efficiency are improved when using stacked patch structures [42,43], metamaterials, and magnetic conductors. In the stacked etched structures [42], the overall height of the antenna measures a thickness of about 4.8 mm. These structures improve the axial ratio and bandwidth, with an overall increase in the size of the antenna. From the comparisons of Table 6.1, metamaterialbased structures show improved performance for energy harvesting as they do not degrade the performance.

6.2.3

reConfiguraBility

Reconfigurable antennas are highly attractive and preferable in energy harvesting due to their advantage in selecting the operating frequency and polarization. Reconfigurability aids to choose from the wideband frequencies to select the desired frequency. Reconfigurability is achieved by using switching devices such as RF electromechanical systems, PIN diodes, or photoconductive switches with biasing elements for switching ON and OFF the respective switching elements. By turning the switch ON and OFF accordingly, the antenna is switched between two different frequency bands. Pal H et al. [44] proposed a frequency-reconfigurable antenna by using diodes to resonate at two different frequencies (2.4 GHz when the diodes are OFF and 1.6 GHz when the diodes are ON). Figure 6.9 illustrates antenna with frequency reconfigurability using diodes for switching between two different frequencies. S. W. Cheung et al. [45] proposed a reconfigurable antenna

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FIGURE 6.9

93

Reconfigurable antenna with diodes for switching between frequencies.

using slots and pin diodes and achieved an axial ratio bandwidth of 7.2% and a wide impedance bandwidth of 19.5%.

6.2.4

HarmoniC rejeCtion

The rectifying circuits convert the incoming RF signals to DC signals [46]. However, these circuits have nonlinear components such as Schottky diodes and PIN diodes, which induce harmonics and hence reduce the overall performance of the harvesting system. These harmonics create a mismatch in the impedance between the antenna and the rectifying circuits and results in a poor PCE. Hence, low pass filters (LPFs) should be added between the rectifying diodes and antenna to suppress and reject the harmonics, thereby increasing the efficiency of the system. Numerous antenna designs incorporate harmonic rejection capability for RFEH systems. To improvise the maximum power to be received by the antenna, several structural modifications such as stub, meandered slits, and DGS are adopted to reject the harmonics and to improve the PCE. An example of harmonic rejection [47] is the circular slot antenna with a DGS structure inscribed at the ground plane, which performs the filtering function in rejecting higher-order harmonics (stop band) and omits the need for an additional filter. The rectifying diode [48] with the microstrip patch induces harmonics of 4.8 and 7.2 GHz at the resonating frequency of 2.4 GHz. However, with a simple circular sector angle of 240° and a feed angle of 30°, it blocks the second and third harmonics, rejecting re-radiation and eliminating the need for a filter. The harmonic rejection feature by these antennas also helps in improving the antenna gain and overall PCE.

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6.3

DESIGN CONSIDERATIONS FOR MATCHING CIRCUITS

This section briefs the importance of matching circuits and their design considerations for RFEH systems. The degradation of the efficiency lies in the mismatching of the impedance between the antenna and the non-linear components in the rectifying circuits. Impedance mismatch echoes the incident wave back to the source and results in poor efficiency. Impedance matching improves the efficiency in converting RF power into the desired DC voltage resulting in the maximum PCE. Matching circuits can also be called as LPFs, which help in rejecting the higher-order harmonics that is twice or thrice the fundamental resonant frequency [49]. A good matching circuit should possess the following characteristics: 1. The impedance matching must be attained between the antenna and the load impedance (rectifier circuit along with the load) over a wide frequency range and input power level. 2. The matching circuit should be relatively small, because it should not increase the overall form factor. Usually, matching circuits are designed using lumped circuits such as T-network, PI-network, L-network, shunt inductor, gamma matching network, band pass filter, or distributed microstrip lines. A simplified design of an L-shaped matching network is discussed followed by a PI-type matching network. Figure 6.10a shows an L-shaped matching network for low power range. L-shaped matching networks provide good impedance matching with minimum loss [50]. However, this type of network suffers from narrow bandwidth due to the high value of Q. Assuming source impedance as 50Ω, Lm and Cm values are calculated as Lm =  

Cm = 

Rin ω 0 (Q + ω 0Cin Rin ) Rin 1 Lm (   Rin −   Rs )  2 1   ω 0   −    L C  m in

(6.4)

(6.5)

The L-section matching network has its limitations on tuning both Lm and Cm. The limitations of the above matching network can be overcome by a PI-network or T-network. Figure 6.10b illustrates a PI-shaped matching network.

FIGURE 6.10

(a) L-section matching network and (b) PI-section matching network.

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FIGURE 6.11 Comparison of L and PI networks: (a) output voltage as a function of frequency and (b) variation of L and C with Q. (Adapted from Sachin Agrawal, Jawar Singh and Manoj S. Parihar. 2015. Performance Analysis of RF Energy Harvesting Circuit with Varying Matching Network Elements and Diode Parameters, IET Microwaves, Antennas & Propagation, vol. 1, pp. 6–18)

The design equation for a PI network is given as

    1 1   Z in = ( RL −   jX L ) ||  + j  ω   L  || ω j   ω C1 ) j   C ( 2)  (  

(6.6)

Figure 6.11 compares L and PI matching networks with 6.11a illustrating the output voltage as a function of frequency. The parasitic losses allied with the passive elements fluctuate with frequency. Figure 6.11b depicts the frequency-dependent nature, as the frequency increasing the capacitive behavior is changed into inductance. As Q increases, the value of L decreases drastically, so high Q circuits can be designed with low values of L [14]. However, for frequencies greater than 1 GHz, lumped elements due to parasitic losses are not suitable. Hence, at high frequencies, microstripbased lines are designed to match the complex input impedance of the antenna and the rectifier circuit at a particular input power level [51]. Song et al. [52] designed an impedance matching network suitable for a load range of 1–10 kΩ. A broad band impedance matching circuit [53] with its upper branch consists of a radial stub and a shorted stub and a 6nH chip inductor is presented for frequency ranges from 1.8 to 2.5 GHz, and its lower branch consists of a shorted bent shaped stub and a 1.8nH chip inductor for matching frequency range at 2.1 GHz.

6.4 RECTIFIER CIRCUITS Rectifier circuits, typically with one or more diodes, must have a high PCE. The selection of diode is of primary importance as it directly relates to the conversion efficiency of the rectifier circuits. A typical diode with poor performance leads to a poor conversion efficiency. The PCE [54] is influenced by the (i) series resistance of

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FIGURE 6.12

(a) Single-stage voltage doubler circuit and (b) Dickson charge pump.

the diode (Rs), (ii) zero-bias junction capacitance (Cj0), (iii) diode breakdown voltage (Vbr), (iv) high switching frequency of the rectifying diode, and (v) low threshold voltage (V T). Figure 6.12 shows the parameters such as harmonics, parasitic effects, reverse breakdown voltage (Vbr), and threshold voltage (V T) that affects the conversion efficiency [55]. Rectifier circuits are classified based on the components used in the rectifier circuits. They are (i) diode-based rectifier circuits and (ii) MOSFET-based rectifier circuits.

6.4.1

diode-BaSed reCtifier CirCuitS

Diodes prove to be an efficient candidate for rectifier circuits for a high PCE. Schottky diodes are widely used for rectenna applications [56]. Rectifiers can be classified into two types: (i) a simple rectifier and (ii) a voltage doubler rectifier. Figure 6.12a illustrates a single-stage voltage doubler circuit, which is widely used to double the output voltage for low-and medium-power applications. A two-stage Cockcroft Walton voltage doubler [57] is capable of generating output voltage three times the input voltage but results in a low voltage gain of the circuit due to its high-coupling voltage drop. Figure 6.12b shows an n-stage Dickson charge pump [58] for doubling

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the input voltage. The important requirement of the charge pump is the need for the clock pulses in every stage, which limits its applications for high voltages.

6.4.2

moSfet-BaSed reCtifier CirCuitS

The advantage of MOSFET lies in its fast switching speed. However, MOSFETS require a high threshold voltage, which limits the efficiency of the RFEH system [59]. Also, the high voltage drop across the device and the reverse leakage current further degrade the efficiency of the system [60]. To compensate for the high threshold voltage, the cross-coupled voltage multiplier [61] technique is used, as illustrated in Figure 6.13. This circuit complexity improves efficiency at the cost of making the multiplier circuit more bulky during the increase in the number of stages. Another method used to compensate the threshold voltage is by implementing cascaded crosscoupled multipliers. This circuit combines a voltage doubler with a cross-coupled voltage multiplier circuit. As the number of stages gets increased, voltage ripples increase, which degrade the efficiency of the system [62]. Various rectifier topologies are reviewed in Table 6.2. Schottky-based diodes show a better efficiency compared to CMOS and MOSFET-based rectifiers. HSMSbased diodes from Agilent and SMS-based diodes from Skyworks are preferred in most of the works compared to the MOSFET-based rectifiers. In the HSMSbased diodes, HSMS8101 is preferred due to its high-power handling capability, low threshold voltage, and wide input frequency range. In the range of SMS diodes, SMS 7630–061 is preferred due to its low threshold voltage, low junction capacitance, and high series resistance [63].

FIGURE 6.13

Single-stage cross-coupled voltage multiplier [61].

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TABLE 6.2 Various Rectifier Topologies Used in the RFEH System

Antenna

Resonant Frequency (GHz)

Shorted ring slot antenna [61] Patch antenna [62]

2.45 2.45

Patch antenna [63]

2.45

Patch antenna [64]

2.45

Patch antenna [65]

2.45

Patch antenna [66]

2.45

Patch antenna [67]

2.45

Rectifying Diode Series HSMS2850 diode HSMS2852 Series Schottky diode (HSMS2850) Bridge rectifier with four Schottky diodes Shunt SMS7630079LF diode HSMS282C diode pairs Voltage doubler (HSMS2860)

Input Power Level

Efficiency (%)

Load RL (Ω) Vdc (V)

0–20µWcm−2 69 @ 20W cm−2 –30 to 15 83 @ 0dBm dBm --63 @ 0.525mW cm−2 0–16 dBm 61 @ 10dBm

2500

1.1

1400

3.75

1600

2.82

1050

3.64

−20 to −40 dBm −10 to −20 dBm 0–20 dBm

1500

7.36

1000

11.42

900

3.5

33.7 @ −11.2 dBm 82.3 @ 22 dBm 72.5 @ 13 dBm

6.5 CONCLUSION Energy harvesting from RF energy for WSN–IoT nodes is an attractive strategy, which is challenging in the design aspects such as low power output levels, less conversion efficiency, and broadband matching, creating keenness among the researchers to ponder in this research area. IoT era, where billions of devices are to be added to the Internet energy harvesting, will play a keen role in the near future. This chapter has given an overview of the selection and designing compact antenna by applying various miniaturization techniques, designing of the matching circuits, various rectifier topologies built with diodes, and MOSFETS to build an efficient RFEH system for real-time applications.

REFERENCES

1. Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H., and Zhao, W. 2017. A survey on Internet of Things: Architecture, enabling technologies, security and privacy, and applications, IEEE Internet of Things Journal, vol. 4, no. 5, pp. 1125–1142. 2. Mitra, R. N. and Agrawal, D. P. 2015. 5G mobile technology: A survey, ICT Express, Vol. 1, no.3, pp. 132–137. 3. Gohil, A., Modi, H., and Patel, S. K. 2013. 5G technology of mobile communication: A survey, International Conference on Intelligent Systems and Signal Processing (ISSP), Gujarat, 2013, pp. 288–292. 4. Park, S. and Jayaraman, S. 2003. Enhancing the quality of life through wearable technology, IEEE Engineering in Medicine and Biology Magazine, vol. 22, no. 3, pp. 41–48.

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5. Leonov, V., Torfs, T., Fiorini, P., and Van Hoof, C. 2007. Thermoelectric converters of human warmth for self-powered wireless sensor nodes, IEEE Sensors Journal, vol.7, no. 5, pp. 650–657. 6. Guo, L. and Lu, Q. 2017. Potentials of piezoelectric and thermoelectric technologies for harvesting energy from pavements, Renewable and Sustainable Energy Reviews, vol. 72, pp. 761–773. 7. Zhao, L. and Yang, Y. 2017. Toward small-scale wind energy harvesting: Design, enhancement, performance comparison, and applicability, Shock and Vibration, vol. 2017, pp. 1–31. 8. Invernizzi, F., Dulio, S., Patrini, M., Guizzetti, G., and Mustarelli, P. 2016. Energy harvesting from human motion: Materials and techniques, Chemical Society Reviews, vol. 45, no. 20, pp. 5455–5473. 9. Albrni, M. A., Sampe, J., Islam, M. S., and Majlis, B. Y. 2016. Ultra-low power energy harvester using hybrid input for wireless communication devices - A review, Journal of Theoretical and Applied Information Technology, vol. 86, no. 3, pp. 365–376 10. Chen, Z., Guo, B., Yang, Y., and Cheng, C. 2014. Metamaterials-based enhanced energy harvesting: A review, Physica B: Condensed Matter, vol. 438, pp. 1–8. 11. Hassan N., Ahmad, B., Abdul Malek, M. F., Aziz, M. Z. A., Bakar, H. A., Azizi, M. S. N., and Ismail, M.. 2018. Radio frequency (Rf) energy harvesting using metamaterial structure for antenna/rectenna communication network: A review, Journal of Theoretical and Applied Information Technology, vol. 96, no. 6, pp. 1538–1550. 12. Devi, K. K. A., Hau, N. C., Chakrabarty, C. K., and Din, N. M. 2014. Design of patch antenna using metamaterial at GSM 1800 for RF energy scavenging, IEEE Asia Pacific Conference on Wireless and Mobile, pp. 157–161. 13. Devi, K. K. A., Sadasivam, S., Din, N. M., and Chakrabarthy, C. K. 2011. Design of a 377 Ω patch antenna for ambient RF energy harvesting at downlink frequency of GSM 900, The 17th Asia Pacific Conference on Communications, pp. 492–495, Sabah, Malaysia. 15. Valtchev, S., Borges, B. V., Brandisky, K., and Klaassens, J. B. 2005. Efficient resonant inductive coupling energy transfer using new magnetic and design criteria, Proceedings of IEEE PESC’05, pp. 1293–1298, Recife, Brazil. 16. Liu, H. 2011. Maximizing efficiency of wireless power transfer with resonant inductive coupling, International Baccalaureate Program. 17. Kurs, A., Karalis, A., Moffatt, R., Joannopoulos, J. D., Fisher, P., and Soljacic, M. 2007. Wireless power transfer via strongly coupled magnetic resonances, Science, vol. 317, no. 5834, pp. 83–86. 18. Jonah, O. and Georgakopoulos, S. V. 2013. Wireless power transfer in concrete via strongly coupled magnetic resonance, IEEE Transactions on Antennas and Propagation, vol. 61, no. 3, pp. 1378–1384. 19. Lu, X., Wang, P., Niyato, D., Kim, D. I., and Han, Z. 2015. Wireless networks with RF energy harvesting: A contemporary survey, IEEE Communications  Surveys & Tutorials, vol. 17, no. 2, pp. 757–789. 20. Visser, H. J. and Vullers, R. J. M. 2013. RF energy harvesting and transport for wireless sensor network applications: Principles and requirements, Proceedings of IEEE, vol. 101, no. 6, pp. 1410–1423. 21. Kim, S., Vyas, R., Bito, J., et al. 2014. Ambient RF energy-harvesting technologies for self-sustainable standalone wireless sensor platforms, Proceedings of IEEE, vol. 102, no. 11, pp. 1649–166.

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23. Andia Vera, G., Georgiadis, A., Collado, A., and Via, S. 2010. Design of a 2.45 GHz rectenna for electromagnetic (EM) energy scavenging, IEEE Radio and Wireless Symposium (RWS). 25. Guha, D, Biswas, S, Antar, Y. M. M. 2011. Microstrip and Printed Antennas: New Trends, Techniques and Applications. Chichester, West Sussex: John Wiley &Sons; p. 481. 26. Varma, R., Sharma, S. K., and Singh, B. 2014. Miniaturization of microstrip patch antenna using metamaterial, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering (IJAREEIE), vol. 3, pp. 9652–9661. 27. Prajapati, P. R. 2015.Application of defected ground structure to suppress out of-band harmonics for WLAN microstrip antenna, International Journal of Microwave Science and Technology, vol. 2015, pp. 1–9. 28. Veselago, V. 1968. The electrodynamics of substances with simultaneously negative values of ε and µ, Soviet Physics Uspekhi, vol. 10, no. 4, pp. 509–514. 29. Dawar, P., Raghava, N. S., and De, A. 2019. UWB metamaterial-loaded antenna for C-band applications, International Journal of Antennas and Propagation, vol. 2019, 1–13. 30. Yoo, M. and Lim, S. 2014. Polarization independent and ultra-wideband metamaterial absorber using a hexagonal artificial impedance surface and a resistor-capacitor layer, IEEE Transactions on Antennas Wireless Propagation, vol. 62, pp. 2652–2658. 31. Chen, L., Li., J., Pan, H., Yi, X. Q. 2010. An efficiency-improved power amplifier using split-ring resonator defected ground structure, Asia-Pacific Symposium on Electromagnetic Compatibility (APEMC), pp. 1421–1423, Beijing, China. 32. Choi, J.and Seo, C. 2008. Broadband VCO using electronically controlled metamaterial transmission line based on varactor-loaded split-ring resonator, Microwave and Optical Technology Letters, vol. 50, no. 4, pp. 1078–1082. 33. Almoneef, T. S. and Ramahi, O. M. 2014. Can Split Ring Resonators be viable for electromagnetic energy harvesting? IEEE Antennas and Propagation Society, AP-S International Symposium (Digest), pp. 424–425, Memphis, TN, USA. 34. Fowler, C. and Zhou, J. 2017.A highly efficient polarization-independent metamaterialbased RF energy-harvesting rectenna for low-power applications. 35. Almutairi, A.F., Islam, M.S., Samsuzzaman, M., Islam, M.T., Misran, N., Islam, M.T. 2019. A complementary split ring resonator based metamaterial with effective medium ratio for C-band microwave applications, Results in Physics, vol. 15, pp. 1–15. 36. Yang, F. and Samii, Y. R. 2008. Electromagnetic Band Gap Structures in Antenna Engineering. Cambridge, UK: Cambridge University Press. 37. Ripin, N., Awang, R. A., Sulaiman, A. A., Baba, N. H., and Subahir, S. 2012. Rectangular microstrip patch antenna with EBG structure, IEEE Student Conference on Research and Development, pp. 266–271, Pulau Pinang, Malaysia. 39. Shrestha, S, Noh, S. K., and Choi, D. Y. 2013. Comparative study of antenna designs for RF energy harvesting, International Journal  of  Antennas  and  Propagation, vol. 385260, p. 10. 41. Chowdhury, M. H., et.al. Single feed circularly polarized crescent-cut and extended corner square microstrip antennas for wireless biotelemetry, International Journal of Electrical and computing engineering, vol. 9, no. 3, pp. 1902–1909.

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42. Falade, O. P., Rehman, M. U., Gao, Y., Chen, X., and Parini, C. G. 2012. Single feed stacked patch circular polarized antenna for triple band GPS receivers, IEEE Transactions on Antennas and Propagation, vol. 60, no. 10, pp. 4479–4484. 43. Kumar, S., Kanaujia, B. K., Khandelwal, M. K., and Gautam, A. K. 2015. Single-feed circularly polarized stacked patch antenna with small-frequency ratio for dual-band wireless applications, International Journal of Microwave and Wireless Technologies, vol. 8, no. 08, pp. 1207–1213. 44. Pal, H. and Choukiker, Y. H. 2016. Design of frequency reconfigurable antenna with ambient rf-energy harvester system, International Conference on Information Communication and Embedded System, Chennai, India. 45. Cheung, S. W., Zhou, C. F., Li, Q. L., and Yuk, T. I. 2016. A simple polarizationreconfigurable antenna, 10th European Conference on Antennas and Propagation (EuCAP), Davos, Switzerland. 46. Allane, D., Vera, G. A., Duroc, Y., Touhami, R., and Tedjini, S. 2016. Harmonic power harvesting system for passive RFID sensor tags, IEEE Transactions on Microwave Theory and Techniques, vol. 64, pp. 2347–2355. 47. Yeo, J. and Kim, D. 2009. Harmonic suppression characteristic of a CPW fed circular slot antenna using single slot on a ground conductor, Progress In Electromagnetics Res earch (PIER) Letters , vol. 11, pp. 11–19. 48. Park, J. Y., Han, S. M., and Itoh, T. 2004. A rectenna design with harmonic-rejecting circular-sector antenna, IEEE Antennas and Wireless Propagation Letters, vol. 3, no. 1, pp. 52–54. 49. Kundu (Datta), P., Acharjee, J., and Mandal, K. 2017. Design of an efficient rectifier circuit for RF energy harvesting system, International Journal of Advanced Engineering and Management, vol. 2, no. 4, pp. 94–97. 50. Sun, Y. and Fidler, J. 1994. Design of impedance matching networks, Proceedings in IEEE International Symposium on Circuits and Systems (ISCAS94), London, UK. 51. Saeed, W., Shoaib, N., Cheema, H. M., and Khan, M. U. 2018. RF energy harvesting for ubiquitous, zero power wireless sensors, International Journal of Antennas and Propagation, pp. 1–16. 52. Song, C., Huang, Y., Carter, P. et al. 2016. A novel six-band dual CP rectenna using improved impedance matching technique for ambient RF energy harvesting, IEEE Transactions on Antennas and Propagation, vol. 64, no. 7, pp. 3160–3171. 53. Song, C., Huang, Y., Zhou, J., Zhang, J., Yuan, S., and Carter, P. 2015. A high-efficiency broadband rectenna for ambient wireless energy harvesting, IEEE Transactions on Antennas and Propagation, vol. 63, no. 8, pp. 3486–3495. 54. Ladan, S., Ghassemi, N., Ghiotto, A., and Wu, K. 2013. Highly efficient compact rectenna for wireless energy harvesting application, IEEE Microwave Magazine, vol. 14, no. 1, pp. 117–122. 55. Valenta, C. R. and Durgin, G. D. 2014. Harvesting wireless power: survey of energyharvester conversion efficiency in far-field, wireless power transfer systems, IEEE Microwave Magazine, vol. 15, no. 4, pp. 108–120. 56. Hindawi Article. Development of new cascade voltage multiplier. https://www.hindawi. com/journals/cje/2014/948586/. Accessed June 12, 2018. 57. Kailuke, A. C., Agarwal, P., and Kshirsagar, R. V. 2013. Design and implementation of low power Dickson charge pump in 0.18m CMOS process, International Journal of Scientific Engineering and Research, vol. 4, pp. 1941–1944. 58. Hashemi, S. S., Sawan, M., and Savaria, Y. 2012. A high-efficiency low-voltage CMOS rectifier for harvesting energy in implantable devices, IEEE Transactions on Biomedical Circuits and Systems, vol. 6, p. 326335. 59. Rectifier comparison, http://investigacionescun.weebly.com/uploads/4/7/2/4/47242737/ comparaci %C3%B3n de circuitos.pdf. Accessed September 14, 2018.

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60. Moisiais, Y., Bouras, I., and Arapoyanni, A. 2002. Charge pump circuits for low-voltage applications, VLSI Design, vol. 15, p. 477483. 61. Takhedmit, H., Cirio, L., Bellal, S., Delcroix, D., and Picon, O. 2012. Compact and efficient 2.45 GHz circularly polarised shorted ring-slot rectenna, Electronics Letters, vol. 48, no. 5, pp. 253–254. 62. Sun, H., Guo, Y.-x., He, M., and Zhong, Z. 2012. Design of a high efficiency 2.45GHz rectenna for low-input-power energy harvesting, IEEE Antennas and Wireless Propagation Letters, vol. 11, pp. 929–932. 63. Harouni, Z., Cirio, L., Osman, L., Gharsallah, A., and Picon, O. 2011. A dual circularly polarized 2.45-GHz rectenna for wireless power transmission, IEEE Antennas and Wireless Propagation Letters, vol. 10, pp. 306–309. 64. Takhedmit, H., Merabet, B., Cirio, L., et al. 2010. A 2.45-GHz low cost and efficient rectenna, Proceedings of the Fourth European Conference on Antennas and Propagation, pp. 1–5, Barcelona, Spain, IEEE. 65. Hong, H., Cai, X., Shi, X., and Zhu, X. 2012. Demonstration of a highly efficient RF energy harvester for Wi-Fi signals, International Conference on Microwave and Millimetre Wave Technology (ICMMT), pp. 1–4, Shenzhen, China, 2012, IEEE. 66. Chou, J.-H., Lin, D. B., Weng, K. L., and Li, H. J. 2014. All polarization receiving rectenna with harmonic rejection property for wireless power transmission, IEEE Transactions on Antennas and Propagation, vol. 62, no. 10, pp. 5242–5249. 67. Nie, M.-J., Yang, X. X., Tan, G. N., and Han, B. 2015. A compact 2.45-GHz broadband rectenna using grounded coplanar waveguide, IEEE Antennas and Wireless Propagation Letters, vol. 14, pp. 986–989.

7

Sustainable and Renewable Isolated Microhydropower Generation Using a Variable Asynchronous Generator Controlled by a Fuzzy PI AC–DC–AC Converter and D-STATCOM P. Devachandra Singh and Sarsing Gao North Eastern Regional Institute of Science and Technology

CONTENTS 7.1 7.2 7.3

Introduction................................................................................................... 104 System Description........................................................................................ 105 CAG and Variable Turbine Model for the Proposed VMHPG System ........ 106 7.3.1 Hydro Turbine Model........................................................................ 106 7.3.2 CAG Model ....................................................................................... 107 7.4 AC–DC–AC Converter Control .................................................................... 108 7.5 Fuzzy PI D-STATCOM Control ................................................................... 110 7.6 Simulation Results and Discussion ............................................................... 110 7.6.1 Case I: Performance of the Proposed VMHPG Model under R-Load and RL-Load ............................................................. 112 7.6.2 Case II: Performance of the Proposed MHPG Model under a Non-linear Load ............................................................................. 115 7.7 Conclusion..................................................................................................... 117 Appendix ................................................................................................................ 118 References .............................................................................................................. 119 103

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7.1 INTRODUCTION Every effort to increase penetration of renewable energy resources can contribute to the reduction in the consumption of fossil fuels. In the last two decades, the contribution of energy generation from hydropower is about 17% [1]. A large amount of share among the renewable energy generation is seen from small-scale hydropower generation in the last many years. Until the last few years, the paradigm of renewable energy generation is shifted toward wind and solar sources [1]. Most studies, at present, are focused on these two technologies; however, the scope of microhydropower generation (MHPG) and small hydropower is high, provided a suitable technology is being implemented. The continuous monitoring of operation and maintenance required for conventional microhydropower plants is the major setback [2]. This chapter focuses on the variable MHPG (VMHPG) technology for extracting energy from hydro potential, which is similar to the extraction of wind power from its variable wind. Hence, the study considers an MHPG system using three-phase capacitor-excited asynchronous generators (CAG) operating at variable speeds fed by an uncontrolled hydro turbine. The primary advantage of CAG is their rugged brushless construction, and no DC field supply is required. They are reliable, economical, and available in a wide range of capacity [3]. The application of such generators in wind power systems requires gearbox arrangement to cope with high wind speed, which will increase the maintenance cost [3]. However, in microhydro applications, CAG without a gearbox arrangement may be used as the velocity of water is much less than that of wind. It also has an inherent capability to operate at variable speeds and the ability to take any type of load [3]. Another advantage includes natural protection against short circuit fault, which makes it perform better than Permanent Magnet Synchronous Generator (PMSG) at medium-speed operation [4]. Furthermore, the proposed scheme considers variable voltage constant frequency by the implementation of an active front-end converter for efficient extraction of power. The technology implemented in the proposed model is closely related to that of the wind energy generation system as reported by many researchers [5–7]. Different classifications of hydropower based on capacity are pico (25,000 kW) [8]. However, different countries may have different classifications. The study considers a 7.5 kW capacity VMHPG system. It can also be classified based on the type of installation such as impoundment, diversion, and pump storage, and based on the type of water turbine, it may be a reaction or impulse type [8]. The proposed model of MHPG is designed considering a diversion type of installation, which is also sometimes known as the “run of the river” type. This installation allows water to be diverted from the mainstream and only the required amount of water can be fed to the turbine. To avoid the complicacy of controlling the diverted water to the turbine, the proposed system is designed to accept free-flowing water. This will run the turbine as well as the generator at variable speeds. Hence, the generator will give variable voltage and frequency in its output terminal. To control this output voltage, the controllers such as static VAR compensator, neural network technique, and so on have been proposed; however, the frequency is not controlled [9,10]. Some topologies and architecture are popular for DFIG, PMSG, and so on [3–10], but these are

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limited to application for very large wind variation and highly unpredictable systems. In this article, two types of controls have been implemented in the proposed VMHPG system. First, a fuzzy PI-based PWM AC–DC–AC converter is used to convert the generated variable AC output to DC output, which is further converted to controlled AC output and thereby maintaining frequency constant. A two-level IGBT-based VSC converter with a DC-link capacitor is used to obtain constant frequency and voltage output. Such control offers more flexibility in power flow control and hence, its efficiency is improved. The harmonics generated during the conversions are duly taken care of by determining the optimum modulation index required [11]. Second, a fuzzy PI-based D-STATCOM is connected in the load terminal to regulate the voltage fluctuations due to non-linear and unbalanced loadings. D-STATCOM is powerful in eliminating harmonics, which otherwise will affect connected loads and cause heating of generator windings [12]. Its operation requires properly designed control algorithms such as synchronous reference frame theory, instantaneous reactive power theory, Icos Ø algorithm, Adaline algorithm, and so on [13–15]. A fuzzy PI-based one has shown superior performance as compared to other conventional types in terms of better undershoot, overshoot, and robustness [16–18]. The subsequent sections present the modeling and simulation of the proposed model and analysis of performances based on simulation results under (i) resistive and inductive loads and (ii) non-linear and unbalanced loadings.

7.2 SYSTEM DESCRIPTION The proposed VMHPG system consists of a three-phase 7.5 kW CAG fed by a variable turbine, a capacitor bank to provide reactive power required by CAG, a controllable IGBT-based fuzzy PI-PWM 2-level AC–DC–AC converter with a DC-link capacitor to maintain constant frequency at AC output and filters, and fuzzy PI D-STATCOM for controlling voltage and load as shown in Figure 7.1. The excitation capacitors of CAG are selected to obtain rated voltage at its output. The saturation parameters of the CAG are obtained from its open circuit test [19]. The detailed parameters are given in the Appendix. The detailed working of the AC–DC–AC converter is also presented in the subsequent sections. Fuzzy PI-based PWM control 2

Fuzzy PI-based PWM control 1

Hydro turbine

LC Filter

Filter

Variable input

LOAD

Rs Ls

Excitation capacitors

FIGURE 7.1

Vdc

LC

CAG

Generator-side converter

Schematic of the proposed VMHPG.

Load-side converter

Fuzzy PI D-STATCOM

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7.3 CAG AND VARIABLE TURBINE MODEL FOR THE PROPOSED VMHPG SYSTEM 7.3.1

Hydro turBine model

The turbine is modeled considering a run-of-the-river scheme MHPG with a velocity comparatively smaller than that of the wind turbine. By determining the torque-speed characteristics of the turbine with the changing flow rate (m3s−1) of water, the turbine model can be developed. If Pm (Nm) denotes the mechanical power output of the turbine or prime mover, Tm is the torque in Nm, and ω r (rads−1) is the rotor speed, then

Pm = Tmω r

(7.1)

In terms of coefficients of torque (τ 0), speed (n0), angular speeds (ω 0 ), and angular synchronous speed, ω s in rads−1, Tm can be rewritten as

Tm = τ 0 − n0

ωr ωs

(7.2)



Tm = τ 0 − ω 0ω r

(7.3)



where ω 0 = n0 ω s

(7.4)

For run-of-the-river, if η , ρ , Q, and v denote the efficiency of turbine, density of water (kgm−3), flow rate (m3s−1), and velocity of water (ms−1), respectively,

1 Pm = ηρQv 2 2

(7.5)

Combining Eqs. (7.1) and (7.5), the mechanical torque can be written as

Tm =

1 ηρQv 2 2ω r

(7.6)



Here

v=Q A

(7.7)

where “A” is the swept area of the turbine blades in m2. Combining Eqs. (7.4) and (7.5) and solving the quadratic equation of angular speed, it can be seen that ω 0 is a function of flow rate (Q). Therefore, the variable turbine is modeled with respect to varying ‘Q’. Now, Eq. (7.4) can rewritten as

Tm = τ 0 − f (Q) ⋅ ω r

(7.8)

This equation gives turbine torque-speed characteristics with respect to varying ‘Q’ and the same is used to model the variable turbine as shown in Figure 7.2.

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FIGURE 7.2 CAG and variable turbine model.

7.3.2

Cag model

The dynamic model of a three-phase CAG can be obtained using stationary d–q axes references frame, where the voltage and current equations are given by [19] [ v] = [ R][i] + [ L ]p[i] + ω r [G ][i]



(7.9)

Taking current derivatives, Eq. (7.9) can be written as

p[i] = [ L ]−1{[ v] − [R][i] − ω r [G ][i]}

(7.10)

where  Ls + Lm  0 [ L] =   Lm  0 



 0  0 [G ] =   0  Lm 

0 0 − Lm 0

0 Ls + Lm 0 Lm 0 0 0 Lr + Lm

Lm 0 Lr + Lm 0 0 0 Lr + Lm 0

0 Lm 0 Lr + Lm      

     

(7.11)

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[ v] =  vds 

vqs

[ R] = diag[ Rs

vqr  ; [i] =  ids  

vdr Rs

Rr

iqs

idr

T

iqr  ; 

Rr ]



The shaft torque of a CAG is given by [17]

Tshaft = Te + J (2 / P) pω r

(7.12)

pω r = { P ( 2J )}( Te − Tshaft )

(7.13)

Rearranging Eq. (7.12),

The shaft torque or the mechanical torque of the prime mover is given in Eq. (7.11). Since the CAG operates in the saturation region and it has non-linear magnetizing characteristics, the magnetizing current can be calculated in integral steps of stator and rotor currents as given by:

Im =

( idr + ids )2 + ( iqs + iqr )

2

(7.14)

The synchronous speed test of the 7.5 kW CAG gives the relationship between Lm and Im as given below [20]: Lm = 0.134 (for I m < 2.8A)

Lm = 9e −5Tm 2 − 0.0087 I m + 0.1643 (for 2.8A < I m < 14.2A)

(7.15)

Lm = 0.068 (for I m > 14.2A)

7.4 AC–DC–AC CONVERTER CONTROL As shown in Figure 7.1, the AC–DC–AC converter is placed in between the CAG and the load along with LC filters on both sides. The generator-side IGBT-controlled rectifier bridge with the LC filter changes the variable AC voltage to DC. The dc voltage changes with changing the speed of the CAG and is adapted to a DC-link capacitor. The switching pulses of 2 kHz are utilized to trigger the switches as required by the control signal. The high switching frequency is selected to have a much higher bandwidth than that of the generator. The DC-link capacitor stores energy and is released to the dc link bus instantaneously at the time of requirement [21]. The IBGTbased VSI installed at the load side converts the DC voltage to AC voltage at rated frequency. It requires six pulses to trigger the bridge switches synchronously, which is generated by a PWM generator. The output AC is a rectangular shaped wave and hence, LC filters with suitable values of L and C are used to smoothen the waveforms. The control algorithms of the generator-side converter and the load-side converters are developed using the most popular d–q decoupling techniques [22] and are depicted in Figures 7.3 and 7.4.

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Isolated Microhydropower Generation Vabc

θ

PLL

Vdq_mes

abc dq0 Iabc

id id-ref

-

ΔVid ΔViq

Vdc Vac_nom

Vdc

Reference voltage generation dq0 to abc

PWM Generator

VSC

Vdc-ref

+

Control scheme of a generator-side converter.

Discrete Virtual PLL 50HZ

Vabc(pu)

-

+

iq

FIGURE 7.3

+

PI Controller

abc dq0

iq-ref

PI/Fuzzy PI Current Regulator

Sin_Cos

abc dq0

Vd-

Vq

PI/Fuzzy PI Controller

+

Vd-ref(pu) +

∆Vd

PI/Fuzzy PI Controller

∆Vq

Vdc Vac_nom

θ Reference voltage generation dq0 to abc

PWM Generator

VSC

Vq-ref(pu)

FIGURE 7.4 Control scheme of a converter at the load side.

FIGURE 7.5 (a) Fuzzy PI control structure applied to GSC and LSC. (b) Fuzzy PI control structure.

To make the generator and load side converters robust and perform better, fuzzy PI controllers are implemented [22], as shown in Figure 7.5. Triangular membership functions of seven scales are implemented for input and output variables. The comparisons of each of the d-axis and q-axis components are fed as input to fuzzy PI controllers,

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TABLE 7.1 Fuzzy Rule Base Δe A. e

NB

NM

NS

Z

PS

PM

PB

NB NM NS Z PS PM PB

NB NB NB NB NM NS Z

NB NB NB NM NS Z PS

NB NB NM NS Z PS PM

NB NM NS Z PS PM PB

NM NS Z PS PM PB PB

NS Z PS PM PB PB PB

Z PS PM PB PB PB PB

as shown in Figures 7.3 and 7.4. IF-THEN Mamdani rule of size 7 × 7 is used for the control system, as shown in Table 7.1 and Figure 7.6.

7.5 FUZZY PI D-STATCOM CONTROL D-STATCOM maintains the load voltage constant by feeding any additional reactive power requirement due to changes in generations, loads, faults, and so on by injecting a leading or lagging current. It consists of a voltage source converter (VSC), DC-link capacitor, and AC inductors connected in shunt with the line. The detailed operation of a D-STATCOM is depicted in Figure 7.7. It requires the decoupling of sensed voltage into d–q components. To maintain load voltage, control of the q-axis component is required. The d-axis components help to maintain the voltage across the DC-link capacitor. The proposed fuzzy PI controller aims at optimizing the fuzzy PI controllers each for the terminal voltage regulator and the current regulators, as shown in Figure 7.7. The design strategies of the fuzzy PI controller remain the same as that of the GSC and LSC. The error and change in the error of input voltages or currents are taken as inputs to the fuzzy PI controller.

7.6

SIMULATION RESULTS AND DISCUSSION

The MATLAB/Simulink model of the proposed MHPG scheme with the variable-CAG system is shown in Figure 7.8. A three-phase 7.5 kW CAG excited with a star-connected capacitor bank and a turbine continuously varying over time is used. The turbine speed and torque vary as the flow rate of the water varies over a minimum discharge of 0.45 m3s−1 and a maximum discharge of 0.88 m3s−1 as shown in Figure 7.9. Two cases of loading conditions are considered each for R/RL load and non-linear/unbalanced load. In both the cases, the waveforms of frequency, generator speed, various voltages, and current are analyzed. In addition, the THD analysis is carried out for voltage waveforms.

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111

FIGURE 7.6 (a) Membership functions for input and output variables. (b) IF-THEN Mamdani rule and (c) surface view of the IF-THEN Mamdani rule.

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V abc(load) PLL

θ

V dq_mes

id -

abc

Voltage Amplitude Computation

dq

id-ref

+

PI Controller

Iabc(load) abc

dq Vt

+

iq +

Fuzzy PI Controller

iq-ref

-

Vdc

ΔVid ΔViq Reference voltage generation

PWM Generator

V dc V ac_nom

VSC

dq0 to abc

V dc-ref

-

+

V t-ref

FIGURE 7.7

Fuzzy PI Current Regulator

Block diagram of the D-STATCOM control algorithm.

Flow Rate (cubic m/sec)

FIGURE 7.8 Simulink model of the proposed MHPG with variable turbine-CAG.

0.8 0.7 0.6 0.5 0.4 0

1

2

3

4

5 Time (sec)

6

7

8

9

10

FIGURE 7.9 Variation of water flow to the turbine.

7.6.1

CaSe i: performanCe of tHe propoSed vmHpg model under r-load and rl-load

In this case, the proposed model is tested for R-load (5 kW) and RL (5 kW and 2 kVAR) at different simulation times. When the R-load is connected to the system at 1 s, the D-STATCOM is switched ON at 1.2 s and again switched OFF at 1.4 s.

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CAG Speed (rpm)

Load frequency (Hz)

Generator frequency (Hz)

Second, the RL-load is connected at 1.5 s and D-STATCOM at 1.6 s, and disconnected at 1.8 s. Figures 7.10–7.13 show the transient waveforms of three-phase VMHPG speed, frequency and load frequency, generator voltage, load voltage, and dc-link voltage, generator current, load current, and D-STATCOM currents, respectively. In Figure 7.10, the generator frequency and speed are deviated from the rated 50 48 46 44 50 48 46 44

1600 1400 1200 1000

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

2

Time (Sec)

DC Link Voltage (V)

Load Voltage (V)

CAG Voltage (V)

FIGURE 7.10 Profiles of generator frequency, load frequency, and generator speed for Case-I.

800 500 0 -500 -800 800 500 0 -500 -800 800 600 400 200 0

0

FIGURE 7.11 for Case-I.

0.2

0.4

0.6

0.8

1

Time (Sec)

1.2

1.4

1.6

1.8

2

Waveforms of generated voltage, load terminal voltage, and DC link voltage

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D-STATCOM D-STATCOM D-STATCOM current-B ph (A) current-Y ph (A) current-R ph (A)

Load current Generator current (A) (A)

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20 0 -20 20 0 -20 20 0 -20 20 0 -20 20 0 -20

0

0.2

FIGURE 7.12

0.4

0.6

1

Time (Sec)

1.2

1.4

1.6

1.8

2

Waveforms of various currents during different phases of operation for Case-I.

FFT Analysis at RL load

Mag (% of Fundamental)

0.8

Fundamental (50Hz) = 557.9 , THD= 3.84%

3 2 1 0

0

100

200

300

400 500 600 Frequency (Hz)

700

800

900

1000

800

900

1000

FFT Analysis at R load

Mag (% of Fundamental)

Fundamental (50Hz) = 586.6 , THD= 3.86% 3 2 1 0

0

100

200

300

400 500 600 Frequency (Hz)

700

FIGURE 7.13 FFT analysis of load terminal voltage at different cases for R and RL-load.

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Isolated Microhydropower Generation

values due to the variable nature of the turbine input, whereas the load frequency is found to be maintained around the rated frequency except during switching of D-STATCOM. Figure 7.11 shows that the D-STATCOM can regulate the terminal voltage of the generator near the rated value. Charging and discharging actions of the DC-link capacitor is observed, which shows the compensating aspect of the D-STATCOM. The injection of D-STATCOM and generator currents for reactive and active power compensation at different operating points are also depicted in Figure 7.12. The %THDs for R load and RL-load with D-STATCOM are found to be 3.84% and 3.86%, respectively, as shown in Figure 7.13.

7.6.2

CaSe ii: performanCe of tHe propoSed mHpg model under a non-linear load

CAG frequency (Hz)

50

CAG Speed (rpm)

50

Load frequency (Hz)

In this case, the proposed VMHPG is tested for non-linear and unbalanced loads. The transient waveforms of all the performance parameters with three-phase rectifier DC load and single-phase rectifier load are shown in Figures 7.14–7.17. At 1.0 s, the three-phase rectifier load with DC resistive load is connected, while the D-STATCOM is switched ON from 1.2 to 1.4 s. The unbalanced rectifier load is connected at 1.5 s, while the D-STATCOM is switched ON again at 1.6 s. Figure 7.14 shows that the generator frequency and speed vary as per the turbine’s variation while the load frequency is an observer to be maintained at around rated frequency with slight fluctuation during switching of D-STATCOM. As seen from Figure 7.15, the voltage waveforms of the generator, load, and DC-link capacitance are found to be quite stable. Slight overshoots and undershoots are observed in load voltage and DC link voltage during switching of D-STATCOM, which get settled in a few cycles.

45

40

45

40 1600 1400 1200 1000

0

0.2

0.4

0.6

0.8

1

Time (Sec)

1.2

1.4

1.6

1.8

2

FIGURE 7.14 Profiles of generator frequency, load frequency, and generator speed for Case-II.

Load Voltage (V)

Generator Voltage (V)

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1000 500 0 -500 -1000 1000 500 0 -500 -1000

DC link Voltage (V)

1000 800 600 400 200 0

Time (sec)

D-STATCOM D-STATCOM D-STATCOM B-ph current (A) Y-ph current (A) R-ph current (A)

Load Current (A)

Generator current (A)

FIGURE 7.15 Waveforms of generated voltage, load terminal voltage, and DC-link voltage for Case-II. 20 0 -20 20 0 -20

20 0 -20 20 0 -20 20 0 -20 0

0.2

0.4

0.6

0.8

1

Time(Sec)

1.2

1.4

1.6

1.8

2

FIGURE 7.16 Waveforms of various currents during different phases of operation for Case-II.

117

Isolated Microhydropower Generation FFT analysis for Single phase rectifier load without D-STATCOM

FFT analysis for Single phase rectifier load without D-S

Fundamental (50Hz) = 480.9 , THD = 15.76%

Fundamental (50Hz) = 558.1

Mag (% of Fundamental)

Mag (% of Fundamental)

15

10

5

0

0

200

400 600 Frequency (Hz)

800

FFT analysis of 3-ph rectifier load without D-STATCOM

8 6 4 2 0

1000

0

Fundamental (50Hz) = 532.5

Mag (% of Fundamental)

Mag (% of Fundamental)

4 3

400 600 Frequency (Hz)

FFT analysis of 3-ph rectifier load without D-STAT

Fundamental (50Hz) = 337.6 , THD = 7.56% 5

200

1.5 1

FIGURE 7.17 FFT analysis of load terminal voltage at different cases for three-phase and 2 single-phase rectifier loads. 1

0.5

The DC-link capacitor tries to maintain the DC load voltage constant, which leads 0 0 to changing D-STATCOM currents to 1000 adjust the reactive and200 active 400 0 200 400and generator 600 800 0 600 power requirements respectively as depicted in Figure 7.16. The %THDs of the loadFrequency (Hz) Frequency (Hz) voltage during three-phase rectifier and unbalanced loads without D-STATCOM are found to be 7.51% and 15.76% respectively whereas those with D-STATCOM are found to be 4.12% and 4.61% respectively as shown in Figure 7.17. This shows that the D-STATCOM also helps to improve the harmonics.

7.7

CONCLUSION

As depicted from the simulation results and Table 7.2, the developed three-phase VMHPG model is capable of operating with satisfactory performance while feeding linear and non-linear loads under transient conditions. The load frequency can be maintained at the rated frequency by using a fuzzy PI-based AC–DC–AC converter. The terminal voltage of the generator is compensated by fuzzy PI D-STATCOM during different loading conditions and is maintained constant at rated values as observed from the simulated results. When rectifier loads are connected, harmonics are generated, which are able to maintain within the permissible limits by using fuzzy PI D-STATCOM. It is also observed that the fuzzy PI D-STATCOM helps in balancing load but sensing all the generator currents, load currents, and the fuzzy PI D-STATCOM currents. The use of fuzzy PI controllers limits the overshoot and

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TABLE 7.2 Summary of Performance Analysis of the VMHPG System Performance Parameters during Loading

Type of Load R RL 3-ph rectifier 1-ph rectifier

Load Frequency (Hz) 50 50 50 50

RMS Voltage (V) Without D-STATCOM

With D-STATCOM

398.2 387.4 407.4 401.7

%THD of Load Voltage Without D-STATCOM

With D-STATCOM

5.46 5.37 7.56 15.76

3.86 3.84 4.12 4.71

582.7 586.3 586.4 587.2

undershoot of the waveforms and also makes the system more robust. Hence, it can be concluded that the fuzzy PI D-STATCOM acts as a voltage compensator, load balancer, and harmonic reducer. Similar studies can be extended for a multimachine system and other intelligent techniques may be implemented to optimize its performance. Therefore, in all, the proposed VMHPG system performs satisfactorily as depicted from the simulation results.

APPENDIX MACHINE PARAMETERS: Squirrel Case Induction Machine: 7.5 kW, 3 phase, 415 V, 29 A, 50 Hz, Y-Connected, 4-Pole, Rs = 0.9 Ω, Rr = 0.66 Ω, Xls = Xlr = 0.00457 H and J = 0.1384 kgm2

PRIME MOVER CHARACTERISTICS: Tm = τ 0 − f (Q) ⋅ ω r



where

τ 0 = 1242, f(Q) varies with the varying flow rate.

GENERATOR SIDE CONTROLLER PARAMETERS: DC voltage regulator: Kp = 0.15, Ki = 0.0002 Current regulator: Kp = 0.6, Ki= 0.009

LOAD SIDE CONTROLLER PARAMETERS: Kp = 0.38, KI = 0.0002

Isolated Microhydropower Generation

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FILTER PARAMETERS: Generator side: R = 5 Ω, L = 40 mH, C = 160 μF Load side: R = 1.13 Ω, L = 22 mH, C = 300 μF

DC-LINK CAPACITOR: C = 10,000 μF, Vdc = 380 V

REFERENCES 1. Renewables 2019, Global Status Reports. Available at https://www.ren21.net/wp-content/uploads/2019/05/gsr_2019_full_report_en.pdf (accessed September, 2020). 2. Singh, D. 2009. “Micro Hydro Power Resource Assessment Handbook.” Asian and Pacific Centre for Transfer of Technology of the United Nations – Economic and Social Commission for Asia and the Pacific (ESCAP), 69.(accessed September, 2020) 3. Ofualagba, G, and E. U. Ubeku. 2011. “The Analysis and Modelling of a Self-Excited Induction Generator Driven by a Variable Speed Wind Turbine.” Fundamental and Advanced Topics in Wind Power, 249–68. doi:10.5772/18159. 4. Alnasir, Z., and M. Kazerani. 2014. “Performance Comparison of Standalone SCIG and PMSG-Based Wind Energy Conversion Systems.” Canadian Conference on Electrical and Computer Engineering, 1–8. doi:10.1109/CCECE.2014.6900923. 5. Hazra, S., and P. S. Sensarma. 2010. “Self-Excitation and Control of an Induction Generator in a Stand-Alone Wind Energy Conversion System.” IET Renewable Power Generation 4 (4): 383–93. doi:10.1049/iet-rpg.2008.0102. 6. Simões, M. G., B. K. Bose, and R. J. Spiegel. 1997. “Fuzzy Logic Based Intelligent Control of a Variable Speed Cage Machine Wind Generation System.” IEEE Transactions on Power Electronics 12 (1): 87–95. doi:10.1109/63.554173. 7. Harrabi, N., M. Souissi, A. Aitouche, and M. Chaabane. 2018. “Intelligent Control of Grid-Connected AC-DC- AC Converters for a WECS Based on T-S Fuzzy Interconnected Systems Modelling.” IET Power Electronics 11 (9): 1507–18. doi:10.1049/ iet-pel.2017.0174. 8. Nababan, S., E. Muljadi, and F. Blaabjerg. 2012. “An Overview of Power Topologies for Micro-Hydro Turbines.” Proceedings -2012 3rd IEEE International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2012, 737–44. doi:10.1109/PEDG.2012.6254084. 9. Ahmed, T., E. Hiraki, M. Nakaoka, and O. Noro. 2003. “Three-Phase Self-Excited Induction Generator Driven by Variable-Speed Prime Mover for Clean Renewable Energy Utilizations and Its Terminal Voltage Regulation Characteristics by Static VAR Compensator.” Conference Record - IAS Annual Meeting (IEEE Industry Applications Society) 2: 693–700. doi:10.1109/ias.2003.1257593. 10. Zouggar, S., Y. Zidani, M. L. Elhafyani, T. Ouchbel, M. Seddik, and M. Oukili. 2012. “Neural Control of the Self-Excited Induction Generator for Variable-Speed Wind Turbine Generation.” Smart Innovation, Systems and Technologies 12: 213–23. doi:10.1007/978-3-642-27509-8_17. 11. Kabalci, E., E. Irmak, and I. Çolak. 2011. “Design of an AC-DC-AC Converter for Wind Turbines.” International Journal of Energy Research 35 (2): 169–75. doi:10.1002/er.1770. 12. Kant, K., B. Singh, and V. C. Sekhar. 2016. “DSTATCOM Supported Induction Generator for Improving Power Quality.” IET Renewable Power Generation 10 (4): 495–503. doi:10.1049/iet-rpg.2015.0200.

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8

Phytoconstituents of Common Weeds of Uttarakhand Proposed as Bio-pesticides or Green Pesticides with the Use of In-Silico and In-Vitro Techniques Somya Sinha, Kumud Pant, Manoj Pal, and Devvret Verma Graphic Era (Deemed to be) University

Ashutosh Mishra Uttarakhand Council of Science and Technology

CONTENTS 8.1 Introduction................................................................................................... 122 8.2 In-Vitro Methodology.................................................................................... 123 8.2.1 Plant Sample Collection.................................................................... 123 8.2.2 Aqueous Extract Preparation............................................................. 123 8.2.3 Phytoconstituent Analysis................................................................. 123 8.2.4 Assessment for Alkaloids.................................................................. 123 8.2.5 Test for Steroids and Sterols.............................................................. 124 8.2.6 Assessment of Anthraquinones......................................................... 124 8.2.7 Assessment of Flavonoids.................................................................. 124 8.2.8 Assessment of Saponins.................................................................... 124 8.3 In-Silico Methodology................................................................................... 124 8.3.1 Sequence Retrieval............................................................................ 124 8.3.2 Comparative Modeling...................................................................... 124 8.3.3 Evaluation of the Model..................................................................... 125

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8.3.4 Construction of a Ligand................................................................... 126 8.3.5 Preparation of Protein ....................................................................... 126 8.3.6 Molecular Interaction........................................................................ 129 8.3.7 Toxicity Examination........................................................................ 129 8.3.8 Docking Analysis.............................................................................. 129 8.4 Conclusion .................................................................................................... 133 Acknowledgment ................................................................................................... 133 References .............................................................................................................. 134

8.1 INTRODUCTION The biochemical pesticides are the major outcome of human activities and agriculture that generally remains as the disseminated ecological contaminants and stay open to strict regulations, which helps in safeguarding the biome and well-being of humans in India, USA, Europe, and other countries of the world.1 Disparity in the biochemical structures of these insect repellents illustrates the sub-families of the pesticides, insecticides, herbicides, and antifungal agents that are clustered together according to the disastrous action.2 The pesticides, herbicides, insecticides, and fungicides have an enormous possibility of exercising detrimental impacts on humans, animals, birds, and other living organisms through different modes of inhalation, consumption of food, breathing, and skin contact.3 They are of the origin for the turmoil of the biodiversity of the atmosphere. Subsequently, defined realizable variations may take place on exposure such as hematological disorders, malignancy, respiration problem, and impairment of the reproductive body parts. Diverse modes of entrance points and the efficacy of interaction of pesticides with the actual target protein have been taken into reflection. Pesticides, chiefly organophosphorus, are the utmost used pesticides in the world with applications extending from marketable to home-grown use and agronomic use for governing disagreeable bug’s population.4 Organophosphate pesticides stay lethal as they possess the capability of fixing to inhibit physiological enzymes like glutathione S-transferases, acetylcholinesterase (AChE), protein kinase C, and cytochrome P450, triggering neurotoxicity among the population.5,6 Existence of AChE protein in innumerable mammals, insects, flora and fauna could give a classification of pesticides noxiousness in the direction of the unintended target for the dislocation of the metabolic gastral system in anthropoid.5,7 Likewise, insecticides, namely pyrethroids,6 are commonly utilized as they are formed from the flowers of Chrysanthemum cinerariaefolium that shows their deadly properties in contrast to the bed bugs.8 These insecticides consist of cypermethrin, α-cyano-3-phenoxybenzyl, and cyfluthrin9 that are lethal for gadflies, bees, and the other craniates. They pose a huge riddle to the marine organism (for instance, fishes) at fairly minuscule levels. Wildflowers are principally the plants that generally grow in erroneous areas like farm fields, pools, lawns, and so on.10 Most of the bellicose species or non-native classes are identified that show insecticidal and pesticidal effects against numerous pest inhabitants. Among the non-native kinds of species of Uttarakhand Parthenium hysterophorus,11 Alternanthera sessilis,12 and Lantana camara,13 shown in Figure 1.1, and several more are revealed to exhibit insect repellent properties in contradiction of various insect inhabitants.14,15 Various reports propose that L. camara leaves turn to

Phytoconstituents of Common Weeds

123

be a possible insect repellent for the supervision of the stored grain insect pests.16,17,18 Assessment of the inherent oil properties of the leaf of L. camara has been achieved that showed its insecticidal part.12,19,20 P. hysterophorus has also been used against Callosobruchus chinensis on chickpea.21,22 This chapter focusses on the target AChE, which is a significant enzyme existing in the bug’s nervous structure that will pass the neurotransmitter into acetic acid and choline, which results in the ending of nerve impulse;23,24 numerous reports and papers we endowed says that this AChE will turn out to be a target for the carbamates and organophosphates.25 Hence, these insect repellents are the source of major menace to insects as they have the aptness to disable the enzyme’s catalytic center.5 Consequently, these insect repellents fix to the active site of the protein and cause the suppression of its activity by phosphorylating serine, which is a polar amino acid residue in the bug’s catalytic center important in plant development in addition to directing into the swift quavering of the muscles and recurrent dismissal of the electric signals ensuing in the death.26 Moreover, the computational approach employs interaction studies of the pesticides with the target AChE protein along with the phytochemical constituents taken from the common weeds.

8.2 8.2.1

IN-VITRO METHODOLOGY plant Sample ColleCtion

Leaves from the given plants P. hysterophorus, A. sessilis, and L. camara should be collected. The collected leaves need to be thoroughly cleaned to shed dry and pulverized to a powdered form using a blender for additional use.

8.2.2

aqueouS extraCt preparation

The aqueous extract was prepared by dissolving powdered leaves in distilled water. The mixture was heated on a hot plate with continuous agitation. Then the water extract was sieved through filter paper. The filtrate was kept in a beaker and allowed to dry by heating in a water bath. The adhesive residue obtained was used for the evaluation of the percentage yield, the remaining marc left and the behavior of the leaf powder was extracted with water and used for the qualitative analysis.

8.2.3

pHytoConStituent analySiS

The extracts are examined for the occurrence of biologically active complexes by implementing standardized measures and actions of the drug powder with dissimilar chemical reagents.

8.2.4

aSSeSSment for alkaloidS

Wagner’s test is performed for the extract by adding a few drops of iodine solution in potassium iodide. The appearance of a reddish-brown precipitate will show the presence of alkaloids.

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teSt for SteroidS and SterolS

The Salkowski test is performed by adding a few drops of chloroform and concentrated sulphuric acid to the extract. The turning of the bluish red color to cherry red will indicate the presence of steroids and sterol.

8.2.6

aSSeSSment of antHraquinoneS

Borntrager’s test is carried out in which the extract is boiled upon addition of dilute sulphuric acid and is filtered and to the benzene that is being filtered, chloroform is added and is properly shaken, after which the biological layer is detached to which ammonia is cautiously added.

8.2.7

aSSeSSment of flavonoidS

To the crude stock extract, a few drops of dilute sodium hydroxide are added. The yellow color that appears in the plant extract will become colorless after the addition of a few drops of diluted acid, which will indicate the existence of flavonoids.

8.2.8

aSSeSSment of SaponinS

For testing the presence of saponins, the extract is taken in a test tube and then diluted with distilled water after which shaking is performed; after shaking, the appearance of a foam layer on the top of the test tube will indicate the presence of saponins.

8.3

IN-SILICO METHODOLOGY

8.3.1 SequenCe retrieval A three-dimensional structure of the studied pesticides was obtained from Research Collaboratory through structure Bioinformatics, Protein Data Bank (PDB) accessible at www.rcsb.org.27 Owing to the inaccessibility of the threedimensional structure for the target protein AChE of the beetles and aphids in the protein data bank, FASTA sequences containing AChE of Rhopalosiphum padi padi, Myzus persicae, Acyrthosiphon pisum aphids and Onthophagus taurus, Leptinotarsa decemlineata, Agrilus planipennis beetles remained stood from protein database of NCBI that is accessible at www.ncbi.nlm.nih.gov as seen in Table 8.3.1.

8.3.2 Comparative modeling AChE sequences of the pests for beetles and aphids were taken in FASTA format from the protein database of NCBI accessible at www.ncbi.nlm.nih.gov.in, and the three-dimensional structure of these pests was made as specified in Table 8.3.2

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Phytoconstituents of Common Weeds

TABLE 8.3.1 Description of the Reviewed Protein Group

Organism

Coleopteran Beetle

Aphidoidea Aphids

Onthophagus taurus Agrilus planipennis Leptinotarsa decemlineata Myzus persicae Acyrthosiphon pisum Rhopalosiphum padi

Entry ID

Enormousness

XP_022919632 XP_018327670 AAB00466 XP_022160417 XP_029344443 AII01418

630 aa 633 aa 629 aa 822 aa 663 aa 672aa

TABLE 8.3.2 SCF-BIO Toxicological Evaluation of Phytoconstituents

S. No. 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.

Phytoconstituents Lantadene A γ-Gurjenone β-Sitosterol Parthenin Caffeic acid β-Caryophyllene Kaempferol Campesterol Stigmasterol Lupeol Germacrene D Valencene

Molecular Mass

Hydrogen Bond Donor

Hydrogen Bond Acceptor

552 204 414 262 312 204 286 400 426 426 204 204

1 0 1 1 5 0 4 1 1 1 0 0

5 0 1 4 6 0 6 1 1 1 0 0

LOGP 7.9297 4.5811 8.0248 1.3904 −0.0531 4.7252 2.3053 7.6347 8.0248 8.0248 4.7252 4.7252

Refractivity 156.375931 66.672981 128.216751 68.111786 77.145782 66.742981 72.385681 123.599747 130.64975 130.64975 66.742981 66.742981

LOGP, lipophilicity

with the aid of SWISS-MODEL server28,29 for the forecasting of the formation of protein structure, as shown in Figure 8.3.1,30,31 which depicts the structure of pests.

8.3.3

evaluation of tHe model

A comprehensive model of the AChE protein of beetles and aphids was generated by means of SWISS-MODEL.32 Various parameters and algorithms were used to access the models. Validation was performed to check the conformation through a Raman plot obtained by a structure analysis and verification server. Evaluation of the stereochemical quality of the protein structure was performed through

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FIGURE 8.3.1

Three-dimensional structure of the modeled protein structure.

PROCHECK by validating and analyzing the geometry of the structure residue by residue available at https://servicesn. mbi.ucla.edu/SAVES/33 as illustrated in Table 8.3.3 that shows the identity of the protein.

8.3.4

ConStruCtion of a ligand

Phytoconstituents from the communal weeds were designed in the ligand form. PubChem was used to download phytocompounds in the .sdf format and they are changed to the Protein Data Bank format by Open Bable.34 Open Bable is a software that converts the file form in regard to the phytocompounds.34 The synthetic pesticides that are recognized diazinon, methamidophos, and cypermethrin remained for the study.35,36 Lipinski’s filter was used to test the toxicity of the compounds as specified in Table 8.3.4 that depicts the toxicity potential of the pesticides and the phytoconstituents.

8.3.5 preparation of protein FASTA sequences for the pest’s AChE protein have been obtained from the National Centre for Biotechnology Information protein database (https://www.ncbi.nlm.nih.gov/ protein) and those were taken in SWISS-MODEL, which is a web server for homology modeling and protein structure.28,31 The protein that is modeled was taken in the .pdb format from SWISS-MODEL, which was further taken for supplementary work.

Poorly soluble Very soluble

Cypermethrin

Soluble

Soluble

Βeta Caryophyllene Kaempferol

Gamma gurjenone Parthenin

Poorly soluble Moderately soluble Very soluble

Lantadene A

Methamidophos

Soluble

Diazinon

Complexes

Aqueous Solubility Estimation Class

Moderately soluble Soluble

Moderately soluble Very soluble

Poorly soluble

Very soluble

Moderately soluble Poorly soluble

Value of log S

Soluble

Extortionate

Low-slung

Extortionate

Soluble Soluble

Low-slung

Low-slung

Extortionate

Extortionate

Extortionate

Gastrointestinal Absorption

Poorly soluble Soluble

Poorly soluble Soluble

Soluble

Silicos-IT Class

TABLE 8.3.3 Pesticides and Phytoconstituent Analysis Through ADMET

Not at all

Absolutely yes Not at all

Not at all

Not at all

Not at all

Not at all

Not at all

Blood–Brain Barrier

Not at all

Not at all

Not at all

Not at all

Yes

Not at all

Not at all

Not at all

Pgp Substrate

Absolutely yes

Not at all

Not at all

Not at all

Not at all

Absolutely yes Not at all

Not at all

Cytochrome P450 3A4 Inhibitor

(Continued )

Lethal dose 50–17 mg kg−1, Class-2 Lethal dose 50–25 mg kg−1, Class-2 Lethal dose 50–8 mg kg−1, Class-2 Lethal dose 50–79 mg kg−1, Class-2 Lethal dose 50–5000 mg kg−1, Class-5 Lethal dose 50–125 mg kg−1, Class-3 Lethal dose 50–5000 mg kg−1, Class-5 Lethal dose 50–3919 mg kg−1, Class-4

Protox-II Toxicity and Class

Phytoconstituents of Common Weeds 127

Poorly soluble Moderately soluble

Campesterol

Germacrene-D

Caffeic acid

Lupeol

Stigmasterol

Valencene

Poorly soluble Moderately soluble Poorly soluble Poorly soluble Very soluble

Beta-Sitosterol

Complexes

Aqueous Solubility Estimation Class

Moderately soluble

Poorly soluble

Soluble

Insoluble

Moderately soluble Poorly soluble

Poorly soluble

Value of log S

Moderately soluble Soluble

Moderately soluble Poorly soluble Soluble

Poorly soluble Soluble

Silicos-IT Class

Low-slung

Low-slung

Extortionate

Low-slung

Low-slung

Low-slung

Low-slung

Gastrointestinal Absorption

TABLE 8.3.3 (Continued ) Pesticides and Phytoconstituent Analysis Through ADMET

Not at all

Not at all

Not at all

Not at all

Not at all

Not at all

Not at all

Blood–Brain Barrier

Not at all

Not at all

Not at all

Not at all

Not at all

Not at all

Not at all

Pgp Substrate

Not at all

Not at all

Not at all

Not at all

Not at all

Not at all

Not at all

Cytochrome P450 3A4 Inhibitor

Lethal dose 50–890 mg kg−1, Class-4 Lethal dose 50–5000 mg kg−1, Class-5 Lethal dose 50–890 mg kg−1, Class-4 Lethal dose 50–2000 mg kg−1, Class-4 Lethal dose 50–2980 mg kg−1, Class-5 Lethal dose 50–890 mg kg−1, Class-4 Lethal dose 50–5000 mg kg−1, Class-5

Protox-II Toxicity and Class

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Phytoconstituents of Common Weeds

TABLE 8.3.4 SAVESv5.0 Server Protein Analysis

Template Sequence Identity

8.3.6

AChE A. planipennis

AChE O. taurus

AChE L. decemlineata

AChE M. persicae

1qo9.1. A 64.84 %

5hcu.1. A 43.05 %

1qo9.1. A 61.27 %

1qo9.1. A 57.33 %

AChE R. padi

AChE A. pisum

6ary.1. A 6arx.1. A 64.38 % 42.86 %

moleCular interaCtion

Studies stood fixed out with iGEMDOCK, which exists as an automatic system for drugs basically pre-owned for docking, broadcasting, and analysis of the positions that are docked.37 This software permits the uploading of the ligands and the protein target that will generate the interaction profile.38 The exploration table comprises detailed evidence portraying the free Energy as illustrated in Table 8.3.5. LigPlus and PyMol were used to create the most favorable docked pose with the target protein as illustrated in Figure 8.3.239,40 that shows the interaction of the phytoconstituents with the receptor protein.

8.3.7

toxiCity examination

SWISS-ADME is used to check the “dispersal” “absorption” “excretion” and characteristics in humans. It usually measures the pharmacology and pharmacokinetics and also defines the nature of a drug or a chemical inside a plant, virus, or an animal. This is gained using http:// www.swissadme.ch/. ProTox server is used to check the toxicity of the phytoconstituents accessible at http://tox.charite.de/ protox_II/ given in Table 8.3.441 aimed for the investigation of the toxicity potential of the phytoconstituents.

8.3.8

doCking analySiS

The studies were conducted considering the phytoconstituents as the ligand fragments and the bugs taken as a constructive switch for learning. The phytoconstituent present in P. hysterophorus was kaempferol, which showed the least binding attraction with AChE (M. persicae), AChE (R. padi), and AChE (A. pisum), i.e., −92.4516, −102.38, and −98.64 kilocalories per mol. Therefore, the synthetic pesticides comprising methamidophos, diazinon, and cypermethrin were cast-off contrary to the phytoconstituents that exhibited lesser binding affinity when distinguished with kaempferol and the additional phytoconstituents, which were −90.9694, −91.3296, and −90.9694 kcal mol−1, as shown in Table 8.3.5. The principle phytoconstituent present in L. camara is Lantadene A, which on interaction with the target protein AChE of O. taurus and L. decemlineata showed minimum energies of −87.4555 kcal mol−1 and −98.6 kcal mol−1.42 The key component present in A. sessilis is stigmasterol that has shown the least binding affinity with

Phytoconstituents

Methamidophos Diazinon Cypermethrin Lupeol Gamma gurjenone

Lantadene A Germacrene-D Parthenin Caffeic acid Beta-caryophyllene Kaempferol Stigmasterol Beta sitosterol Valencene Campestrol

S. No.

1. 2. 3. 4. 5.

6. 7. 8. 9. 10. 11. 12. 13. 14. 15.

AChE (O. taurus) −48.3491 −77.7962 −87.4555 −84.82 −64.1266 −90.9896 −76.17 −75.3631 −79.59 −65.43 −88.89 −87.09 −86.9 −69.61 −83.83

AChE (A. planipennis) −57.9 −80.6 −91.7 −93.45 −67.0385 −86.92 −77.6 −85.17 −75.732 −65.4154 −97.45 −100.47 −97.7277 −71.04 −86.3941

−58.6 −84 −98.6 −81.7 −63.8858 −99.3161 −75.5528 −83.9 −74.8889 −65.4517 −89.4697 −92.1 −98.0451 −69.1 −78.1968

AChE (L. decemlineata) −48.5813 −77.3719 −90.9694 −89.41 −66.18 −85.61 −73.7 −79.08 −76.3779 −63.1982 −92.4516 −87.58 −82.1903 −67.68 −80.93

AChE (M. persicae) −54.6947 −81.3328 −91.3296 −95.4344 −64.5 −90.65 −64.66 −89.9 −75.4162 −62.3067 −102.38 −82.3 −83.56 −71.03 −80.9

AChE (R. padi )

−52.4783 −88.0724 −96.959 −75.69 −66.32 −85.74 −76.6688 −80.53 −80.28 −66.79 −98.64 −77.4695 −94.8527 −66.0873 −81.01

AChE (A. pisum)

TABLE 8.3.5 Interaction Energy of the Phytoconstituents and Synthetic Pesticides with AChE Protein in kcal mol−1

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FIGURE 8.3.2 Interaction poses of the possible herbicides with the known target protein.

Phytoconstituents of Common Weeds 131

132

FIGURE 8.3.3

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Structural quality of the studied proteins through Ramachandran plot. (Continued )

Phytoconstituents of Common Weeds

FIGURE 8.3.3

133

(CONTINUED) Structural quality of the studied proteins through

Ramachandran plot.

AChE protein of A. planipennis, i.e., −100.47 kcal mol−1. However, the pesticides that were considered for the study showed very little interaction energy in contradiction with the phytoconstituents on interactivity by the AChE protein of A. planipennis as given in Table 8.3.5 illustrating the docking energies with the receptor protein.

8.4 CONCLUSION Comparative modeled structures, interaction studies, pernicious analysis, and ADMET have revealed good outcomes. Among the known phytoconstituents, the weed phytoconstituents can prove to be an effective bio-pesticide. Similar to Lantadene A, kaempferol and stigmasterol showed minimal interaction affinity with AChE protein of the identified coleopteran beetles and sap-sucking insects. Hence, the aforementioned phytoconstituents could be well-thought-out means for the forthcoming studies that are intended for the complete eradication of the noxious pests.

ACKNOWLEDGMENT The authors are grateful to the Department of Biotechnology and Department of Life Sciences Graphic Era Deemed to be University for giving us proper direction in completing this study.

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REFERENCES 1. Aktar, W., Sengupta, D., and Chowdhury, A. Impact of pesticides use in agriculture: Their benefits and hazards. Interdisciplinary Toxicology. 2 no. 1 (2009): 1–12. 2. Ongley, E.A. Control of water pollution from agriculture – FAO irrigation and drainage paper 55. Rome: Food and Agricultural Organization of the United Nations. (1976). 3. Sharma, A.K., Gaur, K., et al. Computational interaction analysis of organophosphorus pesticides with different metabolic proteins in humans. Journal of Biomedical Research. 5 (2011): 335–357. 4. Sultatos, L.G. Mammalian toxicology of organophosphorus pesticides. Journal of Toxicology and Environment Health, 43 (1994): 271–89. 5. Fukuto, T.R. Mechanism of action of organophosphorus and carbamate insecticides. Environmental Health Perspectives. 87 (1990): 245–54. 6. Liu, N. Pyrethroid resistance in insects: genes, mechanisms, and regulation. In: Perveen, F. ed., Insecticides - Advances in Integrated Pest Management. Shaheed Benazir Bhutto University, (2012): pp. 457–68. ISBN- 978-953-307-780-2 7. Ffrench-Constant, R.H., Daborn, P.J., Le Goff, G., et al. Genetics and economics of insecticide resistance. Trends in Genetics. 20 no.3 (2004): 163–70. 8. Goddard, J. Bed Bugs (Cimex lectularius) and clinical consequences of their bites. The Journal of Medical American Association. 301 no.13 (2009): 1358–66. 9. Laskowski, D.A. Physical and chemical properties of pyrethroids. Review of Environmental Contamination and Toxicology. 174 (2002): 49–170. 10. Janick and Jules. Horticultural Science (3rd Ed.). San Francisco, CA: W.H. Freeman. (1979): p. 308. 11. Picman, J. and Picman, A.K. (1984). Autotoxicity in Parthenium hysterophorus and its possible role in control of germination. Biochemical Systematics and Ecology. 12 no.3 (1984): 287–292. 12. Rawani, A., Ghosh, A., Chandra, G. Mosquito larvicidal potential of four common medicinal plants of India. Indian Journal of Medical Research. 140 no.1 (2014): 102–108. 13. Rajashekar, Y., Ravindra, K.V., and Bakthavatsalam, N. Leaves of Lantana camara Linn. (Verbenaceae) as a potential insecticide for the management of three species of stored grain insect pests. Journal of Food Science Technology. 51 no.11 (2014): 3494–3499. 14. Ordanza-Cortez, A. Insecticidal effects of leaves, flowers and fruits of Lantana camara Linn. (Philippine Kantutay) on Musca Domestica L. (Philippine Houseflies). Journal of Natural Product and Plant Resources. 5 no.5 (2015): 26–35. 15. Ogendo, J.O., Deng, A.L., Belmain, S.R., et al. Effect of insecticidal plant materials, Lantana camara L. and Tephrosia Vogelii Hook, on the quality parameters of stored maize grains. The Journal of Food Technology in Africa. 9 no.1 (2012): 29–36. 16. Reddy, S.G.E., Dolma, S.K., Verma, P.K. et al. Insecticidal activities of Parthenium hysterophorus L. extract and parthenin against diamondback moth, Plutella xylostella (L.) and aphid, Aphis craccivora Koch. Toxin Reviews. 37 no.2 (2018): 161–165. 17. Kumar, A.K. Review on weed Parthenium hysterophorus (L.). International Journal of Current Research and Review. 10 (2018): 23–32. 18. Ogendo, J.O., Belmain, S.R., Deng, A.L., et al. Comparision of toxic and repellent effects of Lantana camara L. with Tephrosia vogelii Hook and a synthetic pesticide against Sitophilus zeamais Motschulsky (Coleoptera: Curculionidae) in stored maize grain. Insect Science and Application. 23 no.2 (2003): 127–135. 19. Zoubiri, S. and Baaliouamer, A. Chemical composition and insecticidal properties of Lantana Camara L. leaf essential oils from Algeria. Journal of Essential Oil Research. 24 (2014): 377–383. 20. Chalannavar, R.K., Hurinanthan, V., Singh, A., et al. The antimosquito properties of extracts from flowering plants in South Africa. Tropical Biomedicine. 30 no.4 (2013): 559–69.

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21. Tesfu, F. and Emana, G. Evaluation of Parthenium hysterophorus L. powder against Callosobruchus chinensis L. (Coleoptera: Bruchidae) on chickpea under laboratory conditions. African Journal of Agricultural Research. 8 no.44 (2013): 5405–5410. 22. Sahrawat, A., Sharma, J, Rahul, S.N. et al. Parthenium hysterophorus current status and its possible effects on mammalians- a review. International Journal of Current Microbiology and Applied Science. 7 no.11 (2018): 3548–3557. 23. Bourguet, D., Fournier, D., Toutant, J.P., et al. Acetylcholinesterase and insecticide resistance in the mosquito Culex pipiens. Journal of Neurochemistry. 67 no.5 (1996): 2115–2123. 24. Kelly, R.B., Deutsch, J.W., Carlson, S.S., et al. Biochemistry of neurotransmitter release. Annual Review of Neuroscience. 2 (1979): 399–466. 25. Matsumura, F. Toxicology of Insecticides (2nd Ed.). Plenum: New York. (1985). 26. Aldridge. Some properties of specific cholinesterase with particular reference to the mechanism of inhibition by diethyl p-nitrophenyl thiophosphate (E 605) and analogues. Biochemistry Journal. 46 (1950): 451–460. 27. Berman, H.M., Westbrook, J., Feng, Z., et al. Protein data bank. Nucleic Acids Research. 28 no.1 (2000): 235–242. 28. Waterhouse, A., Bertoni, M., Bienert, S., et al. Swiss-Model: Homology modelling of protein structures and complexes. Nucleic Acids Research. 46 no. W1 (2018): W296–W303. 29. Kelley, L.A., Mezulis, S., Yates. C.M., et al. The Phyre2 web portal for protein modeling, prediction and analysis. Nature Protocols. 10 (2015): 845–858. 30. Kelley, L.A. and Sternberg, M.J. Protein structure prediction on the web: A case study using the Phyre server. Nature Protocol. 4 (2009): 363–371. 31. Lipinski, C.A. Lead-and drug-like compounds: The rule-of-five revolution. Drug Discovery Today: Technologies. 1 no.4 (2004): 337–341. 32. Fiser, A. and Sali, A. Modeller: Generation and refinement of homology-based protein structure models. Methods in Enzymology. 374 (2003): 461–91. 33. Laskowski, R.A., Rullmannn, J.A., MacArthur, M.W., et al. Aqua and Procheck-NMR: Programs for checking the quality of protein structures solved by NMR. Journal of Biomolecular NMR. 8 no.4 (1996): 477–86. 34. O’Boyle, N.M., Banck, M., James, C.A., et al. Open Babel: An open chemical toolbox. Journal of Cheminformatics. 3 no.1 (2011): 33. 35. DeLano, W.L. Pymol: An open-source molecular graphics tool. CCP4 Newsletter on Protein Crystallography. 40 no.1 (2002): 82–92. 36. Kim, S., Thiessen, P.A., Cheng, T., et al. PUGView: Programmatic access to chemical annotations integrated in PubChem. Journal of Cheminformatics. 11 no.1 (2019): 1–11. 37. Devvret, K.P., Thapliyal, A., and Tufchi, N. In Silico docking analysis of Mycobacterium Tuberculosis potential targets AftB and EmbA with selected phytochemicals. International Journal of Pharmacy Research and Technology. 7 no.2 (2017): 15–22. 38. Weininger, D. SMILES, a Chemical Language and Information System. 1. Introduction to Methodology and Encoding Rules. Journal of Chemical Information and Computer Scientists. 28 (1988): 31–36. 39. Yang, J.M. Chen, C.C. GEMDOCK: A generic evolutionary method for molecular docking. Proteins: Structure, Function and Bioinformatics. 55 (2004): 88–30. 40. Laskowski, R.A and Swindells, M.B. LigPlot+: Multiple ligand-protein interaction diagrams for drug discovery. Journal of Chemical Information and Modeling. 51 no.10 (2011): 2778–86. 41. Ewing, T. J., Makino, S., Skillman, A.G., et al. DOCK 4.0: Search strategies for automated molecular docking of flexible molecule databases. Journal of Computer-Aided Molecular Design. 15 no.5 (2001): 411–428. 42. Sinha, S., Pant, K., Manoj, P., et al. “Molecular docking studies of phytocompounds in common weeds with AChE proteins of aphids and beetles. Journal of Biologically Active Products from Nature. 10 no. 1 (2020) 18–33.

9

On Energy Harvesting in Green Cognitive Radio Networks Avik Banerjee Madanapalle Institute of Technology and Science

Santi P. Maity Indian Institute of Engineering Science and Technology

CONTENTS 9.1 Introduction................................................................................................... 137 9.2 Literature Review ......................................................................................... 139 9.3 System Model ............................................................................................... 140 9.4 Energy Harvesting and Secondary Data Transmission................................. 143 9.5 Mathematical Solution to Throughput Maximization .................................. 145 9.6 Numerical Results......................................................................................... 146 9.7 Conclusions and the Scope of Future Work.................................................. 149 References .............................................................................................................. 149

9.1 INTRODUCTION Recently, green communications emerge as an innovative research area with several radio networking solutions to cater to the diverse applications in 5G (Khoshabi Nobar et al.2016). An increase in the demand for wireless broadband services and dataintensive applications meeting the diverse quality of service (QoS) requirements lead to the scarcity of the traditional two communication resources, spectrum bandwidth and power (Khoshabi Nobar et al. 2016, Li et al. 2018, Li et al. 2015, Zou et al. 2016). Recent reports state that an estimate of 2% of global carbon dioxide (CO2) emission and 3% of the global energy consumption is generated from the information and communication technology. It was also predicted that by the year 2020, about 20 billion wireless nodes are to be connected worldwide and approximately 320 million tons of carbon emission would be from the mobile networks and connected infrastructure, of which 50% is expected to be emitted from mobile transmission (Cao et al. 2018). This growing concern in energy consumption is due to the unprecedented usage of wireless devices leading to the scarcity in the radio spectrum and its pollution as data congestion while creating an impact on the global heating and carbon emission in the physical environment. 137

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The primary cause of radiofrequency spectrum scarcity is the current static mode of its allocation showing its inefficient and underutilization reported by the studies of the several research groups and regulatory bodies like the Federal Communications Commission (FCC 2002) and Office of Communications (OFCOM 2007). To address the spectrum scarcity issue, a concept called the “cognitive radio” (CR) emerges where a wireless node, before data transmission, senses the surrounding radio environment and accordingly changes its data transmission and reception to make it adaptive to the available radio resources (Haykin 2005). In other words, a secondary user (SU) or a CR node senses the licensed/primary user (PU) spectrum (bandwidth), finds the spectrum hole, and transmits data opportunistically without creating any interference to PU data transmission. This mode of spectrum access is called “interweave” and was the origination of the CR concept. In due time, there have been other modes of spectrum sharing reported, namely, underlay where PU and SU share the spectrum simultaneously with a compulsion from SUs to meet a target interference threshold to the PU receiver. An overlay mode is one where the PU and SU share the spectrum in time slots, i.e., SU avails an exclusive data transmission slot in exchange of its cooperation in the PU data transmission. Wireless nodes are mostly battery-driven and they need provisioning of recharging or replacement leading to the problem of limited network lifetime. The issues of the network lifetime and mutual multiple access interferences among the wireless nodes are the driving forces for the low-power energy-efficient wireless communication system design over the last two decades. The trend in reduction in energy consumption motivates to design an energy-efficient “Green” communication (Khoshabi Nobar et al. 2016). Thus, on one hand, cognitive radio networks (CRNs) (Khoshabi Nobar et al. 2016, Li et al. 2018, Li et al. 2015, Zou et al. 2016) promise to overcome the spectrum scarcity problem, and, on the other hand, the issue of energy crisis can be addressed through energy harvesting (EH). Hence, EH-CRNs are promising for future wireless communications wherein a SU or CR node opportunistically accesses the spectrum holes of the PU, with the enhanced network lifetime through EH leading to green communication technologies (Zou et al. 2016, Pratibha et al. 2015, Lu et al. 2014, Yin et al. 2013). CR is green because it manages the scarce resource “radio spectrum” in the consumer society and addresses the problems of having an impact on the environment: energy efficiency, energy savings, electromagnetic radiation, pollution, and so on. Spectrum sensing (SS) and SU data transmission are the two key operations in CRN. Cooperative spectrum sensing (CSS) involves several SUs to participate in the PU sample acquisition and then either forwards their local sensing decision (hard decision) or sends the sensed samples of the PU signals to the fusion center (FC) (Banerjee et al. 2019). CSS overcomes data collision with PU and fast spectrum access opportunity by the secondary nodes at the cost of the higher energy consumption (Bhowmick et al. 2016) through the involvement of the several SU nodes in SS operation. Energy consumption also occurs due to SU data transmission. As mentioned earlier, these wireless nodes, in general, are batterydriven. Hence, one critical design issue is to conserve the energy for enhancing the network lifetime (Banerjee and Maity 2019). In this practical scenario, EH enhances the battery lifetime of the wireless nodes (minimizes hardware failure)

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139

and overcomes the problem of periodic battery replacement offering a sustainable green communication system (Zou et al. 2016, Pratibha et al. 2015, Lu et al. 2014, Yin et al. 2013). EH can be accomplished either from RF signals (Pratibha et al. 2015, Lu et al. 2014, Yin et al. 2013, Banerjee et al. 2019, Bhowmick et al. 2016) or renewable sources such as solar, thermal, wind, and so on (Dondi et al. 2008). Since electromagnetic waves have the potential to carry both power and information, recent literature explores the simultaneous wireless information and power transfer (SWIPT) technology (Li et al. 2015) as a potential means of EH. SWIPT-enabled CR node operating in power splitting (PS) mode (Banerjee et al. 2019, Banerjee and Maity 2019) splits the received RF signal with an adjustable power to enable SS (to know about the PU spectrum state) and EH operation to work simultaneously. Another mode of EH operation in CRN is the time switching (Banerjee and Maity 2019). Here the SS and EH operations are carried out in non-overlapping slots with a full proportion of the received RF signal used in individual work and is the research issue of this chapter. This study aimed to maximize the SU throughput using the harvested energy. The organization of the chapter is as follows: literature review on EH-CRN is made in Section 9.2. Section 9.3 then briefly introduces an “EH-CRN” system model. Section 9.4 discusses EH and secondary data transmission while the mathematical analysis in throughput maximization is performed in Section 9.5. Simulation results are presented in Section 9.6 while conclusions and scope of future works are reported in Section 9.7.

9.2 LITERATURE REVIEW The literature on EH-CRN is rich (Li et al. 2015, Zou et al. 2016, Pratibha et al. 2015, Lu et al. 2014, Yin et al. 2013, Banerjee et al. 2019, Bhowmick et al. 2016). Li et al. (2015) developed the SWIPT technology in CRNs, and the optimal power allocation and PS ratio are calculated analytically to maximize the energy efficiency of the SUs. In the study of Zou et al. (2016), a priority-based sleeping scheduling algorithm for the sensing nodes is reported in the framework of EH-CSS while maintaining the primary and secondary data collision and energy causality constraints. An optimal SS policy was developed in the work of Pratibha et al. (2015) along with the throughput maximization of the SU under PU collision and energy causality constraints. Lu et al. (2014) considered a two-way relay-based CRN using the SWIPT technology where the relays harvest energy from the received signals for assisting the information exchange process between the two secondary nodes. Yin et al. (2013) considered a typical frame structure that consists of three non-overlapping time frames for sensing, harvesting, and data transmission purposes. An optimization framework is developed to maintain a target throughput in a mixed-integer non-linear programming framework and a differential evolution algorithm is used to derive an optimal sensing strategy. In the study of Banerjee et al. (2019), a sum secondary throughput maximization problem is formulated under the constraints of SS reliability, energy causality, PU cooperation data rate, SU outage probability, the sum, and the individual SU secrecy outage probability. An optimal SS time is found in the study of Bhowmick et al. (2016) to maximize the harvested energy and the performance is

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investigated in terms of harvested energy by the SU, SU data outage, and throughput of the CRN. The optimization problem also considers the SS time duration, the number of time frames, and data collision probability as design variables. The above literature review highlights the importance of EH in SS, maintaining the target throughput during data transmission and enhancing network lifetime to develop a sustainable green CRN. To this aim, this work proposes a typical frame structure for green CRN consisting of two non-overlapping time slots, one for CSS and the other for data transmission or EH (based on the CSS decision) purposes. The system model includes a PU, K number of SU nodes, a single secondary receiver, and an FC. In the first time slot, i.e., during SS, each SU senses the signal samples of PU and after that amplifies the individual received signals and forwards them to the FC. FC then uses the energy detection (ED) technique to identify the transmission state of the PU. Secondary data transmission between a single selected SU transmitter and receiver pair occurs if the CSS decision goes in favor of the non-transmission state of the PU. Now if PU is found to be transmitted in that particular time slot, then all the SUs harvest energy from the interfering signal of the PU. An optimization framework to maximize the sum SU throughput is formulated while maintaining SS reliability and energy causality constraints. The energy causality implies that the required energy for SS, reporting, and data transmission by the SU be met from the harvested energy. The optimal values of SS duration and the fraction of the EH used for data transmission are derived. The research outcomes of this work can be summarized as follows: • SU throughput is shown to attain a global maximum value at some values of the sensing duration and fraction of the energy harvested used in data transmission. • Increase in the number of cooperative users enhances the secondary throughput value. • A performance gain on ~12.68% in secondary throughput over the existing work is shown through simulation results.

9.3 SYSTEM MODEL Figure 9.1 shows a simple CR system model that consists of a primary transmitter (PUT)–receiver pair (PUR), K number of SUs, and an FC. Depending on the demand and the instantaneous channel fading condition, a selected secondary transmitter (SUT) (among the K number of SUs) communicates with its intended destination (SUR) as shown in Figure 9.1. The data transmission between one SUT and SUR pair is considered in our analysis. A list of symbols are given in Table 9.1. The frame structure, shown in Figure 9.2, consists of three non-overlapping time slots, i.e., sensing, reporting, and data transmission or EH. During the first slot of duration ατ, all SUs sense the received signal of PU and forward the same to the FC using amplifying gain β. The duration of the reporting slot is kept (1 − α) τ. Intentionally, the durations of the sensing and the reporting are kept unequal and is dependent on α (α can be a design parameter; however, its optimal value is not determined here). FC combines all the received signals and makes a resultant signal

Energy Harvesting in Green Radio Networks

FIGURE 9.1

141

System model: green CRN model.

TABLE 9.1 List of Symbols Symbols N • • • • •

K Tf Α Τ fs

Φ P(H1), P(H0) Pdet, Pfa xpr(n) • Pp •β Λ •η hpri, hrci, hsd dpri, drci, dsd

ψ pri ,ψ rci ,ψ sd vri(n), vc(n) σns2, σc2, σnd2 γ

Description Total number of samples Total number of Secondary users Total frame duration Fraction of the time slot for spectrum sensing Sensing time duration Sampling frequency Binary indicator variable for PU’s transmission and non-transmission phase Probability for PU’s transmission and non-transmission state Detection, false alarm probability of PU PU transmitted signal PU transmitted power Amplifying the gain factor of SUi for reporting PU signal sample to the fusion center Fraction of the harvested energy used for SU data transmission Energy conversion efficiency of the harvester circuit Fading gains of the channels between PU and SUi, SUi and FC, and SUT and SUR Distance between PU and SUi, SUi and FC, and SUT and SUR Link loss exponent between PU and SUi, SUi and FC, and SUT and SUR Circularly symmetric complex Gaussian noise at SUi and noise at FC Noise variance at the receiver of SUT, FC, and SUR Detection threshold for cooperative spectrum sensing

based on which a global decision of the transmission or non-transmission state of PU is determined using ED. Depending on the CSS decision, SUs perform EH (if PU is found to be in the transmitting state) or a selected SU transmits its own data to its intended destination (if CSS decision goes in favor of the non-transmission state of

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FIGURE 9.2 Frame structure.

the PU). Although all SUs perform EH from the interference signal of the PU, yet the amount of energy harvested by that selected SU is considered in the calculation of the residual energy. Since other SUs are not participating in the data transmission process, energy causality by the selected SU is maintained only if the harvested energy of the latter is sufficient enough to meet the power required for its own data transmission. a. Received signal and channel modeling: During ατ, the signal received by the ith SU is mentioned below

yri (n) = φ h pri x pr (n) + vri (n)

(9.1)

where n = 1 to N = ατfs. The symbol fs denotes the sampling frequency. The symbol ‘N’ denotes the total number of the samples and α denotes the fraction of duration for SS. The parameters P (H1) = P(ϕ = 1) and P(H0) = P(ϕ = 0) denote the stationary probabilities of PU’s transmission and non-transmission states. The PU signal is circularly symmetric complex phase-shift keying modulated and is represented as xpr(n) with 0 mean and variance Pp, i.e., E[|xpr(n)|2] = Pp. PU transmitted power is denoted by the symbol ‘Pp’. The symbol vri(n) denotes the circularly symmetric complex Gaussian (CSCG) noise at SUi and is assumed to be independent and identically distributed random process with zero (0) mean and variance E[|vri(n)|2] = σns2. The combined signal at FC, during (1 − α)τ, can be represented as follows: K



Yc (n) =

∑

βi hrci   yri   (n) + vc (n)

(9.2)

i =1

vc(n) denotes the CSCG noise at the receiver of FC with zero mean and variance E[|vc(n)|2] = σc2. The wireless channels (i.e., both the sensing and the reporting channels) are modeled as CSCG Rayleigh flat fading channels, where the symbols hpri and hrci denote the flat fading coefficients of the links between PU to SUi and SUi to FC, respectively. The fading coefficients −ψ −ψ rci statistics are given as hpri ~ CN(0, d pri pri) and hrci ~ CN(0, drci ). The path loss exponents for the links between PU to SUi and SUi to FC are denoted by ψ pri and ψ rci. The symbols dpri and drci indicate the distance from PU to SUi and SUi to FC, respectively.

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b. Cooperative SS: FC uses an energy detector to perform CSS on the combined signal Yc(n). The test statistics (Tst) is given by ατ fs

Tst =



∑ Y (n)

2

(9.3)

c

n=1

Using the central limit theorem, for a sufficiently large number of N, ‘Tst’ follows a Gaussian distribution under both the hypotheses H1 and H0 (Banerjee et al. 2019). The STs being co-located, the assumption dpri = dpr, drci = drc, ψpri = ψpr, ψrci = ψrc, βi = β looks reasonable for all i = 1 to K (Banerjee and Maity 2019, Huang et al. 2012). The mean values of Tst under H1 and H0 are expressed as E(Tst1) = ατfsµ1 and E(Tst0) = ατfsµ 0, respectively. Here, µ1 = Kβr1 + σc2, −ψ pr −ψ rc µ 0 = Kβr0 + σc2, r1 = d pr drc Pp + r0, and r0 = drc−ψ rc σ ns2 . The variances of Tst under H1 and H0 are expressed as Var(Tst1) ≈ ατfsµ12and Var(Tst0) ≈ ατfsµ 02 (Banerjee and Maity 2019, Huang et al. 2012). The cooperative detection and false alarm probabilities can be expressed as

 γ − µ1ατ fs   γ − µ0ατ fs  Pdet = Q  , Pfa = Q     µ1 ατ fs   µ0 ατ fs 

(9.4)

where γ denotes the detection threshold for CSS. Some amount of energy is consumed by each SU for the sensing and the reporting operations, which can be expressed as Es = Ps ατ and Ec = βSατ, respectively, where S = P(H1) −ψ pr (d pr Pp + σ ns2 ). Ps is the power consumed for SS. The duration for the broadcasting CSS decision, which seems to be negligible compared to the sample acquisition and reporting slots, is not considered in the mathematical analysis (Banerjee et al. 2019).

9.4 ENERGY HARVESTING AND SECONDARY DATA TRANSMISSION This section presents EH and SU data transmission briefly. a. Linear energy harvesting model: At the end of sensing time (τ), all SUs harvest energy when CSS decision goes in favor of PU’s transmission state. However, since only one SU is selected for data transmission/harvesting purposes, the amount of harvested energy by that particular SUT during (Tf − τ) can be expressed as mentioned below.

(

)

Eharvest = η ( T f − τ )  Pdet P ( H1 ) d p−rψ pr Pp + σ ns2 + Pfa P ( H 0 )σ ns2 

= η (Tf − τ ) B

(9.5)

−ψ pr where B = PdetP(H1)(d pr Pp +σ ns2 ) + Pfa P(H0)σ ns2 and ‘η’ denotes the energy conversion efficiency of the harvester circuit (0 < η < 1). It is worth mentioning

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that the linear EH model (Bhowmick et al. 2016) is considered here; however, the non-linear EH model can also be considered (Banerjee et al. 2019). Furthermore, the choice of particular SU T − SU R in participating in the data transmission process is governed by some priority algorithm. b. SU data transmission: Some amount of power, say Pst, is required (a portion of the harvested power) by the SUT to perform its own data transmission, and it can be expressed as Pst =



λ Eharvest λη ( T f − τ ) B = = λη B (Tf − τ ) (Tf − τ )

(9.6)

The power required for the SUT to run the harvester unit, being small, is not considered in the mathematical analysis. The entire portion of the harvested energy is not utilized by SUT, rather some amount of the latter, say (1 − λ ) portion, is kept to support the energy required for SS and reporting signal samples to the FC. Here, the symbol λ (0 < λ < 1) denotes the portion of the harvested energy used for SU data transmission. c. Secondary throughput calculation: The instantaneous spectrum efficiency (SE) of SU T while transmitting data to SU R can be expressed as given below.

Rsr =

(Tf − τ )  P ( H )(1 − P ) log  1 + | hsd |2 Pst    0 2 f    T σ2  

f

nd



(9.7)

The instantaneous channel-fading coefficient of the link between SUT and SUR is expressed as hsd. The modeling of a channel is similar as discussed 2 above. The noise variance at the receiver of SUR is expressed as σ nd . The frame structure is repetitive, which leads to the individual SE of SUT averaged on a large set of instantaneous channel gains. SE averaged over large frames (10,000) can be expressed as Rsravg =

(Tf − τ )  P ( H )(1 − P ) log  1 + dsd−ψ Pst    0 fa 2   T σ 2   sd



f



=

nd

(Tf − τ )C  log Tf

 

2

 d sd−ψ sd λη B     1 + 2      σ nd  



(9.8)

where the mean channel power gain of | hsd |2 averaged over 10,000 frames is calculated as E[hsd 2] = d sd−ψ sd , since, hsd ~ CN(0, d sd−ψ sd ), C =  P ( H 0 )(1 − Pfa ), and Pst = λη B. Here dsd and ψ sd denote the distance and the path loss exponent between the link SUT and SUR, respectively.

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9.5

MATHEMATICAL SOLUTION TO THROUGHPUT MAXIMIZATION

In this section, the problem formulation and its mathematical solution are mentioned. The mathematical analysis as an optimization problem is given below.  ( T f − τ ) C   d −ψ sd λη B    log2  1 + sd 2 max Rsravg ≡ max    τ ,  λ τ σ nd    Tf   



(

(9.9)

)

s.t. (i) Pdet  ≥ Pdet  , Pfa  ≤ Pfa  ⇒ Kβ RQ + ( r0 − r1 ) ατ fs + σ c2 BQ = 0 (ii) Eharvest ≥ Es + Ec + Pst ( T f − τ ) ⇒ η B ( T f − τ ) ≥   Psατ + β Sατ +   λη B ( T f − τ ) ⇒ (1 − λ )η B ( T f − τ ) − ( Ps + β S )ατ ≥  0,

(

)

−ψ where B = Pdet P(H1)(d pr pr Pp + σ ns2 ) + Pfa P(H0)σ ns2 and C =  P ( H 0 ) 1 − Pfa  Constraint (i) in Eq. (9.9) represents the SS reliability, where Pdet  and Pfa  denote the detection and the false alarm probability thresholds, respectively. Here, RQ = (r0 Q−1 Pfa  − r1Q−1(Pdet )) and BQ = (Q−1(Pfa ) − Q−1(Pdet )) in constraint (i) in Eq. (9.9). To find out the optimal value of τ that maximizes the throughput, the SS reliability constraints are set to equality, i.e., Pdet  =   Pdet  , Pfa  = Pfa . The detailed steps for the modification of this constraint are similar to those of Banerjee and Maity 2019 and are not mentioned here due to space constraint. The energy causality constraint mentioned in Eq. (9.9) (ii) ensures that the total harvested energy of SUT is sufficient to provide the necessary energy required for SS, reporting, and data transmission. Lagrange multiplier and Karush–Kuhn–Tucker (KKT) are used to solve this optimization problem. The Lagrangian of this optimization problem can be expressed as

( )

L=

(Tf − τ )C  log  

Tf

(

2

 d sd−ψ sd λη B      1 + σ 2 nd 

)

+ ε1  Kβ RQ + ( r0 − r1 ) ατ fs + σ c2 BQ 

+ ε 2 (1 − λ )η B ( T f − τ ) − ( Ps + β S )ατ  = 0  

(9.10)

where ε1 and ε 2 are the Lagrange multipliers and ε1 ,  ε 2 > 0. KKT is used to solve the optimization problem mentioned in Eq. (9.10). It is found ∂L ∂ L ∂L ∂ L that putting = 0 (partial derivatives of L w.r.t τ ,  λ ,  ε1 ,  ε 2) and = =  = ∂τ ∂λ ∂ε1 ∂ε 2 considering the case ε1 ≠  0,  ε 2 ≠  0, one feasible optimal solution can be found as mentioned below.

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A11Cd sd−ψ sd ( Ps + β S )α η B ( T f − A11 )σ n2d

λ* = 1 −

τ* =



(9.11)

1  B 

1 − 2 11

2

 C   d −ψ sd λ *η B    − A    log2  1 + sd 2  − 1 σ nd       Tf  1 − 2 11

2

(

2

(9.12)

)

1 − λ * η BT f 1  Kβ RQ + σ c2 BQ  and B = . 11   α fs  Kβ ( r1 − r0 )  1 − λ * η B + ( Ps + β S )α The detailed mathematical analysis is not shown due to the space constraint.

where A11 =

(

)

9.6 NUMERICAL RESULTS Numerical results are presented that show the performance of the secondary throughput ( Rsravg ) with the variation of different system parameters. Monte Carlo simulations over 10,000 runs consider the randomness in channel characteristics while numerical results are obtained. The simulation parameters are set as follows: Pp = 1 W, Ps = 0.1 W, Pdet  = 0.95, Pfa  = 0.05, P ( H1 ) = 0.7, P ( H 0 ) = 0.3, T f = 10 ms,  fs = 10 kHz, K = 10, β = 1.4, η = 0.6, d pr = 1.2 m, ψ pr = 3.5, drc = 1.3 m,  ψ rc = 3.8, 2 = 0.5 W to have a fair comparison. d sd = 1.4 m, ψ sd = 4, σ ns2 = σ c2 = 0.6 W, σ nd Figures 9.3 and 9.4 show the variation on the secondary throughput ( Rsravg ) with the fraction of EH used by SUT for its data transmission (λ ) and SS duration (τ ), respectively for α = 0.3 and 0.4. We see that the throughput increases initially as the λ value increases since the increase in the latter enhances the power requirement (Pst given in Eq. (9.6)) for data transmission. However, when the λ   value is further increased, the Rsravg value decreases after reaching a maximum point. This is because an increment in the λ value leads to a decrease in the data transmission time slot (Tf − τ) to maintain the energy causality and the fixed data rate requirement of SUT. It is worth mentioning that a fixed data transmission rate is maintained by SUT; however, data rate constraint in the form of outage probability is not included in the optimization problem, which may be considered as an extended work. Furthermore, it is observed that Rsravg  (α   = 0.4 ) > Rsravg (α   =   0.3). An increase in the α value decreases τ (for maintaining a target SS reliability constraint) and enhances the transmission time slot (Tf − τ), which not only increases the required time slot for data transmission but also enhances the slope of EH during that enhanced slot. This in overall increases the secondary throughput of SUT. Figure 9.3 shows that the simulation and the analytical values match well. Figure 9.4 shows that with the initial increment in the sensing duration (τ ), the secondary throughput of SU T ( Rsravg ) increases. This is because an increase in the τ value decreases the required data transmission slot (Tf − τ) and increases the fraction of harvested energy λ . An increase in the λ  value increases the secondary throughput. With further increase in the τ   value, Rsravg decreases after attaining a maximum point since the (Tf − τ) slot reduces significantly and the scope of EH

Energy Harvesting in Green Radio Networks

(

)

147

FIGURE 9.3 Secondary throughput Rsravg versus the fraction of EH for data transmission (λ ).

(

)

FIGURE 9.4 Secondary throughput Rsravg versus SS duration (τ ).

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also diminishes with this fact. It is found that the maximum values of Rsravg are 0.2457 MbpsHz−1 and 0.2312 MbpsHz−1 (at λ * = 0.2045, τ * =  0.04 s) at α = 0.4 and α = 0.3, respectively. A comparative performance analysis of the secondary throughput between the proposed approach and that of the work reported by Banerjee et al. 2019 is shown in Figure 9.4. Since the work reported by Banerjee et al. 2019 analyszes the sum secondary throughput consisting of K number of cooperative transmit–receive pairs (unlike the present system model, which consists of a single transmit–receive pair to be active at a time in data transmission), the value of the former is averaged (total throughput divided by K number of users) and is compared with the present work to have fairness in the analysis. It is found that the value of Rsravg for the present work is ~12.68% greater than the corresponding value of Rsravg reported by Banerjee et al. 2019 at α = 0.4. It is also observed that the value of τ * obtained by Banerjee et al. 2019 is greater than the same for the present work. An increase in the τ * value reduces both the data transmission slot and the scope of EH for the work reported by Banerjee et al. 2019, which decreases the overall value of Rsravg compared to the present work. Figure 9.5 shows the variation on Rsravg with the fraction of time slot for SS (α ). It is observed that with the increase in α , the secondary throughput value increases. This is because an increase in the α  value leads to a decrease in the τ value (less amount of τ is required to meet the SS reliability constraint) and an increase in the data transmission slot (Tf − τ) value. This increased (Tf − τ) value increases the overall

FIGURE 9.5

(

)

Secondary throughput Rsravg versus the fraction of time slot for SS (α ).

Energy Harvesting in Green Radio Networks

149

throughput of SUT. However, after attaining a maximum point when the value of α is further increased, the Rsravg value decreases since the (Tf − τ) value increases significantly, which reduces the λ value (for maintaining the target data rate and energy causality constraint). The maximum value of Rsravg is observed to be 0.3816 MbpsHz−1 for λ * = 0.3525,  τ * =  0.03251 s at α = 0.5. Thus, α may be a design parameter in this optimization problem, however, not explored to keep mathematical analysis simple. It is also observed that Rsravg ( K = 12 ) > Rsravg ( K = 8 ) (of about ~ 21.15% more) at α = 0.5. An increase in the value of cooperative SUs (K) decreases the sensing duration τ (less amount of τ required to meet the SS reliability constraint) and increases the data transmission slot (Tf − τ). An increase in (Tf − τ) also enhances the chance of EH and increases the secondary throughput.

9.7 CONCLUSIONS AND THE SCOPE OF FUTURE WORK In this chapter, a simple EH-CR model is proposed that maximizes the throughput of the selected secondary transmitter while maintaining SS reliability and energy causality constraints. The main findings of the work reported in this chapter can be summarized as follows: (i) a maximum value of the secondary throughput (Rsravg ) is found for both the optimal values of the sensing duration (τ) and the fraction of the harvested energy for the data transmission (λ), (ii) the maximum value of Rsravg is found to be ~ 12.68% greater over the value obtained in the study of Banerjee et al. 2019 at a fixed value of the fraction of the time slot for SS (α) = 0.4, (iii) Rsravg is found to attain a maximum value for a fixed value of α, and (iv) an increase in the value of cooperative users (K) is found to have benefit in secondary throughput (about ~ 21.15% more for K = 12 over K = 8) at α = 0.5. • The work reported in this chapter may be extended as the same system model involving multiple pairs of secondary transmitter and receiver with the inclusion of secondary outage probability as a constraint in the same optimization framework. • The issue of security (another important research issue in present-day wireless communication) can be included considering the presence of an eavesdropper and may be counter measured through the presence of jamming signals. • The proposed system model and its various extended models can be made or tuned to have an application-specific design like the Internet of Things, unnamed aerial vehicles, and intelligent transport systems. • The issue of time and energy consumption in SS operation can be avoided through PU spectrum prediction using a machine learning scheme.

REFERENCES Banerjee, A. and Maity, S. P. 2019. “On residual energy maximization in cooperative spectrum sensing with PUEA.” IEEE Wireless Communications Letters 8(6): 1563–1566, (December 2019). DOI 10.1109/LWC.2019.2927482. Banerjee, A., Maity, S. P., and Das, R. K. 2019. “On throughput maximization in cooperative cognitive radio networks with eavesdropping.” IEEE Communications Letters 23 (1): 120–123, (January 2019). DOI 10.1109/LCOMM.2018.2875749.

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Bhowmick, A., Roy, S. D., and Kundu, S. 2016. “Throughput of a cognitive radio network with energy-harvesting based on primary user signal.” IEEE Wireless Communications Letters 5 (2): 136–139, (April 2016). DOI 10.1109/LWC.2015.2508806. Cao, X., Liu, L., Cheng, Y., et al. 2018. “Towards energy-efficient wireless networking in the big data era: A survey.” IEEE Communications Surveys & Tutorials 20 (1): 303–332, (First quarter 2018). DOI 10.1109/COMST.2017.2771534. Dondi, D., Bertacchini, A., Larcher, L., et al. 2008. “A solar energy harvesting circuit for low power applications.” IEEE International Conference on Sustainable Energy Technologies, Singapore: 945–949. DOI 10.1109/ICSET.2008.4747143. Federal Communications Commission. 2002. “Spectrum policy task force.” Rep. ET Docket no. 02–135: (November 2002). Haykin, S. 2005. “Cognitive radio: Brain-empowered wireless communications.” IEEE Journal on Selected Areas in Communications 23 (2): 201–220, (February 2005). DOI 10.1109/ JSAC.2004.839380. Huang, S., Chen, H., Zhang, Y., et al. “Energy-efficient cooperative spectrum sensing with amplify-and-forward relaying.” IEEE Communications Letters 16 (4): 450–453, (February 2012). DOI 10.1109/LCOMM.2012.021612.112143. Khoshabi Nobar, S., AdliMehr, K., and MuseviNiya, J. 2016. “RF-Powered Green Cognitive Radio Networks: Architecture and Performance Analysis.” IEEE Communications Letters 20(2): 296–299. DOI 10.1109/LCOMM.2015.2500897. Li, B., Xu, W., and Gao, X. 2015. “Energy-efficient simultaneous information and power transfer in OFDM-based CRNs.” IEEE 81st Vehicular Technology Conference (VTC Spring): 1–5, (May 2015). DOI 10.1109/VTCSpring.2015.7145819. Li, S., Liu, Y., Lin, L., et al. 2018. “Millimeter-wave channel simulation and statistical channel model in the cross-corridor environment at 28 GHz for 5G wireless system.” International Conference on Microwave and Millimeter Wave Technology (ICMMT), Chengdu: 1–3. DOI 10.1109/ICMMT.2018.8563957. Lu, X., Xu, W., Li, S., et al. 2014. “Simultaneous wireless information and power transfer for cognitive two-way relaying networks.” IEEE 25th Annual International Symposium on Personal, Indoor, and Mobile Radio Communication (PIMRC): 748–752, (September 2014). DOI 10.1109/PIMRC.2014.7136264. OFCOM 2007. “Digital dividend review, a statement on our approach towards awarding the digital dividend.”: (December 2007). Pratibha, M., Li, K. H., and Teh, K. C. 2015. “Energy-harvesting cognitive radio systems cooperating for spectrum sensing and utilization.” IEEE Global Communications Conference (GLOBECOM): 1–6, (December 2015). DOI 10.1109/GLOCOM.2015.7417161. Yin, S., Zhang, E., Yin, L., et al. 2013. “Saving-sensing-throughput tradeoff in cognitive radio systems with wireless energy harvesting.” IEEE Global Communications Conference (GLOBECOM): 1032–1037, (December 2013). DOI 10.1109/GLOCOM.2013.6831210. Zou, Y., Peng, J., Liu, K., et al. 2016. “Energy-efficient cooperative spectrum sensing for cognitive sensor networks with energy harvesting.” Chinese Control and Decision Conference (CCDC): 2373– 2378, (May 2016). DOI 10.1109/CCDC.2016.7531382.

10

Mitigation on the Impact of Electric Vehicle Charging Stations by Splitting the Capacity and Optimally Locating on a Reconfigured RDS M. Satish Kumar Reddy and K. Selvajyothi IIITDM

CONTENTS 10.1 Introduction................................................................................................... 152 10.2 Load Flows ................................................................................................... 152 10.2.1 Modified NR Load Flow................................................................... 153 10.2.2 F/B Load Flow................................................................................... 153 10.2.3 Branch Incidence Matrix Load Flow................................................ 153 10.3 Analysis of a Sample Distribution System ................................................... 156 10.4 IEEE 16 Bus RDS ......................................................................................... 160 10.4.1 Bus Voltage Profile ........................................................................... 161 10.4.2 Branch Current Profile....................................................................... 162 10.4.3 Reconfiguration of 16 Bus RDS........................................................ 162 10.4.4 Modeling of Loads at RDS ............................................................... 163 10.5 Optimization Techniques .............................................................................. 164 10.6 Optimal Placement of EVCS Using PSO...................................................... 165 10.6.1 Optimal Reconfiguration of 16 Bus RDS without EVCS ................. 166 10.6.2 Optimal Placement of EVCS without Reconfiguration (Scenario 1) ....................................................................................... 167 10.6.3 Optimal Placement of EVCS before Reconfiguration (Scenario 2) ....................................................................................... 170

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10.6.4 Optimal Placement of EVCS after Reconfiguration (Scenario 3) ....................................................................................... 171 10.6.5 Optimal Placement of EVCS along with Reconfiguration (Scenario 4) ....................................................................................... 172 10.7 Conclusion..................................................................................................... 175 References .............................................................................................................. 175

10.1 INTRODUCTION Conventional transportation is the second culprit for global warming, which leads to unseasonal rains, unpredictable climatic changes, forest fires, and so on. However, sudden replacement of vehicles with electric vehicles (EVs) (Ahmadi, et al. 2019) based on internal combustion engines is not recommended without an infrastructure for them. The low specific energy of EVs could not motivate people for choosing EVs. Therefore, the electric vehicle charging stations (EVCS) erected at optimal locations with the latest charging technologies would change the mindset of customers choosing the EVs (Alsabbagh, et al. 2019). Here, a major problem is welcoming the power system engineers for the sudden implementation of EVCS (Catalbas, et al. 2017) at the domestic and commercial levels. The incorporation of EVCS (Hatef & Ghaffarzadeh 2020, Huiling, et al. 2018, Yazdi, et al. 2019) to the existing radial distribution systems (RDS) adds an extra burden to the power system, affecting the stability, as well as increasing the losses of the system. The problem can be easily solved with the erection of additional generating stations at a cost of additional investment. Even then, with the RDS reconfiguration system, losses can be reduced with EVCS in the system. The analysis of the RDS is needed to determine its performance with and without reconfiguration through load flow studies. The load flow studies can be implemented in the distribution system by modeling its components like lines, transformers, loads, and so on. The EVCS are located in such a way that they must have the least impact and are minimized further with their proper reconfiguration. Optimization strategies accomplish the proper reconfiguration and minimal impact of EVCS placement. In this chapter, particle swarm optimization (PSO) (Yenchamchalit, et al. 2008) is applied for the optimal location of EVCS and reconfiguration for minimizing the losses of the RDS.

10.2 LOAD FLOWS Load flow studies are used to analyze either transmission or distribution system for calculating the bus voltages, branch currents, and losses. These are formulated using numerical methods that start with assumed initialized values for the variables chosen. Load flows are also used for power system planning, short circuit studies, and so on. Load flows that are used for analyzing the transmission system are not directly used for analyzing the distribution system due to their radial nature and low X/R. Hence, distribution load flows are formulated with some modifications in conventional load flows like modified Newton-Raphson (NR) load flow, modified Gauss-Siedel load flow, and modified fast decoupled load flows. The load flows that are formulated with forward current and backward voltages of the RDS are called forward/backward

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153

(F/B) load flow. The load flows that are formulated with branch incidence matrix (BIM) to determine the performance of RDS are called as BIM load flow.

10.2.1

modified nr load floW

The commercial NR load flow is unable to analyze the RDS because of its radial nature and low X/R ratio. Therefore, Xu et al., in 2009, proposed modifications in the conventional NR method to study the performance of RDS. The Jacobian matrix in the process of solving increases its complexity. The complexity further increases with unbalanced RDS.

10.2.2

f/B load floW

The F/B load flow is formulated for studying the RDS (Hajimiragha, et al. 2010). This method is started with a flat voltage profile for all buses. The current of each branch is calculated as per the bus voltage and bus powers in the forward direction and from the end bus, the voltage is calculated to the starting bus. Thus, it is called F/B load flow.

10.2.3

BranCH inCidenCe matrix load floW

In this chapter, a new load flow algorithm is proposed, which handles the distribution system accurately, efficiently, and adequately. All the conventional load-flows that are used to solve the distribution system are started with a uniform voltage profile of 1.0 pu, which took one additional iteration for converging the algorithm. However, the proposed load flow converges the system without a flat voltage stage. It is formulated using simple vector algebra and a minimum number of equations inferior to the commercial load flows. BIM is the backbone of this load flow, so it is named BIM load flow. The flow chart of BIM is shown in Figure 10.1. The load flow is further solved after the formation of BIM of the RDS. The starting step of any load flow is to convert the connected loads to pu values using the base voltage and base power of the system. The base voltage can be taken as an operating voltage at the distribution level, whereas the base power can be taken as the maximum load of the RDS. However, IEEE has prescribed different base values for different bus systems. An important step in this load flow is introducing a new variable ‘β ’, which is used to calculate the receiving end voltages of the system. K is used to evaluate the branch current Ibr from the bus current Ib (Huiling et al. 2018) as mentioned below.

I br = K −1I b

(10.1)

The complex branch powers and bus powers are represented in terms of the K matrix, and losses via (10.2) and (10.3). The system’s Lbr branch power losses produce a voltage difference between the sending and receiving end buses.

Sb = K [ Sbr − Lbr ]

(10.2)

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FIGURE 10.1 Flow chart representing formulation of BIM (K).

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Electric Vehicle Charging Stations Impact



Sbr = K −1Sb + Lbr

(10.3)

Considering the power flow between lth and mth buses, the complex power in branch ‘lm’ can be written as:

* Slm = Plm + jQlm = Vl I lm =



Slm = Plm + jQlm = βlmYlm*

VlVlm* = Vl (Vl* − Vm* )Ylm* * Zlm (10.4)

where

βlm = Vl (Vl* − Vm* )

(10.5)

* βlm = Slm Zlm

(10.6)

* βlm Vl*

(10.7)

From (10.4) From (10.5)

Vm = Vl −

Taking conjugate on either side of (10.4), the branch current can be calculated as:

 β*  I lm =  lm*  Ylm  Vl 

(10.8)

The total losses of the system can be calculated as:

Llm =

∑L

r lm

(10.9)

Hence, the apparent power of the receiving end branch is obtained as: Sbrrec = Sbrsend − Lloss

(10.10)



Lloss = Lrlm = Smspecr−1 − Vmr −1I m*

(10.11)



Max ( Lrlm ) ≤ €

(10.12)

where

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where r is the iteration count. Llm is lm branch losses. Smspec is specified apparent power at the mth bus. Vm is the mth bus voltage. Im is the mth bus current. Sbr is the branch apparent power. €  is the very small positive value. As per (10.12), the maximum losses in the system are considered to be finite and small. The algorithm will perform faster for less complex systems.

10.3

ANALYSIS OF A SAMPLE DISTRIBUTION SYSTEM

The formation of BIM is explained using a sample 7 bus system, as shown in Figure 10.2. According to the algorithm explained in Section 10.2, the branches ‘2b’ and ‘5b’ are augmented with ‘1b’ through the 2nd bus, and the rest of the branches are not augmented with branch ‘1b’. So, a ‘−1’ and ‘0’ are filled at the corresponding space in the matrix as shown in Table 10.1. Similarly, the remaining rows of the matrix are also filled. The diagonal elements of the matrix are generated by the augmented self-branches and are represented by ‘1’. Assumed values of line data and load data required for solving the load flow problem of sample 7 bus system are shown in Table 10.2.

FIGURE 10.2

Single line diagram of sample 7 bus system.

TABLE 10.1 BIM of Sample 7 Bus System Branch

1b

2b

3b

4b

5b

6b

1b

1

2b 3b

−1 1 0

0

0

0 0

−1 1

0

−1 0 0

−1 0

4b 5b 6b

0 0 0

0 0 0

0 0 0

0 1 0

0 0 1

−1 1 0 0

0

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TABLE 10.2 Line Data and Load Data of the Sample Bus System Sending End Bus 1 2 3 4 2 3

Receiving End Bus

Resistance (Ω)

Reactance (Ω)

Real Power (kW)

Reactive Power (kVAR)

2 3 4 5 6 7

1.093 1.184 2.095 3.188 1.093 1.002

0.455 0.494 0.873 1.329 0.455 0.417

600 400 500 300 200 550

600 300 550 300 150 550

Step 1: Convert the loads to pu choosing a base value of 100 MVA. Bus no.

P (pu)

Q (pu)

2 3 4 5 6 7

0.006 0.004 0.005 0.003 0.002 0.0055

0.006 0.003 0.0055 0.003 0.0015 0.0055

Step 2: Assuming an 11 kV distribution system, convert the branch impedances to pu choosing a base impedance of 1.21 Ω. Branch

R (pu)

X (pu)

1 2 3 4 5 6

0.903 0.978 1.731 2.634 0.903 0.828

0.376 0.408 0.721 1.098 0.376 0.346

Step 3: Form the BIM (K) using the procedure given in Section 10.3 Branch

1b

2b

3b

4b

5b

6b

1b 2b 3b 4b 5b 6b

1 0 0 0 0 0

−1 1 0 0 0 0

0

0 0

−1 0 0 0 1 0

−1 0 0 0 1

−1 1 0 0 0

−1 1 0 0

0

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Step 4: Calculate pu apparent power. Iteration 0: Let the total losses TL of the system be initialized as zero. Now, calculate the branch power using Sbranch = [ K ]−1 Sbu

Branch

Apparent Power (pu) 0.042 + j0.033 0.017 + j0.013 0.008 + j0.008 0.003 + j0.003 0.019 + j0.014 0.005 + j0.005

1 2 3 4 5 6

Step 5: Calculate the new variable β branch using

β branch = Z * Sbranch



Branch 1 2 3 4 5 6

β branch 0.0504 + j0.0104 0.022 + j0.0058 0.0196 + j0.0081 0.0112 + j0.0046 0.0224 + j0.0055 0.0059 + j0.0024

Step 6: Calculate the bus voltages and bus currents using the equations Vb = Va − (β /Va)* and Ib = Y(β /Va)* respectively. where ‘a’ and ‘b’ are the sending end bus and receiving end bus of branch ‘ab’. Bus 2 3 4 5 6 7

Bus Voltages (pu)

Bus Current (pu)

0.949 − j0.0140 0.926 − j0.0019 0.905 − j0.028 0.892 − j0.032 0.925 − j0.0195 0.920 − j0.022

0.042 + j0.033 0.0181 + j0.0135 0.0088 + j0.0085 0.0034 + j0.0032 0.0218 + j0.0149 0.0055 + 0.0053i

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Step 7: Calculate the branch currents from the bus currents and K using equation Ibranch = KIbus. Branch 1 2 3 4 5 6

Current (pu) 0.0021 − 0.0047i 0.0038 + 0.0003i 0.0054 − 0.0052i 0.0034 − 0.0032i 0.0037 − 0.0015i 0.0055 − 0.0053i

Step 8: Calculate the sum of the branch losses using the equation TL1 = This is valid only for zero iteration. Branch 1 2 3 4 5 6

∑I V . br br

Branch Losses 0.0019 + 0.0045i 0.0035 − 0.0002i 0.0047 + 0.0049i 0.0029 + 0.0030i 0.0034 + 0.0014i 0.0050 + 0.0050i

Step 9: Calculate the difference between the losses TL = TL1 − TL0. Since TL0 is zero at the beginning of the iteration, the difference is TL1 only. Branch

Loss Difference

1 2 3 4 5 6

0.0019 + 0.0045i 0.0035 − 0.0002i 0.0047 + 0.0049i 0.0029 + 0.0030i 0.0034 + 0.0014i 0.0050 + 0.0050i

Step 10: Check the convergence of TL such that it satisfies less than or equal to 0.0001 and displays the bus voltages, bus currents, and losses, else go to step 4. With this detailed explanation of the procedure for solving the sample 7 bus system, let us see the simulation studies for a standard IEEE 16 bus RDS in the succeeding section, which has three feeders connected to the substation.

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FIGURE 10.3 Single line diagram of IEEE 16 bus RDS.

10.4

IEEE 16 BUS RDS

The single line diagram of IEEE 16 bus RDS is shown in Figure 10.3, which consists of 16 buses with three feeders each maintaining 1.0 pu due to direct connection with the substation (zero load buses). The line and load data for the system are given in Tables 10.3 and 10.4 respectively. The base values of voltage and MVA for the buses are assumed to be 23 kV and 100 MVA respectively. The firm line represents sectionalized switches and the tie line is represented with dotted lines. The system has a real power load of 28.7 MW and a reactive power load of 14.8 MVAR. The switches to be opened for basic load flow are 5, 11, and 16. The system has three feeders named feeder-1, feeder-2, and feeder-3. The total loads at feeder-1, feeder-2, and feeder-3 are 7.5 + j6.1 MVA, 15.1 + j8.7 MVA, and 5.1 − j0.1 TABLE 10.3 The Load Data of Buses Connected to Feeders 1, 2, and 3 of the 16 Bus System Feeder 1

Feeder 2

Active Power Bus No. (MW)

Reactive Power (MVAR)

4 5 6 7

2 3 2 1.5

1.6 1.5 0.8 1.2

Total

7.5

6.1

Feeder 3

Bus No.

Active Power (MW)

Reactive Power (MVAR)

8 9 10 11 12 Total

4 5 1 0.6 4.5 15.1

2.7 3 0.9 0.1 2 8.7

Bus No.

Active Power (MW)

Reactive Power (MVAR)

13 14 15 16

1 1 1 2.1

0.9 −1.1 0.9 -0.8

Total

5.1

−0.1

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TABLE 10.4 The Line Data of Branches Connected to Feeders 1, 2, and 3 of the 16 Bus System Feeder 1 Br No.

Feeder 2 Seb-Reb

(R + jX) Ωkm−1

1–4 0.075 + j0.1 4–5 0.08 + j0.11 4–6 0.09 + j0.18 6–7 0.04 + j0.04 5 5–11 0.04 + j0.04 Red belongs to tie-lines

1 2 3 4

Br No.

Seb-Reb

6 7 8 9 10 11

2–8 8–10 8–9 9–11 9–12 10–14

Feeder 3 (R + jX) Ωkm−1

Br No. Seb-Reb

(R + jX) Ωkm−1

3–13 0.11 + j0.11 12 0.11 + j0.11 13–15 0.11 + j0.11 13 0.08 + j0.11 13–14 0.08 + j0.11 14 0.09 + j0.12 15–16 0.11 + j0.11 15 0.04 + j0.04 7–16 0.08 + j0.11 16 0.09 + j0.12 0.04 + j0.04 Black belongs to main lines

MVA, respectively. Buses 4, 5, 6, and 7 are connected to feeder-1, buses 8, 9, 10, 11, and 12 are connected to feeder-2, and buses 13, 14, 15, and 16 are connected to feeder-3.

10.4.1

BuS voltage profile

Figure 10.4 shows the bus voltage profile of the system using the proposed method. It can be observed that the voltage profile of feeders 1, 2, and 3 are 1.0 pu as they are the starting buses directly connected to the substation. The voltage profile is dropped from 1.0 pu to 0.97 pu at the 9th bus. The buses 4, 5, 6, and 7 are connected to the 1st feeder so the voltage profile is dropped to 0.985 at the 7th bus and the buses 8, 9, 10, and 11 are connected to the 2nd feeder, where also the voltage profile is dropped to 0.97 pu at the 9th bus and raised to 0.976 at the 10th bus as 1.000

without EVCS

Voltage profile (p.u)

0.995 0.990 0.985 0.980 0.975 0.970 0.965

0

2

4

6

8

10

Bus numbers

FIGURE 10.4

Voltage profile of 16 bus RDS.

12

14

16

18

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Branch Current profile (p.u)

0.16 0.14 0.12 0.10 0.08 0.06 0.04 0.02 0.00

1

2

3

4

5

6

7

8

9

10 11 12 13 14 15 16

Branch numbers

FIGURE 10.5 Branch current profile of 16 bus RDS.

it has less load. The buses 11 and 12 are connected to the 9th bus so the voltage profile is further dropped from 0.976 pu to 0.969 at the 12th bus. Finally, at the 3rd feeder, buses 13, 14, 15, and 16 are connected, which has the least loads among these three feeders. So, the voltage profile is improved compared to the 12th bus. The 15th bus and 16th bus voltage profiles are dropped from that of the 14th bus as they have more loads in feeder-3.

10.4.2

BranCH Current profile

The current through the branches of RDS is shown in Figure 10.5. The branches 5, 11, and 16 are open in the RDS. Hence, the current through those branches is zero as shown in Figure 10.5. The current through the 6th branch is the maximum because it is directly connected to the 2nd feeder having a pu voltage of 10. The 8th bus load is the highest among the buses 4, 8, and 13, which are directly connected to the feeders. The losses of the 16th bus RDS are calculated using the bus voltage and branch current profile. The systems’ real power losses are obtained to be 511.43 kW.

10.4.3 reConfiguration of 16 BuS rdS Reconfiguration is the method of changing topology without disrupting its radial nature. The major challenge for reconfiguration of RDS is the selection of tie lines. The number of tie line switches depends on the number of loops formed by the tie line switches. In the case of 16, 33, 69, and 119 bus RDS, 3, 5, 5, and 15 loops, respectively, are formed according to their layout. If the reconfiguration gives minimum losses among all switching combinations, then that reconfiguration is called optimum reconfiguration. The losses of the system for various switching combination of opening of tie and sectionalized switches of 16 bus RDS are given in Table 10.5.

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TABLE 10.5 Real Power Losses of 16 Bus RDS with Different Reconfigurations Opened Switches 5 9 9 5 9 9 2 9 2 8 5

11 14 11 11 7 11 11 14 14 14 11

Losses (kW) 16 3 3 13 13 13 13 15 13 15 3

511.4352 560.5099 547.6767 574.7994 524.9117 559.3434 724.6371 548.5673 758.8787 775.4750 568.6186

The losses are found to be minimum when switches 5, 11, and 16 are opened. Hence, an optimization algorithm is necessary to optimize the switching configuration of tie line and sectionalized switches to reduce the total losses of RDS and hence to improve the voltage profile. In this chapter, PSO is used to optimize the 16 bus RDS.

10.4.4

modeling of loadS at rdS

The loads are classified into three types based on their behavior. They are as follows: Constant power load: The constant power load is the load that draws variable current for maintaining the constant power for its high performance. For example, the motor draws current from the grid to maintain its power as constant. The constant power loads are modeled as (14) for analyzing the distribution system via load flows substituting n = 0. Constant current load: The constant current load is the load that draws constant current from the grid. For example, the battery draws constant current from the mains during its charging. The constant current loads are modeled as (14) for analyzing the distribution system via load flows substituting n = 1. Constant impedance load: The constant impedance load is the load that draws the current from the mains to maintain its impedance as constant. The constant impedance loads are modeled as (14) for analyzing the distribution system via load flows substituting n = 2.

 P + j  Qk  n Ik =  k  (Vk )  Vk

(10.13)

The EV battery behaves as a constant current load until it reaches 80% of its rated power, and then, it acts as a constant power load during the constant current constant

164

FIGURE 10.6

Green Engineering and Technology

Modeling of EVCS at a connected bus.

voltage (CCCV) method of charging (Moghaddam et al. 2018). However, in this chapter, the EVCS is considered as an additional constant power load connected to RDS as is shown in Figure 10.6.

10.5 OPTIMIZATION TECHNIQUES The need for an optimization algorithm is described in Section 10.4 to reduce the losses through optimal reconfiguration and placement of EVCS. There are various techniques such as genetic algorithm (GA), game-theoretical framework, fuzzy logic, and so on available in the literature to optimally place the EVCS. In the study of Sanchari Deb et al. 2017, the optimum position of the EVCS is determined by a GA that does not analyze losses and conductor thermal limits, affecting the reliability of the grid connectivity distribution system. Including device parameter limits, such as voltage, current, and temperature rise in the conducts, can regulate the effect of EVCS. The EVCS optimal placement not only influences system performance but also adds extra cost to the distribution grid through charging devices (Karmaker 2019), land, and extra conductors, and so on. A game theoretical framework (Sanchari Deb, et al 2018) is used for finding the optimal location of EVCS. The power quality issue is also one of the problems faced by the distribution grid with the incorporation of EVCS without proper planning. Charging methodologies, vehicle density at EVCS, and so on are the influencing factors for EVCS to impact the distribution grid. In this chapter, the PSO optimization algorithm is used for placement and reconfiguration of RDS. The initialization plays a key role in formulating and further processing the algorithm. The size of the initialization particle matrix depends on the number of particles and dimensions of the problem. In the case of EVCS placement in RDS, the number of EVCS locations resemble the dimensions of the algorithm, whereas in the case of reconfiguration of RDS, tie line switches resemble the dimensions of the algorithm. The algorithm for PSO to solve the optimization problem is as follows: Step 1: The particles are initialized with a random number by considering the constraints



 x11   x 21 X= −  −   x np1

x12 x 22 − − x np 2

      

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Step 2: Initialize the PSO parameters. Step 3: Determine the fitness for every particle at each iteration. Step 4: Verify the minimum fitness for every generation. Step 5: Particles at every iteration is updated with (10.14). X (Update) = W × X + C1 × rand(1) × ( Pbest − X ) + C2 × rand(1) × ( Gbest − X )

(10.14)

where W = Wmax −



(Wmax − Wmin ) × (iter − 1) (ng − 1)

Step 6: Set the updated positions within its constraints. Step 7: is iter= = ngif yes display optimum values else go to step 3 with iter = iter + 1.

10.6 OPTIMAL PLACEMENT OF EVCS USING PSO The placement of EVCS using PSO for IEEE 16 bus RDS system is analyzed by dividing the problem formulation into four scenarios and at each scenario, the impact of EVCS is studied by choosing three different cases as mentioned below: Scenario 1: Placement of EVCS on an existing RDS (single-step optimization process) Scenario 2: Placement of EVCS before reconfiguration on an existing RDS (two-step optimization process) Scenario 3: Placement of EVCS after reconfiguration on an existing RDS (two-step optimization process) Scenario 4: Placement of EVCS along with reconfiguration on an existing RDS (simultaneous two-step optimization process) The particle matrix (X) chosen for each scenario is illustrated in Table 10.6. The cases at each scenario are as follows: Case 1: Single charging station of 3 MW placed at the optimal location. Case 2: Single charging station of 4.5 MW placed at the optimal location. Case 3: Two charging stations of 3 and 1.5 MW are placed at optimal locations. The formulated algorithms for load flow and reconfiguration of RDS are used as objective function with desired current and voltage constraints, which is mentioned as follows. n branch

fobj = min



∑ TL

lm

lm=1

where lm is the branch number 0.95 < Ibranch < 1.05 0.95 < Vbus < 1.05

n branch

=

∑V

* lm lm

lm =1

I

(10.15)

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TABLE 10.6 Initialization of Particles at PSO for Each Scenario

Scenario 1

 x11   x 21  x X =  31  −  −  x np1 

x12 x 22 x32 − − x np2

        

Scenario 2

 x11   x 21  x X =  31  −  −  x np1 

x12 x 22 x32 − − x np2

        

Scenario 3

 x11   x 21  x31 X=  −  −  x np1 

x12 x 22 x32 − − x np2

Scenario 4

10.6.1

Particle Initialization at the Second Step

Particle Initialization at the First Step

Scenario Type

x13 x 23 x33 − − x np3

 x11   x 21  x X =  31  −  −  x  np1

         x12 x 22 x32 − − x np 2

x13 x 23 x33 − − x np3

 x11   x 21  x X =  31  −  −  x  np1

x12 x 22 x32 − − x np2

 x11   x 21  x X =  31  −  −  x np1 

x12 x 22 x32 − − x np2

x14 x 24 x34 − − x np4

x15 x 25 x35 − − x np5

x13 x 23 x33 − − x np3

        

        

        

optimal reConfiguration of 16 BuS rdS WitHout evCS

Section 5 outlines the reconfiguration of the 16-bus RDS. Using PSO to mitigate losses, optimal reconfiguration is achieved in this section. As explained earlier, choosing the number of switches based on the number of loops in the system is the major challenge in RDS reconfiguration. The major requirements for reconfiguration of RDS are that the switches that are supposed to be opened should not be part of other loops without disturbing the systems’ radial nature. The above requirements can be easily met by using a loop matrix (LM), which has the elements as the branch numbers. Thus, the derived loop matrix for the IEEE 16 bus RDS is as follows:

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 2 LM =  3  7



5 4 11

8 13 14

9 15 0

0 16 0

   

In the LM, each row indicates the formation of the loop with distribution branches. The first loop is formed between feeder-1 and feeder-2. Branches 1 and 6 are not selected since the substation is disconnected while opening these branches. The second loop is formulated with feeder-1 and feeder-3. In this loop, also branch 12 is not selected as it disconnects the substation from the other part of the RDS. The third loop is formed between feeder-2 and feeder-3. Finally, by observing the LM, branch 10 is also not selected because it makes the bus 12 idle. Therefore among 16 branches, 12 branches are effectively used for formulating optimal reconfiguration of RDS. In other words, 12C3 combinations need to be analyzed to optimize the reconfiguration of RDS. Thus, there is a necessity of a heuristic algorithm to optimize the reconfiguration of RDS. The PSO is successfully implemented for the optimal reconfiguration of RDS select branches 7, 9, and 16, reducing the losses from 511.43 to 466.12 kW. The voltage profile was found to be improved, which resembles that at a minimum voltage bus. The minimum voltage is improved from 0.969 to 0.971 pu, which is shown in Figure 10.7. The current through the optimized branches is also reduced. By reconfiguration, the load is shifted from feeder 2 to feeder 1 and feeder 3. Therefore, the current through the branches 1 and 2 increases from the value of the existing RDS (Figure 10.8).

10.6.2 optimal plaCement of evCS WitHout reConfiguration (SCenario 1) The optimization algorithm begins with the initialization of the parameters of PSO like the number of particles, initial velocity, final velocity, minimum inertia, maximum inertia, and a maximum number of generations. The particle matrix dimension

1.000

Without EVCS With Reconfig

Voltage Profile(p.u)

0.995 0.990 0.985 0.980 0.975 0.970 0.965

0

2

4

6

8

10

Bus number

12

14

16

18

FIGURE 10.7 Voltage profile of 16 bus RDS with and without reconfiguration.

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Branch Current profile(p.u)

0.16

Without Reconfig WithReconfig

0.14 0.12 0.10 0.08 0.06 0.04 0.02 0.00

1

2

3

4

5

6

7

8

9

10 11 12 13 14 15 16

Branch numbers

FIGURE 10.8 Comparative analysis of branch currents of 16 bus RDS with and without reconfiguration.

is equal to the number of variables of the objective function, which have lower and higher limits. In this case, the number of tie line switches is the variables of the system. The particles that are initialized earlier are updated at the end of each generation by using inertia. At every generation, a minimum value of the objective function is determined, and finally, the minimum value of the objective function is determined among all generations. The dimensions for the existing problem are the number of EVCS. Here, in this scenario, the placement of EVCS on an existing RDS is planned such as to reduce the losses. The EVCS optimal location is obtained as bus 13 corresponding to cases 1 and 2 using PSO. While in case 3, the additional EVCS is assigned at bus 4. The voltage profile of 16 bus RDS with and without charging station is shown in Figure 10.9. By connecting the 3.0 MW charging station at bus 13, the real power losses increase from 511.4 to 556.14 kW. In comparison to feeders 1 and 2, feeder 3 has a lesser load as shown in Table 10.7. Therefore, the PSO algorithm chooses a bus nearer to feeder-3. The same is true under case 2 where 4.5 MW EVCS is to be optimally located but with more losses. The losses further rise to 586.320 kW. To reduce the losses in the system, case 3 is introduced, which divides large EVCS between the two feeders 1 and 3 locating them at bus numbers 4 and 13. The load among the three buses is balanced after incorporating charging stations (CS) at less sensitive buses (Hajimiragha et al. 2010). The real power loss is 577.65 kW in case 3, which is higher than case 1 and lower than case 2. The bus voltages of RDS are given in Figure 10.9. Figure 10.10 illustrates the branch currents in the above-mentioned three cases. It can be observed that the 1st (under case 3) and 12th (under cases 1, 2, and 3) branch currents are showing a higher value, which enhances the losses of the system since the EVCS are connected to buses closer to these branches.

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1.000

Voltage profile(p.u)

0.995 0.990 0.985 0.980 0.975 0.970 0.965

FIGURE 10.9

0

2

4

6

8

10

Bus number

12

14

16

Voltage profile of 16 bus RDS under various cases of scenario 1.

TABLE 10.7 Comparative Analysis of 16 Bus RDS with EVCS and without Reconfiguration Cases

Open Switches

Real Power Losses (kW)

Minimum Voltage (bus)

Without EVCS Case 1: 3 MW EVCS Case 2: 4.5 MW EVCS Case 3: 3 MW and 1.5 MW EVCS

5, 11, 16 5, 11, 16 5, 11, 16 5, 11, 16

511.43 556.14 586.30 577.64

0.9693(12) 0.9693(12) 0.9693(12) 0.9693(12)

0.18

13 13 13 and 4

Without EVCS With 3.0 MW EVCS With 4.5 MW EVCS With 3.0 MW and 1.5 MW EVCS

0.16

Branch current profile (p.u)

Optimal EVCS Bus Location

0.14 0.12 0.10 0.08 0.06 0.04 0.02 0.00 -0.02

0

2

4

6

8

10

Branch numbers

12

14

16

18

FIGURE 10.10 Comparative analysis of branch currents of 16 bus RDS under various cases of scenario 1.

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10.6.3

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optimal plaCement of evCS Before reConfiguration (SCenario 2)

The EVCS that are optimally placed in the system act as additional loads to the system during charging of EV that ultimately hikes current through the branches. The increase in current through the branches increases the voltage drop across the branch and increases the losses of the system. It draws additional current from the grid, which affects the performance. Hence, after including the EVCS and other additional compensating devices, the system should improve its performance in terms of voltage profile and reduction of losses. Therefore, in this scenario, RDSs are examined further and incorporated reconfiguration to reduce the losses after optimally locating the EVCS. In the previous scenario, EVCS are incorporated optimally without reconfiguration. In this scenario, EVCS are optimally located before reconfiguration of IEEE 16 bus RDS. The voltage profile corresponding to different cases is shown in Figure 10.11. The real power losses of the RDS can be reduced even after locating the EVCS optimally through optimal switching or reconfiguration. Compared to the previous scenario, it is observed that the voltage profile is improved, and real power losses are reduced with reconfiguration. Similarly, the branch currents also are adjusted with reconfiguration as illustrated in Figure 10.12 for various cases considered for this study. As shown in Figure 10.12, the 1st and 2nd branch currents are increased after reconfiguration of the RDS. Branches 5, 11, and 16 are not carrying any current while placing the EVCS. However, during reconfiguration, the branches 7, 9, and 16 do not carry any current as these branches contribute to the optimally selected path. This change in topology reduces the real power losses and improves the bus voltages of RDS. The variation in the losses, minimum voltage of the system, and optimal opened switches for each case are shown in Table 10.8. Without EVCS With 3.0 MW EVCS With 3.0 MW EVCS and Reconfig With 4.5 MW EVCS With 4.5 MW EVCS and Reconfig With 3.0 MW EVCS and 1.5 MW EVCS With 3.0 MW EVCS and 1.5 MW EVCS and Reconfig

1.000

Bus Voltage Profile (p.u)

0.995 0.990 0.985 0.980 0.975 0.970 0.965

FIGURE 10.11

0

2

4

6

8

10

Bus number

12

Bus voltages for various cases at scenario 2.

14

16

18

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Branch current profile (p.u)

0.20

0.15

0.10

0.05

0.00 0

FIGURE 10.12

2

4

6

8

10

Branch number

12

14

16

18

Branch currents for various cases under scenario 2.

TABLE 10.8 Comparative Analysis of Losses at Scenario 2 Cases

Open Switches

Real Power Losses (kW)

Without EVCS Case 1: 3 MW EVCS Case 1: Reconfiguration with EVCS Case 2: 4.5 MW EVCS Case 2: Reconfiguration with EVCS Case 3: 3 MW and 1.5 MW EVCS Case 3: Reconfiguration with EVCS

5, 11, 16 5, 11, 16 7, 9, 16 5, 11, 16 7, 9, 16 5, 11, 16 7, 9, 16

511.43 556.14 517.90 586.30 551.68 577.64 540.81

10.6.4

Minimum Optimal EVCS Voltage (Bus) Bus Location 0.9693(12) 0.9693(12) 0.9716(12) 0.9693(12) 0.9716(12) 0.9693(12) 0.9716(12)

13 13 13 13 13 and 4 13 and 4

optimal plaCement of evCS after reConfiguration (SCenario 3)

The system performance can be improved by reconfiguration, a cost-effective solution, without using any additional compensating devices like distributed generations (DGs). This optimizes the available resources. The performance of the distribution system is thoroughly investigated under this scenario. The bus voltages are reduced with increased losses with the inclusion of EVCS at optimal locations in the previous scenario. Hence, the voltage profile has to be enhanced through reconfiguration in the existing topology of the RDS. Here, PSO finds the optimal location of EVCS and the optimal reconfiguration path to reduce the losses thereby improving the voltage profile of the buses in the balanced radial distribution systems (BRDS). The voltage profile and the branch currents of IEEE

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1.000

Voltage Profile(p.u)

0.995 0.990 0.985 0.980 0.975 0.970 0.965

FIGURE 10.13

0

2

4

6

8

12

14

16

18

Bus voltage profile of IEEE 16 bus RDS for various cases. 0.18

Without EVCS With Reconfig With 3.0 MW EVCS With 4.5 MW EVCS With 3.0 MW and 1.5 MW EVCS

0.16

Branch Current profile (p.u)

10

Bus number

0.14 0.12 0.10 0.08 0.06 0.04 0.02 0.00 -0.02

FIGURE 10.14

0

2

4

6

8

10

Branch number

12

14

16

18

Branch currents for various cases.

16 bus RDS corresponding to the three cases are shown in Figures 10.13 and 10.14, respectively. The voltage profile is improved as shown in Figure 10.13, and the losses are reduced to 466.12 kW with reconfiguration (Table 10.9).

10.6.5

optimal plaCement of evCS along WitH reConfiguration (SCenario 4)

To study the performance of the RDS, the optimal placement of EVCS is furnished along with the reconfiguration. The algorithm for placing EVCS along with optimal reconfiguration of RDS is given in Table 10.10.

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TABLE 10.9 Optimal Placement of EVS before Reconfiguration Cases

Open Switches

Real Power Losses (kW)

Minimum Optimal EVCS Voltage (Bus) Bus Location

Without EVCS Reconfiguration

5, 11, 16 7, 9, 16

511.43 466.12

0.9693(12) 0.9716(12)

-

Case-1: 3.0 MW EVCS Placement Case-2: 4.5 MW EVCS Placement Case-3: 3.0 MW and 1.5 MW placements

7, 9, 16 7, 9, 16 7, 9, 16

515.50 545.56 538.78

0.9716(12) 0.9716(12) 0.9716(12)

4 4 4 and 13

TABLE 10.10 Optimal Placement of EVS Along Reconfiguration Cases Without EVCS Case 1: 3.0 MW EVCS and reconfiguration Case 2: 4.5 MW EVCS and reconfiguration Case 3: 3.0 MW and 1.5 MW EVCS and reconfiguration

Open Switches

Real Power Losses (kW)

Minimum Optimal EVCS Voltage (Bus) Bus Location

5, 11, 16 7, 9, 16

511.43 515.50

0.9693(12) 0.9716(12)

4

7, 9, 16

545.56

0.9716(12)

4

7, 9, 16

538.78

0.9716(12)

and 13

In this scenario, optimal placement of EVCS and reconfiguration are carried out simultaneously. In the previous scenarios 2 and 3, the optimal placement of EVCS and optimal combination of open switches for reconfiguration are operating sequentially, which consumes more computation time for predicting the correct result. Hence, the major focus here is not only on optimal placement of EVCS but also on optimal reconfiguration to reduce the computation time of the system using a proper algorithm. The voltage profile corresponding to each case of increasing capacity of EVCS is shown in Figure 10.15. The change in branch currents at every case is shown in Figure 10.16. The currents through branches 5, 11, and 16 are zero, which is the condition in the existing RDS. Similarly, the currents in branches 7, 9, and 16 are zero as they are an optimal combination of reconfiguration of the system. The current in branches 1 and 2 have increased as they are directly connected to the bus 4, which is the optimal bus location for EVCS. The impact of EVCS on the RDS in terms of real power losses and minimum voltage is shown in Table 10.10. The losses of the system are controlled by optimal placement of EVCS along with reconfiguration. The real power loss for the existing RDS is 511.43 kW, which is raised to 515.50 kW with the simultaneous placement of EVCS and reconfiguration. But the selection of the optimal location is similar to scenario 2 and is different from scenarios 1 and 3. Therefore, the real power losses are lesser compared to scenario 2.

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FIGURE 10.15 Bus voltages for scenario 4.

FIGURE 10.16

Branch currents at every case for scenario 4.

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10.7 CONCLUSION The proposed method is validated with standard three feeder IEEE 16 bus balanced RDS considering four scenarios. Each scenario has been studied with possible three cases optimally locating the EVCS. The objective of each scenario is not only to reduce the losses but also to improve the voltage profile and thermal stability. The optimal placement of EVCS onto the existing BRDS has increased the losses. With the increase in capacity of the charging infrastructure, the losses are increasing. However, by distributing the infrastructure, the losses decrease with optimal placement of EVCS to two different feeders with deterioration in the voltage profile along with higher branch currents. Then, the two-step optimization method is proposed to optimize the location of EVCS before and after the optimized reconfiguration. Here also, distributed EVCS has proven reduced losses at the cost of computational complexity. Finally, the simultaneous two-step process of optimally placing EVCS along with reconfiguration shows minimum losses with less computation time, but higher than an existing system with stable current and voltage limits. Thus, the proposed scheme gives the optimal solution for improving the electric transportation infrastructure without using external compensating devices. Furthermore, the algorithm can also be extended to the dynamic load of the system. As the loads are not static, the EVCS can be scheduled as per the load curve. If the EVCS works during the light load conditions of the load curve, then the load factor of the system will increase, which, in turn, will decrease the generation cost at generating stations. Similarly, when EVCS is operated during heavy load conditions of the load curve, DGs need to be implemented to flatten the load curve with respect to the distribution grid. Furthermore, all these schemes can be extended to practical unbalanced RDS.

REFERENCES A. Ahmadi, A. Tavakoli, P. Jamborsalamati, N. Rezaei, M.R. Miveh, F.H. Gandoman; A. Heidari; A.E. Nezhad, ‘Power quality improvement in smart grids using electric vehicles: a review’, IET Electrical Systems in Transportation, 2019, 9, (2), pp: 53–64 (https://digital-library.theiet.org/content/journals/10.1049/iet-est.2018.5023). A. Alsabbagh, H. Yin and C. Ma, ‘Distributed charging management of multi-class electric vehicles with different charging priorities’, IET Generation, Transmission & Distribution, 2019, 13, (22), pp. 5257–5264 (https://digital-library.theiet.org/content/journals/10.1049/ iet-gtd.2019.0511). M. C. Catalbas, M. Yildirim, A. Gulten and H. Kurum, ‘Estimation of optimal locations for electric vehicle charging stations’, Proceedings of IEEE International Conference on Environment and Electrical Engineering and IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), Milan, July 2017, pp. 1–4. S. Deb; K. Kalita; P. Mahanta, ‘Impact of electric vehicle charging stations on reliability of distribution network’, Proceedings of International Conference on Technological Advancements in Power and Energy (TAP Energy), Kollam, India, Dec. 2017, pp 1–6 (https://ieeexplore.ieee.org/document/8397272). S. Deb, K. Tammi, K. Kalita and P. Mahanta, ‘Impact of electric vehicle charging station load on distribution network’, Energies, MDPI, 178, 2018, pp. 2–25.

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A. Hajimiragha, C.A. Caizares, M.W. Fowler, and A. Elkamel, ‘Optimal transition to plug-in hybrid electric vehicles in Ontario, Canada, considering the electricity-grid limitations’, IEEE Transactions on Industrial Electronics, 2010, 57, (2), pp. 690–701 (https://ieeexplore.ieee.org/document/5129873). M. Hatef, and N., Ghaffarzadeh, ‘A new method for electric vehicle charging in stochastic smart grid operation with RES units and fuel cells’, IET Electrical Systems in Transportation, 2020, 13, pp. 1–13 (https://digital-library.theiet.org/content/journals/ 10.1049/iet-est.2019.0013). T. Huiling, W.U. Jiekang, W.U. Zhijiang, and C. Lingmin, ‘Two-stage optimization method for power loss and voltage profile control in distribution systems with DGs and EVs using stochastic second-order cone programming’, Turkish Journal of Electrical Engineering & Computer Sciences, 2018, 26, pp. 501–517 (https://journals.tubitak.gov.tr/elektrik/ issues/elk-18-26-1/elk-26-1-42-1602-65.pdf). A. K. Karmaker, ‘Analysis of the Impact of Electric Vehicle Charging Station on Power Quality Issues’, Proceedings of International Conference on Electrical, Computer and Communication Engineering (ECCE), Cox’s Bazar Zila, Bangladesh, Feb 2019, pp. 22–27 (https://ieeexplore.ieee.org/document/8679164). D. Meyer, and J. Wang, ‘Integrating ultra-fast charging stations within the power grids of smart cities: a review’, IET Smart Grid, 2018, 1, (1), pp. 3–10 (https://digital-library. theiet.org/content/journals/10.1049/iet-stg.2018.0006). Z. Moghaddam, I. Ahmad, D. Habibi, and Q. V. Phung, ‘Smart charging strategy for electric vehicle charging stations’, IEEE Transactions on Transportation Electrification, 2018, 4, (1), pp. 76–88 (https://ieeexplore.ieee.org/document/8679164). F. Xu, G.Q. Yu, L.F. Gu, and H. Zhang, ‘Tentative analysis of layout of electrical vehicle charging stations’, Proceedings of East China Electric Power, 2009, 37, (10), pp. 1677–1682 (https://scinapse.io/papers/2351191775). L. Yazdi, R. Ahadi and B. Rezaee, ‘Optimal Electric Vehicle Charging Station Placing with Integration of Renewable Energy’, Proceedings of Iran International Industrial Engineering Conference (IIIEC), Yazd, Iran, May 2019, pp. 47–51. K. Yenchamchalit, Y. Kongjeen, K. Bhumkittipich and N. Mithulananthan, ‘Optimal sizing and location of the charging station for plug-in electric vehicles using the particle swarm optimization technique’, Proceedings of International Electrical Engineering Congress (IEECON), Krabi, Thailand, March 2018, pp. 1–4.

11

Parameter Estimation of a Single Diode PV Cell Using an Intelligent Computing Technique Shilpy Goyal, Parag Nijhawan, and Souvik Ganguli Thapar Institute of Engineering & Technology

CONTENTS 11.1 Introduction .................................................................................................. 177 11.2 Problem Formulation .................................................................................... 179 11.3 Proposed Optimization Technique ............................................................... 180 11.3.1 Various Phases of HHO .................................................................... 181 11.3.1.1 Diversification Phase.......................................................... 181 11.3.1.2 Turning from Diversification to Intensification ................. 182 11.3.1.3 Intensification Phase .......................................................... 182 11.4 Results and Discussions ................................................................................ 182 11.5 Conclusions ................................................................................................... 205 References .............................................................................................................. 205

11.1 INTRODUCTION Due to many factors, such as the price of fossil fuel and its possibility of depletion and social and environmental issues, renewable energy systems, also coined as green energy systems, have gained much attention universally. General observation reveals that energy harvesting from solar energy is increased to meet the demand for electricity in developing countries, carbon dioxide release obligations, and price reduction of photovoltaic (PV) modules [1]. Solar systems are thus commonly used in the form of major PV modules in the production of electricity [2]. The simulation of solar PV models usually consists of two stages that are the formulation of the mathematical model and the selection of parameters. Moreover, the real presence of PV modules is generally influenced by the unspecified parameters that can be error-prone and unbalanced when regularly encountering aging, degradation, and unpredictable operating conditions of the system. Precise

177

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recognition of PV module parameters is, therefore, a crucial step in advance to further simulate and configure PV systems [3]. Presented methods are generally categorized into three types, namely, analytical methods, numerical methods, and metaheuristic methods. In the datasheet, the voltage at the maximum power point (Vmp), current at the maximum power point (Imp), short-circuit current (ISC), and open-circuit voltage (VOC) are provided [4]. The correctness of parameter estimation by analytical methods is mainly dependent on the accurate location of these established parameters on the characteristics of solar PV production [3]. In estimating specific electrical parameters of solar PV, the Newton-Raphson (NR) method [5], Lambert W function [6], and Gauss-Seidel (GS) methods have often been considered among the numerous existing numerical methods. Although these statistical methods are more accurate than the analytical methods, these methods also bear long calculation and convergence time. They converge to local maxima rather than global in case of incorrect selection of the first value, particularly in NR and GS methods. Also, in the past year, more attention has been paid to metaheuristic algorithms due to their theoretical and mathematical simplicity, resulting in some complex problems used effectively and flexibly. Another factor is that if an appropriate balance between fundamental modes can be obtained, then they can conduct a very efficient response with a relatively quick and efficient search [7]. Metaheuristic techniques of various models, therefore, used to evaluate parameters of the PV system, such as the artificial bee colony (ABC), were used to define parameters of solar cell models [8, 9]. Furthermore, for this problem, a modified ABC was introduced, and relevant results were achieved. An enhanced opposition-based whale optimization algorithm (WOA) was used for the parameter estimation of the solar module and the tests and comparisons have verified the implementation of the planned method [10]. Moth-flame optimization (MFO) was used for the extraction of solar cell parameters [11]. A new hybrid Bee pollinator Flower Pollination Algorithm (BPFPA) was used for the parameter estimation of both single diode model (SDM) and double diode model of PV cells [12]. Convergence Particle Swarm Optimization (GCPSO) was used to determine the PV parameters of both the single-diode and the double-diode models [13]. The hybrid Jaya-NM algorithm was used for identifying parameters of the single diode PV model [14]. An Ant Lion Optimizer (ALO) with Lambert W function had been used for parameter estimation of a SDM of PV cells [15]. A Multiple Learning Backtracking Search Algorithm (MLBSA) was used to improve the parameter identification of a different single diode, double diode, and PV module of PV cells [16]. An Improved Shuffled Complex Evolution (ISCE) algorithm was used for parameter extraction of a SDM and double diode model of PV cells [17]. An integrated Firefly and Pattern Search (HFAPS) algorithm was developed, in which pattern search served as a local optimization method with the Firefly algorithm as a global optimizer to improve this algorithm [18]. An improved version of the WOA that used opposition-based learning to enhance the exploration of the search space was used [10]. The Salp Swarm Algorithm (SSA) was used for extracting the parameters of the electrical equivalent circuit of a double-diode model of the PV cell [19]. The Performance-guided JAYA (PGJAYA) algorithm was used for

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extracting parameters of a single diode, double diode, and PV module of PV models [20]. The least root means square error was obtained using MFO’s implementation. As these algorithms could produce relatively better results compared to analytical and numerical methods, certain drawbacks, for example, requiring much time and premature convergence, were observed. Nevertheless, further advances are still possible for most of those heuristic-based approaches. HHO is a newly proposed metaheuristic method. HHO’s main inspiration is the cooperative actions, and Harris hawks’ chasing style in nature is called surprise pounce. This work mathematically imitates these intricate patterns and behaviors to create an optimization algorithm [21]. The paper article is presented in the following way: Section 11.2 explains the problem formulation, Section 11.3 explains the proposed optimization technique, Section 11.4 describes the results and discussion, and Section 11.5 gives the conclusion and future scope.

11.2 PROBLEM FORMULATION Figure 11.1 shows the PV cell’s single diode equivalent model. I−V characteristics of a single diode PV cell model is given by Eq. (11.1) [22]:

  q (V + IRS )   V + IRseries I = I PC − I S  exp  −1 −  α KT   Rparallel 

(11.1)

where IPC is the photocurrent, IS is the reverse saturation current, Rparallel and Rseries are the equivalent parallel and series resistances, α is the ideality factor, q is the absolute value of charge on an electron, T is the temperature in Kelvin, and K is the Boltzmann constant. At open circuit, I = 0 and V = VOC; then Eq. (11.1) becomes

qV V   0 = I PC − I S  exp  OC  − 1 − OC   α KT   Rparallel

(11.2)

V qV   I PC = I S  exp  OC  − 1 + OC   α KT   Rparallel

(11.3)

Therefore,

FIGURE 11.1

Representation of a single diode model.

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At the short circuit, V = 0 and I = ISC; then Eq. (11.1) becomes

qI R   I R I SC = I PC − I S  exp  SC series  − 1 − SC series   α KT  Rparallel 

(11.4)

qI R   I R I PC = I SC + I S  exp  SC series  − 1 + SC series  α KT   Rparallel 

(11.5)

Therefore,

At a maximum power point, V = Vmp and I = Imp; then Eq. (11.1) gives

  q (Vmp + I mp Rseries )   Vmp + I mp Rseries I mp = I PC − I S exp   − 1 − Rparallel α KT    

(11.6)

The goal is to estimate accurate parameters of PV cells for all three main conditions (open circuit, short circuit, and maximum power point). Therefore, to minimize the errors, an optimization algorithm is needed at these three points: Equation (11.3) calculates error at the open circuit:

qV V   errOC = I S  exp  OC  − 1 + OC − I PC  α KT   Rparallel 

(11.7)

Equation (11.5) calculates error at the short circuit point:

qI R   I R errSC = I SC + I S  exp  SC series  − 1 + SC series − I PV   α KT   Rparallel

(11.8)

Equation (11.6) calculates error at the maximum power point:

  q (Vmp + I mp Rseries )   Vmp + I mp Rseries errmp = I PC − I S  exp  − I mp  − 1 − Rparallel α KT    

(11.9)

The optimization objective is known to be the sum of the squared errors, and the algorithm should obtain the lowest error or preferably zero error as well as the objective function given in Eq. (11.10):

11.3

2 2 2 err = errOC + errSC + errmp

(11.10)

PROPOSED OPTIMIZATION TECHNIQUE

The Harris hawks optimization (HHO) algorithm was encouraged by the collective discipline, together with the Harris hawks’ chasing manner [21]. For various scientific applications, this algorithm has been used successfully. Hawks, to surprise their prey,

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181

swooped on them cooperatively from different paths. Furthermore, Harris hawks could select the type of chase based on distinct prey flight patterns. HHO has three base stages, including excellent pounce, prey tracking, and other attacking tactics of different kinds [23]. Various phases of HHO are shown in Figure 11.2. The first stage is called “Diversification” in a glance and is modeled on waiting, searching, and discovering the desired hunt mathematically. This algorithm’s second stage is turning exploration to exploitation based on a rabbit’s external energy. Finally, in the third phase called “Intensification” considering the prey’s residual heat, hawks usually take on a soft and sometimes difficult surrounding to hunt the rabbit from new directions.

11.3.1

variouS pHaSeS of HHo

11.3.1.1 Diversification Phase In this phase, the desired hunt is calculated to wait, search, and discover mathematically. The iter + 1 (Harris hawks’ position) is mathematically described as follows [24]: if q ≥ 0.5 X rand (iter) −   r1 X rand (iter) −   2r2 X (iter)  X(iter) + 1 =  (11.11) ( X rabbit (iter) −   X m (iter) ) −   r3 ( LB +   r4 (UB − LB) ) if q < 0.5 where Xrabbit refers to the rabbit position, iter refers to the current iteration, Xrand refers to the randomly selected hawk in the given population, ri,i = 1, 2, 3, 4, q are

FIGURE 11.2 Different phases of Harris hawks optimization (HHO) [21].

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random numbers in the range [0, 1]. Xm represents the average hawk position and is determined as follows:

X m (iter) =

1 N

N

∑ X (iter) (11.12) i

i =1

where Xi shows the hawk’s position and N is the hawk’s length. 11.3.1.2 Turning from Diversification to Intensification Taking T as the maximum repetition size and E0 ∈ (−1,1) as the initial energy in each step, HHO measures the rabbit fleeing energy (E) by Eq. (11.13). Diversification and intensification can be modified for this quality.

iter   E = 2 E0  1 −  (11.13)  T 

In this sense, if |E| ≥ 1, the diversification phase begins; otherwise, the neighborhood of the solutions will be exploited [24]. 11.3.1.3 Intensification Phase The hawks may find a soft or hard siege to capture it from different directions, depending on the prey’s residual strength. A so-called parameter “r” is used to calculate the escaping chance of the prey. According to that, r < 0.5 represents a successful escape. Moreover, when |E| ≥ 0.5, HHO takes soft surroundings and hard surroundings are applied when |E| < 0.5 is used. It is worth noting that even if the prey can escape (i.e., |E| ≥ 0.5), it also depends on r for its success. The attack technique is conditioned by the prey and hawks’ escape and chase strategy, respectively [24].

11.4

RESULTS AND DISCUSSIONS

The HHO algorithm is used to solve the five parameters of SDM and then compare it with other well-known algorithms such as the SSA, Grey Wolf Optimization (GWO), Sine Cosine Algorithm (SCA), and Dragonfly Algorithm (DA). Different datasets were used and are reported in Table 11.1 with the manufacturer name and model numbers, where Vm is the maximum power voltage, Im is the maximum power current, VOC is the open-circuit voltage, ISC is the short circuit current, Ns is the number of cells, and T is the temperature. The number of search agents used in this work is 50, and the number of iterations is 1000. Different algorithms are used to solve five parameters (IPV, α1, RS, Rp, I01) of SDM. The boundaries of parameters are listed in Table 11.2 where IPV is the PV current, α1 is the ideality factor of the first diode, RS is the series resistance, Rp is the parallel resistance, and I01 is the reverse saturation current of the first diode. Table 11.3 gives the estimated parameters and error of SDM with different algorithms for the model named KC200GT. The parameters are calculated with 30 runs so that we perform the statistical analysis of the error function.

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TABLE 11.1 Datasheet of Three Marketable PV Modules at STC [4,25] Company

Kyocera

Canadian Solar

Model Cell type Vm [V] Im [A] VOC [V] ISC [A] Ns [cells] T [°C]

KC200GT Multi-crystal 26.3 7.61 32.9 8.21 54 25

CS6K-280M Mono-crystalline 31.5 8.89 38.5 9.43 60 25

Schutten Solar

Solar World

STM6 40-36 Pro. SW255 Mono-crystalline Poly-crystalline 16.98 30.90 1.50 8.32 21.02 38.00 1.663 8.88 36 60 51 25

TABLE 11.2 Ranges of the Decision Variables Parameters

Lower Bound Upper Bound

IPV(A) (photovoltaic current) α1 (ideality factor) RS(Ω) (series resistance) Rp(Ω) (parallel resistance) IS(µA) (reverse saturation current)

0.001 0.5 0.001 0.01 0

15 7 0.5 500 1

TABLE 11.3 Optimal Parameters of SDM Using Different Algorithms for KC200GT Run 1

2

3

Algorithm

IPV

α1

RS

Rp

I01

Error

HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA

8.2171 9.8292 9.2196 9.3365 8.4887 8.2815 9.6927 9.2194 9.4067 8.4052 8.1918 9.4346 9.2129 9.2797 8.4341

1.4032 4.5570 5.0000 5.0000 1.4919 1.3089 4.7621 5.0000 5.0000 1.3219 1.4822 5.0000 5.0000 5.0000 1.4547

0.1759 0.1869 0.0081 0.0187 0.2571 0.2618 0.2166 0.0071 0.0018 0.3221 0.1045 0.2007 0.0026 0.0010 0.3044

404.55 4.5703 5.0096 5.0321 82.173 290.00 4.7719 5.0079 4.9945 191.18 345.81 5.0083 5.0084 4.9860 396.72

3.69e-07 2.15e-08 5.95e-08 0.00000 1.00e-06 1.09e-07 1.84e-07 8.25e-08 0.00000 1.31e-07 9.05e-07 5.36e-07 4.93e-07 1.51e-08 6.87e-07

4.59e-05 23.3709 21.3711 21.5546 0.16687 0.00916 23.3093 21.3630 21.4392 0.07718 0.00094 22.9400 21.3275 21.3731 0.10780 (Continued )

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TABLE 11.3 (Continued ) Optimal Parameters of SDM Using Different Algorithms for KC200GT Run 4

5

6

7

8

9

10

11

Algorithm

IPV

α1

RS

Rp

I01

Error

HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA

8.4378 10.108 9.2115 9.2871 8.9533 8.2125 9.7614 9.2139 9.3441 8.9140 8.1730 9.3095 9.2228 9.3018 8.4080 8.3164 9.4307 9.2181 9.3202 8.3511 8.2144 10.684 9.2268 9.1830 8.4082 8.1975 10.338 9.2151 9.1498 8.1950 8.1353 9.2131 9.2158 9.2053 9.0462 8.3452 9.7316 9.2220 9.3069 8.2720

1.3481 4.1296 5.0000 5.0000 1.4314 1.4887 4.9362 5.0000 5.0000 1.4492 1.3866 4.9954 5.0000 5.0000 1.4161 1.4802 5.0000 5.0000 5.0000 1.4502 1.4281 3.9520 5.0000 5.0000 1.4249 1.4911 4.3783 5.0000 5.0000 1.4889 1.3756 5.0000 5.0000 5.0000 1.4658 1.4573 4.6761 5.0000 5.0000 1.3627

0.2489 0.0109 0.0011 0.0027 0.5000 0.0696 0.4407 0.0010 0.0054 0.5000 0.1381 0.0863 0.0085 0.0016 0.3051 0.2125 0.2011 0.0104 0.0010 0.2043 0.2037 0.2147 0.0112 0.0025 0.3139 0.1136 0.4446 0.0032 0.0062 0.1078 0.0586 0.0010 0.0018 0.0030 0.5000 0.2535 0.2100 0.0078 0.0048 0.2182

68.736 4.1511 5.0088 5.0687 250.94 150.13 4.9448 5.0083 5.0178 500.00 406.22 5.0039 5.0081 5.0225 322.91 275.37 5.0083 5.0082 5.0041 133.47 408.14 3.9737 5.0089 5.0314 439.51 418.05 4.3937 5.0083 4.9807 370.86 207.62 5.0086 5.0081 5.0151 138.50 163.08 4.6879 5.0084 4.9907 222.03

1.80e-07 4.33e-07 5.05e-08 0.00000 5.46e-07 9.58e-07 5.30e-07 3.05e-07 0.00000 6.71e-07 3.00e-07 3.90e-08 8.21e-07 8.11e-07 4.38e-07 8.95e-07 4.30e-07 1.68e-10 0.00000 6.36e-07 4.95e-07 8.03e-07 2.56e-07 7.36e-07 4.87e-07 9.98e-07 1.96e-07 2.83e-08 0.00000 9.74e-07 2.58e-07 7.36e-07 6.61e-07 1.21e-07 8.02e-07 6.90e-07 8.24e-07 7.40e-09 0.00000 2.23e-07

0.08940 23.1954 21.3160 21.7352 1.35290 3.67e-06 25.1216 21.3150 21.4109 1.34930 0.00351 21.9964 21.3738 21.3682 0.08888 0.02395 22.9431 21.3892 21.3486 0.03258 0.00976 25.5834 21.3954 21.3798 0.10177 0.00046 26.0418 21.3330 21.4603 0.00064 0.01226 21.3149 21.3219 21.3354 1.60650 0.07451 23.3644 21.3688 21.3944 0.00638 (Continued )

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TABLE 11.3 (Continued ) Optimal Parameters of SDM Using Different Algorithms for KC200GT Run 12

13

14

15

16

17

18

19

Algorithm

IPV

α1

RS

Rp

I01

Error

HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA

8.4061 10.060 9.2172 9.3872 8.2724 8.3829 9.6485 9.2260 9.1270 8.3048 8.1327 9.9299 9.2226 9.3916 8.4103 8.2034 9.3307 9.2158 9.3282 8.4262 8.3244 9.8638 9.2151 9.2798 8.1681 8.2205 9.8754 9.2238 8.9550 8.2416 8.1770 9.5578 9.2182 9.2957 8.4917 8.3417 10.163 9.2194 9.2548 8.3559

1.4662 4.6030 5.0000 5.0000 1.4341 1.4687 4.8190 5.0000 5.0000 1.4404 1.2591 4.6585 5.0000 5.0000 1.4227 1.3889 5.0000 5.0000 5.0000 1.3545 1.4669 4.7477 5.0000 5.0000 1.2720 1.3801 4.7245 5.0000 5.0000 1.4912 1.4771 4.8996 5.0000 5.0000 1.4715 1.4179 4.5062 5.0000 4.9018 1.4926

0.2737 0.4136 0.0037 0.0033 0.0016 0.2539 0.2189 0.0093 0.0010 0.1972 0.1313 0.3639 0.0076 0.0034 0.2930 0.1717 0.1099 0.0035 0.0010 0.3042 0.2110 0.3746 0.0019 0.0089 0.1936 0.1935 0.3769 0.0110 0.0048 0.1666 0.0977 0.2312 0.0021 0.0017 0.2615 0.2772 0.4152 0.0051 0.0085 0.1286

207.21 4.6149 5.0097 4.9676 59.706 206.77 4.828 5.0085 5.0291 396.85 241.84 4.6702 5.0082 5.0027 237.20 455.03 5.0085 5.0093 4.9943 126.00 208.13 4.7581 5.0090 5.0091 478.44 452.87 4.7355 5.0080 5.0514 284.13 418.28 4.9086 5.0084 4.9967 92.040 456.66 4.5194 5.0089 4.9258 92.390

7.71e-07 1.71e-07 8.43e-07 0.00000 5.04e-07 7.91e-07 3.77e-08 3.89e-07 0.00000 5.78e-07 5.24e-08 7.80e-08 6.27e-07 0.00000 4.72e-07 3.10e-07 6.20e-08 4.56e-07 0.00000 2.01e-07 7.71e-07 6.46e-07 2.61e-07 0.00000 6.42e-08 2.78e-07 5.57e-07 2.10e-08 0.00000 1.00e-06 8.57e-07 7.79e-07 1.07e-07 0.00000 8.06e-07 4.46e-07 3.93e-08 1.40e-07 0.00000 1.00e-06

0.08894 25.2904 21.3365 21.5601 0.00740 0.06414 23.2626 21.3803 21.3728 0.00851 0.01413 24.7416 21.3668 21.4247 0.08528 0.00023 22.1848 21.3347 21.3672 0.09804 0.02579 24.7148 21.3225 21.3872 0.00531 0.00014 24.7642 21.3937 21.6887 0.00744 0.00266 23.2800 21.3243 21.3504 0.14166 0.04581 25.4834 21.3476 21.4789 0.02427 (Continued )

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TABLE 11.3 (Continued ) Optimal Parameters of SDM Using Different Algorithms for KC200GT Run 20

21

22

23

24

25

26

27

Algorithm

IPV

α1

RS

Rp

I01

Error

HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA

8.4861 9.8583 9.2140 9.2091 8.3382 8.1897 10.086 9.2160 9.3834 8.4433 8.2664 9.7644 9.2161 9.4574 8.4477 8.2751 9.5416 9.2173 9.0715 8.5222 8.2440 9.7224 9.2152 9.3240 8.3160 8.3655 9.7216 9.2142 8.9913 8.4439 8.4441 9.5734 9.2220 9.2295 8.2824 8.3171 9.7512 9.2206 9.2973 8.3360

1.4538 4.8354 5.0000 5.0000 1.3861 1.3579 4.3722 5.0000 4.8989 1.4349 1.4909 4.9569 5.0000 5.0000 1.4904 1.3823 4.7046 5.0000 5.0000 1.4138 1.4431 5.0000 5.0000 5.0000 1.4899 1.3685 4.9012 5.0000 5.0000 1.0923 1.4391 4.7732 5.0000 5.0000 1.3194 1.4830 4.9633 5.0000 4.9883 1.4026

0.2259 0.4473 0.0045 0.0033 0.2331 0.1610 0.2096 0.0011 0.0027 0.3082 0.1687 0.4618 0.0012 0.0726 0.2659 0.2377 0.0305 0.0017 0.0014 0.3947 0.1794 0.4824 0.0041 0.0010 0.2389 0.3243 0.3829 0.0010 0.0067 0.4029 0.1126 0.1369 0.0075 0.0012 0.2314 0.2261 0.4707 0.0043 0.0018 0.2481

130.52 4.845 5.0089 5.0208 500.00 315.84 4.3873 5.0086 4.9201 333.17 294.77 4.9653 5.0085 5.0075 142.56 454.37 4.7153 5.0087 5.0291 500.00 369.17 5.0088 5.0089 5.0349 500.00 499.13 4.9102 5.0088 5.0141 85.788 44.089 4.7835 5.0081 5.0162 167.88 430.19 4.9721 5.0082 4.9683 219.26

6.74e-07 9.16e-07 6.79e-07 0.00000 3.05e-07 2.08e-07 2.82e-07 1.88e-07 0.00000 5.49e-07 9.99e-07 3.67e-07 2.28e-09 3.28e-08 1.00e-06 2.88e-07 2.96e-07 8.25e-08 0.00000 4.32e-07 5.90e-07 3.63e-07 3.77e-08 0.00000 1.00e-06 2.44e-07 4.06e-07 7.19e-07 0.00000 2.96e-09 5.32e-07 1.20e-07 7.31e-07 0.00000 1.25e-07 9.28e-07 9.44e-07 7.32e-07 0.00000 3.68e-07

0.07367 25.2803 21.343 21.3484 0.01570 0.00131 23.9775 21.3165 21.4539 0.10616 0.00630 25.3001 21.3167 21.9663 0.11557 0.00863 21.8689 21.3206 21.4095 0.33451 0.00227 25.4573 21.3400 21.4359 0.03757 0.08086 24.6197 21.3149 21.5144 0.12163 0.09151 22.6242 21.3658 21.3240 0.00848 0.02647 25.3766 21.3412 21.4211 0.03006 (Continued )

187

Parameter Estimation of PV Cells

TABLE 11.3 (Continued ) Optimal Parameters of SDM Using Different Algorithms for KC200GT Run 28

29

30

Algorithm

IPV

α1

RS

Rp

I01

Error

HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA

8.2296 9.4408 9.2190 9.1117 8.3197 8.3263 10.011 9.2159 9.0436 8.2342 8.2044 9.2138 9.2182 8.9968 8.6188

1.4766 5.0000 5.0000 5.0000 1.4679 1.2787 4.6978 5.0000 5.0000 1.3816 1.3448 5.0000 5.0000 5.0000 1.4897

0.1438 0.2094 0.0074 0.0144 0.1263 0.3183 0.4567 0.0019 0.0010 0.2239 0.0158 0.0010 0.0022 0.0095 0.3503

316.89 5.0083 5.0079 5.0088 95.223 444.54 4.7086 5.0082 5.0160 500.00 76.210 5.0086 5.0084 5.0104 107.49

8.54e-07 9.65e-07 3.15e-07 0.00000 7.62e-07 7.21e-08 1.65e-07 2.21e-07 0.00000 2.84e-07 1.69e-07 5.91e-07 2.13e-07 0.00000 1.00e-06

0.00061 23.0134 21.3655 21.4608 0.01556 0.02918 25.5435 21.3226 21.4022 0.00475 0.00010 21.3149 21.3247 21.5338 0.38944

It is very much clear from Table 11.3 that HHO gives the least error out of the other algorithms, whereas DA also provides the error with near to HHO, but the different three algorithms SSA, GWO, and SCA give the very high error value. The convergence graph is shown in Figure 11.3. It is very much clear from the convergence graph that HHO outperforms all the other algorithms; also, the HHO converges to the least error value. DA also exhibits good convergence behavior. SSA, GWO, and SCA curves are somehow managing to perform well. Table 11.4 gives the estimated parameters and error of SDM with

FIGURE 11.3 Convergence curve for SDM (Kyocera KC200GT).

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Green Engineering and Technology

TABLE 11.4 Optimal Parameters of SDM Using Different Algorithms for CS6K-280M Run 1

2

3

4

5

6

7

8

Algorithm

IPV

α1

RS

HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA

9.4715 11.383 10.763 9.8939 9.5900 9.5273 10.913 10.770 9.6139 9.7010 9.5383 10.744 10.207 10.320 10.088 9.6287 11.439 10.199 10.150 9.4995 9.5731 11.022 10.203 10.915 9.7904 9.4530 11.160 10.194 9.8715 9.3887 9.5365 11.016 10.187 10.220 9.5714 9.5722 11.281 10.204 10.348 9.8393

1.5461 4.4894 5.0000 6.1470 1.5142 1.3821 5.0000 5.0000 6.7527 1.4827 1.3952 5.0000 5.6945 5.7007 1.4931 1.5510 4.9215 5.7024 5.8517 1.3893 1.4851 5.0000 5.6995 5.2059 1.5536 1.5532 5.0000 5.7052 6.0868 1.3790 1.4866 5.0000 5.713 5.7490 1.5566 1.4685 5.0000 5.7093 5.3126 1.3839

0.0254 0.0208 0.0012 0.0058 0.1214 0.1453 0.1234 0.0045 0.0010 0.2730 0.1455 0.0010 0.0069 0.0016 0.3892 0.0742 0.4363 0.0010 0.0017 0.0871 0.0593 0.2140 0.0041 0.0021 0.2217 0.0012 0.3076 0.0018 0.0043 0.0017 0.1170 0.1932 0.0010 0.0028 0.0488 0.1512 0.4398 0.0138 0.0033 0.2782

Rp

I01

Error

400.84 8.98e-07 0.0035 4.5105 6.62e-07 32.317 5.0128 8.26e-07 30.754 6.1193 0.00000 30.539 264.65 6.42e-07 0.0529 281.33 1.32e-07 0.0236 5.0130 5.78e-08 32.089 5.0124 3.77e-08 30.790 6.7593 0.00000 30.889 500.00 4.58e-07 0.3759 328.75 1.57e-07 0.0238 5.0135 1.56e-07 30.753 5.7000 0.00000 30.316 5.7254 0.00000 30.352 360.02 5.27e-07 1.4582 481.34 9.63e-07 0.0390 4.9342 1.86e-07 35.964 5.7075 1.34e-08 30.254 5.8297 0.00000 30.354 188.50 1.43e-07 0.0090 112.20 4.54e-07 0.0408 5.0131 5.51e-07 33.121 5.7060 5.04e-07 30.286 5.2094 0.00000 30.821 414.68 1e-06 0.2591 379.40 9.63e-07 0.0011 5.0125 8.41e-07 34.228 5.7107 6.70e-07 30.261 6.0789 3.87e-12 30.423 284.59 1.25e-07 0.0034 253.88 4.69e-07 0.0417 5.0124 6.09e-07 32.882 5.7196 4.36e-07 30.253 5.7819 0.00000 30.367 185.88 1e-06 0.0285 478.98 3.86e-07 0.0467 5.0140 9.18e-07 35.854 5.7150 6.45e-07 30.389 5.2889 0.00000 30.613 114.41 1.36e-07 0.3812 (Continued )

189

Parameter Estimation of PV Cells

TABLE 11.4 (Continued ) Optimal Parameters of SDM Using Different Algorithms for CS6K-280M Run 9

10

11

12

13

14

15

16

Algorithm

IPV

α1

RS

Rp

HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA

9.4891 11.823 10.201 10.481 9.4451 9.5346 10.913 10.195 10.137 9.8902 9.5484 11.944 10.212 9.5896 9.7013 9.5565 11.354 10.196 10.428 9.6272 9.5077 10.994 10.192 10.307 9.6247 9.5807 10.959 10.202 9.9382 9.5053 9.4693 10.989 10.201 10.158 9.5803 9.4783 10.887 10.199 9.6578 9.5853

1.5430 4.6158 5.6924 5.3740 1.5039 1.5050 5.0000 5.7017 5.8337 1.4755 1.5557 4.5162 5.6928 6.2737 1.4683 1.2700 4.5833 5.7051 5.6306 1.4346 1.4446 5.0000 5.7062 5.6771 1.5293 1.4435 5.0000 5.6976 6.0050 1.4772 1.5550 4.9998 5.7053 5.7483 1.4881 1.4391 5.0000 5.6965 6.6495 1.4467

0.0097 0.4636 0.0014 0.0018 0.0092 0.1045 0.1257 0.0018 0.0139 0.2957 0.0925 0.4722 0.0108 0.0678 0.2013 0.1875 0.0511 0.0014 0.0010 0.1107 0.0710 0.2009 0.0042 0.0021 0.1424 0.0791 0.1560 0.0042 0.0017 0.1000 0.0208 0.1790 0.0087 0.0012 0.1292 0.0697 0.0958 0.0018 0.0069 0.0529

207.25 4.6333 5.6980 5.3513 278.29 476.65 5.0130 5.7073 5.8179 415.01 429.88 4.5363 5.6979 6.2840 180.99 110.31 4.6009 5.7110 5.6902 500.00 194.83 5.0134 5.7115 5.7016 289.90 107.59 5.0128 5.7031 5.9788 500.00 421.09 5.0125 5.7103 5.7695 284.03 287.25 5.0128 5.7016 6.6179 89.64

I01

Error

8.62e-07 0.0071 9.01e-07 37.074 7.53e-07 30.257 0.00000 30.454 5.66e-07 0.0010 5.81e-07 0.0232 6.23e-07 32.115 3.71e-07 30.262 0.00000 30.447 4.30e-07 0.4651 1.00e-06 0.0318 2.46e-07 37.510 4.14e-07 30.357 0.00000 31.437 3.84e-07 0.1701 2.62e-08 0.0684 2.51e-07 32.325 5.75e-07 30.257 5.53e-07 30.688 2.61e-07 0.0407 2.86e-07 0.0118 4.92e-07 32.970 1.90e-07 30.287 0.00000 30.335 7.60e-07 0.0804 2.79e-07 0.0438 9.84e-07 32.456 6.19e-07 30.287 2.29e-08 30.433 4.24e-07 0.0139 9.85e-07 0.0032 3.32e-07 32.719 1.80e-07 30.335 0.00000 30.282 4.81e-07 0.0469 2.69e-07 0.0045 8.66e-07 31.782 7.00e-07 30.262 0.00000 30.951 2.88e-07 0.0450 (Continued )

190

Green Engineering and Technology

TABLE 11.4 (Continued ) Optimal Parameters of SDM Using Different Algorithms for CS6K-280M Run 17

18

19

20

21

22

23

24

Algorithm

IPV

α1

RS

Rp

HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA

9.6168 10.768 10.200 10.105 9.9281 9.4112 10.763 10.209 10.139 9.4548 9.6413 10.917 10.206 10.079 9.7131 9.5651 11.109 10.188 10.346 9.5461 9.5709 11.099 10.197 10.566 9.5686 9.5084 10.771 10.199 10.190 9.5475 9.6057 10.781 10.204 9.9923 10.051 9.5953 10.765 10.193 9.5719 9.4771

1.5387 5.0000 5.6992 5.6862 1.5523 1.3743 5.0000 5.7031 5.7360 1.4853 1.5360 5.0000 5.6905 5.9179 1.5558 1.5290 5.0000 5.7149 5.6545 1.5520 1.5579 4.7590 5.7032 5.8864 1.5349 1.2873 5.0000 5.6929 5.5192 1.5555 1.4001 5.0000 5.6947 5.7696 1.5302 1.5429 5.0000 5.7111 6.4141 1.5573

0.1173 0.0010 0.0010 0.0032 0.2590 0.0442 0.0010 0.0156 0.0014 0.0974 0.1612 0.1166 0.0057 0.0023 0.2495 0.0956 0.2671 0.0035 0.0183 0.0834 0.0297 0.0152 0.0032 0.0017 0.1178 0.1454 0.0368 0.0012 0.0107 0.0470 0.1912 0.0010 0.0051 0.0023 0.3505 0.1142 0.0010 0.0074 0.0010 0.0010

195.33 5.0129 5.7050 5.6698 382.99 355.26 5.0131 5.7089 5.7151 500.00 400.55 5.0127 5.6967 5.9139 202.40 195.70 5.0124 5.7209 5.6461 500 117.94 4.7738 5.7090 5.9014 500.00 189.91 5.0139 5.6986 5.5222 456.25 203.57 5.0125 5.7005 5.7188 500.00 183.08 5.0130 5.7176 6.3939 218.71

I01

Error

8.33e-07 0.0739 8.48e-07 30.752 7.87e-07 30.252 3.64e-07 30.369 1.00e-06 0.4227 1.18e-07 0.0008 9.01e-08 30.752 1.24e-07 30.408 2.90e-07 30.336 4.62e-07 0.0209 8.20e-07 0.0998 4.61e-07 32.014 1.33e-07 30.304 0.00000 30.311 1.00e-06 0.4746 7.47e-07 0.0488 1.28e-07 33.745 2.17e-08 30.280 0.00000 30.483 9.64e-07 0.0230 9.99e-07 0.0408 8.79e-07 31.419 6.40e-07 30.276 0.00000 31.061 8.06e-07 0.0417 3.45e-08 0.0111 8.34e-07 31.140 4.20e-09 30.256 0.00000 30.456 1.00e-06 0.0140 1.66e-07 0.0922 4.48e-09 30.753 4.03e-08 30.297 0.00000 30.703 7.97e-07 1.0410 8.67e-07 0.0776 2.12e-07 30.752 1.49e-07 30.321 9.83e-07 30.725 1.00e-06 0.0060 (Continued )

191

Parameter Estimation of PV Cells

TABLE 11.4 (Continued ) Optimal Parameters of SDM Using Different Algorithms for CS6K-280M Run 25

26

27

28

29

30

Algorithm

IPV

α1

RS

Rp

I01

Error

HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA

9.4712 11.110 10.198 10.045 9.5366 9.4783 11.152 10.202 9.8958 9.9049 9.5321 10.960 10.193 10.211 10.191 9.5912 11.066 10.196 9.8345 9.4107 9.5477 10.872 10.201 9.9263 9.8660 9.6238 11.145 10.204 10.053 10.053

1.5072 5.0000 5.7021 5.8901 1.4867 1.4071 4.9259 5.6925 5.9688 1.5197 1.5534 5.0000 5.7033 5.6364 1.5509 1.3906 5.0000 5.7048 5.8001 1.5112 1.5442 5.0000 5.7011 5.9358 1.5048 1.3725 5.0000 5.7005 5.6321 1.5559

0.0304 0.3167 0.0010 0.0057 0.1139 0.0021 0.2226 0.0010 0.0342 0.2642 0.0632 0.1762 0.0013 0.0017 0.3629 0.0153 0.2491 0.0033 0.0040 0.0010 0.0889 0.0898 0.0063 0.0590 0.2605 0.2235 0.2916 0.0050 0.0100 0.2221

269.92 5.0142 5.7077 5.8788 453.02 116.17 4.9387 5.6989 5.926 402.87 288.08 5.0135 5.7091 5.6111 331.45 63.858 5.0131 5.7103 5.7861 500.00 360.61 5.0130 5.7067 5.9525 158.59 474.91 5.0123 5.7063 5.5966 65.107

5.88e-07 4.52e-07 5.54e-08 0.00000 4.74e-07 1.77e-07 5.12e-07 4.58e-11 1.17e-08 7.06e-07 9.70e-07 9.05e-07 4.78e-09 0.00000 1.00e-06 1.41e-07 6.62e-07 4.52e-07 0.00000 6.15e-07 8.85e-07 4.72e-08 6.70e-07 0.00000 5.88e-07 1.18e-07 6.60e-07 3.97e-09 0.00000 1.00e-06

0.0041 34.340 30.253 30.358 0.0246 0.0046 33.357 30.253 30.978 0.3891 0.0214 32.686 30.256 30.373 1.3091 0.0506 33.531 30.278 30.616 0.0004 0.0291 31.716 30.309 31.003 0.4360 0.0876 34.037 30.296 30.691 0.6103

different algorithms for CS6K-280M. The parameters are estimated with 30 runs so that we do the statistical analysis of the error function. It is very much clear from Table 11.4 that HHO gives the least error out of the other algorithms, whereas DA also tries to give the error near to HHO but the other three algorithms SSA, GWO, and SCA give inferior results. The convergence curve of the PV model is provided in Figure 11.4. It is clear from Figure 11.4 that HHO converges faster in comparison to the other algorithms. However, DA also tries to reach the least fitness function value.

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Green Engineering and Technology

FIGURE 11.4 Convergence curve for SDM (Canadian Solar CS6K-280M).

GWO, SCA, and SSA do not perform well for this model. Table 11.5 gives the estimated parameters and error of SDM with different algorithms for STM6 40-36. The parameters are estimated with 30 runs so that we perform the statistical analysis of the error function. It is very much evident from Table 11.5 that HHO gives the least error out of the other algorithms, whereas DA also tries to give the error near to HHO but the other three algorithms SSA, GWO, and SCA give very poor results. The fitness value versus the number of iterations is plotted in Figure 11.5.

TABLE 11.5 Optimal Parameters of SDM Using Different Algorithms for STM6 40-36 Run 1

2

3

Algorithm

IPV

α1

RS

Rp

I01

HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA

1.6892 3.9565 2.8704 2.9655 1.6562 1.7297 3.8032 2.9324 2.6589 1.7125 1.7514 3.5797 2.8727 2.8257 1.7040

1.4288 4.5202 7.0000 7.0000 1.2496 1.4190 4.6538 7.0000 7.0000 1.4664 1.3629 5.0000 7.0000 7.0000 1.4662

0.1993 0.4380 0.0603 0.0012 0.4669 0.3264 0.0774 0.4996 0.0099 0.3211 0.2133 0.0088 0.0740 0.0069 0.2350

174.27 4.5430 7.0112 6.9952 500.00 101.08 4.6756 7.0106 7.0227 142.49 73.756 5.0189 7.0114 6.9890 152.15

6.89e-07 1.26e-07 1.51e-07 0.00000 8.71e-08 6.03e-07 4.65e-08 5.11e-07 0.00000 1.00e-06 3.17e-07 2.28e-07 7.13e-08 1.19e-07 1.00e-06

Error 0.0012 7.0660 2.5743 2.6371 0.0001 0.0085 6.8034 2.5326 2.7137 0.0045 0.0133 5.7581 2.5728 2.6351 0.0029 (Continued )

193

Parameter Estimation of PV Cells

TABLE 11.5 (Continued ) Optimal Parameters of SDM Using Different Algorithms for STM6 40-36 Run 4

5

6

7

8

9

10

11

Algorithm

IPV

α1

RS

Rp

I01

HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA

1.8328 3.6805 2.9354 3.0665 1.6731 1.8058 3.1917 2.8708 2.8460 1.6844 1.6832 2.9403 2.9368 2.7798 1.6499 1.6693 3.7687 2.8649 3.0672 1.6984 1.7284 3.1546 2.8708 2.9888 1.6895 1.7013 3.1847 2.8632 2.9122 1.7685 1.6764 3.0344 2.9007 2.8388 1.6404 1.6699 3.0318 2.8765 2.7408 1.6772

1.4064 5.0000 7.0000 7.0000 1.4462 1.2959 6.1215 7.0000 7.0000 1.4205 1.4387 6.8909 7.0000 7.0000 1.4634 1.4375 4.9146 7.0000 7.0000 1.3691 1.4029 6.0333 7.0000 7.0000 1.4177 1.4420 5.9938 7.0000 7.0000 7.0000 1.3499 6.5085 7.0000 7.0000 1.1890 1.4591 6.4308 7.0000 7.0000 1.4423

0.1155 0.4982 0.5000 0.0022 0.0798 0.1063 0.4043 0.0520 0.1654 0.4327 0.4516 0.3350 0.5000 0.1171 0.0417 0.2182 0.4783 0.0185 0.0108 0.1930 0.0165 0.0496 0.0725 0.0258 0.5000 0.2674 0.0387 0.0034 0.0017 0.0618 0.0707 0.2055 0.2501 0.5000 0.0190 0.1913 0.0478 0.0978 0.0032 0.3435

43.754 5.0182 7.0117 7.0110 222.12 47.614 6.1342 7.0112 7.0142 328.93 394.53 6.9022 7.0112 7.0000 500.00 332.06 4.9328 7.0113 7.0213 130.94 82.948 6.0467 7.0113 7.0094 376.97 147.61 6.0072 7.011 7.0402 17.570 164.55 6.5204 7.0113 7.0248 273.04 330.96 6.4429 7.0112 7.0120 357.37

4.68e-07 3.58e-07 3.59e-08 0.00000 8.28e-07 1.33e-07 4.11e-07 3.06e-07 0.00000 6.54e-07 7.92e-07 9.52e-07 2.95e-07 0.00000 1.00e-06 7.72e-07 7.88e-07 7.05e-07 0.00000 3.57e-07 4.94e-07 9.67e-07 1.31e-08 0.00000 6.40e-07 7.82e-07 9.11e-07 1.00e-06 0.00000 6.77e-07 2.89e-07 9.87e-07 1.02e-07 0.00000 3.59e-08 9.57e-07 1.58e-07 7.33e-08 0.00000 8.16e-07

Error 0.0488 5.5353 2.5325 2.7071 0.0001 0.0345 3.4546 2.5753 2.5686 0.0007 0.0007 2.6385 2.5325 2.6119 0.0002 6.06e-05 5.7809 2.5790 2.7186 0.0022 0.0080 3.6524 2.5730 2.6243 0.0009 0.0030 3.7135 2.5808 2.6750 0.8317 0.0003 3.0262 2.5547 2.5777 0.0010 7.83e-05 3.1392 2.5703 2.6243 0.0003 (Continued )

194

Green Engineering and Technology

TABLE 11.5 (Continued ) Optimal Parameters of SDM Using Different Algorithms for STM6 40-36 Run 12

13

14

15

16

17

18

19

Algorithm

IPV

α1

RS

Rp

I01

HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA

1.6809 3.0924 2.9182 2.8987 1.6538 1.6779 3.2348 2.8935 2.9464 1.6499 1.7174 3.9724 2.9359 2.8915 1.6717 1.6541 3.5776 2.9017 2.8338 1.7993 1.6752 3.2921 2.9048 2.8875 1.6750 1.6556 3.0691 2.8618 2.7953 1.6720 1.6598 2.8996 2.9351 3.0015 1.7997 1.6928 3.1988 2.9363 3.0135 1.7540

1.3832 6.3335 7.0000 7.0000 1.4179 1.4636 5.9678 7.0000 7.0000 1.4635 1.3426 4.6797 7.0000 7.0000 1.4053 1.2527 5.3492 7.0000 7.0000 7.0000 1.4455 5.6144 7.0000 7.0000 1.3974 1.3512 6.4120 7.0000 7.0000 1.4634 1.4132 6.9635 7.0000 6.9881 3.3538 1.3332 6.0811 7.0000 7.0000 1.4480

0.0189 0.0744 0.3713 0.0010 0.0963 0.0912 0.2025 0.2161 0.0034 0.0537 0.1680 0.3659 0.4954 0.0285 0.2085 0.0569 0.3312 0.2841 0.0021 0.0010 0.1475 0.0026 0.2802 0.0032 0.0694 0.0223 0.2002 0.0081 0.0010 0.1333 0.2264 0.1264 0.5000 0.0010 0.0448 0.1187 0.1953 0.4998 0.2672 0.0756

150.96 6.3456 7.0109 7.0023 368.15 209.79 5.9809 7.0111 7.0271 500.00 97.200 4.6986 7.0113 7.0163 297.48 220.39 5.3639 7.0114 7.0176 17.013 237.54 5.6295 7.0110 7.0459 186.35 274.72 6.4241 7.0112 7.0208 326.06 479.33 6.9747 7.0112 6.9974 17.054 126.00 6.0937 7.0111 7.0330 68.248

4.18e-07 7.87e-07 1.61e-08 0.00000 6.26e-07 9.82e-07 7.07e-07 2.23e-07 0.00000 1.00e-06 2.57e-07 3.63e-07 2.30e-08 0.00000 5.52e-07 8.76e-08 9.51e-07 9.36e-09 0.00000 1.11e-07 8.26e-07 7.87e-07 1.27e-07 0.00000 4.94e-07 2.99e-07 2.20e-07 3.48e-08 0.00000 1.00e-06 6.04e-07 7.87e-07 2.07e-09 0.00000 8.31e-08 2.34e-07 2.40e-11 4.41e-09 0.00000 7.69e-07

Error 0.0006 3.2519 2.5434 2.5928 0.0001 0.0004 3.7142 2.558 2.6295 0.0002 0.0052 6.6066 2.5329 2.5827 6.61e-05 0.0001 4.8021 2.5514 2.5868 0.8247 0.0002 4.3559 2.5518 2.7042 0.0002 0.0001 3.1347 2.5802 2.6022 7.11e-05 3.38e-05 2.5984 2.5325 2.6477 0.8290 0.0016 3.5548 2.5325 2.6425 0.0167 (Continued )

195

Parameter Estimation of PV Cells

TABLE 11.5 (Continued ) Optimal Parameters of SDM Using Different Algorithms for STM6 40-36 Run 20

21

22

23

24

25

26

27

Algorithm

IPV

α1

RS

Rp

I01

HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA

1.6736 3.0904 2.9375 2.7661 1.6782 1.8232 2.8893 2.9377 2.8394 1.6879 1.7130 2.9049 2.9365 2.9424 1.6772 1.6623 3.776 2.9001 2.8406 1.6797 1.6710 2.9817 2.8803 2.9265 1.6593 1.6602 2.9356 2.9288 2.9059 1.6476 1.7533 3.0545 2.9103 2.7988 1.6636 1.7987 2.9371 2.9365 2.9762 1.7897

1.2615 6.4086 7.0000 7.0000 1.4458 1.2189 6.9498 7.0000 7.0000 1.4536 1.3537 6.8903 7.0000 7.0000 1.4642 1.3724 4.9293 7.0000 7.0000 1.3451 1.4637 6.6319 7.0000 7.0000 1.4631 1.4237 6.9044 7.0000 7.0000 1.2005 1.4400 6.4830 7.0000 7.0000 1.3198 1.4122 6.8988 7.0000 7.0000 5.7989

0.1732 0.1791 0.5000 0.1540 0.1824 0.0367 0.0524 0.5000 0.0037 0.1860 0.1128 0.1926 0.5000 0.3345 0.2272 0.1732 0.3965 0.2611 0.0013 0.2643 0.1303 0.1577 0.1183 0.4225 0.0813 0.2174 0.2967 0.4478 0.0046 0.4142 0.1798 0.0895 0.3438 0.0036 0.2644 0.4042 0.2029 0.5000 0.0011 0.0089

164.61 6.4204 7.0108 7.0088 232.68 41.710 6.9610 7.0109 6.9917 185.04 97.153 6.9017 7.0107 6.9957 260.61 297.82 4.9469 7.0109 6.989 195.37 276.58 6.6436 7.0108 7.0328 443.01 485.03 6.9157 7.0113 7.0275 500.00 75.599 6.4948 7.0108 7.0174 500.00 59.603 6.9100 7.0111 7.0248 17.215

9.76e-08 8.77e-07 6.26e-08 0.00000 8.29e-07 4.65e-08 8.99e-07 1.22e-07 0.00000 8.88e-07 2.92e-07 2.63e-07 3.95e-07 2.85e-07 1.00e-06 3.83e-07 5.24e-07 0.00000 0.00000 2.78e-07 9.94e-07 3.22e-07 8.67e-07 0.00000 1.00e-06 6.75e-07 9.46e-08 1.25e-07 0.00000 4.37e-08 7.25e-07 2.20e-07 3.95e-07 0.00000 2.13e-07 5.32e-07 6.41e-07 7.71e-08 0.00000 2.53e-09

Error 0.0001 3.1453 2.5325 2.6071 0.0004 0.0437 2.6185 2.5325 2.6211 0.0011 0.0048 2.6550 2.5325 2.5726 0.0005 5.58e-06 5.7890 2.5536 2.6327 0.0004 0.0001 2.9038 2.5681 2.5863 8.71e-05 2.73e-05 2.6308 2.5368 2.6139 0.0006 0.0144 3.0766 2.5459 2.5957 0.0006 0.0310 2.6467 2.5325 2.6414 0.8256 (Continued )

196

Green Engineering and Technology

TABLE 11.5 (Continued ) Optimal Parameters of SDM Using Different Algorithms for STM6 40-36 Run 28

29

30

Algorithm

IPV

α1

RS

Rp

I01

Error

HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA

1.6807 3.3502 2.9082 2.9164 1.6541 1.6981 3.2473 2.8871 2.8768 1.6346 1.6990 2.9839 2.8644 2.8554 1.7484

1.4221 5.8369 7.0000 7.0000 1.3795 1.1725 5.9757 7.0000 7.0000 1.2697 1.1620 6.7252 7.0000 7.0000 1.4694

0.3537 0.2450 0.3195 0.5000 0.1694 0.1516 0.2550 0.1750 0.0972 0.0209 0.2054 0.1763 0.0225 0.0328 0.5000

269.34 5.8497 7.0108 7.0046 500.00 108.31 5.9887 7.0117 7.0109 469.65 101.96 6.7365 7.0112 7.0108 94.994

6.57e-07 8.56e-07 3.63e-07 0.00000 4.20e-07 2.70e-08 5.40e-07 3.12e-08 0.00000 1.11e-07 2.27e-08 4.91e-07 5.35e-08 0.00000 1.00e-06

0.0007 3.9244 2.5481 2.5380 0.0003 0.0014 3.6914 2.5622 2.5703 0.0016 0.0020 2.8101 2.5786 2.5778 0.0163

FIGURE 11.5 Convergence curve for SDM (Schutten Solar STM6 40-36).

In the graph, HHO converges well and also at the least error value. However, DA nears the HHO convergence curve. SSA, GWO, and SCA have the worst convergence curve. Table 11.6 gives the estimated parameters and error of SDM with different algorithms for Pro. SW255. The parameters are estimated with 30 runs so that we do the statistical analysis of the error function. It is quite clear from Table 11.6 that HHO gives the least error out of the other algorithms, whereas DA also tries to give the error near to HHO but the other three algorithms SSA, GWO, and SCA give very poor results. The convergence characteristics are thus shown in Figure 11.6.

197

Parameter Estimation of PV Cells

TABLE 11.6 Optimal Parameters of SDM Using Different Algorithms for Pro. SW255 Test Run 1

2

3

4

5

6

7

8

Algorithm

IPV

α1

RS

HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA

8.8861 9.9565 9.6311 9.3624 9.1838 8.9565 10.122 9.6135 9.7342 8.7735 9.0630 9.1946 9.6148 9.5424 9.2269 9.0774 11.199 9.6320 9.6278 8.9491 8.9801 10.121 9.6220 9.2150 9.0886 8.9531 9.9244 9.6087 9.6639 9.3485 8.9754 10.101 9.6238 9.8469 8.8707 8.9289 9.4935 9.6160 9.6197 9.0744

1.5099 5.6226 5.9067 6.3181 1.5253 1.5269 5.6668 5.9123 6.0241 1.3686 1.5045 6.7866 5.9173 6.0477 1.4968 1.5448 4.7518 5.9045 5.9234 1.3832 1.5347 5.5248 5.9248 5.8842 1.5070 1.4444 5.8069 5.9208 5.7948 1.5412 1.3550 5.3524 5.9071 5.7053 1.5195 1.3843 6.6113 5.9253 5.9098 1.4961

0.0010 0.1338 0.0118 0.0011 0.2419 0.0611 0.3280 0.0011 0.0223 0.0020 0.2070 0.1107 0.0012 0.0068 0.2217 0.0780 0.4728 0.0150 0.0015 0.0709 0.0833 0.2124 0.0172 0.0014 0.2210 0.0964 0.2362 0.0010 0.0084 0.2212 0.0534 0.0596 0.0049 0.0077 0.0023 0.1840 0.3410 0.0077 0.0139 0.2211

Rp 192.83 5.6303 5.9122 6.3229 331.48 168.81 5.6744 5.9187 6.0135 500.00 331.95 6.7899 5.9231 6.0190 92.325 78.751 4.7678 5.9099 5.9057 109.80 174.78 5.5333 5.9304 5.9108 278.81 166.22 5.8136 5.9272 5.8099 91.722 78.191 5.3622 5.9123 5.6613 264.27 296.27 6.6155 5.9316 5.9011 422.05

I01 7.00e-07 9.06e-07 4.49e-07 4.15e-07 8.59e-07 8.43e-07 8.64e-07 2.09e-07 7.24e-19 1.29e-07 6.78e-07 7.58e-07 1.11e-07 0.00000 6.13e-07 9.99e-07 6.68e-07 9.56e-07 0.00000 1.55e-07 9.19e-07 2.77e-07 4.96e-07 0.00000 6.97e-07 3.35e-07 8.79e-07 2.08e-07 0.00000 1.00e-06 1.05e-07 5.22e-07 8.76e-07 0.00000 7.79e-07 1.60e-07 4.42e-09 2.83e-07 0.00000 6.21e-07

Error 7.76e-05 27.434 26.262 26.263 0.1641 0.0116 29.274 26.167 26.466 0.0193 0.0861 27.506 26.168 26.345 0.2277 0.0815 32.105 26.290 26.227 0.0080 0.0199 28.228 26.310 26.705 0.1169 0.0098 28.339 26.167 26.252 0.2911 0.0171 26.945 26.201 26.527 0.0001 0.0234 29.498 26.226 26.303 0.0931 (Continued )

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Green Engineering and Technology

TABLE 11.6 (Continued ) Optimal Parameters of SDM Using Different Algorithms for Pro. SW255 Test Run 9

10

11

12

13

14

15

16

Algorithm

IPV

α1

RS

HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA

8.8869 9.5184 9.6170 9.1089 9.6171 8.9080 10.368 9.6380 9.7058 9.1136 8.9461 10.019 9.6159 9.7920 9.0369 9.0398 9.4757 9.6151 9.6952 8.8711 8.9111 9.9059 9.6089 9.0272 9.3165 8.9265 10.946 9.6235 9.1026 8.9198 8.9615 9.6368 9.6305 9.5610 9.1985 8.9837 10.074 9.6041 10.031 8.8619

1.4665 6.0616 5.9061 6.3617 7.0000 1.4535 5.3910 5.9132 6.0789 1.5158 1.3737 5.5972 5.9129 5.5671 1.5414 1.4665 6.8299 5.9140 5.9431 1.4245 1.4436 5.8465 5.9176 6.3793 1.5406 1.4760 4.9142 5.9172 6.3331 1.4508 1.5387 5.8640 5.9108 5.6863 1.5066 1.4232 5.4691 5.9276 5.6496 1.5175

0.0399 0.0038 0.0025 0.0010 0.0010 0.0898 0.3432 0.0201 0.0082 0.2163 0.1707 0.1583 0.0011 0.0017 0.1251 0.1807 0.4788 0.0041 0.0069 0.0776 0.0716 0.2518 0.0016 0.0057 0.2993 0.0217 0.3938 0.0165 0.0018 0.1279 0.1104 0.0010 0.0128 0.0020 0.4732 0.1528 0.1147 0.0024 0.0017 0.0031

Rp 229.20 6.0669 5.9116 6.4014 5.9162 311.08 5.4007 5.9188 6.0702 298.46 345.73 5.6047 5.9187 5.6318 262.68 204.31 6.8344 5.9192 5.9546 500 190.08 5.8531 5.9236 6.3753 133.26 128.62 4.9272 5.9229 6.4033 500.00 435.83 5.8704 5.9167 5.7039 500.00 207.04 5.4776 5.9336 5.6298 312.67

I01 4.33e-07 8.78e-07 1.92e-08 0.00000 5.20e-07 3.75e-07 5.44e-07 5.14e-07 0.00000 7.69e-07 1.40e-07 1.36e-07 0.00000 0.00000 1.00e-06 4.39e-07 3.58e-07 9.31e-07 0.00000 2.66e-07 3.31e-07 1.43e-07 4.31e-07 0.00000 1.00e-06 4.77e-07 6.37e-07 9.08e-07 0.00000 3.66e-07 9.69e-07 9.50e-07 2.77e-07 0.00000 6.73e-07 2.62e-07 5.49e-07 4.63e-07 1.63e-07 7.64e-07

Error 6.15e-05 26.205 26.180 26.546 26.166 0.0014 29.615 26.336 26.365 0.1109 0.0088 27.676 26.167 26.583 0.0329 0.0562 30.923 26.194 26.251 0.0006 0.0025 28.480 26.172 26.633 0.4693 0.0042 30.881 26.303 26.858 0.0048 0.0136 26.168 26.271 26.343 2.2924 0.0211 27.351 26.179 26.440 0.0006 (Continued )

199

Parameter Estimation of PV Cells

TABLE 11.6 (Continued ) Optimal Parameters of SDM Using Different Algorithms for Pro. SW255 Test Run 17

18

19

20

21

22

23

24

Algorithm

IPV

α1

RS

HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA

8.9067 9.7100 9.6217 9.4306 8.9308 8.9719 10.623 9.6122 9.0348 9.7403 9.0108 9.9606 9.6347 10.062 8.8669 8.8932 10.720 9.6159 9.0498 9.0933 8.8684 9.2586 9.6192 9.4172 9.1670 8.9857 9.7705 9.6210 9.5526 9.5067 8.9750 9.9485 9.6243 9.4794 8.8547 8.9649 9.6854 9.6106 9.4056 9.2166

1.5421 6.3416 5.9073 6.2764 1.2725 1.4928 5.1297 5.9186 6.7598 1.4681 1.4360 5.9474 5.9094 5.3791 1.5167 1.4394 5.1163 5.9208 6.7433 1.5406 1.4918 6.5868 5.9191 6.2250 1.3078 1.4667 5.8402 5.9106 5.9752 1.5190 1.4641 5.6655 5.9094 6.2387 1.4779 1.4938 6.2221 5.9202 6.1562 1.5393

0.0649 0.3963 0.0047 0.0012 0.2125 0.1391 0.3205 0.0044 0.0131 0.5000 0.1857 0.3628 0.0211 0.0028 0.0060 0.0935 0.4152 0.0083 0.0014 0.2022 0.0075 0.0608 0.0066 0.0010 0.3617 0.1560 0.1125 0.0051 0.0010 0.3863 0.0615 0.1473 0.0078 0.0027 0.0010 0.0637 0.2909 0.0046 0.0017 0.2787

Rp 485.03 6.3465 5.9136 6.2876 415.60 388.65 5.1408 5.9248 6.7430 346.33 286.19 5.9534 5.9158 5.3749 269.88 411.29 5.1282 5.9263 6.7640 341.81 241.70 6.5906 5.9242 6.222 277.26 323.70 5.8467 5.917 5.9305 84.507 110.04 5.6727 5.9153 6.2739 253.32 83.122 6.2273 5.9263 6.2079 425.47

I01 9.99e-07 6.38e-08 3.51e-08 5.32e-09 3.38e-08 5.93e-07 7.85e-07 1.66e-08 0.00000 4.71e-07 3.08e-07 7.66e-07 9.06e-07 0.00000 7.56e-07 3.18e-07 1.58e-07 2.85e-08 0.00000 1.00e-06 5.75e-07 8.59e-07 2.60e-07 0.00000 5.79e-08 4.41e-07 7.00e-07 4.79e-07 0.00000 7.91e-07 4.16e-07 7.51e-07 2.79e-07 4.90e-07 4.92e-07 5.73e-07 6.34e-07 0.00000 1.55e-08 1.00e-06

Error 0.0019 29.920 26.199 26.256 0.0046 0.0179 29.721 26.196 26.714 2.3072 0.0361 29.548 26.345 26.439 0.0002 0.0002 30.700 26.231 26.572 0.0996 0.0002 26.932 26.216 26.231 0.2853 0.0233 27.174 26.202 26.431 1.1948 0.0172 27.542 26.226 26.360 0.0013 0.0558 28.874 26.198 26.432 0.2648 (Continued )

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Green Engineering and Technology

TABLE 11.6 (Continued ) Optimal Parameters of SDM Using Different Algorithms for Pro. SW255 Test Run 25

26

27

28

29

30

Algorithm

IPV

α1

RS

HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA

8.9410 10.430 9.6154 9.3620 8.9829 8.9598 9.7609 9.6206 9.3531 9.0007 8.8386 9.7284 9.6290 9.4693 9.0655 8.9427 10.181 9.6122 9.6726 8.9436 9.0626 9.5522 9.6263 10.038 9.1090 9.0739 9.5809 9.6177 9.2447 9.1204

1.5420 5.1178 5.9144 6.1901 1.3687 1.3849 6.0461 5.9224 6.2088 1.3079 1.4703 5.7544 5.9147 6.4629 1.5185 1.5356 5.5480 5.9231 5.7603 1.5429 1.4635 6.2461 5.9096 5.5139 1.4996 1.4383 6.2589 5.9124 6.2499 1.5074

0.0835 0.1689 0.0034 0.0032 0.2037 0.1585 0.2406 0.0125 0.0037 0.0608 0.0143 0.0010 0.0134 0.0016 0.1939 0.0096 0.2787 0.0029 0.0012 0.0320 0.1963 0.1647 0.0118 0.0013 0.1788 0.2244 0.2054 0.0057 0.0063 0.2473

Rp 351.76 5.1300 5.9206 6.2180 391.79 167.45 6.0516 5.9279 6.2019 67.808 380.46 5.7612 5.9207 6.4427 377.89 127.80 5.5561 5.9291 5.7740 191.48 203.90 6.2509 5.9152 5.5002 132.20 210.80 6.2638 5.9185 6.2904 500.00

I01 9.99e-07 9.09e-07 3.29e-08 6.26e-08 1.32e-07 1.60e-07 1.84e-07 8.70e-07 0.00000 5.45e-08 4.53e-07 5.24e-07 4.06e-10 0.00000 7.91e-07 9.15e-07 1.52e-07 7.28e-07 0.00000 1.00e-06 4.25e-07 8.08e-07 8.47e-07 0.00000 6.34e-07 3.17e-07 3.49e-09 2.73e-07 2.29e-07 7.06e-07

Error 0.0076 28.257 26.188 26.300 0.0206 0.0230 28.368 26.268 26.279 0.0232 0.0030 26.184 26.276 26.484 0.0723 0.0080 28.855 26.183 26.194 0.0049 0.0711 27.701 26.262 26.390 0.1012 0.0912 28.081 26.208 26.475 0.1430

HHO converges faster in comparison to the other algorithms. However, DA almost reaches the least fitness function value. The SSA, GWO, and SCA do not perform well. Table 11.7 gives a statistical analysis of the error function of SDM with different algorithms. The table gives the best, worst, mean, and standard deviation of error values of Tables 11.3–11.6. From Table 11.7, we have observed that HHO obtained the best results among all algorithms in terms of all measurements of the sum of square error values, showing that HHO has this capacity to show stable and reliable efficiency compared to the other algorithms. Furthermore, it is not adequate to carry out only the statistical measures like best, worst, mean, and standard deviations as reflected in Table 11.7. Some non-parametric assessments also need to be performed to validate

201

Parameter Estimation of PV Cells

FIGURE 11.6 Convergence curve for SDM (Solarworld Pro. SW255).

TABLE 11.7 Statistical Analysis of Error Function for SDM Case KC200GT

CS6K-280M

STM6 40-36

Pro. SW255

Algorithm

Best

Worst

Mean

Std. Dev.

HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA HHO SSA GWO SCA DA

3.67e-06 21.314 21.314 21.324 0.0006 0.0008 30.752 30.252 30.282 0.0004 5.58e-06 2.5984 2.5325 2.5380 6.62e-05 6.16e-05 26.168 26.167 26.194 0.0006

0.0915 26.041 21.395 21.966 1.6065 0.0998 37.510 30.790 31.437 1.4582 0.0488 7.0660 2.5808 2.7186 0.8290 0.0912 30.923 26.345 26.858 26.166

0.0262 23.909 21.345 21.456 0.2149 0.0352 32.905 30.321 30.595 0.2748 0.0075 3.9492 2.5527 2.6212 0.1121 0.0238 28.483 26.226 26.422 1.1507

0.0326 1.4102 0.0261 0.1368 0.4256 0.0294 1.8317 0.1289 0.2829 0.3892 0.0135 1.3882 0.0184 0.0461 0.2855 0.0276 1.5065 0.0530 0.1652 4.7618

202

Green Engineering and Technology

FIGURE 11.7 Kruskal–Wallis test performance for SDM (KC200GT).

the results obtained in Table 11.7. The Kruskal–Wallis test [26] is thus carried out on the sampled values. For the comparison of more than two different samples, the Kruskal–Wallis test is a popular technique in some instances. Our test cases are no exception to this situation. In our investigation, four different PV models have been tested with the proposed method, namely the HHO. It is being compared with some standard heuristic algorithms like SSA, GWO, SCA, and DA, respectively. Hence, the Kruskal–Wallis test is a perfect fit. Usually, the significant p-values would be less than 0.05 for a 95% confidence interval considered in this work. The graphical representation of the mean ranks for the KC200GT model is shown in Figure 11.7. In the figure, the HHO group of results is represented by using blue color while the other groups are marked with red lines. Similar results are also presented for the other models, viz. CS6K-280M, STM6 40-36, and Pro. SW255 in Figures 11.8–11.10, respectively. It is clear from Figures 11.7–11.10 that the mean rank of group 1 (namely the proposed technique) is found to be distinct as compared to the mean ranks of groups 2–4, viz. the results of SSA, GWO, and SCA but the mean rank of group 5, viz. the results of DA, overlaps the mean rank of group 1 so the result for DA is non-significant. Moreover, the Wilcoxon rank-sum test [27] is also performed on the available data test for the proposed methodology in comparison to the other heuristic techniques. All the PV-models are once again considered for this investigation as well. The p-values for this non-parametric test are shown in Table 11.8. For this study, 95% confidence interval is taken up, meaning that values greater than 0.05 will be considered to be insignificant. The insignificant values are underlined in the table. The results obtained in Table 11.8 are thus found to be highly significant as all the p-values reported in the table are quite less than 0.05 except for the p-value of DA in the case of STM6 40-36. Therefore, the results for DA in the case of STM6 40-36

Parameter Estimation of PV Cells

FIGURE 11.8

Kruskal–Wallis test diagram for SDM (CS6K-280M).

FIGURE 11.9

Kruskal–Wallis test outcomes for SDM (STM6 40-36).

203

are non-significant. To further confirm the validity of the results, Holm–Bonferroni [28] suggested a correction for the p-values following the Wilcoxon test. The results are thus provided in Table 11.9. Once again, the insignificant results are underlined. From Table 11.9, it is quite clear that all the corrected p-values reported are having values of quite less than 0.05 except for the p-value of DA in the case of STM6 40-36. Thus, the results found by the proposed technique are all significant when compared to the metaheuristic algorithms, namely, SSA, GWO, SCA, and DA, but the results for DA in the case of STM6 40-36 are non-significant.

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FIGURE 11.10

Kruskal–Wallis test results for SDM (Pro. SW255).

TABLE 11.8 Wilcoxon Rank-Sum Test Results for SDM for Different PV Models PV Models

Proposed Method

KC200GT CS6K-280M STM6 40-36 Pro. SW255

SSA

HHO HHO HHO HHO

3.0180e-11 3.0180e-11 3.0199e-11 3.0199e-11

GWO 3.0199e-11 3.0180e-11 2.8646e-11 3.0199e-11

SCA

DA

3.0199e-11 3.0199e-11 3.0180e-11 3.0199e-11

4.7138e-04 0.0199 0.9705 0.0049

The underlined value is insignificant.

TABLE 11.9 Corrected p-Values for SDM for the Wilcoxon Test Adding Holm–Bonferroni Corrections PV Models

Proposed Method

KC200GT CS6K-280M STM6 40-36 Pro. SW255

HHO HHO HHO HHO

The underlined value is insignificant.

Corrected p-Values 10 × [0.000000120720.000000120720.000000090590.47138] [0.00000000012070.00000000012070.00000000009050.0199] [0.00000000009050.00000000011450.00000000011450.9705] [0.00000000012070.00000000012070.00000000009050.0049] −3

Parameter Estimation of PV Cells

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11.5 CONCLUSIONS This chapter presents a new HHO algorithm to adequately estimate the parameters of the system of solar cells and PV modules. Several conclusions can be drawn based on competitive and statistical experimental results. HHO is advantageous when compared to the other SSA, GWO, SCA, and DA algorithms, as it has fewer errors according to the experimental results. HHO is capable of producing better convergence than the SSA, GWO, SCA, and DA algorithms. This chapter is unique in the sense it carries an optimized approach for the parameter assessment of some popular solar cell models. However, the single diode is not able to define the different current components of the solar cell; therefore, the future demands double and three diode models. The single diode had less convergence time and fewer parameters to be identified in comparison to the double and three diode models. Thus, in the future, parameter estimation of higher diode models can be carried out. Improved versions of the HHO technique can also be employed to obtain even better results. New hybrid algorithms with HHO can also be proposed for the parameter identification problem of solar cell modeling.

REFERENCES

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12

On the Dynamics of Cellular Automata-based Green Modeling toward Job Processing with Group-based Industrial Wireless Sensor Networks in Industry 4.0 Arnab Mitra Siksha ‘O’ Anusandhan (Deemed to be University)

Avishek Banerjee Asansol Engineering College

CONTENTS 12.1 Introduction...................................................................................................207 12.2 Related Works................................................................................................ 210 12.3 Proposed Work.............................................................................................. 214 12.4 Results and Discussions ................................................................................ 219 12.5 Conclusions ................................................................................................... 221 Acknowledgment ................................................................................................... 221 References .............................................................................................................. 221

12.1 INTRODUCTION Current developments in the manufacturing industry have initiated a direction toward a systematic deployment of CPS (Cyber-Physical Systems), where information from all associated perceptions is considered toward an efficient synchronization between the manufacturing line and the cutting-edge computational techniques. In addition, with the use of several cutting-edge information technologies (ITs), such networked machines in a manufacturing line are found to be able to perform effective and in resilient collaboration. Thus, earlier days manufacturing has evolved into the next 207

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generation production technology, popularly known as Industry 4.0 (Lee, Bagheri, and Kao 2015). With efficient incorporation of the CPS with the existing manufacturing line, essential services, and logistics, today’s manufacturing houses may have a true potential to be transformed into Industry 4.0. Since CPS is at the preliminary stage toward its effective development, several guidelines have been presented by researchers toward its successful implementation. Among several others, a 5C (i.e., connection, conversion, cyber, cognition, and configuration)-based CPS architecture was presented in the study of Lee, Bagheri, and Kao (2015). A typical 5C-based architecture is shown in Figure 12.1. A sequential workflow may be observed in Figure 12.1 that describes the construction of CPS from data acquisition to final value creation through traversal of levels (Lee, Bagheri, and Kao 2015). The associated attributes for each level are placed adjacent to that level (refer to Figure 12.1). It was presented in the study of Lee, Davari, and Singh et al. (2018) that the efficiency of CPS may be further enriched with the proper incorporation of industrial artificial intelligence (AI). For this reason, an incorporation of AI technologies at each defined level of 5C-based CPS architecture (Figure 12.1) was presented in their study (Lee, Davari, and Singh et al. 2018). In a different work, an enhancement toward the existing 5C-based CPS architecture of Lee, Bagheri, and Kao (2015) was further presented by Jiang (2018). An 8C-based architecture was introduced over the existing 5C-based CPS architecture. We find that three new Cs (i.e., customer, coalition, and content) were considered additionally along with

Attributes: self-configuration for resilience, self-adjustment for variation, self-optimization for disturbance

Level 5. (Configuration) Level 4. (Cognition) Level 3. (Cyber) Level 2. (Conversion of data to information)

Attributes: integrated simulation

and synthesis, remote visualization, collaborative diagnosis, and decisions

Level 1. Connection(smart)

Attributes: plug-and-

play, communication, sensor networks

Attributes: smart

analytics for machine health, multidimensionaldata correction, performance and degradation

Attributes: twin model for

components machines, variation identification and memory; similarity-based cluster formation

FIGURE 12.1 A typical 5C-based CPS architecture with important attributes. (Lee, Bagheri and Kao 2015.)

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the existing 5Cs (i.e., connection, conversion, cyber, cognition, and configuration) (Lee, Bagheri, and Kao 2015). We find that most CPS-based applications may be supported with the 5C-based architecture of Lee, Bagheri, and Kao (2015). For this reason, we prefer to continue with our choice for a 5C-based CPS architecture for our chapter. In our studies, we find that Big Data, smart analytics, and industrial IoT (Internet of Things) have potentials for use in CPS (Lee, Kao, and Yang 2014; Cheng, Chen, and Tao et al. 2018) and are well suited with the 5C-based CPS architecture. In this regard, a categorical framework toward Industry 4.0 was presented by Qin, Liu, and Grosvenor (2016). A detailed survey of the existing technologies, applications, and open research areas was presented by Lu in 2017. Besides, uses of wireless sensor networks (WSNs) toward CPS in the Industry 4.0 scenario may be found in some studies (Udgata, Sabat, and Mini 2009; Pyun and Cho 2009; Lee, Kwon, and Song 2009; Shu, Wang, and Niu et al. 2015; Lin, Deng, and Chen et al. 2016). We find that a major emphasis of past researchers (Udgata, Sabat, and Mini 2009; Pyun and Cho 2009; Lee, Kwon, and Song 2009; Shu, Wang, and Niu et al. 2015; Lin, Deng, and Chen et al. 2016) was on the energy-efficient deployment and/or sleep scheduling of WSNs at a smart assembly line. Unfortunately, in our studies, we have not found a dynamic modeling-based investigation toward said deployment and/or sleep scheduling of WSNs in a smart assembly line. We believe that such investigation may further explore its true potential as a green computing model with a possibility for cheap physical implementation. Hence, we find that there exists a scope for further studies toward the exploration of detailed dynamics of such an event in a view of its true energy-efficient modeling at a low-cost physical modeling. For this reason, we continued our investigation with cellular automata (CA)-based modeling. Reasons for the choice of CA as an investigating tool for our presented chapter are further described in Section 12.2. The major contributions in this chapter are as follows: i. It presents a theoretical background toward the deployment of a low-cost Green model in Industry 4.0; ii. It presents a detailed systematic investigation toward a CA-based design to facilitate the modeling of an energy-efficient deployment and scheduling with industrial WSNs in Industry 4.0; iii. An efficient, though being simple and cost-effective, CA-based design with three cells only is finally discovered, which is potentially more suitable for low resource constraint components, e.g., WSNs, IoT, and so on, for uses in Industry 4.0; and iv. Attention on different fixed boundary conditions-based investigation examines its true latent nature (if any) toward the cost-efficiencies in view of hardware and software implementation. The rest of the chapter organization is as follows: Related works are surveyed in Section 12.2; the proposed CA-based model is described in Section 12.3; results and related discussions are presented in Section 12.4; finally, the conclusive discussion is presented in Section 12.5.

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12.2 RELATED WORKS It is already presented in some reports (Lee, Bagheri, and Kao 2015; Lee, Davari, and Singh et al. 2018; Jiang 2018) that cutting-edge ITs are used in today’s CPS/Industry 4.0 to enrich the production/manufacturing lines. From our studies, we found that sensor networks (i.e.,WSNs) are crucial for the implementation of CPS (Lee, Kao, and Yang 2014; Lee, Bagheri, and Kao 2015; Qin, Liu, and Grosvenor 2016, Lu 2017; Lee, Davari, and Singh et al. 2018; Jiang 2018; Cheng, Chen, and Tao et al. 2018) and manufacturing lines (Udgata, Sabat, and Mini 2009; Pyun and Cho 2009; Lee, Kwon, and Song 2009; Shu, Wang, and Niu et al. 2015; Lin, Deng, and Chen et al. 2016). For this reason, several researchers have focused on the enhancements of WSNs involving several aspects of WSNs, e.g., sensor deployment (Udgata, Sabat, and Mini 2009), energy efficiency (Pyun and Cho 2009), and so on. A brief discussion on WSNs is presented next. WSN (Castelli, Silva, and Manzoni et al. 2014) is a network constructed by many sensor nodes generally called nodes. The nodes are configured with several units like processing unit, storage unit, communicating unit, sensing unit, and power backup unit. Those units are very tiny because WSN nodes are tiny devices. The storage unit is used to store important data of WSNs. At first, the raw data are sensed by sensors and transferred to the processing unit. This unit is involved to process raw data and through the communicating unit, the processed data are transferred to the local server generally called the sink node. The WSN nodes are deployed within the target to collect various sorts of important information and transfer that information to the sink node. The state-of-the-art literature review presented that enhancements in view of Industry 4.0 rely on the inherent enhancements of CPS and cutting-edge IT. Thus, enrichments in Big Data processing technologies, networking technologies, WSNs, and so on play a significant role toward the advancements in modern day’s manufacturing line/Industry 4.0 (Lee, Kao, and Yang 2014; Lin, Deng, and Chen et al. 2016; Cheng, Chen, and Tao et al. 2018). A brief discussion on Industry 4.0-related issues and scopes is already presented in Section 12.1. Among several others, we found an interesting research in the study of Lin, Deng, and Chen et al. (2016) related to the uses of industrial WSNs for an energy-efficient deployment along with sleep time scheduling in Industry 4.0, which is briefly presented next. A schematic flow for the reported work of Lin, Deng, and Chen et al. (2016) with GIWSN (group-based industrial wireless sensor network) is presented in Figure 12.2. In the study of Lin, Deng, and Chen et al. (2016), GIWSN uses were presented in the Industry 4.0 framework to enhance scalability, heterogeneity, and flexibility in the dynamic industrial environment. It was mentioned in their study that the lifetime enhancement is always a challenging task in the field of GIWSNs. To solve this problem, the researchers presented an enhanced lifetime for GIWSN with proper inclusion of sleep schedules at the manufacturing lines. Energy consumption for said structures was further taken care off with the uses of a hybrid algorithm known as IGHSA (improved geometric selective harmony search algorithm), which is an enhancement over existing geometric selective harmony search (GSHS) as presented by Castelli, Silva, and Manzoni et al. (2014).

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I n p u t

Robot1

Robot2

Robot3

Robot4

Robot5

O u t p u t

(a) Initial GIWSN with 5 groups of robots Robot1’

Robot2’

Robot3’

(b) Reduced GIWSN with 3 groups of robots

Robot1’3’

Robot2’’

(c) Reduced GIWSN with 1.5 groups of robots using theory of symmetries

FIGURE 12.2 A typical diagram for the simplification method of GIWSN in manufacturing lines. (Lin et al. 2016.)

It was discussed in some studies (Callaway Jr 2003; Wu, Tan, and Xiong 2016; Jaigirdar and Islam 2016) that the sink node acts as an administrator and controls the communicative node in WSN. Depending upon the moving nature of WSN, it is classified into two types and those are static WSN (Wang, Cao, and Ji et al. 2017) and dynamic WSN (Ahmad, Rathore, and Paul et al. 2015). In the case of static WSN, the whole unit is mounted and fixed to a certain fixed point (co-ordinate regarding the sink node). In the case of dynamic WSN, the node is dynamic, though the sink node is generally mounted to a fixed coordinate. Now depending upon the need and purpose, the node is selected. In our experiment, static nodes were used, where the coordinate of the sink node and typical nodes are fixed and permanent (Elhoseny, Yuan, and El-Minir et al. 2016). In the case of a typical WSN design, the sensor nodes are deployed to cover the target area (Alnawafa and Marghescu 2018). The sensor nodes are deployed to sense the required data like weather information or enemy-related information and transfer it to the sink node maybe directly or via another sensor node. Enhancements in WSNs always have been a focus among researchers. Among several efforts, in the study of Han, Liu, and Jiang et al. (2015), the authors have concentrated on the enhancement of reliability optimization. Several optimization techniques have been explored and identified as the key factors to achieve better reliability toward energy optimization, coverage area optimization, duty cycle optimization, and so on. Furthermore, in the study of Banerjee, Garvrilas, and Ivanov et al. (2015), advanced works were presented on energy optimization to enhance

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the reliability of WSNs; WSN systems were presented as complex bridge systems and further investigations were carried out with a fuzzy-linguistic system toward its decision in the assessment of reliability redundancy allocation. In some studies (Banerjee, Garvrilas, and Grigoras et al. 2015; Banerjee, Chattopadhyay, and Mukhopadhyay et al. 2016), the use of a fuzzy-ACO (ant colony optimization) algorithm was presented to improve reliability in WSNs. In our studies, we found that WSNs play significant roles toward the implementation of manufacturing lines in Industry 4.0. Among several others, we focused on a model for “joint energy-efficient deployment and scheduling in group-based” (Lin, Deng, and Chen et al. 2016) industrial WSNs toward uses in Industry 4.0. A brief discussion on the study of Lin, Deng, and Chen et al. (2016) is presented in Section 12.2. Though a detailed study was presented by these authors, unfortunately we have not found a cost-effective approach to investigate the dynamics along with low-cost physical implementation capability. For this reason, we plan to model one such WSNs-based design with CA and to investigate further. We believe that a proper dynamic modeling at low-cost physical modeling should be beneficial in view of Industry 4.0. On the other hand, state-of-the-art literature review explored the uses of CA in Industry 4.0 (Bertacchini, Bilotta, and Caldarola et al. 2016; Demarco, Bertacchini, and Scuro et al. 2019). A brief description on CA is presented next. CA (Wolfram 1984a, b; Chaudhuri, Chowdhury, and Nandi et al. 1997) is an effective mathematical tool toward the modeling of several complex and scientific problems. The basic units of CA are commonly known as cells (CA cells) and are arranged in the form of a lattice. CA progresses over discrete space and discrete time. Evolution of CA may be realized at single or multiple dimensional CA configurations. Simple CA configuration may be obtained with a three-neighborhood (dependencies with only left neighbor, self (self-cell), and right neighbor) at periodic boundary (PB) or null boundary (NB) condition in one-dimension. Said CA arrangement is recognized as elementary CA (ECA). The next state of an ECA is computed by using Eq.(12.1) (Chaudhuri, Chowdhury, and Nandi et al. 1997).

Sit +1 = fi ( Sit−1 , Sit , Sit+1 ) (12.1)

where ‘ fi’ is considered as the next state function; ‘Sit ’ is considered as the present state value of the ‘i th’ cell at time ‘t’; ‘Sit−1’ is considered as the present state value of the ‘i − 1th’ cell at time ‘t’; ‘Sit+1’ is the present state value of the ‘i + 1th’ cell at time ‘t’ and ‘Sit +1’ is the future state value of the ‘i th’ cell at time ‘( t + 1)’. Important ECA terminologies with reference to our presented chapter are briefly discussed next. Reader(s) may further go through Chaudhuri, Chowdhury, and Nandi et al. (1997) to have more reading on CAs. ECA Rule (CA rule): By convention, ECA rules are read by Wolfram codes (from CA rule 0 to CA rule 255). ECA rule may be described with its 8-bit binary form. Equivalent decimal number of said 8-bit binary interpretation is known as the CA rule. Null boundary CA (NBCA): The leftmost edge of the left-cell and the rightmost edge of the right-cell are grounded in such configuration.

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Periodic boundary CA (PBCA): The rightmost-cell and leftmost-cell are linked together in such configuration. Uniform (homogeneous) CA: Identical CA rule is applied at each CA cell for some CA configuration. Hybrid (heterogeneous) CA: More than one different CA rule are applied for some CA configuration. Linear or non-linear CA rule: If the CA rule function incorporates only XOR/ XNOR logic, then it is considered as the linear CA rule. On the other hand, if the CA rule function incorporates AND/OR logic, then it is considered as the nonlinear CA rule. A typical diagram is shown in Figure 12.3 to illustrate NBCA and PBCA scenarios. As presented in some studies (Wolfram 1984a, b; Chaudhuri, Chowdhury, and Nandi et al. 1997), CAs are found to be effective discrete mathematical tools toward several complex problems. CA-based modelings are popular among researchers as inherent parallel computing capabilities and easy integrations to VLSI (Very LargeScale Integration) are found with CAs at the cost of D-FFs (flip-flops) (Chaudhuri, Chowdhury, and Nandi et al. 1997). For said advantages, CA-based models were considered for data security applications (Chaudhuri, Chowdhury, and Nandi et al. 1997), Industry 4.0 uses (Bertacchini, Bilotta, and Caldarola et al. 2016; Demarco, Bertacchini, and Scuro et al. 2019; Xu, Xu, and Li 2018; i Casas 2019), MapReduce design toward Big Data processing (Mitra, Kundu, and Chattopadhyay et al. 2018), authentications in cloud environment (Mitra, Kundu, and Chattopadhyay et al. 2017), PageRank validation (Mitra and Kundu 2015; Mitra and Kundu 2017), communications (Mitra 2016), and so on. For the capabilities toward modeling of a large number of complex and scientific applications, several studies may be found over literature to explore the true dynamics of CA. Among others, ECA dynamics for several homogeneous and heterogeneous CAs at different fixed boundary scenarios were examined (Teodorescu 2015; Mitra 2016; Mitra and Teodorescu 2016). In this regard, it is worthy to mention that NBCA may be considered as one simple case derived from different fixed boundary conditions of Mitra and Teodorescu (2016). Besides, energy consumptions by CA-based models were investigated by Mitra and Kundu (2017). It was concluded by Mitra and Kundu (2017) that power consumption for D-FFs (i.e., CA cells) varies in the range from 1.20E − 05 watt to 1.17E − 07 watt at different CMOS technologies, which is very low. Detailed discussion related to the power consumptions by CAs may be further studied from Mitra and Kundu (2017). It is mentioned in Section 12.1 that we are interested with the research on industrial WSNs for an energy-efficient deployment along with scheduling in Industry 4.0

(i-1)-th cell

(i)-th Self NBCA

(i+1)-th cell

(i-1)-th cell

(i)-th self cell

(i+1)-th cell

PBCA

FIGURE 12.3 Typical diagram for NBCA and PBCA. (Mitra and Kundu 2015.)

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(Lin, Den, and Cheng et al. 2016) and found that CA-based modeling may be possible to investigate the detailed dynamics, which may offer a cost-effective and green (low  power consuming) model in view of Industry 4.0. To the best of our knowledge, we are first to incorporate an ECA-based investigation toward energy-efficient deployment and scheduling with Industrial WSNs in Industry 4.0. Our proposed CA-based modeling is presented in Section 12.3.

12.3 PROPOSED WORK In our proposed approach, we considered GIWSN with n-groups of robots at the manufacturing line in an Industry 4.0 scenario. Our proposed approach for n-groups is an enhanced model as compared to the existing model for five groups of robots (Lin, Den, and Cheng et al. 2016). A complete schematic flow along with brief discussions for our proposed approach is presented in Figure 12.4. Initially for design simplicity, we restricted our CA-based modeling at NBCA configuration only. We observed that simplification and dynamics of GIWSN may effectively be modeled with only three cells at the NBCA configuration. No CA-based modeling compatibility was found at the PBCA scenario as first layer and last layer are not directly connected to each other (refer Figure 12.2). Selection of ECA rule(s) for said NBCA configuration is presented in Table 12.1. Let us follow the following conventions to understand the CA-based design on a binary scale. Value ‘0’ represents no value or absence of input, and value ‘1’ represents First layer

I n p u t

Intermediate layer

Robot2

Robot1

Robot3... Robotn-2

Last layer

Robotn-1

Robotn

O u t p u t

(a) Initial GIWSN with n- groups of robots Robot1’

Robot2’

Robot3’

(b) Reduced GIWSN with 3 groups of robots Left cell at NBCA

Self-cell at NBCA

Right cell at NBCA

(c) Mapping into NBCA for reduced GIWSN with 3 groups of robots

FIGURE 12.4 Proposed mapping for reduced GIWSN with three groups of robots at NBCA configuration. (a) Initial GIWSN with n-groups of robots. (b) Reduced GIWSN with three groups of robots. (c) Mapping into NBCA for reduced GIWSN with three groups of robots.

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Dynamics of Green Modeling

TABLE 12.1 Rule Assignment Cell

CA Rule

First cell (left-cell) Intermediate cell (self-cell) Last cell (right-cell)

R0 R1 R2

valid value or, presence of input. Inputs from ‘Input’ (refer Figure 12.4a) will be processed into outputs at ‘Output’ (refer Figure 12.4a. Thus, following sequences of patterns may be observed at reduced GIWSN with three groups of robots (refer Figure 12.4b) for a continuous processing of input to output (flow is from left to right). The different patterns achieved at reduced GIWSN with three groups of robots are processed further with the achieved NBCA design (Figure 12.4c and Table 12.2) to obtain the transition diagram for the proposed system. Detailed processing is presented in Table 12.3. The left-hand side of ‘left-cell’ and right-hand side of ‘rightcell’ in Table 12.3 represent initialization at ground (NBCA configuration). For more illustrations related to similar processing as presented in Table 12.3, readers may refer Mitra and Kundu (2015). Transition diagram based on the computation as discussed in Table 12.3 is presented in Figure 12.5. Let us follow the notation to understand the transition diagrams (directed graph) presented in Figures 12.5–12.7. Circle indicates a state, decimal value inside the circle indicates state value (number), and arrow indicates state transition from one state (source) to another state (destination).

TABLE 12.2 Different Patterns Observed at GIWSN with Three Groups of Robots Robot1 0 1 1 1 0 0 0 1 0 0 0 1 1

Robot2 0 0 1 1 1 0 0 0 1 0 1 0 1

Robot3 0 0 0 1 1 1 0 0 0 1 0 1 0

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TABLE 12.3 Exploration of State Transition at the NBCA Configuration Left-Cell

Self-Cell

Decimal Value to be Assigned in State

Right-Cell

0 0 0 0 0 0

0 1 1 1 0 0 0

0 0 1 1 1 0 0

0 0 0 1 1 1 0

0 0 0 0 0 0

0 4 6 7 3 1 0

0 0

1 0 0

0 1 0

0 0 1

0 0

4 2 1

0 0

0 1 1

1 0 1

0 1 0

0 0

2 5 6

2 5

0

FIGURE 12.5

6

4

7

1

3

Proposed transition diagram.

5

3

FIGURE 12.6

4

2

1

6

0

7

Modified transition diagram after conflict removal in RMT.

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Dynamics of Green Modeling

5

0

4

2

1

7

5

3

7

6

0

2

1

3

(a)

4

6

(b)

FIGURE 12.7 Transition diagram achieved under several fixed boundary conditions. (a) At 0…0/0…1 fixed boundary and (b) at 1…0/1…1 fixed boundary.

The RMT (Rule MinTerms, the decimal values 0, 1, 2, …, 9 mentioned within the set of parentheses in Tables 12.4 and 12.6) (Kundu, Dutta, and Mukhopadhyay 2008; Mitra and Kundu 2015) for said system is presented in Table 12.4. Please note that value ‘D’ in Table12.4 represents ‘Don’t care condition’. It is observed from Table 12.4 that there are conflicts at the ‘000’-th column,‘001’-th column,‘010’-th column, and ‘011’-th column of the ‘R0’-th row. For the same reason, the CA rule cannot be computed. Hence, the mentioned conflicts need to be removed. Detailed removal of conflict is presented in Table 12.5 and thereafter, conflict removed RMT is presented in Table 12.6. Transition diagram based on the computation as discussed in Table 12.5 is presented in Figure 12.6. Hence, we explored that CA rule 10, CA rule 170, and CA rule 0 in hybrid CA configuration at null boundary CA (NBCA) configuration are capable to model the manufacturing lines with industrial WSNs in Industry 4.0. In addition, we found that power consumption at the physical level by CA is very low (1.20E − 05 watt to1.17E − 07 watt at different CMOS technologies) (Mitra and Kundu 2017). Thus, an energy efficiency is also ensured with the presented design, which is particularly important in view of limited energy resource constraint components such as WSNs, IoTs, and so on. In addition, we may predict the sleep schedule (which is an important criterion toward efficient energy management). We observed a self-loop at state ‘0’ in Figure 12.6. By convention, state ‘0’ indicates end of processing (no presence of input at all three states/ robots). Thus, a system might be scheduled with a sleep to reduce energy consumption. Detailed properties of achieved each CA rule (refer Table 12.6) are enlisted in Table 12.7. Presented summarized information of Table 12.7 may further be explored TABLE 12.4 Proposed RMT Construction at NBCA Configuration CA Rule (in Decimal) R0 R1 R2

111(7)

110(6)

101(5)

100(4)

011(3)

010(2)

D 1 D

D 1 1

D 1 D

D 1 1

1/0 0 D

1/0 0 0

001(1) 000(0) 1/0 0 D

1/0 0 0

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TABLE 12.5 Conflict Removal at NBCA Configuration Left-Cell

Self-Cell

Decimal Value to be Assigned in State

Right-Cell

0

0 0

0 0

0 0

0

0 0

0 0 0

0 0 1 0

0 1 0 0

1 0 0 0

0 0 0

1 2 4 0

0 0

0 1 1

1 1 0

1 0 0

0 0

3 6 4

0

1 0

0 1

1 0

0

5 2

0

1 1

1 1

1 0

0

7 6

TABLE 12.6 Conflict Resolved RMT CA Rule (in Decimal) R0 (10) R1 (170) R2 (0)

111(7)

110(6)

101(5)

100(4)

011(3)

010(2)

D 1 D

D 0 0

D 1 D

D 0 0

1 1 D

0 0 0

001(1) 000(0) 1 1 D

0 0 0

in detail from Elementary Cellular Automata in “http://atlas.wolfram.com/01/01/ rulelist.html”. It is observed from Table 12.7 that achieved rule 0 and rule 170 were additive CA rules, and rule 10 was a non-linear CA rule (Demarco, Bertacchini, and Bilotta et al. 2019; Additive Cellular Automaton in “https://mathworld.wolfram.com/

TABLE 12.7 Detailed Information for Achieved CA Rules of Table 12.6 CA Rule

Equivalent Binary Representation

Next State Evaluating Logic Function (Refer Figure 12.2)

Rule 0 Rule 10 Rule 170

00000000 00001010 10101010

0 NOT (i−1) AND (i+1) (i+1)

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AdditiveCellularAutomaton.html”). Proposed design is further investigated in detail to explore its true dynamics and cost-effectiveness, which is presented in Section 12.4.

12.4 RESULTS AND DISCUSSIONS ECA dynamics at several fixed boundary conditions were examined (Mitra and Teodorescu 2016) to explore its true behavior and advantage toward its implementations (both at the software and hardware level). It was discussed by some authors (Teodorescu 2015; Mitra 2016; Mitra and Teodorescu 2016) that ECA dynamics may vary at different fixed boundary conditions. The different notations used for several fixed boundary conditions (Teodorescu 2015; Mitra 2016; Mitra and Teodorescu 2016) were 0 0 , 01, 1 0 , and 11. The left-most and right-most values at those boundary conditions represent fixed initialization at said boundary positions. Thus, we find that the NBCA scenario is a single instance from the set of all fixed boundary conditions. In the presented report, we followed the same notation of some previous studies (Teodorescu 2015; Mitra 2016; Mitra and Teodorescu 2016) for representing different fixed boundary conditions. We further simulated our design at different fixed boundary conditions. For simulation, we used ‘C’ environment at Intel® Celeron® CPU N 2480 @ 2.16 GHz and 2.16 GHz computing facility at 2.00 GB installed memory. Obtained results in the form of transition diagrams are presented in Figure 12.7. It is observed from Figure 12.7 that identical transitions were achieved for hybrid CA of rule 10, rule 170, and rule 0 at 0 0 and 01 fixed boundary; similarly, another identical transition diagram is achieved for the same hybrid CA at 1 0 and 11 fixed boundary. Hence, it may be concluded that the said hybrid CA dynamics was not dependent on the left-hand boundary values; it might be fixed with boundary value 0 or 1 (refer to Figure 12.7a and b). Thus, it may be further concluded that “there is no need for assigning program memory for software implementations” (Mitra and Teodorescu 2016) for said hybrid CA, which in our belief is very important in view of low resource constraint components such as WSNs, IoTs, and so on. A comparative study related to the IWSNs (Industrial WSNs) was presented (Lin, Deng, and Chen et al. 2016). In the study of Lin, Deng, and Chen et al. (2016), it was described that in the past, an energy-efficiency toward connected coverage strategies in view of several types of time, i.e., network life, coverage, ratio of dead node(s), and average energy consumption, were examined (Han, Liu, and Jiang et al. 2015). In another research, delay-aware and energy-efficient computing in IWSNs were presented (Suto, Nishiyama, and Kato et al. 2015) at reduced latency. A lightweight packet error discriminator concept-based design in IWSNs was presented by Barac, Caiola, and Gidlund et al. (2014) to reduce interference and to increase information precision. Besides, to enhance efficiencies, a data fusion-based approach was presented (Hou and Bergmann 2012), tree-based data gathering algorithm was presented (Zhu, Wu, and Han et al. 2015) toward hotspot problem in local/full deployment area. Unfortunately, we have not found any CA-based approach toward the investigation of dynamics of an energyefficient deployment and scheduling with IWSNs in Industry 4.0. For this reason, the presented approach was compared with reference to the comparative studies of Lin, Deng, and Chen et al. (2016), and key findings are presented in Table 12.8.

Proposed CA-based modeling

Han, Liu and Jiang et al. (2015) Suto et al. (2015) Barac et al. (2014) Hou and Bergmann (2012) Zhu et al. (2015)

Yes

Interference

Yes

Yes

Latency

Yes Yes Yes

Yes

Reliability

TABLE 12.8 Comparative Study Toward Several IWSN-Based Works

Yes Yes Yes Yes

Yes Yes

Power Efficiency

Yes Yes

Yes

Deployment

Yes Yes

Scheduling

Not reported Not reported Not reported Not reported Not reported Not reported Yes

Parallel Computing Compatibility

Not reported Not reported Not reported Not reported Not reported Not reported Yes

VLSI Integration Capability

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It is observed from Table 12.8 that the proposed CA-based approach has advantages of power efficiency, deployment, scheduling along with parallel computing compatibility, and VLSI integration capability.

12.5 CONCLUSIONS An efficient ECA-based modeling is presented with only three CA cells at different fixed boundary scenarios toward an energy-efficient deployment and scheduling with industrial WSNs in Industry 4.0. The presented hybrid CA-based design ensures an enhancement toward simplification of n-groups of robots over existing 5-groups of robots at the low power-consuming model. It is estimated that the proposed CA-based design requires very low power (1.20E − 05 watt to 1.17E − 07 watt) consumption at each physical level implementation of a CA cell (D-FFs); additional investigations explored that it also enjoys low cost toward its software implementation as there is no need to focus on its left boundary value at different fixed boundary conditions. Thus, it ensures a complete cost-efficient implementation both at the hardware (physical) and software level, which, in turn, is very much advantageous toward incorporation with low resource constraint components e.g., WSNs, IoTs, and so on. Hence, we conclude that the presented design is an energyefficient and low-cost design, and it truly exhibits its true potentials toward easy and efficient integration in Industry 4.0.

ACKNOWLEDGMENT The authors sincerely thank the anonymous reviewers for their helpful suggestions, which have further enhanced the quality of the chapter.

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Lin, Chun-Cheng, Der-Jiunn Deng, Zheng-Yu Chen, and Kwang-Cheng Chen. “Key design of driving industry 4.0: Joint energy-efficient deployment and scheduling in group-based industrial wireless sensor networks.” IEEE Communications Magazine 54, no. 10 (2016): 46–52. Lu, Yang. “Industry 4.0: A survey on technologies, applications and open research issues.” Journal of Industrial Information Integration 6 (2017): 1–10. Mitra, Arnab, and Anirban Kundu. “Page ranking validation using cellular automata in cloud.” International Journal of Cloud Applications and Computing (IJCAC) 5, no. 3 (2015): 1–19. Mitra, Arnab. “On the selection of cellular automata based PRNG in code division multiple access communications.” Studies in Informatics and Control 25, no. 2 (2016): 217–226. Mitra, Arnab, and Horia-Nicolai Teodorescu. “Detailed analysis of equal length cellular automata with fixed boundaries.” Journal of Cellular Automata 11, no. 5–6 (2016): 425–448. Mitra, Arnab, and Anirban Kundu. “Energy efficient CA based page rank validation model: A green approach in cloud.” International Journal of Green Computing (IJGC) 8, no. 2 (2017): 59–76. Mitra, Arnab, Anirban Kundu, Matangini Chattopadhyay, and Samiran Chattopadhyay. “A cost-efficient one time password-based authentication in cloud environment using equal length cellular automata.” Journal of Industrial Information Integration 5 (2017): 17–25. Mitra, Arnab, Anirban Kundu, Matangini Chattopadhyay, and Samiran Chattopadhyay. “On the exploration of equal length cellular automata rules targeting a map reduce design in cloud.” International Journal of Cloud Applications and Computing (IJCAC) 8, no. 2 (2018): 1–26. Pyun, Sung-Yeop, and DongHo Cho. “Power-saving scheduling for multiple-target coverage in wireless sensor networks.” IEEE Communications Letters 13, no. 2 (2009): 130–132. Qin, Jian, Ying Liu, and Roger Grosvenor. “A categorical framework of manufacturing for industry 4.0 and beyond.” Procedia CIRP 52 (2016): 173–178. Shu, Lei, Lei Wang, Jianwei Niu, Chunsheng Zhu, and Mithun Mukherjee. “Releasing network isolation problem in group-based industrial wireless sensor networks.” IEEE Systems Journal 11, no. 3 (2015): 1340–1350. Suto, Katsuya, Hiroki Nishiyama, Nei Kato, and Chih-Wei Huang. “An energy-efficient and delay-aware wireless computing system for industrial wireless sensor networks.” IEEE Access 3 (2015): 1026–1035. Teodorescu, Horia-Nicolai. “On the regularities and randomness of the dynamics of simple and composed CAs with applications.” Romanian Journal of Information Science and Technology, Romanian Academy 18, no. 2 (2015): 166–181. Udgata, Siba K., Samrat L. Sabat, and S. Mini. “Sensor deployment in irregular terrain using artificial bee colony algorithm.” In 2009 World Congress on Nature & Biologically Inspired Computing (NaBIC), pp. 1309–1314. IEEE, Coimbatore, India, 2009. Wang, Jin, Jiayi Cao, Sai Ji, and Jong Hyuk Park. “Energy-efficient cluster-based dynamic routes adjustment approach for wireless sensor networks with mobile sinks.” The Journal of Supercomputing 73, no. 7 (2017): 3277–3290. Wolfram, Stephen. “Computation theory of cellular automata.” Communications in Mathematical Physics 96, no. 1 (1984a): 15–57. Wolfram, Stephen. “Cellular automata as models of complexity.” Nature 311, no. 5985 (1984b): 419–424. Wu, Mou, Liansheng Tan, and Naixue Xiong. “Data prediction, compression, and recovery in clustered wireless sensor networks for environmental monitoring applications.” Information Sciences 329 (2016): 800–818. Xu, Li Da, Eric L. Xu, and Ling Li. “Industry 4.0: State of the art and future trends.” International Journal of Production Research 56, no. 8 (2018): 2941–2962. Zhu, Chuan, Shuai Wu, Guangjie Han, Lei Shu, and Hongyi Wu. “A tree-cluster-based data-gathering algorithm for industrial WSNs with a mobile sink.” IEEE Access 3 (2015): 381–396.

13

Green Cloud Computing: An Emerging Trend of GIT in Cloud Computing Satya Sobhan Panigrahi and Bibhuprasad Sahu BPUT

Amrutanshu Panigrahi SOA University

Sachi Nandan Mohanty ICFAI Tech

CONTENTS 13.1 Introduction .................................................................................................. 226 13.2 Basic Concepts .............................................................................................. 227 13.2.1 Green Cloud Computing ................................................................... 227 13.2.2 Requirements of Green Computing on Cloud Computing ......................................................................... 228 13.2.3 Challenges of Green Cloud Computing ............................................ 228 13.2.4 Virtual Machine Migration............................................................... 229 13.2.5 Genetic Algorithm............................................................................. 231 13.3 Literature Review.......................................................................................... 232 13.4 Motivation and Objectives............................................................................. 235 13.5 Problem Statement ........................................................................................ 235 13.6 Proposed Work ............................................................................................. 236 13.6.1 Proposed Ant Colony Optimization (ACO) Algorithm for the Selection of the Most Appropriate VM ................................. 236 13.6.2 Pseudo Code of the Proposed Work.................................................. 237 13.6.3 Flow Chart of the Proposed Work .................................................... 238 13.7 Implementation and Result ........................................................................... 238 13.8 Conclusion and Future Work ........................................................................240 References ..............................................................................................................240

225

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INTRODUCTION

Cloud computing is a standard in the field of computation. The systems are large in numbers that are connected in public and private networks. The reason behind using cloud computing is to provide an infrastructure for applications that should be dynamically scalable and that have been used for storing data and files. The invention of cloud computing has reduced the cost to a much extent and along with it, it reduced the time required for application hosting, content storage, and delivery. Applications and services are accessed by common Internet protocols that run on a distributed network using a virtualized server. It is growing fast because it is easy to use and cheap and has attractive features such as on-demand services and the pay as use scheme. In cloud computing, different systems are connected in public, private, and hybrid networks. Because applications, data, and file storages are dynamically scalable by the inventions of cloud computing, the cost of computation, application hosting, data storage, and delivery has been reduced much. Cloud services are offered by either the cloud service provider (CSP) or Internet service providers (ISP). In general, three types of services are offered by cloud providers, i.e., platform as a service (PaaS), software as a service (SaaS), and infrastructure as a Service (IaaS). Today, there are many reasons for an organization to force cloud computing; they pay as the amount of resource consumption and easily meet the needs of changing the markets rapidly to ensure that they are always on the leading edge for their consumers. Several data centers are available in cloud computing. As the interest in the data center gradually increases, it devours a high measure of energy and it prompts more power utilization and carbon emission [1]. Services delivered from cloud computing will be deployed in any of the cloud deployment models: public cloud, private cloud, community cloud, and hybrid cloud. The physical machine (PM) in the data center will be virtualized in any type of deployment model. Virtualization is the heart of cloud computing multiple applications and operating systems are run on the same servers at the same time [19]. Different researchers are achieving green cloud computing to limit the utilization of energy and effective processing and exploiting the computing infrastructure. Presently, green cloud computing is the developing innovation in the cloud that concentrates on how people can run with environments eco-friendly and deals as we are capable to think about the climate from harm and to reduce loads between data centers by using virtual machine migration (VMM) techniques [1]. Using VMM techniques, this article focuses on load balancing, fault tolerance, and reduces energy consumption between different physical machines. A genetic algorithm is a guideline of delicate processing, which depends on the idea of common hereditary qualities and advancement. It deals with the standards of biological evolution, which are easy to build, and its usage does not need a lot of capacity, subsiding on them an adequate decision for optimization issues [10]. The remainder of this chapter is presented as follows. Section 13.2 deliberates the basic concepts required for understanding the green cloud computing and the work of the chapter. In Section 13.3, the authors present the Literature Review. In this section, the previous studies related to VM migration in green cloud computing are discussed. The authors present their novel methods, which help in eliminating

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this problem. In Section 13.4, motivation and objectives are highlighted, which are achieved in this research. Section 13.5 discusses problem statements. In this section, the problem statements are described, and problems that existed in the previous system were discussed so that a new approach could be developed for the virtual machine. Section 13.6 is on Proposed Work. In this chapter, the proposed work is discussed, which can make the system more efficient by using the proposed scheme. In Section 13.7, the authors describe the implementation and results. Finally, Section 13.8 gives the conclusion and future direction.

13.2 BASIC CONCEPTS 13.2.1

green Cloud Computing

Cloud computing is a profoundly adaptable and low-cost basis for running several applications, for example, high-performance computing (HPC) undertaking and web applications. Notwithstanding, there is one major basic issue in cloud computing, which has been developing because of its growing curiosity, which has expanded the utilization of energy in the data center. The issue of high utilization not just expands the operation cost, which decreases the benefit of cloud providers, but also influences nature as the high utilization of vitality prompts high discharge of carbon. Subsequently, vitality effective arrangements are needed to limit the effect of cloud figuring on the earth [1]. The goal of making a cloud, which is environmentally friendly, can be accomplished by the utilization of green cloud computing [1]. The environmentally friendly structure, which is proficient regarding giving energy solutions and ends up being financially savvy, is the other need of a cloud provider in the wake of being an improvement in the current innovation [2]. The overall administrations and information have been given to the client through green cloud computing, which set various data centers in various areas. To limit the utilization of energy-efficient, accomplishing proficient preparing and using the computing framework, green cloud computing has been utilized by various experts [5]. The use of energy will increment to an enormous sum if the current cloud computing would not have the option to satisfy the requirements of increment in front-end customer gadgets that are collaborating with the data center accessible in the back end. The eco-accommodating utilization of PC and related assets is known as green computing. To accomplish this objective, energy-efficient central processing units have been applied as well the servers, peripherals, and a decrease in resource consumption are essential along with appropriate dumping of electronic waste [3]. The computer, servers and other subsystems that are associated with it are disposed of, designed, and manufactured by green computing. All the requirements have been fulfilled using it without putting any impact on the environment in terms of getting effective and efficient results [8]. There are numerous methods to green computing, i.e.: • • • •

Proficiency of the algorithm, Allocation of the resource, Server virtualization, and Management of power.

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Green Engineering and Technology

requirementS of green Computing on Cloud Computing

Current data centers, working under the cloud computing model, are introducing a variety of applications [9]. The requirement to deal with many applications in a data center makes the contest of on-demand resource provisioning and allocation in reply to time-varying assignments. The average data center consumes as much energy as 25,000 households [11]. Data centers are costly to keep up with, yet also antagonistic to nature [21]. Cloud computing stretches with high adaptability and performance. The data center is kept from other clusters of physical machines. The physical machine will be virtualized to make virtual machines [1]. Virtual machines will be assigned to the user. The services of cloud computing are rendered by the cloud service provider (CSP), due to heavy demand of data centers leading to huge energy consumption and CO2, this leads to huge power charge and power emissions. Hence, green computing is desired to cut power charge and CO2 emissions and raise profits. To report this issue, data center resources should be accomplished by using less energy to initiate green cloud computing (Figure 13.1).

13.2.3 CHallengeS of green Cloud Computing • The necessity of a novel optimization procedure: There is a necessity to make an adjustment between temperature and energy that helps in accomplishing the elite target [1]. • Minimize architecture complexity: To minimize the power required to start a system, the dependency of different components between each other needs to be reduced. • The necessity of competent data centers: In request to spare a lot of vitality, they have to utilize extremely restricted IT apparatus that further diminish the need for enormous data centers [1]. • Cooling of the data center: A major role has been played by sensor networks in handling data centers’ power consumption. • Green IT: A green software movement has become a key area of research by the advancement in the growth of IT industries, but still there is a need to take some more initiatives to fulfill the current needs. • Performance deprivation: The power consumption and throughput of energy have been increased by degrading the performance of servers. • Platform management: To deploy applications in a scalable environment and to maintain it, there is a need to manage server density. The cost of operation is higher related to energy that needs a large amount of server density that supports running applications of the cloud. • Round the clock cloud services: The data centers want constant power because of the accessibility of clients around the globe. • High reliability: A reliable power supply is required. • Cost-efficiency: To build less costly computation techniques.

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FIGURE 13.1 Requirements of green cloud computing.

• Virtualization of servers: It has been used to reduce energy consumption and to improve efficiency effectively. • Energy efficiency: The overall energy consumption rate has been minimized using it. • CO2 emissions: Reduce the CO2 emission rate from cloud resources.

13.2.4

virtual maCHine migration

In cloud computing, virtual machine migration (VMM) is a technique for migrating operating systems and various jobs between physical systems and is also used to reduce the load, fault management, and reduce the amount of energy consumed across multiple physical machines. Different problems are there in green cloud computing. By increasing the cloud resource utilization level with the use of virtualization, technology cloud operation costs get reduced to consierably [7]. However, if the use of virtualization is not done properly in cloud data centers, then the performance of the cloud can degrade too. VMM is a technique that supports cloud service providers to proficiently accomplish loud resources while abolishing the necessity of human regulation [6,19] (Figure 13.2). A. Needs of Virtual Machine Migration VMM is required for providing a hassle-free virtual environment to the client-side so that the clients can execute their tasks without any failure. The basic requirement of the VMM is depicted as follows [21]: • Load balancing: There are several numbers of DCs that are deployed in a cloud environment to successfully execute the user request. The main objective of cloud computing is to provide an environment where the

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FIGURE 13.2

Virtual machine migration.

users can execute their tasks regardless of the computational requirements. Sometimes, the case may arise where one DC will have multiple user requests simultaneously, which will make the DC more prone to failure. So, the cloud service provider checks the load on each deployed DC and tries to equalize the load among all active DCs by transferring some load from a heavy-loaded DC to the lightly loaded. • Maintenance and servicing: VMs are being migrated from one host to another by the cloud service provider with an objective to reboot the original host for better performance and a longer lifeline. • Improve resource utilization: VMM is performed to utilize the available resources effectively. The cloud computing environment allows resource sharing among different DCs and VMs. Therefore, the service provider manages the task from one VM to another depending upon the resource utilization to reduce the communication cost for sharing the resource. • Power management: Once the request is being submitted to the VM, it will first check for resource availability. If the resource is not available locally, then it will go for remote access of the resource from another VM and the requesting VM has to be active along with the server condition until the resource is being granted. Therefore, the VMM can help in managing the power by transferring requests to the VM, which has the requested resource.

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VMM is performed based on the threshold value depending upon the resource utilization, which is being decided by the service provider or the client in service legal agreement (SLA) [13]. In general, the high and low degree of resource utilization is set as the high threshold and lowest threshold. VMM can be done in two ways based on over and under resource utilization. VMM can be categorized in two ways as follows: • Static migration: This type of migration process pauses the current execution and searches for the less loaded DC to which the VM can be transferred. The benefit is an easy way and the drawback is long downtime [7,15]. • Dynamic migration: Dynamic migration allows the service provider to transfer the ongoing task in a VM without any halt from one DC to another and the primary objective of this process is to hide the process from the client-side. The benefit of dynamic migration is downtime, which is not visible to the user with a speedy network [4,7,15].

13.2.5

genetiC algoritHm

A genetic algorithm (GA) is a random search algorithm that is not just a one-point search, yet, in addition, consolidates a multipoint search highlight. It is tough to discover a fit receiver that is set up to get a supplementary assignment when a system becomes overloaded. Therefore, the GA is applied to decide the destination processor that can receive a task. Various viewpoints are desired in load balancing with a GA such as load measure, fitness function, coding strategy, and an algorithm [2]. • Load measure: There are three-level measure schemes that are used to denote the load state of the processor: lightly loaded processor, normally loaded processor, and heavily loaded processor. • Coding methods: It is projected that a likely response to an issue might be indicated as a set of parameters. These parameters (recognized as genes) are united to form a string of values (often referenced as a chromosome). • Fitness function: In dynamic load balancing, the fitness function consists of a fitter node. Moreover, a task can be transformed in such a manner that its time of completion and the cost of communication are less and system throughput and the use of processor are high. • Selection: During the successful completion of each generation, some part of the existing population takes part to breed new peers. This is based on the survival of the fittest mechanism. Individual solutions are chosen, where fitter solutions are bound to be chosen. • Crossover: Crossover opts for genes from parent chromosomes and creates a new offspring. Here, two individuals are selected and crossover sites are picked arbitrarily. • Mutation: Mutation is cast-off to alteration of the genes randomly in a chromosome. Mutation of a bit involves reversing it changing 0 to 1 and vice versa with a small probability.

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There are three different segments of GA, which are: GA is selected, in this chapter, to measure the load between virtual machines because the GA is not only a one-point search but also combines a multipoint search between different virtual machines and selects the more loaded one.

13.3 LITERATURE REVIEW Literature review includes previous studies related to green cloud computing and the major problems arising within them due to the occurrence of VMM. Various researchers have proposed numerous solutions to this problem, which are studied in this section. More et al. [11] have investigated dealing with reviewing many procedures, and algorithms, for competent green cloud computing by using virtualization procedures. Numerous procedures are related to power saving, which can also help in enhancing the efficiency of the systems based on the server and network involved. All such strategies are to be studied here to present a study on the existing methods [11]. The utilization of the same procedures will however not be possible once there is a huge increase in bandwidth and network connectivity in the data centers in the future. There is a need to understand the complete process of power mechanisms occurring within the data centers in order to control the abovementioned concern. The network devices such as servers, CPUs, and switches are the ones that consume the highest power. To design modern algorithms, research is still being carried out. New techniques with enhanced energy efficiency are being evolved, which also include the QoS (quality of service), SLA, and VM consolidation in these systems. They did not work on the ratio of computation and power, which helps in utilizing the resources in a better way along with minimum consumption of energy. Ehsan Arianyan [12] has proposed consolidation as a new practice for energy redeemable in cloud data centers. The main disadvantage of recent research on consolidation solutions is that they emphasize only one criterion and disregard the rest of things. According to the modified analytic hierarchy process (AHP) method, this study proposed a new multi-objective consolidation result. Three objectives have been considered in this, namely, energy consumption, SLA violation, and the number of migrations in the decision process. The comparisons are made among various approaches, and their results are evaluated in terms of simulation parameters. There is minimization in the energy consumption within the results achieved through the proposed method. By executing the proposed method in real cloud infrastructure management products, the experiments were carried out in this study [12].

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Larumbe et al. [13] have presented in this article that the response time of the systems is less for the users that are near to the VMs. This results in enhancing the QoS for the users due to the distribution of VMs near to those users. The impact of the maximization of energy consumption of the cloud is very negative. It might also affect the global warming of the planet. The solution to this issue is provided by placing the VMs within the data centers, which utilize the sources of green energy in the systems. A comprehensive optimization modeling system is provided for managing the applications, which include such dynamic demand. An efficient search heuristic is developed here to resolve the issues. As per the results achieved by implementing the proposed technique, there is a reduction in the communication delay, the power consumption is saved, and there is a minimization of the CO2 emissions as well. The meta-scheduler execution time is maintained here in the proposed approach, which helps in providing an efficient execution time. Chonglin et al. [14] have recommended that for research utilization, virtual machine consolidation is the best solution found. Once the power consumption for each VM is known, more power can be saved here. There are numerous modeling methods proposed here to estimate the power consumption as it is not easily calculated directly. When the multi-VMs compete with the resources on the same server, the performance of current models is not very accurate. There is a correlation between the resource features to provide modeling. A tree regression-based method is proposed in this paper, which helps in computing the power being consumed by the VMs on similar hosts of the systems. The dataset will be partitioned as per the advantages of this method. Here, each dataset is an easy-modeling subset for the other. In various applications that run on VMs, the accuracy achieved by applying this proposed method is around 98% as per the experimental results. The accuracy of individual VMs was however not computed in this study. HAN et al. [15] studied the cooperative behavior of multiple cloud servers and proposed the hierarchical cooperative game model for improving energy efficiency in green clouds. An innovative technique is anticipated in this work, which provides a change in the multiplexed methods that are needed for initial optimal solutions for various users. This results in reducing the loss of efficiency in these systems. Both optimization and fairness have been considered by this algorithm. In a public cloud environment to improve efficiency, the game theory is applied to virtual machine deployment. The drawback of this scheme is that when the resource allocation game has feasible solutions, only then Nash equilibrium exists. Marotta et al. [16] demonstrated that big data centers need to minimize consumption along with the utilization of virtualization technology. This is mainly due to the environmental pollution caused by them as well as the other economy-related issues arising within these systems. The virtual machines consolidation method is one of the many methods, which help in reducing the energy consumption within these systems. In this article the major objective is to maximize the cost-efficiency along with the minimization of the number of active nodes that are being utilized currently in a system. A novel technique is proposed here to consolidate the problem. The attractiveness of the possible VM migrations can be evaluated based on the

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simulated annealing-based algorithm. The other issues related to the topology and traffic of the VM are not highlighted in this article. Wadhwa et al. [17] have proposed a novel approach that will help in minimizing the carbon being emitted and the energy being consumed within the distributed cloud data centers. Within the distributed architecture of the cloud, the proposed architecture utilized the data centers' footprint rate of the carbon. The authors have also added the method of virtual machine allocation and migration to reduce the emission from carbon and consumption of energy in the federal cloud systems. The simulation results of the proposed work show that this novel technique helps in minimizing the emission of harmful rays of CO2 and also the energy being consumed in the cloud data centers. The results are also compared here with the earlier proposed scheduling method, which includes round-robin VM scheduling within their cloud data centers. The drawback of this article is that using the proposed technique works well for a small number of VM requests, but consumption of energy increases for a large number of VM requests. Huang et al. [18] have proposed two new dynamic VM migration algorithms. The potentially over-utilized servers have been predicted by applying a method of local regression and then from migration best fit VMs combination has been found using 0–1 knapsack dynamic programming. The results of this algorithm have been analyzed in terms of complexity of time and have seen that they are highly scalable as compared to existing algorithms in terms of different performance parameters. The energy consumption and the number of VM migrations need to reboot servers that have been reduced by much extent using two new heuristic schemes. Therefore, from all the results, it has been concluded that by the use of the proposed two heuristic algorithms, the green cloud computing can be achieved. In this article the problem of SLA violation caused by overloaded servers is not resolved by the proposed algorithm. Kinger et al. [19] have concluded that by continuous consolidation of VMs, the objective of saving energy can be achieved. The cloud computing thermal state current utilization has been used for moving toward the consolidation of green computing. Both consolidation and resource management have some relation. The energy has been saved to a much extent in the cloud using VMM. In this study, workload management has been used for migration that helps in keeping the temperature power consumption within the limit. Finding the target and source machine is the main challenge faced in migration. Rasouli et al. [20] have recommended that the use of large data centers results in problems such as emissions of greenhouse gases and an increase in the cost of energy. So, it has become a key topic for researchers to provide an efficient method that reduces the data centers' energy consumption. The main aim of this approach is to force idle nodes to go in sleep mode while there is live migration. The real-world workload situation has been considered to assess the performance of the anticipated method. The energy consumption has been reduced by much extent using the proposed method but still, it is not proved to be efficient in improving the whole efficiency of the system.

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13.4 MOTIVATION AND OBJECTIVES Cloud computing is the architecture that stores data on the virtual servers, which has the least consumption of network resources. The cloud provides flexibility to users to store and process that data. Due to the decentralized architecture of the network, various issues get raised, which are energy consumption, fault occurrence, security, and so on. Green cloud computing is the advanced version of cloud computing that uses the least number of network resources for data storage and processing. The fault occurrence is the main problem in green cloud computing, which may arise due to the wrong assignment of the cloudlets to virtual machines. The fault-tolerant approach is required for green cloud computing, which recovers occurred fault at the least amount of time and consumes the least amount of network energy. Following are the various objectives of this chapter:

4. Implement the proposed algorithm and compare it with the existing metaheuristic algorithm in terms of various parameters.

13.5 PROBLEM STATEMENT Green cloud computing is an efficient approach that reduces energy consumption for data storage on clouds. In the network architecture, virtual servers, brokers, and cloud service providers are involved in the data communication. The brokers are the third party that assigns cloudlets to the most capable virtual machines. In this chapter, the technique of virtual machine migration will be proposed, which reduces the chances of fault occurrence and also reduces resource consumption in the network. Green cloud computing is the improved version of traditional cloud architecture, which increases the efficiency of the network in terms of their energy consumption. Overloading is the main issue of green cloud computing and techniques, which are proposed to handle overloading that can increase the number of migrations. An efficient approach is required, which can reduce the number of migrations in the network. This chapter takes the research challenges with VMM techniques on green cloud computing based on a GA. Particuarly, the subsequent research problems are explored: • How to make cloud computing environmentally friendly? CC gives a profitable setup with high scalability and performance [1]. However, the growing demands of cloud infrastructures user lead to the high energy consumption of data center, which results in carbon emission to the environment and it is not environmentally friendly. Therefore, working

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on the cloud to save energy and reduce carbon emissions leads to go green while saving time and money. How to design and build green data centers in a cloud? Cloud computing stores and runs different infrastructures on data centers. To satisfy the user's needs, different physical machines on the data center will be virtualized. There are two ways to build green data centers [21]: (i) use green elements in the design and construction process of data centers and (ii) greenify the course of running and functioning a data center on a daily basis. When, which, and why VMM is required? There are two-migration processes. First, migrating virtual machines from an overloaded server to avoid performance degradation. Second, migrating virtual machines from underloaded servers to advance resource consumption and minimizing energy utilization. A vital verdict that must be made in both situations is to determine the best time to migrate virtual machines to minimize energy consumption, which satisfies reduction of CO2 emission. Before migration, identifying which physical machine is overloaded and preparing the destination physical machine where the migrated job can be assigned should be performed. Where to migrate the VMs nominated for migration? Determining the finest settlement of new VMs or the selected VMs nominated for migration to other servers is an added vital characteristic that influences the excellence of the system. When and which physical server to switch ON/OFF? To optimize energy consumption by the system and avoid carbon footprint, it is required to tactfully decide the place, identification of physical server for deactivation. By using the proper order of deactivation, it saves energy or restarts to handle an increase in the demand of resources.

The basic problems that this chapter solves are the following: • More power costs and high CO2 emissions occur due to the high amount of power needed in the data center. • Properly UN assigned jobs on virtual machines make overloads/under loads. • Due to the decentralized architecture of the network, various fault occurrence issues are raised on the network.

13.6 PROPOSED WORK 13.6.1

propoSed ant Colony optimization (aCo) algoritHm for tHe SeleCtion of tHe moSt appropriate vm

Green cloud computing is a less energy consumption tactic used to supply and execute the data from the userbase. Fault tolerance is a key subject of green cloud computing. In this chapter, the authors use the ACO algorithm for two purposes such as execution and assignment of the task to various DCs for processing. Three phases of the ACO algorithm are as follows:-

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floW CHart of tHe propoSed Work (figure 13.3)

FIGURE 13.3 Flow chart of the proposed work.

13.7

IMPLEMENTATION AND RESULT

The proposed work has been simulated in a cloud sim and cloud analyst environment. Initially, we have taken ten user bases or UBs, which are being situated in six different regions. Each UB is being equipped with a different number of users. The population has been decided randomly and the users of UB are responsible for generating the requests. To handle the requests, some data centers have been implemented with different computational capacities. For experimental purposes, we have implemented 60 numbers of VMs. We have taken the various numbers of requests such as 100, 200, 300, 400, and so on. The closest data center has been taken as the service broker policy, which is wholly responsible for handling the requests coming from the UB

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Average response time for the proposed work.

FIGURE 13.5 DC energy consumption for the proposed work.

toward the DC. For performance measurement, the average energy consumption and response time have been considered with respect to the number of requests. It has been noticed that according to the traffic, the response time and the energy consumption for the DCs are also increasing (Figures 13.4 and 13.5).

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CONCLUSION AND FUTURE WORK

This chapter proposes a combined approach to tackle the power utilization issue. A task-oriented resource allotment strategy (ACO) is proposed; it can decrease the energy consumption of the data center focus adequately on the reason for execution ensure. To approve the adequacy of the proposed strategy, the simulations have been worked on cloud sim and cloud analyst. This can be implemented using various meta-heuristic algorithms [22–31]. In future work, the authors will attempt to consider another sort of model, which can be incredibly powerful for breaking down specific issues in the cloud data center including heterogeneous tasks scheduling and flaw diagnosing, and authors may take more factors into thought; for instance, not just time furthermore, power utilization yet additionally, the condition of the hosts can impact the energy of the data center. Another promising future work heading is to attempt to utilize other biocomputing strategies to tackle a few issues in green cloud computing.

REFERENCES 1. Rao, G. Jagadeeswara, and G. Stalin Babu. “Energy analysis of task scheduling algorithms in green cloud.” In 2017 International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), pp. 302–305. IEEE, Bangalore, 2017. 3. Ibrahim, Abdallah Ali ZA, Dzmitry Kliazovich, Pascal Bouvry, and Ariel Oleksiak. “Virtual desktop infrastructures: Architecture, survey and green aspects proof of concept.” In 2016 Seventh International Green and Sustainable Computing Conference (IGSC), pp. 1–8. IEEE, Hangzhou, 2016. 4. Agrawal, Shalabh, Rana Biswas, and Asoke Nath. “Virtual desktop infrastructure in higher education institution: Energy efficiency as an application of green computing.” In 2014 Fourth International Conference on Communication Systems and Network Technologies, pp. 601–605. IEEE, Bhopal, 2014. 5. Kochut, Andrzej. “Power and performance modeling of virtualized desktop systems.” In 2009 IEEE International Symposium on Modeling, Analysis & Simulation of Computer and Telecommunication Systems, pp. 1–10. IEEE, London, 2009. 6. Kaur, Gaganjot, and Sugandhi Midha. “A preemptive priority based job scheduling algorithm in green cloud computing.” In 2016 6th International Conference-Cloud System and Big Data Engineering (Confluence), pp. 152–156. IEEE, Noida, 2016. 7. Acharya, Shreenath, and Demian Antony D’Mello. “A taxonomy of Live Virtual Machine (VM) Migration mechanisms in cloud computing environment.” In 2013 International Conference on Green Computing, Communication and Conservation of Energy (ICGCE), pp. 809–815. IEEE, Chennai, 2013. 9. Masoudi, Meysam, Behzad Khamidehi, and Cicek Cavdar. “Green cloud computing for multi cell networks.” In 2017 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–6. IEEE, San Francisco, CA, 2017.

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14

Internet of Things for Green Technology Saurabh Bhattacharya and Manju Pandey NIT

CONTENTS 14.1 14.2 14.3 14.4

Introduction .................................................................................................. 243 Carbon Emission........................................................................................... . 245 Green IoT ...................................................................................................... 247 Steps to Achieve Green Technology for the Internet of Things (GIoT) ........................................................................................... 249 14.4.1 Design and Develop Energy-Efficient Hardware.............................. 249 14.4.2 Usage of Power Management Technology ........................................ 249 14.4.3 More Preference for Virtualization Technology............................... 249 14.4.4 More Dependency on the Cloud Computing Technology ................ 249 14.4.5 Optimizing Data Center for Energy Efficiency ................................ 250 14.4.6 More Usage of Efficient Displays ..................................................... 250 14.4.7 Managing e-Wasteby Recycling the Systems ................................... 251 14.4.8 Encourage Work From Home ........................................................... 251 14.5 Issues and Challenges in Implementing GIoT .............................................. 253 14.5.1 Interoperability ................................................................................. 253 14.5.2 Evolution of 5G ................................................................................. 254 14.5.3 Issues with WSN ............................................................................... 255 14.6 Conclusion .................................................................................................... 256 References .............................................................................................................. 257

14.1 INTRODUCTION Climate scientists presented the most alarming recent research on the greenhouse effect. The accumulation of greenhouse gases (GHG) in the atmosphere is increasing at a faster speed as predicted. “Green Technology” or “Going Green” is the most discussed topic and trend in the business world, as the thrust for adopting eco-friendly practices gain momentum for every industry. The pioneer of the IT (information technology) industry is searching for a path to green technology and more environmentally friendly inclusion. How they can improve awareness and minimize costs by being environmentally friendly? It covers most of the carbon emission industries, including road transport, airlines, and generating electricity to IT industry. The main focus of implementing is to provide eco-friendly, correct implementation to make operations more cost-effective and 243

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efficient, and to reduce carbon emission. An optimized way of using energy resource is encouraging the usage of the biodegradable product or recyclable product and minimizing the involvement of hazardous materials without affecting the production. The fast-growing IT industry or information and communication technology (ICT) [R-3,4] recently with the advancement of wireless technologies, sensors, actuators, and remote monitoring contribute a drastic increase in carbon emission, which is as similar as the aviation industry. Due to phenomenal demand for computing devices, software and services, the IT industry or ICT (Figure 14.1) shows rapid growth of carbon emissions each year. The ICT industry plays a crucial role in designing and deploying solutions to provide a low carbon emission system with other sectors. According to climate group - SMART2020 ICT sector’s own emissions are expected to increase, in a business as usual (BAU) scenario, from 0.53 billion tonnes (Gt) carbon dioxide equivalent (CO2e) in 2002 to 1.43 GtCO2e in 2020. But specific ICT opportunities identified in this report can lead to emission reductions five times the size of the sector’s own footprint, up to 7.8 GtCO2e, or 15% of total BAU emissions by 2020.

The Internet of Things (IoT) has been designed to demonstrate numerous developments, and analysis imparts global accessibility over various physical devices. Empowering advances like radio-frequency identification (RFID), sensor organizations, biometrics, and nanotechnologies are presently getting normal, bringing the IoT into actual executions tending to differing applications, including smart cities, smart health, smart logistic, smart traffic, or transportation. They mention an energizing future that intently interconnects our physical world through green organizations [10]. Green organizations in IoT will add to decreasing outflows and contaminations and limiting operational expenses and power consumption. The green Internet of Things (G-IoT) [2,3,12,13] is anticipated to present critical changes in our day-by-day life and would help us understand the vision of "green

FIGURE 14.1 Information and Communication Technology (ICT).

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encompassing knowledge”. These new brilliant items will likewise be setting mindful and ready to play out specific capacities like self-ruling, calling for new types of green correspondence among ‘individuals and things’, and between ‘things themselves’, where power utilization is improved and data transmission usage is boosted. This advancement would be pertinent not exclusively to analysts yet in addition to enterprises and people the same. The point of this particular problem of reducing GHG was to minimize in on both hypothetical and usage approaches in green cutting-edge organizations that can use IoT to provide green frameworks to empower innovations. IoT has already revolutionized the digital world and is reshaping the traditional business concept. The primary reason for adopting IoT in various sectors such as smart health, smart city, smart logistic, smart agriculture, smart sensing, and smart technology is that it reduces energy consumption and carbon dioxide emission in multiple scenarios [7,14,17,18]. IoT helps in realizing the real vision of green technology.

14.2 CARBON EMISSION According to NASA, carbon dioxide (CO2) comprises 411 ppm of the earth’s atmosphere as of July 2019. Because CO2 is a greenhouse gas that traps heat, there is a strong correlation between the amount of CO2 contained in the earth’s atmosphere and climate; the more CO2 there is in earth’s atmosphere, the hotter it gets.

Carbon is the most common element for life on earth, from the air we breathe in to the growing of crops. Gases that trap the heat in the atmosphere to provide suitable climate on earth are known as GHG. About 81% of GHG consist of carbon dioxide and remaining are methane, nitrous oxide, and fluorinated gases. Whenever we discuss carbon emission, we are primarily focusing on carbon dioxide. Carbon dioxide exchanges between oceans and atmosphere are the largest source of natural carbon emission [11]. Plants and animals emit CO2 through respiration process. The carbon cycle supports life on earth through maintaining continuous process cycle over air, water, and soil. Nature does not receive similar treatment from humans. When fossil fuel is used as a significant source of energy, it releases a tremendous amount of carbon dioxide and other GHG into the atmosphere—year by year global carbon dioxide emission increases. By the year 2020, it raised nearly to 34,000 million metric tons, and by 2040 it is estimated that it will cross 40,000 million metric tons. In recent years, human actions led to vast deforestation and excess usage of fossil fuels to compete with the essential requirement for living caused an increase in the amount of carbon dioxide in the earth’s atmosphere. Climate change due to carbon emission is a crucial concern for every country. As shown in Figure 14.2, the adverse effect of carbon emission in our lives and well-beings is directly and adversely affected by it because of rapid growth in the emission of CO2. By 2040, it is expected to cross 40,000 million metric tons (MMTs) of carbon dioxide emission. As shown in Figure 14.3, in 2018, China (10.06GT), United States (5.14GT), India (2.65GT), Russian Federation (1.71GT), and Japan (1.16GT) are top five countries in CO2 emission [11].

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FIGURE 14.2 Global carbon dioxide emission, 1850–2040. (Carbon di oxide Information Analysis center (Oak Ridge National Laboratory, 2017), World Energy Outlook (International Energy Agency, 2019).)

FIGURE 14.3 Greenhouse gas emission for Major Economics, 1990–2030. (World Energy Outlook (International Energy Agency, 2019), CO2 Highlights (International Energy Agency, 2019), International Non-CO2 Projections (U.S. Environmental Protection Agency, 2012).)

Crucial carbon emission saving apprehended in the developing world. Developing and deploying low-cost and low-power devices make precision to various industries leading to modernizing countries much quicker. This also helps in conserving water, soil, air, and fossil fuels. Countries are very much seeking a resolution for the hastening and the continuing threat of changing climate, which causes global warming. However, technologies like IoT already help in reducing carbon emission significantly

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and boosting energy efficiency across various industries. IoT impacts significantly on dipping carbon footprint. Dependency on ICT rapidly increases in a short duration of time. When dealing with carbon emission, the websites used daily produce an ample amount of carbon emission. A regular website releases about 4.61 g of CO2 every single visit: popular websites having tremendous views per month release 533 kg of CO2. There are various tools available to calculate the carbon emission from websites, mobiles, laptops, data centers, and sensors (www.websitecarbon.com for calculating website carbon emission, www.openei.org for calculating carbon emission of mobile phones). Every visit to google produces 0.30 g of carbon though it is cleaner than 80% as compared to other websites. Considering the calculation of website CO2 emission, it depends on five primary factors: 1. 2. 3. 4. 5.

Data transmitted over the wire. Source of energy used by the data center. Website traffic. Carbon emission of electricity. Energy consumed by the interconnected network, data center handling the request and response, consumption of electricity at the user’s end while using a computer/mobile.

14.3 GREEN IoT G-IoT is a procedure adopted for energy efficiency, reducing the greenhouse effect, reducing energy consumption, and CO2 emission [4,6]. GIoT provides sustainability to make the world smarter and safer. Figure 14.4 shows the combination of G-IoT, which comprises all components based on green technology. The main aim of GIoT is to provide the energy-efficient technology to smart home, smart grid, smart health, smart logistics, smart agriculture, smart traffic, and reducing GHG emission. According to EPA.gov transportation (29%), electrical production (28%) and industry (22%) are the significant contributors as the largest emitters of carbon dioxide. Turning every stone to reduce the carbon emission requires tremendous effort from every single person, business organization, and government to curb emission by exploring efficient and optimal usage of energy without disturbing the production. Artificial intelligence (AI) and IoT provide tremendous opportunities to lower the carbon emission level by proper utilization of energy storage and optimization of energy efficiency as most of the IoT devices depend on electrical support to perform. In recent trends, solar energy, tidal energy, and wind energy are used to harvest their own energy for processing, leading to a reduction in the usage of battery. The number of interconnected devices increases rapidly from 50 billion by 2020 [8] to expected 100 billion by 2030[R-3], which generates a tremendous amount of data. Constantly generating massive data from various devices and actuators consumes electricity and produces heat, which lead to an increase in carbon emissions in the environment. Due to the tremendous amount of CO2 emissions causing health and environmental concerns, green technology or renewable technology is considered as an attractive research area in the progression of technology. According to the world

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FIGURE 14.4 Green Internet of Things.

economic forum, using IoT and AI could cut carbon by 15%. IoT and AI utilized to reduce the carbon emission improve resource efficiency, provide optimized production and stimulate innovation and thinking. The significant aspects of designing GIoT [5,15]: 1. To design and develop green computing devices, green processing, green interconnected network, and green communication protocols to achieve optimized power consumption along with maximizing bandwidth utilization. 2. To dispose of green computing devices efficiently to reduce carbon emission and pollution. 3. To enhance the working and energy efficiency of devices without affecting productivity. People have become more aware of the adverse effects of environmental degradation and more concerned over GHG emissions causing global warming. IoT provided the services using things like sensors, RFIDs, and actuators using data centers where data are transmitted over the network. Recent advancement in the field of carbon footprinting paves the path of new upgradation from IoT to GIoT. GIoT ensures the efficient use of things to maintain carbon and GHG emission in the environment. It contributes to reducing emissions, pollution, operational cost, and power consumption without affecting the services.

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Energy efficiency is the critical factor for designing and developing GIoT. GIoT helps to reduce GHG emission in the existed system by minimal changes or to create a completely new green system by focusing on green design, green production, green implementation, green utilization, and green recycling/ disposal with a minimal impact on the atmosphere. Designing and developing green systems help to optimize the IoT greenhouse footprint, providing green smart cities, green smart health services, green smart traffic, green smart education, green smart logistic, green smart grid, and green smart home.

14.4 14.4.1

STEPS TO ACHIEVE GREEN TECHNOLOGY FOR THE INTERNET OF THINGS (GIoT) deSign and develop energy-effiCient HardWare

The critical aspect for attaining GIoT is to develop and design hardware, which is more energy-efficient as compared to existing hardware. Major IT industry vendors including laptops/desktops, servers, and workstations are evolving to meet the standard of EPA’s energy star guidelines. These guidelines use the standard set by the IEEE to measure environmental performance, developing multicore processors to increase in the output without affecting energy usage, high-efficiency power supplies, low-voltage processor, and efficient cooling techniques for data centers. Efficient energy criteria “Energy Star 4.0” set by Electronic Product Environmental Assessment Tool (EPEAT – www.epeat. com) is a worldwide ecolabel for the IT industry.

14.4.2

uSage of poWer management teCHnology

Advanced configuration and power interface (ACPI) is used by the latest operating system to incorporate efficient power management. It allows us to manage and monitor the power of the system over a while. Various hardware vendors provide their own power management software to provide optimal usage of energy.

14.4.3

more preferenCe for virtualization teCHnology

Virtualization technology helps to reduce the number of physical servers and hence provides efficient energy consumption. As virtualization supports to run multiple servers on a single server, it virtually reduces energy consumption and helps to maintain carbon emission. The virtualization facility provided by VMW claims that it decreases the energy cost by almost 80%. It helps in reducing carbon emission by achieving high virtualization.

14.4.4

more dependenCy on tHe Cloud Computing teCHnology

As per the Pike Research, a clean technology market intelligence firm claims that cloud computing could emerge as a significant role changer in reducing energy consumption. Cloud computing provided on-demand service facility to the users,

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making it more energy-convenient. Shifted to cloud computing from tradition mode helps to reduce the energy usage in 2020 by 38% in the world’s data center. Furthermore, these reductions of energy usage will result in a drop of 28% in GHG emission.

14.4.5

optimizing data Center for energy effiCienCy

According to the International Energy Agency (IEA) during COVID-19 lockdown and containment measures, the demand for video streaming, video conferencing, online movies/ games, and social networking increased tremendously. During COVID-19, Internet traffic surge worldwide increased by 40%. Data centers manage most of the Internet protocol in the world, higher traffic demand higher energy consumption. Worldwide Internet traffic surge is increased rapidly and expected to double by the year 2022 to 4.2ZB per year. The number of mobile Internet users and IoT connections are rapidly increasing putting pressure on data centers. In 2019, data center’s energy consumption demand is around 200 TWh worldwide. In the current scenario, where “Green” is the main objective, it is necessary to redesign the infrastructure of the data center to cope up with increasing data traffic efficiently with minimizing the carbon emission. The data center must look for an alternate mode of power supply like solar, wind, water, and tidal energy instead of the traditional power generation method where fossil fuels are used. The more the demand, the more the energy required to serve. Massive consumption of energy needed high cooling facilities in the data center. Redesigning of the data center should consider more energy convenient infrastructure including building design, geographical area, alternative cooling techniques like liquid cooling and evaporative cooling, an alternative source of energy, low powered servers, efficient and uninterrupted power supplies to minimize the GHG.

14.4.6

more uSage of effiCient diSplayS

One of the significant parts of the computer that consumes lots of energy for working is the monitor. Selecting proper monitors can save up to 60%–70% of energy cost. Old cathode ray tube (CRT) monitors consume more power as compared to liquid crystal display (LCD) and light-emitting diode (LED) monitors. High-efficient monitors are used to reduce the GHG emission. High-efficient LCDs are available from several vendors. Figure 14.5 shows the comparison between CRT, LCD, and LED, and it clearly shows that energy consumption of CRT is comparatively very high as compared to LCD and LED monitors. Many vendors are developing energy-efficient monitors to ensure minimum carbon emission. Nowadays, monitor manufacturing companies are using product carbon footprinting to monitor and reduce the impact of carbon emission at different stages of developmental phases. A product carbon footprint is restricted as the aggregated amount of GHG are emitted directly and indirectly by a product over its lifetime. It incorporates outflows from materials extraction, production, appropriation, use, and end-of-life.

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FIGURE 14.5

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Energy efficiency comparison between CRT, LCD, and LED [16].

managing e-WaSteBy reCyCling tHe SyStemS

Each year, obsolete, unwanted, and unused electronics items such as computers, printers, mobiles, computer peripherals, networking devices, and IT products sum up of 20–25 tons of e-waste. Now disposing of e-waste turns to be a global problem. No standard protocols or facility is available to destroy the e-waste. Burning/ incineration of e-waste is used as a standard method. When electronic devices are burnt, they release highly toxic chemicals like polybrominated biphenyl and polybrominated biphenyl ethers. Released toxins get accumulated in the atmosphere and harm the environment and our health. Burning/incineration causes the release of methane, which is 25 times more potent to trap heat in the atmosphere. Going green must redesign the electronics for a longer life span to sustain. Reusing the electronics parts, repurposing, and recycling the old systems save the environment from hazardous chemicals. Companies such as Dell, HP, Panasonic, and Asus take back computers and electronic peripherals for proper recycling and reusing. Recycling and reusing not only save money but also help to maintain a green environment.

14.4.8

enCourage Work from Home

ICT during COVID-19 provides “work from home” as a new dimension of reducing GHG emission. Many tech giants encourage their employees to work remotely. Reducing public and personal transport and reducing energy consumption in offices show a notable decline in emission, and it puts the world toward attaining long sustainable energy goals and reducing GHG emission. Due to lockdown, major cities showed the foremost downfall in road traffic from 50% to 75% around the world (Figure 14.6).

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FIGURE 14.6 Average rush-hour traffic congestion in selected cities in 2019 and during lockdowns. (International Energy Agency.)

FIGURE 14.7 Change in global CO2 emissions and final energy consumption by fuel in the “home-working” scenario. (International Energy Agency.)

Work from home helps in reducing total energy demand through transport, power consumption to maintain office infrastructure, and fuel consumption. Figure 14.7 shows a dramatic downfall in annual CO2 emission by 24Mt and helps to sustain green technology effectively.

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ISSUES AND CHALLENGES IN IMPLEMENTING GIoT

With the continuous development of IoT, the number of sensors, actuators, RFID, and devices is also increased, which consume more energy unprecedently. IoT devices are deployed under a continuous working environment that makes them to be considered as a high-energy guzzler in ICT. One of the primary reasons for more energy consumption in IoT devices is using heterogenous devices with non-standard protocols. While focusing on successful implementation of IoT for green technology, focus should be on low power consumption to maintain GHG emission. Interoperability, the evolution of 5G, and developing green wireless sensor networks are the significant challenges in the successful implementation of IoT in green technology [1].

14.5.1

interoperaBility

Interoperability facilitates real-time communication with a similar type of device. In the case of IoT, where the number of heterogeneous devices is high, compiling with different standards and protocols makes interoperability a big issue. To resolve interoperability issues, additional hardware or software are needed to be installed, which will increase GHG emission. Significant interoperability issues to be determined to attain efficient and effective GIoT are as follows:

b. Network interoperability: IoT devices are generally heterogeneous and multi-service in nature; they rely on short-range wireless communication, which makes them unreliable and intermittent. Network interoperability is able to handle various issues such as data routing, addressing, quality of service, and security due to heterogeneous and dynamic network environment. c. Platform interoperability: Presently, many operating systems are developed especially for IoT devices such as Ubuntu core, Fushsia, RIOT, Contiki, and TinyOS each having their versions and services for users. Even every IoT platform provider uses separate data structures and programming languages like Google brillo uses Weave, Amazon AWS uses SKD, and Apple uses swift for their Apple HomeKit. This non-uniformity creates a hindrance to developing a successful and energy efficient cross-platform IoT application. d. Semantics interoperability: The W3C defines semantic interoperability as enabling different agents, services, and applications to exchange information, data, and knowledge in a meaningful way, on and off the Web. IoT

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devices use various API, use a different mode of communication, generate data in different forms like XML, CSV, or JSON, and use different schemas and data model. This semantic irreconcilability makes it difficult to create an interoperable environment for smooth data exchange.

14.5.2

evolution of 5g

The International Telecommunication Union – responsible for radio communication (ITU-R) – has defined three main application areas for the enhanced capabilities of 5G. They are Enhanced Mobile Broadband (eMBB), Ultra-Reliable Low Latency Communications (URLLC), and Massive Machine Type Communications (mMTC). Only eMBB deployed in 2020; URLLC and mMTC taken quite a while to use in many areas.

Figure 14.8 shows the various usage scenario of 5G in various dimensions. Enhanced Mobile Broadband (eMBB) is an advance version of 4G LTE mobile broadband services, which provides a faster connection with more capacity for data handling. Ultra-Reliable Low-Latency Communications (URLLC) is one of the use cases used by 5G new radio standard. URLLC cater multiple advanced services that require reliable, robust, and uninterrupted data exchange. The main aim to develop 5G is to support a wide range of devices, services, and applications by extending its

FIGURE 14.8 5G Usage Scenario. (www.edn.com.)

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network capabilities to extreme performance. Machine-type communication (MTC) is a communication paradigm to connect numerous devices, which are connected to the network and communicate with each other with very little or no human intervention. In the case of 5G, a massive number of devices are connected to serve a huge number of things hence called massive machine type communication (mMTC). Still, developing countries like India have to wait for 5G technologies, which include few issues and challenges for successful implementation: The major issues with 5G:



14.5.3

iSSueS WitH WSn

There are several issues and challenges in the successful implementation of the green wireless sensor networks (green-WSN/ GWSN) [9], which act as the backbone of implementing GIoT. Sensor nodes require a constant supply of non-renewable energy for working. Implementing GWSN, alternative source of energy, must be considered to reduce GHG emission. Efficient protocols must be designed from the beginning to provide efficient energy and management to devices. Let us discuss the major individual design issues in GWSN – fault tolerance, scalability, production cost, hardware constraint, sensor network topology, transmission medium, and power consumption.



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d.

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and power consumption. It needs to look for an alternative source of energy, which supplies uninterruptedly and without emitting GHG. Maintaining the performance of the network entirely relies on the high-resolution data transmission within a huge infrastructure, potentially without affecting productivity. Production costs: Using disposable devices as sensor nodes increases the production cost. This led to one more issue of adequately disposing devices as many hazardous elements and metals such as iron, alumina, and zirconia are used in various physical, chemical and biological sensors release, which increase the GHG emission in the atmosphere. Redesigning and redeveloping devices under reuse policy make production cost high. Hardware constraints: Every sensor node in a network requires a sensing device/unit, a processing unit, a power supply, and a transmission unit. These require a constant power supply. Any additional functionality increases the production cost and power consumption. Thus, any additional functionality must be within cost and efficient power consumption. Sensor network topology: Maintaining and redesigning topology is one of the most important factors to reduce energy consumption in GWSN. Any deployed topology requires several protocols, techniques, and algorithms to efficiently balance the constant supply of energy, proper usage of memory, data transmission, and communication capabilities with various devices. Transmission media: Most of the devices interconnected with each other in a network are connected by radio communication. However, many network structures use infrared communication and optical fibers as 11a primary communication mode. They must provide an interference-free environment to transmit data over the network. Power consumption: One of the most challenging issues in implementing green technology in any area is power consumption. Either it is a smart city, smart home, smart traffic, smart logistics or a smart grid, efficient and effective implementation of power consumption is always remaining a cumbersome task. Proper implementation to release minimum GHG gases hardware and software used must be redesigned to ensure effective execution of energy policy to deliver efficient energy usage in the network.

14.6 CONCLUSION Technology is tremendously changing at a fast pace. ICT is evolving as a backbone for every industry. Phenomenal technology increases sophistication as well as challenges of GHG. To make a more sustainable earth, green technology must be adapted with IoT. With the expansion of technology, IoT is turning to be the Internet of Every Things (IoET), providing the facilities of anyone communicating at anytime from anywhere with anything. IoT indisputably reformed technological advancement toward energy-efficient technology to accomplish green technology. Green technology initiatives are essential to reduce GHG. Following should be performed to achieve the vision of GIoT.

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Green Health: Making Green Healthcare Using Reinforcement Learning in Fog-assisted Cloud Environment Saeed A. L. Amodi and Sudhansu Shekhar Patra KIIT Deemed to be University

Om Prakash Jena Ravenshaw University

Suman Bhattacharya KIIT Deemed to be University

Nitin S. Goje Tishk International University

Rabindra Kumar Barik KIIT Deemed to be University

CONTENTS 15.1 Introduction .................................................................................................. 260 15.2 Literature Review ......................................................................................... 262 15.3 Reinforcement Learning ............................................................................... 262 15.4 Q-Learning ................................................................................................... 264 15.5 System Model ............................................................................................... 265 15.6 Dynamic Consolidation of VMs Based on Reinforcement Learning .......... 266 15.7 Learning Agent ............................................................................................. 267 15.8 Simulation Results ........................................................................................ 268 15.9 Conclusion .................................................................................................... 270 References .............................................................................................................. 270

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15.1 INTRODUCTION Healthcare facilities involve doctors’ offices, outpatient centers, medical labs, telemedicine centers, clinical healthcare facilities, surgical facilities, community healthcare centers, and so on. In the US, approximately 5% of the total area is occupied by healthcare commercial buildings and consumes around 10.5% of the energy consumed. The energy consumption in the healthcare sector is due to various parameters including cooling, space heating, lighting, medical equipment usage, ventilation, and so on. Finally, the information and communications technology (ICT) services for the healthcare sector is consuming the maximum [1]. In the healthcare field, a large volume of data are collected every day from various sources that may be from patient history or record keeping. Many of the data are structured, unstructured, and semi-structured [2,3]. In today’s digital world, each patient's data including their disease history, medical records, and medical reports should be digitized [4]. For better prediction, better treatment of the patient, and also for the future research field, the data should be properly preserved and analyzed effectively with minimum cost. Therefore, there is a need for technology, which should help us in this area by taking care of the large dataset. Big data analytics help in giving the valuable inside from the data patterns by using data mining [5], data science, and machine learning algorithms [6,7]. After collecting the data, it has to be processed, and then only the important information is mined from the data. Big data bring a lot of revolution in the healthcare industry. The data can only be processed if it is stored securely. For storing the data, the industry is taking the help of the cloud providers [8]. Making the healthcare industry energy-efficient [9, 10], and green is a brilliant idea for solving climate change issues. There is an urgent need for green computing by raising awareness among the users through less electricity consumption. Globally, this sector has growing importance, has a crucial role in the current scenario, and affects the world’s economy. Nowadays, the healthcare sector is using all the technological advancements in its uses such as advanced surgical equipment, remote healthcare monitoring system, modern digital equipment, digital record keeping, and so on and for all this, the sector is using advanced ICT. Figure 15.1 shows the use of a fog-assisted cloud computing environment with data mining in healthcare. The major reason for energy inefficiency in the data centers carrying healthcare data is the servers running at low load. Many times, with low utilization of CPU such as with around 10% CPU usage, the energy consumption is 50% of the peak power. The VM consolidation is one of the efficient techniques for green health in which turning off the servers will have low CPU utilization. Through VM consolidation, the QoS (quality of service) is achieved, which was mentioned in the SLA. Virtualization is another power management technique in data centers through which multiple VMs can run on a single physical server where each VM is running multiple tasks. A big challenge in the field is how to design the best tools and techniques for optimizing the energy consumption, availability, reliability, and sustainability of

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FIGURE 15.1 Use of fog-assisted cloud computing with data mining techniques in a ­healthcare system.

the fog-assisted cloud system [11,12]. In the last few years, the researchers are trying to optimize the energy consumption done by the data centers along with fog centers, which helps in building efficient and cost-effective green IT solutions to the hospitals. Considering the complexities of the healthcare industries, there is a need for extensive study of how big data, data science, and machine learning techniques should be used in the improvement of green healthcare. Hospitals and healthcare sectors should take extensive measures using machine learning to improve the green healthcare. RL is a part of ML in which the software agents take action in the environment to maximize the cumulative reward. After implementing the decision, the feedback was received, which indicates the quality of the action taken. The agent’s aim is to learn the policy for the selection of the best alternative action out of all possible actions. This chapter helps in improving the energy efficiency in their ICT in hospitals and healthcare sectors by using big data, data science, data analytics, and fog technologies. This chapter uses the RL [11–13] model and optimizes the number of active hosts by predicting the current resource requirement, which can save energy of the fog healthcare system and optimizes the cost of data processing used by the healthcare sectors done in the fog-assisted cloud environment [14]. The system can take an intelligent decision whether to be the physical machine is in active or sleep mode. The  proposed method is utilizing a learning agent. Using Q-learning, the agent learns the power model from the environment, and the knowledge the agent gained is utilized to get the efficient procedure for the assigned task. This chapter is organized as follows. Section 15.1 gives the introduction to the chapter, Section 15.2 depicts the literature survey work, reinforcement learning and Q-learning technique are depicted in Sections 15.3 and 15.4, respectively, and the proposed model and the VM consolidation technique are shown in Sections 15.5 and 15.6 respectively. Section 15.7 describes the learning agent and its work in the dynamic consolidation of VMs based on reinforcement learning, Simulation results are shown in Section 15.8 and finally, Section 15.9 concludes the chapter.

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15.2 LITERATURE REVIEW In the recent past, many research works have been carried out for minimizing the energy consumption in the cloud and fog centers. Wu et al. [15] suggested an energy-efficient technique for saving the energy consumption in IoT and fog computing. Meta-heuristic algorithms play an important role in solving the task and scheduling problems in fog servers [16–18]. Goswami et al. [19] studied the performance of the system, which depends on the queue length to scale the VMs along with improving the QoS parameters of the system. Patra et al. [20] designed algorithms for profit maximization by saving energy and spotting allocation quality guaranteed services in a cloud environment. ML approaches have been studied by many researchers for power management in data centers, cloud centers, and fog centers. The task consolidation technique [7] using machine learning executes the assigned tasks with a minimum number of active VMs and, in turn, reduces energy consumption. In some studies [21, 22], an online ML technique is suggested that selects dynamically various experts to take the management decisions and minimizes the power consumption. In some studies [23, 24], an RL technique is used for resource allocation in the data centers. Considering the existing ML techniques in power management and RL techniques in resource management, this work can explore RL-based learning in power management.

15.3 REINFORCEMENT LEARNING RL is one of the types of ML, which is growing in the past few years. RL is learning through interaction. Learning is done by doing and from scratch. RL's goal is to control large-scale stochastic environments with partial knowledge. RL systems have two major constituents: agent and environment. The latter one is dynamic/stochastic, non-stationary where, the agent acts on, and the agent is the RL. The RL process starts as the environment senses the state of the agent. Then, the agent takes some action depending on observations. In turn, the environment sends the next state with respective reward back to the agent. The agent updates its knowledge with the reward returned by the environment and its uses are to evaluate its previous action. This loop continues till it reaches the terminal state, this means the agent completes its tasks and gets the reward. An RL agent influences the state distribution. Hence, it makes decisions/takes actions to maximize reward or minimize punishment. In general, the framework for RL consists of the following: • State space S: the number of states an agent can take perception out of the environment. • Action space A: the different actions the agent performs. • Reinforcement signal r: The environment sends the reinforcement signal to the agent. The signal may be a reward or punishment. The signal received by the agent reflects the next action to be taken by the agent, considering the success or failure of the system. The agent tries to minimize the penalties in the learning process. Figure 15.2 shows the process of reinforcement learning. RL is all about controlling the environment means to find the tradeoff between exploration (look for knowledge) and exploitation (use your knowledge).

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FIGURE 15.2

The process of reinforcement learning.

RL agnate influences the state distribution. Hence, it makes decisions/takes actions to maximize a reward or minimize a punishment. S ∈S: states a ∈A: activities R: reward function δ: transition probability defined by δ s1 a → s2 

(

)

This is an MDP (Markov decision process). Taking an action ‘a’ in state ‘S’ causes the transition δ(s,a,s′) with probability p = 0.8,δ(s, a, s″) with probability p = 0.2. We can write it with conditional probability P(S′ | S, a) = 0.8 and P(S″ | S, a)= 0.2. The reward function for the action ‘a’ taken in state S = R(S, a) (indirect guidance) R(S, a)= +10 with probability p = 0.1 R(S, a)= +5 with probability p = 0.3 R(S, a)= −5 with probability p = 0.6 Hence, we have to follow trajectories. RL goal: Find π* : S → a Using R(S, a) and a suitable return horizon. But do we know δ ( S , a, S′)? Two potential approaches Two RL schemes:

1. Policy Iteration (Monte Carlo, Temporal Differencing TD (λ)) (Evaluate the policy to Improve it) 2. Value Iteration (Q-Learning) (Maximize accumulate reward for (S,a))

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15.4 Q-LEARNING It is a popular RL method that is used in many researches. In the Q-learning algorithm, the system uses Q-values (known as action values), which iteratively improves the learning agent’s behavior. 1. Q-values or action values: Q values are a set for both states and actions. Q(S,a) is an estimation to be evaluated how good of applying an action ‘a’ at the state S. The estimation of Q(S,a) is computed through an iterative process using the TD-update rule. 2. Rewards: An agent in its course of a lifetime from a start state to reach its next state from the current state makes several transactions based on the environment in which the agent is interacting and the choice of action the agent is taking. At each step of the transition, the agent observes the reward from the environment due to the state of action and then takes the next step. If at any instance of time, the agent reaches the terminating state, then there is no further transition. The episode will be completed there. 3. TD update: The TD update formula is depicted as: Q( S , a) = Q( S, a) + α ( R + γ min aA€ Q ( S ′, a′ ) − Q( S , a) )



(15.1)

Here S: Agent’s current state a: The current action based on certain policy S′: Next state in which the agent ends a′: Using the estimated current Q-value, the next best action to be taken R: The current reward predicted looking the environment w.r.t the current action • γ: Since future rewards have less value than the current rewards they have to be discounted. 0 < γ < = 1 is the discounting factor for the current reward. • α is the step length taken for updating the estimation of Q(S,a). • • • • •

The policy π that selects the best action in state s is given by:

Π(s) = min a € A Q( S , a)

(15.2)

The learning agent’s aim is to search for the optimal policy. 4. €-greedy policy for the action By using the Q-value estimation, this policy is a simple one to choose the action. It is taken as follows: • having probability (1 − €), the action has to be taken, which has a maximum Q-value. • having probability (€), an action at random can be chosen.

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Algorithm 1: Q-Learning 2. for every episode 3. do Initialize state ‘S’ 5. do Select ‘a’ from ‘S’ by the action procedure 6. take an action ‘a’ 9. update S = S′ 10. until S terminal

15.5 SYSTEM MODEL Let us consider a fog center with m heterogeneous fog nodes. The processor of each node is of multi-core and the processing capacity is defined in MIPS. Along with this, each node has a network bandwidth, processing capacity, and memory. The m fog nodes have n number of VMs. Users send their requests for provisioning the VMs. Each user requirement is characterized by CPU performance, RAM, storage, and network bandwidth. Since the VMs handle dynamic workloads, the CPU utilization of a VM varies over time. The length of each task is denoted by MI. To optimize the energy consumption and SLA violations, the VMs are consolidated on a minimum number of hosts. When the resource utilization on a particular host is low, the VMs are migrated to other hosts and the host may be switched to idle mode. Similarly, when a host gets overloaded, certain VMs are migrated to other hosts to reduce the SLA. The VM algorithm efficiency can be improved by using the learning agent. The efficiency of the resource allocation algorithm with the energy consumption measures the learning agent using Q-learning. Figure 15.3 shows the use of reinforcement learning in fog centers for VM consolidation to handle green healthcare. The proposed dynamic consolidation steps are carried out as follows: 2. Depending on the learning agent instruction, the VM consolidation is done. The VM consolidation optimizes the VM placement based on power mode. When a host becomes overloaded, the VMs get migrated to other hosts. Similarly, when the host goes to idle mode, all the VMs are migrated to other hosts. The allocation map decides the allocation of VMs to the hosts. 3. The VMMs send migration commands to VMs to migrate to other hosts by the allocation map received through the VM consolidation step.

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FIGURE 15.3 Use of reinforcement learning in VM consolidation for green healthcare.

15.6 DYNAMIC CONSOLIDATION OF VMs BASED ON REINFORCEMENT LEARNING This chapter proposes a dynamic consolidation VM allocation method to minimize the energy of data centers as well as SLA violation using reinforcement learning. The  algorithm adapts the necessary number of hosts depending on the workload to the datacenter. The algorithm decides whether there is a need for more hosts in the system, some hosts may be put into the sleep mode and save energy and verifies whether or not the number of allocated hosts are sufficient. For this purpose, a learning agent is necessary to take the decisions of the system. The proposed algorithm is represented in algorithm 2 as the DC-VM-RL algorithm and is as follows: Algorithm 2: DC-VM-RL 2. If the power mode of a host =sleep but current mode ≠sleep then migrate the VMs to other selected host and the host will be switched to sleep mode.



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to different hosts to avoid SLA violation. The VM selection algorithm identifies which VM should be migrated to different hosts. When the DC-VM-RL algorithm selects a host for allocating a VM, the VM allocation procedure has been used. This procedure helps to locate those hosts that are not overloaded at the current as well as after the allocation of the VM. This means the hosts have idle resources that may be shared among VMs. Thereafter, a host has been chosen from the hosts, which is not overloaded, and hence the power increasing is minimized after the VM allocation. The VM allocation procedure is depicted in the following algorithm. Algorithm 3: VM Allocation Procedure

15.7 LEARNING AGENT The number of active hosts can be reduced depending on the current workload. This needs intelligent decisions as to when to put the hosts in the sleep or active power mode. For this, a learning agent is required and in the algorithm DC-VM-RL, the learning agent is a crucial part. Based on the agent decision, the algorithm switches each host to the specified power mode and calculates the penalty as the reinforcement signal. The main objective of the DC-VM-RL algorithm is to minimize the energy consumption including the SLA violation, the penalty, and time t can be calculated as

Pt = Pt (SLA) + Pt (Power)

(15.3)

The SLA violation penalty is defined as the requested MIPS (Ur) − actually allocated MIPS (Ua) over a time slot. n



SLAt =

∑(U

ri

− U ai )

(15.4)

i =1

Here, n is the number of VMs. The penalty for the SLA violation is calculated as the ratio between the SLA before performing the action and the SLA after the action.

Pt (SLA) = ( SLA t +1 SLA t )

(15.5)

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The penalty for the power consumption is measured as the ratio between the consumed power at the current time slot and the consumed power of the previous time slot. m



Pt (Power) =

∑ Power Power

t +1

i =1

(15.6)

t

Here, the summation is for the total power consumption penalty for m hosts. Then Eq. (15.1) can be rewritten as

Q ( st , a ) = Q ( st , a ) + 0.5 Pt + 0.7min Q(s′, a′) − Q ( st , a ) a ∈ A

(15.7)

The algorithm for the learning agent is shown in algorithm 4. Algorithm 4: Learning Agent

During the initialization state at the beginning of the learning stage and when the current state has not visited earlier, the action taken is on a lower threshold. If the utilization of the host is >0.4 of the available CPU capacity, then the sleep mode of the agent is set to active mode by the agent. Otherwise, the host is under-loaded and will be put in sleep mode.

15.8 SIMULATION RESULTS To evaluate the performance of the proposed system, iFogSim [25] has been considered. It is a popular tool kit for the fog computing community because of its flexibility, scalability, and efficiency. In our simulation, we have taken five scenarios and the number of VMs for each scenario is distinct, which is shown in Table 15.1. We compared the DC-VM-RL algorithm with the state-of-the-art algorithms [26]. They adopt utilization threshold dynamically based on IQR (Inter Quartile Range), LR (Linear Regression), and MAD (Median Absolute Deviation) for estimating the CPU utilization. Also, we have considered the THR (threshold method), which after monitoring the CPU utilization, migrates a VM as soon as the current CPU utilization is more than 80% of the capacity of the VM. There are two matrices such as avg. SLA violation % and energy consumption that has been used for measuring the proposed dynamic VM consolidation by adopting Q-Learning. Table 15.2 shows the

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TABLE 15.1 The Number of VMs Scenario

Number of VMs

I II III IV V

1465 1356 1245 1056 1034

TABLE 15.2 Avg. SLA Violation Percentage Scenario

DC-VM-RL (%)

MAD (%)

THR (%)

LR (%)

IQR (%)

8.34 8.45 9.02 9.64 9.82

10.12 10.05 10.11 10.06 10.46

10.08 10.26 10.09 10.22 10.76

10.07 10.17 10.42 10.46 11.25

10.07 10.02 10.17 10.18 10.35

I II III IV V

TABLE 15.3 Power Consumption of Selected Servers Server

0%

10% 20% 30%

HP Proliant DL 380 G7 HP Proliant DL 380p G8

88

89.5

93.6

98

40%

92.7 97

50%

60%

70% 80%

99.7 102.5 10.6.2 108.6 112.2

101.5 105.6

110.5 116

121.7 127

130

90% 100% 115

118

124

136

percentages of average SLA violations. The DC-VM-RL algorithm leads to less SLA violation than the other algorithms, because the DC-VM-RL algorithm switches the host from sleep to the active mode before any SLA violation occurs. Table 15.3 shows the power consumption for the selected servers used during the simulation. The total energy configuration by a physical node is denoted by:

E=

∫

t1

P(U (t )) dt

(15.8)

t0

Figure 15.4 shows that the RL brings higher energy saving as compared to the other algorithms. In scenario III, it can be observed that for the enabling of the learning algorithm DC-VM-RL, there is a reduction of 12.6%, 19.4%, 22.7%, and 28% by comparing LR, MAD, THR, and IQR, respectively.

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Comparison of energy consumption by DC-VM-RL and other benchmark

15.9 CONCLUSION Healthcare data are voluminous, and a huge processing of data is required in the future. This chapter suggests a dynamic consolidation method using reinforcement learning to minimize the power consumption as well as SLA violation in the fog center. Reinforcement learning helps the agent to learn the host power mode policy without the knowledge of the environment. The proposed method is simulated using iFogSim and compared with other benchmark algorithms, and it has been found that the proposed learning-based dynamic consolidation method performs better when compared to the existing benchmark methods.

REFERENCES

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16

Smart Agricultural Robot Vimal Kumar M. N. R.M.D. Engineering College

Aakash Ram S. KPIT Technologies Limited

Bennet Niffin N. SciComm India

CONTENTS 16.1 Introduction .................................................................................................. 274 16.2 Background/Related Works .......................................................................... 276 16.2.1 Literature Review ............................................................................. 276 16.2.2 Method of Existing Agricultural Robot............................................ 277 16.2.3 Summary of Related Works.............................................................. 277 16.3 Methodology ................................................................................................. 277 16.3.1 Hardware .......................................................................................... 278 16.3.1.1 NodeMCU .......................................................................... 279 16.3.1.2 DHT11 Sensor ....................................................................280 16.3.1.3 Soil Moisture Sensor ..........................................................280 16.3.1.4 L293D Module ................................................................... 281 16.3.1.5 Relay Module ..................................................................... 281 16.3.1.6 Servo Motor ....................................................................... 281 16.3.1.7 Buzzer ................................................................................ 281 16.3.1.8 Raspberry Pi ...................................................................... 281 16.3.1.9 Raspberry Pi Camera ......................................................... 281 16.3.2 Software ............................................................................................ 283 16.4 Experimental Results ....................................................................................284 16.4.1 Mobile Application ...........................................................................284 16.4.2 Live Stream ....................................................................................... 285 16.4.3 Robot................................................................................................. 287 16.5 Future Works ................................................................................................ 288 16.6 Conclusion .................................................................................................... 290 Acknowledgment ...................................................................................................290 References ..............................................................................................................290

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16.1 INTRODUCTION Agriculture is one of the oldest and prime activities of the human race. It is an art of cultivating plants and raising livestock. Water is a very precious resource and a driving force in irrigation. The water requirement of the crop depends on the type of soil, crop, and environmental parameters like temperature and humidity. The activities involved in agriculture are painstaking and demanding manpower throughout the process. It is important to know the effects of the physical parameters in crop cultivation. It includes relief, drainage, climate, soil, and water resources. All these factors are affecting the growth and distribution of crops in a particular area. The physical parameters determine the crop pattern and agricultural operations to be undertaken. Thus, physical parameters influence the type of crops growing, the degree of threat involved in agriculture, and its development. The temperature and humidity of an environment are the primary factors affecting the rate of development of plants. Temperature is the degree of heat present in an environment. Warmer and extreme temperatures potentially impact plant productivity. In photosynthesis, plants use carbon dioxide to produce oxygen and in respiration, plants use oxygen to produce carbon dioxide. It happens due to the effects of heat on photosynthesis. There are limits in temperature for each plant, and they are classified as maximum, optimum, and minimum temperature limits. These limits are important as they directly depend on the plant growth. It should neither go below the minimum limit nor above the maximum limit. Humidity is the amount of water vapor present in the atmosphere. It affects the opening of stomata present underside of the leaves in plants. It also has some limits for the healthy growth of plants. When the humidity is high, the plants cannot draw nutrients from the soil. If it sustains for a prolonged time, then the plant gets rotten. The optimum level of humidity for the production of crops is 50%–70% RH. Table 16.1 provides the temperature and humidity level of wheat and rice with the minimum, optimum, and maximum limits. Soil moisture is the most important parameter in agriculture. It is the level of water stored in the soil. It is influenced by factors such as temperature, characteristics of soil, precipitation, and so on. By using these factors, the type of biome present in the soil and the suitable growing crops for the land can be determined. Crops’ health depends on soil nutrients and an adequate supply of moisture. A decrease in the availability of moisture in soil results in disruption in functionality and growth of a plant. Also, crop yield will be reduced. Moisture availability will become more variable during the climatic changes. Irrigation is carried out in agriculture to cultivate the crops and to maintain moisture in the soil. Soil conditions of the field vary and will not remain constant. Water-holding capacity varies for different types of soil and terrains. Over-irrigation blocks the air supply to the roots increasing the salinity and under irrigation disrupts the crop growth. Table 16.2 shows the amount of minimal soil moisture and the level for irrigation practices of clay, loamy, and sandy soil. Water acts as a critical input in agriculture to irrigate and cultivate the plants. The potentiality of seeds and fertilizers fail to achieve whether plants are not optimally watered. In plants, water is used for growth, and the usage of water by crops is known as evapotranspiration. It is the sum of evaporation and transpiration from the land and ocean surface of the atmosphere. Transpiration is the process of exhalation of

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TABLE 16.1 Temperature and Humidity Limits Crop Type

Humidity (RH)

Wheat

50%–60%

Rice

60%–80%

Temperature (°C)

Limits

3–4 25 30–32 10–12 30–32 36–38

Minimum Optimum Maximum Minimum Optimum Maximum

TABLE 16.2 Irrigation Practice Level Soil Type Fine (Clay) Medium (Loamy) Coarse (Sandy)

No Irrigation Needed

Irrigation to Be Applied

Low Soil Moisture

80–100 88–100 90–100

60–80 70–88 80–90

Below 60 Below 70 Below 80

water vapor through stomata and evaporation is the movement of water to the air from the soil and plant surface. Considerable evaporation is possible only when the plant canopy or top layer of the soil is wet. Evaporation decreases sharply when the soil surface is dried out. The process of considerable evaporation occurs only after rain or irrigation. The root system is responsible for the extraction of water from the soil and stored water in the soil is extracted by plants for tissue building and soil evaporation. There are a variety of crops available for cultivation, but water requirement and the growth duration of each crop vary. Table 16.3 shows the duration of growth and total water requirement for rice, sugarcane, groundnut, sorghum, and maize. Thus, the agricultural practices are tedious and painstaking due to the unpredictable weather conditions and constant monitoring of the field is required. Conventional agricultural practices involve continuous monitoring of the water level, temperature, and humidity. The crops are at a risk of damage due to the entry of wild animals. TABLE 16.3 Duration and Water Requirement of Crops Crop Rice Sugarcane Groundnut Sorghum Maize

Duration (Days) 110 360 105 105 100

Total Water Requirement (MM) 1250 2200 510 500 500

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The  monitoring water level of the soil improves the existing irrigation practices. Invasion of livestock into the field destroys the crops. Therefore, we call for a system to resolve the challenges in the agricultural sector by monitoring and controlling the physical status of the landforms and reducing the manpower involved in the process. Thus, we propose an IoT (Internet of Things)-based semiautomated mobile agricultural robot equipped with sensors and actuators interfaced with NodeMCU, which delivers the data derived from sensors to the mobile application through the Internet.

16.2 BACKGROUND/RELATED WORKS In this section, the existing work related to an agricultural robot is discussed. It contains the literature review, the method of an existing agricultural robot, and the summary of related works.

16.2.1

literature revieW

An autonomous robot with ultrasonic sensors has been used in agricultural land to reduce manpower and to increase productivity. The design includes ploughing the land, sowing the seed, spraying the fertilizer, and navigation. Obstacles are detected by an ultrasonic sensor, thereby controlling the movement of the robot (Usha, Maheswari, and Nandagopal 2015). There is a need to find new ways for the developed agriculture to improve efficiency. A new range of agricultural equipment can be developed by making small machines to be smart (Blackmore, Stout, Wang, and Runov 2005). Farming and robotic system are the two important fields that are being researched by the scientists. The advancement and combination of these fields can efficiently solve many problems. Agriculture with a robotic system can be used in more complex and dynamic systems (Dattatraya, Mhatardev, Shrihari, and Joshi 2014). It provides optimum solutions and a wide range of production with their own merits and demerits (Nithin and Shivaprakash 2016). An Agribot is designed to reduce the manpower but to increase the speed and accuracy of the work (Celen, Onler, and Kilic 2015). It performs agricultural activities such as spraying, seeding to improve application, and ensures safety (Ankit, Abhishek, Akash, and Sumeet 2015). A walking robot with hexapod body is used to walk in any direction by using an ultrasonic proximity sensor. It can dig a hole to plant a seed and spray fertilizers (Danfeng, Yan, Xiurong, and Huaimin 2010). It can communicate with nearby robots through Wi-Fi (Amer, Mudassir, and Malik 2015). Due to labor scarcity and expensive manpower, the output in agriculture is gradually declining. The cost of seeds and fertilizers is increasing exponentially. It demands the use of modern technology for the efficient use of fertilizers in a less cost (Agarwal and Thakur 2016). An automated robot has been designed to perform the farming activities for the rhizome plants (Sampoornam, Dinesh, and Poornimasre 2017). The robot is designed with the aim of increasing productivity and reducing labor by executing the basic farming functions (Chalwa and Gundagi 2014) (Abba, Lee and Liz Crespo 2019). It includes plantation, sowing seeds, watering, and spraying of fertilizers (Suraj, Anilkumar, Pooja, Usha, and Sheetal 2017). Human efforts have been reduced using tools and livestock in the agricultural process. A multitasking robot in agriculture focuses on plantation

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and fertilization without any human intervention. It overcomes the drawbacks of the traditional method (Karan, Agrawal, Dubey, and Chandra 2012). The overall efficiency of the agricultural process can be increased by the application of automation and robotics in the field of agriculture (Shivaprasad, Ravishankara, and Shoba 2014). An IoT-controlled multipurpose autonomous agricultural robot has been designed for seeding and spraying of pesticides (Swarna, Jerusha, Tanwar, and Singhal 2020). It reduces the human intervention in the cultivable land. Resources have been utilized efficiently and ensured a high yield. This is a propel technique to sow, water, and harvest with the least labor by using a multipurpose robotic vehicle. Voice commands are used to control the vehicle through a smartphone through Bluetooth. The vehicle is capable of cultivating, sowing, watering, and harvesting. Anyone can operate this vehicle without any technical knowledge (Ravi, Praveen, Rajesh, Kuruva, and Thippa 2019). A robot for greenhouse monitoring has been designed, constructed, and validated. Its functionality is to measure CO2 concentration, air, temperature, and humidity in a greenhouse environment using the appropriate sensors.

16.2.2

metHod of exiSting agriCultural roBot

In the existing method, the robot works on an ATmega16 microcontroller containing components such as RF transmitter, RF receiver, dc motor, remote, battery, and solar panel. The remote provides an operating command to an RF transmitter that transmits the signal to the robot. This signal will be received by the RF receiver in the robot and performs the desired action. The DC motor will actuate and the motor gets revolution as per receiving commands such as reverse, forward, left, and right with respective buttons present on the remote. When the power supply is turned ON, a command signal is sent to the receiver using the transmitter. As soon as it receives the signal, the robot performs the required operation as per the given commands. Teeth are up and down during ploughing operation and during seeding operation, the hopper is closed and opened. Thus, the existing system is based on the RF technology, and it uses the data collected from different sensors and parameters to take accurate actions and to better predict the crop productivity and quality.

16.2.3 Summary of related WorkS Thus, the Agribots are either completely manual or automated controlled. Though the existing system has certain features, it is still insufficient to perform other agricultural activities. These systems are heavy and consume more space, which makes it difficult to make through the narrow crop lines. All features are made in manual control mode, which demands the farmer’s attention on the mobile operation.

16.3 METHODOLOGY The proposed system is capable of driving over any terrain for surveillance, monitoring the field, and controlling farm equipment. It measures the moisture content of the soil using a soil moisture sensor, which is attached to a robotic arm. The robot also measures the parameters, temperature, and humidity level of the farm.

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The obtained values of moisture, temperature, and humidity are sent to a centralized microcontroller. This helps the end-users to achieve proper irrigation by pumping the contained water using a motor, thus averting the adverse effects of over-irrigation and under irrigation. A single soil moisture sensor is sufficient to perform the entire process. The microcontroller delivers these observations to the end-user via a mobile application. This data is then sent from the microcontroller to the mobile application via a cloud server. A camera is attached to a Raspberry Pi for live streaming. An audio device is equipped to drive away the invading animals from the field. Figure 16.1 shows the block diagram of the proposed system. The proposed system is broadly classified into hardware and software.

16.3.1

HardWare

Hardware consists of an embedded system that uses a NodeMCU as a microcontroller and delivers sensor input data to the microcontroller. Controller logic is used to perform the processing and notify the user about the parameter values received from the sensors. The sensors that are incorporated with the system are to detect the events or changes in the environment and send the information in analog or digital format to NodeMCU. Some of the sensors in the proposed system are as follows: • DHT11 sensor: To detect the temperature and humidity level in the atmosphere. • Soil moisture sensor: To measure the soil moisture content by detecting the dielectric permeability of water in the soil. • Raspberry Pi camera: To record live video of the field. Some of the actuators in the proposed system are as follows: • • • • •

L293D module: To control the speed and motion of the DC-geared motors. Servo motor: To dip the soil moisture sensor into the soil. Relay module: For ON/OFF control on water pump. Buzzer: To amplify the alert sound to drive away the invading wild animals. Water pump: To pump out water for irrigation.

The project is based on monitoring and collecting the data regarding the physical parameters of the field and delivering it to the mobile application through the Internet. Figure 16.2 shows the different hardware components used in a smart agricultural robot. NodeMCU acts as a microcontroller unit. DHT11 sensor is used to detect the temperature and humidity of the atmosphere. The soil moisture sensor is equipped along with the servo motor to dip and measure the soil moisture content of the soil. The DC-geared motors are used along with each wheel to enable bot movement. The Raspberry Pi camera is interfaced to gain visual aid of the field. The relay module is set up to control the power supply to the water pump for irrigation purposes.

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Block diagram of a smart agricultural robot.

16.3.1.1 NodeMCU It is an open-source firmware and a low-cost device mainly used in the IoT platform for designing and developing the prototypes. Lua scripting language is used for this firmware. The surface-mounted board contains MCU and antenna, which functions as a dual in-line package. This device is also known as ESP8266. A Wi-Fi transceiver

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FIGURE 16.2 Hardware components.

is integrated with the device by which it can connect to the available network nor create its own network to allow other devices to directly connect to it. The operating voltage of the device is 3– 3.6 V. An LDO voltage regulator is mounted on the board to keep a steady voltage of 3.3 V. It pulls 80 mA during RF transmission where the supply up to 600 mA is reliable. A regulated 5V power supply to ESP8266 is supplied through a MicroB USB connector or VIN pin. For external components, a power source up to 3.3 V can be powered from NodeMCU. 16.3.1.2 DHT11 Sensor It is a low-cost digital sensor used to measure temperature and humidity of the environment. The output of the sensor is sent to the data pin where the temperature is calculated using a negative temperature coefficient thermistor, which causes a decrease in its resistance value with an increase in temperature and humidity with capacitive electrodes where a change in capacitance is detected with two electrodes of a moisture-holding substrate. The change is measured and processed by IC to generate a digital output. 16.3.1.3 Soil Moisture Sensor The moisture content of the soil is measured using a soil moisture sensor. It is achieved by measuring the volumetric water content within the soil, where dielectric permittivity is a function of water. This sensor uses capacitance to measure the dielectric permittivity of the surrounding medium in the soil.

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16.3.1.4 L293D Module L293D is commonly known as a motor driver, which is used to control the speed and direction of a motor. It is a 16 pin IC, which can control two motors simultaneously and provides a wide supply range from 5 to 36 V. 16.3.1.5 Relay Module Relay is an electrical device that incorporates an electromagnet for switching the devices or circuits. It controls the circuit electromechanically when the relay gets energized and consists of two terminals, namely normally closed (NC) and normally open (NO). Relay is energized when an input electrical signal is applied. When the relay is in its energized state, the circuit connected to NO will be closed and NC will be open. When the relay is in its original state, the circuit connected to NC will be closed and NO will be open. 16.3.1.6 Servo Motor A Servo motor is an actuator device that is used to rotate or push an object at a specific angle or distance. It works on the principle of pulse width modulation and contains some gears to provide high torque with great precision. A potentiometer in the motor is responsible to calculate the angle and stop the shaft position on the required angle. 16.3.1.7 Buzzer It is a low-cost electronic device used to produce sound with a simple construction. A piezo buzzer can be split into piezo and electromechanical devices. When the voltage is applied, the piezoelectric vibration plate in the buzzer starts to vibrate and sound is emitted from the buzzer through piezo-piezo sounders. 16.3.1.8 Raspberry Pi It is a credit card sized, small single-board computer, mainly used for research purposes. It uses Broadcom BCM2835 SoC with ARM as a central processing unit. The board contains GPIO pins, RCA, AUX, HDMI, Ethernet, USB, and SD card slot. It is open source and runs in any environment. The OS should be loaded in an SD card and inserted in the Raspberry Pi for booting the device. The operating voltage of Raspberry Pi is 5V. 16.3.1.9 Raspberry Pi Camera The Raspberry Pi Camera Module is interfaced with Raspberry Pi through a flex cable connector. The flex cable connector is a flat flexible cable that contains 15 pins. The camera can take Full HD (1080p) photos and videos. It can be controlled from Raspberry Pi through a terminal window or programs. The circuit diagram of a smart agricultural robot is shown in three phases. Figure 16.3 illustrates the circuit diagram of the robot, Figure 16.4 illustrates the circuit diagram of animal alert and irrigation system, and Figure 16.5 illustrates the circuit diagram of a live stream monitoring system.

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FIGURE 16.3

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Robot.

FIGURE 16.4 Animal alert and irrigation.

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FIGURE 16.5

16.3.2

283

Monitoring system.

SoftWare

The software system is a blueprint for developing the project and executing the necessary tasks. The robot is designed to be remotely controlled from any location through the Internet. Therefore, it is developed with the IoT architecture involving NodeMCU as the microcontroller of the robot and Firebase as the cloud server. The software architecture of IoT is shown in Figure 16.6. Mobile application is developed with user interface for controlling the robot from remote location. It is named as multi-altran. NodeMCU and mobile application can communicate with each other through the cloud server. Edge computing techniques are used to implement the proper communication protocols. Parameters are created in Firebase realtime database. The mobile application is connected to Firebase with each parameter through the Internet and

FIGURE 16.6

Software architecture of IoT.

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FIGURE 16.7

Connectivity process.

NodeMCU is connected with each parameter of Firebase through the Internet. The mobile application fetches the values of sensor parameters from Firebase and displays the value in the user interface. Actuators of the robot are controlled from a mobile application by sending the values to Firebase. NodeMCU fetches the values of actuator parameters from Firebase and undergoes the required action. Sensors in the robot sense the value and the data are sent to the Firebase through NodeMCU. The connectivity process in Figure 16.7 demonstrates how data are collected and reported by the sensors and mobile application to the Firebase service in the cloud. A Raspberry Pi camera is used for live monitoring of the field. Raspberry Pi is programmed to host the live video in a webpage through the Internet for remotely monitoring and controlling the robot. Initially, mobile application (Multi-Altran) and NodeMCU, and Raspberry Pi in the robot need to connect to the Internet through Wi-Fi access point or network data. Once the system is online, NodeMCU and mobile application (Multi-Altran) establish the connection with Firebase. Raspberry Pi starts to stream the live video of the field in the webpage. NodeMCU is configured using Arduino IDE. Mobile application is developed with Android Studio. Raspbian OS is loaded in SD card and inserted in Raspberry Pi for booting the device. Python program is used in Raspberry Pi for streaming the live video of the field in webpage. By implementing IoT technology, all the monitoring and control activities are undertaken from the mobile application through the Internet. The flowchart illustrated in Figure 16.8 describes the working of the robot and Figure 16.9 describes the working of an animal alarm and irrigation system.

16.4 EXPERIMENTAL RESULTS The proposed system for remotely monitoring and controlling the agricultural parameters through the Internet via a mobile application has been implemented. The developed prototype and the outcome of the smart agricultural robot are shown in this section.

16.4.1

moBile appliCation

A mobile application with user interface has been developed for the user to comfortably monitor the field as shown in Figure 16.10. It includes the navigation control of the robot and triggering of water pump and animal alarm.

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FIGURE 16.8 Flowchart of the robot.

16.4.2

live Stream

The webpage has been hosted from the Raspberry Pi that can be viewed from any browser as shown in Figure 16.11. It contains the live stream video from the robot. The movement of the robot can be controlled by watching the live stream of the field.

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FIGURE 16.9 Flowchart of animal alarm and irrigation system.

FIGURE 16.10

Mobile application.

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FIGURE 16.11

16.4.3

287

Live stream.

roBot

The robot is capable to drive over any terrain for surveillance, monitoring, and control. It measures the moisture content of the soil, atmospheric temperature, and humidity. A smart agricultural robot is shown in Figure 16.12. The front view, back portion, and front portion of the robot are shown in Figures 16.13–16.15, respectively. The results of the robot along with the expected and actual outcomes are provided in Table 16.4.

FIGURE 16.12

Smart agricultural robot.

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FIGURE 16.13

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Front view of the robot.

FIGURE 16.14 Back portion of the robot.

16.5 FUTURE WORKS By introducing artificial intelligence, the bots can detect weeds based on position and edge feature technique. The solar panels can be used to power the robot. GPS modules can be equipped to track the location and control from any remote location. A pH sensor can be added to detect the salinity level of the soil. An automatic seed sowing technique can be implemented to contain the seeds and bury them into the soil.

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FIGURE 16.15

Front portion of the robot.

TABLE 16.4 Expected and Actual Outcomes of the Robot S. No.

Features

1.

Temperature

2.

Humidity

3.

Soil Moisture

4.

Control

5.

Water Pump

6.

Animal Alarm

Expected Outcome DHT11 sensor detects the atmospheric temperature and delivers the value to the mobile display. DHT11 sensor detects the atmospheric humidity and delivers the value to the mobile display. A soil moisture sensor is attached to the servo motor. For “Downward”, the motor control horn is tilted down for the sensor to make ground contact, and for “Upward”, the control horn shifts back to the actual position. The sensed moisture value is delivered to a mobile application. L293D module is interfaced with four wheels in pairs and the wheel speed and direction are controlled. The options drive the robot in the selected direction based on the precoded speed and direction. Relay module ON/OFF the water pump according to the option selected on the mobile display. Buzzer is ON/OFF according to the option selected on the mobile display.

Actual Outcome Same as the expected outcome Same as the expected outcome Same as the expected outcome

Same as the expected outcome

Same as the expected outcome Same as the expected outcome

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16.6 CONCLUSION Agriculture in collaboration with robotics and IoT technology makes the farmer get better yield and production. The purpose of agricultural robots is to reduce the problems faced in the agricultural sector. Thus, a smart agricultural robot is designed, developed, and tested for temperature, humidity and soil moisture sensing, live streaming, mobile monitoring, water irrigation, and animal alert system in real-time on actual field. The outcome has promising values with acceptable delay. Thus, this robot functions on all terrains to monitor, maintain, and cultivate the land.

ACKNOWLEDGMENT The authors would like to thank Dr. K. Helenprabha, Professor and Head, Department of Electronics and Communication Engineering, R.M.D. Engineering College, for her support that made this research work possible.

REFERENCES S. Abba, J. A. Lee and M. Liz Crespo. “Design and Performance Evaluation of a Low-Cost Autonomous Sensor Interface for a Smart IoT-Based Irrigation Monitoring and Control System.” Sensors 19(2019): 3643–3668. N. Agarwal and R. Thakur. “Agricultural Robot: Intelligent Robot for Farming.” International Advanced Research Journal in Science, Engineering and Technology 3 no. 8 (2016): 177–181. G. Amer, S. M. M. Mudassir and M.A. Malik. “Design and Operation of Wi-Fi Agribot Integrated System.” in International Conference on Industrial Instrumentation And control (ICIC) (2015): 207–212. College of Engineering Pune, Pune, Maharastra, India. B. S. Blackmore, W. Stout, M. Wang and B. Runov. “Robotic Agriculture – The Future of Agricultural Mechanisation.” in 5th European Conference on Precision Agriculture (2005): 621–628. Uppsala, Sweden. I. H. Celen, E. Onler and E. Kilic. “A Design of an Autonomous Agricultural Robot to Navigate between Rows.” 2015 International Conference on Electrical, Automation and Mechanical Engineering (2015): 349–352. Phuket, Thailand. V. N. Chalwa and S. S. Gundagi. “Mechatronics Based Remote Controlled Agricultural Robot.” International Journal of Emerging Trends in Engineering Research 2 no.7 (2014): 33–43. S. Chavan, A. Dongare, P. Arabale, U. Suryanwanshi and S. Nirve. “Agriculture Based Robot (AGRIBOT).” International Journal of Advance Research and Innovative Ideas in Education (IJARIIE) 3 no. 1 (2017): 1416–1420. D. Danfeng, M. Yan, G. Xiurong and L. Huaimin. “Research on a Forestation Hole Digging Robot.” in 2010 International Conference on Intelligent Computation Technology and Automation (2010): 1073–1076. Changsha, China. G. D. Dattatraya, M. V. Mhatardev, L. M. Shrihari and S. G. Joshi. “Robotic Agriculture Machine.” International Journal of Innovative Research in Science, Engineering and Technology 3 no. 4 (2014): 454–462. P. V. Nithin and S. Shivaprakash. “Multipurpose Agricultural Robot.” International Journal of Engineering Research 5 no. 6 (2016): 1129–1254. K. P. Ravi. 2019. Agribot. In Architecture and Security Issues in Fog Computing Applications, ed. M. Praveen Kumar Reddy, K. Rajesh, L. Kuruva and G. Thippa Reddy, pp. 151–157. IGI Global, Hershey, Pennsylvania, USA.

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K. P. Sampoornam, T. Dinesh and J. Poornimasre. “Agriculture Robot (Agribot) for Harvesting Underground Plants (Rhizomes).” AgricEngInt 19 no. 2 (2017): 62–67. B. S. Shivaprasad, M. N. Ravishankara and B. N. Shoba. “Design and Implementation of Seeding and Fertilizing Agriculture Robot.” International Journal of Application or Innovation in Engineering & Management (IJAIEM) 3 no. 6 (2014): 251–255. K. Singh, K. Agrawal, A. K. Dubey and M. P. Chandra. “Development of the Controller-based Seed and Fertilizer Drill.” in 2012 12th Internal conference on Intelligent Systems Design and Applications (ISDA) (2012): 369–374. Kochi, India. A. Singh, A. Gupta, A. Bhosale and S. Poddar. “Agribot - An Agriculture Robot.” International Journal of Advanced Research in Computer and Communication Engineering 4 no. 1 (2015): 317–319. K. Swarna Krishnan, K. Jerusha, P. Tanwar and S. Singhal. “Self-Automated Agriculture System Using IoT.” International Journal of Recent Technology and Engineering (IJRTE) 8 no. 6 (2020): 758–762. P. Usha, V. Maheswari and V. Nandagopal. “Design and Implementation of Seeding Agricultural Robot.” Journal of Innovative Research and Solutions (JIRAS) 1 no. 1 (2015): 138–143.

17

A Survey of Lightweight Cryptography for Power-Constrained IoT Devices: Security Challenges and Issues Sunil Kumar and Dilip Kumar National Institute of Technology Jamshedpur

CONTENTS 17.1 Introduction .................................................................................................. 294 17.1.1 Criteria of the IoT Design ................................................................. 295 17.1.1.1 Energy Monitoring............................................................. 295 17.1.1.2 Resource Management ....................................................... 295 17.1.1.3 Interoperability .................................................................. 296 17.1.1.4 Interference Management .................................................. 296 17.1.1.5 Safety Currently ................................................................. 296 17.1.2 Contribution ...................................................................................... 296 17.1.3 Organization of the Chapter ............................................................. 296 17.2 Fundamentals of Lightweight Cryptography Techniques ............................ 297 17.2.1 Techniques of Lightweight Block Ciphers ........................................ 297 17.2.2 Lightweight Hash Functions ............................................................. 299 17.2.3 Lightweight Stream Cipher Algorithms ........................................... 299 17.2.4 High-Performance Systems .............................................................. 299 17.2.5 Assessment of Low-Constrained Devices ........................................ 300 17.2.6 Present Research Work ..................................................................... 300 17.3 IoT Security Issues and Strategies of Prevention.......................................... 301 17.3.1 Issues and Research Challenges ....................................................... 301 17.3.1.1 Issues .................................................................................. 301 17.3.1.2 Challenges .......................................................................... 301 17.3.2 Strategies and Preventive Measures for IoT ..................................... 302 17.3.2.1 Asymmetric LWC Method for IoT ..................................... 302 17.3.2.2 Symmetric LWC Method for IoT ....................................... 302

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17.3.3 Pseudocode for a Lightweight Encryption System ........................... 303 17.3.4 Pseudocode for the Decryption System ............................................ 304 17.4 Suggested Lightweight Cryptographic System for IoT ................................. 305 17.4.1 Framework of LWC .......................................................................... 305 17.5 Problems and Discussion .............................................................................. 307 17.5.1 The Algorithms of Cryptography and Cipher Design ...................... 307 17.5.2 The Issues Associated with Block Size and Key Size ...................... 307 17.5.3 Modern Threats ................................................................................308 17.5.4 Security and Privacy ......................................................................... 308 17.6 Conclusion .................................................................................................... 308 References .............................................................................................................. 309

17.1

INTRODUCTION

IoT is a device whose aim is to communicate with everyday things and to share knowledge to achieve a specific objective. This simple concept allows for a wide range of applications, including smart towns, smart farms, industrial automation, defense, medical services, entertainment, and so on. In the field of sophisticated interactive media connectivity, the IoT is a new world view. IoT is a worldwide initiative that combines more interesting and valuable citizens, information, processes, and things than ever before. This involves interconnected machines such as radio-frequency identification (RFID) tags, sensors, actuators, microcontrollers, mobile phones, and wireless computer transceivers that can communicate and recognize through the Internet to achieve their objectives and individuals can transfer data across a network without contact with other humans [1, 2]. Gartner research [3] estimated that by 2020, IoT will deliver revenue of over $300 billion, excluding laptops, tablets, and smartphones. Furthermore, by 2020, smartphones and tablets are projected to exceed 7.3 billion units. Such systems can create an immense and complicated network, with significant data transmission through the communication channel. When IoT grows quickly, it poses challenges and threats including handling massive data volumes, handling information processing with electricity, addressing safety issues, and encoding/decoding big data. Because multiple smart nodes are linked from IoT devices, in embedded systems, the demand for the required cryptographic approach is increased. Nevertheless, intelligent machines typically have limited resources or may be considered low-resource equipment in terms of their low processing capacity, existence of batteries, small sizes, small memory, and reduced energy supply. Standard primitives could also not be ideal for smart lowresource systems. For example, in RFID tags [4], the 1204-bit RSA algorithm cannot be applied. Moreover, the strict constraints involved in the mass production and production of smart devices prevent the need for a new lightweight cryptography algorithm, which provides necessary protection, cryptography, low power interfaces, and other features for the new technology allowed by IoT. Cloud platforms are used to provide relevant data to users and receive requests to convert them; Internet accessibility makes them available almost all the time and every moment. However, this openness often makes them vulnerable because they

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can be accessed and attacked from anywhere in the world. If successful, then any attacker can access personal, medical, financial, or location information and use the actuator to damage the device as well as the health of the consumer [5]. Vulnerability analysis and future attacks are discussed in some studies [6, 7]. Another problem is that they are widely seen as less ingenious devices with limited resources and have difficulties in implementing security services. Traditional security systems are not viable due to limited IT and energy resources [8, 9]. With the massive growth in IoT (trillions expected), data were exchanged on related items in the near future [10]. There is also an incredible increase among IoT devices. IoT device designers face many challenges and complications, except for the capacity for energy [11] and preservation of data [12] and networked security in the application layer protocal [13], certain threats and in general, whenever resources are limited, challenges are more severe critical data exchange devices. In addition, a power attack might potentially drain the battery of an IoT system and trigger shutdown of the computer [12]. The NIST notes that lightweight cryptography is a subset that offers solutions for fast-growing applications that use clever low-energy devices [14]. It targets many applications and can be used in software and hardware. A typical cryptographic algorithm works well on computers, servers, sensor networks, embedded systems, and digital devices. The bottom ends of the spectrum are appliances such as RFID tags, sensor systems, and implanted devices. The applications and communication channels need lightweight cryptographic techniques. Implementation of lightweight cryptography algorithms includes the wireless sensor networks, RFID, WBAN, IoT network, smart cards, and so on [15,16]. IoT also uses a cloud computing model with many security concerns [17–20]. Besides these problems, resource-constrained devices with less processing capacity, reduced battery life, limited storage, and limited network require efficient protection keys that will not crunch IoT resources.

17.1.1 Criteria of tHe iot deSign Every year, IoT is rising exponentially and a critical environment requires devices activated by IoT and five conditions exist for potential technologies. 17.1.1.1 Energy Monitoring The IoT-enabled intelligent devices are constantly capable of sensing, receiving, running, and processing information to make smart decision making possible [21]. The collection and distribution of vast quantities of data limits on energy supply play a crucial role in IoT infrastructure. Therefore, power use is one of the prominent requirements for improving network systems and life. 17.1.1.2 Resource Management Modern IoT network systems can be remotely accessed and configured to achieve sustainable communications to connected resources, should therefore share and modify the real-time system beyond [22]. The task handled by this method should also adequately balance to ensure accurate communication between the user and devices powered by IoT.

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17.1.1.3 Interoperability Interoperability is one of the fundamental requirements of the implementation of IoT, which can interact between systems or devices without taking into account the technological or product requirements. Bringing the latest IoT-enabled systems should enable adapting and communicating with different Wi-Fi techniques to enhance the versatility of the IoT system [23]. 17.1.1.4 Interference Management Wireless technologies are linked across the Internet with a growing number of connected devices in IoT architecture day after day. Therefore, disruption can vary between two radio transmissions among multiple nodes and must be remedied in the forthcoming IoT node [24]. 17.1.1.5 Safety Currently The crucial and challenging security problems in the IoT contexts include privacy, secure management and storage, authentication and communication, and user authentication. The size of IoT devices and utilities contributes to many bugs and node attacks. Because of the limited processing power, conventional security approaches suffer from multiple pitfalls and frequently do not identify the physical network threats [21,25]. Consequently, IoT safety must be improved through ensured connectivity, only allow the approved client to access the information and regularly modify for a smart device.

17.1.2 ContriBution This article aims to address a thorough analysis of lightweight cryptography technologies for IoT devices with low power, IoT design security issues, research criteria, and challenges to protected wireless communication in power-constrained IoT devices like sensors, actuators, RFID tags, and so on. This article contributes to another era of secure wireless communication with power-constrained IoT devices. A detailed review to defend the data from attacks and power efficiency in the grid, region, confidentiality, authentication, and block-chain wireless communication schemes shall be reviewed in a secured communication channel. In addition, different approaches focused on many types of lightweight cryptography approaches to transferring information have been extensively tested for communication networks. Modern approaches have been explored for secured communication in IoT and will support researchers for transmission of information through a variety of power-constrained IoT devices. Ultimately, we identify the obstacles and open issues to secure IoT sensor devices through lightweight cryptography algorithms in communication networks.

17.1.3

organization of tHe CHapter

The is chapter is organized as follows: Section17.1 addresses briefly the introduction on the power-constrained IoT devices and the paragraph also contains the article’s

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inspiration and contribution. Section 17.2 addresses the fundamentals of lightweight cryptography techniques in IoT transmission, offering performance matrix low cost, throughput, latency, and also describes existing research work. Section 17.3 discusses the security challenges and prevention methods for IoT that include safety standards and open issues that should be considered in future studies. Section 17.4 suggests a lightweight cryptographic system for IoT. Section 17.5 describes and discusses problems and finally, Section 17.6 provides the conclusion.

17.2 FUNDAMENTALS OF LIGHTWEIGHT CRYPTOGRAPHY TECHNIQUES This chapter deals with the different elements of the lightweight algorithms as defined in Figure. 17.1 and we summarize some lightweight algorithms in Table 17.1, according to its frame size, key size, design, and number of rounds.

17.2.1

teCHniqueS of ligHtWeigHt BloCk CipHerS

Lightweight WG-8 cipher is an encryption method optimized for small-constrained systems of the Welch–Gong family. A variety of block ciphers have been suggested to give good results for things like RC-5[26], AES-1 [27,28], TEA [29], and XTEA [30]. In general, some have been modified and intended to improve efficiency by simplifying traditional ciphers. For example, DESL [31,32] is often referred to as lightweight DES. The round function in DESL uses one S box rather than eight rounds, so that the initial and end permutation is permitted to raise the execution of the hardware SIMON and SPECK [33] that come in different widths and key sizes with

FIGURE 17.1 Classification of various LWC algorithms.

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TABLE 17.1 Current Lightweight Ciphers Ciphers RECTANGLE ITUBee AES HEIGHT PRESENT mCrypton TEA SEA LEA DES SEED TWINE DESL 3DES TDEA Hummingbird2 Camelia KHAZAD

Design

Key Size

SPN Feistel SPN GFS SPN SPN Feistel Feistel Feistel Feistel Feistel Feistel Feistel Feistel Feistel SPN + Feistel SPN + Feistel SPN

80/128 80 128/192/256 128 80/128 64/96/128 64 96 128,192,256 54 128 80/128 54 56/112/168 64 128 128/192/196 128

Block Size 64 80 128 32 64 64 64 96 128 64 128 64 64 64 64 64 128 64

No. of Rounds 25 20 10/12/14 32 31 12 64 93 24/28/32 16 16 32 16 48 48 4 18/24/24 3

block ciphers. Both platforms are flexible and perform well in a variety of lightweight frameworks [34]. Certain lightweight block ciphers are described below. Smaller block sizes: Block size should be minimal, so that the efficiency of the lightweight block ciphers is achieved and money is saved. It should be less than 64-bit instead of 128-bit. As the size of the block decreases, the plain text size is decreased. Smaller key size: A lightweight block cipher must be small in order to obtain minimum battery life and power usage. PRESENT [35], for example, is 80-bit and Twine [36] is 80/128-bit. Simpler rounds: Lightweight cipher blocks targeting low-resource restricted devices inherently involve simple computation compared to traditional block cipher techniques. Lightweight methods can reduce the number of rounds. For example, 4-bit S-Boxes were used for lightweight rather than 8-bit boxes for traditional cryptography for a single S-Box. Some easier lightweight algorithms for cryptography are as follows: PRESENT uses, 4-bit S-boxes with just four rounds of Hummingbird2 [37], and Iceberg [38]. Simpler key schedules: A program for a given key that measures round subkeys. Complex keys use additional storage and resources to execute them. Lightweight block ciphers use easier key schedules, so that subkeys can be produced. For example, the TEA block cipher splits the 128-bit into four bit blocks of 32-bits.

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17.2.2

299

ligHtWeigHt HaSH funCtionS

A traditional hash function seems to have a broad internal factor and high-power consumption, which cannot be preferred without a lightweight hash function in the RFID protocol [39]. A lightweight feature based on lightweight block ciphers is, therefore, presented [40]. Some simple hash functions are PHOTON [41], Quark [42], SPONGENT [43], and Lesamnta-LW [44]; some are easily available. Smaller output sizes are very high for applications needing hash collision resistance. Interior and balance sizes can be used where no collision resistance is required. This hazardous role should have the same protection against pre-image, second image, and impact attacks where collision safety hash skill is required, and this can decrease the internal state spectrum. Typical hash capacity for smaller message sizes will be used to boost the large 264-bit contribution. For most detached guidelines about the ability of the lightweight hash, the standard size of the information is much smaller (like 256 bits at most). Therefore, hash functions improved for short messages may be more suitable for lightweight implementation.

17.2.3

ligHtWeigHt Stream CipHer algoritHmS

The European Network of Excellence for Cryptology (eSTREAM) was designed to identify modern stream statistics that would be ideal for unconditional adoption [45]. Rivalry finalizers with three grain ciphers have been announced in 2008 [46], MICKEY [47] and Trivium [48].

17.2.4

HigH-performanCe SyStemS

The elite framework uses unique crypto-engines to satisfy three critical needs: performance, flexibility, and safety. Some weaknesses such as area and power are regarded to a limited extent [49]. Some of the high-performance requirement systems are being addressed below. Modified cryptographic CPU and crypto ALU processors use a CPU to execute cryptography algorithms that have been designed for use. An Instruction Set Architecture typically incorporates instructions that are cryptographically oriented. Due to the different cryptography algorithms, selecting these types of instructions is difficult. The machine software must be changed to something like a compiler to use innovative instruction [50]. Cryptographic co-processor improves the cipher speed and is done through the system unit; it is dedicated to the cryptographic processor, a cryptography company, and this is managed by the host processor. Which control of downtimes and the Co-processor decides the general execution of data [51]. Cryptographic arrays by using parallel methods to further enhance efficiency, an encryption array of processing unit and a multicore encryption processor was created and it is similar to computational tasks and includes the mapping topology for transmitting information among entities and storage. It provides an extremely encrypted data rate or multiple ciphers simultaneously.

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Cryptographic multicore in comparison, the cryptographic multicore processor does not rely on a method. The authors [52] have revealed a multichannel cryptographic and multi-standard processor (MCCP) for 8-core systems. The key FPGA system for AES cryptography was introduced. The AES core can be easily changed by other block ciphers by rearranging the FPGA hardware.

17.2.5

aSSeSSment of loW-ConStrained deviCeS

The lightweight cryptographic algorithm offers an interplay between power and resource to achieve the same degree of security when considering efficiency metrics of low constrained devices. The output may also be communicated along with energy use, duration or delay, and flow capacity. The following two types of cipher implementation are available in limitedconstrained devices. Software implementations: The lightweight cipher algorithm can be implemented using a computer machine that can be a low-cost 8-bit and 16-bit microcontroller on the CPU. The program code is written may be machinedependent or independently such as C/C++ and Java. When encryption algorithms implement on a low constrained device, which is based on strength, speed, and storage, the calculation of software-specific concentrates on the required number of gate registers for RAM and ROM. Hardware implementation: Hardware design and deployment tools are typically represented in terms of the gate area and the complete use of customizable ASICs or FPGAs. The architecture in FPGA offers advantages such as reducing costs for production and increasing versatility. There are search tables, flip-flops, and multiplexers [53]. On the other hand, ASIC’s custom design relies on an automatic design process to minimize the design time.

17.2.6

preSent reSearCH Work

The current methods, for instance, use AES as a lightweight block cipher. The objective is to build AES into some kind of lightweight block cipher by considering duration and intensity values into account. The technique they proposed applied to RFID tags and sensor nodes [54]. The QTL structure is fundamentally changed in the Feistel design to increase the gradual diffusion of the current Feistel design. In QTL, cryptography and decryption approaches are similar. QTL also inhabits fewer regions in limited application areas and reduces the cost of energy consumption when using hardware [55]. A key Feistel modification scheme protects the cipher from relevant key attacks. Since a Feistel cipher is vulnerable to such a form of attack without a key schedule, the cipher focuses on the main attacks. They have shown that it is simple to create safe ciphers against related key attacks by using the AKF scheme [56]. A recent lightweight-embedded security cryptography algorithm design was proposed [57]. Their work offered an overview of the process of a bit substitution system based on another lightweight and compact cipher framework. We create confusion using

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PRESENT S-boxes as no such confusion occurs with bits and permutation commands in all existing algorithms. The current S-boxes for compact algorithms and a new hybrid method, which offers more compact results, are proposed in this article for both memory spaces. Many proposed data safety and security mechanisms for sensor networks such as AES, LED, KATAN, and TWINE. These protection measures, however, have disadvantages, security flaws, and machine complications. These problems were tackled and lightweight chips were suggested with messy maps and genetic interventions. Their proposed scheme uses elliptical curve points to classify interacting nodes [58]. The proposed system provides security and protection of privacy along with multi-level certainty managing. The whole system will quickly improve processing strength and capacity so that large quantities of PHI (Individual Health Data) are reported, while at the same time restricting disclosure security in medical services [59]. A lightweight cryptography tool has been built in the android platform [60]. They built the user-friendly NCRYPT tool for such a system. NCRYPT provides the option to encode certain data or picked sensitive files. Lightweight 8-round iterative UAN communication block cipher algorithms were based on the principle of chaos rather than the S-box. This scheme will guard against brutal attacks and opponents [61]. An ultra-lightweight cryptography concept was suggested for general computing [62]. ANU, is available in 25 rounds and supports 80/128-bit planning. ANU ciphers such as MITM, Zeroday, and Biclique are resistant to simple and advanced attacks [63]. This article deals with how secure data can be held and distributed through constrained devices to protect data from threats, along with data changes by unauthorized users.

17.3 IoT SECURITY ISSUES AND STRATEGIES OF PREVENTION 17.3.1

iSSueS and reSearCH CHallengeS

17.3.1.1 Issues IoT emergence in public facilities, business organizations, workplaces, and so on is facing safety and privacy issues, which are a major inconvenience when designing the IoT platform. Conventional cryptography algorithms are not ideally suitable in the IoT scenario due to many resource constraints and conditions such as power, low battery, real-time execution, and so on. Lightweight cryptography is also more consistent with IoT. There are several small cryptographic algorithms in symmetry and asymmetry categories. However, these lightweight algorithms may not ensure realtime security, runtime, energy consumption, and memory requirements. Symmetric algorithms are not authenticated, although the asymmetric ones are less important, consuming more memory. This affects the collection and processing of information in real-time and wastes IoT resources. 17.3.1.2 Challenges In IoT applications, the key challenge is to ensure privacy, safety, and data integrity. The main problem is, however, related to the authorization, authentication,

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and management mechanism. IoT relies therefore on the ability and efficiency of communications on future IPv6 conventions, which fulfill the inclination and versatility requirements. More challenges are related to the IoT framework: Less human interference can lead to physical and logical assaults. Several research studies on the security faults in wireless IoT networks have already resulted in many attacks including DoS/DDoS, response attacks, and more. Another challenge is to restrict resources for energy usage, less battery life, bandwidth, diverse system, and complex safety strategies that can delay system performance.

17.3.2

StrategieS and preventive meaSureS for iot

17.3.2.1 Asymmetric LWC Method for IoT In general, RSA is not part of the LWC method due to its maximum key size. Due to the use of two wide modulo operations and prime numbers, RSA ensures better security and user privacy. Compared with the RSA algorithm, the ECC needs a lower key size, speed, and storage space. Then it is implemented in the small hardware area, leading to smoother real-time calculations [64]. The 6LoWPAN nodes use the ECC algorithm that can be used for restricted systems. The lightweight symmetrical and asymmetrical IoT environment algorithms are calculated by the size of the code, the block size, number of rounds, the key sizes, internal structures, and potential attacks in Tables 17.2 and 17.3. 17.3.2.2 Symmetric LWC Method for IoT NIST says that AES includes three variants: AES-128, AES-192, and AES-256. It is used with a CoAP solution in the application layer. The cryptography feature includes a four 128-bit block matrix. The internal status is arranged by sub bytes, move rows, mix columns, and add round keys. TWINE: The Feistel structure is used to call eight times a round and to apply the 4×4 S-box XOR on the subkey. TWINE is a complex permutation and mixture for optimizing propagation comparison to CLEFIA and HIGHT. In TWINE, just half of the cycle is changed for a single sub-block separation and it involves a permutation to disperse all sub-blocks. TABLE 17.2 Asymmetric LWC Methods Asymmetric Method RSA ECC

Code Size

Block Size

Potential Threats

900 8838

1024 160

Modules attack Timing attack

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TABLE 17.3 Symmetric LWC Method Symmetric Algorithm

Technique

Code Length

HEIGHT TEA PRESENT RC5

GFS Feistel SPN ARX

5672 1140 936 Not fixed

No. of Rounds 32 32 32 20

Key Size 128 128 80 16

Key Length 64 64 64 32

Potential Attacks Saturation attack Related key attack Differential attack Differential attack

HIGHT: The high protection and lightweight height (HIGHT) operation of the Feistel network uses very simple and basic operation. During cryptography and decryption, this key is created. The authors suggested a parallel implementation that would need fewer resources and a small number of codes, and that the RFID system would be improved [65]. HIGHT has susceptibility to degradation threats. PRESENT: This depends on the substitution and permutation network, which consists of 31 rounds. PRESENT is used as a lightweight cryptography algorithm. It is 64-bit long with two 80-bit and 128-bit keys. It is applied to the embedded system on the substitution layer using 4-bit input and output of the S-box.

17.3.3

pSeudoCode for a ligHtWeigHt enCryption SyStem

The cryptography algorithm takes a plain text input as a 64-bit fixed-size block and then divides it into two halves of 32-bit fragments. The Feistel function F () operates in each round of the cryptography scheme along with a secret key size ranging from 64-bit to 128-bit. The incorporation of H function H (), which is an invertible function, operates at electronic speed to generate a 32-bit cipher at each round of the Feistel function. The resultant two halves of 32-bit are then swapped and merged to get the desired 64-bit ciphertext straight after the end of 14 rounds of the Feistel function of the cryptography algorithm. The following is the description of the cryptography algorithm shown in algorithm 1. Algorithm 1.0: Pseudocode for Encryption System Plaintext input if 64-bit (PT) Splits PT into two 4 bytes: PTL, PTR For i = 1 to 14: PTL = PTL XOR P (i) PTR = PTR XOR (P (i) XOR F (PTL)) XL = XL XOR H (XR) Switch PTL and PTR End For

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17.3.4

pSeudoCode for tHe deCryption SyStem

The decryption algorithm is a reverse engineering process of the cryptography system in which the plain text is generated using the same shared secret key and process for 14 rounds in the Feistel function. Therefore at the end of all rounds, the two halves of 32-bits are joined to produce original plaintext data of 64-bit. The following is the description of the decryption algorithm shown in algorithm 2. Algorithm 2: Pseudocode for the Decryption System

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17.4 SUGGESTED LIGHTWEIGHT CRYPTOGRAPHIC SYSTEM FOR IoT 17.4.1

frameWork of lWC

The existing LWC framework for IoT combines asymmetric and symmetric cryptography methods. The lightweight asymmetric method has a greater level of protection than symmetric algorithms, while they are machine complex and have larger key sizes in a constrained IoT sense [65]. Thus, a resource-based IoT environment is developed to deliver asymmetric and symmetric lightweight methods, with tiny key size, fewer calculation time, consumption of low power, minimum storage size, and equal protection. In addition, intelligent space involves several devices with limited power and memory, but several devices have ample amounts of battery power, computation processing, and storage space. The suggested technique, therefore, incorporates all aspects of cryptography, considering all computer parameters applicable to an IoT paradigm mentioned in Figure 17.2. Figure 17.2 shows a flow diagram that accepts IoT system variables to be an input, as well as its result, and proposes versatile data cryptography for this intelligent device. LWC comprises four research steps using four input parameters: storage space (SS), information space (IS), battery power (BP), and processing power (PP) [66].

FIGURE 17.2 Framework of LWC.

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The proposed LWC system incorporates the principle of hierarchy. Every smart home device combines, data collection processors, provide sufficient guidance, and articulate the system goal information that proves a hierarchical organized model [67]. The LWC framework offers two output cryptography algorithms (slight symmetric and asymmetrical cryptography), depending on the examination of device parameters. The lower number, key size, key length, and code size indicate the lightweight release of conventional algorithms. This shows that the proposed scheme is acceptable and suited for IoT devices such as RFID, the WSN, and so many more. The desired efficiency, in this case, is lightweight symmetric cryptography as per current research [68,69]. Otherwise, the next processing step continues, which is the variable check of the device battery of IoT. Formulas (17.1) and (17.2), will impact the computing capacity, the storage size, and the capacity of batteries of the IoT device [70], respectively. The notation of those equations is given in Table 17.4. If the estimated battery power value is less than the threshold and is based on research [71], lightweight symmetric cryptography is suggested and advised by the proposed method. Even if the IoT device has enough battery power, it goes through the process of analyzing the memory space during the next step. For evaluation of memory capacity, the metric component depends on the platform, like code length, and is connected to the chosen processor with its instructions. The amount of iteration or/and XOR inclusion may also affect storage space. The cryptography method does not need more storage if substitution boxes are not used. Equation(17.1) shows the design performance:

Throughtout =

NB NB × F = CB TB

(17.1)

The process of design frequency is described by throughput. Current number (cycles) associated with the command set stored in the memory of the processor. Each processor, therefore, has a different frequency and number of cycles. a capacity booking for information stored on IoT systems is different from restricted capacity and adequate memory. The LWC framework verifies whether the computer has

TABLE 17.4 Symbols and Metric Symbols KS GE TB A CB F Th

Metric Key size Gate Equivalent Time for encoding 1 block Design area Cycles Nb. for encoding 1 block Frequency Throughput

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a limited memory for analytical calculation capacity; otherwise, it is lightweight asymmetric cryptography [72,73]. The measurement systems power is considered as the effectiveness metric determined in the report of the flow rate determined with a defined clock frequency for the area during the last stage of LWC studies [74]. This variable also calculates the area costs and specifications for handling a single cipher text bit. The efficiency is demonstrated in the following formula.

Efficiency =

Th NB N  ×F = = B A TB  × A C B × A

(17.2)

An LWC relates the threshold value to the computer power of a device by taking certain efficacy levels of the device into account. According to the current quest in some of the studies [68,75], if the calculation power value reaches the threshold level, the suggested technique for this system is lightweight asymmetric cryptography, and then the framework shows lightweight symmetric cryptography.

17.5 PROBLEMS AND DISCUSSION The importance of a number of contributions to the cryptographic system and its application areas will have to be examined in future studies. In the previous chapters, we have surveyed and summarized many current lightweight algorithms for lowresource IoT devices. This chapter focuses on research subjects related to lightweight and traditional encryption. In this chapter, we further define the problems mentioned as follows.

17.5.1

tHe algoritHmS of CryptograpHy and CipHer deSign

The design of cryptography examined in this work allows the global output of various cryptographic designs to be exposed. However, a reliance on technology and resources deforms the findings and ends with big discrepancies between research, and so, it is better to understand different ways by proposing a new cryptography comparable with the current traditional cryptography. The new model improves the accuracy of lightweight encryption. To enhance cryptographic energy, authors have suggested a hardware design power, which has achieved optimal power in 32 rounds from Katan cipher [71]. In addition, they proposed compacting their model by considering the physical, architectural, and algorithmic problems. The framework includes a traditional Feistel structure with slow cipher diffusion. Adjustment between both the complexities of a round also raises the number of rounds, pipelines, and roll-ups. In addition, a middleware system name PalCom to exchange lightweight data for the IoT environment was suggested [76].

17.5.2

tHe iSSueS aSSoCiated WitH BloCk Size and key Size

The development of a lightweight, resource constrained technique emphasizes on the considerable size and characteristics of the block. Even as the key length is increased,

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the size of ciphertext immediately increases, and as a result, the computing power becomes more essential. It also applies for block size. Intruders may use a certain key to kill the algorithm with a multi-key attack. The privacy property is breached, if the hackers obtain the key.

17.5.3

modern tHreatS

Most methods are proposed to prevent and recognize threats that affect system security and break the models implemented [76]. Therefore, the cipher capacity must be modernized to be vulnerable to such attacks. The major problem with the energyrestrained method is Hardware Trojan. A substitute unresolved issue necessitates creating a universal pattern that consolidates the design of HT and hardware to determine the complexities and tradeoffs of protection.

17.5.4 SeCurity and privaCy The security and privacy for constrained devices and their resource structure can be adapted, which enhances the attention and interest in safety metrics. In general, no safety metric can reliably estimate the security and privacy for the cryptographic field. Encryption is a subject for decryption, which is to break the encryption process by using a list. The security level may be rated as less safe, secure, or moderate, relying on the effective list of attacks. Nevertheless, common security systems still require upgrades and more clear-cut safety requirements for cryptography algorithms for resource-constrained devices in IoT systems still require updates and anticipate criteria for key cryptographic security.

17.6 CONCLUSION We have worked in-depth on the latest branch of traditional cryptography called lightweight encryption techniques; however, many low constrained devices perform computational methods and are constrained in terms of personal-sufficiency, energy usage, and memory. We often face the additional issue of security and safety and consider the way IoT employers maintain safety. In addition, we explored different types of lightweight cryptographic algorithms that can also be applied with software or hardware implementation. Different types of algorithms lead to the creation of safe, powerful, lightweight, key-sized encryption algorithms, reducing the calculation strength and rapid process. In this study, we have introduced a new method that can be used in a smart city. We have also addressed the opening points on block size, key size, cipher structure, deployment, security measurements, and new attacks. Our expectation will research the cost of these solutions and the probability of integration into restricted IoT systems. Furthermore, we plan to develop the algorithm used to calculate the threshold value for an individual system parameter previously provided in this work.

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REFERENCES 1. Perera, Charith, Arkady Zaslavsky et al. “Context aware computing for the Internet of things: A survey.” IEEE Communications Surveys & Tutorials 16, no. 1 (2013): 414–454. 2. Mahmoud, Rwan, Tasneem Yousuf, Fadi Aloul et al. “Internet of things (IoT) security: Current status, challenges and prospective measures.” In 2015 10th International Conference for Internet Technology and Secured Transactions (ICITST), pp. 336–341. IEEE, 2015. 3. STAMFORD (2013) “Gartner says the Internet of things installed base will grow to 26 billion units by 2020.” http://www.gartner.com/newsroom/id/2636073. Accessed 26 Aug 2020. 4. Padmavathi, B., and S. Ranjitha Kumari. “A survey on performance analysis of DES, AES and RSA algorithm along with LSB substitution.” IJSR, India 2, (2013): 2319–7064. 5. Arias, Orlando, Jacob Wurm, Khoa Hoang, and Yier Jin. “Privacy and security in Internet of things and wearable devices.” IEEE Transactions on Multi-Scale Computing Systems 1, no. 2 (2015): 99–109. 6. Nawir, Mukrimah, Amiza Amir, Naimah Yaakob, and Ong Bi Lynn. “Internet of Things (IoT): Taxonomy of security attacks.” In 2016 3rd International Conference on Electronic Design (ICED), pp. 321–326. IEEE, Phuket, Thailand, 2016. 7. Papp, Dorottya, Zhendong Ma et al. “Embedded systems security: Threats, vulnerabilities, and attack taxonomy.” In 2015 13th Annual Conference on Privacy, Security and Trust (PST), pp. 145–152. IEEE, Izmir, Turkey, 2015. 8. Stankovic, John A. “Research directions for the Internet of things.” IEEE Internet Things J 1 (2014): 3–9. 9. Roman, Rodrigo, Pablo Najera et al. “Securing the Internet of things.” Computer 44, no. 9 (2011): 51–58. 10. Wang, Jie, Xin Kang, Ying-Chang Liang et al. “An energy harvesting chain model for wireless-powered IoT networks.” In 2018 10th International Conference on Wireless Communications and Signal Processing (WCSP), pp. 1–6. IEEE, Hangzhou, 2018. 11. Sheng, Zhengguo, Shusen Yang, Yifan Yu, et al. “A survey on the IETF protocol suite for the Internet of things: Standards, challenges, and opportunities.” IEEE Wireless Communications 20, no. 6 (2013): 91–98. 12. Singh, Saurabh, Pradip Kumar Sharma, Seo Yeon Moon, and Jong Hyuk Park, “Advanced lightweight encryption algorithms for IoT devices: Survey, challenges and solutions,” Journal of Ambient Intelligence and Humanized Computing (May 2017): 1–18. doi:10.1007/s12652-017-0494-4. 13. Shu, Zhaogang, Jiafu Wan, Di Li, Jiaxiang Lin, Athanasios V. Vasilakos, and Muhammad Imran, “Security in software-defined networking: Threats and countermeasures.” Mobile Network Application 21, no. 5 (2016): 764–776. 14. McKay, Kerry A., Larry Bassham et al. “Nistir 8114: Draft report on lightweight cryptography.” Available on the NIST website: http://csrc.nist.gov/publications/drafts/ nistir-8114/nistir_8114_draft.pdf (2016). 15. Yick, Jennifer, Biswanath Mukherjee et al. “Wireless sensor network survey.” Computer Networks 52, no. 12 (2008): 2292–2330. 16. Latré, Benoît, Bart Braem, Ingrid Moerman et al. “A survey on wireless body area networks.” Wireless Networks 17, no. 1 (2011): 1–18. 17. Sajid, Anam, Haider Abbas et al. “Cloud-assisted IoT-based SCADA systems security: A review of the state of the art and future challenges.” IEEE Access 4 (2016): 1375–1384.

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18

Nanogenerator-based Sensors for Human Pulse Measurement Ammu Anna Mathew, S. Vivekanandan, and Arunkumar Chandrasekhar Vellore Institute of Technology

CONTENTS 18.1 Introduction .................................................................................................. 315 18.2 Working and Fundamentals Mechanism of NGs ......................................... 316 18.2.1 Piezoelectric Nanogenerator ............................................................. 316 18.2.2 Triboelectric Nanogenerator ............................................................. 317 18.2.3 Pyroelectric Nanogenerator .............................................................. 318 18.3 Choice of Materials ...................................................................................... 318 18.4 Applications of NGs in Pulse Measurement................................................. 319 18.4.1 PENG-Based Sensors ....................................................................... 319 18.4.2 TENG-Based Sensors ....................................................................... 322 18.5 Conclusion .................................................................................................... 324 Conflict of Interest ................................................................................................. 325 References .............................................................................................................. 325

18.1 INTRODUCTION Self-powered systems that can be operated without any external power source are a topic of great interest in the last few decades [1]. The technology of generating electricity by converting the mechanical/thermal energy is called a nanogenerator (NG). The mechanical/thermal energy used for conversion may be due to some small-scale deformations. Displacement current is the driving force for conversion, irrespective of the material being used. As the NGs have the capability to operate without external power, they can extend their application to different fields, mainly contributing to the biomedical and healthcare devices [2]. NGs are classified into four types: piezoelectric nanogenerator (PENG), triboelectric nanogenerator (TENG), pyroelectric nanogenerator (PYNG), and thermoelectric generator (TEG). PENG and TENG make mechanical to electrical energy conversion whereas the latter converts thermal energy to electrical energy. NGs harvest energy from small-scale deformations and large-scale deformations in different fields, as shown in Figure 18.1. Nanotechnology incorporated with energy harvesters produces 315

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FIGURE 18.1

Nanogenerator technology.

more sensitive and accurate NGs. The most commonly used NGs are PENG, TENG, and PYNG, which are explained below in detail. The physiological signals are to be monitored, evaluated, and analyzed in appropriate time to remain healthy. The advancements in the field of biomedical and healthcare, considering factors such as power consumption, biocompatibility, and nano-materials, have provided a healthy, risk-free life to many individuals [3]. Figure 18.2 shows a graphical representation of various NG classifications and their application in various fields. NGs find their application in numerous fields but this chapter concentrates on pulse monitoring in the biomedical field. This chapter provides a brief description of the working mechanism of NGs followed by parameters to be considered while designing an NG. An overview of PENG and TENGbased pulse sensors for health monitoring has also been discussed.

18.2

WORKING AND FUNDAMENTALS MECHANISM OF NGS

Maxwell’s displacement current theory is the basis of NGs. The output characteristics from which the output current and output voltage equations are derived are based on Maxwell’s equation. The three main NG classifications are explained below.

18.2.1

piezoeleCtriC nanogenerator

The type of energy harvesters that converts the external kinetic energy to electrical energy by means of nanostructured piezoelectric materials is called a PENG. PENG is fabricated using materials having the piezoelectric effect. The phenomenon of inducing electric potential as a result of generated electric dipole movement due to the stress is called the piezoelectric effect. In addition to the material, stretchable substrates and

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FIGURE 18.2 Classification and major applications of nanogenerators.

connecting electrodes are also used. With respect to biomedical applications, the choice of biocompatible piezoelectric materials, device design, and encapsulation are of primary concern. The PENG has three different geometrical configurations: (i) vertical nanowire integrated nanogenerators (VING), (ii) lateral nanowire integrated nanogenerators (LING), and (iii) nanocomposite electrical generators (NEG).

18.2.2

triBoeleCtriC nanogenerator

The type of energy harvesters that converts the external mechanical energy into electrical energy by combining contact electrification with electrostatic induction is called a TENG. The charge transfer occurs between the two thin organic/ inorganic films with opposite tribo-polarity in the inside circuit and electron flow between the electrodes attached to the films to balance the potential that occurs outside the circuit. The triboelectric effect can be observed in day-to-day life for mechanical energy collection. The simple and miniature design, flexibility and portability, cost-effectiveness, higher sensitivity, accuracy, and material availability are the promising points of TENG-based designs. There are four different modes of operation for TENG: (i) vertical contact – separation (CS) mode, (ii) lateral sliding (LS) mode, (iii) single electrode (SE) mode, and (iv) freestanding triboelectric – layer (FT) mode.

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pyroeleCtriC nanogenerator

The type of energy harvesters that converts the external thermal energy to electrical energy by means of nanostructured pyroelectric materials is called a PYNG. The heat energy wasted is harvested by this technique for conversion to electric energy. The working principle includes primary pyroelectric effect (charge developed under strain-free conditions) and secondary pyroelectric effect (charge developed under strain conditions). This type of NGs find their application in fields where time-dependent temperature variation occurs such as in active sensors. The output voltage of the PYNG will be high but with a very less output current.

18.3

CHOICE OF MATERIALS

Materials used for constructing NGs are the primary concern in the design. The materials should be chosen such that they should have the ability to produce the NG effect. The dynamic charge transfer ability based on capacitance characteristics, NG efficiency in terms of surface charge density, work function of energy required for electron movement, and so on are also to be considered while finalizing the materials [4]. While choosing materials for PENGs, usually materials with wurtzite structures and perovskite structures are considered with the advantage of simplicity and cost-effectiveness in fabrication. ZnO, CdS, and GaN are examples of wurtzite structure-based materials. Several experiments with different techniques were carried out to improve the piezoelectric effect on nanowires, thus developing new wurtzite structure-based piezoelectric materials like p-type ZnO nanowires. The p-type structures developed more output signal than that of n-type wurtzite structures. The perovskite structures displayed more piezoelectric effect compared to wurtzite structures. Barium titanate (BaTiO3) nanowire is an example of a perovskite structure, which has 16 times more output signal than a ZnO nanowire. The other materials include PVDF, ultra-long potassium niobate (KNbO3) nanofibers, and lead magnesium niobate-lead titanate (PMN-PT). PMN-PT was further improved to a single-crystal PMN-PT nanobelt to obtain a higher piezoelectric constant [5]. Almost all materials such as metal, polymer, wood, and silk can be used to demonstrate the triboelectric effect. TENG has a wide material choice compared to PENG. Electron gaining or losing ability determines the material charge, thereby determining the polarity. Material that gives up electrons is assigned positive polarity and that which gains electrons is given negative polarity. Based on this principle, a list of materials called triboelectric series are made assigning positive and negative charges with neutral materials in the middle. The position of materials in the series may vary slightly depending on the environmental and surface conditions. To get a better triboelectric effect, the materials will be chosen such that more positively charged materials will be combined with the most negative material. Surface morphology and functionalization are also considered to improve the triboelectric effect [4].

Sensors for Human Pulse Measurement

18.4

319

APPLICATIONS OF NGS IN PULSE MEASUREMENT

Mutual and immediate communication of physiological signals has been established through the development of real-time biomedical monitoring systems. This development has brought incredible advancement in the medical field, which has aided automatic medical analysis. Wearable self-powered devices based on the NG approach are capable of detecting body signals with greater sensitivity and accuracy. Pulse sensors are one such devices capable of examining and monitoring the circulatory system activities as the pulse is very closely related to heart. The essential signals of the body such as respiration rate, heartbeat, and blood pressure are to be monitored from time to time, and the absence or variation from normal signals may lead to a life-threatening situation or mortality [6]. Many sensors with the NG approach have been introduced to monitor the cardiovascular conditions based on the human pulses. Here we have classified the sensors based on two types of NGs: PENG and TENG.

18.4.1

peng-BaSed SenSorS

Self-powered sensors have become a promising category to resolve the issue of power consumption in wearable healthcare devices. The vulnerability problem and abrasion resistance are overcome in piezoelectric sensors. The conversion of mechanical deformations to electrical energy in flexible energy harvesters to improve the efficiency of output current is an important property of the piezoelectric effect. The piezoelectric capability mainly depends on the piezoelectric charge coefficient of the material utilized in fabrication, and it should be high for the desirable effect. This section deals with some of the existing piezoelectric-based pulse sensors used for health monitoring [7]. J. McLaughlin et al. in 2003 introduced a reliable non-invasive technique for measurement of pulse wave velocity (PWV) from the human peripheral artery (arterial pulse wave velocity (APWV)) using a piezoelectric pressure sensor by an ultrasound Doppler. The reproducible results used an analysis program where measured data are filtered for calculating the APWV. Different techniques were used for analyzing the wave velocity, which includes peak-to-peak detection (PPAPWV), foot-to-foot detection (FFAPWV), cross-correlation PWV, and apparent arterial pulse wave velocity (AAPWV). The mean value of all these was considered as the true value of PWV. The pulses in two different locations are measured simultaneously in PWV measurement using two piezoelectric sensors, which produced an output voltage when mechanical deformation occurs at the output contacts. The stress/strain (piezoelectric strain constant = 23 × 10 −12mV−1) is converted to proportional electrical energy (piezoelectric voltage constant = 216 × 10 −3mN−1) using piezoelectric materials, which has a wide range of frequency ranging from 0.001 to 109 Hz. In addition, such materials have low acoustical impedance and less moisture resistance. They are flexible, lightweight, and thin but mechanically strong. The experimentation was validated by human trials with different age groups. Here PVDF is made electrically conductive by depositing a nickel-copper alloy on either sides of PVDF. The proposed instrument finds its clinical application when it converts the PWV (6–15 m s−1) to the value of peripheral

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artery wall elasticity. This technique was found to be beneficial for peripheral arterial disease patients and pre- and post-surgery/ treatment patients [8]. GT. Hwang and co-workers in 2014 introduced a self-powered, flexible, and highly efficient artificial pacemaker made of single-crystalline rhombohedral piezoelectric 1.7 cm × 1.7 cm thin film of 0.72 PMN-0.28 PT onto an ultraviolet light-cured polyurethane-coated PET substrate (110 μm). The Cr/Au film deposited on the PMN-PT plate acts as a bottom electrode and is attached to the Si wafer. Cr/Au (10 nm/100 nm) was deposited as the top electrode. Deposition was done using a DC sputtering technique on an 8.4 μm PMN-PT plate as shown in Figure 18.3. The SU-8 (Microchem) layer acts as a protective layer. This thin film converts the biomechanical motion to electrical energy with maximum values of current and voltage to be 0.223 mA and 8.2 V respectively. A stress-controlled exfoliating method is performed to transfer the PMN-PT thin film onto a flexible substrate. This method utilizes the inherent stress available in a 20 μm nickel film. The real-time simulation was done on the cardiac muscles of a live rat. The proposed device can be used as an energy source for recharging batteries. The work was extended by performing 3D stacking of piezoelectric films onto a single PET substrate and the porcine organ movement is harvested [9]. In 2015, A. Bongrain and others reported two different versions of an ultrathin AlN piezoelectric sensor, which is capable of measuring the micro-deformations. This sensor finds its application in the measurement of ECG-correlated cardiac pulse wave signal, thus predicting cardiovascular diseases from the wave shape. The CMOS companionable hygienic processes involved (i) piezoelectric AlN deposition

FIGURE 18.3 Piezoelectric flexible PMN-PT sensor. (a) Graphical representation of the fabrication process and its biomedical application. (b) Digital images of a power generation sensor in its original, bending, and release positions. (Reproduced with permission [9]. Copyright John Wiley & Sons.)

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on the first electrode (200 nm) as an aluminum electrode by DC magnetron sputtering (first version) and (ii) piezoelectric AlN deposition on the first electrode as platinum by lift-off (second version). The second electrode (200 nm) in both versions is aluminum. The second version was found to be more effective in exhibiting piezoelectric properties, though the production yield was higher in the first version. A consistent biocompatible parylene layer ( 0.05). The following formula was used to determine the test value:

χ = 2



k

l

i =1

j =1

∑∑

(n

ij

− nij nij

)

2

in which: k = total number of columns, i = a single column, l = total number of rows, and j = a single row. Degree of freedom = (k − 1)*(l − 1) The χ2 test is the most important nonparametric test. Using the χ 2 independence test, one can verify the hypothesis that there is no relationship between two qualitative or quantitative variables (Sobczyk 2006). No correlation analysis was carried out, as the given data were not figures but merely an indication of elements having the same non-measurable characteristics. The independence test allowed for checking whether there was a relationship between elements with the same feature, that is, whether there was an impact on the number of examined features. Data Sample – 100 employees The presence of solutions implemented as part of Green IT Yes – 50 employees No (including the lack of knowledge about the existence of such a system) – 50 employees The degree of employee agility Very good – 21 employees, Good – 51 employees, Satisfactory – 26 employees, and Sufficient – 2 employees.

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Degree of employee agility Presence of solutions implemented as part of Green IT Yes No

Very good

Good

Satisfactory

Sufficient

9 11

21 30

20 6

0 3

The theoretical value table: Degree of employee agility Presence of Green IT solutions YES NO

Very good

Good

Satisfactory

Sufficient

10 10

25.5 25.5

13 13

1.5 1.5

Test value: χ2 = 12.32 At the significance level of p < 0.05, it can be concluded that there is a relationship between the presence of Green IT in Nadolny MM and the level of its agility.

20.7 USE OF GREEN IT IN TEAL ORGANIZATIONS – CONCLUSIONS Today's businesses have come to operate in a turbulent, unpredictable market environment where sustainability policy continues to take on importance. They have been forced to adopt strategic orientation, which would allow the company to face the growing demands of a changing reality. Each organization must therefore adapt to changes. These changes include the necessity to implement ‘green’ IT solutions that aim to eliminate toxic substances from products, as well as the collection and recycling of outdated or damaged equipment. The implementation of the above is only possible if a company develops organizational agility attributes, which are an important part of the idea of Teal management. This chapter examined the case of Nadolny MM. The company is successfully introducing and applying the Green IT technology, such as Workflow software that enables full integration with the service management systems implemented by the company’s partners, to integrate printing production processes performed by the company's partners into a coherent whole. The chapter also tries to answer the question of the reasons for the company's success. The research conducted under the direction of Prof. Ph. D. Agnieszka Rzepka has positively confirmed the thesis put forward in the introduction of the chapter and proved that Nadolny MM undeniably possesses several qualities found in agile organizations that operate according to the idea of Teal management. The research, the case study, and the presence of statistical correlations presented above make it possible to conclude that the development of Green IT in a company is part of the business strategy found in Teal organizations. These results are consistent with those of other researchers.

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FIGURE 20.2 Determinants of an agile organization implementing Green IT solutions. (own elaboration.)

It seems that Kidd (1995) was right in assuming that only those companies that have developed agile qualities and can adapt to the conditions of the new industry 4.0 era can successfully implement innovative IT solutions (including Green IT). Based on this knowledge, some strategies can be proposed that would lead to worker agility. The first proposed strategies relate to the way a company is managed in response to the challenges of Industry 4.0. This requires the use of new Green IT technologies. Such smart and flexible technologies create technological agility. However, a company achieves holistic agility through the skills and experience of people who create an agile workforce. A representation of these dependencies is shown in Figure 20.2. A company can be considered agile on the condition that it develops a flat structure, which is a core idea of the Teal model of management. For the effective implementation of flexible and intelligent information technologies in the Green IT, the ability of employees to adapt quickly to the requirements of new equipment is essential. An organization achieves agility by integrating multiple sources into a coordinated, interdependent system consisting of flexible organizational structures. For this reason, developing the skills of employees and increasing their environmental awareness become an essential part of running an agile company, managed according to the methods characteristic of the Teal management model. The considerations presented herein may provide inspiration for further research. It would seem particularly interesting to examine the relationship between providing employees with autonomy and their willingness and commitment to introduce Green IT solutions.

20.8 PRACTICAL RECOMMENDATIONS

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assets that determine its uniqueness. It must therefore develop qualities that are the answer to the impulses that come from the market environment. Organizational agility is undoubtedly one such attribute. 3. It is a good idea for a company to form an interdisciplinary team of specialists whose task is to identify problems related to sustainable development and to improve the electricity consumption in the company. 4. A good business practice is to include Green IT in the company's business strategy. 5. It is important to remember that the company’s management methods have a direct impact on employee agility, which helps raise awareness and alter employee behavior to protect the natural environment.

REFERENCES Allen, J., van der Velden, R., 2005. The flexible professional in the knowledge society: conceptual framework of the REFLEX project. REFLEX Working paper. alumniportal-deutschland.org, 2020. [online]Available at: [Accessed 12 September 2020]. Bełz, G., Barbasz, A., 2014. Research Papers. Wrocław: Wydawnictwo Uniwersytetu Ekonomicznego we Wrocławiu. Breu, R., Hafner, M., Weber, B., Novak, A., 2002. Model driven security for inter-organizational workflows in e-government, ISCA: In Proc. TCGOV. Brzozowski, T., 2012. Zrównoważony rozwój szansą dla organizacji obecnie przyszłości. In: A. Stabryła, K. Woźniak (eds.), Determinanty potencjału rozwoju organizacji. Kraków: MFiles.pl. Dahmardeh, N., Banihashemi, S. A., 2010. Organizational agility and agile manufacturing, European Journal of Economics, Finance and Administrative Science: 27(1): 3–12. Doś, A., 2011. Współczesne koncepcje celu przedsiębiorstwa w aspekcie implementacji zasad zrównoważonego rozwoju. In: T. Famulska, J. Nowakowski (eds.), Kontrowersje wokół finansów. Warszawa: Difin. Doz, Y., Kosonen, M., 2008. The dynamics of strategic agility: Nokia’s rollercoaster experience. California Management Review: 50(3): 95–118. Drucker, P., 1992. Innowacja I Przedsiebiorczosc. ̨ ́ ́ Warszawa: Panstwowe ́ Wydawnictwo Ekonomiczne. Gorczyńska, M., 2010. Stabilność finansowa a zrównoważony rozwój przedsiębiorstwa. Zarządzanie i Finanse: 2(2): 2–5. Grudzewski, W.M., Hejduk, I.K., Sankowska, A., Wańtuchowicz, M., 2010. Sustainability w biznesie, czyli przedsiębiorstwo przyszłości – zmiany paradygmatów i koncepcji zarządzania. Warszawa: Poltext. Gunasekaran, A., 1999. Organisational quality ‐ a cognitive approach to quality management. The TQM Magazine: 11:180–187. Herzenberg, S.A., Alic, J.A., Wial, H., 1998. New Rules for a New Economy: Employment and Opportunity in Postindustrial America, New York: Cornell University Press. Hopp, W., Tekin, E., Van Oyen, M., 2004. Benefits of skill chaining in serial production lines with cross-trained workers. Management Science: 50:83–98. Jeznach, A., Eichelberger, W., 2017. Szef, Który Ma Czas. Gliwice: Wydawnictwo Helion. Juchnowicz, M., 2016. Firmy Samoangażujące - Utopia Czy Biznesowy Realizm. Zarządzanie i Finanse: 2(10):171–174. Kidd, P., 1995. Agile Manufacturing. Wokingham, England: Addison-Wesley. Kisielnicki, J., 2009. MIS - Systemy Informatyczne Zarzadzania. ̨ Warszawa: Placet.

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Mazur-Wierzbicka, E., 2005. Koncepcja zrównoważonego rozwoju jako podstawa gospodarowania środowiskiem przyrodniczym. In: D. Kopycińska (ed.), Funkcjonowanie gospodarki polskiej w warunkach integracji i globalizacji. Szczecin: Wydawnictwo Uniwersytetu Szczecińskiego. Pearce, J.M., 2012. The case for open source appropriate technology. Environment, Development and Sustainability 3: 425–431. Raport-erp.pl. 2020. Raport ERP 2020- Porównanie Systemów ERP, CRM - APS - Zaawansowane Planowanie I Harmonogramowanie. [online] Available at: [Accessed 12 October 2020]. Raschke, R., 2010. Process-based view of agility: The value contribution of IT and the effects on process outcomes. International Journal of Accounting Information Systems 11: 297–313. Rogall, H., 2010. Ekonomia zrównoważonego rozwoju. Teoria i praktyka. Poznań: Zysk 4 i S-ka. Rzepka, A., 2018. Relacje miedzyorganizacyjne ̨ i kapitał Intelektualny jako czynniki rozwoju mikro i małych przedsiebiorstw. ̨ Warszawa: Difin. Rzepka, A., 2019a. Innovation, inter-organizational relation, and co-operation between enterprises in Podkarpacie region in Poland. Procedia Manufacturing, 30, pp.642–649. Rzepka, A., 2019b. Soft management factors and organizations – outcome of research. In: L. Mihalcova, et al (ed.), Production Management and Business Development. Boca Raton, FL: Taylor & Francis Group, pp. 195–200. Sobczyk, M., 2006. Statystyka. Lublin: Wydawnictwo Uniwersytetu Marii Curie-Skłodowskiej. ̨ Sudoł, S., 2006. Przedsiebiorstwo. Warszawa: Polskie Wydawnictwo Ekonomiczne. De Sutter, J., 2007. Potega ̨ Technologii Informatycznych. Warszawa: VIZJA Press & IT. Szkudlarek, P., Milczarek, A., 2014. Rola społeczeństwa informacyjnego w kreowaniu zrównoważonego rozwoju. Ekonomia i Środowisko: 3(50): 12–16. Turban, E., Leidner, D., McLean, E., Wetherbe, J., 2006. Information Technology for Management, Transforming Organizations in the Digital Economy. New York: John Wiley & Sons. Zailani, S., Jeyaraman, K., Vengadasan, G., Premkumar, R. 2012. Sustainable supply chain management (SSCM) in Malaysia: A survey. International Journal of Production Economics: 5:140.

21

Design of a Pentagon Slot-based Multi-band Linear Antenna Array for Energy-efficient Communication: Future Challenges and Applications in Green Technologies Satheesh Kumar P. and Balakumaran T. Coimbatore Institute of Technology

CONTENTS 21.1 21.2 21.3 21.4 21.5 21.6

Introduction .................................................................................................. 357 Planar Array ................................................................................................. 358 Simple Patch Design ..................................................................................... 358 Design of Pentagonal Slot Antenna .............................................................. 360 Dual-Band Antenna ...................................................................................... 361 1 × 2 Linear Pentagonal Slot Array............................................................... 363 21.6.1 Simulated Results ............................................................................. 363 21.7 Tri-Band Antenna ......................................................................................... 363 21.8 Conclusion .................................................................................................... 364 References .............................................................................................................. 367

21.1 INTRODUCTION Radiation elements for an advanced wireless scenario for a smart green environment should have certain features such as high directivity, decent bandwidth (BW), and beam steering [1] achieved by side lobe-level control (SLL) for a space populated by embedded systems, sensors, use terminals, actuators, and other wireless communicating services. The above features cannot be accomplished by a single 357

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antenna element, as bad SLL and BW management. The antenna arrays would be capable of providing the ideal better BW radiation pattern, half control BW and SLL with proper geometric and electrical array properties [2]. The impulse for the highly probable communication systems is introduced in this chapter. The simplest kind is a linear array, in which all elements are arranged in a direct line – straight line. For different array geometries, many standard digital techniques are deceived. These standard methods are time-consuming and are challenging numerical tests. The antenna arrays are evenly classified into two groups [3]. Antenna arrays of non-uniform antenna components consist of the first type and linear antenna arrays that consists of the second type often consider a group of unevenly spaced antenna arrays with an odd or even number of antenna elements of uniformly distanced antenna arrays to synthesize efficient arrays. Therefore, an additional scope for planar antenna arrays to obtain different array geometries by preserving those edges in the FALSE condition [4] and the overall SLL of the resulting arrays can then be further reduced by the optimization of the array element and inter-element distances. The article reports two array geometries by eliminating several elements from a quadratic antenna array, followed by optimizing the element location to minimize the SLL in the resulting array geometry.

21.2 PLANAR ARRAY Figure 21.1 displays the symmetrical plane sequence of isotropic elements (2M + 1) × (2N + 1) aligned in the XY plane. Then, the pattern of the 2D array can be represented as follows [5,6]:

 N  M   AF(u, v) = A(m, n)  exp ( jkd x (m)u ) exp ( jkd y (n)v )    n = − N  n = − M  

∑ ∑

(21.1)

For symmetric array,

21.3

d − m = −d − m 1 < m < M d − n = −d − n 1 < n < N

(21.2)

SIMPLE PATCH DESIGN

The equations used for designing an antenna are mentioned below. The thickness of the middle layer substrate regulates the gain and directivity of an antenna. The thickness of the substrate depends on the resonant frequency of an antenna. The thickness of the substrate is chosen within the range for antenna configuration [7]. The antenna gain is determined by the substrate thickness. The substrate thickness depends on the antenna’s resonating frequency. The substrate height is calculated based on 0.003λ < h < 0.05λ.

λ = C fr ,

(21.3)

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FIGURE 21.1

Equally spaced planar array geometry.

where C = velocity of light and fr = resonant frequency. For the frequency of 2.4 GHz, the thickness of the substrate is 1.6 mm. The relative permittivity is a parameter, which varies depending on the substrate material used. The material used depends on the application of the antenna. The εr = 4.4 for the FR4 material, and the length (L sub) and width (Wsub) of the substrate [8] should be Wsub = 6h + W



(21.4)

Leff = L + 2∆L



(21.5)

The equivalent (effective) dielectric constant is calculated by using,



ε eff =

h εr + 1 εr − 1  +  1 + 12  2 2 w

−0.5

(21.6)

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The extra length due to the fringing effect is



∆L = h(0.412)

w + 0.264   h w   − 0.258 )  + 0.8 h 

( ε eff + 0.3)  ( ε eff

(21.7)

ΔL = Length extension due to the fringing effect.

21.4

DESIGN OF PENTAGONAL SLOT ANTENNA

For the best axial ratio results on an infinite ground level, the patch geometry and the probe positions are optimized and subsequently extended to finite ground planes of various forms to get better fidelity. With the largest side of λ and other sides of equal ratio to the radiating pentagonal patch, the finite pentagonal plane was constructed. In addition, longitudes of radius h were also planned and simulated. Here, h is the frequency of infinite ground planes with the lowest axial ratio effect. In Figure 21.2, the dotted line on the opposite side is a 50 Ω microstrip line. The proposed antenna is fabricated on commercially available FR4 substrates with h = 1.8 mm, εr = 4.4, and tan ∂ = 0.02. The width of the feed lines, corresponding to the standard impedance of 50 Ω, is chosen as 3 mm to streamline design and discussion [9]. The antenna is fed through a proximity electromagnetic coupling through a microstrip thread. For matching, a quarter-wave circuit is used. The high-frequency structure simulator 3D electromagnetic field tool is used for antenna design [10]. The side length for the slot is 23.5 mm, g = 0.8 mm, and d = 2.4 mm. When Lf = 26 mm and Wf = 0.5 mm, the corresponding impedance condition is optimized. In this case, the length is adjusted, when fastening b to 0.7 mm.

FIGURE 21.2 Single-band pentagonal slot antenna.

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FIGURE 21.3 Return loss of a single-band pentagon-shaped antenna, resonating at 4.8 GHz.

FIGURE 21.4 4.8 GHz.

Radiation pattern of a single-band pentagon-shaped antenna resonating at

Figure 21.3 describes assessing, return loss of simulated with measured return loss at 4.8 GHz fabricated with optimized measurements, RT/duroid 6010/6010LM. The 70.7% beam width for a single-band patch is noticed in the E-plane far-field radiation pattern at 4.8 GHz as shown in Figure 21.4.

21.5 DUAL-BAND ANTENNA The outer and inner rings were made to resonate at 4.2 and 5.8 GHz. The prototype model of the slot antenna, as shown in Figure 21.5, gives geometric parameters. The antenna has the geometric dimension L1 = 17.4 mm, L2 = 16.7 mm, a1 = 0.4 mm, a2 = 0.3mm, b1 = 0.1 mm, b2 = 0.1 mm, g1 = 0.1 mm, g2 = 0.1 mm, Lf = 23.5 mm, and w = 3 mm. Figure 21.6 indicates the frequency comparison reactions for the antenna being tested and simulated. For the two resonant frequencies, the practically attained impedance BW (10-dB) is 14.3% and 8.1%. From simulation to measurement, the frequency transition

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FIGURE 21.5

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Pentagonal slot antenna for a dual band.

FIGURE 21.6 (a) Far-field radiation pattern radiating at 4.2 GHz. (b) Far-field radiation pattern radiating at 5.8 GHz.

is lower than 76 MHz. The results show that the dual-band CP mode of the antenna at 4.2 GHz is mainly supported by the broad pentagonal slot [11,12]. Radiation patterns at the two resonant frequencies are calculated with their parameters, as shown in Figure 21.6. The slot antenna is a two-way radiator; it has identical radiation patterns on either side. The structural configurations in the upper half of space radiate a circular (LHCP) left-hand wave and in the under-half circular (RHCP) wave. The opposite circular polarising radiation can be done when the feeding line is on the right. The peak gains measured are 3.7 and 3.2 dBi radiating for 4.2 and 5.8 GHz, respectively.

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21.6 1  ×  2 LINEAR PENTAGONAL SLOT ARRAY The antenna element’s laminate is configured and calibrated. The array architecture dimension constitutes h = 3.2 mm, εr = 2.4, and tan δ = 0.0012. The 1 × 2 linear array arrangement with the same elements is shown in Figure 21.7.

21.6.1 Simulated reSultS It is important to note that for both linear array and linear bands analyzed multiband impedance BW and gain values were obtained. Parametric analysis is carried out to determine the impact of the element size and the inter-element distance between the components. In terms of impedance variance, response, and BW, the simulation observation was performed. For the linear array, this generates 47 and 80 MHz of BW for the dual bands of 4.2 and 5.8 GHz, respectively. The proposed approach is used to work with multiple frequencies using array geometry and also for advanced wireless technology applications. Figure 21.8 shows the comparative analysis of reflection coefficients (S11) for slotted pentagon, 2 x 1 linear array and measured results and Table 21.1 shows the comparative returns loss results for different configurations. Gain vs. frequency response is shown in Figure 21.9 for a flat array configuration that resonates in phi = 90° in E-plane. In high and low bands, the display gives better patterns, but inferior patterns seen in the middle band of the spectrum. This is because of the misleading effects of inset fed and also because of the transforming impedance at the element joints.

21.7 TRI-BAND ANTENNA Figure 21.10 shows the prototype model of the planar slot antenna. The outer, middle, and inner rings resonate for 2.4, 3.5, and 5.8 GHz. The prototype model of the slot antenna has its geometrical parameters weighed as L1 = 17.4 mm, L2 = 16.7 mm, a1 = 0.4 mm, a2 = 0.3 mm, b1 = 0.1 mm, b2 = 0.1 mm, g1 = 0.1 mm, g2 = 0.1 mm, Lf = 23.5 mm, w = 3 mm, L 4 = 18.1 mm, and g3 = 0.1 mm [13,14].

FIGURE 21.7 Prototype of 1 × 2 linear pentagonal slot array.

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FIGURE 21.8 Comparative analysis of reflection coefficients (S11) for the slotted pentagon, 2 × 1 linear array, and measured results.

Figure 21.11 indicates the frequency comparison reactions for the antenna being tested and simulated. For the three resonating frequencies, the measured result shows impedance BWs (−10-dB) of 14.3%, 8.1%, and 12.4%. From simulation to measurement, the frequency transition is lower than 76 MHz. The results show that the antenna is clearly in the CP mode of the lower band and is mainly from the major pentagon at 2.4 GHz. It is important to see that the pentagonal slotformed antenna feature influences multi-bands, impedance, and gaining values studied for linear and planar array models. To assess the effect of the element size and the inter-element distance between the components, a parametric analysis is performed. The simulation observation was carried out in terms of BW, impedance variance, and response.

21.8 CONCLUSION A multi-band pentagonal slot antenna has been experimentally studied and analyzed that the antenna suites better for smart green energy-efficient communication. The proposed antenna supports the new generation of wireless sensor networks, either locally or in a wide region, with collaboration and communication entities and community mobility. The pentagonal model of the proposed design is successfully implemented and the results of single-band pentagonal slot, dual-band pentagonal slot, 1 × 2 linear array, and tri-band pentagonal slot antenna actively engage green environmental communication. On comparing the gain, linear 1 × 2 arrays offer better directive gains of 6 and 5.4 dBi, which are better than those of the planar antenna, the compactness offered here in the planar slotted antenna is better. The proposed antenna provides a fascinating solution for mobile communication systems, thanks to its compactness and circular polarization. Finally,

58.0 × 64.0 78.3 × 84.0

130.0 × 78.3

76.1 × 91.0

1 × 2 linear array

Tri-band pentagonal slot

• Size of the Antenna (mm2)

Single-band pentagonal slot Dual-band pentagonal slot

Approach 4.8 4.2 5.8 4.2 5.8 2.4 3.5 5.8

Simulated 4.9 4.3 6.3 4.3 6.3 2.4 3.6 5.9

Measured

Frequency in GHz

TABLE 21.1 Comparative Analysis of Linear Array and Slotted Multi-band Antenna

−18 −25 −38 −23 −22 −32 −25 −15

Simulated −17 −30 −35 −30 −35 −34 −18 −22

Measured

Return Loss (S11) in dB 3 3.7 3.2 6 5.4 2.5 3.1 3

Gain in dBi

550 120 90 47 80 72 95 81

−3dB % Bandwidth (MHz)

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366

FIGURE 21.9

FIGURE 21.10

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Far-field radiation pattern at the frequency of 5.8 GHz.

Prototype of the tri-band pentagon-shaped slotted antenna.

a smart pentagon-shaped array antenna achieves peak antenna gains of 5, 6, and 9 dBi for multiple resonating frequencies of 2.4, 3.5, and 5.8 GHz respectively. From the results of the simulation, point-to-point communication systems are an important observation of energy efficiency. Maximum energy efficiency requires more transmission power as the power consumption of the antennas increases. In addition, the improvement in energy efficiency output is neutralized by the power consumption to equip more antennas.

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FIGURE 21.11 Comparative analysis of simulated and measured results of reflection ­coefficients (S11) for the tri-band planar pentagon-shaped slotted antenna array.

REFERENCES

Index access 3–6, 14, 18 accuracy 317, 319 acetylcholinesterase (AChE) 122, 123, 124, 125, 129, 133 ACO 235–236, 240 AC-DC-AC converter 105, 108, 117 action space 262 activated 73, 74 action values 264 active front-end converter 104 actuators 276, 278, 281, 284 Acyrthosiphon pisum 124, 125, 129 adaptable 227 advantages of PV electricity 338 advancement 10, 14, 16–17 AES 297, 298, 301 agile 353, 354 agile organization 354 agility 344, 352, 354 agriculture 274, 276–277, 290 AHP 232 algorithms 1, 8–9, 11–12, 22–23, 27, 30 Alternanthera sessilis 122, 133 AMQP 43 amplifying gain 140 analytical 2, 15 animal alarm 284, 289 antenna gain 93 aperiodic 367 applications 315, 317, 319, 325 applications of a solar cell 338 ARCore 69 array 357, 358, 359, 360, 361, 364, 365, 367 array geometry 358, 359, 363 artificial intelligence (AI) 85, 208, 247, 288 AR, VR mechanism 60 assessment of low-constrained devices 300 asymmetric LWC method for IoT 302 automation 2, 17–18, 351 axial ratio 92, 93, 360 bandwidth 88–90, 92–94, 357, 365 battery 81, 87 beam steering 357 big data 2, 5, 9–10, 12, 14, 16–19 biocomputing 240 biodegradable 72 biomass 72 biomedical 315–317, 319–320, 322, 324–325 bio-pesticides 122, 133

bio-waste material 72 blend 75, 78, 82 block ciphers 296, 297, 298 bottlenecks 24 branch current profile 162 branch incidence matrix 153 bus voltage profile 161 business 344 buzzer 278, 281, 289 camera 278, 281, 284 capacitance 280 capacitor-excited asynchronous generators 104, 105, 107, 108, 110 carbon 72, 78, 80, 82 carbon-neutral cycle 72 carbon emission 244, 245 cellular automata (CAs) 209, 212, 213 channel fading 140, 144 characteristics of a solar cell 337 characteristics of IoT 40 chitosan 81 circular polarization 364, 367 circularly symmetric complex Gaussian (CSCG) 142 clay 274–275 climate 331 cloud 294 cloud computing 2, 4–6, 14, 16, 19, 249 cloud server 278, 283 cloud sim 238, 240 cloudlets 235 CoAP 43 CO2 2, 12, 228–229, 233–234, 236, 247 coding 231 cognitive radio network 137–138, 149–150 computing 177, 205–206 communication. 40 community 59 communication systems 358, 364, 367 computational tools 22–25, 28–30 conductivity 75–77, 80–82 conflict 216, 217, 218 constant current constant voltage charging 163 constant current load 163 constant impedance load 163 constant power load 163 context-awareness 41 control 277–278, 281, 284, 287–289 conversion 315, 318–319, 322–323 conversion efficiency 87, 90, 95–98

369

370 convergence 178–179, 187, 191–192, 196, 201, 205–206 converter 104, 105, 108–110, 112, 117 cooperative spectrum sensing 138, 141, 149–150 cooperative users 140, 149 cost-efficiency 209 coefficients of torque and speed 106 CPS architecture 208 CPU utilization 260 crossover 231 crop yield 274 cryptographic arrays 299 cryptographic co-processor 299 cryptographic multicore 300 CSP 226, 228 cut-throat 22 cyber-physical system (CPS) 207, 208, 209 cypermethrin 122, 126, 127, 130 data analysis in the IoAT architecture 48 data applications 2, 17 data center 1, 9, 19, 226–229, 232–236, 238, 240, 249 data layer 2, 12, 13–14 data transmission 138, 140–144, 146, 148–149 database 283 data-driven 12 dataset 233 DC 229–231, 236–237, 239 DC-geared motor 278 DC-link capacitor 105, 108, 110, 115, 117 DC-link voltage 113, 116 DDS 43 decision variable 183 defective ground structures 89, 90 deformation 315, 319–321, 323 density 72, 74, 78, 80 density of water 106 deployment 220, 221 designing GIoT 248 destinations of green innovations 329 detection threshold 141, 143 devices 319, 322, 324 DFIG 104 DHT 11 278, 280, 289 diagnosis 322 diazinon 127, 130 dielectric 88, 89, 91 dielectric constant 359 dielectric permittivity 280 different types of energy 334 digital technologies 344 diode-based rectifier 96 direction 276, 281, 289 directivity 357, 358 discrete event simulation (DES) 24 diseases 320, 322 dispose 248

Index disposing 251 diversification 177, 181–182 docking 129 dopant 76, 78 Dragonfly Algorithm (DA) 182 d-STATCOM 105, 110, 112–118 dual-band 357, 361, 362 dynamic consolidation 266 dynamic model 23, 25–31 EBG 90, 91 ECA dynamics 219 ECA rule 212, 213 eco-friendly 71, 78, 80, 82, 226 eco-friendly umbrella 330 ecological development 345 economic development. 22 economic growth 22 ecosystem 22 ecosystem 344 effect 316–319, 323, 325 effective 5, 6, 8 efficiency 141, 143, 318, 319, 323 efficient display 250 electric power generation in space 338 electrode 78, 79 electrolytes 74, 75, 82 electromagnetic 138, 139 elementary cellular automata (ECA) 212 EMBB 254 embedded 278 emission 22, 29 energy 315–326 energy consumption 226, 228–229, 232–235 energy detection 140 energy efficiency 260, 347, 366 energy harvesting 137–139, 141, 143, 145, 147, 149–150 energy harvesting methods 85–87 energy monitoring 295 energy proficiency 333 energy savings 138 energy star 4.0 249 energy system 347 energy utilization 24, 26–27 energy-efficient 209 environment 219 environmental 22, 24, 28–29 e-plane 361, 363 ESP8266 279–280 evapotranspiration 274 e-waste 251 existing works 44 experimental model 23 fabrication 80 facilities 2, 6, 10, 16 far-field 361, 362, 366

Index fault occurrence 235–237 fault tolerance 256 fidelity 363 fillers 76–78 fitness function 231 firebase 283–284 5G 85, 254 fixed boundary conditions 219 flex cable 281 flexible organizational structures 355 flow rate 106, 110, 112 fog-assisted cloud system 261 forward and backward sweep load flow 153 fossil fuels 104 FR4 substrate 360 framework 2, 3, 12–17, 19 framework of LWC 305, 306 free-flowing water 104 frequency response 363 fringing effect 360 functional blocks of IoT 38 fundamentals 315–316 fusion center 138, 141 future 72, 78, 82 fuzzy PI 105, 109, 110, 112, 117, 118 GA 231–232, 235 gain 358, 362, 363, 364, 365, 000 gearbox 104 gelatin 81 generator 104, 105, 107–110, 112–117 generator side converter 105, 108, 109 genetic algorithm 226, 231, 235 geometric selective harmony search (GSHS) 210 global warming 246 Going Green 1, 9 green advancements or eco-developments 332 green city framework 59 green cloud computing 226–229, 231, 232, 234–236, 240 green communication 137–139 green computing 1, 9, 209, 214 green creations 328 green data center 1, 9 green developments 332 green engineering 2, 4, 6, 8, 10, 12, 14, 16, 18 greenhouse gases 243 greening 60 Green Innovation Applications 332 green innovations 329, 330 green IoT 2–3, 5–8, 9–11, 13, 15, 17–19 green IoT 244, 247 green IT 345, 353, 355 green model 209 green smart healthcare 1, 6 green smart home 1, 5 green smart transport 1, 7

371 green Technology (GT) 243, 329 green-energy 364 greenhouse gas (GHG) 334 Grey Wolf Optimizer (GWO) 182 ground plane 360, 367 group-based industrial wireless sensor network (GIWSN) 210 GT advantages and disadvantages 333 GT opportunities in India 334 GT significant goals 329 hardware constraint 256 hardware implementation 300 hardware platforms used in the IoAT architecture 48 Harris Hawks Optimization 180, 181, 206 harmonic 105, 117, 118 harmonic rejection 93 harvesters 316–319 health 316, 319, 321–322, 324–325 healthcare 315–316, 319, 322–325 high-performance systems 299 HIGHT 303 humidity 274–275, 277–278, 280, 287, 289–290 hybrid (heterogeneous) CAs 213 hydro turbine 104, 106 ICT 260 IEEE 16 bus BRDS 160 IGBT 105, 108 impedance 87–90, 93–95 implementation 352 improved geometric selective harmony search algorithm (IGHSA) 210 independent and identically distributed (IID) 142 indoor navigation 58, 62, 68 industrial automation 330 industrial IoT (IIoT) 209 industrial sector 23, 26 industry 4.0 208, 344, 351, 354 initial population 232, 237 information technologies (ITs) 207 innovation 328, 331, 345 innovative applications of GT 332 inorganic 72, 73, 75, 76 in-silico 122, 124 integrated into the information network 41 intelligent information 354 intelligent decision-making capability 41 intensification 177, 181–182 Inter Quartile Range(IQR) 268 interdisciplinary team 351 interference management 296 international energy agency 250 interior modeling 60 Internet 276, 278, 283–284 Internet of Things 276

372 Internet of Things in agriculture 46 interoperable communication protocols 41 interoperability 253, 296 introduction, problem statement 58 invading animals 278 in-vitro 123 IoAT applications 53 IoAT architecture 46 IoT 85, 86, 98, 294, 295, 302 IoT applications 44 IoT enabling technologies 44 IoT protocols 42 IoT protocols stack 42 irrigation 274–276, 278, 281, 284, 290 issues and research challenges 301 IT 345 IWSNs (industrial WSNs) 219, 220 Kruskal–Wallis test 202–204 Karush–Kuhn–Tucker (KKT) 145 L293D 278, 281, 289 Lagrange multiplier 145 Lantana camara 122, 123, 129 LC filter 105, 108 lightweight cryptography 295, 297 lightweight hash functions 299 light-weight stream cipher algorithms 299 line data of 16 bus BRDS 160 linear band 363 linear regression 268 lithium 72, 76, 81, 82 live stream 278, 281, 285, 290 load 104, 105, 108, 110, 112–118 load balancer 118 load data of 16 bus BRDS 161 load flows 152 loop matrix 166–167 load side converter 105, 108, 109 loamy 274–275 low pass filters 93 low power 221 low-cost 212, 221 LWC 305, 306, 307 machine learning techniques 49 Mamdani rule 111 management 40 man power 22 manufacturing layout 22, 27, 30 manufacturing line 210 market competition 22 market environment 353 Markov decision process 263 matching circuit 87, 94, 95 materials 315–319, 321, 323 maximum power point 178, 180

Index mean 139, 142–144 meandered slits 89, 91, 93 measurement 315, 319–324 mechanism 77–81, 316 Median Absolute Deviation (MAD) 268 melt casting method 76 metaheuristic 177–178, 203 metamaterial 89, 90–92 methamidophos 127, 130 methane 245 microcontroller 277–278, 283 microhydropower generation (MHPG) 104, 106, 110, 112, 115 microstrip 88, 92–95, 360, 367 MIMO 257 miniaturization 88, 89, 98 mobile application 276, 278, 283–284, 289 modeling 23–24, 26–31 modeling of distribution system 156 modeling of EVCS 163 modern threats 308 modified Newton Raphson loadflow 153 money 22 model 345 monitor 250, 275–278, 281, 284, 287, 290 monitoring 316, 319, 322, 325 Monte Carlo simulations 146 MOSFET-based rectifier 96, 97 Moth Flame Optimization (MFO) 178, 206 MQTT 42 MTC 255 multi-Altran 283–284 mutation 231 nanogenerator 315–318, 325–326 nanotechnology 315 national benefits for energy generation 331 national GT policy tools 334 natural environment. 355 navigation 61 network 253 network layer 2, 13 neural network 104 NavMesh 69 NG 315–319, 322–325 nodeMCU 276, 278–280, 283–284 noise 141, 142 non-parametric test 202 null boundary CA (NBCA) 212 objective function 165 objectives of Green Technologies 329, 330 obsolete technologies 24 open circuit 179–180 open-source 279 optical air mass 337 optimal reconfiguration 166

373

Index optimal placement of EVCS 167, 170 - 172 optimization 22, 26–31, 139, 140, 145, 146, 149 optimization techniques 164 organizations 344, 348 organization’s view 23 organizational agility 351 output 316, 318–319, 321–325 overloaded 231, 234, 236 painstaking 274–275 Parthenium hysterophorus 122–123 parameter estimation 177–178, 205–206 Particle Swarm Optimization 165 PENG 315–316, 318–319 path loss exponent 142, 144 pentagonal slot 358, 360, 362, 363, 364, 365 performance 228–230, 233–236, 239 periodic boundary CA (PBCA) 213 Permanent Magnet Synchronous Generator 104 permeability 90 permittivity 90 perturbation 92 Photovoltaic Energy Conversion Applications 334 photovoltaic impact 335 physical level implementation 221 physical parameters 274, 278 phyto-constituents 122, 123, 126, 127, 129, 133 piezoelectric 315–316 pillars of GT 331 pin diodes 92, 93 PI-Network 94 planar array 358, 359, 364 platform 253 polarization 90–92 Polish companies 344 porous 73, 79, 80 potable water 330 power consumption 256 power density 72, 77, 78, 80 power efficiency 220, 221 power management 249 power management unit 86, 87 power supply 250 power transfer 139, 150 power utilization 226, 240 PRESENT 301, 303 primary user 138, 150 probability of detection 141, 143, 145 probability of false alarm 141, 143, 145 probe-feed 367 product lifecycle management (PLM) 23 production 210 production cost 256 production operations 22 productivity 22, 25–26, 31 prototype 359, 363, 364 proximity electromagnetic coupling 360

pseudocode for a lightweight encryption system 303, 304 pseudocode for the decryption system 304 PSO algorithm 164, 165 PSO update equation 165 PV cell 177–180, 206 PV effect 335 PV module 177–179, 183, 205 PWM 105, 108, 109, 112 PYNG 315–316, 318 pyroelectric 315, 318 q-learning 264 QoS 232–233 qualities 348 q-values 264 radiation field 367 radiation pattern 358, 361, 362, 366 random process 3 rapid growth 22 raw materials 22, 26–28, 30–31 Rayleigh 3–7, 11–13, 15 reconfigurability 92 reconfiguration of 16 bus RDS 162 rectifying circuit 87, 93, 94 redox potential 75 reflection coeffecients 363, 363, 367 refractive index 90 reinforcement learning 262 relationship 353 relay module 278, 281, 289 reliability 228 reliability optimization 211 remote 277, 283–284, 288 renewable 6, 7 renewable 72, 82 renewable energy 104 reporting slot 142 resonating frequency 358 resource management 295 resource utilization 22–25, 28 REST HTTP 43 returnloss 361, 365 reverse saturation current 179, 182–183 rewards 264 RF energy 86, 87, 95, 98 RFID 244, 294, 295, 296, 303 RL load 110, 112–114 Rule Min Term (RMT) 217 rule base 110 run of the river 104, 106 rural electrification 338 safety currently 296 Salp Swarm Algorithm (SSA) 178, 206 sampling frequency 141, 142

374 sandy 274–275 scalable 226, 228, 234 scalability 256 scheduling 24, 220, 221 Schottky diodes 93, 96, 98 secondary receiver 140 secondary transmitter 140, 149 secondary user 138, 141 security 40 security and privacy 308 security issues 53 segmental motion 74, 78, 82 segments 15–16 selection 231–232, 236 self-adapting and dynamic 40 self-configuration 40 self-powered 315, 319–320, 322–326 semantic 253 sensing duration 140, 146, 149 sensing slot 141 sensor network topology 256 sensors 2–6, 8, 11–13, 17–18, 244, 276, 278, 284 sensors used in the IoAT architecture 48 services 40 Servo motor 278, 281, 289 shaft torque 108 short circuit 180, 182 shorted posts 89 side lobe level 357 signals 316, 319, 322 significant goals 329 simpler key schedules 298 simpler rounds 298 simulation 23–26, 28–31 Sine Cosine Algorithm (SCA) 182 single feed 91, 92 single-band 360, 361 single diode 177–179, 205 size reduction 89–91 SLA 231–232, 234, 265 sleep scheduling 209 slot antenna 360, 361, 362, 363, 364, 367 slots 89, 91, 93 smaller block sizes 298 smaller key size 298 smart cities 1, 2, 8–13, 18–19 social 22, 24, 26, 28 sodium salt 72, 73, 76–78 soft 73 software 351 software implementations 300 soil 274- 278, 280, 287–290 soil moisture 274–275, 277–278, 280, 289–290 solar cell application 336 solid state 72 solution 352 sources 74

Index specific capacitance 80, 81 spectrum 363 spectrum efficiency 144 spectrum sensing 138, 141 speed 276, 278, 281, 289 split ring resonator 90 stacked patch 92 starch 81 static VAR compensator 104 state space 262 strategies 348, 349 structure 74 statistical analysis 182, 191–192, 196, 200–201 substrate thickness 358 subsystem 347 sum of square error (SSE) 200 supercapacitor 72, 78, 81 surveillance 277, 287 sustainability 22, 24–28 sustainability policy 353 sustainable 1, 2, 5, 7, 9–10, 15, 18–19, 71, 72 sustainable development 23, 344, 346 sustainable energy 330 switching combinations 162, 167 symmetric LWC method for IoT 301, 302 synchronous speed 106, 108 synchronous speed test 108 syntatic 254 swept area 106 teal 345 techniques of lightweight block ciphers 297, 298 technology 315, 322–325 TD update 264 temperature 274–275, 277–278, 280, 287, 289–290 TENG 315–319, 322–323 terrain 274, 277, 287, 290 THD 110, 114, 115, 117, 118 3D 58, 59, 60, 67 threshold method 268 throughput 137, 139, 140, 144–150 tie-lines 162 T-Network 94 tools of GT 334 total losses of the system 155 transformation process 22 transforming impedance 363 transition diagram 215, 216, 217 transmission media 256 transmission power 366 tri-band 357, 361, 363, 364, 367 triboelectric 315, 317 triangular membership function 109 triple bottom line (TBL) 23 turbine model 106, 107 TWINE 301, 302 types of innovative GT 333

375

Index UB 238 UGU engine 67, 68 ultra-lightweight cryptography 301 uniform (homogeneous) CA 213 unity 69 unity engine 64, 65 UnityEngine.AI 64, 65 unique identity 41 URLLC 254 usable goods 22 variable MHPG 104–106, 113, 115, 117, 118 variable voltage 104 variance 141–143 various IoT-Based cloud service platforms 48 virtual manufacturing 24 velocity of water 106 virtualization 226–227, 229, 232–233 virtualization 249 VM 226, 231–234, 236 VM allocation 267 VM consolidation 260 VM manager (VMM) 226, 229–232, 234–236, 265

VM migration 265 VM selection 267 voltage source converter (VSC) 105, 109, 110, 112 voltage multiplier 87, 97 waste 72 water pumping 338 wearable electronics 85 web sockets 44 whale optimization algorithm (WOA) 178. 205 wide-band 367 Wilcoxon rank-sum test 203–204 wind power 104 wireless sensor networks (WSNs) 209, 210, 211 wireless technologies used in the IoAT architecture 48 work from home 251 work station 26 working of photovoltaic cell 335 WSN 255 WSN node 87 XMPP 43

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