Intelligent Green Communication Network for Internet of Things 1032234628, 9781032234625

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Intelligent Green Communication Network for Internet of Things The text covers the advanced and innovative concept of green communication networks using the Internet of Things in different fields including cloud technology, agriculture, the automobile sector, and robotics. It will also help readers in learning the efficient use of sensors and devices in the Internet of Things networks. The text covers 5G communication and its application for intelligent and green network-enabled Internet of Things. This book • Discusses intelligent and green networking-enabled Internet of Things • Covers architectures and models for intelligent and green communication networks-enabled Internet of Things • Discusses designing Internet of Things devices that help in reducing the emissions of CO2 in the environment and energy consumption • Highlights green computing approach and green communication network designs and implementations for Internet of Things ecosystem • Includes studies on energy-aware systems, technologies, and green communication This book comprehensively discusses recent advances and applications in the area of green Internet of Things communication in a single volume. It will serve as an ideal reference text for senior undergraduate and graduate students, and academic researchers in the fields of electrical engineering, electronics and communication engineering, computer engineering, and information technology.

Intelligent Green Communication Network for Internet of Things

Edited by

Rajan Patel, Nimisha Patel, Linda Smail, Pariza Kamboj, and Mukesh Soni

First edition published 2023 by CRC Press 6000 Broken Sound Parkway NW, Suite 300, Boca Raton, FL 33487-2742 and by CRC Press 4 Park Square, Milton Park, Abingdon, Oxon, OX14 4RN CRC Press is an imprint of Taylor & Francis Group, LLC © 2023 selection and editorial matter, Rajan Patel, Nimisha Patel, Linda Smail, Pariza Kamboj and Mukesh Soni; individual chapters, the contributors 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 [email protected] 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: Patel, Rajan, editor. | Patel, Nimisha, editor. | Smail, Linda, editor. | Kamboj, Pariza, editor. | Soni, Mukesh, editor. Title: Intelligent green communication network for internet of things / edited by Rajan Patel, Nimisha Patel, Linda Smail, Pariza Kamboj and Mukesh Soni. Description: First edition. | Boca Raton : CRC Press, 2023. | Includes bibliographical references and index. Identifiers: LCCN 2022053482 (print) | LCCN 2022053483 (ebook) | ISBN 9781032234625 (hardback) | ISBN 9781032443072 (paperback) | ISBN 9781032443072 (ebook) Subjects: LCSH: Internet of things. | Electronic digital computers—Energy conservation. | Green technology. | Artificial intelligence. Classification: LCC TK5105.8857 .I5335 2023 (print) | LCC TK5105.8857(ebook) | DDC 004.67/80286—dc23/eng/20230111 LC record available at https://lccn.loc.gov/2022053482 LC ebook record available at https://lccn.loc.gov/2022053483 ISBN: 9781032234625 (hbk) ISBN: 9781032443072 (pbk) ISBN: 9781003371526 (ebk) DOI: 10.1201/9781003371526 Typeset in Sabon by codeMantra

Contents

Editors vii List of Contributors xi 1 Green IoT: Need, architecture, applications, challenges, and future scope

1

DULARI BHATT, MADHURI CHOPADE, ALPA OZA, RAJAN PATEL, AND NIMISHA PATEL

2 Green IoT: Future direction and open challenges

13

MENKA PATEL, RAJAN PATEL, AND NIMISHA PATEL

3 Enabling sustainable technologies using the Internet of Things for Industry 4.0

35

POULAMI DALAPATI AND SAURABH KUMAR

4 IoT deployment: What Cloud has to offer?

57

SANJAY T. SINGH, MAHENDRA TIWARI, AND JYOTI MISHRA

5 A new optimal protocol for Green IoT communication

71

SAEED DOOSTALI AND BEHZAD SOLEIMANI NEYSIANI

6 Pharmaceutical supply chain management system using Blockchain and IoT technology

87

GARGI CHAUHAN, BHARGAV PATEL, NIKUNJ PRAJAPATI, SHAILENDRA RAJ, SHLOK GADRE, AND SAURABH PATEL

7 Intelligent green communication network for IoT applications M. SUGACINI, N.R. GAYATHRI, P.N.M. KAMALIKA, AND G. NANTHA KUMAR

97

vi Contents

8 Scalable and energy-efficient intelligent schemes for Green IoT

125

BHAVSAR RAKESH, YESHA PATEL, AND MOHIT BHADLA

9 Energy-efficient clustering protocol for IoT-based unmanned aerial vehicles

141

PALVINDER SINGH MANN, SHAILESH D. PANCHAL, AND SATVIR SINGH

10 Comprehensive study on next-generation IoT: Energy-efficient green IoT

155

MAITRI PATEL, PARITA SHAH, RAJAN PATEL, PRIYA SWAMINARAYAN, AND RAHUL VAGHELA

11 Integrating IoT technology for effective agriculture monitoring: An approach to smart farming system

171

UMAR FAROOQ, AQIB AMIN RATHER, AND NASIR SHAREEF TELI

12 Enforcement of IoT for potent oversight of toxic levels in the construction and demolition waste at an industrial vicinage 189 G.K. MONICA NANDINI

13 Smart water management system for water level and quality detection, monitoring, and control in residential structures 203 HIRAL M. PATEL AND RUPAL R. CHAUDHARI

14 A novel approach for vehicle detection to avoid accidents in the construction area

221

DIYA VADHWANI, DEVENDRA THAKOR, AND DARSHANA PATEL

15 Smart home security

233

ANIRUDH GOEL AND CHINTAN PATEL

Index 249

Editors

Dr. Rajan Patel is currently a full-time professor in the Department of Computer Engineering at Gandhinagar Institute of Technology (GIT) and Dean (Engineering and Technology) of Gandhinagar University, Gandhinagar, India. He has more than 18 years of teaching experience in the fields of Computer Science and Engineering. He earned his PhD in Computer Engineering from RK University, Rajkot, India. He earned his MTech in Computer Engineering from S.V. National Institute of Technology (NIT), Surat, India, and BE in Computer Engineering from Saurashtra University, Rajkot, India. He has more than 60 collaborative publications in journals and conferences and presented 17 articles in national/international conferences including IEEE, Science Direct, Springer, and Elsevier. He has published one Indian patent and has been granted two Australian patents. As a co-author, he published and edited Springer and CRC Press books. He received an appreciation certification from the Dewang Mehta Innovation IT award and an Honored Code certificate in the Technical Communication Workshop from IIT, Bombay. He also received two awards: paper presented at an international conference (2017, 2018–2019) of CSI. He received the certificate of excellence for organizing a two-week sponsored FDP from IIT Bombay. He also worked for ISEAP-sponsored MHRD project during his postgraduation period at NIT, Surat, India. He acted as a Reviewer/ Editorial board member/TPC/Advisory/Session chair/invited guest of IEEE, Springer, IET, and other international/national conferences and journals. He is also associated with other universities as examiner, supervisor, and board member. He is a recognized PhD research supervisor, and under his guidance, two PhD scholars were awarded degrees. He has also guided more than 16 PG students. His main areas of interest are intelligent applications and its security, information security, IoT, and cloud computing. Dr. Nimisha Patel is a Professor in the Department of Information Technology and Head (CE/IT) at Gandhinagar Institute of Technology (GIT), constituent Institute of Gandhinagar University, Gujarat, India. vii

viii Editors

She has worked in different positions in her 18-year academic journey as a Dean, Associate Dean (R&D), and Head. She completed her PhD in Computer Science & Engineering with Cloud Computing as the domain of research. She earned her MTech in Computer Engineering from S.V. National Institute of Technology (SVNIT), Surat, and BE in Information Technology from HNGU, Patan. She has done more than 50 research publications in various conferences and journals of international and national repute. Being a recognized PhD supervisor, she is guiding six PhD scholars. She has delivered expert talks, acted as session chair, and also reviewed research publications. Her areas of interest are cloud computing, parallel processing, security, and networking. Dr. Linda Smail is an associate professor in the Department of Mathematics and Statistics at Zayed University, Dubai, United Arab Emirates, where she teaches Mathematics and Statistics courses. She earned her PhD in Applied Mathematics from Marne-La-Vallée University, France, in 2004. Her research interests include Bayesian networks and machine learning. Dr. Smail is particularly interested in inference in Bayesian networks and learning in graphical models, with a focus on exact inference algorithms for Bayesian networks. Her primary scholarly goal is to conduct research and use Bayesian networks in different fields from medical, entrepreneurship, to decision making under uncertainty as well as intelligent systems. Dr. Pariza Kamboj is currently associated with Sarvajanik College of Engineering & Technology (SCET), Surat, as professor and head of department in the Computer Engineering Department. She has more than 25 years of teaching experience in various reputed engineering colleges in India. She earned her PhD (Computer Engineering) from MDU Rohtak in 2011. She earned her MTech (Computer Science and Engineering) with distinction from Kurukshetra University (KU), Kurukshetra, Haryana, India, in 2006. Her research interest areas are deep learning, machine learning, IoT, Big Data analytics, data science, Python for data science, WSN, mobile ad-hoc networks, computer networks, and network security. She has published a total of 47 research papers in various international and national journals, international and national conferences of repute. She is a member of various professional bodies, such as Institution of Engineers, Computer Society of India, Indian Society of Technical Education (ISTE), IFERP, ISRD, and International Association of Computer Science and Information Technology (IACSIT). She has published/filed five patents in India and Australia. She has done consultancy work and provided a cost-effective and efficient solution to the problem titled “Optimizing IVF Predictions and Procedure for Better Outcomes”.

Editors ix

Mr. Mukesh Soni is pursuing his PhD in Computer Science and Engineering from Jagran Lakecity University, Bhopal, India. He has completed his Master’s in Computer Science and Engineering from NIT Bhopal. He is currently an assistant professor in the Department of Computer Engineering at Smt. S.R. Patel Engineering College, India. He has received “Young Scientist award”, “Best Teacher Award”, “Award for Contribution to Student Development”, among others. He is also a reviewer for many peer-reviewed journals. He has written many articles in SCIE/Scopus journals and also presented his research in Springer/ IEEE conferences. He has qualified for GATE Examination several times and for UGC-NET Examination. He has also published three patents in India.

List of Contributors

Mohit Bhadla Ahmedabad Institute of Technology Ahmedabad, India Dulari Bhatt Department of Big Data Analytics Adani Institute of Digital Technology Management Gandhinagar, India Rupal R. Chaudhari Department of Computer Engineering Sankalchand Patel College of Engineering Sankalchand Patel University Visnagar, India Gargi Chauhan Information Technology Department Sardar Vallabhbhai Patel Institute of Technology Vasad, India Madhuri Chopade Department of Information Technology Gandhinagar University Gandhinagar, India Poulami Dalapati Department of Computer Science & Engineering The LNM Institute of Information Technology Jaipur, India Saeed Doostali Department of Software Engineering, Faculty of Engineering, Mahallat Institute of Higher Education, Mahallat, Iran xi

xii  List of Contributors

Umar Farooq Department of Electronics and Communication Islamic University of Science and Technology Awantipora, India Shlok Gadre Information Technology Department Sardar Vallabhbhai Patel Institute of Technology Vasad, India N.R. Gayathri Department of Information Technology Sri Venkateswara College of Engineering Chennai, India Anirudh Goel School of Information Technology - Artificial intelligence and cybersecurity Rashtriya Raksha University Lavad, India P.N.M. Kamalika Department of Information Technology Sri Venkateswara College of Engineering Chennai, India Saurabh Kumar Department of Computer Science & Engineering The LNM Institute of Information Technology Jaipur, India Palvinder Singh Mann Graduate School of Engineering and Technology Gujarat Technological University Ahmedabad, India Jyoti Mishra University of Allahabad Allahabad, India G. K. Monica Nandini Department of Civil Engineering Sona College of Technology Salem, India

List of Contributors  xiii

G. Nantha Kumar Department of Information Technology Sri Venkateswara College of Engineering Kancheepuram, India Behzad Soleimani Neysiani Department of Computer Engineering, Isfahan (Khorasgan) ranch, Islamic Azad University, Isfahan, Iran Alpa Oza Department of Information Technology SAL College of Engineering Ahmedabad, India Shailesh D. Panchal Graduate School of Engineering and Technology Gujarat Technological University Ahmedabad, India Bhargav Patel Information Technology Department Sardar Vallabhbhai Patel Institute of Technology Vasad, India Chintan Patel School of Information Technology - Artificial Intelligence and Cybersecurity Rashtriya Raksha University Lavad, India Darshana Patel Department of Information Technology V.V.P. College of Engineering Rajkot, India Hiral M. Patel Department of Computer Engineering Sankalchand Patel College of Engineering Sankalchand Patel University Visnagar, India Maitri Patel Department of Computer Engineering - GIT Gandhinagar University Gandhinagar, India

xiv  List of Contributors

Menka Patel Department of Computer Engineering Ganpat University Mehsana, India Nimisha Patel Department of Information Technology Gandhinagar University Gandhinagar, India Rajan Patel Department of Computer Engineering Gandhinagar University Gandhinagar, India Saurabh Patel Electronics and Communication Department Sardar Vallabhbhai Patel Institute of Technology Vasad, India Yesha Patel Ahmedabad Institute of Technology Ahmedabad, India Nikunj Prajapati Information Technology Department Sardar Vallabhbhai Patel Institute of Technology Vasad, India Shailendra Raj Information Technology Department Sardar Vallabhbhai Patel Institute of Technology Vasad, India Bhavsar Rakesh Computer Engineering Department Ahmedabad Institute of Technology Ahmedabad, India Aqib Amin Rather Department of Electronics and Communication Islamic University of Science and Technology Awantipora, India

List of Contributors  xv

Parita Shah Department of Computer Engineering Sarva Vidyalaya Kelavani Mandal managed Vidush Somany Institute of Technology & Research Kadi, India Sanjay T. Singh Department of Computer Science & Information Technology Sam Higginbottom University of Agriculture, Technology and Sciences (formerly Allahabad Agricultural Institute) Allahabad, India Satvir Singh Department of Electronics & Communication I.K. Gujral Punjab Technical University Punjab, India M. Sugacini Department of Information Technology Sri Venkateswara College of Engineering Chennai, India Priya Swaminarayan Faculty of IT & CS Parul University Vadodara, India Nasir Shareef Teli Department of Electronics and Communication Engineering Islamic University of Science and Technology Awantipora, India Devendra Thakor Department of Computer Engineering Uka Tarsadia University Surat, India Mahendra Tiwari Department of Electronics and Communication University of Allahabad Allahabad, India

xvi  List of Contributors

Diya Vadhwani Department of Computer Engineering Uka Tarsadia University Surat, India Rahul Vaghela Department of Information Technology Engineering – GIT Gandhinagar University Gandhinagar, India

Chapter 1

Green IoT Need, architecture, applications, challenges, and future scope Dulari Bhatt Adani Institute of Digital Technology Management

Madhuri Chopade Gandhinagar University

Alpa Oza SAL College of Engineering

Rajan Patel and Nimisha Patel Gandhinagar University CONTENTS 1.1  Introduction of Green IoT 1 1.2  Need for Green IoT 3 1.3  Architecture of Green IoT 3 1.4  Applications of Green IoT 4 1.4.1  Smart home 4 5 1.4.2  Industrial automation 1.4.3  Smart healthcare 5 1.4.4  Smart grid 5 1.4.5  Smart cities 5 1.4.6  Smart agriculture 6 1.5 Challenges 7 1.6  Future Scope 9 1.7 Conclusion 10 References 10 1.1  INTRODUCTION OF GREEN I oT The Internet of Things (IoT) has transformed how we work and live due to rapid technological advancements. Despite the obvious benefits of IoT, which, in turn, benefits our society, it’s worth remembering that IoT also uses energy, contributes to harmful contamination, and harms the DOI: 10.1201/9781003371526-1

1

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environment. E-waste puts additional strain on the ecosystem. The world is becoming smarter. To enhance the advantages while lowering the costs, despite the dangers of IoT, there is a growing desire to relocate. Green IoT is widely viewed as the IoT’s future and good for the environment. In order to achieve this, a variety of strategies need to be implemented to minimize carbon emissions: Reduce your carbon footprint, preserve resources, and improve efficient energy use. It is the cause for the relocation toward a green IoT, where machines, communications, and data are connected. Along with electricity, there are sensors, clouds, and the Internet. Increasing efficiency and lowering carbon emissions are two goals we have set for ourselves. This chapter provides an in-depth examination of the current state of affairs, and the work on green IoT research and possible technologies provide some hints for future green IoT research. The Internet transforms the globe into a little town where everything is connected and the rest of the world through global communication networks (TCP/IP) protocol. Things controlled by wireless communication networks include communication devices and physical items such as ­vehicles, computers, and household appliances. As a result, the Internet has profoundly altered how we live and connects many aspects of our lives, from professional to social interactions (Atzori, 2010). The Internet of Things consists of smart networking of existing networks and context-aware computing employing system resources (IoT). As a result, the Internet of Things (IoT) is everything around us that needs to be communicated “anytime, anyplace, any media, and anything.” The lifecycle of green IoT starts and ends with green design to green disposal. One can achieve satisfactory results with green IoT only if they are following green IoT life cycle as shown in Figure 1.1.

Figure 1.1 Green IoT lifecycle.

Green IoT  3

1.2  NEED FOR GREEN I oT IoT technologies make machines smarter every day, allowing them to handle data intelligently and improve communication. Furthermore, IoT refers to the ability of a range of objects (devices) to connect and collaborate for shared purposes (Atzori, 2010; Gubbi et al., 2013). Examples include radio frequency identification (RFID), sensors, actuators, mobile phones, drones, etc. As a result, a wide range of real-time monitoring applications, such as e-healthcare, home automation, environmental monitoring, transportation autonomy, and industrial automation, would be affected (Popa, 2017; Prasad, 2013). Energy consumption is becoming state-of-the-art to secure the dependability of the Internet of Things and the deployment of an intelligent world. To reach a sustainable, inventive society, the IoT must be energy efficient to reduce greenhouse gas emissions and carbon dioxide (CO2) emissions from sensors, devices, apps, and services. Big data necessitates a lot of storage, cloud computing, and channel bandwidth for transmission, which is why IoT is so popular. However, big data processing uses much energy. Multiple energy needs will, in turn, put additional strains on society and the environment. Green IoT is being developed to meet the smart world’s development and sustainability goals by reducing carbon emissions and electricity usage. 1.3  ARCHITECTURE OF GREEN I oT There is not a single green IoT architecture. Instead, it has various architectures depending on its application. To facilitate communication across multiple applications and heterogeneous networks with a broad range of devices, a common architecture is required for IoT, such as the ISO OSI model or the TCP/IP model. Furthermore, it is critical to comprehend how to incorporate energy efficiency across the whole architecture. The equipment that is used to communicate and the protocols that are used to communicate should be energy efficient. Similarly, the applications should be energy efficient to have a low total environmental effect (Shaikh et al., 2017). Several researchers have proposed various IoT architectures. However, there is no broadly agreed-upon architecture for the Internet of Things (Wang, 2011). It was first used in the early phases of this field’s research. It consists of three layers: perception, network, and application (Khan et al., 2012).

i. The physical layer of the perception layer comprises sensors for perceiving and gathering information about the environment. It recognizes some physical parameters and, more than likely, other smart objects in the environment.

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Figure 1.2  Three-layer architecture of IoT.

ii. Connecting to other smart things, network devices, and servers is the responsibility of the network layer. Its capabilities are also employed to transmit and process sensor data (Khan et al., 2021). iii. The application layer is in charge of providing the user with application-specific services. It defines several applications in which the Web of Things is frequently used, such as smart homes, smart cities, and smart health (Figure 1.2). 1.4  APPLICATIONS OF GREEN I oT Green IoT helps IoT explore new energy sources, is environmentally benign, and reduces IoT’s impact on the environment. As a result, the diverse applications of green IoT are significant in terms of economic, environmental, and social sustainability, the preservation of natural resources, and the improvement of human health. The Internet of Things (IoT) converts our regular actions and circumstances into intelligent choices that enhance our quality of life. The following are some of the applications.

1.4.1  Smart home A green IoT allows a mobile or PC to handle home-based lighting, complex heating, setting alarms, and other gadgets remotely. It distinguishes between inhabitants for individualized actions and replies, and it combines the TV, computer system, and mobile into a single device, among other things. To reduce the environmental effect, the life cycle of green IoT should be considered, which includes green design, green use, green manufacture,

Green IoT  5

and, lastly, green disposal/recycling. Aslam et al. (2016) used channel allocated optimization to build a model for desired QoS facility of mixed IoT devices in a smart home.

1.4.2  Industrial automation Based on the Internet, industries have been mechanized using machines that can automatically execute the task without or with little human interaction. Kulkarni (2017) briefly describes automation in industries based on green IoT.

1.4.3  Smart healthcare For capturing, analyzing, and tracing the human body, various biometric actuators and sensors have been used in smart healthcare (Kalarthi, 2016; Islam et al., 2015). The IoT uprising in healthcare leads to new and improved sensors linked to the Internet to create crucial data in real time (Niewolny, 2013; Ullah et al., 2017). The benefits of efficient healthcare facilities include improved care quality, more admittance to treatment, and lower prices.

1.4.4  Smart grid Similar to the Internet of Things, the smart grid’s efficiency is based on fairness. It refers to the grid’s capacity to change and re-adjust d ­ ynamically to distribute electricity at the highest quality and lowest cost possible. Consumers may participate in the solution via a smart grid. Yanti (2015) discussed communication sensor networks in IoT and smart grid applications. Yang et al. (2015) developed a low-cost remote memory attestation for the smart grid. In addition, Liu et al. (2017) offered various methods for improving the data validity of IoT-level data loss in smart cities. The smart grid of the future will be able to track and share energy use and construct whole energy systems (Karnouskos, 2010).

1.4.5  Smart cities Smart cities are one of the most encouraging and well-known IoT applications (Petrolo, 2015; Heo et al., 2014). IoT may be defined as the efficient use of energy to create a sustainable smart world (Sathyamoorthy, 2015). As a result, it is advocated that machines be provided with extra sensory and communication add-ons to make the environment smarter. In a metropolis, machines can perceive their surroundings and interact. Smart parking (Ramaswamy, 2016), smart light lamps (Popa, 2012), highquality air, smart vehicles (Maria et al., 2013), smart traffic management (Su et al., 2011), and so on are all part of the smart city (Maksimovic and

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Omanovic-Miklicanin, 2017). The key to innovative technology and big data success in smart cities, where the quality of life will increase while pollution decreases (Maksimovic and Omanovic-Miklicanin, 2017), was summarized. The notion of smart cities (Sun et al., 2016) has developed into smart and linked communities. Song et al. (2017) and Jiang et al. (2016) examine the ideas and uses of smart cities.

1.4.6  Smart agriculture Smart agriculture will help farmers deal with the immense hurdles they are up against. The sector should think about coping with water constraints, cost management, and restricted land availability while developing new products. Green IoT applications for agriculture were proposed by Nandyala et al. (Maksimovici, 2017). In healthcare and agriculture, the combination of IoT and CC helps to lower the power consumption of the CC and IoT combo. Therefore, green IoT and green nanotechnology seem viable options for developing smart and sustainable agriculture and food production (Maksimovici, 2017). Figure 1.3 indicates various areas where smart agriculture can be applied.

Figure 1.3  Application areas of smart agriculture.

Green IoT  7

1.5 CHALLENGES Organizations are currently moving toward green IT rehearses. Dell, Apple, and HP are the organizations considered green tech pioneers by GreenFactor (a worldwide innovation and natural exploration drive). Dell is attempting to diminish the energy prerequisites of its server farms and equipment. It is attempting to diminish unsafe substances in its PC as well. Apple guarantees that its scratchpad meets the new Energy Star 5.0 details and uses without mercury LED illuminated showcases sans PVC parts. The act of green and reasonable processing in medical services faces many difficulties. The most well-known issue confronted is by the clients. Clients don’t know about the current green innovations. There is a need to improve the consciousness of ecological issues. Mindfulness about utilizing the innovation to decrease natural issues and decrease fossil fuel by-products should be broad. The difficulties also incorporate expense, quality, and reach, including treatment and usable expenses, expanding the span of medical care benefits, and working on the quality (further developing diagnostics and better results from patient therapies) while keeping up with green and maintainable practices (Ramsay, n.d.). Handling power is crucial for business, and its interest is truly expanding with the dramatic ascent in the number of clients. However, having amicable climate practices is restricted by the foundation limits. Challenges are confronted by IT gear clients as well as by merchants. In reality, carrying out green server farms is an extremely challenging errand because of the expansion in energy prerequisites and the expanding energy costs. The hardware life cycle of the executives additionally becomes hard for green procedures. Removal of e-squander is another issue because of the absence of procedures and foundation for it. Information science and big data examination are exhaustively utilized in the medical services industry due to their intricacy. Clinics and medical care can add to the green bearing by joining enormous information examination, IoT, and distributed computing. E-waste should be arranged appropriately and reused as a feature of a manageability program. Energy-proficient frameworks can supplant old frameworks. These include virtual servers, virtual information stockpiling, and productive application and data set construction. They can prompt decreased IT power utilization. Another test can be the utilization of environmentally friendly power. Even though they are prompted for diminished fossil fuel by-products, their arrangement is costlier than ordinary network energy. Another issue they face is that environmentally friendly power isn’t dependable and may likewise deal with the issue of accessibility. One more issue is the exactness of existing energy assessment devices. Such devices incorporate Nokia Energy Profiler, PowerTutor, and Trepn Profile. They are utilized to gauge energy utilization. Unfortunately, they use cell phones, and their exactness is low, directly resulting from the low accuracy of “fuel check sensors utilized in

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cell phone batteries.” New green advances such as GreenHadoop face challenges while assessing a task’s energy and time necessities. Booking choices are made on this premise which might endure. The assignment of waste hotness usage measures in server farms requires expensive warm hotness trade materials. Green estimates reception might end up being troublesome with cost-effective business activities. While utilizing distributed computing as green innovation, the principal challenge is a decrease in energy use alongside the giving nature of administration fulfilling the necessities. Changes made to the current frameworks to join green innovation might affect the accessibility and nature of administration. In medical care applications, accessibility and quality are vital necessities that can’t be compromised. The nature of administration (QoS) based on asset choice and provisioning is important since asset determination and arrangement can bring about energy proficiency (Godbole, n.d.). Another interoperability issue is that numerous public cloud frameworks are not intended for communication. There is an absence of industry norms to permit the plan of interoperable cloud stages. Energy-mindful unique asset allotment can likewise cause issues. Their exorbitant power cycling could impact the unwavering quality of servers. The interference of energy in the cloud climate influences the nature of the offered support. VM movements utilized may likewise prompt significant expenses for asset solidification over long stretch organizations. In “enormous information” for medical care, challenges incorporate expanding reception rates for electronic clinical records while having an amicable climate framework. In green IoT, the difficulties confronted in incorporating framework green IoT models range from the board, correspondence, security, and QoS. There is an issue of association between heterogeneous organizations, containing various sorts of gadgets, running on various stages and for various uses. Presentation of energy productivity in them might be a test. Energy-productive correspondence conventions of organizations for energy effective correspondence between IoT ought to be without compromising the unwavering quality of availability. For safety and security, calculations that are energy effective set the weight of calculation on IoT gadgets. This may indeed cause more energy utilization. Operational expenses of frameworks decline while taking on green figuring rehearses. Green figuring exercises apportion IT assets in low framework power and inactive states, which should guarantee no decrease in administration and accessibility, particularly in continuous applications. The administration of maturing and old obsolete gadgets, frameworks, and different assets is a not kidding action. More established equipment gadgets have expanded power utilization and require asset substitutions and removals. The endeavors toward green processing are in restricted regions and spotlight on diminished energy utilization and e-squander. However, the eventual fate of green figuring will rely upon productivity and green items.

Green IoT  9

There is a need for government approaches that give impetuses to green distributed computing business suppliers and clients. Likewise, assessment is significant in sustainable power innovation and power utilization estimation and requires better strategies and examination around here. High assessment precision with restricted assessment upward is the prerequisite. Programming operational expense-based assessment ought to be improved for precise assessment of stockpiling code area. Each of these is a forthcoming exploration region requiring new and inventive arrangements. 1.6  FUTURE SCOPE The brilliant fate of green IoT will change our future climate to become better and green, extremely high QoS, socially and ecologically maintainable and monetarily. These days, the most interesting regions center around greening things such as green correspondence and systems administration, green plans and executions, green IoT administrations and applications, energy-saving techniques, coordinated RFIDs and sensor organizations, portability, organization of the executives, the participation of homogeneous and heterogeneous organizations, savvy items, and green restriction. The accompanying exploration fields should have been investigated to ­foster ideal and productive answers for greening IoT: 1. There is a requirement for UAVs to supplant countless IoT gadgets, particularly in horticulture, traffic, and checking, which will reduce power utilization and contamination. UAV is a promising innovation that will prompt green IoT with minimal expense and high productivity. 2. Transmission information from the sensor to the portable cloud be more helpful. Sensor-cloud incorporates the remote sensor organization and portable cloud. It is an extremely hot and guaranteed innovation for greening IoT. A green interpersonal organization as assistance (SNaaS) may explore for energy effectiveness of the framework, administration, WSN and cloud technology. 3. M2M correspondence assumes a basic part to diminish energy use and risky outflows. Smart machines must be more intelligent to empower computerized frameworks. Machine robotization should be limited in the event of traffic, making an essential and quick move. 4. Configuration green IoT might be acquainted with points of view accomplishing superb execution and high QoS. Tracking down reasonable methods for upgrading QoS boundaries (i.e., bandwidth, postponement, and throughput) will contribute viably and productively to greening IoT. 5. While going toward greening IoT, it will be needed for less energy, searching for new assets, limiting IoT adverse consequence on the

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soundness of humans, and upsetting the climate. Then, at that point, green IoT can contribute fundamentally to a supportable brilliant and green climate. 6. To accomplish energy-adjusting for supporting green correspondence between IoT gadgets, the radio recurrence energy reap ought to be thought about. 7. More exploration is expected to foster the plan of IoT gadgets which assists with lessening CO2 emanation and energy use. The most basic assignment for savvy and green ecological life is saving energy and diminishing the CO2 outflow. 1.7 CONCLUSION The phenomenal technological advancements of the 21st century provide several benefits. However, the advancement of technology necessitates a large amount of energy, accompanied by deliberate e-waste and dangerous emissions. In this study, we examine and identify the most crucial technologies utilized for green IoT to make our environment and society smarter and greener. The ICT revolution (i.e., FRID, WSN, M2M, communication network, Internet, DC, and CC) has significantly improved IoT greening capabilities. The things around us will become smarter to perform specific tasks autonomously based on critical factors of ICT technologies, resulting in a new type of green communication between humans and things, as well as among things themselves, where bandwidth utilization is maximized, hazardous emissions are mitigated, and power consumption is optimally reduced. Future proposals for enhancing the efficiency and effectiveness of green IoT-based applications have been discussed. This study is useful for anybody interested in learning more about studies on green IoT. The future of green IoT is discussed with the current trends. REFERENCES Aslam, S., S. Aslam, N. Ul Hasan, A. Shahid, J. W. Jang and K.-G. Lee. (2016). Device centric throughput and QoS optimization for IoTsin a smart building using CRN-techniques. Sensors, 16, 1647. Atzori, L., Antonio Iera and Giacomo Morabito. (2010). The Internet of things: A survey. Computer Networks, 54, 2787–2805. Godbole, N. S. (n.d.). Using data science & big data analytics to make healthcare green. 12th International Conference & Expo on Emerging Technologies for a Smarter World (CEWIT). Melville, NY, IEEE, 1–6. Gubbi, J., R. Buyya, S. Marusic and M. Palaniswamia. (2013). Internet of Things (IoT): A vision, architectural elements, and future directions. Future Generation Computer Systems, 29, 1645–1660.

Green IoT  11 Heo, T., K. Kim, H. Kim, C. Lee, J. H. Ryu, Y. T. Leem, J. A. Jun, C. Pyo, S.-M. Yoo and J. G. Ko. (2014). Escaping from ancient Rome! Applications and challenges for designing smart cities. Transactions on Emerging Telecommunications Technologies, 25, 109–119. Islam, S. R., D. Kwak, M. D. Humaun Kabir, M. Hossain and K.-S. Kwak. (2015). The Internet of things for health care: A comprehensive survey. IEEE Access, 3, 678–708. Jiang, D., P. Zhang, Z. Lv and H. Song. (2016). Energy-efficient multi-constraint routing algorithm with load balancing for smart city applications. IEEE Internet of Things Journal, 3, 1437–1447. Kalarthi, Z. (2016). A review paper on smart health care systems using the Internet of Things. International Journal of Research in Engineering and Technology (IJRET), 5, 80–83. Karnouskos, S. (June 2010). The cooperative Internet of things enabled the smart grid. Proceedings of the 14th IEEE International Symposium on Consumer Electronics (ISCE2010), 07–10. Khan, N., A. A. B. Sajak, M. Alam and M. S. Mazliham. (2021). Analysis of Green IoT. Journal of Physics. Conference Series, 1874, 012012. Khan, R., R. Khan, S. Ullah Khan, R. Zaheer and S. Khan. (December 2012). Future Internet: The Internet of things architecture, possible applications and key challenges. 10th International Conference on Frontiers of Information Technology (FIT’ 12), 257–260. Kulkarni, N. (2017). Green industrial automation based on IOT: A survey. International Journal of Emerging Trends in Science and Technology, 4, 5805–5810. Liu, Y., X. Weng, J. Wan, X. Yue, H. Song and A. V. Vasilakos. (2017). Exploring data validity in transportation systems for smart cities. IEEE Communications Magazine, 55, 26–33. Maksimovic, M. (2017). The role of Green Internet of Things (G-IoT) and Big Data in making cities smarter, safer, and more sustainable. IJCDS Journal, 6(4), 175–184. Maksimovic, M. and E. Omanovic-Miklicanin. (2017). Green Internet of Things and Green nanotechnology role in realizing smart and sustainable agriculture. VIII International Scientific Agriculture Symposium “AGROSYM 2017”, Jahorina, Bosnia and Herzegovina. Maria, A., M. Biagi and R. Cusani. (2013). Smart vehicles, technologies and main applications in vehicular ad hoc networks. Vehicular Technologies - Deployment and Applications. doi: 10.5772/55492 Niewolny, D. (2013). How the Internet of things is revolutionizing healthcare. White Paper, NXP, 1–8. Petrolo, R., V. Loscrì and N. Mitton. (2015). Towards a smart city based on cloud of things, a survey on the smart city vision and paradigms. Transactions on Emerging Telecommunications Technologies, 28(1), e231. Popa, D., D. Popa and M. M. Codescu. (2017). Reliability for a Green Internet of Things, Buletinul AGIR nr. 45–50. Popa, M. and A. Marcu. (2012). A solution for street lighting in smart cities. Carpathian Journal of Electronic and Computer Engineering, 5(1), 91–96. Prasad, S. S. and C. Kumar. (2013). A green and reliable internet of things. Communications and Networks, 5, 44–48.

12  Intelligent Green Communication Network for Internet of Things Ramaswamy, P. (2016). IoT smart parking system for reducing greenhouse gas emissions. 2016 International Conference on Recent Trends in Information Technology (ICRTIT), IEEE, 1–6. Ramsay. (n.d.). Case Studies from GGHH Members. Retrieved from https://www. greenhospitals.net/case-studies-energy, www.kooweeruphospital.com.au Sathyamoorthy, P. and E. C.-H. Ngai. (2015). Energy efficiency as an orchestration service for mobile Internet of Things. IEEE 7th International Conference on Cloud Computing Technology and Science (CloudCom), IEEE, 155–162. Shaikh, F. K., S. Zeadally and E. Exposito. (June 2017). Enabling technologies for Green Internet of Things. IEEE Systems Journal, 11(2), 983–994. Song, H., R. Srinivasan, T. Sookoor and S. Jeschk. (2017). Smart Cities: Foundations, Principles, and Applications. John Wiley & Sons. Su, K., J. Li and H. Fu. (2011). Smart city and the applications. International Conference on Electronics, Communications, and Control (ICECC), IEEE, 1028–1031. Sun, Y., H. Song, A. J. Jara and R. Bie. (2016). Internet of things and big data analytics for smart and connected communities. IEEE Access, 4, 766–773. Ullah, F., M. A. Habib, M. Farhan, S. Khalid, M. Y. Durrani and S. Jabbar. (2017). Semantic interoperability for big data in heterogeneous IoT infrastructure for healthcare. Sustainable Cities, and Society, 2017, 90–96. Wang, H. N. (2011). Future Internet of things architecture: like mankind neural system or social organization framework? IEEE Communications Letters, 15(4), 461–463. Yang, X., X. He, W. Yu, J. Lin, R. Li, Q. Yang, and H. Song. (2015). Towards a lowcost remote memory attestation for the smart grid. Sensors, 15, 20799–20824. Yanti, H. (2015). The applications of WiFi-based wireless sensor network in internet of things and smart grid. Buletin Inovasi ICT & Ilmu Komputer, 2(1).

Chapter 2

Green IoT Future direction and open challenges Menka Patel Ganpat University

Rajan Patel and Nimisha Patel Gandhinagar University

CONTENTS 13 2.1 Introduction 2.1.1  Internet of Things 14 2.1.2  IoT architecture 15 2.1.3  Green IoT 16 2.1.4  Green IoT principles 17 2.1.5  Green IoT technologies 18 2.1.6  Advantages and disadvantages of Green IoT 18 2.1.7  Life cycle of Green IoT 19 2.2  Application of Green IoT 20 2.2.1  Smart cities 21 2.2.2  Smart E-health 22 2.2.3  Smart agriculture 23 2.2.4  Smart home 23 2.2.5  Smart grid 24 2.2.6  Smart industry and manufacturing 24 2.3  Green IoT Implementation Strategies 24 2.4 Open Challenges and Future Research Directions 27 2.4.1  Future research direction 29 2.4.2  Tools and technology 30 2.5 Conclusion 30 References 30 2.1 INTRODUCTION With the advancement of science and technology, the world is growing smarter by associating people automatically with various devices. To achieve automation, IoT technologies are being developed at a rapid pace. Through an adaptive communication network, processing, analysis, and storage, IoT components DOI: 10.1201/9781003371526-2

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become smarter. The Internet of Things (IoT) seeks to link the physical and digital worlds by utilizing devices; for instance, cameras, sensors, RFID, actuators, drones, and cell phones to perceive, gather, and transfer data from the environment via the Internet (Poongodi et al., 2021, Almalki et al., 2021). IoT devices can provide an extensive range of real-time observing applications with such components and communication technologies, as seen in environmental monitoring (Tellez et  al., 2016, Tawab et al., 2016, Shah & Mishra, 2016), e-healthcare (Chen et al., 2018, Kong et al., 2017), transportation autonomy, industry digitization and automation (Popa et al., 2017, Prasad & Kumar, 2013), and home automation (Pavithra & Balakrishnan 2015, Kodali et al., 2016). IoT is capable of obtaining and disseminating massive quantities of data that can be analyzed for smart decision-making due to state-of-the-art communication technology. To make IoT pervasive, the big data requirements of IoT necessitate storage capacity (Min Gu et al., 2014), cloud computing (Hashem et al., 2015), and a broad bandwidth for transmission. In IoT devices, this large amount of data processing and transmission uses a lot of energy. However, by utilizing efficient and smart technology known as Green IoT, it is feasible to cut power consumption and thereby aid in pollution management, energy demand reduction, and utilization of resources. It can be aided by adopting green regulations and guidelines that encourage users to use environmentally friendly things. Green IoT also encompasses the environmental inter-dependability component, energy efficiency cost-effectiveness, and collective cost of investment including both the cost of disposal and recycling (Poongodi et al., 2021).

2.1.1  Internet of Things The IoT is a group of connected electronic gadgets that are communicating without human–machine interaction. Figure 2.1 shows a few applications of IoT. IoT has been defined as (Smith, 2012): A dynamic global network infrastructure with self-configuring capabilities based on standard and interoperable communication protocols where physical and virtual “things” have identities, physical attributes, and virtual personalities and use intelligent interfaces, and are seamlessly integrated into the information network, often communicating data associated with users and their environments. According to Juniper Research’s most recent forecast, the number of IoT devices will surpass 46 billion by 2021. In comparison to 2016, this is a 200% increase. According to Martech Advisor, this sum is expected to cross 125 billion by 2030. The key components of the IoT network are hardware, middleware, Internet, and presentation. The integrated sensors, actuators, or other embedded hardware, as well as RFID tags, are part of the hardware. Actuators translate electrical signals to physical

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Figure 2.1 Application of IoT.

output, whereas sensors transfer physical parameters to electrical output. In the hardware layer, wireless sensor networks are crucial. Data is stored and processed on cloud infrastructures called as middleware, which provides distinct impacts by performing various analytics on the data. The Internet section allows for connection and collaboration between other parts. The Presentation section is about the processing, interpretation, and visualization of data (Poongodi et al., 2021).

2.1.2  IoT architecture The multi-layered IoT architecture emphasized devices that consume a lot of energy in each layer. It is a combination of hardware and software technologies, communication protocols, and various processing technologies. This layered architecture attempts to achieve ubiquity and pervasiveness by sensing, analyzing, communicating, and processing enormous amounts of data (Whitmore et al., 2015). The current architecture focuses on the nodes with the highest power consumption, protocols, middleware components, and applications. As shown in Figure 2.2, the IoT architecture is divided into five layers (Poongodi et al., 2021). 1. Perception layer: It is also known as the device layer. It contains dissimilar devices such as sensors and actuators, which gather useful information, for instance, temperature, health records, intruder

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Figure 2.2 Five-layered IoT architecture.

detection, location, air moisture level, and transmit to the network layer. 2. Network layer: It is known as the transmission layer. The primary task of this layer is to connect various network devices, smart objects, and servers. It processes the sensor data and sends it to the processing layer over a transmission media such as Wi-Fi, Bluetooth, 3G, etc. 3. Middleware layer: It is the core processing layer of the IoT architecture. It receives a gigantic volume of data from the network layer and stores, analyzes, and processes it. In addition to that, managing the services and maintaining the database connectivity are responsibilities of the middleware layer. 4. Application layer: The primary function of this layer is to provide application-oriented services to the end-users of IoT. This layer can communicate with the end-user application by using application layer protocols. 5. Business layer: In the end, this layer has command of the entire IoT system, which includes business models, user privacy, and app. It is concerned with the production of various business models, graphs, and flowcharts based on the data obtained from the above layer. Furthermore, IoT technology implementation is dependent on welldesigned business models. Different components of the IoT are working on each of the layers as shown in Figure 2.3.

2.1.3  Green IoT Green IoT implies the development of the Internet of Things utilizing strategies that lessen the carbon footprint in the environment. It entails employing the most power-efficient technologies and algorithms to maximize energy efficiency in computing, communicating, and controlling. Over the last

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Figure 2.3 Energy-aware IoT components (Tahiliani & Dizalwar, 2018).

several years, energy efficiency in the IoT has piqued the interest of researchers and designers for the following reasons: The National Intelligence Council (NIC) anticipates that by 2025, Internet nodes would be found in everyday objects, which include food storage, furniture, printed documents, and more. By 2025, there will be 75 billion connected IoT devices. The IT industry, as well as every computer user, should green its equipment and adapt how they are used to create a more ecologically friendly environment. Green IoT employs technologies that save energy in IoT applications to either minimize or mitigate greenhouse gas emissions. The fundamental of Green-IoT is Green Computing (GC) technology, which includes a study and development of environmental sustainability, which comprises the research and practice of an efficient and successful design, manufacture, usage, and destruction of computing components with minimal or no environmental impact.

2.1.4  Green IoT principles In the era of automation, Green IoT is a contemporary technology in ICT research because the requirement for energy increases continuously and the conventional sources of energy are diminishing. To achieve the advantages of Green IoT, the following principles are suggested: 1. Reduce network size: To save energy, nodes in the network must be strategically placed, and efficient routing techniques must be established. 2. Selective sensing: It captures only the data that is required, preventing energy loss from unwanted or irrelevant data.

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3. Hybrid architecture: Depending on the application, deploying passive and active sensors can help to save energy. 4. Policymaking: In many IoT application domains, effective policies must be created to reduce energy usage. 5. Smart trade-off: Trade-offs must be made based on the situation. Cost, communication, and processing all must be prioritized smartly.

2.1.5  Green IoT technologies Green Internet technology: Green Internet technologies demand the usage of low energy consumption hardware and software to save energy while ensuring the same level of performance. This encompasses things such as gateways, routing devices, and communication protocols. Green RFID tags: RFID is a small electronic component containing a range of RFID tags and small tag readers. It recognizes and tracks RFID-tagged things automatically and can store the data of connected objects. Active RFID and passive RFID are the two main types of RFID. Inactive RFID tags, integrated batteries are allowed to continuously transmit their signal, whereas passive RFID tags do not. Numerous ways have been suggested to reduce the energy ­consumption of RFID tags, for instance, to minimize the usage of non-­biodegradable substances used in RFID tags by reducing their size of it. Green Wireless Sensor Network (WSN): Green WSNs are made possible by green energy conservation methods, radio optimization methods, and green routing methodologies, all of which result in WSNs using less mobility energy. Smart data algorithms can also be developed to save storage space and minimize the size of data sent through WSN. Sensor nodes in the WSN can be enabled only when they are needed to save energy. Green cloud computing: To decrease energy consumption, hardware and software can be used in Green cloud computing, as well as certain additional controls are used to increase the energy efficiency of the underlying processes. Green data centers: All sorts of data, as well as applications, are stored, managed, processed, and disseminated through data centers. Data centers should be built to make use of renewable energy sources.

2.1.6  Advantages and disadvantages of Green IoT Based on the awareness, development, and cost there are certain advantages and disadvantages of Green IoT as listed below: Advantages: It does not produce any detrimental substances to the environment. It has grown in popularity as technology users become more ecologically conscientious. This will benefit investors in the long run

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in some sectors. It is less expensive to maintain. It decreases operating costs; therefore, the overall cost will be reduced in the long run. We will never run out of essential resources such as water or electricity because they are derived from renewable natural resources. It will help to mitigate the consequences of global warming by lowering CO2 emissions. Disadvantage: The initial or implementation expenses are incredibly costly. Due to a lack of awareness of the technology by the people, it will be time-consuming for the large population to adopt. Technology is still growing, and many items are still in the R&D stage. As a result, people are oblivious of performance outcomes. There are insufficient skilled human resources available to develop green technology-based products or system.

2.1.7  Life cycle of Green IoT Green IoT mainly emphasizes the design and strength of IoT networks by enabling green IoT technologies. The design phase of green IoT is considering technologies such as energy savers equipment, the architecture of a network, and communication protocols. The leverage phase is concerned with improving the efficiency of energy and lowering toxic waste. Green IoT helps to protect natural resources by enabling green ICT solutions that reduce harmful emissions, energy usage, pollution, and resource usage. As a result, Green IoT emphasizes green design, building, operation & maintenance, and disposal (Poongodi et al., 2021). The phases of the green IoT life cycle are shown in Figure 2.4.

Figure 2.4 Green IoT lifecycle.

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The following are detailed descriptions of each phase: 1. Green design: Environmentally friendly materials should be prioritized in component design. RFIDs, devices, sensors, and actuators are important components of an IoT system that are designed to save resources and emit less heat without compromising performance. Hardware components could be made smaller and software features guarantee that the environment is not harmed. 2. Green construction: Ecologically responsible IoT devices and sensors, including elements made of biodegradable materials, should be manufactured. The construction industry ensures that green products are employed as the number of devices grows. To analyze the components’ durability and quality, standards and policies must be established. All manufacturing companies should utilize biodegradable materials and design with efficiency in mind. 3. Green operation and maintenance: All gadgets’ energy usage should be decreased by programming them to turn off automatically when they are not in use. Simple adjustments in the way IoT components are used can dramatically improve their utilization. To extend the lifespan of sensors, they can work on the “awake and sleep” principle. Furthermore, device energy consumption must be minimized to reduce the effect of dangerous gases on the environment. 4. Green disposal: Recycling is the process of applying technology to present equipment or resources to create new versions of the same or different devices. To limit the amount of electronic waste, old gadgets and equipment should be recycled and remanufactured. The utilization of decomposable materials in the production of IoT devices is a process of green recycling, and it becomes more crucial as it cuts energy usage and greenhouse gas emission. Consider a mobile phone, which comprises copper, plastic, and non-biodegradable components. According to a survey conducted in Australia, 23 million cell phones are not in use. Furthermore, 90% of the ingredients used in the production of cell phones are decomposable; hence, it is possible to recycle them. The recovery and collection of metals that fall under the EEE-type were recently established by Electric and Electronic Equipment (EEE). Furthermore, authors have recommended using solar energy for charging, which would save roughly 20% of the energy consumed (Zink et al., 2014). 2.2  APPLICATION OF GREEN I oT Green IoT has made noteworthy changes in the environment due to usage of IoT devices is increasing in many cities for different types of applications. The major purpose of green IoT is to conserve the environment and

Green IoT  21

reduce the detrimental effects of contamination and CO2 emissions. Green IoT uses different green ICT technology, which has a tremendous potential to assist environmental sustainability and economic growth, making the world smarter and greener (Gapchup et al., 2017, Arshad et al., 2017). Numerous sensors, machines, devices, and drones are involved in communicating with one another to complete the task in a green environment intelligently. Furthermore, Green IoT makes IoT useful for limiting harm, creating an environmentally friendly atmosphere, and investigating other energy sources. As a result, many Green IoT applications are economically, ecologically, and sustainably beneficial, preserving natural resources and reducing adverse effects on human health. Some Green IoT applications are addressed in detail below.

2.2.1  Smart cities Smart cities are one of the newest Green IoT applications that have gotten a lot of attention in recent years. Smart cities are supported by the Green IoT, which connects smart sensors, gadgets, vehicles, and infrastructure across the city. The quality of human existence has improved as a result of the development of unique technology for smart connected communities and numerous smart city applications. If eco-friendly smart cities are to be developed, green IoT must be implemented. It allows shareholders to reduce chemical emissions and water usage. Hence, the integration of excellent ICTs and data processing into the G-IoT in smart cities is taken into account (Jamal & Butt, 2018). Reduction of harmful gas emission: In the city, numerous sensors, gadgets, and devices are installed for observing and storing information. In addition to that, the number of meters mounted in the city to assess energy consumption, level of waste, and greenhouse emissions. WSN and RFID are the crucial technologies to enable each of the equipment to communicate and interact with other devices in the green IoT. Mainly, WSN is a group of smart sensors and devices that may be put in any location to monitor and store data for many activities that take place there. Innovative improvements in the WSN domain have enabled it to become more energyefficient, and enhanced networking protocols have made WSN more environmentally friendly (Haider et al., 2020). The primary goal of G-IoT is to control energy usage and emissions (Gupta et al., 2020). The G-IoT is set up to achieve green industry, green correspondence, green preparation, green energy consumption, and green waste management. In addition to that, it is involved in air vitalization, traffic management, managing the parking slot, etc. This can be ­accomplished by the use of intelligent measuring devices and autonomous monitoring (Abdul-Qawy & Srinivasulu, 2019) such as smart actuators that can monitor and adjust energy usage on their own, for that the use of a smart grid is required. For continuous and major development in the

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smart grid, it is required to implement green power consumption, which converts the smart grid into a Green smart grid. Green SG is automated, self-healing, self-qualitative, cost-effective, and environmentally friendly. It provides an alternate source of energy, for instance, the solar, wind, and water resources, as well as regulates energy distribution between sources (Yang et al., 2021). These all environmental resources produced a huge quantity of data that needs to be handled as big data, and that could be stored in the cloud by G-IoT, to take appropriate actions on that few prediction techniques are applied. Due to cutting-edge controlled and robotic systems of G-IoT, overall energy consumption and wastage can be decreased by 40%–70% (Hassani et al., 2019), specifically, 25% decrease in carbon footprinting, a 30% reduction in energy consumption, and a 25% increase in the efficient distribution of resources between various sectors (Koppe et al., 2020).

2.2.2  Smart E-health The IoT revolution has the potential to benefit the healthcare business the most. Hospitals may increase access to patient care with good quality while lowering operational costs by implementing green IoT-based systems, as well as establishing IoT-based patient monitoring solutions. Mobile applications and wearable gadgets are given to the patients to monitor their health in patient-centric IoT systems. For instance, for athletes who are not preferred to wear a chest strap to track heart rate, Adidas Smart Run, the GPS-enabled running watch with a built-in heart rate monitoring system, is available for them the medical community facing big data challenges by continuous monitoring of heart rates. The heart rate data is analyzed by intelligent algorithms and provide insights that could revolutionize health care and illness prevention methods. The cross-platform program, developed by Softweb Solutions, connects to the Pebble watch and allows patients and caregivers to manage emergency contacts and medication warnings. The caregiver is also supplied with reports on medicine, activities, dementia fence, and fall detection (Kaur et al., 2017). All monitoring devices, systems, machines, and other gadgets should be energy efficient in smart health systems. Hospitals should put in place a system that enables precise environmental management while also incorporating effective control mechanisms. Within hospitals, patients and employees providing direct patient care use interactive equipment to collect, track, and transmit data in real-time. Clinical staff members can now use mobile devices to access the data, allowing for more efficient clinical operations. For example, The Sydney Adventist Hospital in Australia has become a digital hospital, with its electronic health record, virtualized data centers that collect and organize the information, and mobile apps that provide clinical staff and patients with immediate access to data.

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2.2.3  Smart agriculture By 2050, the world’s population is predicted to have grown to 9.6 billion people. In addition to that, due to climate change, extreme weather events are becoming common and the decrease of arable land due to erosion becomes increasingly critical for the farmers to become more productive. Smart agriculture farming based on IoT is only the solution to deal with this problem because it has provided solutions to the farmers to improve their crop yields, optimize irrigation efficiency, and reduce farming costs. Precision farming is now only practiced in industrialized countries. North America controls the lion’s share of the worldwide smart agricultural farming market, with 37.34%. In developing countries, progress has been slower, owing to organizations, such as the World Bank, supporting prototype programs rather than business venture capitalists. PAD (Precision Agriculture for Development) is currently the largest provider of smart agriculture in third-world countries. In India, Kenya, Pakistan, Rwanda, Ethiopia, Uganda, and Bangladesh, the supplier has labs and collaborations. PAD aspires to enhance the lives of the world’s 100 million smallholder farmers by giving them specialized information about their local geography, climate, and other factors. To achieve an eco-friendly process in smart agriculture third green revolution was introduced. It is controlling the agrarian world through a combination of ICT, such as precision gear, sensors, actuators, GPS, UAVs, etc.

2.2.4  Smart home To save the environment, people are becoming more conscious of the energy management of their houses by controlling carbon emissions and power consumption. It will provide new opportunities for builders, architects, and homeowners to build sensors-based smart homes including equipment such as air conditioner (AC), washing machines, lights, microwaves, doors, and so on, which are controllable using smartphones or tablets. For instance, it is possible to power on the AC by sensing the temperature level from outside of the home using smartphones, another example is, that a smart home can have the capability to open and close the windows based on weather forecast information automatically. There are numerous applications launched by various service providers, which are controlled via the web by allowing their users to monitor the houses from anywhere using smartphones. Renowned industries not limited to LG, Google, Apple, etc. have launched smart home applications, for example, home chat, Nest, and Home Kit, respectively. To achieve an ecological smart home, power control is required by determining the peak and ideal times for the consumption of appliances. ­Real-time energy monitoring tools are required, to provide feedback on

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energy usage and its expense, which allows taking crucial decisions to make the green smart home. By installing an intelligent power management system in the houses, it is possible to turn on and off the home appliances automatically. Furthermore, the use of improved indoor thermostats can help to reduce carbon emissions.

2.2.5  Smart grid The green smart grid is a hybrid of a traditional electrical power grid and modern IT technology. To enable better grid control, fast-paced two-way communications are used. A smart grid is a decentralized and web-based system that improves the overall control of grid infrastructure. It connects new assets to current operating systems and uses these new devices to yield the grid with whole new benefits. This combination allows for efficient resource usage and reduced consumption of energy. A smart grid has the capability of exchanging the produced energy and can dispatch electricity based on the requirement. Furthermore, it enables users to utilize energy more efficiently by providing access to comprehensive data on their power usage as well as additional management choices. Due to such a smart feature, the smart grid can operate energy efficiently ( Kaur et al., 2017).

2.2.6  Smart industry and manufacturing Robots are meant to accomplish manufacturing tasks with minimum human interaction and automatically tracked and handled various functionalities in a controlled manner. As a result, machines are operating automatically with little or no involvement from the people (Kulkarni & Abhang, 2017). The industrial Green IoT brings smart production systems and IoT architecture together that allow for remote access and reduce downtime. Furthermore, it facilitates data sharing between industrial enterprises and factory floors, enhancing market agility, equipment efficiency, labor productivity, etc. (Poongodi et al., 2021). 2.3  GREEN I oT IMPLEMENTATION STRATEGIES Implementation of G-IoT is achieved by adopting several strategies as shown in Figure 2.5 (Albreem et al., 2021). Hardware-based approaches: To develop Green IoT, users have to focus on key technologies such as machine-to-machine (M2M), wireless sensor network (WSN), and RFID. M2M technology is used to establish the communication link between connected IoT devices. It is capable of managing autonomous devices without human intervention. Furthermore, to accomplish Green M2M, the following energy-saving mechanisms have to be implemented: Set a threshold to implement automatic control of

Green IoT  25

Figure 2.5 Green IoT implementation strategies.

transmission power, which can identify the minimum scope of power transmission for ensuring the communication. Design power-efficient routing protocols for M2M communication. The network’s inactive nodes should be scheduled to go into sleep mode. As a result, only required nodes will be in use at the time of data collection. Evolve energy harvesting systems that include spectrum sensing and management, as well as interference reduction and power optimization (Poongodi et al., 2021). Due to autonomous sensors that are geographically spread and collectively monitoring the different situations, WSN is utilized for object detection, environmental measurement, fire detection, health monitoring, industrial process monitoring, evolving constraints in the military, and so on. The power and storage capacity of the sensor nodes are limited. They work with other sensors and send receptive data to the base station, and in terms of an ad hoc, it is known as a sink node. On the contrary, the following are the techniques to focus on for Green WSN: To support Green WSN, it is necessary to evolve a power-saving mode, which requires sensors to transmit data only when it is needed. They should be in sleep mode in all other cases (Ye et al., 2002, Anastasi et al., 2013). Energy-efficient routing techniques should be developed, for instance, the nearest routing algorithm, in which the nearest node is active, means to transmit and receive the data (Khalil and Zaidi, 2012). In addition to that multipath routing has multiple paths with reduced replay nodes (Poongodi et al., 2021, Rekha & Sekar, 2016) for data transmission to get benefits such as fault tolerance, and increased bandwidth, improved security, and improved system budget. RFID: RFID is a small device that stores data about connected objects. To achieve a green following are the points to focus on: A reader commences

26  Intelligent Green Communication Network for Internet of Things

an interrogation of RFID tags by delivering a wakeup signal, which helps to avoid overheating issues. To avoid energy consumption by overhearing problems, Reservation Aloha for No Overhearing (RANO) protocol is proposed (Lee et al., 2014). Software-based Approaches: Green software is defined as software that accounts for and optimizes the consumption of resources and their impact on the environment as a result of its use (Ray et al., 2013). By using green design concepts, green software attempts to reduce energy usage in software-intensive systems. The Orchestration Agent (OA) is installed on each client’s device to optimize server selection based on energy usage trends (Peoples et al., 2013). Using energy-efficient scheduling algorithms to switch sensors into ON, pre-OFF, and OFF power modes during their idle states, saves a significant amount of energy (Abedin et al., 2015). Green IoT software solutions are based on a variety of technologies, including data centers, cloud computing, and virtualization (Arshad et al., 2017). Policy-based G-IoT implementation: Large-scale energy conservation can be aided by guidelines and tactics based on operational data from IoT devices. Monitoring (various conditions of energy usage), data collection, user feedback, and automation systems are steps of designing energy efficiency policies. For example, we can use data from multiple areas of a building, such as resident behavior and energy usage fluctuations, to make decisions. After that, we’ll be able to develop guidelines and strategies for various territories inside the same building. Automation systems can assist to identify the location of residents including environmental changes and enable us to make energy-intensive decisions. City Explorer (ZamoraIzquierdo et al., 2010), a solution for home automation, utilized (Moreno et al., 2014) to develop policies, is made up of three layers; all of them are liable for data collecting, processing of data, and services such as energy efficiency. When applied to real-time circumstances, the above policy-based approach could reduce energy use by 20% ( Arshad et al., 2017). Awareness based G-IoT implementation: Awareness campaigns are an important part of reducing energy usage. Because it is impossible to guess or estimate how many people will follow your initiatives, awareness campaigns differ by culture and region are required to arrange. We can use smart metering technology to provide homeowners with current situation-based feedback on their energy usage from various sources in their homes, offices, and buildings, and then advise them on how to control and limit their energy consumption. This can help you save 3%–6% of energy (McKerracher and Torriti, 2013). Changing habits towards G-IoT: By developing basic habits in our daily activities, it is possible to reduce energy consumption and carbon emissions. Although it is a low-scale strategy, when the modest savings are included on an international level, they can add up to a substantial change. One option is to employ automation systems like those described in Occhiuzzi et al. (2013), Fensel et al. (2014), and Moreno-Cano et al. (2013) to monitor

Green IoT  27

energy consumption habits in offices, homes, and factories, and then prevent energy losses in our routine tasks. Recycling: The use of biodegradable materials in the manufacturing of devices in an IoT network can contribute to making it eco-friendly. For instance, cell phones are constructed from some of the world’s most limited natural resources, such as copper, plastic, and non-biodegradable materials, which can contribute to the greenhouse effect if not properly disposed of. According to one estimate, there are 23 million abandoned cell phones available in Australian drawers and cupboards (Foteinos et al., 2013), with 90% of the material in the phones being decomposable; therefore, the demand for recycling is growing all the time, and handle the issue of carbon footprinting and massive energy usage. Although it is an unreasonable hypothesis to regain 90% of the material, it can still make a significant impact in terms of energy savings. As Green IoT is a renowned research area the usage of IoT is growing exponentially. Many researchers have published various techniques to achieve green IoT covered in Table 2.1. 2.4 OPEN CHALLENGES AND FUTURE RESEARCH DIRECTIONS Based on the various parameters, the numerous issues are linked with the transformation of IoT to G-IoT such as hardware, software, policy, awareness, routing algorithm, recycling procedure, etc. Hardware-based approaches are about sensors, WSN, RFID, and M2M technologies, whereas software-based approaches are about the data center, cloud computing, and virtualization technology. The recycling process is about reusing and recycling the waste components of unused devices. To estimate energy consumption smart metering system is used. In the case of changing habits, people have to start to adopt such habits which save energy in daily life. Although G-IoT is still in its beginnings, massive research efforts are in progress to develop green technology and protect the environment still there are significant problems that have to be addressed urgently. To find the best Green IoT solutions few of the key obstacles are: Incorporation of energy-efficient IoT architecture to achieve the desired performance of the entire system. For G-IoT communication, protocols with low power consumption, energy-efficient devices, and energy-saving scheduling techniques should be used. To achieve data security cryptography algorithms are required to implement. Execution of such algorithms increases the load on IoT devices, which is one of the key factors of rising energy usage. This is a less-explored field of research, where application layer functions can be made more energy-efficient. This can be accomplished by adding a variety of methodologies into program design, such as web apps. For example, Blackle energy-saving Internet search has a black background.

28  Intelligent Green Communication Network for Internet of Things Table 2.1  Techniques of Green IoT Reference

Technology

Type

Sarder et al. (2015)

G-IoT network, sensors

SB

Said et al. (2020)

G-IoT network

SB

Lenka et al. (2019)

Wireless sensor networkassisted IoT network

SB

Al-Azez et al. (2015)

Virtualization framework for energy-efficient IoT networks, sensors Controlling the greenhouse effect for precision agriculture IoT sensors

HB

SB

Sathyamoorthy et al. (2015)

Smart phones

SB

Sun and Ryoo (2015)

Sensors

SB

Alvi et al. (2015)

Cloud computing

SB

Helal and Elmougy (2015)

Sensors

HB

Lim et al. (2015)

Sensors

HB

Moreno et al. (2014)

Smart building

PB

Vatari et al. (2016) Eteläperä et al. (2014)

HB SB

Description This paper proposes an energy-efficient approach to enhance the life expectancy of IoT networks. This paper introduces an IoT energy management method. This work introduces and tests an energy-intensive data routing protocol to transfer the data. This study introduces an energy-efficient cloud computing platform for IoT. This research introduces IoT and cloud-based precision agriculture solutions. This research proposes a way for increasing the energy efficiency of IoT sensors. A method for predicting energy consumption for a variety of smartphone applications. To decrease communication between nodes, MAC is used with wireless smart sensors. A method of reducing the data path has been proposed. Creating a path through the network and designating a certain area for each node. Workload distribution among different types of nodes (sensors and relay). Create policies and methods to reduce energy usage.

In the case of IoT device design, a few factors such as reducing energy consumption and making devices more energy efficient with less e-waste must be examined and integrated to save time and money. For the green IoT energy consumption models, trustworthiness and the awareness of context should be improved, as it leads to the creation of reliable and trustworthy G-IoT solutions. To restore a lot of IoT devices in a variety of industries including agriculture and traffic monitoring, aiming to reduce harmful waste and energy consumption UAVs are in demand. Efficient and low-cost

Green IoT  29

Green IoT is led by UAVs. M2M plays a crucial role in reducing harmful substances and energy utilization because of automated systems. To take prompt action in the traffic management system is required to reduce the delay in machine automation. The design of smarter IoT devices reduces energy consumption and CO2 emission to achieve a smart and green environmental life.

2.4.1  Future research direction There is no question that the G-IoT can improve people’s lives and the environment by creating associated technology and infrastructure more environments friendly. The services and application of Green IoT, QoS, energy-efficient models, planning, sewage treatment, etc. have dominated recent G-IoT research. Figure 2.6 depicts the future direction in each of these areas.

Figure 2.6 Future research directions.

30  Intelligent Green Communication Network for Internet of Things Table 2.2  Tools and technology for G-IoT Green technologies Green Cloud Computing Green RFID

The sensor network of the data center

Tools Google Cloud, Microsoft Azure, Amazon Web Services In RFID, materials and removable tags cannot be decomposable or reused; hence, a variety of products and materials are not allowed to recycle. If carbon-based conductive ink is used as a substrate antenna and bio-based plastic is employed in RFID, it is feasible to make it reusable. Zigbee

Reference Oberhaus (2022) Tabassum et al. (2018)

Liu et al. (2016)

2.4.2  Tools and technology Table 2.2 shows the used tools and technology for the G-IoT. 2.5 CONCLUSION In this chapter, the application of IoT, key challenges in IoT, and the components of IoT for various services have been discussed. In addition to that, various ways to deal with environmental issues, energy-intensive IoT, and its technologies are mentioned. Green IoT benefits the ecosystem by reducing carbon footprinting, boosting reusability, and employing carbon-free materials. Furthermore, to achieve Green IoT, principles, Green IoT technologies, and the life cycle of Green IoT have been discussed. Moreover, the application of Green IoT for instance, smart cities, helps human for better, secure, and convenient life by providing information of interest, smart healthcare for continuous monitoring and tracking the patients, and smart agriculture helps the farmers to cultivate the healthy and good amount of crops and increases livestock, smart home providing the luxury to observe and controls the home appliances easily, the smart grid helps to develop energy-efficient system along with control and manage resources, and smart industry and manufacturing helps to achieve automation discussed. Finally, Green IoT implementation approaches along with open challenges and future research directions have been discussed. REFERENCES Abdul-Qawy, A. S. H., & Srinivasulu, T. (2018). SEES: a scalable and energy-efficient scheme for green IoT-based heterogeneous wireless nodes. Journal of Ambient Intelligence and Humanized Computing, 10(4), 1571–1596. https:// doi.org/10.1007/s12652-018-0758-7

Green IoT  31 Abedin, S. F., Alam, M. G. R., Haw, R., & Hong, C. S. (2015). A system model for energy efficient green-IoT network. 2015 International Conference on Information Networking (ICOIN), 177–182. https://doi.org/10.1109/icoin.2015.7057878 Al-Azez, Z. T., Lawey, A. Q., El-Gorashi, T. E., & Elmirghani, J. M. (2015). Virtualization framework for energy efficient IoT networks. 2015 IEEE 4th International Conference on Cloud Networking (CloudNet), 74–77. https:// doi.org/10.1109/cloudnet.2015.7335284 Albreem, M. A., Sheikh, A. M., Alsharif, M. H., Jusoh, M., & Mohd Yasin, M. N. (2021). Green Internet of Things (GIoT): applications, practices, awareness, and challenges. IEEE Access, 9, 38833–38858. https://doi.org/10.1109/ access.2021.3061697 Almalki, F. A., Alsamhi, S. H., Sahal, R., Hassan, J., Hawbani, A., Rajput, N. S., Saif, A., Morgan, J., & Breslin, J. (2021). Green IoT for eco-friendly and sustainable smart cities: future directions and opportunities. Mobile Networks and Applications. https://doi.org/10.1007/s11036-021-01790-w Alvi, S. A., Shah, G. A., & Mahmood, W. (2015). Energy efficient green routing protocol for Internet of Multimedia Things. 2015 IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), 1–6. https://doi.org/10.1109/issnip.2015.7106958 Anastasi, G., Francesco, M. D., Conti, M., & Passarella, A. (2013). How to prolong the lifetime of WSNs. CRC Press. Arshad, R., Zahoor, S., Shah, M. A., Wahid, A., & Yu, H. (2017). Green IoT: an investigation on energy saving practices for 2020 and beyond. IEEE Access, 5, 15667–15681. https://doi.org/10.1109/access.2017.2686092 Chen, X., Ma, M., & Liu, A. (2018). Dynamic power management and adaptive packet size selection for IoT in e-Healthcare. Computers & Electrical Engineering, 65, 357–375. https://doi.org/10.1016/j.compeleceng.2017.06.010 Eteläperä, M., Vecchio, M., & Giaffreda, R. (2014). Improving energy efficiency in IoT with re-configurable virtual objects. 2014 IEEE World Forum on Internet of Things (WF-IoT), 520–525. https://doi.org/10.1109/wf-iot.2014.6803222 Fensel, A., Kumar, V., & Tomic, S. D. K. (2014). End-user interfaces for energyefficient semantically enabled smart homes. Energy Efficiency, 7(4), 655–675. https://doi.org/10.1007/s12053-013-9246-2 Foteinos, V., Kelaidonis, D., Poulios, G., Vlacheas, P., Stavroulaki, V., & Demestichas, P. (2013). Cognitive management for the Internet of Things: a framework for enabling autonomous applications. IEEE Vehicular Technology Magazine, 8(4), 90–99. https://doi.org/10.1109/mvt.2013.2281657 Gapchup, A., Wani, A., Wadghule, A., & Jadhav, S. (2017). Emerging trends of Green IoT for smart world. International Journal of Innovative Research in Computer and Communication Engineering, 5(2), 2139–2148. Gu, M., Li, X., & Cao, Y. (2014). Optical storage arrays: a perspective for future big data storage. Light: Science & Applications, 3(5), e177. https://doi. org/10.1038/lsa.2014.58 Gupta, S., Modgil, S., & Gunasekaran, A. (2019). Big data in lean six sigma: a review and further research directions. International Journal of Production Research, 58(3), 947–969. https://doi.org/10.1080/00207543.2019.1598599 Haider, Z., Jamal, T., Asam, M., Butt, S., & Ajaz, A. (2020). Mitigation of wireless body area networks challenges using cooperation. International Journal of Security and Its Applications, 14(1), 15–30. https://doi.org/10.33832/ ijsia.2020.14.1.02

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Green IoT  33 McKerracher, C., & Torriti, J. (2012). Energy consumption feedback in perspective: integrating Australian data to meta-analyses on in-home displays. Energy Efficiency, 6(2), 387–405. https://doi.org/10.1007/s12053-012-9169–3 Moreno, M., Úbeda, B., Skarmeta, A., & Zamora, M. (2014). How can we tackle energy efficiency in IoT based smart buildings? Sensors, 14(6), 9582–9614. https://doi.org/10.3390/s140609582 Moreno-Cano, M., Zamora-Izquierdo, M., Santa, J., & Skarmeta, A. F. (2013). An indoor localization system based on artificial neural networks and particle filters applied to intelligent buildings. Neurocomputing, 122, 116–125. https:// doi.org/10.1016/j.neucom.2013.01.045 Oberhaus, D. (2019, 10 December). Amazon, Google, Microsoft: Here’s Who Has the Greenest Cloud. Wired. https://www.wired.com/story/amazon-googlemicrosoft-green-clouds-and-hyperscale-data-centers/ Occhiuzzi, C., Caizzone, S., & Marrocco, G. (2013). Passive UHF RFID antennas for sensing applications: principles, methods, and classifications. IEEE Antennas and Propagation Magazine, 55(6), 14–34. https://doi.org/10.1109/ map.2013.6781700 Pavithra, D., & Balakrishnan, R. (2015). IoT based monitoring and control system for home automation. 2015 Global Conference on Communication Technologies (GCCT). https://doi.org/10.1109/gcct.2015.7342646 Peoples, C., Parr, G., McClean, S., Scotney, B., & Morrow, P. (2013). Performance evaluation of green data centre management supporting sustainable growth of the internet of things. Simulation Modelling Practice and Theory, 34, 221–242. https://doi.org/10.1016/j.simpat.2012.12.008 Poongodi, T., Ramya, S. R., Suresh, P., & Balusamy, B. (2021). Application of IoT in Green Computing [E-book]. In Advances in Greener Energy Technologies (1st ed. 2020 ed., pp. 295–323). Springer Medizin Verlag. https://doi. org/10.1007/978-981-15-4246-6 Popa, D., Popa, D., & Codescu, M. M. (2017). Reliability for a Green Internet of Things. Buletinul AGIR nr, 45–50. Prasad, S. S., & Kumar, C. (2013). A Green and reliable Internet of Things. Communications and Network, 05(01), 44–48. https://doi.org/10.4236/ cn.2013.51b011 Ray, S., Sengupta, N., Maitra, K., Goswami, K., Agarwal, S., & Nath, A. (2013). Green software engineering process: Moving towards sustainable software product design. Journal of Global Research in Computer Sciences, 4(1), 25–29. Said, O., Al-Makhadmeh, Z., & Tolba, A. (2020). EMS: an energy management scheme for Green IoT environments. IEEE Access, 8, 44983–44998. https:// doi.org/10.1109/access.2020.2976641 Sathyamoorthy, P., Ngai, E. C. H., Hu, X., & Leung, V. C. M. (2015). Energy efficiency as an orchestration service for mobile Internet of Things. 2015 IEEE 7th International Conference on Cloud Computing Technology and Science (CloudCom), 41–44. https://doi.org/10.1109/cloudcom.2015.90 Shah, J., & Mishra, B. (2016). IoT enabled environmental monitoring system for smart cities. 2016 International Conference on Internet of Things and Applications (IOTA), 383–388. https://doi.org/10.1109/iota.2016.7562757 Smith, I. G. (2012). The Internet of Things 2012 (3de editie). CASAGRAS2.

34  Intelligent Green Communication Network for Internet of Things Sun, K., & Ryoo, I. (2015). A study on medium access control scheme for energy efficiency in wireless smart sensor networks. 2015 International Conference on Information and Communication Technology Convergence (ICTC), 623–625. https://doi.org/10.1109/ictc.2015.7354625 Tabassum, H., Ben Ghorbel, M., Elsawy, H., Guibene, W., & Guruacharya, S. (2018). Green Internet of Things (IoT): enabling technologies, architectures, performance, and design issues. Wireless Communications and Mobile Computing, 2018, 1–2. https://doi.org/10.1155/2018/3747562 Tahiliani, V., & Dizalwar, M. (2018). Green IoT systems: an energy efficient perspective. 2018 Eleventh International Conference on Contemporary Computing (IC3). https://doi.org/10.1109/ic3.2018.8530550 Tellez, M., El-Tawab, S., & Heydari, H. M. (2016). Improving the security of wireless sensor networks in an IoT environmental monitoring system. 2016 IEEE Systems and Information Engineering Design Symposium (SIEDS), 72–77. https://doi.org/10.1109/sieds.2016.7489330 Vatari, S., Bakshi, A., & Thakur, T. (2016). Green house by using IOT and cloud computing. 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), 246–250. https://doi. org/10.1109/rteict.2016.7807821 Vijaya Rekha, R., & Raja Sekar, J. (2016). An unified deployment framework for realization of Green Internet of Things (GIoT). Middle-East Journal of Scientific Research, 24, 187–196. https://doi.org/10.5829/idosi.mejsr.2016.24.S2.146 Ye, W., Heidemann, J., & Estrin, D. (2002). An energy-efficient MAC protocol for wireless sensor networks. Proceedings: Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies, 3, 1567–1576. https:// doi.org/10.1109/infcom.2002.1019408 Whitmore, A., Agarwal, A., & Da Xu, L. (2015). The Internet of Things—a survey of topics and trends. Information Systems Frontiers, 17(2), 261–274. https://doi. org/10.1007/s10796-014-9489-2 Yang, Z., Jianjun, L., Faqiri, H., Shafik, W., Talal Abdulrahman, A., Yusuf, M., & Sharawy, A. (2021). Green Internet of Things and big data application in smart cities development. Complexity, 2021, 1–15. https://doi.org/10.1155/2021/ 4922697 Zamora-Izquierdo, M. A., Santa, J., & Gomez-Skarmeta, A. F. (2010). An integral and networked home automation solution for indoor ambient intelligence. IEEE Pervasive Computing, 9(4), 66–77. https://doi.org/10.1109/mprv.2010.20 Zink, T., Maker, F., Geyer, R., Amirtharajah, R., & Akella, V. (2014). Comparative life cycle assessment of smartphone reuse: repurposing vs. refurbishment. The International Journal of Life Cycle Assessment, 19(5), 1099–1109. https://doi. org/10.1007/s11367-014-0720-7

Chapter 3

Enabling sustainable technologies using the Internet of Things for Industry 4.0 Poulami Dalapati and Saurabh Kumar The LNM Institute of Information Technology

CONTENTS 3.1 Introduction 35 3.2  Industry 4.0 36 3.2.1  Sustainable development in Industry 4.0 38 3.3  Intelligence in Industrial IoT 41 3.3.1  Artificial intelligence 41 3.3.2  Machine learning 42 3.3.3  Deep learning 45 3.4  Analytics and Data Management in IIoT 46 3.4.1  Big data and advanced analysis 47 3.5 Case Study: Future of Smart Transportation 49 3.5.1 Advantages and applications of IoV in smart transportation 52 3.6 Conclusion 52 References 53 3.1 INTRODUCTION In the last few decades, industrial transformation has been observed, focusing on the introduction of smart manufacturing processes to optimize time and effort. The idea is to reduce the cost of production with additional benefits to both producers and consumers. These benefits are the result of the outcomes of sustainable industrial operations. Sustainable industrial activities support the functions to be performed so that energy efficiency, conservation of resources, and low-waste production can be achieved (Chandima Ratnayake and Markeset 2012). As a result, intelligent decision-making (Gupta, Forgionne, and Mora 2007) has evolved to be a critical process in industrial operations. Intelligent decision-making requires that both the machines and the processes involved in the operations must be automated. The automation, in turn, can be achieved with the effective implementation of an intelligent decision support system (Gupta, Forgionne, and Mora 2007). The automation can be performed through integrated communication and computation

DOI: 10.1201/9781003371526-3

35

36  Intelligent Green Communication Network for Internet of Things

among the smart devices deployed in the region of interest. These devices are responsible for performing operations such as sensing, actuation, transmission, analysis, and storage of data at both the local and global levels (Pandey and Zaveri 2016a). The local level is at the device and global level may be either within the network or among different networks which exist in the operation. However, there is a need to address the issues of interoperability and portability of these devices and the data being sensed and actuated upon by these devices. The Internet of Things (IoT) environment provides a ubiquitous platform to integrate all the sensors, actuators, and other computational devices involved in the smart industrial processes to address the issues discussed above. The IoT acts as an enabler for the sustainable development of products and processes by providing a framework where the embedded devices, sensors, and other IoT components may co-exist, irrespective of their heterogeneous characteristics and behavior (Kumar et al. 2020). Moreover, there is a need to address the challenges associated with implementing IoT to automate traditional manufacturing and industrial processes using innovative modern technologies under the umbrella of Industry 4.0 (Lasi et al. 2014). The sustainable development in Industry 4.0 revolves around the utilization of Artificial Intelligence (AI) (Sergi et al. 2019), Cyber-Physical System (CPS) (Lu 2017), and Machine Learning (ML) (Zhou, Liu, and Zhou 2015) using high-speed Internet technology. As a result, a huge amount of data is generated in the process of sensing and communication of the sensed events. It requires a massive amount of computation to be performed, either by the deployed devices or at the terminal stations. The real-time communication and computation of data may further help in profound decision-making by the decision support systems. It requires continuous improvement in industrial processes to realize further these processes’ energy-efficient transformations for intelligent production scheduling. The three main aspects of a successful realization of Industry 4.0 are planning, controlling, and improving quality in the industrial methods (Zhou, Liu, and Zhou 2015). In this regard, the successful implementation of sustainable manufacturing processes needs to address the real-time analysis of big data. In this context, this chapter introduces the idea behind Industry 4.0 and the need for sustainable development to achieve its goals. As an enabler, the use of IoT in smart manufacturing is discussed with different issues and challenges associated with the process. Further, the roles of AI, CPS, machine learning, deep learning, and big data analysis in Industry 4.0 are also discussed. 3.2  INDUSTRY 4.0 In the last 300 years, the lives of people around the world have changed with better living and happiness indices. The industrial revolution has played one of the most crucial roles in achieving the same. The first industrial

Enabling sustainable technologies  37

revolution was focused on mechanization, the second industrial revolution focused on mass production, and the third industrial revolution was purely in the field of inventions of computers and automation. The fourth industrial revolution is the current phase, which is sometimes also known as Industry 4.0. The focus of Industry 4.0 is on intelligent things and entities. It can positively change the different industrial sectors such as electronics, manufacturing, defense, transportation, chemical, automotive, construction, etc. In the past decade, research in IoT has proved it to be an effective technique to achieve the vision of the fourth industrial revolution (Wittenberg 2016). The Industrial Internet of Things (IIoT) is considered the network of objects or things embedded with computation and communication capabilities to perform industrial operations by exchanging information among themselves. There are four essential components of IIoT: smart objects, networking infrastructure, business intelligence, and people-in-the-loop (Pandey and Zaveri 2016b). Moreover, there are five expected features in Industry 4.0: efficiency, flexibility, innovation, workforce, and competitiveness, as depicted in Figure 3.1. Efficiency is concerned with the intelligent and efficient use of resources such as raw materials, water, and energy. The flexibility relates to the dynamic supply chains that are responsive to the ever-changing technologies and market conditions. The innovation needs to be fostered by utilizing both the hardware and software solutions

Figure 3.1 Expected features in Industry 4.0.

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on a large scale. Finally, the workforce must be skillful so that competitiveness can be brought among the industries, wherein data-driven and global business modeling is the need of the hour for performance improvement in large-scale production. Similarly, the design philosophy of IIoT for industrial processes discusses four crucial parameters: distributed decision making, information clarity, technical assistance, and interoperability (Frank, Dalenogare, and Ayala 2019). The design philosophy emphasizes enabling intelligent objects to take independent and autonomous decisions. It can be done with transparency of information to empower smart objects to reduce human intervention in the industrial processes. Furthermore, there are four industrial process enablers: virtualization, which deals with the collaboration of intelligent objects; analysis that is concerned with the analytical operations performed with the massive amount of data; visualization for well-informed interpretation of analyzed data and representation in human-readable form; and the use of cloud platforms to reduce the geographical gap in communication and computation services. These process enablers are responsible for improving operational efficiency and emphasize product innovation. The operating efficiency, in turn, has benefits in terms of improved resource utilization, increased productivity, and reduction in the cost of operations. The realization of operational efficiency and innovation in product development results in building an enhanced ecosystem to cater to the dynamic marketplace. Moreover, the parameters mentioned above enable us to create a sustainable environment where the benefits from the fourth industrial revolution can be increased. The sustainable assessment in Industry 4.0 is discussed in the following section.

3.2.1  Sustainable development in Industry 4.0 The term sustainability means the “ability to be maintained at a specific rate or level”. In a sense, sustainability aims to exist at a continuous rate with the introduction of a certain level of improvement in the process involved. In the industries, sustainability can be achieved mainly using energy-efficient techniques, conservation of resources, and minimizing the production of wastes in the operations. Industry 4.0 proposes to include the characteristics of previous industrial revolutions and address the globalization and emerging issues to provide sustainable development (Garbie 2016). Globalization is considered one of the critical drivers of sustainable development in the industries affecting development and manufacturing worldwide. As shown in Figure 3.2, the five essential elements of globalization are Supply Chain Management (SCM), Information and Communication Technology (ICT), energy pricing, emerging markets, and business models. The SCM is considered one of the vital strategic functions at different stages of the industrial operations in the industries. At the global level, the

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Figure 3.2  Five essential elements in the issue of globalization.

issue to be addressed in SCM is related to environmental concerns such as climate change, contamination, and non-renewable resource consumption. The ICT is considered the backbone of any manufacturing industry in the current scenario as it provides a platform to share information among the customers, producers, and suppliers. Some of the examples of the use of ICT are in the Enterprise Resource Planning (ERP), wireless communication technology, Global Positioning System (GPS), and Radio Frequency Identification (RFID) system, etc. Similarly, the production with optimized energy consumption helps in improving the economic advantages. As the increase in energy pricing affects sustainability, there is a need to reduce the energy consumption from non-renewable sources and increase the utilization of renewable sources. In emerging markets, it is difficult to identify the marketing conditions, especially when there is a need to meet the standards of newly developed innovative products. Thus, the business model must be concrete enough to address all the issues discussed above. The business model must meet the expectations of mass customization, where the knowledge is incorporated to include the local and international markets. There is a need to have the business model’s strategic approaches to maximize the enterprises’ economic profits by considering the competitive benefits and promoting the product values.

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Figure 3.3 Emerging issues in Industry 4.0.

Furthermore, the emerging issues in a sustainable development deal with technology, government regulation, population growth, economic crisis, and consumption of natural resources, as depicted in Figure 3.3. The emerging problems address the changes in the manufacturing industries based on the aggressive worldwide competition. One of the crucial issues in sustainability is technological advancements to facilitate high-quality and low-cost products by reducing manufacturing time. The task is to convert the traditional system to an automated system and introduce agility and flexibility concepts. However, the technology must adhere to the guidelines of government agencies around the world. The government regulation prevents unfair competition and provides laws for suitable environments for the employees in the industries. The government regulation deals with employment, advertising, labor, environment, safety, health, and business privacy. For the business model’s success, there is a need to monitor the population growth worldwide continuously. Population growth affects industrial growth, food supply, fertility, sociology, economy, polity, industry location, and available land for industrial operations and development. The three different categories of countries based on population growth are developed, emerging, and developing. There is a need to amalgamate the needs of these countries using emerging technologies. Moreover, the economic crisis and the availability

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of resources for growth are also important factors associated with emerging issues of sustainable development. The technologies are the basic building block to achieve the goals of Industry 4.0 sustainably. The advent of IoT and the use of artificial intelligence, machine learning, and big data analytics to perform autonomous communication and computation operations must be understood to be implemented concerning the standards and guidelines of Industry 4.0. The role and significance of AI in achieving automated intelligence are discussed in the following section. 3.3  INTELLIGENCE IN INDUSTRIAL I oT Industrial IoT deals with the implementation of IoT in industrial processes. The idea is to automate the industrial operations with the aim of energy management and safety management with improved manufacturing standards using Machine-to-Machine (M2M) intelligence (Alam, Nielsen, and Prasad 2013). Different goals can be achieved with the use of IoT in industrial operations. Some of them are energy management, pollution monitoring, environmental monitoring, plant safety, surveillance, information security, environmental safety, machine internetworking, system health diagnosis, production efficiency, remote management, supply chain automation, logistics, motion control, storage, and parking, and building smart home/office (Xu, He, and Li 2014; Thibaud et al. 2018). The IoT environment uses sensors equipped with machinery and wireless communication with audio-visual reality applications to interact with the customers. It requires a long-term analysis of data to provide an intelligent view of the needs of the customers. In this context, there is a need to understand the scope of intelligence from the viewpoint of its realization, as discussed in the following sections.

3.3.1  Artificial intelligence There are different ways to express the idea behind AI. According to Patterson (Patterson 1990), AI is considered a branch of computer science that deals with studying and creating computer systems that exhibit some form of intelligence. Eugene Charniak et al. (2014) mention AI as the study of mental faculties through computational models. The term artificial corresponds to something human-made, and intelligence corresponds to the thinking power. Thus, AI can be defined as the creation of software having intuitive decision-making capabilities. Therefore, it addresses the use of computer programs to embed human-made intelligence in the machines. John McCarthy first proposed the phrase artificial intelligence in 1956 (McCarthy 1989). Since its inception, there has been a significant level of research and developments observed in this field.

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A computer program without AI uses an extensive database for algorithmic search to achieve the goal. In contrast, the computer program with AI uses a large knowledge base to utilize heuristic search methods to perform a task effectively. The development of a knowledge base is one of the crucial tasks in the field of AI. The knowledge can be categorized as procedural or operational, declarative or relational, and heuristic. The scope of AI-based techniques deals with its implementation in games, theorem proving, Natural Language Processing (NLP), vision and speech processing, robotics, and expert systems (Müller and Bostrom 2016). AI has significant roles in different fields. It acts as an enabler in realizing Industry 4.0 by supporting human–computer interaction, cyber-physical systems, cloud computing, cognitive computing, etc. It provides a virtualized instance of physical objects in a smart factory for interaction with one another. It further has cost-effective benefits as the simulation scenario will help understand the actual situation and reduce the damage of essential components. The role of AI is either being implemented or being studied to be realized in machine safety, efficient product lifecycle, efficient manufacturing processes, etc. The use of AI helps the machines and equipment to communicate and relay information with one another. Some of the examples include computer vision, robotics, NLP, among others. With the help of AI, industries can process the large amount of data generated during operations. It helps in improving the prediction of yield and quality of the product in manufacturing. However, there are some challenges associated with using AI in industrial IoT, such as efficient connectivity among the devices, development of a full-proof method to understand the data in real time, training the machines, and making these trained machines act in dynamically changing real-world conditions (Sergi et al. 2019). Moreover, there are many advantages associated with AI, such as improved efficiency, cost-saving, improved security, augmented performance, and resource boosting with optimized usage. With these fundamental advantages, researchers and developers are utilizing AI in the fields of agriculture, education, manufacturing, aerospace, transportation, among many other areas. All these fields help in realizing the vision of using artificial intelligence on machines. However, learning serves as one of the basic building blocks to build a concrete knowledge base and achieve intelligence.

3.3.2  Machine learning Machine learning is sometimes considered a subset of AI. It imparts the capabilities to machines to perform decision-making functions based on the experiences gained rather than being explicitly programmed. However, a certain level of programming is needed to make the machines capable of performing the operations mentioned above. The machine learning activities empower the machines by using data to answer questions related to

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the domain of operation (Candanedo et al. 2018). There are two phases of machine learning: training which is performed initially utilizing the set of data available concerning the domain, and prediction, conducted by answering the questions related to the operational activities in the same domain. The dataset used may be labeled or unlabeled and trained using standard machine learning algorithms to create a model. The model, thus formed, serves as the basis for the prediction of different activities. In the prediction process, the testing dataset is utilized which consists of variety of data, in addition to the data as that in the training dataset. A few examples of the domains where machine learning can be applied are image recognition, speech recognition, traffic prediction, product recommendation, selfdriving cars, email spam and malware filtering, virtual personal assistants, online fraud detection, weather prediction, prediction of natural calamities, etc. (Candanedo et al. 2018). Machine learning algorithms can be classified into three categories: unsupervised, supervised, and reinforcement learning (Kotsiantis, Zaharakis, and Pintelas 2006), as shown in Figure 3.4. Unsupervised machine learning technique is used to identify similar groups of data. Such identification of data is sometimes also known as clustering. Data segregation is done on the unlabeled dataset, utilizes the inner structure of data, and is not concerned with the specific outcome. Clustering can be performed in two ways: hard clustering, in which the data points belong to one cluster entirely, and soft clustering, in which the data points belong to more than one cluster. One of the examples of hard clustering is the K-means method, while that of soft clustering is Fuzzy c-Means (FCM) method. The K-means algorithm

Figure 3.4 Categories of machine learning algorithms.

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may not be as fast as FCM, but FCM is extremely faster than the K-means algorithm. Similarly, the supervised machine learning technique is used to classify the dataset by performing the mapping function from the labeled dataset. There are two categories of supervised learning: regression and classification. The regression is performed for the dataset where the output variable is a real number, such as the cost of a product. In contrast, classification is performed when the output variable is considered as a category, such as the color of the product. Finally, reinforcement learning is regarded as the machine learning algorithm that enables machines to improve their performance through automated learning performed for the ideal behaviors of an environment domain. The environment consists of a set of states, actions, and rewards. An agent is responsible for looking into the current state and goal state and performing actions to receive the reward for reaching the goal state. The key aspect of agent–environment interaction is to allow the agents to maximize the rewards during the execution of different tasks in the domain. For instance, if there is a fire situation in a room of any building, the agent is supposed to be programmed so that it will stop the fire. The agent senses the fire condition using the onboard sensing unit, uses its knowledge base to find the location of water in the building, and then tries to sprinkle the water to stop the fire. Two crucial points can understand the difference between reinforcement learning and supervised learning (Kotsiantis, Zaharakis, and Pintelas 2006). First, there is no external supervisor to guide the agents in reinforcement learning, whereas an external supervisor in supervised learning guides the agents. In supervised learning, the external supervisor knows the environment in which the actuation needs to take place. Second, there is no reward function in supervised learning, whereas the reward function in reinforcement learning provides the feedback to the agent for further activities. Similarly, there are two key differences between reinforcement learning and unsupervised learning (Kotsiantis, Zaharakis, and Pintelas 2006). First, there is a mapping between input and output for reinforcement learning which is not the case in unsupervised learning. Second, reinforcement learning builds a knowledge graph from the constant feedbacks of the corresponding actions, whereas unsupervised learning focuses on finding the underlying pattern in the available dataset. In the current scenario, many industries are either trying or utilizing the benefits of IIoT with machine learning, such as healthcare, retail, finance, travel, and social media. For instance, Pfizer explores the use of IBM Watson for drug discovery which has helped speedy research and develop the Covid-19 vaccine. In finance, machine learning can be used to detect different kinds of fraud by targeting the focused account holders. Machine learning is helping the retail business by providing improved customer service through various levels of product recommendations. The support of

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dynamic price setup and sentiment analysis support has allowed the travel business to thrive by innovating newer strategies to attract travelers worldwide. Some of the common examples of utilizing learning in social media are that of face tagging in photographs and job suggestions by leading job websites.

3.3.3  Deep learning As explained previously, machine learning is considered the subset of AI that can learn automatically from the available object features. There are two primary limitations associated with machine learning. First, the machine learning algorithms are ineffective or not valid for high-dimensional data. Second, the features have to be explicitly mentioned in the case of machine learning algorithms. To reduce the effect of these limitations, deep learning plays a significant role. Deep learning can be considered the subset of machine learning that can learn automatically by searching the objects’ features on its own. Deep learning tries to mimic the functionality of neurons present in the human brain. When the amount of data is enormous, deep learning gives better performance in terms of accuracy (Goodfellow, Bengio, and Courville 2016). The working principle of deep learning utilizes the signals which travel among the neurons in the artificial neural network (LeCun, Bengio, and Hinton 2015). In the neural network, each neuron is assigned weightage values. The neuron which has the highest weightage has more effect on the subsequent layers than the others. The final layer combines all the weighted inputs to produce the results. The term deep refers to the number of hidden layers in the network. The working of deep learning is based on the concept of nested hierarchy, i.e., breaking complex tasks into simpler modules. For instance, if there is a problem of recognizing the fruit orange, first, the shape is checked, then the color is checked, and then the taste is checked to come up with the conclusion that the given fruit is orange. The impact of deep learning on IIoT can be understood in terms of improvement in the speed and accuracy of operations (Liang et al. 2020). There are two important reasons due to which deep learning is considered to be helpful. First, it requires a large amount of labeled data. Second, it requires high-end computational power for processing. Thus, the large quantity of data and high quality and accuracy of reliable data is crucial for deep learning algorithms. The strength of deep learning, as a technology, can be understood by its implementation performed by Toshiba in collaborative distributed deep learning technology between the edge and the cloud (Toshiba Digital Solutions Corporation, 2021). In this technology, the learning process is performed on the cloud for high processing, and the inference process is conducted at the edge for real-time processing. This has helped Toshiba improve the yield and productivity in semiconductor

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industries, adopt a drone navigation control system to find the damage in power transmission lines, prediction of behavior of workers in warehouses through wearable devices, and forecast power generation in solar power systems. Some other agencies, such as H 2O, Intel’s Nervana, and Zebra medical vision, apply deep learning techniques in their respective fields to realize their individual visions (Ameri Sianaki et al. 2019).

3.4  ANALYTICS AND DATA MANAGEMENT IN II oT Throughout the discussions in the chapter, it has already been established that intelligence is essential for the effective utilization of different parameters in industrial IoT. Also, gaining experiences and learning from these experiences require a concrete model that is necessitated by using a huge amount of data accessed from the operational domain. Further, these data must be processed to extract useful information that will help inform decision-making for industrial operations. In this context, the analysis of data becomes one of the crucial pillars of IIoT. The IIoT analytics serves as the bridge between industrial assets and information systems. The industrial assets consist of all the machines and shopfloor devices in the industries to perform the processing of products. On the other hand, information system is accessed by the business processes and people for different activities related to the industries. The industrial assets can be considered as the hardware and information system is a software application used to interact with the hardware. This interaction needs data that is provided through IIoT analytics. The analysis operation requires connectivity data as an input and provides new insights and knowledge as an output to the information system. It further helps to perform optimized decision making and intelligent operations by the information system on the industrial assets. Thus, industrial IoT analytics can be defined in terms of connectivity and data analytics to achieve the vision of Industry 4.0. The data are systematically analyzed to extract meaningful information. This information can be utilized in two ways. First, it is used to understand the operational states, performance, and environment of operation. Second, it helps to identify the emerging information patterns to reduce inefficiency. Further, this may help in the optimization of dynamic operations, prognostic maintenance, and real-time data analysis. IIoT analytics is of three types: descriptive, predictive, and prescriptive (Lee and Lee 2015). The descriptive analytics address the questions such as when, where, and what happened. Predictive analytics deals with the questions such as what next and what is the pattern. Similarly, prescriptive analytics addresses the questions such as what is the best action and should we try this action. Moreover, there are critical challenges associated with analytics, such as data distribution, semantics, data streaming, automation

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of analytics, context, safety, correctness, and timing. Based on the size and other distinct characteristics of data and its applications, the concept of analytics must be understood with respect to data science and its role in big data analysis, as discussed in the following section.

3.4.1  Big data and advanced analysis To better understand analytics, the concept of data science must be explored, which leads to the realization of different techniques to handle the enormous amount of data being generated in the real-time application in the current scenario. Data science is an interdisciplinary field that uses scientific methods by virtue of statistical research, data processing, and machine learning to extract knowledge from the different types of data available in various application domains (Sagiroglu and Sinanc 2013). As shown in Figure 3.5, data science uses the concepts of computer science, mathematics, and domain expertise to develop models for the analysis of

Figure 3.5 Data Science from the viewpoint of computer science, mathematics, and domain expertise.

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the data. However, the challenge comes when the amount of data becomes huge. This huge amount of data results from a wide variety of applications that exist in our day-to-day lives worldwide. Big data refers to the data which are too big to be handled by either the processing tools or traditional databases. Such data consists of both structured and non-structured data. The structured data can be easily organized, stored in relational databases, managed by the query languages in databases, and accounts for only 20% of the total data available in the world. On the other hand, the unstructured data do not possess any predefined model, cannot be processed by the traditional databases, enhances the insight to huge datasets, and accounts for 80% of the total data available in the world. Some examples include website blogs, Facebook chats, images, news, tweets, comments on social media, etc. According to NIST (Ekbia et al. 2015), big data represents the data of which acquisition speed, data volume, or data characterization restricts the capacity to use conventional associated methods to manage successful analysis or the data that can be successfully operated with critical horizon zoom technologies. The characteristics of big data can be explored in terms of volume, variety, and velocity of data (Sagiroglu and Sinanc 2013). Some of the researches also suggest characterizing big data in terms of variability, veracity, value, and virtualization (Ekbia et al. 2015). The volume addresses the quantity of data created continuously from different sources. For example, uploading more than 100 hours of videos on YouTube, galactic pictures captured using the telescope, etc. The velocity addresses the speed of data generation and outlines the reduction of data processing time to provide real-time services. For example, according to a survey conducted in 2020 (Internet Live Stats, 2020), there are 500 million tweets per day on average, the New York stock exchange measures 1TB of data during every exchanging session, etc. The variety deals with the different categories of data with no restriction on the input data format. However, it must be pointed out here that most of the data generated and non-structured. Some examples of the variety in data are text, audio, video, images, web, GPS data, sensor data, documents, flash data, etc. The term variability is slightly different than variety. Variability suggests that the meaning of data changes constantly. Such data appear as an indecipherable mass without structure, such as language processing, hashtags, geo-spatial data, multimedia, sensor events, etc. The veracity indicates biases in data, visualization provides the power to identify the new patterns in data continuously, and value is related to extracting meaningful business information from the scattered data. There are different sources from which massive amounts of data are generated, such as online trading, production and inventory, sales, industries, healthcare, agriculture, gene sequencing, medical clinics, computational biology, astronomy, nuclear research, etc. In the rapidly changing world concerning digitization, it has been observed that the data are getting generated from anywhere and at any time and are movable from one system to

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another. Since some of these data are irrelevant for the domain of operations, data analytics is performed to know about the relevancy of data. Data analytics help in appropriate product recommendations, improve market effectiveness, protect from wrong decisions, help join an activity that allows expanding businesses, and is a handy tool to learn about the competitors. To understand the role of big data analytics in Industry 4.0, let us consider a situation. Suppose an event-driven scenario in which the events are detected continuously by the deployed sensors. In such a case, the detected events are sent to the operational recorder. The operational recorder is a database that stores these data, which are optimized further using querying. The amount of data generated depends on the number of events occurring in the environment. Thus, the industrial Internet requires an approach to manage and process the data received by the millions of sensors for perception analysis. In this case, the IIoT can be considered one of the biggest benefactors of big data, thus needing new technologies to manage vast data. Some of the essential features of big data analytics in the current scenario are on-demand self-service, complete network access, method grouping, fast and flexible, and measured services. It must be understood that with the advent of the fourth industrial revolution, the scope of digitization has increased in multiple folds. In such a scenario, the demand is to perform automated operations for the apparent reasons discussed in the chapter. However, the automated processes are aligned with embedding intelligence in machines to learn from their experiences. Effective learning can only be achieved once past experiences can be mapped to the real world, and data analytics play a key role in realizing the same. Many applications around us require such technologies. However, focus must be given to optimizing the performance parameters related to these technologies to gain maximum from their implementation in different domains of the industries. One such domain in the field of smart transportation is discussed in the subsequent section. 3.5 CASE STUDY: FUTURE OF SMART TRANSPORTATION In the 21st century, with the growing number of things with higher communication potentials and the eventual evolution of the fifth-generation concept (5G), heterogeneity in existing networks is being redefined. Many different devices from our day-to-day life are getting interconnected as part of the new era of IoT. Besides, IoT also enables the implementation of intelligent concepts in the transportation system. The past few decades have observed enormous growth in urbanization, which immediately impacts transportation in intra-connection and interconnection among cities. To cope with this, smart and intelligent transportation systems (ITS) (Rhoades and Conrad 2017) have captured the

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spotlight in the transportation research domain. The emerging technologies focus on smart mobility and reducing congestion within the cities. With the budding concept of IoT, today’s world is entering into a new era of mobility named ITS, which is supposed to be an application combining new technologies to guide and manage transportation systems with enhanced safety and efficiency. By integrating the concept of ML with IoT, ITS can reduce direct human interventions during accidents to ensure safety. The emerging concept of the Internet of Vehicles (IoV) (Mollah et al. 2020) has become a key research aspect in recent days due to enormous advancements in the vehicular domain, IoT, cyber-physical systems, and modern satellite communication. With the help of IoT, IoV (Muthuramalingam et al. 2019; Qureshi et al. 2020; Ji et al. 2020) allows the amalgamation of the Internet and vehicles, which incorporates environmental components such as infrastructures, other vehicles, and sensors. It also allows developing a common platform to exchange information within the environment. The general system structure of IoV is shown in Figure 3.6. The concept of IoV flourishes to support the ever-increasing demand for advanced technologies in the transportation domain and keep up the

Figure 3.6 Structure of the Internet of vehicle system.

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Figure 3.7 Vehicle-to-Everything (V2X) communication model of IoV.

increasing need for autonomy in vehicles. These smart vehicles are connected using the Internet to converge the ITS into reality. In addition, IoV is also meant to enable interconnection among autonomous vehicles, traffic infrastructures, and pedestrians to evolve ITS into Vehicle-to-Everything (V2X) models, such as Vehicle-to-Vehicle (V2V), Vehicle-to-Roadside (V2R), Vehicle-to-Infrastructure (V2I), Vehicle-to-Pedestrian (V2P), and Vehicle-to-Sensor (V2S). The main goal of IoV is focused on efficient traffic management, avoidance of accidents, and providing runtime information services. These characteristics are depicted in Figure 3.7. The key technologies behind the implementation of IoV are mainly centered around vehicle intelligence and vehicle networking. Vehicle intelligence focuses on using advanced equipment and technologies such as artificial intelligence, machine learning, computer vision, big data analysis, multiagent-based systems, etc., on achieving the objective of intercommunication between vehicles, people, and infrastructures of information sharing. In contrast, vehicle networking refers to the onboard information services, short-range intercommunication between vehicles, which helps gather data regarding position, navigation, etc. These ensure a wide range of communication among the vehicles in the smart transportation scheme.

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3.5.1 Advantages and applications of IoV in smart transportation The advantages of IoV are mainly twofold. Firstly, as the architecture of IoV is heterogeneous, it allows cooperation with vehicular communication networks and other infrastructures and/or communication networks. Moreover, last but not least, most of the current era communication devices are compatible with the technology of IoV. The increased reliability, upgradation in information processing systems, and use of AI technologies allow vehicles to choose actions autonomously for better performance and ensure the stability of network connectivity. The application area of IoV is multidimensional. The focus of the applicability of the same is different in different countries. Most countries in America are concerned with the safety applications related to smart transportation. In contrast, IoV is inclined to complex road infrastructure and navigation systems in European countries and Japan. Again, China is focused on the driver experience in the smart urban transportation system. The applications of IoV are focused but not limited to, mainly on safety application and service application. The safety applications plan to increase the safety measures of vehicle drivers, passengers on board, and other passers-by. V2X communications are enabled among vehicles and other communication devices to enable information sharing. These ensure safety among traffic entities and allow emergency warnings to avoid collision scenarios. The safety application is categorized into public safety, road information warning system, and intelligent traffic management with this background. The main objective of service applications is to enhance the experience and communication of drivers and passengers of the vehicles. Both can have various service information, like real-time weather updates, dynamic navigation, online news updates, entertainment, etc., by accessing the network itself. Service applications are also meant to provide different diagnostic services regarding the vehicle condition and runtime solutions to reported problems to the users.

3.6 CONCLUSION The realization of Industry 4.0 is not possible without the components and technologies of IoT. Also, the fourth industrial revolution is concerned with embedding intelligence in industrial operations, irrespective of the domains. It helps in the continuous improvement of processes to achieve sustainability. Sustainable development in Industry 4.0 revolves around utilizing technologies such as artificial intelligence, cyber-physical systems, machine learning, and deep learning. These technologies require high-speed Internet to provide real-time service delivery. As a result, a tremendous amount of data

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is generated, which needs computation and communication to achieve the goals of the decision support systems. However, transmitting such a massive amount of data becomes a challenge. Thus, data analytics serves as the key concept to segregate the essential data from non-essential ones for efficient transformation of the industrial processes for profound decision making. A significant amount of research is needed to benefit from amalgamating all the technologies as mentioned earlier in real-world applications. REFERENCES Alam, Mahbubul, Rasmus H Nielsen, and Neeli R Prasad. 2013. “The Evolution of M2M into IoT.” In 2013 First International Black Sea Conference on Communications and Networking (BlackSeaCom), 112–15. Ameri Sianaki, Omid, Ashkan Yousefi, Azadeh Rajabian Tabesh, and Mehregan Mahdavi. 2019. “Machine Learning Applications: The Past and Current Research Trend in Diverse Industries.” Inventions 4 (1): 8. Candanedo, Inés Sittón, Elena Hernández Nieves, Sara Rodríguez González, M. Teresa Santos Martín & Alfonso González Briones. 2018. “Machine Learning Predictive Model for Industry 4.0.” In International Conference on Knowledge Management in Organizations, 501–10. Chandima Ratnayake, R M, and T Markeset. 2012. “Asset Integrity Management for Sustainable Industrial Operations: Measuring the Performance.” International Journal of Sustainable Engineering 5 (2): 145–58. Charniak, Eugene, Christopher K Riesbeck, Drew V McDermott, and James R Meehan. 2014. Artificial Intelligence Programming. Psychology Press. Ekbia, Hamid, Michael Mattioli, Inna Kouper, Gary Arave, Ali Ghazinejad, Timothy Bowman, Venkata Ratandeep Suri, Andrew Tsou, Scott Weingart, and Cassidy R Sugimoto. 2015. “Big Data, Bigger Dilemmas: A Critical Review.” Journal of the Association for Information Science and Technology 66 (8): 1523–45. Frank, Alejandro Germán, Lucas Santos Dalenogare, and Néstor Fabián Ayala. 2019. “Industry 4.0 Technologies: Implementation Patterns in Manufacturing Companies.” International Journal of Production Economics 210: 15–26. Garbie, Ibrahim. 2016. Sustainability in Manufacturing Enterprises: Concepts, Analyses and Assessments for Industry 4.0. Springer. Goodfellow, Ian, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. MIT press. Gupta, Jatinder N D, Guisseppi A Forgionne, and Manuel Mora. 2007. Intelligent Decision-Making Support Systems: Foundations, Applications and Challenges. Springer Science & Business Media. Internet Live Stats. 2020. “Twitter Usage Statistics.” https://www.internetlivestats. com/twitter-statistics/. Ji, Baofeng, Xueru Zhang, Shahid Mumtaz, Congzheng Han, Chunguo Li, Hong Wen, and Dan Wang. 2020. “Survey on the Internet of Vehicles: Network Architectures and Applications.” IEEE Communications Standards Magazine 4(1): 34–41. Kotsiantis, Sotiris B, Ioannis D Zaharakis, and Panayiotis E Pintelas. 2006. “Machine Learning: A Review of Classification and Combining Techniques.” Artificial Intelligence Review 26(3): 159–90.

54  Intelligent Green Communication Network for Internet of Things Kumar, J Sathish, Saurabh Kumar, Meghavi Choksi, and Mukesh A Zaveri. 2020. “Collaborative Data Acquisition and Processing for Post Disaster Management and Surveillance Related Tasks Using UAV-Based IoT Cloud.” International Journal of Ad Hoc and Ubiquitous Computing 34 (4): 216–32. Lasi, Heiner, Peter Fettke, Hans-Georg Kemper, Thomas Feld, and Michael Hoffmann. 2014. “Industry 4.0.” Business & Information Systems Engineering 6 (4): 239–42. LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. 2015. “Deep Learning.” Nature 521 (7553): 436–44. Lee, In, and Kyoochun Lee. 2015. “The Internet of Things (IoT): Applications, Investments, and Challenges for Enterprises.” Business Horizons 58 (4): 431–40. Liang, Fan, Wei Yu, Xing Liu, David Griffith, and Nada Golmie. 2020. “Toward Edge-Based Deep Learning in Industrial Internet of Things.” IEEE Internet of Things Journal 7 (5): 4329–41. Lu, Yang. 2017. “Cyber Physical System (CPS)-Based Industry 4.0: A Survey.” Journal of Industrial Integration and Management 2 (03): 1750014. McCarthy, John. 1989. “Artificial Intelligence, Logic and Formalizing Common Sense.” In Richmond H. Thomason (ed) Philosophical Logic and Artificial Intelligence, 161–90. Springer. Mollah, Muhammad Baqer, Jun Zhao, Dusit Niyato, Yong Liang Guan, Chau Yuen, Sumei Sun, Kwok-Yan Lam, and Leong Hai Koh. 2020. “Blockchain for the Internet of Vehicles towards Intelligent Transportation Systems: A Survey.” IEEE Internet of Things Journal 8 (6): 4157–85. Müller, Vincent C, and Nick Bostrom. 2016. “Future Progress in Artificial Intelligence: A Survey of Expert Opinion.” In Fundamental Issues of Artificial Intelligence, 555–72. Springer. Muthuramalingam, S, A Bharathi, N Gayathri, R Sathiyaraj, B Balamurugan, and others. 2019. “IoT Based Intelligent Transportation System (IoT-ITS) for Global Perspective: A Case Study.” In Internet of Things and Big Data Analytics for Smart Generation, 279–300. Springer. Pandey, Saurabh K, and Mukesh A Zaveri. 2016a. “Hierarchical Tree-Based Optimized Communication for Real Time Event Driven Internet of Things.” In Proceedings of the 9th Annual ACM India Conference, 77–83. Pandey, Saurabh K, and Mukesh A Zaveri. 2016b. “Localization for Collaborative Processing in the Internet of Things Framework.” In Proceedings of the Second International Conference on IoT in Urban Space, 108–10. Patterson, Dan. 1990. Introduction to Artificial Intelligence and Expert Systems. Prentice-Hall, Inc. Qureshi, Kashif Naseer, Sadia Din, Gwanggil Jeon, and Francesco Piccialli. 2020. “Internet of Vehicles: Key Technologies, Network Model, Solutions and Challenges with Future Aspects.” IEEE Transactions on Intelligent Transportation Systems 22 (3): 1777–86. Rhoades, Benjamin B, and James M Conrad. 2017. “A Survey of Alternate Methods and Implementations of an Intelligent Transportation System.” In Proceedings of the IEEE SoutheastCon, 1–8. Sagiroglu, Seref, and Duygu Sinanc. 2013. “Big Data: A Review.” In 2013 International Conference on Collaboration Technologies and Systems (CTS), 42–47.

Enabling sustainable technologies  55 Sergi, Bruno S, Elena G Popkova, Aleksei V Bogoviz, and Tatiana N Litvinova. 2019. Understanding Industry 4.0: AI, the Internet of Things, and the Future of Work. Emerald Group Publishing. Thibaud, Montbel, Huihui Chi, Wei Zhou, and Selwyn Piramuthu. 2018. “Internet of Things (IoT) in High-Risk Environment, Health and Safety (EHS) Industries: A Comprehensive Review.” Decision Support Systems 108: 79–95. Toshiba Digital Solutions Corporation. 2021 “Artificial Intelligence Discovering Values of IoT Data: Analytics towards Deep Learning.” https://www.global. toshiba/ww/company/digitalsolution/articles/tsoul/20.html. Wittenberg, Carsten. 2016. “Human-CPS Interaction-Requirements and HumanMachine Interaction Methods for the Industry 4.0.” IFAC-PapersOnLine 49 (19): 420–25. Xu, Li Da, Wu He, and Shancang Li. 2014. “Internet of Things in Industries: A Survey.” IEEE Transactions on Industrial Informatics 10 (4): 2233–43. Zhou, Keliang, Taigang Liu, and Lifeng Zhou. 2015. “Industry 4.0: Towards Future Industrial Opportunities and Challenges.” In 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), 2147–52.

Chapter 4

IoT deployment What Cloud has to offer? Sanjay T. Singh Sam Higginbottom University of Agriculture, Technology and Sciences

Mahendra Tiwari and Jyoti Mishra University of Allahabad

CONTENTS 4.1 Introduction 57 4.2 Significance of Cloud for IoT deployment 58 4.3 Cloud platforms for IoT deployment 59 4.3.1 Amazon Web Services (AWS) IoT 59 4.3.2 Microsoft Azure IoT 60 4.3.3 Google Cloud IoT Solution 60 4.3.4 IBM Watson IoT 60 4.3.5 Siemens Mindsphere 61 4.3.6 Bosch IoT platform 61 4.4 Application domain 61 4.4.1 Agriculture 61 4.4.2 Smart City 62 4.4.3 Healthcare 63 63 4.4.4 Smart Home and Smart Metering 64 4.4.5 Smart energy and smart grid 4.5 Green Cloud and Fog Computing as enabling technologies for green IoT 64 66 4.6 Green IoT applications 67 4.7 Conclusion References 68 4.1 INTRODUCTION Internet of Things (IoT) is an intelligent network that has expanded the scope of Internet beyond interconnecting computing and computing devices by interconnecting various things such as digital cameras, smart watches, air conditioners, refrigerators, etc., which are equipped with information sensing equipment such as sensors, with the objective to communicate and DOI: 10.1201/9781003371526-4

57

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exchange valuable information (Stankovic, 2014). Not only it has impacted our homes but it has also impacted our businesses. Few instances of IoT in business are smart grids, supply chain management, traffic management, smart cities, etc. IoT accomplishes tasks such as locating, monitoring, and managing things in an intelligent way. Cloud Computing as a computing platform has attained a considerable amount of maturity over the years. Cloud delivers both software and hardware, over the Internet, as SaaS, PaaS, and IaaS services to customers. These services are characterized by on-demand delivery and has pay-asyou-go model (Armbrust et al., 2009). Many organizations have already drifted to the Cloud because of the inherent benefits it provides (Tyagi & Kumar, 2020). Cloud has eliminated the need of buying and setting up inhouse computing infrastructure and also training the IT staff to manage this infrastructure. Cloud is capable of supplying high performance to a vast group of varied users. Large amounts of computing assets and services can be provisioned at lightning fast speed as per the demand of clients. It also offers tools and technologies to effectively formulate policies to ensure overall security. To realize the dream of a fully linked world, several requirements have to be fulfilled. Some of these requirements are global connectivity and accessibility, dynamic resource management for meeting user requirements, and ensuring maximum utilization of resources to facilitate resource sharing while giving users a personalized experience when they use the IoT services (Biswas & Giaffreda, 2014). Apart from this, all of these features have to be highly reliable and must be scalable. Cloud yields interoperability, reliability, scalability, flexibility, availability, and security which IoT is lacking (Tyagi & Kumar, 2020). As a solution to these shortcomings, the merging of Cloud with IoT can be a very fruitful proposition for both these technologies. This can be accomplished by deploying IoT applications on Cloud platforms with help of services provided by Cloud service providers. In this chapter, we will cover some of the key commercial IoT platforms offered by some of the leading Cloud vendors and some applications resulting from the amalgamation of Cloud and IoT. Further, we will focus on how green Cloud and Fog Computing are the enabling technologies for implementing green IoT. We will sum up this chapter by discussing some intelligent applications of green IoT for saving the environment. 4.2 SIGNIFICANCE OF CLOUD FOR I oT DEPLOYMENT As discussed earlier, IoT lacks interoperability, reliability, scalability, flexibility, availability, and security (Tyagi & Kumar, 2020). All of these features are provided by Cloud. Cloud has proven to deliver seamless data flow, processing of data, and gathering of data at a lesser cost, allowing the

IoT deployment  59 Table 4.1  Factors governing the amalgamation of Cloud and IoT Driving factor Computation Storage

Communication

IoT challenges IoT devices are constrained as they have limited data processing power. IoT produces limitless stream of data which allows data exchange, aggregation, and processing, all of which require security. Limitless data are produced by IoT devices which require high-speed connectivity to communicate, store, and manage this data.

Cloud solutions Cloud offers limitless computing power. Cloud offers virtualized, inexpensive, limitless, on-demand storage, and robust security primitives. Cloud offers an inexpensive and systematic solution for gathering, tracking, and controlling data from any location with the help of applications.

Source: Modified from Tyagi and Kumar (2020).

development of prediction algorithms followed by data-driven decisions. Computation, storage, and communication resources are the three variables that govern the decision to combine these two technologies. Although it is beneficial to merge both IoT and Cloud, it is not without unique challenges of its own. Table 4.1 summarizes the unique deployment challenges posed by IoT followed by the solutions provided by Cloud Computing. 4.3  CLOUD PLATFORMS FOR I oT DEPLOYMENT There are various commercial Cloud platforms available for managing large volumes of data spawned by IoT (Amazon Inc., 2018, 2021; Ray et al., 2019) devices. These Cloud platforms for deploying IoT are shown in Figure 4.1.

4.3.1  Amazon Web Services (AWS) IoT AWS IoT is one of the industry-leading IoT-centered Cloud platform which provides various services such as AWS IoT Device Defender and AWS IoT Device Management for connecting and managing IoT devices from the Cloud, etc. Through these services, (i) IoT appliances can be linked to the AWS Cloud without the hassle of managing servers, (ii) audit your IoT set-ups on a routine basis and protect your fleet of IoT devices, and (iii) register, arrange, monitor, and manage your IoT appliances with ease of scalability. Another useful service that AWS IoT offers is AWS IoT analytics for performing analytics on a large volume of data transferred to the Cloud by IoT devices.

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Figure 4.1 Cloud platforms for IoT deployment (own collection).

4.3.2  Microsoft Azure IoT With the Azure IoT platform, Microsoft enables IoT application development features at the network’s edge. It is founded on a cost-per-use model with no time limit for termination. It relies on robust integration of Azure IoT Edge, which has advantages of near real-time response, Cloud agnostic functionality, and lower solution costs. It’s safe and intelligent edge enables AI and analytics services to be installed at the edge.

4.3.3  Google Cloud IoT Solution Google Cloud IoT Solution is a smart platform for IoT development. There are three layers among which the platform is divided: (i) IoT device, (ii) data analytics, and (iii) data consumption. Google’s IoT services are flexible, and they facilitate developing each layer of the IoT system. Device deployment, communication, administration, end-to-end security, full edge and Cloud intelligence, etc., are all possible with this platform’s features (Google Inc., 2021).

4.3.4  IBM Watson IoT This IoT platform facilitates IoT device connection, collection of data from these devices, and translation of the collected data into actionable insights (IBM Watson Workspace, 2018). Clients may use the Watson IoT

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platform and its add-on services to gather data from devices, equipment, and machines; study that data; and gain meaningful insights that assist them in taking better decisions.

4.3.5  Siemens Mindsphere Mindsphere provides (Siemens, 2021) (i) end-to-end solutions for linking appliances, storing data, and designing and operating applications that are secure, (ii) open application programming interfaces (APIs) and native Cloud accessibility which provides extensive range of data exchange possibilities for industrial IoT as a service, and (iii) facility for rapid development of industry-specific IoT systems which are equipped with enhanced analytics capabilities.

4.3.6  Bosch IoT platform Bosch IoT platform comprises services to design and deploy IoT applications fast, with ease, and with security. It is an open-source and openstandards-based IoT platform which offers a great deal of flexibility. It is built on industry standards and open-source initiatives. It offers services related to three main functional areas which are (i) device connectivity with the help of which devices, sensors, and machines can connect to the Internet from anywhere, (ii) data management and analysis which helps in storing, managing, and processing huge volumes of data from connected devices, and (iii) device management which has the capacity to monitor customer’s devices throughout their useful lives (Bosch, 2021).

4.4  APPLICATION DOMAIN Deploying IoT over Cloud, also termed as Cloud IoT, brings a computing paradigm to life which can have many application domains (Figure 4.2) such as agriculture (Badua, 2015), smart city, healthcare, smart home and smart metering, and smart energy and smart grid (Botta et al., 2014), etc.

4.4.1 Agriculture In agriculture, IoT may be extremely beneficial since it can assist in the selection of more profitable crops with low production costs. Monitoring plants, soil, animals, and managing greenhouse environments are all advantages of deploying IoT in agriculture (Badua, 2015). Soil, water, pesticides, and fertilizers are some of the agricultural inputs. The quantity and quality of these inputs necessary for good yield of crops can be controlled with the application of IoT in agriculture. Also, farmers cannot control vendors

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Figure 4.2 Cloud IoT applications (own collection).

on an individual basis in provincial districts. They need vendors who can deliver a variety of services to them. A farmer may supply crops straight to clients using IoT in both geographically small local areas and across larger areas. This progress will lead toward improved agricultural yields, more efficient food product sales, and the manufacture of food items that will benefit the world.

4.4.2  Smart City IoT can offer a generic middleware for smart city (Ballon et al., 2011; Suciu et al., 2013) services allowing them to gather data from various heterogeneous sensing set-ups, as well as IoT mechanisms such as use of RFID sensors with geo-tagging for 3D representations, and uniformity in disseminating information. Several newly suggested solutions indicate that Cloud topologies are to be used to facilitate the identification, connection, and combining of actuators and sensors, resulting in platforms capable of providing and supporting global connectivity and real-time applications (Mitton et al., 2012). A sensor platform equipped with APIs for detecting and actuating along with a Cloud platform supporting autonomous administration, analysis, and governance of big data from numerous, real-world appliances can be the two indispensable components of these frameworks (Suciu et al.,

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2013). This sophisticated service architecture hides the underlying complexities of Cloud infrastructure while addressing challenging public sector Cloud needs including heterogeneity, configurability, interoperability, scalability, high responsiveness, extensibility, and security (Ballon et al., 2011; Suciu et al., 2013). Furthermore, Cloud-based platforms make it hassle-free for third-party developers to build and distribute plugins for IoT that allow any device to connect to the Cloud (Ballon et al., 2011). While cities have similar issues, such as need to efficiently communicate information inside and across cities and the desire for improved cross-border procedures, they lack a common infrastructure and technique for working (Ballon et al., 2011), and this issue might be caused by taking a holistic approach and can be resolved with the help of Cloud IoT (Suciu et al., 2013).

4.4.3 Healthcare Because of telemedicine and ambient-assisted living (Zhang et al., 2010), multimedia technologies and IoT have made their way into the healthcare profession. Smart gadgets, mobile Internet, and Cloud services all contribute to healthcare’s ongoing and systematic innovation, allowing for costeffective, efficient, quick, and first-class medical facilities to be provided everywhere (Kuo, 2011; Gachet et al., 2012). For this, sensor data produced by prevalent healthcare apps must be appropriately handled for subsequent investigation and processing (Doukas & Maglogiannis, 2012). In this case, Cloud adoption abstracts technical specifics, removing the requirement for technical skills or control over technological infrastructure (Alagöz et al., 2010; Löhr et al., 2010), and thus providing a viable approach for efficiently managing healthcare sensor data (Doukas & Maglogiannis, 2012). It also makes mobile devices suitable for delivering, accessing, and communicating health information while on the move (Nkosi & Mekuria, 2010) and also at the same time improving medical data security, availability, and redundancy (Kuo, 2011; Alagöz et al., 2010). Furthermore, it allows for the execution of secure multimedia-based health services over the Cloud, which solves the issue of executing bulky security and multimedia algorithms on devices with inadequate processing capability and tiny batteries (Nkosi & Mekuria, 2010).

4.4.4  Smart Home and Smart Metering In residential environments, where embedded devices of heterogeneous nature enable the automation of day-to-day in-house tasks, IoT has a great deal of potential. In this context, the Cloud is the greatest option for developing versatile apps with fewer lines of code, making home automation a breeze (Kamilaris et al., 2011). The final system must meet three critical conditions (Ye & Huang, 2011) with the aim of allow varying autonomous

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single-family smart homes to access services which can be reused through the Internet. These services are (i) connectivity of in-house network which implies interconnection of each and every smart appliance in smart homes with any other, (ii) remote control equipped with intelligence which means that smart home appliances and services should be managed intelligently from any device irrespective of the geographical location, and (iii) automation, i.e., networked appliances in the home, should perform their tasks by connecting to smart-home-oriented Cloud services. Many smart home applications suggested in various literature use wireless sensor networks to realize smart metering that enables identification of appliances (Chen et al., 2013), intelligent energy management (Chen et al., 2013), air conditioning, lighting, and heating (Han & Lim, 2010).

4.4.5  Smart energy and smart grid In local as well as wide-area heterogeneous contexts, IoT and Cloud can be efficiently combined to enable intelligent control of energy supply and consumption (Botta et al., 2014). As the first scenario, lighting might be delivered just where and when it is absolutely essential by utilizing the data acquired by different types of nodes (Han & Lim, 2010). Networking, sensing, and processing abilities are all available in these nodes, but resources are constrained. As a result, computing duties should be spread appropriately among them or delegated to the Cloud, where more complicated and comprehensive judgments can be made. For the second scenario, by integrating system data on the Cloud, while offering self-healing, interactive operation and involvement of users, best quality of electricity, decentralized production, and response of demand, the issue of energy alternative and compatible use may be handled (Yun & Yuxin, 2010). 4.5 GREEN CLOUD AND FOG COMPUTING AS ENABLING TECHNOLOGIES FOR GREEN I oT The basic functions of the IoT include detecting the environment and collecting data from the environment with the help of sensor devices. The gathered data is communicated to faraway data centers or the Cloud. This massive data exchange across millions of IoT devices creates a high energy demand and leads to increased energy waste as heat. Green IoT aims to reduce IoT device energy usage which ultimately results in a clean and safe environment (Thilakarathne et al., 2022). Green IoT refers to energy-efficient techniques and methods (both hardware as well as software) used by IoT in order to help minimize the greenhouse effects of applications which already exist and services to lessen the greenhouse impact of IoT in itself. To have almost zero or little environmental effect, the whole green IoT

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life cycle should concentrate on design, manufacture, use, and eventually dumping/recycling with a green perspective (Murugesan & Gangadharan, 2012). As we have already discussed IoT can be deployed with the help of Cloud Computing, which is built on virtualization technologies, with the goal of lowering energy usage as compared to having several computers in data centers (Thilakarathne et al., 2022). Major investment is done in infrastructure and administrative systems by Cloud service providers. They do so in order to bill the service users based on how much time they spend working on the service and how much they utilize it. The data centers that host Cloud applications use a lot of energy, which results in significant operating expenses and a lot of CO2 emission (Buyya et al., 2010). Many organizations are migrating their computing services to Cloud because it offers easy access to a large number of resources. Eventually, to meet new service requests and preserve service quality, the Cloud resources must all be scaled up. As a result of this expansion, more servers are deployed leading to more electricity consumption, resulting in greater environmental concerns and CO2 emissions. Hence, optimized use of Cloud resources is of utmost importance to decrease energy usage. To lower energy consumption, equipment that consume lesser energy, latest virtualization techniques, and self-optimized software application must be used. As a result, the goal of green Cloud Computing is to decrease carbon emissions by decreasing energy consumption, as well as to making optimal use of Cloud resources. Hardware and software that decrease energy usage are the two primary classes of solutions for green Cloud Computing. Hardware solutions aim at devising and fabricating equipment that use less energy without sacrificing performance quality. Software solutions, however, aim at making effective software designs that use less energy while maximizing resource use (Beik, 2012). As mentioned earlier, IoT is a collection of millions of IoT devices with data sensing (e.g., sensors) capabilities. This massive collection of IoT devices generates massive amounts of data which is known as ‘Big Data’ which necessitates well-organized and intelligent storage (Al-Fuqaha et al., 2015). Cloud Computing provides for the smart management of this huge amount of data. However, Cloud Computing has some constraints of its own. The most fundamental constraint is associated to Cloud-to-end-device communication, which is established through the Internet and is inappropriate for a huge number of latency-sensitive Cloud applications. Furthermore, Cloud-based software is typically dispersed and made up of several components (Mouradian et al., 2018). As a result, it’s extremely typical to deploy application components separately across several Clouds. However, the overhead generated by communications across multiple Clouds makes the delay much worse. Fog Computing is a computing paradigm that was created to address these issues.

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Fog Computing is a technology that extends the Cloud Computing architecture to the network’s edge, bringing compute, control, and data storage closer to end users (Chiang & Zhang, 2016). It can connect IoT devices with Cloud storage devices. In Fog Computing, services offered by the Cloud are stretched to network edge devices and in this way Fog Computing act as an extension to Cloud technology. Fog Computing paradigm has lower operating expenses and energy usage than Cloud Computing since the fog layer is located nearer to end users and as a result the distance between users and fog devices may be as little as a few hops (Hu et al., 2017; Mahmud et al., 2018). As a result, communication lag in the fog is reduced. Real-time engagement, however, might be a difficult challenge for Cloud technology because of its limitation of high latency, yet Fog Computing can readily fix this problem (Naha et al., 2018). Due to its proximity to end customers, it has the ability to deliver services with superior latency performance than Cloud data centers. As a result, Fog Computing may be the best option for IoT designers when it comes to implementing the green IoT (Al-Fuqaha et al., 2015). 4.6  GREEN I oT APPLICATIONS Because of the breakthroughs in IoT, noteworthy variations in our surroundings have occurred, and some changes may come shortly. However, because of the growth in e-waste, toxic emissions, and energy demand, the cost of improvements might be enormous. Green IoT is expected to have a significant impact on our future lives, leading to a greener world. We shall see a lot of gadgets, machines, sensors, drones, and other things in our everyday lives in the near future that work and interact with each other to fulfill their duties intelligently for a greener environment. As a result, minimizing energy requirements, lowering CO2 emissions, and minimizing environmental hazards have been the focus of green IoT applications. Not only is green IoT assisting other businesses in decreasing greenhouse gas emissions, but it also minimizes the negative influence of IoT on the environment (Alsamhi et al., 2019). Green IoT helps IoT explore new energy sources, is environmentally benign, and reduces the impact that IoT causes to the environment. The diverse applications of green IoT are significant in terms of economic, environmental, and social sustainability, as well as the preservation of natural resources and the improvement of human health. Haseeb et al. (2021) developed an intelligent computing model based on green IoT which is for sustainable cities. In this work, the authors developed a data transportation technique that reduces liability with regard to data security and energy management. The proposed methodology uses deep learning to provide optimum features for data routing that aids sensors to find the shortest pathways to the edge servers. Additionally, they combined

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distributed hashing and chaining techniques that simplified their security solutions that resulted in a more effective computing system. They experimentally showed that their model reduces energy usage by 21%, along with other benefits, as compared to other such solutions. Kaur et al. (2020) in their work proposed a green IoT-enabled energy-efficient framework for monitoring and predicting wildfire. The objectives of their system are (i) using a variety of IoT sensors with the help of which forest covers are monitored for wildfire causing factors, (ii) energy efficiency pertaining to fog nodes which are resource-constrained resulting in longer sensor network lifespan, (iii) during times of high wildfire susceptibility, an emergency alert is generated, (iv) use of self-organized mapping technology to visualize sensitive forest cover in the event of a forest fire, and (v) information communication to disaster management organizations for expected and estimated outcomes. A Precision Agriculture Monitoring System was proposed and implemented by Ali et al. (2018). Their system uses less energy, emits fewer greenhouse gases, and has a user-friendly interface. Using their solution farmers can utilize their cellphones to monitor the differences in their farm’s parameters (weather, water, soil, insect detection, incursion detection, and fire detection) on a regular basis from anywhere as well as at any time. 4.7 CONCLUSION IoT is a very promising paradigm which aims at realizing the dream of a smart and interconnected world in which objects around us are able to connect and converse with each other. In order to deploy IoT, Cloud Computing which is already a mature computing paradigm can be utilized for managing data produced by IoT devices, and subsequently, intelligent services can be offered to the customers. Merging IoT with Cloud diverts the requirements such as storage, computation, and communication to Cloud which will otherwise be required by IoT service providers to implement separately which has its own unique challenges. Furthermore, both paradigms fill in gaps for each other – Cloud providing unlimited storage capacities to IoT and IoT extending the breadth and applicability of Cloud. Integrating both IoT and Cloud brings about many application domains to reality. However, Cloud may not always be the best choice for all the IoT applications, especially the once which are delay sensitive. Hence, Fog Computing is the choice of computing paradigm for such applications. Fog Computing complements Cloud Computing by extending the computing resources to the network’s edge which is closer to the user. This paradigm is more energy efficient as compared to the Cloud Computing. Greening both Cloud Computing and Fog Computing helps and using them in conjunction to each other results in realizing the green IoT which has the potential to save the environment against the evil of greenhouse effect.

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IoT deployment  69 Chen, S. Y., Lai, C. F., Huang, Y. M., & Jeng, Y. L. (2013). Intelligent home-appliance recognition over IoT cloud network. 2013 9th International Wireless Communications and Mobile Computing Conference, IWCMC 2013, 639– 643. https://doi.org/10.1109/IWCMC.2013.6583632 Chiang, M., & Zhang, T. (2016). Fog and IoT: An overview of research opportunities. IEEE Internet of Things Journal, 3(6), 854–864. https://doi.org/10.1109/ JIOT.2016.2584538 Doukas, C., & Maglogiannis, I. (2012). Bringing IoT and cloud computing towards pervasive healthcare. Proceedings - 6th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, IMIS 2012, 922–926. https://doi.org/10.1109/IMIS.2012.26 Gachet, D., De Buenaga, M., Aparicio, F., & Padrón, V. (2012). Integrating internet of things and cloud computing for health services provisioning: The virtual cloud carer project. Proceedings - 6th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, IMIS 2012, 918–921. https://doi.org/10.1109/IMIS.2012.25 Google Inc. (2021). Google IoT Platform Solutions for IoT Project Development — Digiteum. https://www.digiteum.com/google-iot-platform-iot-development/ Han, D. M., & Lim, J. H. (2010). Smart home energy management system using IEEE 802.15.4 and Zigbee. IEEE Transactions on Consumer Electronics, 56(3), 1403–1410. https://doi.org/10.1109/TCE.2010.5606276 Haseeb, K., Ud Din, I., Almogren, A., Ahmed, I., & Guizani, M. (2021). Intelligent and secure edge-enabled computing model for sustainable cities using green internet of things. Sustainable Cities and Society, 68(December 2020). https:// doi.org/10.1016/j.scs.2021.102779 Hu, P., Dhelim, S., Ning, H., & Qiu, T. (2017). Author’s accepted manuscript technologies, applications and open issues reference. Journal of Network and Computer Applications. https://doi.org/10.1016/j.jnca.2017.09.002 IBM Watson Workspace. (2018). Service Description IBM Watson Workspace. 04, 1–8. https://internetofthings.ibmcloud.com/ Kamilaris, A., Pitsillides, A., & Trifa, V. (2011). The Smart Home meets the Web of Things. International Journal of Ad Hoc and Ubiquitous Computing, 7(3), 145–154. https://doi.org/10.1504/IJAHUC.2011.040115 Kaur, H., Sood, S. K., & Bhatia, M. (2020). Cloud-assisted green IoT-enabled comprehensive framework for wildfire monitoring. Cluster Computing, 23(2), 1149–1162. https://doi.org/10.1007/s10586-019-02981-7 Kuo, A. M. H. (2011). Opportunities and challenges of cloud computing to improve health care services. Journal of Medical Internet Research, 13(3), e1867. https:// doi.org/10.2196/jmir.1867 Löhr, H., Sadeghi, A. R., & Winandy, M. (2010). Securing the e-health cloud. IHI’10 - Proceedings of the 1st ACM International Health Informatics Symposium, 220–229. https://doi.org/10.1145/1882992.1883024 Mahmud, R., Koch, F. L., & Buyya, R. (2018). Cloud-fog interoperability in IoTenabled healthcare solutions. ACM International Conference Proceeding Series. https://doi.org/10.1145/3154273.3154347 Mitton, N., Papavassiliou, S., Puliafito, A., & Trivedi, K. S. (2012). Combining cloud and sensors in a smart city environment. Eurasip Journal on Wireless Communications and Networking, 2012(1). https://doi.org/10.1186/16871499-2012-247

70  Intelligent Green Communication Network for Internet of Things Mouradian, C., Naboulsi, D., Yangui, S., Glitho, R. H., Morrow, M. J., & Polakos, P. A. (2018). A comprehensive survey on Fog computing: State-of-the-art and research challenges. IEEE Communications Surveys and Tutorials, 20(1), 416– 464. https://doi.org/10.1109/COMST.2017.2771153 Murugesan, S., & Gangadharan, G. R. (2012). Harnessing Green It: Principles and Practices. Wiley. https://doi.org/10.1002/9781118305393 Naha, R. K., Garg, S., Georgakopoulos, D., Jayaraman, P. P., Gao, L., Xiang, Y., & Ranjan, R. (2018). Fog computing: Survey of trends, architectures, requirements, and research directions. IEEE Access, 6(c), 47980–48009. https://doi. org/10.1109/ACCESS.2018.2866491 Nkosi, M. T., & Mekuria, F. (2010). Cloud computing for enhanced mobile health applications. Proceedings - 2nd IEEE International Conference on Cloud Computing Technology and Science, CloudCom 2010, 629–633. https://doi. org/10.1109/CloudCom.2010.31 Ray, P. P., Dash, D., & De, D. (2019). Edge computing for Internet of Things: A survey, e-healthcare case study and future direction. Journal of Network and Computer Applications, 140, 1–22. https://doi.org/10.1016/j.jnca.2019.05.005 Siemens. (2021). Siemens | MindSphere | Apps. https://siemens.mindsphere.io/en/ docs/ Stankovic, J. A. (2014). Research directions for the internet of things. IEEE Internet of Things Journal, 1(1), 3–9. https://doi.org/10.1109/JIOT.2014.2312291 Suciu, G., Vulpe, A., Halunga, S., Fratu, O., Todoran, G., & Suciu, V. (2013). Smart cities built on resilient cloud computing and secure internet of things. Proceedings - 19th International Conference on Control Systems and Computer Science, CSCS 2013, 513–518. https://doi.org/10.1109/CSCS.2013.58 Thilakarathne, N. N., Kagita, M. K., & Priyashan, W. D. M. (2022). Green Internet of Things: The Next Generation Energy Efficient Internet of Things. In: Iyer, B., Ghosh, D., Balas, V.E. (eds.), Applied Information Processing Systems. Advances in Intelligent Systems and Computing, vol. 1354, 391–402. Springer. https://doi.org/10.1007/978-981-16-2008-9_38 Tyagi, H., & Kumar, R. (2020). Cloud computing for IoT. Internet of Things (IoT): Concepts andApplications,25–41.https://doi.org/10.1007/978-3-030-37468-6_2 Ye, X., & Huang, J. (2011). A framework for cloud-based smart home. Proceedings of 2011 International Conference on Computer Science and Network Technology, ICCSNT 2011, 2, 894–897. https://doi.org/10.1109/ICCSNT.2011.6182105 Yun, M., & Yuxin, B. (2010). Research on the architecture and key technology of Internet of Things (IoT) applied on smart grid. 2010 International Conference on Advances in Energy Engineering, ICAEE 2010, 69–72. https://doi. org/10.1109/ICAEE.2010.5557611 Zhang, Q., Cheng, L., & Boutaba, R. (2010). Cloud computing: State-of-the-art and research challenges. Journal of Internet Services and Applications, 1(1), 7–18. https://doi.org/10.1007/s13174-010-0007-6

Chapter 5

A new optimal protocol for Green IoT communication Saeed Doostali Mahallat Institute of Higher Education

Behzad Soleimani Neysiani Islamic Azad University

CONTENTS 71 5.1 Introduction 5.2  Network coding 72 5.3  Topology control 75 5.4 Combined network coding and topology control 76 5.5  Considering the receiving energy 80 5.6 Results 82 5.7 Conclusion 84 References 84

5.1 INTRODUCTION As new technology in the communication field, the Internet of Things (IoT) provides a convenient lifestyle for humans, reducing labor and eliminating the chances of human errors. IoT provides the capability of sending data via communication networks such as the Internet. Any creature, including objects, animals, and even humans, can participate in the communication. Based on IoT technology, connected devices can make precise and informed decisions through machine learning and neural networks (Höller et al., 2014). Consider a traffic camera as an intelligent apparatus. This apparatus can monitor the events occurring in a street, such as traffic congestion and accidents, weather conditions, and communicate the aggregated data to a joint gateway. Other cameras also send their data to this gateway, and finally the gateway sends the data further to a City-wide Traffic Monitoring System (CTMS). The cameras can send their data using various methods such as pull or push (Soleimani Neysiani et al., 2012) in a specific topology (Farhadian et al., 2021). Suppose that the Municipal Corporation decides to close a particular road for special issues. This situation can cause traffic congestion on the streets around this road. Hence, these streets become bottlenecks in the city. This decision is sent to CTMS. Let CTMS be an DOI: 10.1201/9781003371526-5

71

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intelligent traffic system, so it can quickly learn and predict patterns in traffic using a learning mechanism such as machine learning. This system could also send live instructions for drivers via smart devices to choose more suitable routes. IoT is an example that creates a self-dependent networked system that leverages real-time control. However, IoT can be used in many applications, including smart homes and cities, self-driven vehicles, agriculture, and telehealth (Doostali et al., 2020). We expect IoT to manage billions of connected objects intelligently everyday. According to this equipment, a wide range of wireless sensor networks (WSNs) will be adopted in various application environments. Note that most WSNs are limited in energy resources, storage, processing capability, and transmission range; hence, in deploying a WSN, these constraints must be satisfied. For example, in the Industrial IoT, there are hundreds of interconnected sensors connected with industrial applications such as manufacturing and energy management. The healthcare system including hospitals, clinics, ambulances, and other related systems have many equipment and tools which need to be managed efficiently (Soleimani Neysiani et al., 2021) and various data such as texts, images, and videos quickly are processed (Soleimani Neysiani & Homayoun, 2022). As another example, in the agriculture production area, IoT-based WSNs monitor the yields condition and helps to automate agriculture precisely using various sensors. The sensors can help farmers through intelligent decisions to improve production yields. However, in these applications, energy consumption is a significant challenge. This need for more intelligent energy solutions doubles when we know that worldwide energy consumption will grow by 40% over the next 25 years (Lueth, 2020). There are more solutions to reduce energy consumption in IoT. One of the most important solutions is to find the optimal or near-optimal topology for the generated network when the positions of IoT nodes are fixed (for example, in the Industrial IoT, smart traffic control system, and so on). However, reducing the number of packets sent to the network or aggregating them together can significantly reduce energy consumption, as the most promising techniques, topology control and network coding, have recently attracted much attention in reducing energy consumption (Deng et al., 2007; Doostali & Babamir, 2020; Jiang et al., 2015; Ka Hung et al., 2010; Khalily-Dermany, 2021a, b; Khalily-Dermany & Nadjafi-Arani, 2019; Khalily-Dermany et al., 2015, 2017, 2019; Khalily-Dermany & Sharifian, 2015). We briefly introduce these two techniques and then present some approaches which combine them to provide an energy-efficient method. 5.2  NETWORK CODING Network coding as a forwarding method can significantly improve the multicast communication throughput in a WSN. Two main phases for applying this method in a network are sub-graph selection and coding-solution-construction.

A new optimal protocol for Green IoT communication  73

In the first phase, network coding selects the best sub-graph to specify the links and the amount of bandwidth used. In the second phase, it defines the operation function for all selected sub-graphs’ nodes. In the coding phase, the flows passed from the links can be defined by a finite algebraic field, Fq . Coding considers an alphabet of size, q = 2m, which means that the information is sent from the sources in packets of m bits. The network nodes treat the m bits as one symbol of the finite field Fq and processed using operations over Fq . Thus, the finite field elements determine which packets must be used and how to be mixed and then pass through the links (Ho & Lun, 2008; Médard & Sprintson, 2012). More precisely, network coding uses the broadcast nature of channels to forward many packets in a transmission. As a result, the total energy consumption is reduced in comparison to the traditional store-and-forward approach (Khalily-Dermany & Nadjafi-Arani, 2019; Xie et al., 2015). Since the memory and computation ability of the middle nodes in an IoT-based WSN are very limited, because there are overheads due to the complexity of linear encoding and decoding. Thus, it is more appropriate to use the XORs operation (Katti et al., 2008; Kok et al., 2015). Sub-graph selection is the problem of determining the coding sub-graph to use, and the techniques used in this phase differ significantly from coding techniques. In the sub-graph selection, we use techniques from networking theory, while in coding, we generally use information theory and coding theory techniques. Sub-graph selection is a network resource allocation problem: There is a limited resource that we wish to allocate to coded packets in such a way as to achieve specific communication objectives. The communication objective, which optimizes the routing’s cost from a source to the destination nodes, is to establish a set of (unicast or multicast) connections at particular, given flow rates subject to flow conservation law and capacity limitation (Ho & Lun, 2008; Khalily-Dermany & Nadjafi-Arani, 2019). Flow conservation law ensures that the sum of incoming flow equals the sum of outgoing flow for all intermediate nodes from the sources and the destinations, and the capacity limitation states that the amount of flow through an edge does not exceed its capacity. For more clarity, the advantage of network coding is illustrated by a simple example presented in Figure 5.1, where nodes A and B want to exchange packets pa and pb via a relay node R. A simple store-and-forward approach requires four transmissions: (i) A sends pa to R, (ii) which forwards it to B,

Figure 5.1  Network coding approach (Doostali & Babamir, 2020).

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and (iii) B sends pb to R, (iv), which forwards it to A. By considering network coding, A and B send their packets to R, which XORs the received packets (i.e., pa and pb ) and broadcasts the generated packet to nodes A and B. Node A can decode the received packet by applying XOR with its packet (i.e., pa ⊕ pa ⊕ pb = 0 ⊕ pb = pb ). Similarly, B can obtain pa . However, the maximum amount of information sent in a communication network from one node to the destination nodes is called maximum transmissible flow (MTF). The method of obtaining this flow is expressed as a classical theorem called max flow-min cut in the graph theory (Fragouli & Soljanin, 2008). This theory shows that finding MTF between the source node and the destination node is equivalent to finding the minimum cut from the source node to the destination. Assume that the edges have a sending capacity in a directed graph; MTF is the maximum flow that can be sent from one node to another so that a flow less than or equal to its capacity passes through each edge. The source and sink (or destination) nodes are at the beginning and end of the flow, respectively. To remove or cut an edge, a cost equal to the capacity of that edge must be paid. A cut in a graph involves removing the edges of a graph to divide the graph into two unconnected graphs. There is only the source node or destination node in each of these graphs. The cutting cost equals the sum of the costs of removing the graph edges. The cut that has the lowest cost is called the minimum cut. Thus, the max flow-min cut theory is equivalent to finding the minimum cut. For example, suppose that in the graph of Figure 5.2, the capacity of all edges is equal to 1. By removing the edges s → 1 and s → 2, the graph is divided into two unconnected graphs so that the right sub-graph has the source node while the left sub-graph has the destination node, where s is the source and the t1 and t2 are the destinations. The cost of this cut is equal to the capacity of the removed edges (i.e., 2). Similarly, the cost of cutting edges 1 → t1, 3 → 4, and 2 → t2 , which divides the graph into two unconnected graphs, will be equal to 3. By applying all possible cuts, as mentioned

Figure 5.2  Achieve MTF (Ho & Lun, 2008).

A new optimal protocol for Green IoT communication  75

above, the minimum cutting cost equals two and is related to cutting edges s → 1 and s → 2. Therefore, MTF in this graph will be equal to 2. Note that MTF is not achieved using conventional storing and sending information. In the conventional store-and-forward methods described above, data flows are assumed to be water flows into the transmission tubes, while in network coding, the intermediate nodes can be processed the received packets. For example, in Figure 5.2, MTF between s and two destination nodes t1 and t2 is 2, meaning that in the maximum situation, two packets can be sent to nodes t1 and t2 at the same time. Using the usual store-and-forward methods, this MTF cannot be achieved, while using network coding it will be possible, because if node s sends packets b1 and b2 to nodes 1 and 2, then node 3 can XOR the received packets and send them to node 4; next node 4 receives this packet and sends it to destinations t1 and t2 . Finally, they receive the data packets using the stated above regarding decoding incoming packets (Ho & Lun, 2008). Destination t1 receives packet b1 from node 1, and packet b2 by XOR-ing the packet received from node 4 and b1. 5.3  TOPOLOGY CONTROL Topology control is a method for managing network configuration to reduce energy consumption and prolong the network lifetime. For managing a configuration, topology control changes the network parameters such as transmission ranges and sensor roles (Karl & Willig, 2007; Shang et al., 2014). In general, two different categories are considered for topology control approaches: (i) hierarchical approaches and (ii) flat approaches. In the first category, approaches select some nodes as the infrastructure for routing data to other nodes, while in the second one, approaches consider the same functionalities for all sensors (Santi, 2005; Vien et al., 2015). Hierarchical approaches cluster nodes and specify one node as a cluster head for each cluster. The cluster heads collect data from cluster members and transmit the aggregated data to the sinks. In contrast, flat approaches consider the transmission range of sensor nodes as the most crucial feature in minimizing the energy consumption of a network. Researchers are always looking for the best value for the transmission range. The work in this area can be considered in two categories: (i) finding the optimal or near-optimal transmission range for all sensors (called critical transmission range) and (ii) finding the optimal transmission range for each sensor individually, which is suitable for heterogeneous topologies, especially in IoT-based network. Figure 5.3 shows the difference between the maximum and restricted transmission ranges of the two topologies. Using the transmission range more petite than the maximum transmission range will reduce the energy consumption of sending and receiving data and reduce network traffic.

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Figure 5.3  Topology with maximum transmission range versus restricted transmission range (Labrador & Wightman, 2009).

5.4 COMBINED NETWORK CODING AND TOPOLOGY CONTROL Despite the benefits of network coding, the performance of this technique depends on network topology. The topology affects the efficiency of network coding if destination nodes cannot receive enough linearly independent network coding-based packets to obtain the original packets (Shang et al., 2014). It has been proved that synchronously using network coding and topology control in a network can provide more advantages, especially for reducing energy consumption. Providing an optimization problem is one of the most important solutions for finding optimal topology when the network coding is also utilized (Ho & Lun, 2008). To this end, the network coding problem is formulated as (5.1), where ai demonstrates the cost per unit rate of node si , and fi is the amount of flows that should be transmitted by si in practice which is called actual multicast flow rate (Ho & Lun, 2008).

minimize   g ( f ) =

∑a f (5.1) i i

si ∈S

The optimal sub-graph can be obtained by minimizing the function in (1). The following constraints restrict the mentioned optimization as (5.2)–(5.4):

∑x( ) − ∑ x( ) = σ ( ) (5.2) t ij

s j ∈N i

t ji

s j ∈Mi

i

t

A new optimal protocol for Green IoT communication  77



fi = Maxs j ∈Ni xij(t ) (5.3)



0 ≤ xij(t ) ≤ cimax (5.4)

where xij(t ) is the virtual multicast flow rate from node si to node s j for the destination node t . This rate demonstrates the amount of flow that the node si transmits and node s j receives. In these equations N i shows the neighbors of si and Mi is the set of nodes whose node si is their neighbor. Suppose that a connection of traffic δ from a source node such as s to a destination node such as t is established. Constraint (5.2) shows the flow conservation law. This law states that the sum of entering flow is equal to the sum of exiting flow for all intermediate nodes (i.e., σ i(t ) = 0 ), and σ i(t ) is equal to δ for the source node because the source node creates the traffic and σ i(t ) is equal to −δ for the destination node because this node receives the traffic. This network is a flow network in which the source nodes generate the flow, and the destination nodes consume it, while the intermediate nodes pass the flows. Constrain (5.3) demonstrates the relation between actual and virtual flows. Virtual flow is the amount of flow that node si sends to one of its neighbors, such as s j. So there is a virtual flow for each neighbor of this node. But the actual flow that must be considered for si is equal to the maximum amount of virtual flows that this node sends to its neighbors to reach destination t . Finally, constraint (5.4) states that the amount of flow on each node is nonnegative and bounded by its maximum capacity (i.e., cimax). As mentioned, determining the best transmission range for each node is a primary topology control method. For transmitting data, the energy consumption of the node si is equal to (5.5) in which η1 and η2 are fixed values​ specified by the channel and transceiver design characteristics, respectively (Deng et al., 2007).

Etx ( ri ) = η1 + η2riα (5.5)

The parameter ri indicates the transmission range of si and α indicates the path loss exponent. The value of α depends on the diffusion medium and usually is in the range of 2–4. The transmission range plays a critical role in this relation. Therefore, the optimization function mentioned above (i.e., g(f )) will change as follows to show the role of the transmission range where r and f are both decision variables (Khalily-Dermany et al., 2017).

minimize   g ( f ,  r ) =

∑r f (5.6) α i i

si ∈S

78  Intelligent Green Communication Network for Internet of Things

In fact, in objective function g ( f ,  r ), we consider the main element of the energy consumption (i.e., ri ) as the cost per unit rate. This function is subject to constraints (5.2)–(5.4), and the following constraints

{

}



xij(t ) ≤ Max ( ri − dij ) ,0 × B (5.7)



0 ≤ ri ≤ r max (5.8)

The first constraint (Eq. 5.7) states that if the distance between two nodes si and s j is greater than the transmission range ri , then node si cannot send any flow to s j. In other words, the amount of virtual flow between them will be zero. Otherwise, this virtual flow can have any positive value. Equation (5.8) indicates that the transmission range is always positive and less than the maximum transmission range. Let us call optimization problems (5.6), (5.2), (5.3), (5.4), (5.7), (5.8) Optimization models for Topology control in Network-coding-based-WSNs (OToNec, which we use this name in the Results section). In mathematical concepts, if an objective function is presented for a problem and its constraints are convex, the program is called convex. Note that there are more general methods from convex optimization that can efficiently and reliably solve even significant problems. Hence, converting a non-convex problem to a convex can be very useful. However, this case can be distinguished by evaluating the second derivative matrix called the Hessian Matrix (HM) while the objective function is twice differentiable. According to a famous theorem, we know that an objective function such as g is convex if and only if the HM of this function is positive semi-definite (Bertsekas, 1998). The optimization (5.6), (5.2–5.4), and (5.7 and 5.8) is non-convex based on the following proof. To do so, consider the following derivatives as (5.9):

∂g ∂g = α riα −1fi ,  = riα (5.9) ∂ri ∂ fi ∂2 g ∂2 g ∂2 g = α (α − 1) riα −2 fi ,  = α riα −1 ,  2 = 0 2 ∂ ri ∂ri ∂ fi ∂ fi

So the second derivative matrix H by order 2n can be obtained as follows (5.10):

M [ i,  j ] = {α (α − 1) riα −2 fi  1 ≤ i = j ≤ n  0 o.w.  (5.10) N [ i,  j ] = {α riα −1  1 ≤ i = j ≤ n  0 o.w. 

A new optimal protocol for Green IoT communication  79

H = [ M   N   N  0  ] Consider an arbitrary vector v = ( x1 , …,  xn ,  y1 , …,  yn ). Clearly vT Hv is depended on v and α (5.11). n

vT Hv =

∑α (α − 1) r

α −2 2 i i i

f x + 2α riα −1xi yi (5.11)

i =1

Thus, matrix H is not semi-definite, and objective function g ( f ,  r ) is nonconvex. Now, the objective function is converted to a convex problem. Suppose that both variables fi and ri are positive. Hence new variables fi′ = ln ( fi ) and ri′ == ln ( ri ) can be defined and function g ( f ,  r ) will be reformulated as (5.12): minimize   h ( f ′,  r ′ ) =



∑ Exp (α r′+ f ′ ) (5.12) i

i

si ∈S

(

)

The HM of  Exp α ri′+ fi ′  concludes that the objective function is convex,   because the summation of convex functions is convex, so the h ( f ′,  r ′ ) is convex. After the same replacements in the mentioned constraints, we have (5.13–5.18):

∑x( ) − ∑ x( ) − σ ( ) = 0 (5.13) t ij

s j ∈N i

t ji

i

t

s j ∈Mi



fi′ = lnln    Maxs j ∈Ni xij(t )    (5.14)



− xij(t ) ≤ 0 (5.15)



xij(t ) − cimax ≤ 0 (5.16)



xij(t ) − Max Exp ( ri′) − dij × B, 0 ≤ 0 (5.17)



ri′− lnln   r max   ≤ 0 (5.18)

{(

)

(

)

}

80  Intelligent Green Communication Network for Internet of Things

Since the objective function is convex and the linear constraints, the optimization problem is convex (Khalily-Dermany et al., 2017). 5.5  CONSIDERING THE RECEIVING ENERGY Considering the energy required to receive data and the energy required to send can bring the optimization problem (proposed in Section 5.4) closer to real-world IoT networks. The receiving energy depends on the distance between the sender and the receiver so that the closer nodes receive a stronger signal than the farther nodes; thus, the closer node needs less energy to detect, receive, and encode the data. Friis relation illustrates this problem as follows (5.19) (Karl & Willig, 2007):

 d0 ( j )  Pijrcvd ( dij ) = Pijrcvd d0 ( j ) ×   (5.19)  dij 

(

α

)

This equation shows the power consumed by s j to receive data from si placed in distance dij which is greater than the reference distance d0 ( j ). In Eq. (5.19), the variable Pijrcvd d0 ( j ) represents the reference power used by s j to receive information from a node placed in the reference distance. The amount of path loss, which is defined as the ratio of the transmitted power to received power, is shown in equation (5.20) which PL d0 ( j ) represents the rate of path loss for the reference distance d0 ( j ).

(

)

(



PL ( dij ) =

Pijrcvd ( dij ) Ptx

)

α

 dij  = PL d0 ( j ) ×   (5.20)  d0 ( j ) 

(

)

This equation shows that when the distance of an arbitrary receiver, such as s j and the transmitter si is being increased, then the power consumption to receive data will also increase. For more clarity, consider α = 2 and three nodes si , s j, and sk which are placed at distances dij = d and dik = 2d . If si sends a signal and s j consumes power equal to p to receive it, then according to Eq. (5.20), sk must consume power equal to 4 p for receiving this signal. Therefore, it can be said that the energy consumed to receive information is directly corresponding to dijα (Khalily-Dermany et al., 2019). However, when a signal is sent by a node such as si , all the neighbors of this node receive it. Therefore, the energy consumption will be related to

∑d

s j ∈N i

α ij

where

N i denotes the neighbors of si . Hence, for considering the receiving energy, we reformulate the objective function (5.1) as follows (5.21) subject to constraints (5.2), (5.3), (5.4), (5.7), and (5.8) (Khalily-Dermany et al., 2019).

A new optimal protocol for Green IoT communication  81



minimize   g ( r ,  f ) =

   riα + dijα  × fi (5.21)   si ∈S  s j ∈N i 

∑

∑

The optimization problem in IoT is like a scheduling between nodes for data sending and receiving based on many parameters such as intervals, congestion control limitations, energy consumptions, and other crucial requirements. The scheduling is a common problem in computer networks and distributed systems such as cloud computing (Soltani Soulegan et al., 2021) and IoT systems. Scheduling can be defined for single or multiple objective functions in which there are many solutions to solve that specially using meta-heuristic algorithms (Soltani et al., 2016). Let us call optimization problems (5.21), (5.2), (5.3), (5.4), (5.7), (5.8) as Genetic Algorithm based Topology Control for Network coding-based multicast WSNs (GA_ ToCNec, which we use this name in the Results section). Similar to the argument made in Section 5.4, we can show that this optimization is nonconvex. To this end, consider the following derivatives (5.22):

∑

∂g ∂g = α riα −1fi ,  = riα + dijα ∂ri ∂ fi s ∈N j



i

∂2 g ∂2 g ∂2 g α −2 α −1 = − r f = r = 0, (5.22) α α 1 ,  α ,  ( ) i i i ∂2 ri ∂ri ∂ fi ∂ri ∂rj ∂2 g ∂2 g = 0,  2 = 0 ∂ri ∂ f j ∂ fi

So the second derivative matrix H by order 2n can be obtained as follows (5.23):

{ N [ i,  j ] = {α r

M [ i,  j ] = α (α − 1) riα −2 fi  1 ≤ i = j ≤ n  0 o.w. 

α −1 i

 1 ≤ i = j ≤ n  0 o.w. 

(5.23)

H = [ M   N   N  0  ] Since the matrix H is precisely the same as the matrix of Eq. (5.10), so for an arbitrary vector v = ( x1 , …,  xn ,  y1 , …,  yn ), vT Hv is dependent on v and α (see Eq. (5.11)). Therefore, matrix H is not semi-definite, and objective function g ( f ,  r ) is non-convex. Contrary to what was stated in Section 5.4, it is not possible to convert the mentioned optimization to a convex problem. Thus, a new algorithm is necessary to solve this optimization with less time complexity like genetic algorithm (GA) (Khalily-Dermany et al., 2019).

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Let Gtotal = ( S,  Ltotal ) be a graph model for the network when all nodes use maximum transmission range, where S is the set of nodes and Ltotal is the set of links. In GA, a population of N pop chromosomes with S gene is considered where each chromosome represents a spanning sub-graph G ⊆ Gtotal, and each gene of a chromosome indicates the transmitting range of the corresponding node. Note that the transmitting range is a non-negative random value between 0 and the maximum transmission range that satisfies the constraint (5.8), while other constraints are considered when the fitness function is calculated. In each iteration of GA, the best sub-graphs are selected for the next generation based on the fitness function. For calculating the suitability of each chromosome, we apply the optimization problem (5.21), (5.2), (5.3), (5.4), (5.7). Since the transmission ranges of all nodes are fixed by a chromosome such as chk, the optimization problem of this chromosome converts to a linear programming problem. Hence, we can solve it effectively with various available solvers. Note that if one of the problem constraints for a chromosome is violated, we assign a large number to its fitness. The new population is obtained by changing selected chromosomes through crossover and mutation. In the crossover phase, N pop × Pc chromosomes are selected as parents where Pc is the crossover rate. The single-point crossover can be used to create new offspring from the selected parents. GA splits two selected parents at a random point and then swaps the first part of these parents to produce two new offspring (see Figure 5.4). The mutation operation selects one gene of N pop × Pm chromosomes randomly where Pm is the mutation rate. Since the value of each gene shows the transmission range of the corresponding node, GA replaced its value with a random value between 0 and the maximum transmission range. However, GA continues generating a new population until a certain number of generations have to be run or the fitness of the best chromosome is converged to an optimal solution. After the termination of GA, the best sub-graph is obtained (for more details about this algorithm, we refer the readers to see (KhalilyDermany et al., 2019). 5.6 RESULTS This section compares the two mentioned approaches, i.e., OToNec and GA_ToCNec. To this end, suppose n sensor nodes are randomly deployed in an area of size 100m × 100m , and four sinks are located at the corner of

Figure 5.4  Single-point crossover (Khalily-Dermany et al., 2019).

A new optimal protocol for Green IoT communication  83

this area. Moreover, at a constant bit rate, each sensor node generates 256bit UDP packets, then it sends this packet to all the sink nodes when the maximum transmission range is 10m,  and the maximum capacity of each sensor node is 10kbps. Note that all transmissions are considered collisionfree. A uniform distribution from 7 to 15 J is utilized for generating the random initial energy for all sensor nodes. Furthermore, rates 0.8 and 0.08 are considered for crossover and mutation, respectively (Khalily-Dermany et al., 2019). First, the energy consumption of these two methods is compared. Table 5.1 shows the consumed energy for transmission in OToNec and GA_ ToCNec for a network with 30, 40, and 50 sensor nodes. As expected, when more nodes are deployed in the network, the consumed energy for transmission is raised in all approaches. However, GA_ToCNec consumes more energy than OToNec since GA_ToCNec uses the genetic algorithm; thus, it behaves randomly and may return a non-optimal solution for the optimization problem, while OToNec solves the proposed optimization problem and generates the optimal sub-graph. Hence, the energy consumption of OToNec is less than GA_ToCNec. Table 5.2 demonstrates the pernode energy consumption. The results state that, contrary to Table 5.1, when the number of sensor nodes is increased in a network, the per-node energy consumption is decreased. The results also show that the per-node energy consumption of GA_ToCNec is greater than OToNec. The time of finding the optimal solution for an optimization problem has a critical role, especially in a network with more sensor nodes. Hence in Table 5.3, the required time for generating the network topology in OToNec and GA_ToCNec is compared. GA_ToCNec can be solved faster than OToNec, since GA_ToCNec uses a genetic algorithm to convert its optimization problem to linear ones. The results show that the required Table 5.1  Energy consumption for transmission Energy consumption #Sensor nodes 30 40 50

OToNec 2.71 3.53 4.16

GA_ToCNec 2.92 3.97 5.20

Table 5.2  Per-node energy consumption Energy consumption #Sensor nodes 30 40 50

OToNec 0.09633 0.09525 0.09500

GA_ToCNec 0.10263 0.10235 0.10220

84  Intelligent Green Communication Network for Internet of Things Table 5.3  R  equired time for obtaining the network topology Time (ms) #Sensor nodes 30 40 50

OToNec 8201 15118 19093

GA_ToCNec 270 370 460

time for obtaining the optimal solution in OToNec increases exponentially as the number of sensor nodes increases. Therefore, it cannot be used for networks with many sensor nodes. 5.7 CONCLUSION This study reviews IoT protocol concepts, including network coding and topology control, as two crucial requirements for improving energy consumption in a green IoT environment. A new method called optimal topology and network coding (OToNec) is proposed, which improves energy consumption by about 7%–20% for small and large networks, respectively. In addition, the scenario should be examined for future work. Besides, other bottlenecks can be analyzed for optimization. REFERENCES Bertsekas, D. (1998). Network Optimization: Continuous and Discrete Models. Athena Scientific. https://books.google.com/books?id=qUUxEAAAQBAJ Deng, J., Han, Y. S., Chen, P.-N., & Varshney, P. K. (2007). Optimal transmission range for wireless ad hoc networks based on energy efficiency. IEEE Transactions on Communications, 55(9), 1772–1782. https://doi.org/10.1109/ TCOMM.2007.904395 Doostali, S., & Babamir, S. M. (2020). An energy efficient cluster head selection approach for performance improvement in network-coding-based wireless sensor networks with multiple sinks. Computer Communications, 164, 188–200. https://doi.org/10.1016/j.comcom.2020.10.014 Doostali, S., Babamir, S. M., Dezfoli, M. S., & Soleimani Neysiani, B. (2020). IoTBased Model in Smart Urban Traffic Control: Graph theory and Genetic Algorithm. 2020 11th International Conference on Information and Knowledge Technology (IKT), Tehran, Iran. Farhadian, N., Barekatain, B., Haroni, M., & Soleimani Neysiani, B. (2021). An intelligent novel hybrid live video streaming method in mesh-based peer-topeer networks. Nashriyyah -i Muhandisi -i Barq va Muhandisi -i Kampyutar -i Iran, 18(4), 261–275. http://rimag.ricest.ac.ir/fa/Article/28458

A new optimal protocol for Green IoT communication  85 Fragouli, C., & Soljanin, E. (2008). Network Coding Applications. Now Publishers Inc. https://books.google.com/books?id=toKNlD7TM4oC Ho, T., & Lun, D. (2008). Network Coding: An Introduction. Cambridge University Press. https://books.google.com/books?id=rKr9B3vn3a4C Höller, J., Tsiatsis, V., Mulligan, C., Karnouskos, S., Avesand, S., & Boyle, D. (2014). From Machine-to-Machine to the Internet of Things: Introduction to a New Age of Intelligence. Academic Press. https://doi.org/10.1016/ B978-0-12-407684-6.00025-5 Jiang, D., Xu, Z., Li, W., & Chen, Z. (2015). Network coding-based energy-efficient multicast routing algorithm for multi-hop wireless networks. Journal of Systems and Software, 104, 152–165. https://doi.org/10.1016/j.jss.2015.03.006 Ka Hung, H., Yalin Evren, S., Dongning, G., & Berry, R. A. (2010, 17–19 March 2010). The maximum stable broadcast throughput for wireless line networks with network coding and topology control. 44th Annual Conference on Information Sciences and Systems (CISS 2010), Princeton, NJ, USA. Karl, H., & Willig, A. (2007). Protocols and Architectures for Wireless Sensor Networks. John Wiley & Sons. https://books.google.com/books?id=170R-1aZsQYC Katti, S., Rahul, H., Hu, W., Katabi, D., Médard, M., & Crowcroft, J. (2008). XORs in the Air: Practical Wireless Network Coding. IEEE/ACM Transactions on networking, 16(3), 497–510. https://doi.org/10.1109/TNET.2008.923722 Khalily-Dermany, M. (2021a). A decentralized algorithm to combine topology control with network coding. Journal of Parallel and Distributed Computing, 149, 174–185. https://doi.org/10.1016/j.jpdc.2020.12.001 Khalily-Dermany, M. (2021b). Transmission power assignment in network-codingbased-multicast-wireless-sensor networks. Computer Networks, 196, 108203. https://doi.org/10.1016/j.comnet.2021.108203 Khalily-Dermany, M., & Nadjafi-Arani, M. J. (2019). Mathematical aspects in combining network coding with transmission range adjustment. IEEE Communications Letters, 23(9), 1568–1571. https://doi.org/10.1109/ LCOMM.2019.2924625 Khalily-Dermany, M., Nadjafi-Arani, M. J., & Doostali, S. (2019). Combining topology control and network coding to optimize lifetime in wireless-sensor networks. Computer Networks, 162, 106859. https://doi.org/https://doi. org/10.1016/j.comnet.2019.106859 Khalily Dermany, M., Sabaei, M., & Shamsi, M. (2015). Topology control in network–coding–based–multicast wireless sensor networks. International Journal of Sensor Networks (IJSNet), 17(2), 93–104. https://doi.org/10.1504/ IJSNET.2015.067864 Khalily-Dermany, M., Shamsi, M., & Nadjafi-Arani, M. J. (2017). A convex optimization model for topology control in network-coding-based-wirelesssensor networks. Ad Hoc Networks, 59, 1–11. https://doi.org/10.1016/j. adhoc.2016.12.010 Khalily-Dermany, M., & Sharifian, S. (2015). Effect of various topology control mechanisms on maximum information flow in wireless sensor networks. The Smart Computing Review (SmartCR), 5(1), 10–18. https://doi.org/10.6029/ smartcr.2015.01.002

86  Intelligent Green Communication Network for Internet of Things Kok, G.-X., Chow, C.-O., & Ishii, H. (2015). Improving network coding in wireless ad hoc networks. Ad Hoc Networks, 33(Complete), 16–34. https://doi. org/10.1016/j.adhoc.2015.04.002 Labrador, M. A., & Wightman, P. M. (2009). Topology Control in Wireless Sensor Networks: With a Companion Simulation Tool for Teaching and Research. Springer Science & Business Media. https://books.google.com/ books?id=JUcBbzo9L7YC Lueth, K. L. (2020, July 8, 2020). Top 10 IoT applications in 2020. Retrieved 4/10/2022 from https://iot-analytics.com/top-10-iot-applications-in-2020/?cv=1 Médard, M., & Sprintson, A. (2012). Network Coding: Fundamentals and Applications. Academic Press. Elsevier. Santi, P. (2005). Topology control in wireless ad hoc and sensor networks. ACM Computing Surveys (CSUR), 37(2), 164–194. https://doi. org/10.1145/1089733.1089736 Shang, T., Huang, F. H., Mao, K. F., & Liu, J. W. (2014). The effect of hexagonal grid topology on wireless communication networks based on network coding. International Journal of Communication Systems, 27(9), 1319–1337. https:// doi.org/10.1002/dac.2782 Soleimani Neysiani, B., & Homayoun, H. (2022). Medical text and image processing: Applications, methods, issues, and challenges. In O. P. Jena, B. Bhushan, & U. Kose (Eds.), Machine Learning and Deep Learning in Medical Data Analytics and Healthcare Applications (1st ed., Vol. 1, pp. 65–90). CRC Press. https://doi. org/10.1201/9781003226147-4 Soleimani Neysiani, B., Neematbakhsh, N., Barekatain, B., Maarof, M. A., & Zamanifar, K. (2012). Understanding pull-based method efficiency in peer-topeer live video streaming over mesh networks. Journal of Basic and Applied Scientific Research, 11(2), 11626. Soleimani Neysiani, B., Soltani, N., Doostali, S., Shiralizadeh Dezfoli, M., Aminoroaya, Z., & Khoda Karami, M. (2021). Data science in health informatics. In M. Mehta, K. Passi, I. Chatterjee, & R. Patel (Eds.), Knowledge Modelling and Big Data Analytics in Healthcare: Advances and Applications (1st ed., Vol. 1, pp. 299–340). CRC Press. https://doi.org/10.1201/9781003142751-20 Soltani, N., Barekatain, B., & Soleimani Neysiani, B. (2016). Job scheduling based on single and multi objective meta-heuristic algorithms in Cloud computing: A survey. International Conference on Information Technology, Communications and Telecommunications (irICT), Tehran, Iran. Soltani Soulegan, N., Barekatain, B., & Soleimani Neysiani, B. (2021). MTC: Minimizing time and cost of cloud task scheduling based on customers and providers needs using genetic algorithm. International Journal of Intelligent Systems and Applications (IJISA), 13(2), 38–51. https://doi.org/10.5815/ ijisa.2021.02.03 Vien, Q.-T., Tu, W., Nguyen, H. X., & Trestian, R. (2015). Cross-layer topology design for network coding based wireless multicasting. Computer Networks, 88, 27–39. https://doi.org/10.1016/j.comnet.2015.06.005 Xie, L. F., Chong, P. H., Ho, I. W., & Guan, Y. L. (2015). A survey of inter-flow network coding in wireless mesh networks with unicast traffic. Computer Networks, 91, 738–751. https://doi.org/10.1016/j.comnet.2015.08.044

Chapter 6

Pharmaceutical supply chain management system using Blockchain and IoT technology Gargi Chauhan, Bhargav Patel, Nikunj Prajapati, Shailendra Raj, Shlok Gadre, and Saurabh Patel Sardar Vallabhbhai Patel Institute of Technology

CONTENTS 6.1 Introduction 87 6.2 Theoretical Framework and Design Consideration of proposed system 88 6.2.1  System diagram 88 6.3  Project Component 92 6.3.1 Blockchain 92 6.3.2 IoT 92 6.3.3  End user 94 6.4 Summary 95 References 95 6.1 INTRODUCTION In 2008, Satoshi Nakamoto (or a group of persons) came up with the idea for the first blockchain. Nakamoto made significant improvements to the concept by employing a Hash cash-like mechanism to timestamp blocks without requiring them to be signed by a trusted entity and by establishing a difficulty parameter to control the rate at which blocks are added to the chain. The design was implemented by Nakamoto as a core component of the cryptocurrency Bitcoin the following year, where it acts as the public ledger for all network transactions. Since then, blockchain technology is being integrated into multiple areas. The primary use of blockchains today is as a distributed ledger for cryptocurrencies, most notably Bitcoin. There are a few operational products maturing from proof of concept by late 2016. Businesses have been thus far reluctant to place blockchain at the core of the business structure (Benchoufi et al. 2017). Blockchain in smart contracts and the supply chain is the best use in logistics as it helps in tracking, transparency and also reduces lot of paperwork. As per our research, the supply of pharmaceuticals is very sensitive as they deal with people’s health (WHO, 2010). Fraud in this chain can result in danger for one’s life (Haq & Esuka, 2018). We are making the DOI: 10.1201/9781003371526-6

87

88  Intelligent Green Communication Network for Internet of Things

whole pharmaceutical chain decentralized where all the information will be stored on a decentralized ledger which will be visible to all people. We will use blockchain to store all information and IoT to fetch location and temperature of the container when the medicines are being shipped between supply chains (Barik, 2019). In view of the above discussion, this chapter introduces the pharmaceutical supply chain management system with the integration of blockchain technology and the IoT technology. Section 6.2 introduces prior work, theoretical framework and design consideration which includes the system diagram and overall system flow. Section 6.3 introduces to project components such as blockchain component, IoT component and end user module which contains the track down to the supply chain. Section 6.4 summarizes the overall idea presented in this chapter regarding innovation. Section 6.5 contains different references which help in detailed discussion of the points discussed in this chapter. 6.2 THEORETICAL FRAMEWORK AND DESIGN CONSIDERATION OF PROPOSED SYSTEM

6.2.1  System diagram In Figure 6.1, the entire possible node that may participate in the supply chain is mentioned. The supply chain will begin as the manufacturer creates a medicine and initiates the transportation. The medicine will be transported to the warehouse from where it will be shipped to different locations across the country or globe as per requirement. The medicines may be shipped via ships, train or cargo plane. At each level of transportation from one place to next in the chain, the record is stored on the distributed ledger.

Figure 6.1 System block design.

Pharmaceutical supply chain management system  89

While transporting in containers, the IoT sensors will notify all the nodes that the location and temperature are taken care of as per the requirement. Finally, the medicine will reach the retailer from the local warehouse. The consumer will buy it from there and check if the medicine is actually manufactured and shipped by the company mentioned or not, which actually provides trust and product’s integrity. Figure 6.2 illustrates the graphical representation of the entire system from the initial creation or manufacturing point to the final product purchase point. The entire process will be initiated from the manufacturing company, where the productions of the different pharma products take

Figure 6.2  Graphical representation of the system.

90  Intelligent Green Communication Network for Internet of Things

place. This initial point will be responsible for deciding the critical bounds and integrity details of the products. At that particular moment, the manufacturer is the prime concern point for instantiating the entire chain process with the help of smart contracts. After that the product will be shipped to the distributor via any medium of shipping via cargo planes, containers and ships. At the distributors’ end, the verification process takes place, and if product validation is positive, then the arriving details will be added to the existing chain. After product arrived is at this node now, same integrity and validation two-way authentication process leads to the transfer of batch between distributor to supplier and supplier to hospitals and medical stores. The system is divided into three parts: manufacturer, transportation and consumer. Manufacturer is responsible for creating all medicines using authenticated raw materials. He/she will also monitor the transportation in the chain. The nodes that work in transportation are responsible to transport medicines from one location to destination and also following all the constraints that should be followed as per the medicines. They have to maintain the temperature criteria and keep medicines from getting damaged. After the medicines reach the retailer shop, the consumer can purchase and easily check the authenticity of the medicine. The consumer can see all the transactions made in the chain. Figure 6.3 represents the system flow process of an entire system. It contains the flow of the entire system including the role of the manufacturer, transporter and consumer. If user is manufacturer and once manufacturer log in to the system successfully, then he/she can create a medicine and add relative information into the smart contracts. Manufacturer is the entity who generates and affixes the QR code on each and every pharma products. After all documentation initialization with product will be done, manufacturer is responsible for the deployment of the same contract on the network. After the deployment step, manufacturer can monitor the shipment process throughout the chain until the product is shipped to the next partial destination (Hasan et al., 2019). If user does not come under the manufacturer group, two more subcategories are there: user is either transporter or consumer. If user is transporter and once transporter enrolls into the system successfully, he/she can validate the details regarding the products which he/she going to supply between two nodes. Once the details are verified successfully, then transporter can provide the unique signature to the actual details and generate new transaction to the existing supply chain. Transporter can also monitor the location of the shipment until it reaches to the final or partial destination node, and once product reaches the destination node, the job of the transporting node will be effectively labeled as successful. Consumer is the main entity, to whom entire system will be centered. Whenever the consumer reaches out to consume or purchase any pharma

Pharmaceutical supply chain management system  91

Figure 6.3 Flowchart of system.

products from a shop or retail shop and if the consumer wants to check the integrity of products, then they just need to scan the QR code labeled on the products. Once they scan the QR code, all details regarding that product will be shown in their device. All details regarding the product such as manufacturing date and time, expiry date, temperature details (if applicable), all intermediate nodes through which the product reaches the hands of the consumer. If user does not belong to either the manufacturer or transporter group, then he/she is considered as the consumer. Remaining flowchart represents the system flow process of the consumer interaction with the blockchain whole supply chain track records of the particular product with just one east scan using smart phones. As our proposed system is having IoT module for the product tracking in pharmaceutical supply chain, we can also enable the device to auto-calculate the minimum travel distance between source to destination while transportation. This will reduce the transportation time; thus, it provides

92  Intelligent Green Communication Network for Internet of Things

significant benefits in terms of green environment by reducing the petroldiesel consumption during the supply of products. Also, blockchain can help in the counterfeit medicines rejection by using proof of concept algorithm. It will provide transparency to consumers. By using latest blockchain algorithms and green smart contracts, we can minimize the computation time of verifying each transactions, thus less energy consumed by the machines. IoT can be also used in the inventory management of medicines to store, manage and update all inventory data without using papers. The AI-enabled IoT will automatically forecast the demand of all medicines, and we can do next production of latest medicines; according to that, it will reduce the wastage of medicines and all resources for making it. So, implementation of IoT and blockchain can efficiently sustain the global ecosystem and provide their participation in eco-friendly green world. 6.3  PROJECT COMPONENT

6.3.1 Blockchain We have chosen the Ethereum blockchain platform (Ethereum, 2017) for our project. Ethereum is a public blockchain network (Jeff, 2015a, b) which provides the functionalities of the smart contract for our decentralized application. We have created a smart contract using the remix. Ethereum IDE which provides the environment to write smart contract compiles them and also supports to deploy them on the real blockchain network. The following functions are included in the smart contract. (1) createMedicine()—which can be only invoked by the manufacturer. (2) transactMedicine()—which will be invoked every time there happens a transaction in the chain. All the transaction information will be stored as a log on the blockchain network. The logs cannot be modified, so once it is deployed on the network, no one can manipulate it. The blockchain is connected to the website using ReactJS framework (Aggarwal, 2018). We are using web3js package provided by the JavaScript that enables us to interact with the blockchain network (Lee, 2019). We have designed our frontend using bootstrap (Cochran, 2012). For testing purpose in the blockchain network, we are currently using ganache which is a local network (Mohanty, 2018) and Ropsten which is an actual Ethereum test network.

6.3.2 I oT The IoT modules concluded with Raspberry Pi 4 Computer Model B 1-GB RAM, DHT11 temperature and humidity sensor (Barik, 2019), Neo 6M GPS tracker. Sensor will connect to the Raspberry Pi with the help of jumper wires. Sensor data will be stored on the ThingSpeak (Pasha, 2016) over the

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internet network, which is an open IoT platform for cloud data collection with MATLAB analysis on the channel stored data. Figure 6.4 represents the basic circuit diagram of the Raspberry Pi and DHT11 sensor. It represents a simple connection between the Raspberry Pi and DHT11 sensor in which power supply (vcc(+)) pin 1 is connected to pin 4 (5 V power supply), data supply(GPILO4) pin 2 is connected to pin 7 (GPIO), and GND pin 3 is connected to pin 20 (ground). Figure 6.5 represents the basic circuit diagram of the Raspberry Pi and Neo 6M GPS sensor. It represents a simple connection between the Raspberry Pi and Neo 6M GPS sensor in which, power supply (vcc(+)) pin 1 is connected to pin 2 (5 V power supply), transmit data (TXD) pin 2 is connected to pin 10, and GND pin 4 is connected to pin 6 (ground).While the above-mentioned connections of Figures 6.4 and 6.5 are for graphical representations only and might differ to hardware being used. With the help of the DHT11 and Neo 6M GPS sensor, humidity, temperature and location data are measured and sent over the internet network to the ThingSpeak private channel for cloud storage with the help of rest API key. We need to create an account to the ThingSpeak and create a channel with related field types for data storage. Once done using the read/write

Figure 6.4 Raspberry Pi and DHT11 sensor connection.

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Figure 6.5 PI and Neo 6M GPS sensor connection.

API key, one can communicate with the data anytime, anywhere manner. The data would be sent to the cloud storage in time intervals of 10 seconds. Whenever the pharma product reached at the intermediate nodes or destination, data would be collected from the ThingSpeak in the JSON format, and the further temperature inaccuracy during the shipment process would be taken care at the point of transaction entry in blockchain (Panarello et al., 2018). Using the latitude and longitude details, they can keep track of the product while on move.

6.3.3  End user With the help of QR code scan, the consumer will get information on which consumer can put trust regarding product integrity and satisfaction that the product is the product which it claims to be. For this purpose, we would prefer mobile applications. We will aim to do such task either by the android application development (Bocek et al., 2017) or by the react native one page mobile application development tools (Eisenman, 2016). We lead to the development of android in Microsoft Windows with minimum 4 GB RAM (8 GB recommended). Two gigabytes of available disk space minimum (4 GB recommended) with 1.5 GB for android SDK and emulator system user. The end user requirement would be android handset with minimum version 4 or higher with available internet access on handset. The consumer can easily scan QR code and fetch all the details (Bocek et al., 2017) from scratch to like manufacturing details, creation of the medicine, temperature and timestamp of entire supply chain process and all on their handset within a minute so that he/she can verify the product and purchase it without any doubt in mind.

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6.4 SUMMARY Blockchain technology has the unrealized promise to help improve the health supply chain, but further study, evaluation and alignment with policy mechanism are needed. The pharmaceutical supply chain management system combines with the IoT module to provide the real-time state acquisition and time-based automatic temperature and location tracker. In our system blockchain technology provides the immutable trusted data storage without access to data modification which provides data transparency and helps in enhancing the security level of pharma product supply chain and also prevents the counterfeit drugs/medicines to reach to the hands of customers or for consumption of those products. End user interacts with the help of the Easy QR code scan with the basic android mobile phones for evaluating the product integrity and maintaining trust for better environment of pharmaceutical engagement between them. REFERENCES Aggarwal S. (2018). Modern web-development using ReactJS. International Journal of Recent Research Aspects, 5(1), 133–137. Barik L. (2019). IoT based temperature and humidity controlling using Arduino and Raspberry Pi. International Journal of Advanced Computer Science and Applications, 10(9), 494–502. Benchoufi M., Porcher R., & Ravaud P. (2017). Blockchain protocols in clinical trials: Transparency and traceability of consent. F1000 Research Ltd. Bocek T., Rodrigues B. B., Strasser T., & Stiller B. (2017). Blockchains everywhere - A use-case of blockchains in the pharma supply-chain. International Symposium on Integrated Network and Service Management (IM), 772–777. Cochran D. (2012). Twitter Bootstrap Web Development How-To. Packt Publishing Ltd., London, UK. Eisenman B. (2016, April 25). Writing Cross-Platform Apps with React Native. www. infoq.com/articles/react-native-introduction. Ethereum. (2017, Nov 14). www.ethereum.org. Haq, I., & Esuka, O. M. (2018). Blockchain Technology in Pharmaceutical Industry to Prevent Counterfeit Drugs. International School of Software Wuhan University, Wuhan, China. Hasan H., AlHadhrami E., AlDhaheri S., Salah K., & Jayaraman R.(2019). Smart contract-based approach for efficient shipment management. Computers & Industrial Engineering, 136, 149–159. Jeff G. (2015a). Public versus Private Blockchains—Part 1: Permissioned Blockchains [white paper]. Jeff G. (2015b). Public versus Private Blockchain—Part 2: Permissionless Blockchains [white paper]. Lee, W. M. (2019). Using the web3. js APIs. In Beginning Ethereum Smart Contracts Programming, 169–198. Apress, Berkeley, CA.

96  Intelligent Green Communication Network for Internet of Things Mohanty D. (2018). Deploying Smart Contracts. In: Ethereum for Architects and Developers. Apress, Berkeley, CA, 105–138. Panarello A., Tapas N., Merlino G., Longo F., & Puliafito A. (2018). Blockchain and IoT integration: A systematic survey. Sensors, 18(8), 2575. Pasha S. (2016). Thingspeak based sensing and monitoring system for IoT with Matlab analysis. International Journal of New Technology and Research (IJNTR), 2(6), 19–23. Remix Ethereum. remix.ethereum.org WHO. (2010). Growing threat from counterfeit medicines. Bulletin of the World Health Organization, 88(4), 247–248.

Chapter 7

Intelligent green communication network for IoT applications M. Sugacini, N.R. Gayathri, P.N.M. Kamalika, and G. Nantha Kumar Sri Venkateswara College of Engineering

CONTENTS 7.1  7.2  7.3  7.4  7.5 

Smart Cities 97 Smart Healthcare 99 Smart Buildings 101 Smart Education 103 Smart Data Center 106 7.5.1 Five basic IoT applications for server farm foundation boards 107 7.6  Smart Parking System 108 7.7  Smart Transport System 110 7.8  Smart Power Supply 111 7.9  Smart Automobile 112 7.10  Smart Industry and Automation 114 7.11  Smart Food and Supply Chain 115 7.12  Smart Agriculture 116 7.13  Smart Resource Management 118 7.14  Smart Waste Management 119 7.15  Smart Water Management System 121 References 122 7.1  SMART CITIES Internet of Things (IoT) has arisen as a promising innovation to work with the progress towards a computerized future and smart communities. Varieties of difficulties and issues connected with IoT have emerged, including intelligent-based framework display and green communication, smart capacity demonstration, smart cities, smart automation, the appearance of IoT frameworks and smart applications functions. It’s critical to give smart answers for these issues and difficulties, by using brilliant gadgets, new methods and new advancements created as an after effect of IoT-based intelligent frameworks and green communications. On account of the new advances in smart IoT frameworks and green communication innovations,

DOI: 10.1201/9781003371526-7

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Figure 7.1 Intelligent system.

IoT is moving towards IoT-based intelligent systems (IoT-BIS) as shown in Figure 7.1. The IoT-BIS frameworks have added too many fields, including IoT-wellbeing, AI-IoT, cutting-edge remote frameworks for IoT and IoT industry and applications. Digital city (Angelo Castellani et al., 2014) uses IoT devices such as associated sensors, lights and meters to collect and analyse information. Smart communities then, at that point, use this information to further develop the framework, public utilities and administrations. IoT is significant for each city. It’s anticipated that more than 60% of the planet’s populace will live in inner city areas by 2030. Large populaces request enormous assets. Occupants will require admittance to water, effective transportation that does not harm the ecosystem, clean air, and viable disinfection and waste administration. With shrewd utilization of resources needed for a savvy city, along with IoT innovation, the urban areas of tomorrow will actually

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want to fulfill the needs of their inhabitants in a compelling and productive way. Connected advancements and enormous information can make brilliant arrangements. The test will create, send, work and stay aware of physical gadgets, programming and organization parts that offer flexibility, security, low power and negligible cost to manage improvement and handiness of the arrangement. As metropolitan regions use more cuttingedge technologies, they become more vulnerable against cyber attacks. A cutting-edge attack on a power organization could really hurt a city and its tenants. Moral requests around the use of individual information, similar to facial affirmation data, moreover surface in a splendid city environment. Obligation and client following furthermore become issues, as client volume augmentations and more people access sensitive data. Joining refined security into the entire data adventure from the very edge over flexible associations across the colossal wired web establishment, the cloud is crucial in keeping insightful metropolitan regions secure. Smart city arrangement designers, planners, integrators and specialist co-ops can advance frameworks with these capacities provided that they influence driving edge cell, Wi-Fi and Bluetooth modules and simple to oversee cell availability plans, information coordination and equip board memberships (Lynch and Law, 2019, Jin et al., 2014). This advancement will permit associations to further develop products and guarantee security while making more astute, greener, reasonable urban communities around the world. Areas where top priorities for a smart city are interconnected transport, traffic planning, sensor integrated headlamp, weather monitoring, air quality/pollution monitoring, smart metering – water, water quality monitoring and many more. The fate of our urban areas is interconnected with the eventual fate of IoT. As regional authorities open the maximum capacity of metropolitan information stages, AI, savvy gadgets and interconnectivity, the requirement for IoT will develop dramatically. This will prompt proficient critical thinking, shrewd portability, maintainability and more. Quite possibly, the most intriguing ways that IoT can help future metropolitan regions is by reducing the prerequisite for private vehicles. With the presence of driverless vehicles, capable public vehicles can be made open to everyone soon, filled by IoT advancement. The vehicles and transports of things to come will really need to work using data imparted by street furniture or streetlights, conveying a useful and predictable traffic stream.

7.2  SMART HEALTHCARE A healthcare system is one that empowers patients and specialists to speak with one another and remotely trade data observed, gathered and analyzed from patients’ day-by-day exercises through the IoT (Al-Mahmud et al., 2020, Ananthula & Vippalapalli 2016, Baker et al., 2017). Smart medical

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services can be characterized as a combination of patients and specialists onto a typical stage for wise wellbeing checks by investigating everyday human activities. Internet of Things (IoT)-enabled empowerment has made remote observations in clinical areas possible, delivering the likelihood to keep patients secured and strong, and drawing in specialists to convey expert opinions. It has moreover extended patient responsibility and satisfaction, as collaborations with experts have become more direct and more capable. Additionally, remote sensing of a patient’s progress helps in decreasing the length of crisis center stay and hinders readmissions. IoT altogether influences reducing clinical consideration costs basically and further creating treatment outcomes. IoT is in actuality changing the clinical benefits industry by reexamining the space of devices and people relationships in conveying clinical consideration plans. IoT has applications in clinical areas that benefit patients, families, specialists, crisis centers and insurance organizations. As medical care advancements are propelling, there is an expanded commitment and cognizance of customers with regard to their wellbeing. The current medical services’ ecosystems are not furnished with technological advances that can work on persistent consideration by refreshing them with continuous patient data and permitting them to go to proactive lengths for therapy. Our IoT answer for the medical care area permits emergency clinics to further develop the quality while zeroing in on by and large use decrease. Patient’s health data: Patient’s health data information is wellbeingrelated data produced or reported by patients or overseers. Patient’s health data includes information relating to a patient’s wellbeing or therapy history, way of life decisions and side effects in addition to other things that are either revealed by patients or gathered from their wearables as well as Internet-empowered clinical gadgets. Chronic disease management: Chronic diseases such as diabetes, heart conditions, disease, weight, joint inflammation and stroke among others are turning out to be exceptionally common. It is significant for individuals experiencing these infections to continually deal with them during their visits to specialists and IoT-based gadgets that can prove to be useful for them. Wearable as well as different gadgets such as glucometers, heart screens and asthma devices among others can assist patients in monitoring their wellbeing. Additionally, the Internet-empowered gadgets save their wellbeing-related information on the cloud, which can then be obtained by clinical experts. Remote patient monitoring: Remote patient monitoring is an aid for individuals living in childcare centers to gather patient wellbeing information, which can be remotely investigated by clinical mentors and experts. This is a successful approach to watching a patient’s wellbeing and proposing course adjustments in the event that a specific treatment ends up being ineffectual. A portion of the different benefits of observing distant patients is diagnosing intense ailments before they get excessively convoluted and recognizing drug inebriation in patients who are getting therapy for cardiovascular issues through digitalis medications.

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Preventive measures: Utilization of Internet of Things in clinical care benefits industry can moreover be significant for healthy people who are not encountering contaminations and who do not wish to hinder issues later on. Prosperity-related data can be obtained by anyone reliably, which can be granted to clinical specialists. This can help in providing proof of even a minor issue and hinder illnesses over an extended time. Short-term care: Momentary consideration can be given to individuals who have been released from the clinic after a medical procedure or subsequent to getting therapy for an intense disease. This kind of care disposes the requirement of visiting the emergency clinic during the recuperation stage, permitting patients to get quality medical services inside the solaces. Home-based care: There is no chance that every senior citizen might have a caretaker at their house. Therefore, healthcare analyzers help unhealthy people to be in their homes and to monitor their health by using IoT devices. By monitoring patients’ health, they can instantly attend to the ones who require it. mHealth – Making healthcare more mobile: The term mHealth is short for portable wellbeing. This term has been characterized by the WHO as “a part of eHealth.” The Global Observatory of eHealth (GOe) has defined mHealth as “clinical and general wellbeing practice upheld by cell phones, patient checking gadgets, individual advanced colleagues (PDAs) and other remote gadgets.” Mobile telephones and different gadgets are utilized to help patients and further develop medical care. Other than utilizing cell phones to make decisions and send instant messages, mHealth likewise incorporates more intricate highlights and applications, for example, general bundle radio assistance (GPRS), third and fourth era versatile media communications (3G and 4G frameworks), GPS and Bluetooth innovation. AAL – Tackling challenges of an aging society: As we move towards the future, the sky’s the limit and there will be an increasingly large proportion of elderly worldwide. Savvy wellbeing solutions must be applied to medical services for more seasoned grown-ups too. Encompassing Assisted Living, in short AAL, is one new methodology that targets in assisting more established individuals live in their homes as autonomously as could be expected. IGI Global characterizes AAL as “a generally new ICT pattern to install canny items in the climate to help individuals (for the most part more seasoned grown-ups) in living freely and checked.” 7.3  SMART BUILDINGS Figure 7.2 shows the splendid construction of a design plan that uses motorized cycles to normally control the design’s assignments including warming, ventilation, cooling, lighting, security and various systems. A splendid construction (Jia et al., 2018, Nugur et al., 2018) uses sensors, actuators and microchips to assemble data and regulate it as demonstrated by business’

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Figure 7.2 Smart building.

abilities and organizations. This system helps owners, executives and office bosses to further develop resolute quality and execution, which diminishes energy use, progresses how spaces are used and restricts the natural impact of structures. New constructions, or more prepared structures that have been changed over to splendid designs, are persistently advancing. They are living creatures that can be associated with quick and adaptable software. The crucial importance in astute level constructions is that it assists

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inhabitants’ lives with lighting, warm comfort, air quality, genuine security, sanitization and more at lower expenses and makes a regular impact than structures that are not connected. Making an adroit structure, or making a design splendid, begins by interfacing with focus systems, for instance, lighting, power meters, water meters, siphons, warming, cautions and chiller plants with sensors and control frameworks. At a further evolved stage, even lifts, access structures and hiding can end up being significant for the system. There is no single set of standards that makes up for what a keen design is, yet what they all offer for all expectations and intentions is a mix. Various new constructions have “wise” advancements and are related and responsive to a splendid power grid. Creating or changing a design into an adroit structure is valuable for both the owner and the affiliations working within. These benefits range from energy savings to convenience gains to supportability. IoT has a wide range of possibilities to robotize numerous parts of buildings, making benefits from natural proficiency to cost administration. Rather than a solitary “killer application” for user experience, we are beginning to see a blend of utilization cases. These use-cases influence IoT, sensors and availability to empower customization of spaces in workplaces and gathering rooms in view of inhabitants’ levels and tenant inclinations, effective versatility all through the structure, and help inhabitants in understanding the area and finding their way – all controllable by portable stages. In particular, they are fit for prescient familiarity with individual necessities. This computerization relies upon joining with the Building Management System (BMS), which can remotely handle data from different sensors and control building capacities. Huge business structures use IT applications to interface different BMSs that work freely during an activity, empowering them to share data and upgrade structure execution. Savvy building capacities has more scope for advances, every one of which can be utilized autonomously or related to others to infer much more advantages. Smart building approach can improve the energy lifetime, enhance smart structure and avoid wasting of resources. 7.4  SMART EDUCATION Educational guidance maybe the most flexible and fruitful in terms of using IoT gadgets to make tutoring more agreeable, instinctive and accessible to all. The IoT contraptions give students easy access as shown in Figure 7.3 to all that from learning materials to correspondence stations to incredible game plans, and they engage teachers to measure the learning progress of students in certifiable time. Speaking of guidance, the COVID-19 pandemic has highlighted the key presence of informative sources in an adequate manner. IoT basically enables making necessary adjustments to a customary procedure and converting it to cutting edge with a couple of additional benefits and extended efficiency. This can be used for showing all subjects

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Figure 7.3 Smart education.

from vernaculars to math to showing sensible capacities such as clinical sciences with the use of plans and movement to chip away at a predominant appreciation of the point. Notwithstanding these wise investment devices, sheets, facilitated ready systems in schools, examination checking mechanical assemblies, cameras, school locks, everything can move from the genuine world to the central structure-based control world a with robotization.

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IoT is enabling tutoring to end up being effectively suitable and in unambiguous cases beat the constraints of the foundations. Distance learning: IoT-based frameworks have a component of putting away and planning information in an application structure with unique programming and as a sign-in element of sites that empowers anybody from anyplace getting to that with a client id and a secret phrase which can be given by the organization to their distance learning students. This can help each and every individual who can’t be a piece of a genuine instructive organization yet need to seek after its instructive course. Live classes, prerecorded classes, online clock-based evaluation questions, and following of time spent on the entryway can all assist in making an extensive methodology for distance students. Safety in premises: Most schools miss the mark on the framework to recognize warnings for burglary, misuse, rape and different wrongdoings and don’t have a legitimate alternate course of action on account of a calamity or crisis. IoT can help in tackling such issues at a huge level, on account of any unbearable movement that gets observed on the camera; it very well may be promptly dealt with because of an organization framework that empowers the camera recording to be shown at different screens in the premises. On account of any fire or short out, IoT-based sensors can enact alerts with the specific region of the issue so there is less issue and risk in settling the issue. Assuming anybody attempts to break into the school, shrewd entryway locks through sensors and alarms can be turned on and help can be called consequently. AR-equipped systems: Augmented reality can be perceived as an upgraded adaptation of this present reality to be introduced in a more justifiable manner with the assistance of mechanized instruments. IoT-based gadgets and frameworks can be made much more effective by the utilization of AR, legitimate markings and subtleties can be introduced to the understudies just by the output of a standardized tag against the theme they are considering. AR with its illustrations and sounds joined with a product framework can give improved subtleties and 3D dreams of the point being instructed, for instance, the life structures of a human ear can be better perceived in an energized manner than by hypothetical clarifications read without holding back in the homeroom. Such review materials can be gradually and steadily be refreshed in the educational systems and entryway by the administration specialists empowering the understudies to find and see vivified portrayals of the points in any place they consider fit. Automated attendance recording: Participation of the understudy is a worry for the instructors and in the schools that is a regular undertaking with no other option. IoT can help in giving an answer for this strong undertaking of recording participation and ascertaining it for different purposes. IoT can help in lessening this undertaking for pretty much every class. Biometric participation or scanner tag based with the character card number of the understudy can be utilized in consequently recording the

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participants as they enter the homeroom. Along these lines, there is scarcely any opportunity for error and capacity. This won’t empower the educators to commit more opportunity to their essential concern which is showing it is understudied yet such frameworks can be made more successful by sending an immediate message to student folks of their nonattendance in the study hall, making them mindful of the circumstance. A similar element can be utilized in participation and speech of the educator; the school’s assisting staff with canning likewise recording their entrance and leaving timing utilizing their id and biometrics so there is a reasonable history of everything. Special education: At some point back, this was extremely difficult and relatively intense for the uniquely able understudies to get the typical and definite instruction. With the consolidation of IoT devices and shrewd gadgets, the instructive educational plan is as a rule uniquely altered and homeroom conditions are being made sound and light-delicate to take care of the extraordinary necessities of the understudies with tangible inabilities. For instance, they can look for help from an arrangement of sensor-associated gloves and a tablet to produce verbal discourse, interpreted from gesturebased communication which the educators can use while showing the ideas stretched out to what exactly is referenced in the books. 7.5  SMART DATA CENTER Data centers have turned into an irreplaceable piece of present-day facilities, registering frameworks, providing business-basic environment and data storage. Major blackouts, information interferences and personal times are expensive. While the sheer volume, speed and design of IoT information are making it difficult for security, stockpiling the board, servers and the organization, IoT has likewise set out to open doors to advance data center foundation of the executives (Fahrianto et al., 2017). IoT sensors and remote IoT availability are being utilized to guarantee the ideal execution of basic server farm gear to augment uptime, increment energy effectiveness, lower working expenses or more and safeguard the information it stores. Currently, server farms are just as dexterous and cutthroat as one can expect. Furthermore, working with cutting-edge and shrewd arrangements can truly make a lot of difference. There is a large number of gadgets that can store information, for example, cell phones, online media, PCs and cloud and undertaking applications. Because of this, there has been an increase in the development of information. Thus, the information being sent to the organizations is expanding as well. The information produced by IoT empowers associations to gather data that can help with their direction. Consequently, it assists better in dealing with their association’s frameworks and processes. Due to this, how much information that should be put away through a data center has been quickly acquired too.

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Adaptability is frequently included during arranging. Be that as it may, to oblige the outstanding development in information, a server farm should anticipate inevitable IoT reconciliation.

7.5.1 Five basic IoT applications for server farm foundation boards Temperature and humidity monitoring: Server farm offices work day in day out, consuming tremendous measures of energy and generating enormous amounts of heat. Temperature control inside the server farm is fundamental to avoid gear overheating, to direct hardware cooling and to quantify general value. Thermistors situated in server storerooms and in key “areas of interest” around the organization’s offices, for example, cooling admission and release vents, can screen heat age and result for scattered, grainy control of the HVAC framework and PC room cooling (CRAC) unit. This is additionally a basic capacity for decreasing energy costs, which addresses the biggest functional cost in server farms. Server farms tend to overcool offices by a few degrees just to decide in favor of an alert. Indoor air quality monitoring: Indoor air quality is another natural peril affecting server farms. Upkeep tasks, framework redesigns and gear makeover for open air use for ventilation, compression or potentially cooling can bring airborne pollutants into the office. An indoor air quality checking arrangement can proactively make groups aware of toxic substances and particulates that influence electronic gear, consume contacts and lessen capacities to unsatisfactory level, which can bring about expensive data center blackouts. Leak detection: Water spills are perhaps the greatest danger to a server farm. Leak recognition sensors inform groups at the absolute first indication of a break, permitting them to make a healing move. For instance, rope sensors can be set in difficult-to-arrive areas, for example, around each CRAC framework, cooling dissemination units, under raised floors and some other break source (such as lines). Spot spill sensors can be used to screen liquid floods in dribble containers, checking in more modest rooms, wardrobes or any low spots. Remote power monitoring: The fundamental requirement for proactive intervention and response to potential disasters, as well as for remote battery and uninterruptible power supply (UPS) checking, is ecological surveillance. Being able to recognize potential issues early and quickly react to imperfections or debasement boost the unwavering quality of UPS battery frameworks and gives associations the flexibility required in the present unique server farms. Security and access control: Security break in a data center could bring about the deficiency of gear; however, the genuine risk has to do with information openness. With organizations gathering actually recognizable data in various structures, customers are turning out to be progressively worried

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about how well that information is being safeguarded. Any kind of danger to an organization’s information can fundamentally affect notoriety and business and result in enormous monetary loss. When it comes to checking and confining admittance to the data center itself, remote sensors can be used in current locations and limited access to individuals such as opening and shutting of entryways or windows. In a similar manner, IoT-empowered locks, card for each user and keypads can additionally screen and control the work force entering and leaving for a multifaceted way to deal with security. IoT for server farm foundation gives executives a comprehensive perspective on current climate conditions, asset utilization and security to boost uptime, increment energy productivity, lower working expenses and forestall information misfortune and openness. 7.6  SMART PARKING SYSTEM In the current situation around us, we see abundant vehicles and the ineffectiveness to oversee them all put together. As the populace builds step by step, the pace of usage likewise increments and adapting up to the numbers turns into an assignment. An inescapable issue all over the planet is observing an automatic vehicle parking system. The overall way to deal with observing a positioning spot is to go around and drive carelessly until a free space is found, as shown in Figure 7.4. Observing a positioning spot will be the least demanding undertaking or it can be the most monotonous one when it includes wide sections of land of circulated space across one level or various levels. The components thus will be used in these ventures are 3D displayed and drafted in Solidworks programming as per the aspects. This provides us with an outline of how the model will care for collecting every one of the parts by utilizing chosen aspects. The plan uses Eagle programming, and it provides us with an outline of the place of parts in the circuit. The infrared beams are conflicting and are available all over. To balance out this irregularity, an IR Emitter is utilized to project the radiation light. The light waves which are discharged shouldn’t be visible in the apparent range. When the discharge becomes predictable, the IR beneficiary gets these radiations and converts them into an electrical sign accordingly, thereby making a possible contrast. As the radiation expands, the voltage builds making more current stream. To acquire the distance, the impression of waves should diminish. To sum up this, the voltage of the circuit increments when any article comes nearer. The model will be run in the expected circumstances and will be tried appropriately. The smart vehicle leaving framework chips away at the straightforward standard of recognizing obstructions and sending visual input. The vicinity sensor is located on the roof of the parking area thus comprises an infrared producer and a recipient. The IR producer discharges infrared beams,

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Figure 7.4 Smart parking system.

and these beams for the most part ricochet off objects. The IR beneficiary gets these beams and converts them into an electrical sign making a possible contrast. The subsequent potential contrast finishes the circuit. The LEDs are put along the carport and switched on in view of the information obtained by the sensor. An edge distance is adjusted using the potentiometer to fix a specific distance in view of the normal stature of vehicles for sending and getting the radiation. Resistors are given to guarantee the protected working of LEDs and IR sensors. For this undertaking, in view of size, a 12 V battery is used to drive every one of the parts. Case 1: When the parking spot is unfilled, the IR producer radiating the beams won’t skip back if an item (vehicle) isn’t distinguished. The beams won’t strike the IR collector, and subsequently there will be no increase in likely distinction. The criticism of this outcome makes the Yellow LED switch turn on showing the accessibility of a parking spot. Case 2: When the parking spot is

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involved, the IR beams produced are ricocheted back as the vehicle height is inside the limit distance and the beams strike the beneficiary, and these waves are changed over into an electrical sign making it a likely distinction. The criticism of this outcome is shown by the Red LED turning on and hence determining the driver that the specific parking spot is filled. There is consistent emanation of IR waves so the criticism is momentary. When the vehicle leaves the parking spot, the beams don’t return and the Yellow LED switches back on. Mathematical Modeling Using the Solid works demonstrating programming, we 3D displayed the parts and collected it to produce a 3D model of the necessary mode. Automation is a positive development for the future in smart transportation. This plan gives a powerful answer for the normal issue talked about. The smart vehicle leaving framework was planned, created and tried which gave precise outcomes at the time of edge distance was aligned; then, the obstacle was distinguished. The exchanging of LEDs in light of the vehicle in the parking spot was immediate in view of no vehicle and vehicle identified. 7.7  SMART TRANSPORT SYSTEM Wireless sensor networks have mechanically grown all the more quickly from there, the sky’s the limit productively, and they have turned into a critical hotspot for the advancement of IoT. They track down applications in practically all regions including brilliant matrix, smart transportation frameworks, smart home, brilliant medical clinics, etc. The accomplishment of the above prompted the smart city advancement as referenced by our Indian Prime Minister. The possibility of web of things (IoT) was created in corresponding to WSNs. The stream graph of the existing framework is displayed. The arrival and passing of vehicles are taken into account. The data along these lines is aligned to the board frameworks. Two different sensors are required here: parking sensors and street sensors. Along these lines 2 m are utilized, for example, existing stopping meters and new stopping meters. The data obtained from the sensors is passed to the sensor board frameworks. Stopping meters send their individual information to meter the board blocks. This framework can likewise be incorporated to give smart lighting of the roads. Here the headlamp is fired up if the road is being used by the vehicles and different times it endures turned off. Digital parking is used as follows. Sensors recognize whether a parking spot is available and communicate information to the focal server. Cell phone applications demand a parking spot and guide the drivers to that free space. Stopping expenses are straightforwardly paid through the cell phone application. Access to loading zones and private stopping zones is confined.

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IoT traffic engineering contains RFID (Suriya et al., 2008), wireless sensor advances, ad hoc systems administration and web-based data frameworks. Insightful traffic IoT is separated into three layers, for example, application layer, acquisition layer and network layer. The application layer is in charge of the board’s shrewd movement, the executives’ astute maneuvering, data collection and observation, and data management. Network layer utilizes Wi-Fi, 3G/4G and WiMax or GPRS. Securing layer utilizes RFID, RFID reader, WSN and intelligent terminals. This part describes the outcomes acquired in the current framework. This overview is for around 15 km around Ooty, Tamil Nadu, India. Here the area data is shipped off to the information base like clockwork because of memory consideration, and this could be decreased. The proposed framework can work with less memory constraints and can send the area data constantly. The proposed framework even gives parking assistance to the drivers on the road. The IoT will play a significant role of continuous monitoring and to observe the live traffic. The present status finds a better approach for traffic signals by greater handling of assets. The traffic organization division can make use of this continuous traffic observing data to identify the risks and along these lines respond by forcing quick activities. Finally, IoT will assume a significant part in rush hour gridlock by working on the effectiveness of traffic security and traveling costs. 7.8  SMART POWER SUPPLY Energy utilization can be observed by using an electric gadget called an energy meter. The expense and the ordinary utilization of power are educated to the client to overcome high bill usage (Yaghmaee & Hejazi, 2018). The energy meter shows many units are consumed and shares the information with both the client and to the electrical board so this helps in reducing man-power. The user can actually look at their power consumption from any place. The IoT is utilized to turn on/off the domestic devices using hand-off and Arduino communicating. The target of this framework is to screen how much power is consumed. Both the wholesaler and the customer will be benefited in the long run resulting in a decrease in the utilization of power. The smart energy meter observing framework is used to read the values automatically. The square outline comprises Arduino, energy meter, Wi-Fi module and IoT, relay and transformer (Ay et al., 2019). Energy regulator used here is the clip energy regulator. 230 V AC mains is the information given to the transformer, and AC mains is changed over to low voltage. Energy meter estimates the live current, voltage and power as far as KW-h. Microcontroller peruses these boundaries and sends it to the cloud. Node MCU is a Wi-Fi gadget which has a microcontroller in it. This associates

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the neighborhood switch through IoT. The situation with these boundaries can be acquired through versatile or PC. Wi-Fi is utilized for information correspondence. Wi-Fi is arranged with Arduino. Choosing a reasonable transformer is critical. The current rating and the auxiliary voltage of the transformer is a key variable. The current rating of the transformer relies upon the current required for the heap to be driven. The information voltage to the 7805 IC ought to be something like 2 V more noteworthy than the expected 2 V yield; consequently, it requires an information voltage near 7 V. Thus, a 6-0-6 V transformer with current rating of 500 mA is used. Hand-off is the three-terminal high-voltage (NC, C and negative) gadget which interfaces with control. Hand-off likewise has three pins with low voltage (ground Vcc and sign) which associate with the Arduino. Transfer, which is a 120–240 switch that is associated inside an electromagnet. The regulator is used for calculating the energy used by electric burden. The energy is the all-out power consumed and used by the heap at a specific time period. Wi-Fi module conveys profoundly coordinated Wi-Fi answers to fulfill clients for the persistent need of effective power usage. The diagram shows the plotted values of time and power units. It provides the energy utilization for a client at a time. The power utilization by the client arrives at 90% and then an alarm message will be shared with the user. Smart energy observing framework incorporates Arduino, Wi-Fi, and energy meter. The framework consequently peruses the energy meter and gives home robotization through an application created and power the executives done through this application. The current framework consumes less energy, and it will decrease manual work. We can get month to month energy utilization from a distant area straightforwardly to the focal office. In this manner, we lessen the human exertion expected to record the meter readings which are until now recorded by visiting the home exclusively. 7.9  SMART AUTOMOBILE The proposed framework in this chapter can handle the car, fuel burglary cases beyond difficult. The proposed framework comprises a keypad to turn on the vehicle. It gives an ongoing observing framework to the client. By utilizing this, one can have the option to control and respond within a brief time frame to avoid mishaps. It comprises two microcontrollers and an alternate arrangement of sensors and actuators to finish the task. The cause for the work is to attain the consideration by controlling the whole management process through powerful square outline making and critical thinking, while at the same time guaranteeing the achievement of explicit cycles, approaches, procedures, strategies and advances. Here, we

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used two microcontroller bores to play out the activity. First is ARDUINO and the second is NodeMCU which is associated with the cloud for continuous checking: (i) Arduino UNO and (ii) node microcontroller unit (MCU) 1. Ultrasonic sensor: The limit of a ultrasonic sensor is to recognize the assortment in fuel level, and it gives variable outcome voltage as per the varieties in level. This ultrasonic sensor can be used for a liquid. So it will, in general, be used for petrol as well as diesel or on occasions it will be used for water level acknowledgment as well. 2. Accelerometer: An accelerometer is an electromechanical gadget used to evaluate speed increment powers. 3. Keypad: Client will enter the mystery expression using the keypad. Different keys of the keypad are according to the accompanying information: (i) 0 to 9, (ii) Enter, (iii) Escape. 4. Buzzer or siren: A ringer is turned on whenever petrol theft occurs or petrol is stolen. Indicators will be turned on when there is a decrease in the petrol level without start key. 5. GPS: The Global Positioning System (GPS) is a “gathering of stars” of around 30 particularly scattered satellites that circle the Earth and make it functional for people with ground recipients to pinpoint their geographic region. The regional accuracy is some place in the scope of 100 to 10 m for most equipment. GPS structures are truly adaptable and can be found in essentially any industry region. They can be used to design forests, help farmers with social event their fields and investigate planes on the ground or in the air. 6. Microcontroller: A microcontroller (MCU for microcontroller unit) is a little PC on a lone metal-oxide-semiconductor (MOS) fused circuit (IC) chip. 7. Vibration sensors: Vibration sensors can’t avoid being sensors for assessing, appearing and taking apart direct speed, evacuating and area, or speed increment. Vibration, in any case unnoticeable and concealed by human resources, means the machine condition. 8. Hall Effect: The hall impact sensor deals with the standard of the hall impact, which communicates that whenever an appealing field is applied towards a way inverse to the advancement of electric stream of a transport, a potential difference is affected. This voltage can be used to distinguish if the sensor is close by a magnet. 9. Servo: Servos are constrained by sending an electrical thump of variable width, or heartbeat width guideline (PWM), through the control wire. There is a base heartbeat, a biggest heartbeat and an emphasis rate. 10. Breadboard: A breadboard is a tight spotless gadget for brief models with equipment and test circuit plans. Most electronic parts in electronic circuits can be interconnected by inserting their leads or terminals into the openings and thereafter making relationship through wires where proper.

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This has numerous extensions in the future which are yet to be found. This means to serve the general public in the number of ways it could be allowed. It is a joint exertion of different divisions of designing to settle a specific problem. Further, the item can be consolidated to a solitary coordinated framework and be utilized in numerous perspectives and offices to tackle serious issues of the general public. 7.10  SMART INDUSTRY AND AUTOMATION The field of mechanization has had an outstanding effect in a wide scope of enterprises after assembling. In order to decrease the human requirement and to develop the product, a designed concept called mechanization (Da Zu, 2014) was introduced. Although motorization furnishes human administrators with apparatus to help them with the strong necessities of work, mechanization extraordinarily diminishes the requirement for human tactile and mental prerequisites. One of the major applications of mechanization is in the bottle and retaining liquid industries, where the fluid must eventually be reliably filled. For these sorts of utilizations, the pattern is creating some distance from the singular gadget or machine towards nonstop robotization arrangements. The entire integrated automation covers the total creation line, from receipt of merchandise, the creation cycle, filling and bundling, to shipment of products. Architecture: The primary part of smart automation thus puts up the business via the web. To control this activity, we make a website page where we give the info that is clicked on the button available on your website page and kicking your hardware off in your industry. The respective web page is displayed to the customer PC and the network is connected by a medium such as Wi-Fi modem. At the beneficiary side, engines are associated with the application to drive them. The engines are controlled with the assistance of a microcontroller. The UI which is utilized to control the mechanical arm is made on a website or an application. The control is given through the web to the Wi-Fi module. This goes about as the beneficiary and gives the go sign to the microcontroller (KL25Z). The microcontroller will go about according to the given guidelines. The regulator board is now associated with the server through the Wi-Fi module. The sign which is given to the modern machine is really sent through the web and thus we can get to it from any spot. Anyway, the website page or the application should require a login ID and a secret key for the sake of security, for an individual to control the modern apparatus. The happenings in the business during the shortfall of manual consideration should be visible through a CCTV camera. In this manner, the apparatus helps the order through a site page by the client and the human or client doesn’t need to control the hardware in the business physically.

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Hardware and Software Components for smart industry and automation: a. AT89S52: The AT89S52 has features such as 8K bytes of Flash, 256 bytes of RAM, 32 I/O lines, Watchdog timer, two data pointers, three 16-bit timer/counters, a six vector two-level interrupt architecture, a full duplex serial port, on-chip oscillator and clock circuitry. b. IR sensor: An electronic gadget that discharges and recognizes infrared radiation to detect a portion of its environmental factors. c. Switch: Switch is an electromechanical gadget consisting of at least one arrangement of portable electrical contacts associated with outside circuits. d. MSP8266: It provides the unmatched ability to embed Wi-Fi capacities inside various systems or to function as a free application, with lesser cost, and essential, unimportant space. Accordingly, by carrying out these frameworks, we can get to the live information and furthermore control the gadget that interacted with our framework. 7.11  SMART FOOD AND SUPPLY CHAIN The use of IoT in food supply chains (FSCs) is one of the promising killer applications. Starting from accuracy in agribusiness to food creation, handling, stockpiling, dispersion and devouring, purported ranch-to-plate, IoT arrangements give promising possibilities to address the perceptibility, porousness and controllability challenges. These secure, proficient and manageable FSCs are satisfactory. The IoT possibly makes it helpful between food producers, shipper organizations and accommodation organizations who can cooperate as never before to guarantee effective conveyance and sanitation. With IoT-based automation arrangements, organizations across the network gain continuous permeability and empower the computerized, smart activities which are expected to guarantee food is of the greatest quality, followed through on time and ready in an ideal situation. The food supply network detectability framework model should be visible. We have fostered a stage that contains three layers: application layer, communication layer and sensing layer. The detecting or application layer is to intend to screen the state of harvests and domesticated animals on ranches in the production network with various programmed recognizable proof and information catch advances, in view of cost viability. RFID labels, for instance, can be utilized to distinguish pigs and steers, and instances of meats and natural products. Instances of minimal-expense natural products can be followed utilizing 1D or 2D standardized tags. Remote sensor organizations can screen temperature, moistness, CO2 ,

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weight metals and other ecological circumstances in fields, nurseries and lodging for pigs and cows, as well as transient things during transport. The correspondence layer is intended to permit different partners to get to production network data. We set up an IoT design in view of Object Name Service (ONS) (Danilo de Donno et al., 2015), so data can be stored and uploaded to the web. Presently, the framework tracks by part level, yet it will actually want to oversee merchandise at the thin level utilizing exceptional identifiers, for example, a Serialized Global Trade Item Number or Global Individual Asset Identifier. The application layer gives the functionalities that are based on top of execution of IoT; it will uphold applications and administrations that could be utilized by ranchers, retailers, the public authority, experts and purchasers. It incorporates a data set containing China’s sanitation guidelines. Inventory network accomplices will actually want to dissect information caught from the RFID labels and standardized identifications to decide item quality and time span of usability. Ranchers will actually want to construct their own applications and administrations. We’ve made a few models, including “my homestead,” “my crow house,” “my store network,” “following and following framework” and “review right hand.” Purchasers will actually want to check item lapse dates, quality assurance, test reports, electronic families, item photographs and recordings, and client assessments. The insurgency of IoT advances has brought out extraordinary possibilities to make the present food store network more secure, more viable and more manageable. 7.12  SMART AGRICULTURE Smart agriculture is developing the leader’s thought using present-day as shown in Figure 7.5 advancement to fabricate the sum and nature of rustic things. Farmers in the twenty-first century approach agriculture through GPS, soil checking, data on the board and IoT developments. The goal of splendid agribusiness research is to ground a unique genuinely steady organization for farm the board. Keen development considers it important to determine the issues of people’s improvement, natural change and work that has gained a huge load of imaginative thought, from planting and watering respects prosperity and social events. In IoT-based splendid agribusiness, a structure is worked for checking the yield field with the help of sensors (light, tenacity, temperature, soil clamminess, etc.) and robotizing the water framework (Liu et al., 2019). IoT in a country setting suggests the use of sensors, cameras and various contraptions to change every part and movement related to developing into data. We truly believe savvy cultivating should expand and make from what it at present is in light of the fact that this preparing will extensively lessen the negative normal externalities of current agriculture. Quick metropolitan regions utilize the IoT devices like related sensors, lights and meters to accumulate and look at data. The

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Figure 7.5  Smart agriculture.

metropolitan networks then use this data to additionally foster a structure, public utilities and organizations, from that point, anything is possible. For farmers, it is difficult for them to understand particular terms and usage of advancement, and moreover, it is a useful endeavor. Implementation in smart agriculture: The whole population is estimated to contact 10 billion by 2051; this makes a major issue for the farming department. The important things to consider are outrageous atmospheric conditions, rising environmental change and cultivating ecological effects, the requirement of food should met. To meet these expanding needs, farming needs to go to new innovation. New shrewd cultivating applications in light of IoT advances will empower the farming business to diminish waste and improve usefulness. In IoT-based smart cultivation is used to check the water requirement, condition of soil of the land savvy cultivating, a framework is worked for checking the harvest field with the assistance of sensors

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(light, stickiness, temperature, soil dampness and so forth). The ranchers can screen the field conditions from any place. Implementation of soil moisture sensor: Soil dampness sensors measure the total water in the soil. Reflected radiation is impacted by the dirt dampness and is utilized for remote detecting in hydrology and agribusiness (Da Xu et al., 2014). Versatile test tools can be utilized by ranchers or landscapers. Soil dampness sensors help great water system boards. Great water systems give better yields, utilizes fewer data sources and builds productivity. Soil dampness sensors help irrigators to get what’s going on in the root zone of a yield. Water level sensor: Water is crucial and a critical component in agricultural and farm creation and is a key to our own fulfillment as well. Checking the water level of a water source, for instance, a water tank or bore well, expects a basic part in cultivation. Checking the water level of a water source, for instance, a water tank or bore well, accepts a key part in watering the chiefs. The observation of the water level in a water source can be used to safeguard water and to focus on the water age. Consequently, noticing water levels is a huge endeavor in cultivation. In this model assessment of the proposed system, an Arduino UNO board close by Ethernet protect for Internet accessibility is used. A water level sensor in this model is simply used for display purposes. In this manner, the smart agriculture framework diminishes the time and assets that are expected while performing it physically. This utilizes the innovation of the IoT. The framework additionally gauges the dampness of soil and the level of water in fields. This functions admirably in the ideal circumstances and further improvement can be made when the circumstances are not ideal like legitimate brightening. 7.13  SMART RESOURCE MANAGEMENT Smart resource allocation or resource management is characterized as a course of allotting assets to the mentioned application/gadget. It is only an arrangement on the best way to utilize the accessible assets, with the goal that shortage of assets doesn’t win. Asset designation is one of the exercises that go under Resource Management System (RMS) as displayed. The function of the resource management system incorporates support of the data set, execution assessment, benefits organization, booking of assets and so forth. Every one of the exercises of RMS is dealt with by the Resource Management Layer (RML). RML is answerable for asset assignment in a successful manner with the end goal that it doesn’t corrupt the framework execution. While apportioning assets, two significant impacts should be kept away from: (i) over provisioning of assets – emerges when assets are appointed more than required and (ii) under provisioning of assets – emerges when assets are doled out not exactly needed or there might be the vulnerability of in distribution.

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Types of resources: computing resource, networking resource, storage resource, and power resource. Resource allocation activities: There are four main activities that come under resource allocation. It includes planning, task mapping, task scheduling, and communication scheduling. a. Planning: It is the principal action in asset designation. Under this stage, the total IoT ecosystem system is inspected to check whether the accessible assets are adequate for the application. b. Task mapping: The subsequent stage in resource allocation is task planning. In this, every hub is relegated to a specific errand and at times hubs consolidate to achieve a specific assignment. c. Task scheduling: After task mapping, the following movement is task booking. It assumes a key part in asset allotment. d. Communication scheduling: The last advance in the asset portion is correspondence planning. The exercises are associated with task execution on the correspondence channels. This tells about resource allocation in IoT. QoS is one of the main viewpoints in resource allocation. As we have effectively examined, because of expansion in heterogeneous gadgets resource management is turning into a significant test step by step in IoT. To beat this issue, various creators have proposed a few thoughts, instruments and calculations according to their point of view. We can utilize these to take care of the asset designation issue and can characterize new plans to defeat this issue. 7.14  SMART WASTE MANAGEMENT Population is increasing rapidly, and with the monetary growth in countries, additionally, there has been a tremendous increase in executive exploitation. There is no genuine right method or appropriate chain framework to track and screen the waste and removal framework. No matter what, the trash cans and recycling are not being used in every metropolitan community, now and again the trash in the receptacles arrives at the point, where it spills outside the trash bucket and spreads out in the nearby region and causes so many medical problems for residents. A few of them proposed for the IoT smart squander framework that consolidates numerous innovations to screen unlawful conduct in garbage removal continuously. The Internet of trash canisters (IoGB) proposed the use of smart cycle containers subsequent to being loaded up with trash squander. This approach assists in tackling the issue of lack of space by putting the trash canisters in an essential manner. A server-based observing framework was used to control an independent vehicle that gathers the waste when required. The IoT-based smart container uses a machine and profound learning model to handle

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the issue of garbage removal and to give information in light of the estimate of air contamination related to the trash receptacle in the climate. An IoT-based server communicates with the trash canister. The Google cloud server calculates and reports the situation with the receptacle and gives the ongoing gauging data. System Architecture: To represent the square graph of the design of the framework. The IoT-based smart waste monitoring and control framework comprises seven units (Light and Abba, 2020). The gadgets comprise the waste bin unit, an ultrasonic sensor unit, a power supply unit, a checking and control unit, a fluid precious stone presentation (LCD) unit, a lightemanating diode (LED) unit, a Wi-Fi network unit and a user interface (UI) unit. The general framework activity is constrained by a solitary microcontroller (checking and control unit). This unit is customized as to how different peripherals and parts of the framework act. Ultrasonic sensors are used to identify the trash distance inside the trash receptacle. The ultrasonic sensor has the ability to quantify the distance to or from an item by utilizing sound waves. The sensor decides and gauges the distance by communicating a sound wave at a specific recurrence and by paying attention to the sound wave to bob back. To decide the distance between the sonar and the item, the time distinction between when the sound wave is communicated and when the wave is bobbed back is registered. In any case, it is vital to realize that a few articles may not be distinguished by ultrasonic sensors. The detected information from the sensors is observed and controlled in the checking and control unit. The light-producing diode unit gives status data on the degree of trash in the trash receptacles. Assuming the level is lesser than half, the green LED will turn on; the status message “purged” will be shown, as well as the deliberate distance. On the off chance that the level is more prominent than half and under 70%, the yellow LED will turn on, and the status message “half-filled” will be shown, as well as the deliberate distance. Assuming the level is above 70% the red LED will turn on, and the status message “filled” will be shown, as well as the deliberate distance. The information processed will be shown on the LCD screen and furthermore sent to the far-off server through the Wi-Fi module, which would store the data in a data set. A web application is planned and carried out using PHP programming language to query the information from the data set through Wi-Fi availability and share the data onto a website page for a remote survey observation. Clients can get to the trashed status or level data through the website. The detected information will be transferred to the information log document and refreshed persistently. The information shown on the website page will be refreshed every 5 minutes. Improper removal and inappropriate upkeep of home-grown waste establish issues in general wellbeing and environment contamination. Consequently, this endeavors to give reasonable arrangements towards dealing with the loss by working together with the utilization of IoT; for example, providing free web space for a specific period of time once the

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waste is unloaded into the container. The proposed framework will assist in resolving some of the major issues connected with waste and keep the environment clean. 7.15  SMART WATER MANAGEMENT SYSTEM Water is a significant asset for all living creatures on the earth. In that, certain individuals are not getting adequate measures of water due to inconsistent appropriation. We can utilize this approach so everybody gets equivalent measure of water. It is additionally used to avoid wastage of water during the appropriation time frame. In the past technique, the representative will go to that spot and open the valve for a specific span; on the other hand, the worker will go to a similar spot and close the valve, which is a misuse of time. The proposed framework is completely robotized. Here, human work and time are saved. Block diagram: The capacity to screen the water level and to shield water from wastage is a significant issue through the fields of the climate as well as designing. Our IoT-based framework comprises two solenoid valves, an ultrasonic sensor for level estimation, regulator, stream rate sensor and sensors for water quality, which actually measure pH and conductivity. The square chart of the planned framework is given. Working: This framework can be carried out on water tanks for protection and squander-less utilization. When water reaches tanks the pH level of water will be checked, and when this is within permissible limits, the conductivity of water will be checked. On the off chance that the pH or conductivity of water is not within safe limits, then the water won’t be provided to family tanks and valves will be shut. The same technique will be followed until water reaches safe limits. After the acceptable quality check of water on the off chance that the tanks are full, valves of the tank will be opened and water will be appropriated. During circulation of water, the pace of stream is estimated so that it is distributed equivalently. This entire information is sent from Wi-Fi to the web page so the framework can be accessed from a distance via a PC. The progression of dissemination and the quality of water will be checked from the web page which can be accessed via the web. The stream outline of the framework is displayed. This exhibits the effective execution of a web-based way to deal with estimating the water quality and utilization consistently. A flow sensor is used for estimating the amount provided, wiping out the disadvantages of customary water metering frameworks. Future upgrades can incorporate prepaid charging and programmed treatment of water in view of the idea of defilement. Water metering frameworks will be utilized for robotized charging, disposing of the downsides of customary water metering frameworks. This clever thought can be additionally stretched out to other categories such as oil and flammable gas.

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REFERENCES Al-Mahmud, O., K. R. Khan, R. Roy, & F. Mashuque Alamgir. (2020). Internet of Things (IoT) based smart health care medical box for elderly people. 2020 International Conference for Emerging Technology (INCET), pp. 1–6. Ananthula, S., & V. Vippalapalli. (2016). Internet of things (IoT) based smart health care system. 2016 International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), pp. 1229–1233. doi: 10.1109/SCOPES.2016.7955637. Ay, D., M. Rk, N. Nandinipriya, V. Subashini, & P.G. Padma Gowri. (2019). Smart power monitoring system using IoT. 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS), pp. 813–816. Baker, S. B., W. Xiang, & I. Atkinson. (2017). Internet of things for smart healthcare: Technologies, challenges, and opportunities. IEEE Access, pp. 1–1. doi: 10.1109/ACCESS.2017.2775180. Castellani, A., A. Zanella, L. Vangelista, M. Zorzi, & N. Bui. (2014). Internet of things for smart cities, IEEE Internet of Things Journal, vol. 1, no. 1. doi: 10.1109/JIOT.2014.2306328. Da Xu, Li, & Shifeng Fang. (2014). An integrated system for regional environmental monitoring and management based on Internet of Things, IEEE Transactions on Industrial Informatics, vol. 10, no. 2, pp. 1596–1605. doi: 10.1109/ TII.2014.2302638. Da Zu, Li. (2014). Internet of Things in industries: A survey, IEEE Transactions on Industrial Informatics, vol. 10, no. 4, pp. 2233–2243. doi: 10.1109/ TII.2014.2300753. de Donno, D., L. Catarinucci, L. Mainetti, L. Palano, L. Patrono, L. Tarricon, & M. L. Stefanizzi. (2015). An IoT-aware architecture for smart healthcare systems, IEEE Internet of Things Journal, vol. 2, no. 6, pp. 515–526. doi: 10.1109/ JIOT.2015.2417684. Fahrianto, F., N. Anggraini, H. B. Suseno, A. Shabrina, & A. Reza. (2017). Smart data centre monitoring system based on Internet of Things (IoT) (study case: Pustipanda UIN Jakarta). 2017 5th International Conference on Cyber and IT Service Management (CITSM), pp. 1–9. doi: 10.1109/CITSM.2017.8089280. Jia, Ruoxi, B. Jin, M. Jin, Y. Zhou, I. C. Konstantakopoulos, Loannis, H. Zou, J. Kim, D. Li, W. Gu, R. Arghandeh, P. Nuzzo, S. Schiavon, A. L. SangiovanniVincentelli, & C. J. Spanos. (2018). Design automation for smart building systems. Proceedings of the IEEE, vol. 106, no. 9, pp. 1680–1699. doi: 10.1109/ JPROC.2018.2856932. Jin, J., J. Gubbi, S. Marusic, & M. Palaniswami. (2014). An information framework for creating a smart city through Internet of Things. IEEE Internet of Things Journal, vol. 1, no. 2, pp. 112–121. doi: 10.1109/JIOT.2013.2296516. Light, C. I., & S. Abba (2020). IoT-based framework for smart waste monitoring and control system: A case study for smart cities, Engineering Proceedings, vol. 2, no. 1, p. 90. doi: 10.3390/ecsa-7-08224. Liu, H., X. Sun, L. Xia, X. An, & Q. Zhao. (2019). State-of-the-art Internet of Things in protected agriculture. Sensors, vol. 19, no. 8, p. 1833. doi: 10.3390/ s19081833. Lynch, J. P., & K. H. Law. (2019). Smart city: Technologies and challenges, IT Professional, vol. 21, no. 6, pp. 46–51. doi: 10.1109/MITP.2019.2935405.

Intelligent green communication network for IoT applications  123 Nugur, A., M. Pipattanasomporn, M. Kuzlu, & S. Rahman. (2018). Design and development of an IoT gateway for smart building applications, IEEE Internet of Things Journal. doi: 10.1109/JIOT.2018.2885652. Suriya, A., N. Jinaporn, P. Nakonrat, & S. Wisadsud. (2008). Security system against asset theft by using radio frequency identification technology. 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, pp. 761–764, doi: 10.1109/ ECTICON.2008.4600542. Yaghmaee, M. H., & H. Hejazi. (2018). Design and implementation of an Internet of Things based smart energy metering. 2018 IEEE International Conference on Smart Energy Grid Engineering (SEGE), pp. 191–194.

Chapter 8

Scalable and energyefficient intelligent schemes for Green IoT Bhavsar Rakesh, Yesha Patel, and Mohit Bhadla Ahmedabad Institute of Technology

CONTENTS 8.1 Introduction 125 8.2  Heterogeneous Wireless Network 128 8.3  Intelligent and Green Sensor 130 8.4 Green Cloud Computing (GCC) Technology 132 8.5  Wireless Sensor Network (WSN) 133 8.6  Green IoT Approaches 135 8.7 Green Machine-to-Machine Technology (M2M) 136 8.8  Green Communication and Networking 137 References 140 8.1 INTRODUCTION The Internet has made the world so small that things are connected to each other and with the world via global communication networks using Transmission Control Protocol/Internet Protocol (TCP/IP). These things include not only communication devices, but also physical objects, such as cars, computers, and home appliances, which are controlled through wireless communication networks. The Internet has changed drastically the way we live and interact with each other in every situation, spanning from professional life to social relationships (Atzori et al., 2010). Smart connectivity of the existing networks and context-aware computation using system resources is a substantial part of the Internet of Things (IoT). Therefore, IoT is everything around us which is used to communicate “anything, anywhere, any time and using any media”. It is going to change a broad range of real-time monitoring applications such as e-healthcare, home automation, environmental monitoring, transportation autonomy, and industrial automation (Bhavsar, 2021). IoT is all about collecting data, using data, and mutual communication among devices. To fulfill the smart world development and sustainability, green IoT is introduced to reduce carbon emission and power consumption.

DOI: 10.1201/9781003371526-8

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Ubiquitous Networking Ubiquitous networking, also known as persuasive networking, is the distribution of communications infrastructure and wireless technologies throughout the environment to enable continuous connectivity. Nowadays, because of technology growth in wireless access networks, machine– machine communication, man–machine interaction, artificial intelligence, and a wide variety of network applications are based on it. The emerging area of ubiquitous networks can be realized anytime, and anywhere communication between object-to-object and person-to-object is possible. The use of ubiquitous networks is increasing massively due to the speedy growth of wireless, broadband, and Internet technologies. To accommodate and provide solutions with increased data rates, the current and emerging wired and wireless technologies are envisioned with the latest development as 6G, which has a major role in the development of wireless communications by taking into consideration the three major aspects as mobile ultra-broadband (terahertz communications), Super IoT (inter-dependent and satellite communications), and artificial intelligence (AI) and machine learning (ML). The critical network that supports any data rates, latency, and throughput is the ubiquitous network. These networks not only support the heterogeneity of networks but also provide that he next-generation service platforms. The ubiquitous networks are the context-aware, zero-touch networks of computing devices that can connect any time and place. They are useful for exchanging information among any devices, networks, people, and objects through the Internet and termed as the IoT. The industrial revolutions replace the term IoT as Internet of Everything (IoE). The challenges in the ubiquitous network are seamless connectivity, context awareness, resource management, and device management. The speedy and massive development of wireless, broadband, and Internet technologies continuously promotes the use of the ubiquitous network to an advanced level with flexible service platforms to access data from everything connected, everywhere, anytime: digital to virtual. According to applications and services, the network has no limits to imagination. The applications used in the ubiquitous networks are smart as shown in Figures 8.1 and 8.2. These applications are limited to homogeneous networks and take care of heterogeneous networks (social to monitoring).

Figure 8.1 Heterogeneous applications of ubiquitous networks.

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Figure 8.2 Applications of ubiquitous networks.

The ultimate goal is to automate human life. The best example of the application of ubiquitous networks is the smart city, where each element needs to be taken care of by considering various components such as infrastructure, health care, transportation, planning, manufacturing, education, security, safety, connectivity, coordination, and cooperation. The practical use case of the connected car using ubiquitous networks is shown in Figure 8.3. V2X, a new way of communication between the user, cars, and infrastructure, permits to envision numerous applications focusing on trusted cooperative communication in virtual ad hoc network (VANET) as heterogeneous communications. The communication is between V2V: Vehicle to Vehicle, V2I: Vehicle to Infrastructure, V2D: Vehicle to Device, V2H: Vehicle to House, V2G: Vehicle to Grid, V2P: Vehicle to Person, and V2N: Vehicle to Network. Most of the D2D communication protocols use mobile nodes for extended data delivery. If the system considers the local service, then direct communication between end-users is preferred. During natural disasters such as earthquakes, the traditional network does not work due to damage. The need arises to develop an ad hoc network that communicates between Device to Device (D2D), Direct to Mobile (D2M), and vice versa. With the involvement of IoT, a hybrid network will be created for interconnection and communication of accurate data. The best example is shown in Figure 8.3. The

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Figure 8.3 Ubiquitous networks.

different challenges in such applications are data traffic volume, number of connected devices, diverse requirements, energy consumption, bandwidth utilization, maintaining privacy and trust, and network connectivity. 8.2  HETEROGENEOUS WIRELESS NETWORK Heterogeneous network also describes wireless networks using different access technologies. Like a wireless network that provides a service through a wireless LAN and is able to maintain the service when switching to a cellular network is called a wireless heterogeneous network. When network architecture migrates from one existing network architecture to another, new equipment must be introduced. A step-by-step migration strategy is required to complete the size of networks which implies that at all times the network will consist of a combination development. This combination development ranges from electrical routers to all optical packet and wavelength switches. It is important to find a suitable architecture in which a new technology (e.g., all optical switches) can be introduced gradually and thereby enable a seamless migration. Single- versus multi-technology architectures: A network can be single technology/protocols in many levels such as IP level. A network architecture based upon one single technology has the advantage of easier maintenance. That’s why the widespread use of IP is the main argument. However, a single technology probably cannot provide the optimal solution in all circumstances. In any case, it is a given fact that most of today’s telecommunication networks are multi-technology networks. Three main reasons behind them are as described below:

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Interconnection: The networks are themselves interconnected networks of different operators. Each operator has full control over its own domain (and adopt a single technology, if possible), but has no control over what technology its neighbor is using. Size: Some networks are very large. Each technology has its advantages. Therefore, different technologies can be optimal in different circumstances and environments, and thus an operator can decide to use the optimal technology solutions in different areas. Upgrade: The upgrade of large networks is done gradually, and during the upgrade phase, multiple technologies are present in the network. This can also occur due to competition in standardization. In multi-technology network environment, it is clear-cut to group all network nodes into domains such that, within each domain, there is only single technology equipment. This is shown in Figures 8.4 and 8.5. The gateways are used to work as adaptation devices, between network domains.

Figure 8.4 Structure of a heterogeneous network.

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Figure 8.5 A mixed-technology network. Due to network evolution, new technologies are popping up as “islands” within the network.

Partitioning plays a vital role in a network which finds a way to interconnect the network domains; thus, the entire network remains fully connected. Therefore, this leads to the requirement of traffic adaptation between the areas, as the reasons are bit rate difference, packet size variation, packet length constraints, transport characteristics, and traffic grooming. 8.3  INTELLIGENT AND GREEN SENSOR Sensors and sensor networks have a significant impact in meeting environmental challenges. Sensor applications in multiple fields such as smart power grids, smart buildings, and smart industrial process control significantly contribute to more efficient use of resources and thus a reduction of greenhouse gas emissions and other sources of pollution. Sensors measure multiple physical properties and include electronic sensors, biosensors, and chemical sensors. Sensors can thus be regarded as “the interface between the physical world and the world of electrical devices, such as computers”. Wireless sensor and actuator networks (WSANs) are networks of nodes that sense and potentially control their environment. They communicate the information through wireless links “enabling interaction between people or

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Figure 8.6 Typical wireless sensor and actuator network.

computers and the surrounding environment”. The data gathered by the different nodes is sent to a sink which either uses the data locally, through, for example, actuators, or which “is connected to other networks (e.g., the Internet) through a gateway. Figure 8.6 illustrates a typical WSAN1. Sensor nodes are the simplest devices in the network. As their number is usually larger than the number of actuators or sinks, they have to be cheap. The other devices are more complex because of the functionalities they have to provide. A sensor node typically consists of five main parts: one or more sensors gather data from the environment. The central unit in the form of a microprocessor manages the tasks. A transceiver (included in the communication module in Figure 8.7) communicates with the environment, and memory is used to store temporary data or data generated during processing. The battery supplies all parts with energy (see Figure 8.7). To assure a sufficiently long network lifetime, energy efficiency in all parts of the network is crucial. Due to this need, data processing tasks are often spread over the network, i.e., nodes cooperate in transmitting data to the sinks. Although most sensors have a traditional battery, there is some early-stage research on the production of sensors without batteries, using similar technologies to passive radio frequency identification (RFID) chips without batteries.

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Figure 8.7 Architecture of a sensor node.

8.4 GREEN CLOUD COMPUTING (GCC) TECHNOLOGY Cloud computing (CC) is an emerging technology used across the Internet. It provides unlimited computational, unlimited storage, and service delivery via the Internet as conceptually shown in Figure 8.8. The combination of CC and IoT together has a broad scope of research. The primary aim of GCC technology is to promote the utilization of eco-friendly products which are facilely recycled and reused. The primary purpose of GCC is to reduce the use of hazardous materials, maximize energy consumption, and enhance the recyclability of old products and wastes. Furthermore, it can be achieved by product longevity resource allocation, paperless virtualization, or proper power management. A Multi-method data delivery (MMDD) for sensor-cloud (SC) users could achieve lower cost and less delivery time. MMDD incorporates four kinds of delivery: delivery from WSN to SC users, delivery from cloud to SC users, delivery from cloudlet to SC users, and delivery from SC users to SC users (Nandyala et al., 2016). Numerous works carried out on GCC and potential solutions are shown as follows (Shaikh et al., 2015): Adoption of software and hardware for decreasing energy consumption. Power-saving using virtual machine (VM) techniques (e.g., VM consolidation, VM migration, VM placement, VM allocation) (Sarathe et al., 2016).

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Figure 8.8 Green cloud computing.

Various energy-efficient resource allocation mechanisms and related tasks. Efficient methods for energy-saving systems and Green CC techniques based on cloud supporting technologies (e.g., communications, networks). 8.5  WIRELESS SENSOR NETWORK (WSN) The combination of wireless communication and sensing has led to wireless sensor networks (WSNs). WSNs represent the critical technology which has made IoT flourish. “A sensor is a combination of an enormous number of small, low-power and low-cost electronic devices” (Prabhu et al., 2017). A large number of sensors and base station (BS) nodes represent the components of WSN. Each sensor node consists of sensing, power, processing, and communication unit which was discussed in Zhu et al. (2017). Sensor nodes are being deployed around the world, measuring local and global environmental conditions such as weather, pollution, agricultural fields, and so on. Each sensor node reads from surroundings such as temperature, sound, pressure, humidity, acceleration, and so on. Sensors also communicate with each other and deliver the needful sensory data to BS using ad hoc technology. They have limited power and low processing, as well as small storage capacity, while a BS node is authoritative. The idea of green IoT is

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Figure 8.9 Sensor modes for green IoT.

supported by studies in which sensor nodes are kept in sleep mode for most of their life to save energy as shown in Figure 8.9. WSNs can be just realized when data communication occurs at ultra-low power. Sensors can utilize energy harvested directly from the environment such as sun, vibrations, kinetic energy, temperature differentials, and so on. WSNs’ technology has to transmit a signal efficiently and allow going to sleep for minimal power usage. The goal of WSN is supplying sufficient energy to enhance the system lifetime and contribute reliable/robust transmission without compromising the overall Quality of Service (QoS). The idea of green WSN for enabling green IoT was supported by a study which focused on increasing energy efficiency, extending network lifetime, reducing relay nodes, and reduction in system budget. The work was implemented in four steps: the creation of hierarchical system frameworks and placement of sensor/actuator nodes, clustering the nodes, creation of optimization model to realize green IoT, and finally the calculation of minimal energy among the nodes. The findings showed that the proposed approach was pliable, energy-saving, and cost-effective when compared with the existing WSN deployment schemes. Therefore, it is well suited for the green IoT. The green designation of the actual operation and application of five WSNs are for minimizing the energy use. The presentation of green design is suitable for monitoring and risk. This type of work contributed to the following (Shaikh et al., 2015): Periodic data collection and notifying in contrast to sensor systems. Timestamp reconciliation and this technique dynamically supports a growing or decreasing sensor population. Real-time visualization in a geographic context. Regarding green WSN technology, the following techniques could be adopted.

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a. Sensor nodes should work only when necessary while spending the rest of their life in a sleep mode to save energy consumption. b. Energy depletion (e.g., wireless charging, utilizing energy harvesting mechanisms which generate power from the environment such as the sun, vibrations, kinetic energy, temperature, and so on). c. Optimization of radio techniques (e.g., transmission power control, cooperative communication, modulation optimization, energy-efficient CR, and directional antennas). d. Data reduction mechanisms and energy-efficient routing techniques. 8.6  GREEN I oT APPROACHES The intention of this subsection is to provide readers with a brief understanding of recent models and techniques that have been developed and proposed toward achieving GIoT. We have categorized them based on the devised GIoT approach, i.e., hardware-based (HB), software-based (SB), and policy-based (PB) as shown in Table 8.1 (Koutitas, 2010). Table 8.1  Table for green IoT approaches Technology

Type

GIoT network

SB

GIoT network

SB

Wireless sensor networkassisted IoT network

SB

Virtualization framework for energy-efficient IoT networks Controlling greenhouse effect for precision agriculture IoT energy management

HB

SB

Data center

SB

Smart home automation

PB/SB

IoT sensors

SB

HB/SB

Description To extend the life expectancy of the IoT networks, an energy-efficient scheme is proposed in this study. An energy management scheme for IoT is introduced in this study. Energy-efficient data routing protocol for data transferring is introduced and experimented in this study. An energy-efficient cloud computing platform for IoT is introduced in this study. IoT- and cloud-based system for precision agriculture is introduced in this study. IoT energy management scheme is proposed in this study. A methodology for context-aware server allocation for energy-efficient data center is introduced through this study. In this study, authors propose various strategies to track different types of energy consumption parameters and reduce energy wastage in smart home environment. A method for improving energy efficiency in IoT sensors is proposed in this study.

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8.7 GREEN MACHINE-TO-MACHINE TECHNOLOGY (M2M) Nowadays, machines are increasingly becoming smarter and able to gather data without human intervention. The idea of an intelligent machine-tomachine (M2M) communication is necessary to be used on a considerable scale. Machines should have good connectivity in order to enhance modern computer machines and other electronic devices for storing large data. They can share the capacity with all physical machines and any other machines around. A machine represents an object which has electrical, mechanical, environmental and electronic properties as shown in Figure 8.10. M2M communication is safe and works efficiently for all kinds of tasks such as home, industrial, medical, and business processes.

Figure 8.10 M2M communication.

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As shown in Figure 8.10, a massive number of machines can communicate intelligently, share information, and collaborate on decision making. M2M is the advanced version of IoT, where machines communicate with each other without human intervention. With the help of IoT, billions of machines can connect, recognize, communicate, and respond to each other. The massive M2M nodes communicate intelligently and collect data, and send data to BS for deploying the M2M domain for wireless network relays. The BS further supports various M2M applications over the network in the application domain. Green M2M and massive machines are involved in M2M communications. They will consume a lot of energy, particularly in the M2M field. Several techniques might be used to increase energy efficiency for greening IoT (Zhu et al., 2015): a. Intelligently adjust power transmission; b. Efficient communication protocols required for distributing the computing techniques; c. Activity scheduling of nodes used to switch some nodes to sleeping mode while keeping the functionality of the original network; d. Energy-saving mechanisms; e. Employ energy harvesting and the benefits of CR.

8.8  GREEN COMMUNICATION AND NETWORKING Green communication is the practice of selecting energy-efficient communications and networking technologies and products, minimizing resources used whenever possible in all branches of communication. The information and communication technology (ICT) sector has experienced tremendous growth in the number of mobile users over the last decade. Recent studies have shown that the number of global mobile subscriptions has increased exponentially from 500 million subscriptions in 2001 to 5 billion subscriptions in 2015 and tends to reach global penetration of 100% after 2021 which is shown in Figure 8.11. Green wireless communication plays a crucial role in green IoT. Green communications and networking refer to sustainable, energy-aware, energy-efficient, and environmentally aware. The idea of green communication network refers to low CO2 emissions, low exposure to radiation, and energy efficiency. Genetic algorithm optimization for developing the network planning, where the findings showed significant CO2 reductions and cost savings and low exposure to radiation, which was proposed with evidence in Koutitas (2010). The feasibility of the combination of soft and green is to investigate through five interconnected areas of research (i.e., energy efficiency and spectral efficiency codesign, rethinking signaling/

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Figure 8.11 Green communication.

control, no more cells, invisible base stations, and full duplex radio). The investigation details of the energy efficiency of 5G mobile communication networks are discussed from three aspects of theory models, technology developments, and applications. The growing technologies need energy efficiency in the next-generation networks (NGNs). The need for adopting energy efficiency and CO2 emission is in order to fulfill the demands for increasing the capacity, enhancing data rate, and providing high QoS of the NGN. Many types of research have been done for saving energy by using solar and enhanced QoS. Applying network coding-based communication and reliable storage is useful for saving energy for green IoT. Utility-based adaptive duty cycle (UADC) algorithm has been proposed to reduce delay, increase energy efficiency, and keep a long lifetime. Nowadays, 5G may expect to impact our environment and life considerably as IoT promised to make it efficient and comfortable as shown in Figure 8.12. 5G always focuses on decreasing energy utilization and leads to green communication and healthy environments which is shown in Figure 8.13. It shows the importance of 5G technology for enhancing the reliability and QoS of the communication between machines and humans. Furthermore, 5G technology enables to provide large coverage connectivity, reduces latency, saves energy, and supports higher data rate and system capacity. The 5G applications and their services for our society include e-health, robotics communication, interaction human and robotics, media, transport & logistics, e-learning, e-governance, public safety, automotive and industrial systems, etc. Tremendous technology development in the 21st century has many advantages. However, the growth of the technology demands for high energy accompanied with intentional e-waste and hazardous emissions. We survey and identify the most critical technologies used for green IoT and

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Figure 8.12 Expected 5G for greening IoT.

Figure 8.13 Developments of energy consumption for green communication.

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keeping our environment and society smarter and green. ICT revolution (i.e., WSN, M2M, communication network, Internet, DC, and CC) has qualitatively augmented the capability for green IoT. All things around us will become smarter to perform specific tasks autonomously, rendering of the new type of green communication between humans and things and also among things themselves, where bandwidth utilization is maximized, hazardous emissions mitigated, and power consumption is reduced optimally. The trends and prospective future of green IoT are provided. REFERENCES L. Atzori, A. Iera, G. Morabito, The Internet of Things: A Survey. Computer Networks, 54 (2010) 2787–2805. R. Bhavsar, Prediction and Monitoring on Secure Edge-Cloud Integrated Privacy Preserving Heath Protecting System (2021). ISSN 2249-6157. G. Koutitas, Green Network Planning of Single Frequency Networks. IEEE Transactions on Broadcasting, 56 (2010) 541–550. C.S. Nandyala, H.-K. Kim, Green IoT Agriculture and Healthcare Application (GAHA). International Journal of Smart Home, 10 (2016) 289–300. B. Prabhu, N. Balakumar, A.J. Antony, Wireless Sensor Network Based Smart Environment Application. IJIRT, 3 (2017) 10p. R. Sarathe, A. Mishra, S.K. Sahu, Max-Min Ant System based Approach for Intelligent VM Migration and Consolidation for Green Cloud Computing. International Journal of Computer Applications, 136 (2016) 13–18. F.K. Shaikh, S. Zeadally, E. Exposito, Enabling Technologies for Green Internet of Things. IEEE Systems Journal, 11 (2015) 983–994. C. Zhu, V.C. Leung, K. Wang, L.T. Yang, Y. Zhang, Multi-Method Data Delivery for Green Sensor-Cloud. IEEE Communications Magazine, 55 (2017) 176–182. C. Zhu, V.C. Leung, L. Shu. E.C.-H. Ngai, Green Internet of Things for the Smart World. IEEE Access, 3 (2015) 2151–2162.

Chapter 9

Energy-efficient clustering protocol for IoT-based unmanned aerial vehicles Palvinder Singh Mann and Shailesh D. Panchal Gujarat Technological University

Satvir Singh I K Gujral Punjab Technical University

CONTENTS 9.1 Introduction 141 9.2  Related Work 142 9.3 Improved Artificial Bee Colony (iABC) metaheuristic 144 9.3.1  Improved initialization phase 145 9.3.2  Improved solution search equation 145 9.3.3  Improved clustering protocol—iABC2 145 9.4  Results and discussion 147 9.5 Conclusion 151 References 152 9.1 INTRODUCTION Over the last few years, UAVs have witnessed tremendous growth due to lowcost, multi-functional capabilities even in diverse and complex environments such as habitant monitoring, deep sea remote sensing to name a few, which is part of Internet of Things (IoT). UAVs contain self-configured, distributed and autonomous sensor nodes (SNs) that monitor physical or environmental activities such as humidity, temperature or sound in a specific area of deployment (Jin et al., 2008; Liu et al., 2012). SNs can have more than one sensor to capture data from the physical environment, wherever deployed. A sensor with limited storage and computation capabilities receives the sensed data through analog-to-digital converter (ADC) and processes it further for transmission to a main location, known as Sink or Base Station (BS), where the data can be analyzed for decision making in variety of applications (Samrat and Udgata, 2010). Every node also acts as a repeater for passing information of other sensor nodes to the sink. The most important part of the sensor node is its power supply, which caters to the energy requirements of sensors, processors and transceiver; however, its limited battery life can lead to premature exhaust of the network due to excessive usage (Chamam and Pierre, 2010). As manual DOI: 10.1201/9781003371526-9

141

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recharging of batteries is not possible in complex deployments, efficient use of energy becomes a tough challenge in applications where a prolonged life of the network is required (Karaboga and Akay, 2009). Researchers are heavily involved in designing of energy-efficient solutions; however, network life can also be extended by planning energy-efficient approaches. It is well accepted that cluster-based hierarchical approach is an efficient way to save energy for distributed WSNs (Abbasi and Younis, 2007; Singh and Mann, 2017a,b), which increase network life by effectively utilizing the node energy and support dynamic WSNs environment. In a cluster-based WSNs, SNs are divided into several groups known as clusters with a group leader known as Cluster Head (CH). All the SNs sense data and send it to their corresponding CHs, which finally send it to the BS for further processing. Clustering has various significant advantages over conventional schemes (Abbasi and Younis, 2007). First, data aggregation is applied on data, received from various SNs within a cluster, to reduce the amount of data to be transmitted to BS; thus, energy requirements decrease sharply. Second, rotation of CHs helps to ensure a balanced energy consumption within the network, which prevent getting specific nodes starved due to lack of energy (Walck, 2007). However, selection of appropriate CH with optimal capabilities while balancing energy-efficiency ratio of the network is a well-defined multi-modal optimization problem in WSNs (Larranaga, 2001). Thus, metaheuristic-based approaches including evolutionary algorithms (EAs), particle swarm optimization (PSO), genetic algorithm (GA) and recently artificial bee colony (ABC) have been used extensively as population-based optimization methods by different researchers for energy-efficient clustering protocols in WSNs (Zhang and Wu, 2011). Results show that the performance of the ABC metaheuristic is competitive to other population-based algorithms with the advantage of employing fewer control parameters with simplicity of use and ease of implementation (Akkaya and Younis, 2005). However, similar to other population-based algorithms, the standard ABC metaheuristic also faces some challenges, as it is considered to have poor exploitation cycle than exploration; moreover, convergence rate is typically slower, especially while handling multi-modal optimization problems (Yick et al., 2008). Therefore, an iABC metaheuristic is presented, with an improved solution search equation, which will be able to search an optimal solution to improve its exploitation capabilities and an improved approach for population sampling through the use of first of its kind compact Student’s t distribution to enhance the global convergence of the proposed metaheuristic. Further, to utilize the capabilities of the proposed metaheuristic, an improved artificial bee colony-based clustering protocol (iABC2) is introduced, which selects optimal cluster heads (CHs) with energy-efficient approach. 9.2  RELATED WORK A large number of clustering protocols have been developed so far (Singh and Mann, 2017a,b). Here we present only the vital contributions of the researchers based on conventional as well as metaheuristic approach;

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however, we underline the significance of metaheuristic, as our proposed protocol is part of this approach. Low-energy adaptive clustering hierarchy (LEACH) (Karaboga and Basturk, 2008) is a conventional clustering protocol which combines energy-efficient cluster-based routing to applicationoriented data aggregation and achieve better lifetime for a WSN. Hybrid Energy-Efficient Distributed (HEED) clustering (Mohammad Ali Moridi, 2018) is another conventional clustering protocol that selects CHs based on the hybridization of node residual energy and node proximity to its neighbors or node degree thus achieving uniform CH distribution across the network. Power-efficient and adaptive clustering hierarchy (PEACH) (Heinzelman et al., 2002) selects CHs without additional overhead of wireless communication and supports adaptive multi-level clustering for both location-unaware and location-aware WSNs, but with high latency and low scalability thus making it suitable only for small networks. T-ANT (Deng et al., 2011), a swarm-inspired clustering protocol, exploits two swarm principles, namely separation and alignment, through pheromone control to obtain a stable and near uniform distribution for selection of CHs. EnergyEfficient Multi-level Clustering (EEMC) (Gaura, 2010) achieves less energy consumption and minimum latency in WSNs by forming multi-level clustering with minimum algorithm overhead. However, the authors ignored the issue of channel collision which happens frequently in wireless networks. Energy-efficient heterogeneous clustered scheme (EEHC) (Al-Karaki and Kamal, 2004) selects CHs based on weighted election probabilities of each node, which is a function of their residual energy and further supports node heterogeneity in WSNs. Multi-path routing protocol (MRP) (Yi et al., 2007) is based on dynamic clustering with ant colony optimization (ACO) metaheuristic. A CH is selected based on residual energy of nodes, and an improved ACO algorithm is applied to search multiple paths that exist between the CH and BS. MRP prolonged the network lifetime and reduces the average energy consumption effectively using the proposed metaheuristic. Energy-efficient cluster formation protocol (EECF) (Walck, 2007) presents a distributed clustering algorithm where CHs are selected based on a three-way message exchange between each sensor and its neighbors while possessing maximum residual energy and degree. However, the protocol does not support multi-level clustering and considers small transmission ranges. Mobility-based clustering (MBC) protocol (Mininno and Cupertino, 2008) supports node mobility; hence, CHs will be selected based on nodes residual energy and mobility, whereas a non-CH node maintains link stability with its CH during set-up phase. UCFIA (Swagatam Das, 2011) is a novel, energy-efficient unequal clustering algorithm for large-scale WSNs, which use fuzzy logic to determine node’s chance to become CH based on local information such as residual energy, distance to BS and local density of nodes. Distributed Energy-Efficient Clustering with Improved Coverage (DEECIC) (Gonuguntla and Mallipeddi, 2015) selects minimum number of CHs to cover the whole network based on nodes local information and periodically updates CHs according to nodes residual energy and distribution. Energy-Aware Evolutionary Routing Protocol (ERP) (Das and

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Abraham, 2009) is based on evolutionary algorithms (EAs) and ensures better trade-off between lifetime and node stability period of a network with efficient-energy utilization in complex WSNs environment. Harmony search algorithm-based clustering protocol (HSACP) (Kumar et al., 2009) is a centralized clustering protocol based on harmony search algorithm (HSA), a music-inspired metaheuristic, which is designed and implemented in real time for WSNs. Yang et al. (2009) present a Linear/Nonlinear Programming (LP/NLP) formulation of energy-efficient clustering and routing problems in WSNs, followed by two algorithms for the same based on a particle swarm optimization (PSO). Their proposed algorithms demonstrate their proficiency in terms of network life, energy consumption and delivery of data packets to the BS. Conventional approaches (Kuila and Jana, 2014) are better in self-organization, load balancing with minimum overhead but average in energy efficiency, whereas metaheuristic is shown to be good in energy efficiency with prolonged network life. Therefore, metaheuristic-based approaches need to be further explored and improved for energy-efficiency solutions in WSNs. 9.3 IMPROVED ARTIFICIAL BEE COLONY (i ABC) METAHEURISTIC Traditional ABC metaheuristic is proposed by D. Karaboga (Yick et al., 2008) for optimizing multi-variable and multi-modal continuous functions, which has aroused much interest in the research community due to less computational complexity and use of less number of control parameters. Moreover, the optimization performance of ABC is competitive to wellknown state-of-the-art metaheuristics (Soroush Abbasian Dehkordi & Farajzadeh, 2020). In ABC, there are three type of bees: employed bees, onlookers and scout bees. The employed bee carries exploitation of a food source and shares information such as direction and richness of food source with the onlooker bee through a waggle dance, and the onlooker bee will select a food source based on a probability function related to the richness of that food source, whereas the scout bee explores new food sources randomly around the hive. When a scout or onlooker bee finds a new food source, they become employed again; however, when a food source has been fully exploited, all the employed bees will abandon the site and may become scouts again. Like traditional ABC metaheuristic, its variants too faces some challenges, such as the convergence rate is typically slow since they find difficulty in choosing the most promising search solution, while solving complex multi-modal optimization problems. To overcome these limitations, we present an iABC metaheuristic with an improved initialization phase for better sampling and improved solution search equation, named ABC/rand-to-opt/1 (Kulkarni et al., 2011) with optimal search abilities.

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9.3.1  Improved initialization phase Population initialization is an important step in evolutionary algorithms as it can affect the convergence rate and quality of the final solution. Moreover, a large amount of the memory is needed either to store the trial solutions or control parameters of the problem. To reduce the memory requirements, the concept of virtual population has been introduced (Hoang et al., 2014) through family of estimation of distribution algorithms (EDA) (Mann and Singh, 2019) framework by considering compact probability density functions (cPDFs). Therefore, we propose Student’s t distribution (Tyagi and Kumar, 2012), a cPDF, which needs only one vector to be stored on memory, thus reducing storage and step-up convergence rate.

9.3.2  Improved solution search equation Differential evolution (DE) (Singh and Mann, 2019) employs most powerful stochastic real-parameter algorithms to solve multi-modal optimization problems with the optimal combination of population size and their associated control parameters. In other words, a well-contrive parameter adaptation approach can effectively solve various optimization problems and convergence rate can improve further if the control parameters are adjusted to appropriate values with improved solution search equations at different evolution stages of a specific problem. There are various DE variants which are different in their mutation strategies, but DE/rand-to- best/1 (Mao and Zhao, 2011) is one of its kind which explores best solutions to direct the movement of the current population and can effectively maintain population diversity as well. DE/rand-to-best/1 : vt = xt + SF 1(xbes − xt) + SF 2(xr − xs)

(9.1)

where SF1 and SF 2 are scaling factors for neighborhood search, we propose a new solution search equations ABC/rand-to-opt/1 as follows: ABC/rand-to-opt/1 : vi j = xi j + φi j(xopt, j − xi j) + ψi j(xr1 j − xr2 j) (9.2) where r 1, r 2 are random variables from 1, 2,..., SN, xopt is the optimal individual solution with optimal fitness in the current population with φij and ψij are scaling factors, respectively. The proposed solution search equation ABC/rand-to-opt/1, which utilizes the information of only optimal solutions in the current population, can improve the convergence rate of the proposed metaheuristic.

9.3.3  Improved clustering protocol—iABC 2 Further inheriting the capabilities of the proposed metaheuristic to solve wellknown multi-modal optimization problem of energy-efficient clustering in UAVs, an iABC 2 protocol is presented with an optimal CH selection ability.

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The detailed algorithm is discussed below: Clustering Protocol—iABC2 Algorithm Input PN Population number, MCN Maximum cycle number, Dimension of vector to be optimized, D Food sources, SN Lower bound of each element, xmin xmax Upper bound of each element, ξ Control parameter, CR Crossover rate. Output

→

Chj ← xopt, j begin round ← 0 for i = 1 → SN do    Generate rε [0, 1] according to uniform distribution.   if r ≤ ξ then    Generate x−i jε [1, 0] according to PDF Pj(x). else    Generate xi jε [0, 1] according to PDF Qj(x).    Evaluate fitness fi(xi j)    trial(s) ← 0    round + +   end if end for repeat until for i = 1 → SN do     Generate vi j according to Eq.    Evaluate fitness fi(vi j) round + +    if fi(xi j) < fi(vi j) then     xi j ← vi j     fi(xi j) ← fi(vi j)     trial(s) ← 0 else     trial(s) ← trial(s) + 1   end if end for if round == MCN then     Memorize the optimal soloution, xopt, j achieved so far and exit repeat.     Chj ← xopt, j end if repeat

Energy-efficient clustering protocol  147 until    for → i = 1 SN do    ← r rand[0, 1]    ≤ if r CR then ui j ← vi j   else ui j ← xopt, j   end if    Evaluate fitness fi(ui j) and fi(xopt, j)   if fi(ui j)≤ fi(xopt, j) then     xi, j ← ui j    if fi(ui j) > fi(xopt, j) then     xopt, j ← ui j    fi(xopt, j ) ← fi(ui j )    trial(s) ← trial(s) + 1   end if   end if   if solution need to be abandoned   replace with a new solution, produced using   round + + end for if round == MCN then   emorize the optimal soloution, xopt, j achieved.   Chj ← xopt, j end if end

9.4  RESULTS AND DISCUSSION We evaluate the performance of proposed protocol with existing ACO (Yi et al., 2007), HEED (Mohammad Ali Moridi & Mostafa Sharifzadeh, 2018) and PEACH (Heinzelman et al., 2002) protocols, the protocols are simulated over two different BS position scenarios to assess their behavior toward energy consumption and network lifetime. The simulation will be performed over standard MAC protocol with Free Space radio propagation and CBR traffic type, considering other parameters as shown in Table 9.1. In the first scenario UAV # 1, a network of sensor nodes ranging from 75 to 450 is deployed randomly over an area of size 500 × 500 m 2 with a BS, located at position (150 m, 300 m), whereas in the second scenario UAV # 2, a BS will be placed at position (250 m, 500 m). First, we execute the protocols to compare energy consumption in the network for both scenarios. Figure 9.1 shows that in scenario UAV # 1, energy consumption of the proposed protocol iABC 2 is approximately 30%, 70%, 130% less than ACO, HEED and PEACH protocols, respectively, which is attributed to the use of compact Student’s t distribution and improved solution search equation to select optimal CHs, thus minimizing energy consumption in the network.

148  Intelligent Green Communication Network for Internet of Things Table 9.1  Simulation parameters Parameter MAC protocol Radio propagation Traffic type εfs εmp Propagation limit Receiver sensitivity Data rate Packet size

Value 802.11 Free space CBR 6 pJ/bit/m 0.0011 pJ/bit/m4 −111 dBm −89 2 Mbps 5,000 bits 500 bits

Figure 9.1 Energy consumption in UAV# 1.

Even in scenario UAV #2 (Figure 9.2), the proposed protocol consumes 40% less energy as compared to its contender ACO, which clearly shows the effectiveness of the proposed metaheuristic iABC. In ACO, all CHs are

Energy-efficient clustering protocol  149

Figure 9.2 Energy consumption in UAV # 2.

inevitably used as a relay node to forward the data packets to the BS, which therefore consumes more energy, whereas HEED and PEACH select CHs based on a pre-defined selective probability of sensor nodes which will not optimize the energy usage in complex UAVs. Further, Figures 9.3 and 9.4 show that iABC 2 extend the average network lifetime by approximately 30% and 20% compared to HEED and ACO in UAV # 1 and UAV # 2, respectively, which is the outcome of nodes surplus energy availability due to less computation coupled with better convergence and an optimal selection of CHs with proposed metaheuristic. The energy thus saved in iABC 2 will prolong the network lifetime and the nodes will be able to transmit data for a longer duration. In ACO, due to un-symmetric data forwarding effects on the CHs, those near to the BS will die quickly thus reducing the network lifetime. PEACH is having the least network lifetime among all its peers, due to the absence of a clear data aggregation and communication framework, especially for UAV # 2 like scenarios.

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Figure 9.3 Network lifetime in UAV #.

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Figure 9.4 Network lifetime in UAV # 2.

9.5 CONCLUSION This chapter presents an improved metaheuristic, which is based on first of its kind Student’s t cPDF and DE-inspired improved solution search equation to improve exploitation capabilities as well as convergence rate of existing ABC metaheuristic. Further, we presented iABC 2 , a clustering protocol based on the proposed metaheuristic for UAVs, which selects optimal CHs based on an improved search equation and an efficient fitness function. Finally, we compare the performance of the proposed protocol with other protocols to prove its validness over various performance metrics.

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REFERENCES A. A. Abbasi & M. Younis (2007). “A survey on clustering algorithms for wireless sensor networks,” Computer Communications, 30(14), 2826–2841. A. A. L. Samrat & S. Udgata (2010). “Artificial bee colony algorithm for small signal model parameter extraction of mesfet,” Engineering Applications of Artificial Intelligence, 11, 1573–1592. A. Chamam & S. Pierre (2010). “A distributed energy-efficient clustering protocol for wireless sensor networks,” Computers & Electrical Engineering, 36(2), 303–312. A. K. S. Das & A. Abraham (2009). “Metaheuristic clustering,” Studies in Computational Intelligence, 178. C. Walck (2007). Statistical Distributions for Experimentalists. Particle Physics Group. D. Hoang, P. Yadav, R. Kumar & S. Panda (2014). “Real-time implementation of a harmony search algorithm-based clustering protocol for energy efficient wireless sensor networks,” IEEE Transactions on Industrial Informatics, 10(1), 774–783. D. Karaboga & B. Akay (2009). “A comparative study of artificial bee colony algorithm,” Applied Mathematics and Computation, 214(1), 108–132. D. Karaboga & B. Basturk (2008). “On the performance of artificial bee colony (ABC) algorithm,” Applied Soft Computing, 8(1), 687–697. D. Kumar, T. C. Aseri & R. Patel (2009). “Eehc: Energy efficient heterogeneous clustered scheme for wireless sensor networks,” Computer Communications, 32(4), 662–667. D. N. E. Mininno & F. Cupertino (2008). “Real-valued compact genetic algorithms for embedded microcontroller optimization,” IEEE Transactions on Evolutionary Computation, 12(2), 203–219. E. Gaura (2010). Wireless Sensor Networks: Deployments and Design Frameworks. Springer. J. L. P. Larranaga (2001). Estimation of Distribution Algorithms: A New Tool for Evolutionary Computation. Kluwer. J. N. Al-Karaki & A. E. Kamal (2004). “Routing techniques in wireless sensor networks: A survey,” Wireless Communications, IEEE, 11(6), 6–28. J. R. M. A. D. Soroush Abbasian Dehkordi & Kamran Farajzadeh (2020). “A survey on data aggregation techniques in iot sensor networks,” Wireless Networks, 26, 1243–1263. J. Yang, M. Xu, W. Zhao, & B. Xu (2009). “A multipath routing protocol based on clustering and ant colony optimization for wireless sensor networks,” Sensors, 10(5), 4521–4540. J. Yick, B. Mukherjee & D. Ghosal (2008). “Wireless sensor network survey,” Computer Networks, 52(12), 2292–2330. J. R. M. A. D. Soroush Abbasian Dehkordi & Kamran Farajzadeh (2020). “A survey on data aggregation techniques in iot sensor networks,” Wireless Networks, 26, 1243–1263. K. Akkaya & M. Younis (2005) “A survey on routing protocols for wireless sensor net- works,” Ad hoc Networks, 3(3), 325–349. K. C. V. V. Gonuguntla & R. Mallipeddi (2015). “Differential evolution with population and strategy parameter adaptation,” Mathematical Problems in Engineering, 78, 145–160.

Energy-efficient clustering protocol  153 M. E. Bayrakdar (2020). “Cooperative communication based access technique for sensor networks,” International Journal of Electronics, 107. P. Kuila & P. K. Jana (2014). “Energy efficient clustering and routing algorithms for wireless sensor networks: Particle swarm optimization approach,” Engineering Applications of Artificial Intelligence, 33, 127–140. P. N. S. Swagatam Das (2011). “Differential evolution: A survey of the state-of-theart,” IEEE Transactions on Evolutionary Computation, 15, 212–225. P. S. Mann & S. Singh (2019). “Improved metaheuristic-based energy-efficient clustering protocol with optimal base station location in wireless sensor networks,” Soft Computing, 23, 1021–1037. R. V. Kulkarni & A. Forster, & G. K. Venayagamoorthy (2011). “Computational intelligence in wireless sensor networks: A survey,” IEEE Communications Surveys & Tutorials, 13(1), 68–96. R. Zhang & C. Wu (2011). “An artificial bee colony algorithm for the job shop scheduling problem with random processing times,” Entropy, 13(9), 1708–1729. S. Deng, J. Li, & L. Shen (2011). “Mobility-based clustering protocol for wireless sensor networks with mobile nodes,” Wireless Sensor Systems, IET, 1(1), 39–47. S. Mao & C.L. Zhao (2011). “Unequal clustering algorithm for WSN based on fuzzy logic and improved ACO,” The Journal of China Universities of Posts and Telecommunications, 18(6), 89–97. S. Singh & P. S. Mann (2017a). “Artificial bee colony metaheuristic for energy-efficient clustering and routing in wireless sensor networks,” Soft Computing, 21, 6699–6712. S. Singh & P. S. Mann (2017b). “Energy efficient clustering protocol based on improved metaheuristic in wireless sensor networks,” Journal of Network and Computer Applications, 83, 40–52. S. Singh & P. S. Mann (2019). “Improved artificial bee colony metaheuristic for energy-efficient clustering in wireless sensor networks,” Artificial Intelligence Review, 51, 329–354. S. Tyagi & N. Kumar (2012). “A systematic review on clustering and routing techniques based upon leach protocol for wireless sensor networks,” Journal of Network and Computer Applications, 12, 92–110. S. Yi, J. Heo, Y. Cho, & J. Hong (2007). “Peach: Power-efficient and adaptive clustering hierarchy protocol for wireless sensor networks,” Computer Communications, 30(14), 2842–2852. W. B. Heinzelman, A. P. Chandrakasan & H. Balakrishnan (2002). “An application- specific protocol architecture for wireless microsensor networks,” IEEE Transactions on Wireless Communications, 1(4), 660–670. Y. Jin, L. Wang, Y. Kim, & X. Yang (2008). “Eemc: An energy-efficient multi-level clustering algorithm for large-scale wireless sensor networks,” Computer Networks, 52(3), 542–562. Y. K. H. D. J. Mohammad Ali Moridi & Mostafa Sharifzadeh (2018). “Development of wireless sensor networks for underground communication and monitoring systems,” Tunnelling and Underground Space Technology, 73, 127–138. Z. Liu, Q. Zheng, L. Xue & X. Guan (2012). “A distributed energy-efficient clustering algorithm with improved coverage in wireless sensor networks,” Future Generation Computer Systems, 28(5), 780–790.

Chapter 10

Comprehensive study on next-generation IoT Energy-efficient green IoT Maitri Patel Gandhinagar University

Parita Shah Vidush Somany Institute of Technology and Research

Rajan Patel Gandhinagar University

Priya Swaminarayan Parul University

Rahul Vaghela Gandhinagar University

CONTENTS 10.1 Introduction 156 10.2  Principles of Energy-Efficient Green IoT 158 10.3 Enabling Energy-Efficient Technologies for Communication and Monitoring159 10.4  Communication Protocols for IoT 162 10.5  Data Visualization for IoT 163 10.5.1 Data visualization in IoT applications using charts 163 10.5.2  Tools for data visualization in IoT applications 163 10.6 Challenges and Issues for Energy-Efficient Green IoT165 10.7  Energy-Efficient IoT Application 165 10.8 Conclusion 167 References 167

DOI: 10.1201/9781003371526-10

155

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10.1 INTRODUCTION Internet of Things (IoT) has boomed recently in fame. IoT defines numerous technologies and research areas which empowers physical objects across the worlds for global connection. IoT objects are able to transmit data and interconnect with each other through environment sensing. IoT tools have great potential for understanding the physical world, reacting to evolving events and anomalies. So, IoT seems like the ultimate solution for researchers to get insight of real-time physical processes in real world (Garg et al., 2021). The information and communication systems are driven inside the forthcoming IoT where technologies such as radio-frequency identification (RFID), sensor networks, biometrics, quick response (QR) codes, and nanotechnologies are the keystone and support to develop real-world applications such as smart grid, e-health, and smart assistance for transportation, and so on (Serpanos & Wolf, 2018). IoT utilizes things and incorporates the required secrecy while providing services for all type of applications. The main idea behind IoT is to sense, connect, and communicate everything (e.g., from small rooms to large buildings, from everyday appliances to sophisticated embedded systems, from manmade artifacts to natural objects) around us on internet. IoT-connected device is identified by its IP address. Sensor provides brainpower to these objects. Sensors are micro-electromechanical systems (MEMS) which is responsible for reacting to changes in temperature, sound, humidity, pressure, motion, light, time, weight, etc., and yielding required action for which it is programmed (Ray, 2016). IoT is an offered setup that enables exchanges of data through connecting the existing and common non-living objects, people and animals in network. Also, it eradicates the interference of humans for exchanging data between the objects and machines. The following Figure 10.1 represents the components of IoT (Hossein Motlagh et al., 2020). Environmental concerns have become increasingly appealing as people have become more aware of the effects of environmental deterioration. The carbon footprint has increased because of recent technological breakthroughs. As a result, the groundwork for a new field known as G-IoT has been laid (Green IoT). Soon, it will provide green assistance for managing different user duties. The G-IoT is expected to make significant changes in everyday life and will aid in the realization of a green ambient intelligent vision that connects the physical world through a green network. Green IoT networks will help to reduce emissions and pollutions, as well as increase environmental conservation and surveillance while cutting operating costs and power consumption (Ouaddah et al., 2017). The following Figure 10.2 represents the concept of green IoT (Alsamhi et al., 2019). G-IoT may be defined as energy-efficient strategies (equipment or programming) accepted by IoT either to speed up the reduction of greenhouse impact of appealing applications and administrations or to reduce the effect

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Figure 10.1 Components of IoT.

Figure 10.2 Green IoT.

of greenhouse impact of IoT itself in the planning and expansion of IoT. In the first situation, IoT will help to reduce greenhouse gas emissions; however, in the second case, further restructuring of IoT greenhouse gas emissions will be considered (Fensel et al., 2014). Green IoT’s whole life cycle would emphasize green removal/reuse, green production, and green planning, all of which have a low or no impact on the climate. Figure 10.3 represents the goals of energy-efficient green IoT (Maksimovic, 2018).

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Figure 10.3 Goals of energy-efficient green IoT.

The demand for energy-efficient systems has increased due to the widespread attentiveness regarding rising energy costs and ecological awareness. Researchers are engrossed in the direction of G-IoT due to the fast emergent demand of attaining energy efficiency. G-IoT shows green climate as well as saves investment. It offers an orderly arrangement that works with green and manageable development of the humanity. It promotes technological advancements while also addressing cultural issues such as smart vehicles, practical cities, and efficient energy use in order to create a G-IoT atmosphere (Ahmad et al., 2019). Hence, G-IoT focuses on two key aspects: (i) development of energy-efficient computing devices (which is also referred to as green designing), networking architecture, and communication protocols and (ii) ensuring the reduction in carbon emissions, pollution, and other environmental damage caused by IoT technologies, and catering to the industrial demands while ensuring energy efficiency. It is requisite to study about the state-of-the art techniques and strategies which can accomplish the energy hunger of billions of devices in order to make the IoT green. This chapter provides a comprehensive overview of energy-saving practices and strategies for the G-IoT (Arshad et al., 2017). 10.2  PRINCIPLES OF ENERGY-EFFICIENT GREEN I oT Figure 10.4 represents the foremost principles which are employed in attaining G-IoT and decreasing carbon footprint (Murugesan, 2008). The principles are defined as follows: Selective sensing: It gathers only important and required data for the respective situation. Thus, elimination of additional data sensing provides high energy efficiency.

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Figure 10.4 Principles of energy-efficient green IoT.

Reduction in network size: In order to provide low energy consumption, the reduced size networks can be utilized by applying perceptive routing mechanisms and by placing the nodes efficiently. Hybrid architecture: Passive and active sensors can be utilized to perform several activities in IoT network system which provides low energy consumption. Intelligent trade-offs: An optimal balance can be preserved between prioritizing costs and energy efficiency through communication techniques such as compressive sensing and data fusion. Policy-making: Productive and efficient policies have a considerable effect on reducing energy consumption. 10.3 ENABLING ENERGY-EFFICIENT TECHNOLOGIES FOR COMMUNICATION AND MONITORING A few green advancements, for example, green M2M (machine-to-machine connection), green RFID labels, green data center, green sensor groups, and green distributed computing organizations, should be incorporated in the green IoT. Figure 10.5 depicts the essential technologies required to create a green IoT system. Green RFID tags: One of the valuable innovations that is reliable and quickly being used to distinguish, track, and follow different products is RFID. The goal of RFID productions is to coordinate the incorporated circuit (IC) into valuable structures for printing and encoding, so any client might tackle your concern. RFID has a wide extent of

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Figure 10.5 Enabling technologies for green IoT.

usage including operations running, modern creation and capacity, distribution center administration, access control, drug and patient following, and retail, etc. (Parashar & Rishishwar, 2017). The applications are spreading into new industries and areas all the time. RFID is one of the most noticeable green IoT wireless solutions. RFID consists of a minimal number of RFID tags and RFID tag readers. RFID tag is a little microprocessor that is connected to a radio recurrence (which can get and convey messages). RFID labels can store information about the merchandise they are connected to. Two types of RFID tags exist: active and passive (Parashar & Rishishwar, 2017). The passive tags are battery-free and rely on the principle of induction to collect energy from the reader signal. Active tags, however, feature a battery that fuels the signal transmission while also increasing the range (Jara et al., 2014). RFID is widely employed in applications that help to promote a better environment by, among other things, improving waste disposal, decreasing energy consumption in buildings, and cutting automobile emissions. RFID tags may store data or information for any things that are attached to them at a low level (Xu et al., 2011). Inbuilt battery is available in active RFID that permits them to constantly communicate their own sign, while inactive RFID labels need capacity for a functioning power supply. Minimization of the RFID tag size might help to reduce non-biodegradable trash (Lee et al., 2014). Green WSN: One of the critical components for enabling green IoT is a green wireless sensor network (WSN). A group of sensors with limited

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power and storage capacity which forms a wireless sensor network: sensors within WSN communicate through short-range radio connections. Several integrated sensors can collect data from the environment in the sensor nodes (Miozzi et al., 2020). Some of the benefits of green IoT can be stated as to make the most of your energy by employing energy-saving measures, maximizing the usage of reusable energy, and by utilizing context-aware algorithms and data, it diminishes the information size which benefits storage minimization, and it utilizes the energy productive routing calculation to restrict versatility power utilization (Zanella et al., 2014). Green Internet technology: Energy-efficient nodes must be used in green communication and networking because it assumes a significant part in IoT. The idea of green internet technology refers to green communication network with low radiation exposure and CO2 emission. To develop smart and green grid internet technology has major contribution. The following ideas can be used for green internet technology: (i) Synchronizing operation of routers and traffic helps to reduce complexity and decreases the power requirement of internet. (ii) Energy consumption in network device can be minimized by using dynamic topology management mechanism (Meng & Jin, 2011). Green M2M: Due to usage of artificial intelligence, machines are becoming smart, and they can collect required data without human help. M2M communication should be deployed on large scale for successful execution of the system. To achieve good communication, machine should have good connectivity with other storage devices. IoT technology helps to connect, recognize, respond, and communicate billions of machines with each other; therefore, it is necessary to minimize power consumption in M2M communication. Listed techniques might be used to minimize power consumption to maximize energy efficiency: (i) Power transmission should be intelligently regulated. (ii) For distributed computing methods, efficient communication protocols are required. (iii) Some of the unused nodes may be switched to sleep mode by scheduling the activity of nodes without affecting the original functionality of node. (iv) Energy harvesting and saving mechanism can be used (Simoes & de Souza, 2016). Green Data Center: Green Data Center (GDC) is a database for data flow, data management, together with data storage. This kind of data is developed by things, devices, and users, and together creates an architecture through which an Orchestration Agent (OA) is employed in a client–server model which oversees context examination involving servers when it comes to reference efficiency, along with info center management. The processed data is usually then sent again toward the client gadgets with the intelligently chosen servers (Fang et al., 2014). However, this specific design demands the installation of OA

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on each customer device as properly, as the want for backup web servers to maintain reliability, which may bring about considerable energy intake. Because of this, it should have a context-aware sensing platform that will employ selective realizing to maximize vitality efficiency (Ramaswamy, 2016). Green Cloud Computing: A virtualization approach that uses the internet is called cloud computing (CC). It offers limitless storage, processing power, and internet-based service delivery. Paperless virtualization, efficient power management, and resource allocation for product lifespan can all help (Vatari et al., 2016). Centrally regulated data replication is essential in CC to provide users with a high degree of service and reliability, but it uses a lot of energy and bandwidth. To reduce connection latency, a solution is required, which might be accomplished by replicating data closer to consumers on cloud services.

10.4  COMMUNICATION PROTOCOLS FOR I oT Various communication protocols such as Wi-Fi, Bluetooth, Zigbee, Z-Wave, NFC, and so on are used to meet specific function requirement of IoT applications (Al-Sarawi et al., 2017). Bluetooth: Bluetooth provides short-range communication and imports key for wearable products which allows communication between the smart phone and wearable product by connecting them. Zigbee: It is a wireless technology created as an open worldwide standard to satisfy the specific requirements of low-cost, low-power wireless IoT networks. It provides low-power operation with robustness and high security used in industrial setting. Z-Wave: It is low-power radio-frequency communication, primarily designed for products like home automation which provides lamp control. Due to simple design of protocol, it enables faster connection and communication for wireless technology such as ZigBee and others. Wi-Fi: Due to availability of Wi-Fi within home environment, it is popular IoT communication protocol for many developers. It has ability of fast data transmission of large amount of data. NFC: It is contactless card technology which enables information sharing at less than 4 cm. It also provides access to connected electronic devices and digital content of network by enabling simple and safe communication between devices such as smart phone, electronic devices which allows users to perform various operation without being physically present.

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10.5  DATA VISUALIZATION FOR I oT IoT has huge effect on the industry for accelerating their business, and due to this, large amount of data are generated around the globe. Generated data has large volume, speed, and varieties so it is challenging task for leading industry to collect, process, and analyze this kind of data. Providing visualization for data interpretation to make accurate decision is important, and it becomes essential part of IoT (Peddoju & Upadhyay, 2020). The way of representing meaningful insights of large volume data is referred as data visualization, and it helps to identify meaningful patterns of latest trends from collected data. Interconnected data is part of IoT system so continuous data collection, storage, and analysis are required so data visualization provides the chance to identify hidden patterns for analysis purpose for fast, accurate decision making. Generated insight hidden patterns provides an opportunity to solve business problem, cost saving and develop new ideas with smarter decision. Group of sensors connected in IoT network produces real-time data and shares it on cloud where it is stored and analyzed for interpretation to make smart decisions. Consider an example of weather analysis where different components identify the speed of wind, temperature, intensity of sunlight, moisture, location and generating data. For quick grasp of these kind of data, visual context helps more effectively compared to textual format to make faster decisions (Peddoju & Upadhyay, 2020).

10.5.1 Data visualization in IoT applications using charts Different types of charts, such as pie charts, bar charts, column charts, and line charts, are available and may be utilized for data visualization in a variety of applications (Peddoju & Upadhyay, 2020), as illustrated in Figure 10.6.

10.5.2  Tools for data visualization in IoT applications IoT data visualization solutions aid in the provision of powerful analytics, data gathered from various IoT devices which must be interpreted to be analyzed and improved business choices made (Peddoju & Upadhyay, 2020). Grafana: Time series data which includes specific time in days, week, etc. is generated and collected in large amounts based on current economic conditions; this type of data visually can be represented using Grafana which is an open-source data visualization tool. Kibana: For analyzing high-volume log data, the open-source visualization tool called Kibana can be used. Elasticsearch and Logstash

Figure 10.6 Data visualization with different types of graphs.

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Comprehensive study on next-generation IoT  165

known as ELK stack technologies are used with Kibana which is log management platform used globally. Power BI: Power BI is a popular Microsoft product which provides analysis report for huge organization and comes with products such as Power BI services for SaaS, Power BI desktop, and mobile Power BI apps. Reports are generated with Power BI Desktop; generated reports are published using Power BI Services and dashboard and reports made available on Power BI mobile app.

10.6 CHALLENGES AND ISSUES FOR ENERGY-EFFICIENT GREEN I oT Severe efforts are ongoing to achieve sustainable and energy-efficient technologies, but G-IoT is still struggling in its initial stages due to multiple challenges. The shift to green IoT poses a serious challenge and creates a bottleneck to the computing and efficiency needs (Albreem et al., 2017). The underlying challenges are performance management after the incorporation of G-IoT models within the existing IoT architecture, concentrating on green design of applications to diminish the environmental effect, consistency of G-IoT after incorporating energy consumption models, energy efficiency of both the devices and the protocols used in communication, handling the expanded intricacy of the green IoT foundation, compromising between productive range the board and dynamic range detecting, green solutions to decrease power consumption by the middleware layer, proficient security mechanisms such as encryption and control commands.

10.7  ENERGY-EFFICIENT I oT APPLICATION The energy-efficient IoT system converts our regular actions and circumstances into intelligent decisions that improve our quality of life (Khan et al., 2021). Some of the applications are as shown in Figure 10.7: Smart water management: Applications such as smart water meter are used to keep a check on water consumption of individuals’ usage. This gadget works by measuring data such as measure of water extricated, energy utilized, temperature, and stream rate. IoT in strategies, Retail: Item tracking and monitoring using smart shelves, fast payment solution, stock monitoring, production network, and checking of different supplies with the assistance of sensors are all possible by IoT systems, and because of IoT these businesses can take advantage of managing supply chain management efficiently (Albreem et al., 2017).

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Figure 10.7 Energy-efficient IoT applications.

IoT in mining industry: IoT development, such as RFIDs, Wi-Fi, and sensors, is gaining popularity in the mining business, which centers around correspondence among minors and agents. Various diseases of minors can also be diagnosed with the help of the sensors. IoT in transportation: Real-time tracking of vehicles has advantages such as safety improvement, minimization of fuel cost, theft recovery, and so on, and all these are possible by use of RFID tags and sensors. It is helpful in the areas of scheduling, optimizing transit times, dependability, controlling equipment concerns, and reacting to client requests; this enhances services and service delivery. IoT in textile: An advancement e-thread gives gathering information from garments. Development such as brilliant shirt has pulse sensors that are perfectly fused, as well as a pocket for a player following sensor on the upper back for superb GPS gathering. The subsequent skin fit permits players to move openly on the field while at the same time keeping a special body dampness framework that keeps them cool regardless the game tosses at them. This kind of savvy shirt additionally channels the unsafe impacts of UV radiation, permitting players to concentration and play serenely in any event, when the game is hot. Smart cities: A clever city is a city of interfacing actual frameworks, social foundations, and business foundations. A city can be clever through an enormous dispersion of IoT (particularly through interchanges of

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machine-to-machine and human-to-machine). Some of the popular smart city technologies are smart grids, smart utility meter, smart transportation, smart air quality management, etc. (Elkhodr et al., 2013). Smart Health: A wireless body area network (WBAN) is an innovation in view of a minimal expense remote sensor network that is utilized to screen frameworks in homes, clinics, and different work areas. WBAN sensors are light weight and have a little battery in the wearable adaptation. They might be put inside or beyond the human body (Parashar and Rishishwar, 2017). The sensors then, at that point, convey by means of ZigBee, CoAP, 6LowPAN, and different conventions. The patient’s blood stream, pulse, blood pH level, and internal heat level may be generally estimated with these sensors. 10.8 CONCLUSION Climate change is a global concern, and addressing it provides chances for industry to develop future businesses that are both sustainable and energy efficient. Green IoT is the future for many enterprises to survive in current times, as a wide range of industries can benefit from green IoT-based applications to optimize energy-efficient digital technologies and solutions. This chapter highlights the essential ideas relating to the creation of energyefficient green IoT systems. It looks at a variety of technologies that are used in green IoT. In addition, an industry application case was detailed, and finally, the biggest open difficulties for developing the next generation of green IoT systems were listed to help future researchers. REFERENCES Ahmad, R., Asim, M., Khan, S. Z., & Singh, B. (2019). Green IoT — issues and challenges. SSRN Electronic Journal. doi:10.2139/ssrn.3350317 Albreem, M. A. M., El-Saleh, A. A., Isa, M., Salah, W., Jusoh, M., Azizan, M., & Ali, A. (2017). Green internet of things (IoT): An overview. 2017 IEEE 4th International Conference on Smart Instrumentation, Measurement and Application (ICSIMA). doi:10.1109/icsima.2017.8312021 Alsamhi, S. H., Ma, O., Ansari, M. S., & Meng, Q. (2019). Greening internet of things for greener and smarter cities: a survey and future prospects. Telecommunication Systems, 72(4), 609–632. doi:10.1007/s11235-019-00597-1 Al-Sarawi, S., Anbar, M., Alieyan, K., & Alzubaidi, M. (2017). Internet of Things (IoT) communication protocols: review. 2017 8th International Conference on Information Technology (ICIT). doi:10.1109/icitech.2017.8079928 Arshad, R., Zahoor, S., Shah, M. A., Wahid, A., & Yu, H. (2017). Green IoT: an investigation on energy saving practices for 2020 and Beyond. IEEE Access, 5, 15667–15681. doi:10.1109/access.2017.2686092

168  Intelligent Green Communication Network for Internet of Things Elkhodr, M., Shahrestani, S., & Cheung, H. (2013). The Internet of Things: Vision & challenges. IEEE 2013 Tencon - Spring. doi:10.1109/tenconspring.2013.6584443 Fang, S., Da Xu, L., Zhu, Y., Ahati, J., Pei, H., Yan, J. & Liu, Z. (2014). An integrated system for regional environmental monitoring and management based on Internet of Things. IEEE Transactions on Industrial Informatics, 10(2), 1596–1605. doi:10.1109/tii.2014.2302638 Fensel, A., Kumar, V., & Tomic, S. D. K. (2014). End-user interfaces for energyefficient semantically enabled smart homes. Energy Efficiency, 7(4), 655–675. doi:10.1007/s12053-013-9246-2 Garg, P., Pranav, S., & Prerna, A. (2021). Green Internet of Things (G-IoT). Green Internet of Things for Smart Cities, 23–46. doi:10.1201/9781003032397–2 Hossein Motlagh, N., Mohammadrezaei, M., Hunt, J., & Zakeri, B. (2020). Internet of Things (IoT) and the energy sector. Energies, 13(2), 494. doi:10.3390/ en13020494 Jara, A. J., Bocchi, Y., & Genoud, D. (2014). Social Internet of Things: the potential of the Internet of Things for defining human behaviours. 2014 International Conference on Intelligent Networking and Collaborative Systems. doi:10.1109/ incos.2014.113 Khan, N., Sajak, A. A. B., Alam, M., & Mazliham, M. (2021). Analysis of Green IoT. Journal of Physics: Conference Series, 1874(1), 012012. doi:10.1088/1742-6596/1874/1/012012 Lee, C.-S., Kim, D.-H., & Kim, J.-D. (2014). An energy efficient active RFID protocol to avoid overhearing problem. IEEE Sensors Journal, 14(1), 15–24. doi:10.1109/jsen.2013.2279391 Maksimovic, M. (2018). Greening the future: green Internet of Things (G-IoT) as a key technological enabler of sustainable development. Internet of Things and Big Data Analytics toward Next-Generation Intelligence, 283–313. doi:10.1007/978-3-319-60435-0_12 Meng, Q., & Jin, J. (2011). The terminal design of the energy self-sufficiency internet of things. 2011 International Conference on Control, Automation and Systems Engineering (CASE). doi:10.1109/iccase.2011.5997619 Miozzi, C., Errico, V., Saggio, G., Gruppioni, E., & Marrocco, G. (2020). Performance evaluations of UHF-RFID flexible antennas fully-integrated with epidermal sensor board. 2020 IEEE International Conference on Flexible and Printable Sensors and Systems (FLEPS). doi:10.1109/fleps49123.2020.9239578 Murugesan, S. (2008). Harnessing green IT: principles and practices. IT Professional, 10(1), 24–33. doi:10.1109/mitp.2008.10 Ouaddah, A., Mousannif, H., Abou Elkalam, A., & Ait Ouahman, A. (2017). Access control in the Internet of Things: big challenges and new opportunities. Computer Networks, 112, 237–262. doi: 10.1016/j.comnet.2016.11.007 Parashar, A., & Rishishwar, S. (2017). Security challanges in IoT. 2017 Third International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB). doi:10.1109/ aeeicb.2017.7972351 Peddoju, S. K., & Upadhyay, H. (2020). Evaluation of IoT data visualization tools and techniques. Data Visualization, 115–139. doi:10.1007/978-981-15-2282-6_7

Comprehensive study on next-generation IoT  169 Ramaswamy, P. (2016). IoT smart parking system for reducing green house gas emission. 2016 International Conference on Recent Trends in Information Technology (ICRTIT). doi:10.1109/icrtit.2016.7569513 Ray, P. P. (2016). A survey on internet of things architectures. EAI Endorsed Transactions on Internet of Things, 2(5), 151714. doi:10.4108/eai.1–122016.151714 Serpanos, D., & Wolf, M. (2018). Industrial Internet of Things. Internet-of-Things (IoT) Systems, 37–54. doi:10.1007/978-3-319-69715-4_5 Simoes, N. A. V., & de Souza, G. B. (2016). A low cost automated data acquisition system for urban sites temperature and humidity monitoring based in Internet of Things. 2016 1st International Symposium on Instrumentation Systems, Circuits and Transducers (INSCIT). doi:10.1109/inscit.2016.7598189 Vatari, S., Bakshi, A., & Thakur, T. (2016). Green house by using IOT and cloud computing. 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT). doi:10.1109/ rteict.2016.7807821 Xu, X., Gu, L., Wang, J., Xing, G., & Cheung, S.-C. (2011). Read more with less: an adaptive approach to energy-efficient RFID systems. IEEE Journal on Selected Areas in Communications, 29(8), 1684–1697. doi:10.1109/jsac.2011.110917 Zanella, A., Bui, N., Castellani, A., Vangelista, L., & Zorzi, M. (2014). Internet of Things for smart cities. IEEE Internet of Things Journal, 1(1), 22–32. doi:10.1109/jiot.2014.2306328

Chapter 11

Integrating IoT technology for effective agriculture monitoring An approach to smart farming system Umar Farooq, Aqib Amin Rather, and Nasir Shareef Teli Islamic University of Science and Technology

CONTENTS 11.1 Introduction 171 11.2  Proposed System 173 11.2.1  Arduino Mega 2560 175 11.2.2  Temperature and humidity sensor (DHT11) 176 11.2.3  Soil moisture sensor (FC-28) 176 11.2.4  PIR sensor (HC-SR501) 176 11.2.5  Ultrasonic sensor (HC-SR04) 178 11.2.6  Flame sensor 178 11.2.7  Sound sensor 179 11.2.8  Water level sensor 180 11.2.9  LDR sensor 180 11.2.10  Node MCU ESP8266 181 11.3  Results and Discussion 181 11.3.1  Simulation results 183 11.3.2  Experimental results 184 11.4 Conclusion 186 References 187 11.1 INTRODUCTION Due to the exponential population growth, there is always an unprecedented need for higher agriculture production. However, the practices and tools used in the agriculture system, especially in developing countries, are traditional and don’t provide efficient results to meet these demands. Besides this, other concerns related to agriculture sector are (Liu et. al, 2021, Vos, 2019) the following:

DOI: 10.1201/9781003371526-11

171

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Agricultural land is incessantly shrinking because of advancement of other sectors such as industrial sector and construction sector. Environment is continuously degrading because of exponentially increasing pollution. Soil features such as texture, pH value, fertility and moisture content which are important factors for farming are degrading. 60% of water used in irrigation is wasted due to inefficient agricultural methods. Global warming poses serious threat to agriculture as changing precipitation results in crop failures. In order to meet the global demands, the agriculture sector should focus on increasing the annual food production. This requires a highly sophisticated agricultural system capable of overcoming all challenges related to food scarcity and security. These concerns can be overcome by using a smart agricultural system structured around Internet of Things (IoT). IoT is a collection of various electronic devices connected via internet and sharing information with each other, thereby enabling a wide range of applications in areas of health care, industry, home automation and smart agriculture monitoring (Balaji et al., 2019, Brahmbhatt et al., 2020, Farooq and Rather, 2021). An IoT-based agriculture system consists of sensors embedded with a MCU for a specific application purpose. Some of the common application scenarios are listed below (Kour and Arora, 2020, Lova and Vijayaraghavan, 2020, Saranya et al., 2019): Crop water management: IoT embedded with sensors sends a message to farmers about water usage. Pest control management: IoT embedded with sensors helps farmers to prevent or reduce losses due to predators. Precise agriculture management: IoT system uses certain types of sensors that closely monitor the parameters which are directly/indirectly linked with our agriculture. Farm security management: IoT system allows real-time monitoring and prediction through sensors to respond quickly to any untoward incident. A plethora of applications of IoT-based systems in agriculture have enabled substantial research attention in this field for the past few years, and multiple approaches and systems have been proposed so far to exploit the benefits of IoT technology in agriculture sector. Prathibha et al. (2017) proposed an IoT-based smart agriculture system to monitor the temperature and humidity of the agricultural field. Camera is also interfaced with the system to capture images of the field and send these to the farmer’s mobile using Wi-Fi. The work proposed by Boobalan et al. (2018) involved in analyzing the soil moisture level and auto-irrigation of the crops. The system

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also senses humidity and temperature and detects any obstacle or human intervention in the crop field. Jha et al. (2017) proposed an IoT-based field monitoring that provides real-time soil moisture, humidity and temperature of the field to the farmers. A farmer would be able to take prompt action to manage the field. An Arduino microcontroller board with soil temperature and humidity sensors is used to collect the data from the field. The work aims to take preventive measures for loss of crops and enables to increase the overall productivity. Gondchawar and Kawitkar (2016) proposed a smart GPS-based remote-controlled robot to perform tasks such as weeding, spraying, moisture sensing, bird and animal scaring besides performing functions such as smart irrigation, temperature and humidity maintenance. Soni et al. (2021) proposed an IoT-integrated image processing system to analyze crop environment. Image processing techniques have been used as a tool to monitor the fruits, right from the plantation to harvesting. Srivastav et al. (2020) proposed crop water management system with the help of soil moisture and water-level sensors. It also gives an insight of the IoT system in improving the irrigation system. Mohanraj et al. (2016) proposed an e-agriculture system consisting of KM-knowledge base and monitoring modules. It gathers data from various sources such as real-time market prices and current production-level stats, as well as primary crop knowledge. The work also advocates incorporating the ICT in the agriculture sector and aims at overcoming the limitations of conventional agricultural techniques by utilizing water efficiently and reducing the labor costs. In this work, an IoT-integrated agriculture system is embedded with a number of sensors for real-time analysis of the agriculture field and triggers any necessary action based on already set thresholds. It is directed at making the system automated and incorporates various aspects of IoT technology. It keeps the farmer updated of the status of various parameters such as temperature, humidity, soil moisture, water level besides any possibility of intrusion/security threat and to take action independently based on already set inputs. It also uses ultrasonic sensor, PIR sensor, flame sensor and other sensors for monitoring various aspects of the agricultural field. This chapter is organized into four sections. Section 11.1 gives an Introduction and discusses the need and motivation of IoT-enabled effective agriculture system. It also gives an overview of the related work in this direction. Section 11.2 discusses the IoT-integrated proposed system in detail. Section 11.3 presents the simulation and experimental results. Finally Section 11.4 concludes this chapter and discusses the possible future work. 11.2  PROPOSED SYSTEM Traditional agricultural systems involves manual checkups of the agriculture field to ensure that parameters such as temperature, soil moisture, water level and humidity are in stable range. This manual intervention is

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time consuming and inefficient in terms of overall agriculture production. IoT-based smart agriculture systems will play an important role in overcoming these problems. The proposed system is an attempt toward this direction. The block diagram of the proposed system is shown in Figure 11.1. For water management in agriculture field, soil moisture sensor and waterlevel sensor are embedded with the IoT system. Similarly for pest control management, sound sensor is used to detect any noise/sound caused by birds and insects in the field. For the purpose of precise agriculture management, DHT11 sensor, LDR sensor and air quality sensor are employed. These sensors measure certain secondary parameters on which quality of crops depend and any change in values of these parameters with respect to threshold value will trigger a system to do a pre-determined action. In the similar way, in order to achieve security management, PIR sensor, flame sensor and ultrasonic sensor are employed to monitor the intrusion detection, fire detection and obstacle detection respectively which are crucial for farm security. These sensors embedded with the IoT system allow farmers to respond quickly to any threat. The microcontroller used in this system is the Arduino Mega 2560, and Wi-Fi module used is ESP8266. Further, the two devices can be communicated via serial communication using UART scheme. The data collected from these sensors is processed, and action is taken by the microcontroller based on the already set thresholds. Whenever there is any deviation of any

Figure 11.1 Block diagram of proposed IoT-integrated system.

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parameter, the system alerts the farmer through a buzzer and also sends a detailed report through an email to the farmer’s mail address using a Wi-Fi module. The status of various parameters is also continuously displayed on an LCD. The data is also stored on the cloud called ThingSpeak. Other devices such as an exhaust fan is used in a green house to maintain a constant temperature and a submersible pump for maintaining the soil moisture level to a stable value. These two high-current devices are connected to MCU through a relay module. In order to make the system energy efficient and to avoid a shutdown in case of a power failure, a solar rechargeable battery along with a DC booster is incorporated in the proposed system. The description of each device used in the proposed system is discussed as follows (Sehrawat and Gill, 2019, Ripka and Tipek, 2013):

11.2.1  Arduino Mega 2560 The Arduino Mega 2560 as shown in Figure 11.2 is a microcontroller board from Arduino family. The board has 70 input/output pins with 16 analog pins used for any analog device connected to these pins and 54 digital pins out of which 15 are PWM in nature used for adjusting LED brightness or adjusting the speed of fan/pump. Besides these pins, there are four UART pins used for serial communication with another device, a 16MHz crystal oscillator, a USB port either used for programming or powering up the Arduino board via PC/laptop, a power jack which supports the external power supply of up to 18 V (recommended is >12 V), an ICSP header pin used to communicate with another board via master slave scheme and a reset button. Talking about the memory of onboard chip, the ATmega 2560 has 8KB of SRAM, 4KB of EEPROM and 256 KB of flash memory all for storing code.

Figure 11.2 Arduino Mega 2560.

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Figure 11.3 DHT11 sensor.

11.2.2  Temperature and humidity sensor (DHT11) The sensor used to monitor temperature as well as a humidity is DHT11 sensor (Figure 11.3). It is a low-cost, simple design digital sensor and measures the parameters instantaneously. It measures temperature with the help of a thermistor and uses a capacitive humidity sensing element for measuring humidity. It is generally used in heating, ventilation and air conditioning systems. In an agricultural field, it can be used in a green house for monitoring temperature as well as humidity. If temperature deviates from the stable range, then the two-way exhaust fan gets activated by sending high to low pulse from Arduino pin until the temperature becomes stable.

11.2.3  Soil moisture sensor (FC-28) It is an analog sensor used for measuring the water content of soil. The one used here (Figure 11.4) measures moisture through electrical properties such as resistance of the soil. The resistance of the soil varies with varying water content of the soil. It finds applications mainly in agricultural fields but can also find other applications such as monitoring sporting fields. If the moisture level goes down enough that soil becomes dry (an undesired condition), then the pump gets activated by sending high to low pulse from Arduino pin until the moisture level becomes stable.

11.2.4  PIR sensor (HC-SR501) PIR stands for passive infrared sensor. PIR sensor as shown in Figure 11.5 is a passive sensor used for detecting any motion in the surroundings. A certain amount of infrared light is emitted by every living being which gets detected by this sensor. PIR itself contains a pyroelectric sensor which generates a sudden electric signal when it receives some heat in the form of IR

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Figure 11.4 Soil moisture sensor.

Figure 11.5 PIR sensor.

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waves. The sensor is covered with a Fresnel lens so that all IR waves get converged toward it. It finds applications in security alarms, smart homes, etc. In an agricultural field, it can be used to detect any intrusion or movement of an animal. If PIR sensor detects any kind of motion, then the farmer receives an alert through the buzzer.

11.2.5  Ultrasonic sensor (HC-SR04) This sensor is used for measuring the distance of an object at which it is from the sensor. It does so with the help of SONAR (sound navigation and ranging) technique, wherein it uses sound waves for the purpose. It generates and senses these waves with a transmitter and a receiver mounted on it. The transmitter sends “trigger” signals and the receiver receives “echo” signals after reflecting from a target. It is generally used for measuring various distances by using formula of radar range. In an agricultural field, ultrasonic sensor as shown in Figure 11.6 is mounted either on farm vehicles such as tractors for automatic machine purposes or on robots which can be used as a guard and fruit pickers. It can also help in detecting any movement and pinpointing the object.

11.2.6  Flame sensor Flame sensor (Figure 11.7) is used to detect the occurrence of a fire or a flame. It uses the flame flash method and is highly sensitive to light within a particular range between 700 and 1,200 nm of the EM spectrum. These sensors find applications in fire alarms, fire fighting robots, etc. In an agricultural field, it would help in reducing the damage in case a fire arises.

Figure 11.6 Ultrasonic sensor.

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Figure 11.7 Flame sensor.

Figure 11.8 Sound sensor.

When the flame sensor gets activated due to a fire in farm fields, then either automatically or manually the sprinkler system gets activated at once. In both cases, the farmer gets alerted via email if automatic mechanism is used or via buzzer if manual system is used.

11.2.7  Sound sensor A sound sensor is used for measuring the intensity of any sound and gives its electrical equivalent signals as output. It works similar to a human ear, having a diaphragm which converts vibrations into signals. It finds applications in security and monitoring systems, home automation and is also embedded in nearly every communication device. In an agricultural field, a sound sensor as shown in Figure 11.8 is used to detect the presence of any animal and will be helpful during pest/bird attack by detecting the sound and alerting the farmer.

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Figure 11.9 Water-level sensor.

11.2.8  Water level sensor Water-level sensor as shown in Figure 11.9 is used to measure the water level, detect rainfall or even a leakage. It has a series of exposed parallel conductors, which together act as a variable resistor. The resistance of these parallel conductors varies according to the water level. It is used to monitor the water level of the storage tank in an agricultural field to prevent any detrimental outcome of a drought-like condition. If level of water in the tank is low, then motor gets activated and remains so until the storage tank is filled.

11.2.9  LDR sensor LDR stands for light-dependent resistor. It is a device used for measuring the intensity of light. As the name suggests, its resistance varies proportionally to the intensity of light that falls on it. It finds applications in home

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Figure 11.10 LDR sensor.

automation, light-intensity meters, etc. In a smart agriculture system, LDR sensor as shown in Figure 11.10 is used to monitor light intensity in a green house.

11.2.10  Node MCU ESP8266 Node MCU is an open-source firmware which allows users to take control of firmware and modify it according to their interest. It is a development board that helps users to build IoT products. It includes firmware that runs on the ESP8266 Wi-Fi and hardware which is based on the ESP-12 module. NodeMCU development board as shown in Figure 11.11 is featured with Wi-Fi capability which allows users to send data over a server, an analog pin which is used to connect any analog sensor and 16 GPIO pins stands for General Purpose Input Output pins which are used to connect other devices. It can be powered using a micro USB jack which supports up to 5 V and VIN pin which supports up to 3.3 V (external supply pin). Node MCU supports UART, SPI, and I2C interface for serial communication with another device. The workflow diagram that illustrates the working of the proposed system is given in Figure 11.12. 11.3  RESULTS AND DISCUSSION The proposed IoT-integrated effective agriculture system has been designed and tested in simulator/software as well as in terms of hardware. The results obtained from simulations and experiments are discussed as follows:

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Figure 11.11 Node MCU ESP8266.

Figure 11.12 Workflow diagram of the proposed diagram.

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11.3.1  Simulation results The software used for the simulation of the proposed system is Proteus 8 software (Brewer et al., 1992). It is a virtual suite used to design and simulate electronic projects such as PCB, circuit design and IoT project design without taking risk of damaging the components. The proposed system discussed in Section 11.2 is designed and simulated in Proteus 8 software. The design of the proposed system in the simulation environment is shown in Figure 11.13. The simulation results obtained from different sensors used in the design setup can be seen in Figure 11.14.

Figure 11.13 Circuit design of proposed system in Proteus 8 software.

Figure 11.14 Virtual terminal displaying various sensor readings in Proteus 8 software.

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11.3.2  Experimental results Experimental results are obtained from the hardware setup of the proposed system which consists of a microcontroller interfaced with various sensors, Wi-Fi module, LCD screen, and peripherals such as DC fan and a submersible motor as shown in Figure 11.15a and b. A solar rechargeable battery has also been added to avoid power failure. In addition to this, there are LEDs to indicate the status of various parameters instantaneously. We have tested its working and also took results showing the system is working properly and can prove highly efficient in real-time farm field monitoring. The experimental results from sensors in the system are given in Figure 11.16. The results are summarized in tabular form and presented in Table 11.1.

Figure 11.15 (a) Hardware design of proposed system (front view). (b) Hardware design of proposed system (back view).

LDR sensor

Flame sensor Sound sensor Water-level sensor

Used in greenhouses to monitor the amount of light intensity crops receive

Used to detect any kind of sound to prevent bird attacks To monitor the status of water storage tank

Precise agriculture management

Farm security management Precise agriculture management Farm security management Pest control management Crop water management

Detect any intrusion in farm by detecting motion Measure the distance between farm vehicle and obstacle, and fruit picker robot and an obstacle Used to detect the fire

Ultrasonic sensor

Crop water management

Monitor the moisture content of soil

Objective Precise agriculture management

Soil moisture sensor PIR sensor

Application

To monitor temperature and humidity in farm

DHT11 sensor

Sensor

900; Max. NA

NA

NA

0.2–40 m

NA

10%–60%

13°C–35°C

Threshold

Table 11.1  S ensor threshold values and readings from the proposed system Action

Inform the farmers via messages and LEDs

Water sprinklers get activated Buzzer and alarm get activated Below 500, pump gets activated

Below 22°C, heater gets activated and above 35°C, fan gets activated Below 10% of soil moisture, pump gets activated Buzzer and Alarm gets activated Buzzer gets activated

Readings

Intensity = 795 lux

Sound level = 573 Intensity = HIGH Water level = Low

Distance = 21 inches Warning: Obstacle ahead No flame detected

Soil moisture = 37.15% Action = Pump OFF & Stable soil moisture No motion detected

Humidity = 74% Temp. = 14.30°C Action; Stable Temp.

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Figure 11.16 Sensor readings of the proposed system.

11.4 CONCLUSION In this work, an IoT-integrated smart agriculture system has been proposed by incorporating various sensors and other peripherals in our agricultural methods in order to improve the quality and quantity of the produce and use our resources more efficiently. The proposed system is used for monitoring, management and security of the farm field. In order to make the system energy efficient and to avoid shutdown in case of a power failure, a solar rechargeable battery is incorporated in the proposed system. The simulation and experimental results show that the proposed system is having high efficiency and accuracy in fetching the data of various parameters from the farm on a real-time basis. The proposed IoTintegrated system will assist farmers in managing agriculture in a much better and more efficient way not only by gaining various inputs from the farm field but also by taking necessary and appropriate action in response to the received inputs. In future, we intend to develop this proposed system into a full-fledged smart farming system by increasing the sensing capability of the system to fetch more data especially regarding air quality, rain intensity, pest control, plant health and also integrating the system with GSM and GPS module. Also, in future, we intend to focus on the miniaturization and cost effectiveness of the system so that it is accessible and affordable to farmers easily.

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REFERENCES Balaji, S., Nathani, K., & Santhakumar, R. (2019). IoT technology, applications and challenges: A contemporary survey. Wireless Personal Communications, 108(1), 363–388. Boobalan, J., Jacintha, V., Nagarajan, J., Thangayogesh, K., & Tamilarasu, S. (2018, April). An IoT based agriculture monitoring system. In 2018 International Conference on Communication and Signal Processing (ICCSP) (pp. 0594– 0598). IEEE. Brahmbhatt, P. V., Patel, R. G., & Patel, N. (2020). IoT-based smart water treatment plant of GIFT city. In Handbook of Research on the Internet of Things Applications in Robotics and Automation (pp. 287–299). IGI Global. Brewer, E. A., Dellarocas, C. N., Colbrook, A., & Weihl, W. E. (1992). Proteus: A high-performance parallel-architecture simulator. Acm Sigmetrics Performance Evaluation Review, 20(1), 247–248. Farooq, U., & Rather, G. M. (2021). Millimeter wave communication networks: Evolution, challenges, and potential applications. International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), 12(3), 138–153. Gondchawar, N., & Kawitkar, R. S. (2016). IoT based smart agriculture. International Journal of advanced research in Computer and Communication Engineering, 5(6), 838–842. Jha, R. K., Kumar, S., Joshi, K., & Pandey, R. (2017, July). Field monitoring using IoT in agriculture. In 2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT) (pp. 1417–1420). IEEE. Kour, V. P., & Arora, S. (2020). Recent developments of the internet of things in agriculture: A survey. IEEE Access, 8, 129924–129957. Liu, Y., Ma, X., Shu, L., Hancke, G. P., & Abu-Mahfouz, A. M. (2021). From Industry 4.0 to Agriculture 4.0: Current status, enabling technologies, and research challenges. IEEE Transactions on Industrial Informatics, 17(6), 4322–4334. Lova Raju, K., & Vijayaraghavan, V. (2020). IoT technologies in agricultural environment: A survey. Wireless Personal Communications, 113(4), 2415–2446. Mohanraj, I., Ashokumar, K., & Naren, J. (2016). Field monitoring and automation using IOT in agriculture domain. Procedia Computer Science, 93, 931–939. Prathibha, S. R., Hongal, A., & Jyothi, M. P. (2017, March). IoT based monitoring system in smart agriculture. In 2017 International Conference on Recent Advances in Electronics and Communication Technology (ICRAECT) (pp. 81–84). IEEE. Ripka, P., & Tipek, A. (Eds.). (2013). Modern sensors handbook. John Wiley & Sons. Saranya, K., Dharini, P. U., Darshni, P. U., & Monisha, S. (2019). IoT based pest controlling system for smart agriculture. In 2019 International Conference on Communication and Electronics Systems (ICCES) (pp. 1548–1552). IEEE. Sehrawat, D., & Gill, N. S. (2019, April). Smart sensors: Analysis of different types of IoT sensors. In 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI) (pp. 523–528). IEEE. Soni, G. K., Gour, S., Agarwal, M. K., Sharma, A., & Shekhawat, C. S. (2021). IOT based smart agriculture monitoring system. Design Engineering, 2243–2253.

188  Intelligent Green Communication Network for Internet of Things Srivastava, R., Sharma, V., Jaiswal, V., & Raj, S. (2020). A research paper on smart agriculture using IoT. International Research Journal of Engineering and Technology (IRJET), 7(07), 2708–2710. Vos, R. (2019). Agriculture, the rural sector, and development. In Asian Transformations, D. Nayyar (Ed.), Oxford University Press, Oxford, pp. 160–185.

Chapter 12

Enforcement of IoT for potent oversight of toxic levels in the construction and demolition waste at an industrial vicinage G.K. Monica Nandini Sona College of Technology

CONTENTS 12.1 Introduction 189 12.2 Introduction: Background and Driving Forces 190 12.3 Usage of LoRaWAN for Waste Collections 192 12.4 Features 194 12.4.1  Ultrasonic sensor 195 12.4.2  GPS module 196 12.4.3 Gateway 196 12.4.4  The server and the GUI 198 12.5 Conclusion 199 References 200 12.1 INTRODUCTION As the Internet of Things (IoT) sensors have become a valuable tool for predicting environmental phenomenon, it has gained much scope as a method to connect and create a network for sending and receiving data at a lower cost (Roostaei, 2018). Despite the current pandemic, it is predicted that 30 billion IoT connections will exist by the end of 2025 and has imprinted its significance in various applications such as smart homes, smart city, agriculture, wearables, smart grids, and so on. Nowadays, real-time monitoring of managing the waste especially from Construction and Demolition (C&D) sites with increased toxic level due to its existence in the vicinage of industrial areas is the need of the hour. In this work, LoRa technology has been used over the C&D waste found at a radius of 10 km from a paint industry located near the outskirts of Coimbatore district, Tamil Nadu, has been experimented with to predict the toxic levels and send a warning sign to the residents and industry for achieving eco-friendly surroundings (Khoa et al., 2020). As the IoT sensors have become a valuable tool for predicting enormous phenomenon, it has gained its popularity in environmental management sector (Vishnu et al., 2021). DOI: 10.1201/9781003371526-12

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12.2 INTRODUCTION: BACKGROUND AND DRIVING FORCES Feasible development of the total world depends on a few components such as economy, quality education, agribusiness, businesses and numerous others, but environment is one of the variables that plays the foremost vital part. Well-being and cleanliness are key components of the maintainability of mankind and advance of any nation, which comes from a clean, contamination-free and perilous-free environment. In this way, its observing gets to be basic so as to guarantee that the citizens of any country can lead a sound life (Kumar et al., 2019). Here comes the role of Environment Monitoring (EM) which portrays the legitimate arranging and administration of catastrophes, controlling diverse contaminations and successfully tending to the challenges that emerge due to unfortunate outside conditions in terms of toxicated waste management (Brous et al. 2020). EM bargains with water contamination, contamination removal methods, perilous radiation, climate changes, seismic tremor occasions, etc. The sources of contamination are contributed by a few components, a few of which are man-made and others due to normal causes, and the part of EM is absolutely kept to address the challenges so far existing alongside the mankind and environment (Ullo & Sinha, 2020). One cannot envision a savvy city without a smart squander administration framework. A city comprises of showcases, workplaces, different little or expansive scale homes and social orders with which the quantity of waste generated (Farooq et al., 2020). The major sources of squander are collected from family units and also the natural or inorganic waste materials delivered out of commercial or family exercises (Mouha, 2021). Dustbin is one such way to gather the squander of household and hold up for civil organizations. Most of the time, the trash canisters or dustbins are placed in public places or before family/social orders within the cities, which portray the reason because of the increase in squander each day. Therefore, improper waste administration makes a serious well-being hazard and leads to the spread of irresistible illnesses conjointly contaminating the surrounding environment, which should be thoroughly studied (Nadu & Nadu, 2018). The different biodegradable squander combinations produces poisonous gasses such as methane if dustbins are left unattended for numerous days which require prompt activity, and also the most serious issue with quickly increasing population within the urban range is day-today biodegradability (Roostaei, 2018). Nowadays, most of the research is written on the applied scale of technology using IoT, which is something that facilitates human life in certain fields, including how improvisation of business capabilities (Hong et al., 2014), and up-to-date research analyzes how data obtained from IoT devices can benefit various aspects. However, watching the disruptive level that spreads across all areas where solid waste management is a major concern and also becomes a subject that’s widely discussed within the future through the 4th technological revolution

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scheme (Akram et al., 2021). It is a gap to write down a science thinking flow as a foundation that addresses this IoT growth and development which will be employed by researchers, developers and industries. The direction of the event of IoT is predicted to be easier to know how the character and habits of the matter occur, and various methods and tools employed by researchers associated with the knowledge domain and industry (Dachyar et al., 2019). As the Internet of Things (IoT) sensors have become a valuable tool for predicting various environmental and societal phenomena, it has gained much scope as a method to connect and create a network for sending and receiving data at a lower cost, especially in Waste Management sector (Roostaei et al., 2020). One major theme inside the shrewd city is shrewd squander administration. When it comes to squander administration frameworks, the communication separate between the squander collection center and the squander collection point could be a major figure in deciding the system’s viability. In any case, accessible communication innovations such as LoRa and SigFox, which work on a low power wide-area arrange (LPWAN), are able to cater to the long-distance communication required by the squander administration framework whereas sacrificing on the rate of information transmission. Ponders within the field of remote communication in IoT have too been quickening (Roostaei et al., 2020). Few literature portray that, then again, communication innovation such as Bluetooth, Wi-Fi and Zigbee offer way better information transmission rates, but these are constrained by their information transmission ranges (Sheng et al., 2020). The use of LoRa (long-range) communication techniques which can function without the service of an internet connection can be particularly used in smart waste management for purposes like intimating authorized persons to get rid of any filled trash cans (Theivanathan et al., 2021). As proposed, the savvy container in working with Web of things is an Arduino Uno-based waste bin along with an extraordinarily put multicontroller which is for the most part connected to the GSM modem, and a special kind of ultrasonic sensor is additionally associated to it in making waste management projects a grand success (Kumar et al., 2019). Also, on the holder the sensor is set at a highest point and the level of edge was balanced based on waste management needs (Vishnu et al., 2021). When at the degree of edges, the junk gets filled the GSM modem is triggered by the sensor and in this way the authorities are sent data within the form of alert to purge the substance of the holder (Kumar et al., 2019). Whereas arranging these savvy containers a few uncommon features are considered such as sensibility, back and sturdiness, etc. (Khoa et al., 2020). All this arranging makes a difference to keep the environment of the city clean and sterile and in such keen cities there’s less spread of infections (Almuhaya et al., 2022). Despite the current pandemic, it is predicted that 30 billion IoT connections will exist by the end of 2025 and have imprinted its significance in various applications such as smart homes, smart city, agriculture, wearables, smart grids, etc. (Jagtap, 2020). Nowadays, a real-time monitoring of managing

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the solid waste especially from Construction and Demolition (C&D) sites with increased toxic level especially when its existence in the vicinage of industrial areas is the need of the hour. In this work, LoRa technology has been used over the C&D waste found at a radius of 10 km from a paint industry located near the outskirts of Coimbatore district, Tamil Nadu, has been experimented with to predict the toxic levels and send a warning sign to the residents and industry for achieving a eco-friendly surroundings. As the IoT sensors have become a valuable tool for predicting enormous phenomenon, it has gained its popularity in environmental management sector. With the increase in population, technology which is helpful in managing waste for achieving Green Smart Society which in turn helps in upliftment in terms of reducing pollutants, conserving, resourcing and reusing the energy is needed in most of the environment related projects. Therefore, few experimental measures as a part of real-time monitoring has been implemented in this work with the help of selected IoT accessories and devices. 12.3 USAGE OF LORAWAN FOR WASTE COLLECTIONS Each waste can in a city features a little module interior which contains the ultrasonic sensors interfaces with the LoRa module. Ultrasonic sensors are utilized for distinguishing the trash level within the junk cans. The trash level recognized by the ultrasonic sensors is at that point sent to the door by shaping a standard LoRa bundle format. In the case of LoRa, there are two systems: open organize and private organize. In an open organize, portals are already introduced. The door can get the information which is at that point sent to the application server. In a private organize, the fetched of doors isn’t required. An Ethernet association is given to the portal by the benefit supplier. There are a secure information transmission and gathering due to the applications organize session key. Based on the information obtained, cautions will be produced such as the waste can being full or purge. From there, the fundamental activities to purge the junk can be taken. With the help of Table 12.1, list of chemical compounds released Table 12.1  Chemical compounds released from paint industry (Pollution Control Law Series 2000–2001) Chemical compounds Lead (Pb) Chromium (Cr) as hexavalent Chromium(Cr) as total Copper(Cu) Nickel(Ni) Total heavy metals

Permissible limits(mg/L) 0.1 0.1 2 2 5 7

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Figure 12.1 Construction and demolition waste recycling protocol.

from a paint industry has been studied in detail for carrying out the work precisely and efficiently with the help of IoT. Figure 12.1 states that how the C&D waste protocol is followed in the recent times paving the way for implementation of IoT. Also a network with reference to the one given in Figure 12.2 that is implemented to execute this work. LoRaWAN (long-range WAN) networking architecture is adopted for the deployment of Bin Level Monitoring Units (BLMU)s for tracking of the hazardous pollutants deposited in C&D wastes from a paint industry chosen for this work. Data regarding the quantity of toxic pollutants deposited in C&D waste has been collected using BLMU with LoRaWAN gateway and uploaded to the server for further processing of the work. Then the information is published into the server from BLMUs through MQTT for having an effective publication of results to the local residents and government officials. Further, the BLMUs placed at various points are connected to a Central Monitoring Server for

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Figure 12.2 Reference of the block diagram of BLMU used in this work.

effective processing of data to reduce the noxious compounds released into the nearby vicinity. The entire projects were carried out on a trial basis after which pilot-scale study was planned to be put forth. 12.4 FEATURES Figure 12.3 is used as a reference for creating an IoT architecture for representing the assimilation of four different components – sensors/devices, connectivity, data processing and a user interface which is meant for successful execution of this project. Therefore, after an intensive study from various literatures, suitable sensors, gateways, server and graphic user interface (GUI) have been used for this particular work, and their function in this project has been explained below. Especially, the ultrasonic sensors deployed in the selected waste dumping areas capture the timelines and send signals to respective personnel’s when the pollutants exceed the permissible limits. The above features are important part of this work so that the efficiency of the results are interconnected with them. More than ten trials related to check on receiving warning signal were done with the nearby

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Figure 12.3 A reference of the network architecture used in this project.

residential people who are the most affected people due to such contaminated environment. Based on the successfulness of this project, next level such as increasing the area will be done to have a massive study.

12.4.1  Ultrasonic sensor The ultrasonic sensor is on the list of most driving gadgets within the IoT stage which will be fulfilling the fill level sensor requirement of this project which identifies the level based on ultrasonic innovation (Nithiavathy & Shree, 2021). A vigorous sensor is set in squander containers and holders and measures the fill level, no matter what has been stored interior. This innovation is qualified for unwavering quality and insights. It consequently adjusts to changing surfaces and diverse sorts of waste. Focus on getting regularly measured information and sensor data are transmitted to the cloud through the portable communication organize for verifying the quality of data for waste management is done. Also, a filling-level sensor is executed with a SIM card which is having an advantage of utilizing existing media transmission systems for information transmission to the nearby neighborhood in the form of alert messages. To have more precision and steady readings, the above sensor is utilized to a maximum extent in this work. When this ultrasonic sensor is utilized, the time taken to send the signal related to toxicity is checked and how much time is required in return and consequently the cleaning action taken is calculated by it. By the condition, the removal of contaminant can be evaluated for having the

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four pins considered in work. Ultrasonic sensor equipped in the BLMU is a MB1010LV – Max sonar EZ which is lightweight, tiny and commercially available for systematic discernment of toxic pollutants level in the C&D waste collected. They are considered to be cost effective and dependable type with high-precision and high-frequency sound waves (25–50 Hz) which is helpful in evaluating how well the toxic contaminants are removed based on the alert signal. A wide range detection of wastes from 0 to 10m is possible with this sensor without any hindrance, and data also recorded on a continuous basis.

12.4.2  GPS module GPS collector module gives yield in standard NMEA (National Marine Hardware Affiliation) string setup which offers yield serially on Tx stick with default 9600 band rate. This NMEA string yield from GPS collector contains diverse parameters isolated by commas like longitude, scope, elevation, time of collection of waste, etc. Each string begins with ‘$’ and closes with carriage return/line nourish sequence. Several GPS modules are arranged over the collection points and to avoid the time consumption during waste collection process. To collect the geolocation coordinates of each trashbin, BLMU is integrated with a PAM7Q GPS antenna module which in turn helped in monitoring the waste dumping sites and reduction in pollutant level.

12.4.3 Gateway LoRa, which is brief for long extend, is one of the major long-range and low-power remote communication frameworks created by Semtech Organization. Whereas LoRa and LoRaWAN are commonly mixed up to be the same thing, they really speak to diverse components of a LoRa-based communication system. LoRa may be a radio recurrence carrier flag based within the physical (PHY) layer that changes over the data it gets to signals. On the other hand, LoRaWAN may be a convention found within the Media Get to Control (MAC) layer that advances LoRa signals to more extensive applications. Figure 12.4 depicts the various applications of LoRaWAN with a most basic function of simply demodulating LoRa packets from end nodes and transferring them to the server, but there are still several key elements you have to be aware of when working with LoRaWAN gateways or LoRaWAN networks in general. The Packet Forwarder is the software that provides the core functionality of a LoRaWAN gateway and defines the method of that is used to receive LoRa packets and transmit them to the network server. Agreeing to the application situation, three classes of LoRaWAN gadgets can be used: Lesson A –Bi-directional end-devices: After an uplink (UL) message, two get windows open to empower for downlink

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Figure 12.4 Applications of LoRaWAN.

(DL). The NS can react in one of these windows but not both. For the rest of the time, end devices (EDs) remain within the rest mode. The EDs of this course expend less vitality but have tall latency. Lesson B – Bi-directional end-devices with scheduled get openings: In expansion to the two get windows, the EDs open additional windows at planned times. In arrange to open additional windows, EDs utilize time-synchronized guides send by the gateways (GWs). The EDs of this lesson have a medium vitality utilization and medium latency. Course C – Bi-directional end-devices with maximal get space: The EDs ceaselessly keep get window open as it were in the event that they are transmitting. The network server (NS) can in this way build up DL communication at any time (Lalle et al., 2021). In this work, wireless communication is accomplished by utilizing Wi-Fi, Bluetooth and cellular systems, but these groups incorporate major problems such as commotion, impedances, organize slack, interruption and inefficiency for trial work to proceed the work after rectifying pros and cons. LoRa innovation is proposed to solve these issues by having a partitioned arrange which enables long-range transmission of more than 10km with low power consumption; and also able to handle tall capacity, that is, millions of messages per base station, which is perfect for public network administrators serving numerous clients. The proposed system comprises LoRa door, a farther symptomatic system, sensors for observing trash amount and a cloud platform. The

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method is done by meddle different modules such as GPS, camera, engines and sensors. Here the sensors are monitored regularly to keep track of trash levels (Sartipi, 2020). A low-power wide-area network (LPWAN) protocol is a part of LoRa, which helps in broadcasting signal between the node and gateway allowing the bandwidths of 110, 200 or 450 kHz used in this work. Each BLMU in this work with IoT-enabled solid waste management system will be monitored from Central Monitoring Station established within college campus meant for this project work, and also the BLMU in a specific region creates the WAN using non-IP-based communication protocols such as the LoRa protocol (Lalle et al., 2021). LoRa gateway configures the network address, IP address, default subnet mask, server IP, server up port and server down port in accordance with system requirements for smooth functioning of the entire setup involved in this work.

12.4.4  The server and the GUI The insight into MQTT and its utilization in IoT applications are needed for this work. As a result, we are going conversation around the solid and powerless focuses of this informing standard and compared it with other competing technologies. MQTT may be a high-usage innovation that was at first utilized to construct connections within a satellite-based arrange. The lightweight convention permitted for low transfer speed and control consumption. The asset proficiency of MQTT played a major part within the framework. The GUI server is dependable for producing the energetic web-based client interface of Netcool/Impact which arrange demands between conclusion users’ web browsers and Netcool/Impact and returns the graphical sees that you simply utilize to work with the information show, administrations and policies. An occurrence of the Tivoli Coordinates Entrance server is made amid the establishment, in the event that you chose to introduce the GUI server as one of the arrangement components. The installer sets all of the default setup properties for the server. After the establishment, we will be able to alter the arrangement of the GUI server by altering its properties files. The Tivoli Coordinates Entrance server begins and stops naturally when the application server, where its components are introduced, is begun or halted, but you’ll be able to moreover begin, and halt it autonomously of the application server whenever needed in this project (Nepomuceno & Pessoa, 2018). The server’s hardware configuration used in this piece of work includes an Intel Core i9 processor, 32 GB RAM and a 1TB hard drive running Ubuntu 20.04.3 LTS. The software package includes the Eclipse Mosquito message broker for implementing the MQTT protocol, which uses a publish/subscribe model to transport data for predicting the toxic levels at regular intervals. The intelligent GUI with this software package is designed using the C Sharp programming language on the.NET platform. The.NET core 3.0 is a lightweight and highly

Enforcement of IoT for potent oversight of Toxic levels  199 Table 12.2  Test results from the study area Pollutant Lead (Pb) Chromium (Cr) as hexavalent Chromium(Cr) as total Copper(Cu) Nickel (Ni) Total heavy metals

Concentration (mg/L) 0.12 0.101 0.11 2.2 2.1 7.1

Warning sign





performant RPC framework that enables real-time message push without polling. After systematic decoding and storage of the bin level data, the representation of the main window, area icons and all trash bin icons is graphically mapped to the BLMU measurements. In addition to cross-referencing the data, amalgamation of data through Blynk Android mobile application, an open-source network is also used. The Blynk application can be easily integrated with Arduino or microcontroller via Wi-Fi, GSM or Ethernet cables. As soon as the application is integrated, it shows whether the trash can is completely, partially full or empty. There you will find all the information you need about waste disposal, and you can also quickly identify the garbage container when waste with a high level of pollutants such as lead (Pb), chromium (Cr) as hexavalent, chromium(Cr) as total, copper(Cu), nickel (Ni) and total heavy metals exists. Once the chemical compounds exceed the permissible limits, warning sign stating the term hazardous as shown in Table 12.2 will be sent to people surrounding the zone to prevent further consequences in the future. 12.5 CONCLUSION Thus, IoT has been effectively used in this work to verify how much reduction in toxicity in the wastes surrounding the paint industry can be achieved on a trial basis, and also appropriate warning to the respective officials is also tested so that pilot-scale study can be initiated. The development and validation of an IoT network architecture approach to efficiently manage trash bins with pollutants exceeding the permissible limits in public places and residential areas are also studied in detail. According to the results obtained, proposed IoT-enabled C&D waste monitoring unit helps to track the hazardous pollutants in the vicinity of industrial areas and thus helps to suppress the pollutant level below permissible limits. Therefore, future works have been planned to expand the area for limiting the pollutants in a vast horizon for safeguarding the environment.

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REFERENCES Akram, S. V., Singh, R., Gehlot, A., Rashid, M., Alghamdi, A. S., Alshamrani, S. S., & Prashar, D. (2021). Role of wireless aided technologies in the solid waste management : A comprehensive review. Sustainability, 13(23), 13104. Almuhaya, M. A. M., Jabbar, W. A., Sulaiman, N., & Abdulmalek, S. (2022). A survey on LoRaWAN technology : Recent trends, opportunities, simulation tools and future directions. Electronics, 11(1), 164. Brous, P., Janssen, M. Herder, P. (2020). The dual effects of the Internet of Things (IoT): A systematic review of the benefits and risks of IoT adoption by organizations. International Journal of Information Management, 51, 101952. Dachyar, M., Zagloel, T. Y. M., & Saragih, L. R. (2019). Heliyon knowledge growth and development : Internet of things (IoT) research, 2006–2018. Heliyon, 5(June), e02264. https://doi.org/10.1016/j.heliyon.2019.e02264 Farooq, M. S., Riaz, S., Abid, A., Umer, T., & Zikria, Y. B. (2020). Role of IoT technology in agriculture : A systematic literature review. Electronics, 9, 319. Hong, I., Park, S., Lee, B., Lee, J., Jeong, D., & Park, S. (2014). IoT-based smart garbage system for efficient food waste management. Scientific World Journal, 2014, Article ID 646953. Jagtap, S. (2020). Waste Management Improvement in Cities using IoT. September. https://doi.org/10.1109/PARC49193.2020.236631 Khoa, T. A., Phuc, C. H., Lam, P. D., Mai, L., Nhu, B., Trong, N. M., Thi, N., Phuong, H., Dung, N. Van, Tan-y, N., Nguyen, H. N., Ngoc, D., & Duc, M. (2020). Waste Management System Using IoT-Based Machine Learning in University. Wireless Communications and Mobile Computing, 2020, Article ID: 6138637. Kumar, S., Tiwari, P., & Zymbler, M. (2019). Internet of Things is a revolutionary approach for future technology enhancement : A review. Journal of Big Data. https://doi.org/10.1186/s40537-019-0268-2 Lalle, Y., Fourati, M., Fourati, L. C., & Barraca, J. P. (2021). Routing strategies for LoRaWAN Multi-hop networks : A survey and an SDN-based solution for smart water grid. IEE Access, 9, 168624–168647. Mouha, R. A. (2021). Internet of Things (IoT). Journal of Data Analysis and Information Processing, 9(2), 77–101. https://doi.org/10.4236/jdaip.2021. 92006 Nadu, T., & Nadu, T. (2018). Iot based waste management in smart city using ir. International Journal of Engineering Research and Advanced Technology, 1984–1986. https://doi.org/10.7324/ijerat.2018.3246 Nepomuceno, T., & Pessoa, T. C. (2018). A GUI-based Platform for Quickly Prototyping Server-side IoT Applications. Smart SysTech 2018; European Conference on Smart Objects, Systems and Technologies, Dresden, Germany, 1–9. Nithiavathy, R., & Shree, R. A. (2021). IOT based tool garbage management system. Journal of Physics: Conference Series, 1916, 012171. https://doi. org/10.1088/1742-6596/1916/1/012171 Roostaei, J. (2018). Smart Wastewater Treatment : Internet of Things (IoT) and Edge Computing Applications in Environmental Engineering. March. Michigan Environmental Health Association’s 2018 Annual Education Conference, Michigan.

Enforcement of IoT for potent oversight of Toxic levels  201 Roostaei, J., Vishnu, S., Ramson, S. R. J., Senith, S., Anagnostopoulos, T., Abumahfouz, A. M., Fan, X., Srinivasan, S., Kirubaraj, A. A., Khoa, T. A., Phuc, C. H., Lam, P. D., Mai, L., Nhu, B., Trong, N. M., Thi, N., Phuong, H., Dung, N. Van, Tan-y, N., … Saragih, L. R. (2020). Household waste management system using IoT and machine learning household waste management system using IoT and machine. Procedia Computer Science, 167(2019), 1950–1959. https:// doi.org/10.1016/j.procs.2020.03.222 Sartipi, F. (2020). Influence of 5G and IoT in construction and demolition waste recycling – conceptual smart city design. Journal of Construction Materials, 1, 4-1. https://doi.org/10.36756/JCM.v1.4.1 Sheng, T. J. I., Islam, M. S., Misran, N., Baharuddin, M. H., Arshad, H., Chowdhury, M. E. H., Rmili, H., & Islam, M. T. (2020). An Internet of Things based smart waste management system using LoRa and Tensorflow deep learning model. IEEE Access, 8, 148793–148811. https://doi.org/10.1109/ACCESS.2020.3016255 Theivanathan, G., T, B. M., Dhinesh, M., Kalaiarasan, S., & Haaslinbilto, L. A. (2021). Smart waste management using Lora. Annals of the Romanian Society for Cell Biology, 25(6), 2011–2017. Ullo, S. L., & Sinha, G. R. (2020). Advances in smart environment monitoring systems using IoT and sensors. Sensors, 20, 3113. Vishnu, S., Ramson, S. R. J., Senith, S., Anagnostopoulos, T., Abu-mahfouz, A. M., Fan, X., Srinivasan, S., & Kirubaraj, A. A. (2021). IoT-enabled solid waste management in smart cities. Smart Cities, 4, 1004–1017.

Chapter 13

Smart water management system for water level and quality detection, monitoring, and control in residential structures Hiral M. Patel and Rupal R. Chaudhari Sankalchand Patel University

CONTENTS 13.1 Introduction 203 13.2  Literature survey 205 13.2.1  Comparisons of various research schemes 206 13.3  Measurement parameters of system 206 13.4  Methodology of the proposed system 206 13.5  System design 209 13.5.1 Software 209 13.5.2  Hardware components 211 13.5.2.1  Arduino UNO 211 13.5.2.2  Ultrasonic sensor 211 13.5.2.3  Turbidity module 212 13.5.2.4  LCD display (16*2 I2C) 213 13.5.2.5  Relay module (JQC-3FF-S-Z) 214 13.6  Data modeling diagrams 214 13.6.1  Sequence diagrams 215 13.6.2  Activity diagrams 217 13.7  Demo of work 218 13.8 Conclusion 219 References 220 13.1 INTRODUCTION Water covers more than 70% of the earth’s surface, but only 3% of that water is pure and safe to drink. Drinking water facilities are currently confronted with new real-world issues. Due to limited drinking water resources, high financial demands, increasing population, urbanization in rural areas, and expanding industry, the water quality available to people

DOI: 10.1201/9781003371526-13

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has deteriorated dramatically. In most residential areas where there is no continuous supply of water, water is kept in large underground tanks and then pumped to a rooftop tank for domestic use. It has been discovered that the current water-lifting technology necessitates a significant amount of manual labor. Due to the human inclination to forget things, this system may not be turned on or off in a timely manner, resulting in water waste and dirt in the surrounding areas. Many times, if the water level falls below a certain threshold, alkali suction can cause damage to complex sanitary ware. Furthermore, current technology lacks the ability to monitor the cleanliness of water stored in a tank. When we monitor the water level in a tank, we pay special attention to two scenarios. The first is a water tank overflow, and the second is a water supply shortfall. Our homes’ water tanks lack a mechanism that displays the volume of water in the tank as well as the cleanliness of the water. At our house, there is no automatic control system for the pressure pump. Due to the lack of a monitoring mechanism, either water overflow may occur, resulting in water waste, or the tank may go empty and remain as such until the operator physically observes it. That is why we are developing a system that will display the water level and also indicate the quality of the water. It will also use a technology that turns the water pump off and on automatically. As a result, we will be able to overcome the issue of water waste. The automatic mode in our suggested smart water tank system will turn on/off the pressure pump depending on pre-defined upper and lower threshold values. Furthermore, at certain intervals, our proposed system will display the purity level of stored water on an LCD panel. Additionally, an alarm will be sounded if the impurity level exceeds a certain threshold. If the system senses a water shortage, it starts the pressure pump and delivers a notification to the home owner through an LED display. Meanwhile, the system continuously measures a number of essential water quality elements and sends warning signals to the home owner if the value of any of these measurements exceeds safe levels. When the water tank level is reached, a certain threshold, the system shuts down the pressure pump and transmits an emergency alert. The proposed system proves to be an excellent replacement for the mechanically controlled systems found in most residential structures. The following is how the rest of this chapter is organized: Section 2 reviews the project’s related work, Section 3 describes the measuring parameters, Section 4 depicts the proposed system with module explanations, Section 5 represents the proposed system’s hardware and software requirements with component explanations, Section 6 shows data modeling diagrams, Section 7 portrays the product demo by showing results with the schematic circuit and how it works, and Section 8 concludes with future scope.

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13.2  LITERATURE SURVEY Pasika and Gandla (2020) have proposed a cost-effective IoT-based solution to monitor water quality. They have used Arduino Mega and NodeMCU target boards with a number of sensors to develop the system. The sensors are interfaced with the microcontroller unit, and further, processing is executed with the aid of using the computer. The information received may be directed to the cloud by using Internet of Things with the help of web-based application named as ThingSpeak to supervise various parameters such as pH value, the turbidity of the water, level of water in the tank, and temperature and humidity of the neighboring atmosphere through the web server. Costa and Emanuel (2020) predicted an IoT-based arrangement that uses devices such as smartphones, computers, and tablets to monitor changes in the level of water in containers. By building a prototype, the author can help water supply operators reduce the time wasted and energy at work. The author pointed out in the work of BRAC University and Paul (2018) about the physical and chemical properties of water in different sources and industrial waste water using a sensor network based on the Internet of Things. Daigavane and Gaikwad (2017, pp. 1107–1116) proposed the design and development of a system consisting of multiple sensors to measure the physical and chemical parameters of water. The measurement values from sensors can be processed by Arduino. Finally, the sensor data can be viewed on the Internet using the Wi-Fi system. Sarkar et al. (2018) provided an intelligent water management system by combining the Internet of Things technology with business processes and decision support systems. They provide a framework to describe in detail the physical scenarios of their inspection implementation, thereby allowing the water management process. Yasin et al. (2021, pp. 42–56) reviewed research using the Internet of Things (IoT) as a communication technology that can control the preservation of available water instead of being wasted by home owners and farmers. Mukta et al. (2019) evolved an IoT-based Smart Water Quality Monitoring (SWQM) machine which allows to monitor the quality of water on the premise of four exceptional parameters such as pH value, temperature, turbidity and electric-powered conductivity. They have coupled water level sensor, turbidity sensor and PH sensor to Arduino UNO to measure the values of parameters under consideration. The information accumulated from all sensors is communicated to a computing device utility that is evolved in the .NET platform. They have used a fast forest binary classifier to show better scrutinizing performance to validate the system’s accuracy and effectiveness in predicting the water quality.

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Olisa et al. (2021) have planned a water level and water quality observing framework that incorporates IoT innovation for ongoing checking of a two-tank framework. They have a coordinated Android portable app and a control framework to survey the water quality, perform level checks in the upward tank, and activate intelligent pumping control. A creative lowquality water outlet valve and control algorithm was coordinated into the framework. This considers simple flushing of low-quality water from the upward tank when distinguished. The flush low-quality water could be utilized for other outside purposes or reused through a treatment interaction. Consequently, they have fostered the instrument to stop water wastage.

13.2.1  Comparisons of various research schemes Table 13.1 provides a brief comparison of the numerous systems offered by various researchers using various parameters and techniques. 13.3  MEASUREMENT PARAMETERS OF SYSTEM There are a variety of factors that may be used to determine the quality of water. However, we have taken into account two parameters in our recommended solution. • The turbidity of the water in the tank • The level of the water in the tank. 13.4  METHODOLOGY OF THE PROPOSED SYSTEM One ultrasonic sensor is used in the proposed system to display the water level in the water tank. When the tank is 95% full, the pump will shut off automatically. We have a single display (LCD) on which we can check how much the tank is filled and the water quality. The pump will automatically switch on if the tank is 30% full or less than 30% full. Another function we added via the turbidity module is the ability to demonstrate the purity level of water by shooting a light beam into the water to be tested, after which it will continue to display pertinent information via LCD. If the turbidity level exceeds 20, the LCD screen will indicate “water dirty” and the buzzer will sound. Based on this information, the user can determine when the tank should be cleaned. Figure 13.1 depicts the suggested system’s block diagram, while Figure 13.2 illustrates the system’s operating principle.

IoT

IoT

Sarkar et al. (2018)

Arduino

Raspberry Pi 3

Arduino Mega 2560

IoT ARTIK (Cloud platform)

Daigavane and Gaikwad (2017, pp. 1107–1116)

Arduino UNO

IoT

Costa and Emanuel (2020) BRAC University and Paul (2018)

Arduino Mega NodeMCU

IoT Cloud computing

Microcontroller

Pasika and Gandla (2020) 

Technology used

Sensors

Temperature sensor Water-level sensor

Analog PH sensor Flow sensor (YF-5201) Ultrasonic sensor Temperature sensor (ds18b20) CO2 sensor Salinity sensor Turbidity sensor

pH sensor DHT-11 sensor Turbidity sensor Ultrasonic sensor Ultrasonic sensor

Table 13.1  Comparison of various existing research schemes

Wi-Fi module

–

Wi-Fi module ESP8266 GSMsim 900A GSM shield SIM808 Wi-Fi module

Wi-Fi Module ESP8266

Communication module

Water level Turbidity CO2 level pH value Water level Water quality

pH value Water-level temperature

pH value Turbidity water-level temperature humidity Water level

Parameters considered

(Continued)

Mobile app

Web-based application

Web-based application

Web-based application

ThingSpeak Mobile App

Mobile/Desktop/ Web application

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IoT ICT

IoT Machine learning

IoT Wireless communication

Yasin et al. (2021, pp. 42–56)

Mukta et al. (2019)

Olisa et al. (2021)

Technology used

ESP32 Module

Arduino UNO

Arduino Mega 2560

Microcontroller

Turbidity sensor SEN0189 pH sensor SEN0161 Temperature sensor DFR0198 Analog sensor DFR0300 Water-level sensor Turbidity sensor pH sensor

Moisture sensor Humidity sensor Turbidity sensor Fabricated sensor Capacity sensor

Sensors

Table 13.1  (Continued) Comparison of various existing research schemes

Wi-Fi module Firebase Bluetooth module

GSM shield SIM900 Wi-Fi module ZigBee Bluetooth Ethernet –

Communication module

Water level Turbidity pH value

pH value Temperature Turbidity Electric conductivity

Water level Water quality

Parameters considered

Mobile app

Desktop application

–

Mobile/Desktop/ Web application

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Smart water management system for water level and quality detection  209

Figure 13.1. System block diagram.

13.5  SYSTEM DESIGN

13.5.1 Software We wrote our code in Arduino integrated development environment (IDE), which is a cross-platform application. The source was written in C and C++ languages. It’s easy to program, and first we tested the sensors each

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Figure 13.2 Working principal of proposed system.

individually and then integrated the whole system. The Arduino IDE supplies a software library from the wiring project, which provides many common input and output procedures. User-written code only requires two basic functions, for starting the sketch and the main program loop, that are compiled and linked with a program stub main() into an executable cyclic executive program with the GNU tool chain, also included with the IDE distribution.

Smart water management system for water level and quality detection  211 Table 13.2  Hardware requirements Item name Arduino UNO R3 5v USB Adapter Ultrasonic sensor HC-SR04 Turbidity module (sensor) LED lights Relay (JQC-3FF-S-Z) LCD (16 × 2 I2C) 1k ohm register Mini water pump kit (battery holder, water pipe, DC water pump) Buzzer Breadboard 9 V battery Jumper Wires Tank

Justification It is an open-source microcontroller board based on the ATmega328 chip Ultrasonic sensors work by emitting sound waves at a frequency too high for humans to hear Used to check water quality Red lights Used as an automatic switch Used to display the output Used to control signals – Used to generate audio signal Connection board For power supply Connection wires To store water

13.5.2  Hardware components The list of components required to make the proposed system is given in Table 13.2. 13.5.2.1  Arduino UNO The Arduino UNO R3 is an open-source microcontroller board based on the ATmega328 chip as shown in Figure 13.3. This board has 14 digital input/output pins, 6 analog input pins, onboard 16 MHz ceramic resonator, port for USB connection, onboard DC power jack, an ICSP header, and a microcontroller reset button. It contains everything needed to support the microcontroller. Using the board is also very easy: simply connect it to a computer with a USB cable or power it with a DC adapter or battery to get started. 13.5.2.2  Ultrasonic sensor Ultrasonic sensors work by transmitting sound waves at a frequency excessively high for people to hear. They then, at that point, wait for the sound to be reflected back, calculating the distance based on the time required. This is often as old as how microwave radar measures the time it takes a radio radiation to return once contacting an object. While a few sensors utilize a different sound terminal and recipient, it’s conjointly possible to

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Figure 13.3 Arduino UNO R3. (ElectronicsComp, 2021.)

Figure 13.4 Working principal of an ultrasonic sensor. (Paramasivam, 2020.)

blend these into one gadget, having a supersonic part switch back and forth between producing and getting signals (Paramasivam, 2020). This kind of sensor can be manufactured in an exceptionally more modest bundle than with independent components that are advantageous for applications of any size at whatsoever premium. Figure 13.4 portrays the working principal of an ultrasonic sensor. 13.5.2.3  Turbidity module Figure 13.5 shows the structure of a turbidity sensor. The Arduino turbidity sensor distinguishes water quality by estimating the degree of turbidity.

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Figure 13.5 Structure of a turbidity sensor. (Keyestudio Wiki, n.d.)

Figure 13.6 An LCD controller. (Circuit Digest, 2018.)

It can determine suspended particles in water by estimating the light conveyance and dissipating rate which changes with how much complete suspended solids (TSS) in water. As the TTS builds, the fluid turbidity level increments. 13.5.2.4  LCD display (16*2 I2C) A 16 × 2 LCD screen pin diagram with I2C interface is shown in Figure 13.6. It is able to display 16 × 2 characters on two lines, white characters on blue background. Usually, Arduino LCD projects will run out of pin resources easily, especially with Arduino UNO. This I2C 16 × 2 Arduino LCD screen is using an I2C communication interface. It means it only needs four pins for the LCD: VCC, GND, SDA, and SCL. We can connect with the jumper wire directly.

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Figure 13.7 Relay module. (Raspberry Pi Stack Exchange, 2017.)

13.5.2.5  Relay module (JQC-3FF-S-Z) Relay as shown in Figure 13.7 is used to provide time delay functions. They are used to time the delay open and delay close of contacts. Relays are used to control high-voltage circuits with the help of low-voltage signals. Similarly, they are used to control high-current circuits with the help of low-current signals. 13.6  DATA MODELING DIAGRAMS Now, using data modeling diagrams such as sequence diagrams and activity diagrams for water-level monitoring, water quality detection, and automatic pump turn on/off as illustrated in Figures 13.8 to 13.12, let’s try to explain the suggested system for smart water management.

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13.6.1  Sequence diagrams

Figure 13.8 Sequence diagram for water-level detection.

Figure 13.9 Sequence diagram for automatic water pump on/off system.

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Figure 13.10 Sequence diagram for water quality detection.

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13.6.2  Activity diagrams

Figure 13.11 Activity diagram for water-level detection and automatic water pump on/ off.

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Figure 13.12 Activity diagram for water quality detection.

13.7  DEMO OF WORK Let’s have a look at some screenshots of the working system in this area. The hardware setup of the entire system is shown in Figure 13.13. Figure 13.14 shows the time when the pump turns on and off as well as the output of the turbidity module, which is used to check the purity of the water in the tank.

Figure 13.13 Hardware setup of the smart water management system.

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Figure 13.14 Results of the working system.

13.8 CONCLUSION To summarize, we can say that our system will be useful in achieving our goal “SAVE WATER, SAVE EARTH” by reducing the water wastage. A large amount of energy, effort, and money spent for water that is made obtainable to us in our home. Our proposed system can also moderate expenses and the amount of energy used to pump water. This lowers energy demand and thus relies less on carbon-intensive power plants, which in turn benefits the environment by reducing their carbon dioxide emissions. By indicating water purity level, our system can improve the health level of family members. Our system will be useful to eliminate the human tendency to forget controlling the system. The proposed solution can be further developed and scaled to be used as a liquid level indicator in huge containers in companies, for fuel tank level gauging, smart jar, and many more.

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REFERENCES BRAC University, & Paul, B. (2018, August). Sensor Based Water Quality Monitoring System. BRAC University. Retrieved from http://dspace.bracu.ac.bd/xmlui/bitstream/handle/10361/10840/11101042_CSE.pdf?sequence=1&isAllowed=y ×  2 LCD with ESP32 using Circuit Digest. (2018, August 9). Interfacing 16  I2C. Retrieved from https://circuitdigest.com/microcontroller-projects/ interfacing-16x2-lcd-with-esp32-using-i2c Costa, J. E. D., & Emanuel, A. W. R. (2020). IOT design monitoring water tank study case: Instituto profissional de canossa (IPDC). International Conference on Science and Applied Science (ICSAS2020). https://doi.org/10.1063/5.0032638 Daigavane, V., & Gaikwad, D. M. A. (2017). Water quality monitoring system based on IOT. Advances in Wireless and Mobile Communications, 10(5), 1107–1116. ElectronicsComp. (2021). Arduino UNO R3 SMD Atmega328P board. Retrieved from www.electronicscomp.com. Keyestudio Wiki. (n.d.). KS0414 Keyestudio turbidity sensor v1.0. Retrieved April 6, 2022, from https://wiki.keyestudio.com/KS0414_Keyestudio_Turbidity_Sensor_ V1.0 Mukta, M., Islam, S., Barman, S. D., Reza, A. W., & Hossain Khan, M. S. (2019). Iot based smart water quality monitoring system. 2019 IEEE 4th International Conference on Computer and Communication Systems (ICCCS). https://doi. org/10.1109/ccoms.2019.8821742 Olisa, S. C., Asiegbu, C. N., Olisa, J. E., Ekengwu, B. O., Shittu, A. A., & Eze, M. C. (2021). Smart two-tank water quality and level detection system via IoT. Heliyon, 7(8), e07651. https://doi.org/10.1016/j.heliyon.2021.e07651 Paramasivam, N. (2020, February 11). How to create a distance measuring system using Arduino UNO R3. Retrieved from https://www.c-Sharpcorner.Com/ Article/How-to-Create-Distance-Measuring-System-Using-Arduino-Uno-R3/. Pasika, S., & Gandla, S. T. (2020). Smart water quality monitoring system with cost-effective using IoT. Heliyon, 6(7), e04096. https://doi.org/10.1016/j.heliyon.2020.e04096 Raspberry Pi Stack Exchange. (2017, October 3). Using single 5V relay (jqc-3ff-s-z) safety advice required. Retrieved from https://raspberrypi.stackexchange.com/ questions/73311/using-single-5v-relay-jqc-3ff-s-z-safety-advice-required Sarkar, S., Sikder, S., Islam Ashik, S., & Siddika, A. (2018). Analysis, design and development of an IoT based water management system for residence. GSJ, 6(10), 398–403. Yasin, H. M., Zeebaree, S. R. M., Sadeeq, M. A. M., Ameen, S. Y., Ibrahim, I. M., Zebari, R. R., Ibrahim, R. K., & Sallow, A. B. (2021). IoT and ICT based smart water management, monitoring and controlling system: A review. Asian Journal of Research in Computer Science, 42–56. https://doi.org/10.9734/ajrcos/2021/ v8i230198

Chapter 14

A novel approach for vehicle detection to avoid accidents in the construction area Diya Vadhwani and Devendra Thakor Uka Tarsadia University

Darshana Patel V.V.P. College of Engineering

CONTENTS 14.1 Introduction 221 14.2  Literature Study 222 14.3  Proposed System 223 14.4  Precautionary measures 223 14.5 Methodology 223 14.6  Algorithm for Proposed System 225 14.7 Implementation 225 14.7.1  Hardware specifications 226 14.7.2  Software specification 226 14.7.3  Raspberry Pi 3 226 14.7.4  Display module 227 14.7.5  Ultrasonic sensor & buzzer 227 14.7.6  GPS receiver 228 14.8  Results and Discussion 230 14.9 Advantages and Disadvantages of the System 230 14.10  Conclusion and Future Work 231 References 231 14.1 INTRODUCTION Intelligent Transportation System (ITS) is a network of interconnected roads, users and vehicles for gathering information. There are many applications of ITS such as Accident Notification System which is also referred as an emergency management system, Traveler Information Services, Parking System, etc. In this study, an accident alert system is developed using the prototype model build using GPS, Raspberry Pi, ultrasonic sensor and buzzer. Road accidents during ongoing construction of a particular site sometimes cause fatal deaths to humans, and in order to avoid accidents DOI: 10.1201/9781003371526-14

221

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or reduce road accidents at hazardous locations such as construction sites, the scenario for accident alert is the construction area. For an accident alert system, the distance of vehicle crossing near the construction area is calculated, to warn the workers and save their life. The prototype developed for a construction site aims to reduce the accidents of workers or persons available at the construction site. The system alerts the vehicle driver to slow down or be careful by ringing the buzzer while passing by such a risky area. If the vehicle is only a few inches close to the construction area, the buzzer gets activated and the driver is alerted. The system also alerts the driver if there is any obstacle in front it. The ultrasonic sensor is a low power device that utilizes less energy. Thus, the low power utilization of an accident alert system is developed using green communication IoT-enabled technology comprising Raspberry Pi, ultrasonic sensor and GPS device. 14.2  LITERATURE STUDY As automobiles increase along the roadside, there is an increase in accidents which cause deaths. Many accident notification systems have been developed for warning the vehicles to avoid accidents. The ITS has various applications areas; one of the applications of an accident notification system is our area of study (Baramy et al., 2016). The system implemented by Baramy et al. (2016) is used to send notification to relatives of persons whose vehicles are involved in accidents using GSM module. In Kumar et al. (2016), the vehicle is detected using GPS tracking module, and the SMS is sent to the registered mobile number. The new intrusion alarm system based on the wireless sensor network was developed by Bhaskar et al. (2017). This intrusion alarm system consists of two elements: the first is the vehicle detector, and the second is warning devices (Bhaskar et al., 2017). The vehicle detector elements are used to monitor the perimeter, while the warning devices are used to alert the workers working in the construction area (Huang et al., 2018). The research study in Nasr et al. (2016) gives an IoT system solution which notifies the Public Safety Organizations’ (PSO) headquarters when an accident occurs using GPS (Nasr et al., 2016). Using MEMS accelerometer, the GPS tracking system is developed to monitor the accidents (Desai et al., 2013; Gaonkar, 1994). The shortest path routing algorithm is used for navigation systems according to research performed in Chang et al. (2008). According to Li et al. (2008), vehicle-based on mobile sensor is used for traffic monitoring. Many systems are developed based on the problems for accident prevention, and this chapter aims to save the ecosystem using intelligent green communication Internet of Things (IoT)-enabled systems (accident alert system) and contributes to the welfare of human lives who become the victim of road accidents every day.

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14.3  PROPOSED SYSTEM The accident alert system is tracks the vehicle’s current location and the location of an accident-prone area using GPS. The system is used to calculate the distance between the running vehicle and the prone area, here the construction area, using the IoT technology. The system ensures that the vehicle crossing nearer to the construction area is alerted as it is risky in order to make the person and vehicle are safe in that area. A cost-effective accident alert system which is an application of intelligent transportation system is developed using green communication IoT-enabled technology comprising Raspberry Pi, ultrasonic sensor and GPS device. In the construction zone, there is tremendous rise in accidents, and to avoid accidents in the construction area, many systems have been developed for alerting the driver of a vehicle. In the proposed system, the driver of a vehicle is alerted by a buzzer when crossing the restricted area in the construction zone. The distance is calculated using a sensor mounted on the vehicle when it crosses the restricted area. The main aim of the accident alert system is to alert the vehicle crossing the accident-prone area, here the construction site. It instructs the vehicle driver to slow down or be careful by ringing the buzzer while passing by such an unsafe or a risky area. If the vehicle is only 4 inches close to the location, then the buzzer gets activated and the driver is alerted.

14.4  PRECAUTIONARY MEASURES Road accidents lead to fatal deaths; this study aims to reduce the accidents at the construction site or risky area and save human lives. Following are some of the precautionary measures to be considered while implementing the scenarios for accident alert system at the construction site or risky areas (Gopalakrishnan, 2012). • Vehicles should be fitted with proper breaks and good lighting. • Driver should be aware of traffic rules. • Installation of necessary GPS modules in vehicles while crossing the construction area or risky area. • There should be proper use of helmet by the driver of the vehicle.

14.5 METHODOLOGY The block diagram for accident alert system is given in Figure 14.1 The proposed system flowchart is shown in Figure 14.2 which describes the complete flow for the accident alert system.

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Figure 14.1 Block diagram of an accident alert system.

Figure 14.2 Flowchart of an accident alert system.

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Figure 14.3 Sequence of steps followed by an accident alert system.

14.6  ALGORITHM FOR PROPOSED SYSTEM Step 1: Request for accurate location from the GPS receiver. (This is the area where the construction is going on.) Step 2: Check for the location from the area detector module. Step 3: If location is detected, then a response is sent to GPS and system. Step 4: Calculate the distance between the area detected and the alert system. Step 5: Send the measured distance to the system through receiver. Step 6: If calculated distance is less than 5 m, then alert the vehicle driver with a buzzer to slow down the speed in order to avoid the accident in the construction area. Using the above-proposed algorithm, the system is implemented. Figure 14.3 shows the relationship of different modules or devices of system. In Figure 14.3, different data values and functions are shown for each module of the proposed system. Figure 14.3 shows the sequence of steps followed by the accident alert system to alert the driver of vehicle in the risky area. 14.7 IMPLEMENTATION In the construction area, there are higher risks to workers. So to avoid accidents in the area, the vehicles are detected if it crosses the restriction area

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and the driver of the vehicle is warned using a buzzer to prevent the accident and save the life of workers. The proposed system is implemented by using the following hardware and software requirements.

14.7.1  Hardware specifications Following are the hardware components used to configure the system. • • • • • •

Raspberry Pi 3 Ultrasonic sensor GPS receiver LCD Connecting wires Breadboard

14.7.2  Software specification • Raspbian Jessie OS

14.7.3  Raspberry Pi 3 Raspberry Pi is a small-sized computer with low cost, which can be connected to the TV or LCD monitor having a keyboard and mouse (Desai et al., 2013; Astudillo-Salinas et al., 2016). Using Raspberry Pi, the shortcode in Python language is built (Bekaroo and Santokhee, 2016). Figure 14.4 shows the Raspberry Pi 3 module.

Figure 14.4 Raspberry Pi 3 module.

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14.7.4  Display module Figure 14.5 shows the display module used in the accident alert system.

14.7.5  Ultrasonic sensor & buzzer Figure 14.6 shows the ultrasonic sensor and buzzer used in the accident alert system.

Figure 14.5 Display module.

Figure 14.6 Ultrasonic sensor and buzzer.

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14.7.6  GPS receiver Figure 14.7 shows the GPS receiver used in the accident alert system. Distance Measurement Using Raspberry Pi 3 module and Ultrasonic Sensor Figure 14.8 shows the connection of ultrasonic sensor, GPS receiver and buzzer with Raspberry Pi 3 module. Alert Module & Distance Measurement Figure 14.9 shows alert module with buzzer. With the help of distance calculated using the ultrasonic sensor and the Raspberry Pi 3, the buzzer can be activated. If the distance calculated is less than the threshold distance value, then the buzzer starts ringing unless the distance between the ultrasonic sensor and the obstacle is increased. Figure 14.10 displays the distance measured.

Figure 14.7 GPS receiver.

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Figure 14.8 GPS receiver with Raspberry Pi 3 module.

Figure 14.9 Alert module.

Figure 14.10 Distance measurement.

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14.8  RESULTS AND DISCUSSION By the configuration of ultrasonic sensor, GPS receiver, buzzer and Raspberry Pi, the distance is calculated as 14.66 which is in the range of prone area or construction area. If distance is greater than or less than the threshold value, the system works accordingly. The distance measured by the ultrasonic sensor is sent to the processing system; the system verifies the distance calculated with threshold value and responses as: If the distance calculated is more than the threshold value, then the worker is safe and the vehicle is far away from the prone area, i.e., say threshold = 20 cm, say distance is 25 cm so 20 cm