WSN and IoT
An Integrated Approach for Smart Applications
1st Edition
Published on 26. April 2024
406 pages
978-1-040-01302-1 (ISBN)
Description
Nowadays, all of us are connected through a large number of sensor nodes, smart devices, and wireless terminals. For these Internet of Things (IoT) devices to operate seamlessly, the Wireless Sensor Network (WSN) needs to be robust to support huge volumes of data for information exchange, resource optimization, and energy efficiency. This book provides in-depth information about the emerging paradigms of IoT and WSN in new communication scenarios for energy-efficient and reliable information exchange between a large number of sensor nodes and applications.
WSN and IoT: An Integrated Approach for Smart Applications discusses how the integration of IoT and WSN enables an efficient communication flow between sensor nodes and wireless terminals and covers the role of machine learning (ML), artificial intelligence (AI), deep learning (DL), and blockchain technologies which give way to intelligent networks. This book presents how technological advancement is beneficial for real-time applications involving a massive number of devices and discusses how the network carries huge amounts of data allowing information to be communicated over the Internet. Intelligent transportation involving connected vehicles and roadside units is highlighted to show how a reality created through the intelligent integration of IoT and WSN is possible. Convergence is discussed and its use in smart healthcare, where only through the intelligent connection of devices can patients be treated or monitored remotely for telemedicine or telesurgery applications. This book also looks at how sustainable development is achieved by the resource control mechanism enabling energy-efficient communication.
A wide range of communication paradigms related to smart cities, which includes smart healthcare, smart transportation, smart homes, and intelligent data processing, are covered in the book. It is aimed at academicians, researchers, advanced-level students, and engineers who are interested in the advancements of IoT and WSN for various applications in smart cities.
WSN and IoT: An Integrated Approach for Smart Applications discusses how the integration of IoT and WSN enables an efficient communication flow between sensor nodes and wireless terminals and covers the role of machine learning (ML), artificial intelligence (AI), deep learning (DL), and blockchain technologies which give way to intelligent networks. This book presents how technological advancement is beneficial for real-time applications involving a massive number of devices and discusses how the network carries huge amounts of data allowing information to be communicated over the Internet. Intelligent transportation involving connected vehicles and roadside units is highlighted to show how a reality created through the intelligent integration of IoT and WSN is possible. Convergence is discussed and its use in smart healthcare, where only through the intelligent connection of devices can patients be treated or monitored remotely for telemedicine or telesurgery applications. This book also looks at how sustainable development is achieved by the resource control mechanism enabling energy-efficient communication.
A wide range of communication paradigms related to smart cities, which includes smart healthcare, smart transportation, smart homes, and intelligent data processing, are covered in the book. It is aimed at academicians, researchers, advanced-level students, and engineers who are interested in the advancements of IoT and WSN for various applications in smart cities.
More details
Series
Edition
1. Auflage
Language
English
Place of publication
London
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
Product notice
Reflowable
Illustrations
30 Tables, black and white; 160 Line drawings, black and white; 6 Halftones, black and white; 166 Illustrations, black and white
ISBN-13
978-1-040-01302-1 (9781040013021)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
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12/2025
1st Edition
CRC Press
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Book
04/2024
1st Edition
CRC Press
€216.00
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Book
04/2024
CRC Press
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Persons
Dr. Shalli Rani is pursuing a postdoctoral from Manchester Metropolitan University, UK from July 2022. She is a Professor at Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India. She has 18+ years of teaching experience. She received an MCA degree from Maharishi Dyanand University, Rohtak in 2004 and an M. Tech. degree in Computer Science from Janardan Rai Nagar Vidyapeeth University, Udaipur in 2007 and a Ph.D. in Computer Applications from Punjab Technical University, Jalandhar in 2017. Dr. Rani has published/accepted/presented more than 100+ papers in international journals /conferences (SCI+Scopus) and edited/authored five books with international publishers. She has worked on Big Data, Underwater Acoustic Sensors, and IoT to show the importance of WSN in IoT applications and has received a Young Scientist award in Feb. 2014 from the Punjab Science Congress, a Lifetime Achievement Award, and a Supervisor of the Year award from Global Innovation and Excellence, in 2021. Her main areas of interest and research are Wireless Sensor Networks, Underwater Sensor networks, Machine Learning, and the Internet of Things.
Dr. Ashu Taneja is an associate professor at Chitkara University Research and Innovation Network (CURIN), Chitkara University, Punjab, India. She has a PhD in Electronics and Communication Engineering with an experience of 12 years serving industry, teaching, education and research. She received Masters of Engineering (M.E.) from Thapar University, Patiala, Punjab in 2011 and Bachelors of Technology (B.Tech) from Kurukshetra University, Haryana, in 2008. She has 50+ publications in international journals /conferences (SCI + SCOPUS). She has filed more than 20 patents. She has also served as reviewer in many repudiated journals of IEEE, Springer, Elsevier, IET, Hindawi and Wiley. Her research interests include Next Generation Wireless Networks, IoT, 5G, 6G, Energy Harvesting, User Selection, Antenna Selection, Intelligent Reflecting Surfaces, and Vehicular communication.
Dr. Ashu Taneja is an associate professor at Chitkara University Research and Innovation Network (CURIN), Chitkara University, Punjab, India. She has a PhD in Electronics and Communication Engineering with an experience of 12 years serving industry, teaching, education and research. She received Masters of Engineering (M.E.) from Thapar University, Patiala, Punjab in 2011 and Bachelors of Technology (B.Tech) from Kurukshetra University, Haryana, in 2008. She has 50+ publications in international journals /conferences (SCI + SCOPUS). She has filed more than 20 patents. She has also served as reviewer in many repudiated journals of IEEE, Springer, Elsevier, IET, Hindawi and Wiley. Her research interests include Next Generation Wireless Networks, IoT, 5G, 6G, Energy Harvesting, User Selection, Antenna Selection, Intelligent Reflecting Surfaces, and Vehicular communication.
Content
1. Introduction to IoT and WSN 2. Role of Sustainable IoT in Present Day Life 3. Designing an Integrated IoT-WSN Framework for Smart City Applications 4. Principles of Artificial Intelligence in Internet of Things 5. Blockchain-Based Communication Framework for Smart Vehicles 6. A Sustainable IoT-Based Smart Transportation System for Urban Mobility 7. Optimizing Content Delivery in ICN-Based VANETs Using Machine Learning Techniques 8. the Role of IoMT Technologies in Revolutionizing Healthcare: A Comprehensive Overview 9. Deep Learning and Its Applications in Healthcare 10. An Explainable Deep Learning Model for Clinical Decision Support in Healthcare 11. Deep Learning Models for Automated Diagnosis of Brain Tumor Disorder in Smart Healthcare 12. 6G and Distributed Computing for IoT: A Survey 13. Designing a Decentralized IoT-WSN Architecture Using Blockchain Technology 14. Privacy-Preserving Machine Learning on Non-Co-Located Data Sets Using Federated Learning: Challenges and Opportunities 15. Deep Learning Method for Hyperspectral Image Classification 16. Towards Smarter Industries: Security Framework Using Attribute-Based Encryption and Systematic Solutions
