IoT and WSN based Smart Cities: A Machine Learning Perspective

ISBN-13: 9783030841843 / Angielski / Miękka / 2023

Opis

This book provides an investigative approach to how machine learning is helping to maintain and secure smart cities, including principal uses such as smart monitoring, privacy, reliability, and public protection. The authors cover important areas and issues around implementation roadblocks, ideas, and opportunities in smart city development. The authors also include new algorithms, architectures and platforms that can accelerate the growth of smart city concepts and applications. Moreover, this book provides details on specific applications and case studies related to smart city infrastructures, big data management, and prediction techniques using machine learning.

Spis treści

Chapter 1. Protocol Design for Earthquake Alert and Evacuation in Smart Buildings.- Chapter 2. Impact of Internet of Things and Clinical Decision Support System in Healthcare.- Chapter 3. Smart Healthcare Support Using Data Mining and Machine Learning.- Chapter 4. Window Functions for Phasor Signal Processing of Wide Area Measurement in Smart Grid Communications.- Chapter 5. Facens Smart Campus Integrated Dashboard: A Use case applied for Energy Efficiency.- Chapter 6. Cloud Internet of Things in Medical and Smart Healthcare Applications.- Chapter 7. Tornado Forecast Visualization for Effective Rescue Planning.- Chapter 8. Situational Awareness for Law Enforcement and Public Safety Agencies Operating in Smart Cities-Part I.- Chapter 9. Situational Awareness for Law Enforcement and Public Safety Agencies Operating in Smart Cities: Part II.- Chapter 10. A Wireless Sensor Architecture for Efficient Water Quality Measurement and Monitoring Using IO.- Chapter 11. Design of a WSN Platform for Internet of Things Applications.- Chapter 12. Performance Analysis of Modulation Techniques over an Smart City Optical Communication Channel under Weak Atmospheric Turbulence.- Chapter 13. Towards Secure Cyber Infrastructure for SmartCities: Learning based Intelligent Solutions.- Chapter 14. Utilizing ICN caching for IoT big data management in WSN based vehicular networks.- Chapter 15. Integration of WSN and IoT: Its Applications and Technologies?.- Chapter 16. Choice Based Recreation Facility for Smart Cities.

O autorze

Dr. Shalli Rani is Associate Professor in CSE with Chitkara University (Rajpura (Punjab)), India. She has 15+ years teaching experience. She received MCA degree from Maharishi Dyanand University, Rohtak in 2004 and the M.Tech. degree in Computer Science from Janardan Rai  Nagar Vidyapeeth University, Udaipur  in 2007 and Ph.D. degree in Computer Applications from Punjab Technical University, Jalandhar in 2017. Her main area of interest and research are Wireless Sensor Networks, Underwater Sensor networks and Internet of Things. She has published/accepted/presented more than 35+ papers in international journals /conferences (SCI+Scopus) and two books with Springer.  She is serving as the associate editor of IEEE Future Directions Letters.  She has worked on Big Data, Underwater Acoustic Sensors and IoT to show the importance of WSN in IoT applications. She received a young scientist award in Feb. 2014 from Punjab Science Congress, in the same field. 

Dr. Vyasa Sai is a Senior Hardware Engineer in the Visual and Machine Learning IP Group @ Intel Corporation, Folsom, CA, USA. He received his PhD from the Department of Electrical and Computer Engineering (ECE) at the University of Pittsburgh, Pittsburgh, PA, USA in 2013. He also has a Master of Science degree in ECE and a Bachelor of technology degree in ECE from USA and India respectively. Dr. Sai is a published author with numerous refereed international publications in the field of electronics and communication engineering that includes IoT, WSN, RFID, Low Power Electronics, Security, among others. Dr. Sai is also a published inventor who holds several US patents along with technical leadership roles on editorial boards, advisory board, technical committees at IEEE, Elsevier, among others. He is also an invited reviewer for many international journals and conferences. Dr. Saiʼs research contributions and accomplishments have won him many international honors that include 2018 Williams award, 2019 outstanding scientist award, 2020 Sheth International Achievement award, among others.

Dr. R. Maheswar has completed his B.E (ECE) from Madras University in the year 1999, M.E (Applied Electronics) from Bharathiyar University in the year 2002 and Ph.D in the field of Wireless Sensor Network from Anna University in the year 2012. He has about 20 years of teaching experience at various levels and presently working as Professor in the Department of ECE, KPR Institute of Engineering and Technology, Coimbatore. He has published around 70 papers at International Journals and International Conferences and published 4 patents. His research interest includes Wireless Sensor Network, IoT, Queueing theory and Performance Evaluation. He has served as guest editor for Wireless Networks Journal, Springer and serving as editorial review board member for peer reviewed journals, and also edited 4 books supported by EAI/Springer Innovations in Communications and Computing book series. He is presently an associate editor in Wireless Networks Journal, Springer and Alexandria Engineering Journal, Elsevier.

Blurb

This book provides an investigative approach to how machine learning is helping to maintain and secure smart cities, including principal uses such as smart monitoring, privacy, reliability, and public protection. The authors cover important areas and issues around implementation roadblocks, ideas, and opportunities in smart city development. The authors also include new algorithms, architectures and platforms that can accelerate the growth of smart city concepts and applications. Moreover, this book provides details on specific applications and case studies related to smart city infrastructures, big data management, and prediction techniques using machine learning.

• ​Explores various applications to show how machine learning is helping to maintain and secure smart cities;
• Includes developments on how applying machine learning to IoT and WSN can help in monitoring, privacy, and reliability of smart cities;
• Provides a platform for researchers and professionals to explore applications of IoT for smart cities.