Iot And Wsn Based Smart Cities A Machine Learning Perspective

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.

This book provides a valuable combination of relevant research works on developing smart city ecosystem from the artificial intelligence (AI) and Internet of things (IoT) perspective. The technical research works presented here are focused on a number of aspects of smart cities: smart mobility, smart living, smart environment, smart citizens, smart government, and smart waste management systems as well as related technologies and concepts. This edited book offers critical insight to the key underlying research themes within smart cities, highlighting the limitations of current developments and potential future directions.

This book exploits the benefits of integration of wireless sensor networks (WSN) and Internet of Things (IoT) for smart cities. The authors discuss WSN and IoT in tackling complex computing tasks and challenges in the fields of disaster relief, security, and weather forecasting (among many others). This book highlights the challenges in the field of quality of service metrics (QoS) in the WSN based IoT applications. Topics include IoT Applications for eHealth, smart environments, intelligent transportation systems, delay tolerant models for IoT applications, protocols and architectures for industrial IoT, energy efficient protocols, and much more. Readers will get to know the solutions of these problems for development of smart city applications with the integration of WSN with IoT.

Artificial Intelligence and Machine Learning in Smart City Planning shows the reader practical applications of AIML techniques and describes recent advancements in this area in various sectors. Owing to the multidisciplinary nature, this book primarily focuses on the concepts of AIML and its methodologies such as evolutionary techniques, neural networks, machine learning, deep learning, block chain technology, big data analytics, and image processing in the context of smart cities. The text also discusses possible solutions to different challenges posed by smart cities by presenting cutting edge AIML techniques using different methodologies, as well as future directions for those same techniques. Reviews the smart city concept and teaches how it can contribute to achieving urban development priorities Explains soft computing techniques for smart city applications Describes how to model problems for effective analysis, intelligent decision making, and optimal operation and control in the smart city paradigm Teaches how to carry out independent projects using soft computing techniques in a vast range of areas in diverse fields like engineering, management, and sciences

The book IoT and Big Data Analytics (IoT-BDA) for Smart Cities – A Global Perspective, emphasizes the challenges, architectural models, and intelligent frameworks with smart decisionmaking systems using Big Data and IoT with case studies. The book illustrates the benefits of Big Data and IoT methods in framing smart systems for smart applications. The text is a coordinated amalgamation of research contributions and industrial applications in the field of smart cities. Features: Provides the necessity of convergence of Big Data Analytics and IoT techniques in smart city application Challenges and Roles of IoT and Big Data in Smart City applications Provides Big Data-IoT intelligent smart systems in a global perspective Provides a predictive framework that can handle the traffic on abnormal days, such as weekends and festival holidays Gives various solutions and ideas for smart traffic development in smart cities Gives a brief idea of the available algorithms/techniques of Big Data and IoT and guides in developing a solution for smart city applications This book is primarily aimed at IT professionals. Undergraduates, graduates, and researchers in the area of computer science and information technology will also find this book useful.

This book introduces machine learning and its applications in smart environments/cities. At this stage, a comprehensive understanding of smart environment/city applications is critical for supporting future research. This book includes chapters written by researchers from different countries across the globe and identifies critical threads in research and also gaps that open up new and challenging lines of research for the future. Recent advances are discussed, and thorough reviews introduce readers to critical domains. The discussion on key research topics presented in this book accelerates smart city and smart environment implementations based on IoT technologies. Consequently, this book supports future research activities aimed at developing future IoT architectures for smart environments/cities.

The rapid growth of IoT and its applications in smart cities pose significant challenges for academic scholars. The increasing number of interconnected devices and the massive amounts of data they generate strain traditional networks, leading to inefficiencies and security vulnerabilities. Additionally, the centralized control plane in Software Defined Networks (SDN) presents a single point of failure, hindering network performance, while IoT devices themselves are susceptible to attacks, compromising user data and privacy. To address these pressing issues, Network-Enabled IoT Applications for Smart City Services offers a compelling solution. Edited by Dr. K. Hemant Kumar Reddy, Dr. Diptendu SinhaRoy, and Tapas Mishra, this book advocates leveraging SDN to handle high-frequency data streams effectively. It also proposes the integration of blockchain technology to enhance security and reliability in IoT applications, offering a roadmap for scholars to improve network efficiency, security, and reliability in IoT and smart city domains. With their extensive expertise, the authors provide academic scholars with a comprehensive and innovative resource that inspires further research and development in this evolving field, enabling them to make significant contributions to the advancement of IoT and smart city technologies.

The Internet of Things (IoT) has numerous applications, including smart cities, industries, cloud-based apps, smart homes, and surveillance. The Internet of Things (IoT) enables smarter living by connecting devices, people, and objects. As networking became a crucial aspect of the Internet, rigorous design analysis led to the development of new research areas. The Internet of Things has revolutionized daily living in countless ways. It enables communication between buildings, people, portable gadgets, and vehicles, facilitating mobility. Smart cities and cloud-based data have transformed corporate practices. With billions of connected gadgets, everything will soon be able to communicate remotely. IoT networks, whether public or private, rely significantly on machine learning and software-defined networking. Indian and other governments have approved various research projects on IoT-based networking technologies. This field of study will significantly impact society in the future. Researchers are concerned about the many application areas and driving forces behind smart cities. The authors aim to provide insights into software-defined networking, artificial intelligence, and machine learning technologies used in IoT and networking. The framework focuses on practical applications and infrastructures. The book includes practical challenges, case studies, innovative concepts, and other factors that impact the development of realistic scenarios for smart surveillance. It also highlights innovative technology, designs, and algorithms that can accelerate the creation of smart city concepts. This resource includes real-world applications and case studies for smart city technology, enormous data management, and machine learning prediction, all with confidentiality and safety problems.