Springer Algorithmic Mechanism Design for Internet of Things Services Market: Design Incentive Mechanisms to Facilitate the Efficiency and Sustainability of IoT Ecosystem

Product Description This book establishes game-theoretical frameworks based on the mechanism design theory and proposes strategy-proof algorithms, to optimally allocate and price the related IoT services, so that the social welfare of IoT ecosystem or the service provider’s revenue can be maximized and the IoT service provision can be sustainable. This book is written by experts based on the recent research results on the interaction between the service providers and users in the IoT system. Since the IoT networks are essentially supported by data, communication, and computing resources, the book focuses on three representative IoT services, including the data analytics services, the cloud/fog computing services for blockchain networks, and the wireless powered data crowdsourcing services. Researchers, scientists, and engineers in the field of resource allocation and service management for future IoT ecosystem can benefit from the book. As such, this book provides valuable insights and practical methods, especially the novel deep learning-based mechanism that can be considered in the emerging IoT technology. From the Back Cover This book establishes game-theoretical frameworks based on the mechanism design theory and proposes strategy-proof algorithms, to optimally allocate and price the related IoT services, so that the social welfare of IoT ecosystem or the service provider’s revenue can be maximized and the IoT service provision can be sustainable. This book is written by experts based on the recent research results on the interaction between the service providers and users in the IoT system. Since the IoT networks are essentially supported by data, communication, and computing resources, the book focuses on three representative IoT services, including the data analytics services, the cloud/fog computing services for blockchain networks, and the wireless powered data crowdsourcing services. Researchers, scientists, and engineers in the field of resource allocation and service management for future IoT ecosystem can benefit from the book. As such, this book provides valuable insights and practical methods, especially the novel deep learning-based mechanism that can be considered in the emerging IoT technology. About the Author Yutao Jiao received the Ph.D. degree in computer science and engineering from Nanyang Technological University, Singapore, in 2020. He is currently an assistant professor in the College of Communication Engineering, Army Engineering University of PLA, Nanjing, China. His research interests include algorithmic mechanism design, blockchain, distributed machine learning, as well as mobile crowdsourcing in the Internet of Things. Ping Wang (Senior Member, IEEE) received the Ph.D. degree in electrical engineering from University of Waterloo, Canada, in 2008. Currently, she is an associate professor in the School of Computer Science and Engineering, Nanyang Technological University, Singapore. Her current research interests include resource allocation in multimedia wireless networks, cloud computing, and smart grid. She was a co-recipient of the Best Paper Award from IEEE Wireless Communications and Networking Conference (WCNC) 2012 and IEEE International Conference on Communications (ICC) 2007. Dusit Niyato (Fellow, IEEE) received the B.E. degree from the King Mongkuk’s Institute of Technology Ladkrabang, Thailand, in 1999, and the Ph.D. degree in electrical and computer engineering from the University of Manitoba, Canada, in 2008. He is currently a professor in the School of Computer Science and Engineering and the School of Physical and Mathematical Sciences, Nanyang Technological University, Singapore. He has published more than 380 technical papers in the area of wireless and mobile networking and is an inventor of four US and German patents. He has authored four books, including “Game Theory in Wireless and Communication Networks: Theory, Models, and Application