This book initially delves into its fundamentals to initiate the exploration of online incentive mechanisms in wireless communications. Three case studies are provided to elaborate details on designing online mechanism design in practical system. For crowdsensing with random task arrivals, this book introduces a linear online incentive mechanism model with insurance of the quality of information for each incoming task. In the context of edge computing systems, the authors model a nonlinear online incentive mechanism with the consideration of mobile users' energy budget constraints. It also explores online incentive mechanism for collaborative task offloading in mobile edge computing to achieve on-arrival instant responses. This book not only disseminates current knowledge but also sheds light on future research directions.

The design of incentive mechanisms in wireless communication systems is of paramount importance as it encourages dormant terminals within networks to contribute their valuable resources. The consideration of randomness of network processes enhances the mechanism design under online settings and decision making on the fly. This book endeavours to bridge existing knowledge gaps by comprehensively presenting and developing fundamental insights into online incentive mechanisms and their design methods in the realm of wireless communications. It's one of the first books to provide a comprehensive understanding of the fundamental principles of online incentive mechanisms and their intricately designed methods in the dynamic world of wireless communications. Future research directions include an investigation in the evolving domain of online incentive mechanism designs within wireless communications.

This book strikes a balance between theoretical knowledge and practical application, making it a valuable resource for both researchers and practitioners in the field of wireless communications and network economics. Advanced-level students majoring in computer science and/or electrical engineering will want to purchase this book as a study guide.

Autorentext

Gang Li (S'19-M'22) received the Ph.D. degree from the Concordia University, QC, Canada, in 2022, and the M.S. degree in information and communication systems with the Guilin University of Electronic Technology, Guilin, China, in 2016. Since Mar. 2024, he is with the College of Computer Science, Inner Mongolia University, Hohhot, China, where he is currently a full research professor. From Jun 2022 to Feb. 2024, he was with the School of Electronic and Electrical Engineering, Wuhan Textile University, Wuhan, China. He was awarded the International Graduate Student Scholarship from University of Manitoba in 2018, and was the recipient of Concordia International Tuition Award of Excellence for 2019-2020. His current research interests include online algorithm, mechanism design, machine learning, and their applications in federated learning, edge computing, B5G.

Jun Cai received the Ph.D. degree from the University of Waterloo, ON, Canada, in 2004. From June 2004 to April 2006, he was with McMaster University, Canada, as a Natural Sciences and Engineering Research Council of Canada (NSERC) Postdoctoral Fellow. From July 2006 to December 2018, he has been with the Department of Electrical and Computer Engineering, University of Manitoba, Canada, where he was a full Professor and the NSERC Industrial Research Chair. Since January 2019, he has joined the Department of Electrical and Computer Engineering, Concordia University, Canada, as a full Professor and the PERFORM Centre Research Chair. His current research interests include edge/fog computing, ehealth, radio resource management in wireless communication networks, and performance analysis. Dr. Cai served as the General Chair of BSC 2023 and Technical Program Committee (TPC) Co-Chair for IEEE GreenCom 2018. He also serviced Track/Symposium TPC Co-Chair for several conferences, including IEEE VTC-Fall 2020, 2019, 2012, IEEE CCECE 2017, IEEE Globecom 2010, and IWCMC 2008. He also served on the editorial board of IEEE Internet of Things Journal, IEEE Wireless Communications Magazine, IEEE Systems Journal, and Wireless Communications and Mobile Computing.

Inhalt

Chapter 1 Introduction of Online Incentive Mechanism in Wireless Communications.- Chapter 2 Linear Online Incentive Mechanism Design Case Study of Crowdsensing with Random Task Arrivals.- Chapter 3 Nonlinear Online Incentive Mechanism Design: Case Study of Edge Computing with Energy Budget.- Chapter 4 Online Incentive Mechanism Design for Real-time Decision Making: Case Study of Collaborative Task Offloading in Mobile Edge Computing.- Chapter 5 Conclusions and Future Research Directions.