04452nam a22006495i 4500 978-981-99-6921-0 DE-He213 20251030110912.0 cr nn 008mamaa 240103s2024 si | s |||| 0|eng d 9789819969210 978-981-99-6921-0 10.1007/978-981-99-6921-0 doi QA76.59 UMS bicssc COM051460 bisacsh UMS thema Li, Youqi. author. aut http://id.loc.gov/vocabulary/relators/aut 69119 Incentive Mechanism for Mobile Crowdsensing [electronic resource] : A Game-theoretic Approach / by Youqi Li, Fan Li, Song Yang, Chuan Zhang. 1st ed. 2024. Singapore : Springer Nature Singapore : Imprint: Springer, 2024. XI, 129 p. 1 illus. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda SpringerBriefs in Computer Science, 2191-5776 Chapter 1: A Brief Introduction -- Chapter 2: Long-term Incentive Mechanism for Mobile Crowdsensing -- Chapter 3: Fair Incentive Mechanism for Mobile Crowdsensing -- Chapter 4: Collaborative Incentive Mechanism for Mobile Crowdsensing -- Chapter 5: Coopetition-aware Incentive Mechanism for Mobile Crowdsensing -- Chapter 6: Summary. Mobile crowdsensing (MCS) is emerging as a novel sensing paradigm in the Internet of Things (IoTs) due to the proliferation of smart devices (e.g., smartphones, wearable devices) in people’s daily lives. These ubiquitous devices provide an opportunity to harness the wisdom of crowds by recruiting mobile users to collectively perform sensing tasks, which largely collect data about a wide range of human activities and the surrounding environment. However, users suffer from resource consumption such as battery, processing power, and storage, which discourages users’ participation. To ensure the participation rate, it is necessary to employ an incentive mechanism to compensate users’ costs such that users are willing to take part in crowdsensing. This book sheds light on the design of incentive mechanisms for MCS in the context of game theory. Particularly, this book presents several game-theoretic models for MCS in different scenarios. In Chapter 1, the authors present an overview of MCS and state the significance of incentive mechanism for MCS. Then, in Chapter 2, 3, 4, and 5, the authors propose a long-term incentive mechanism, a fair incentive mechanism, a collaborative incentive mechanism, and a coopetition-aware incentive mechanism for MCS, respectively. Finally, Chapter 6 summarizes this book and point out the future directions. This book is of particular interest to the readers and researchers in the field of IoT research, especially in the interdisciplinary field of network economics and IoT. Mobile computing. 64148 Cooperating objects (Computer systems). 64303 Data mining. 6941 Computer science Mathematics. 64041 Mathematical statistics. 67233 Algorithms. 58677 Computer science. 58552 Mobile Computing. 64148 Cyber-Physical Systems. 65638 Data Mining and Knowledge Discovery. Probability and Statistics in Computer Science. 67234 Design and Analysis of Algorithms. 67299 Theory and Algorithms for Application Domains. 67030 Li, Fan. author. aut http://id.loc.gov/vocabulary/relators/aut 69120 Yang, Song. author. aut http://id.loc.gov/vocabulary/relators/aut 69121 Zhang, Chuan. author. aut http://id.loc.gov/vocabulary/relators/aut 69122 SpringerLink (Online service) Springer Nature eBook Printed edition: 9789819969203 Printed edition: 9789819969227 SpringerBriefs in Computer Science, 2191-5776 66578 https://doi.org/10.1007/978-981-99-6921-0 ZDB-2-SCS ZDB-2-SXCS 57065 57065