A Game-theoretic Approach Li, Youqi. creator author. aut http://id.loc.gov/vocabulary/relators/aut Li, Fan. author. aut http://id.loc.gov/vocabulary/relators/aut Yang, Song. author. aut http://id.loc.gov/vocabulary/relators/aut Zhang, Chuan. author. aut http://id.loc.gov/vocabulary/relators/aut SpringerLink (Online service) text si 2024 1st ed. 2024. monographic eng

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access XI, 129 p. 1 illus. online resource. 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. 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. by Youqi Li, Fan Li, Song Yang, Chuan Zhang. Mobile computing Cooperating objects (Computer systems) Data mining Computer science Mathematics Mathematical statistics Algorithms Computer science Mobile Computing Cyber-Physical Systems Data Mining and Knowledge Discovery Probability and Statistics in Computer Science Design and Analysis of Algorithms Theory and Algorithms for Application Domains QA76.59 9789819969210 https://doi.org/10.1007/978-981-99-6921-0 https://doi.org/10.1007/978-981-99-6921-0 240103 20251030110912.0 978-981-99-6921-0