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Incentive Mechanism for Mobile Crowdsensing [electronic resource] : A Game-theoretic Approach /

By: Contributor(s): Material type: TextTextSeries: SpringerBriefs in Computer SciencePublisher: Singapore : Springer Nature Singapore : Imprint: Springer, 2024Edition: 1st ed. 2024Description: XI, 129 p. 1 illus. online resourceContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9789819969210
Subject(s): Additional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification:
  • 004.167 23
LOC classification:
  • QA76.59
Online resources:
Contents:
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.
In: Springer Nature eBookSummary: 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.
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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.

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