Incentive Mechanism for Mobile Crowdsensing Incentive Mechanism for Mobile Crowdsensing
SpringerBriefs in Computer Science

Incentive Mechanism for Mobile Crowdsensing

A Game-theoretic Approach

Youqi Li und andere
    • CHF 47.00
    • CHF 47.00

Beschreibung des Verlags

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.

GENRE
Computer und Internet
ERSCHIENEN
2024
3. Januar
SPRACHE
EN
Englisch
UMFANG
140
Seiten
VERLAG
Springer Nature Singapore
GRÖSSE
10.1
 MB
Autonomous Robotics and Deep Learning Autonomous Robotics and Deep Learning
2014
Automatic Design of Decision-Tree Induction Algorithms Automatic Design of Decision-Tree Induction Algorithms
2015
Multilingual Text Recognition Multilingual Text Recognition
2026
Knowledge Distillation in Computer Vision Knowledge Distillation in Computer Vision
2026
Mobile Data Services Mobile Data Services
2026
Computational Infodemiology Computational Infodemiology
2026