Incentive Mechanism for Mobile Crowdsensing: A Game-theoretic Approach

✍ Scribed by Youqi Li, Fan Li, Song Yang, Chuan Zhang

• Publisher: Springer
• Year: 2024
• Tongue: English
• Leaves: 137
• Series: SpringerBriefs in Computer Science
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Preface
Acknowledgments
Contents
1 A Brief Introduction
1.1 Overview of Mobile Crowdsensing
1.2 Incentive Mechanism for Mobile Crowdsensing
1.3 Book Structure
References
2 Long-Term Incentive Mechanism for Mobile Crowdsensing
2.1 Introduction
2.1.1 Motivations
2.1.2 Challenges
2.1.3 Contributions
2.1.4 Related Work
2.2 Game Modeling
2.2.1 Task Model
2.2.2 Platform Model
2.2.3 User Model
2.2.4 Problem Statement
2.2.5 Edge-Cloud Implementation
2.3 Detailed Design
2.3.1 Stage iii: Online Worker Selection
2.3.2 Stage ii: Users' Interests of Tasks Disclosure
2.3.3 Stage i: Online Pricing
2.4 Equilibrium Analysis
2.4.1 Strategy Performance in Stage iii
2.4.2 Strategy Performance in Stage ii
2.4.3 Strategy Performance in Stage i
2.5 Performance Evaluation
2.5.1 Evaluation for Stage III
2.5.2 Evaluation for Stage II
2.5.3 Evaluation for Stage I
2.5.4 Evaluation on Trace
2.6 Conclusion
References
3 Fair Incentive Mechanism for Mobile Crowdsensing
3.1 Introduction
3.1.1 Motivations
3.1.2 Challenges
3.1.3 Contributions
3.1.4 Related Work
3.2 Game Modeling
3.2.1 Overview
3.2.2 User Model
3.2.3 Platform Model
3.2.4 Fairness Model
3.2.5 Privacy-Preserving Model
3.2.6 Problem Statement
3.3 Detailed Design
3.3.1 Stage iii: Platform's Participant Recruitment Strategies
3.3.1.1 UCB-Based Participant Recruitment Algorithm
3.3.1.2 LyaUCB Based Participant Recruitment Algorithm
3.3.1.3 Privacy-Preserving Integration
3.3.2 Stage ii: Users' Interest Set Determination
3.3.3 Stage i: Platform's Reward Pricing Strategy
3.4 Equilibrium Analysis
3.4.1 Strategy Performance in Stage iii
3.4.2 Strategy Performance in Stage ii
3.4.3 Strategy Performance in Stage i
3.5 Performance Evaluation
3.5.1 Benchmarks and Metrics
3.5.2 Evaluation Results
3.6 Conclusion
References
4 Collaborative Incentive Mechanism for Mobile Crowdsensing
4.1 Introduction
4.1.1 Motivations
4.1.2 Challenges and Our Contributions
4.1.3 Related Work
4.2 Game Modeling
4.2.1 User
4.2.2 Platform
4.2.3 App
4.2.4 Payoff Definition
4.2.4.1 Users' Payoff
4.2.4.2 Platform's Payoff
4.2.4.3 App's Payoff
4.2.5 Three-Stage Decision Process
4.3 Detailed Design with Equilibrium Analysis
4.3.1 Stage iii: Tasks Allocation
4.3.1.1 The Case with ri = 0
4.3.1.2 The Case with ri = 1
4.3.2 Stage ii: Incentive and Tagging Determination
4.3.2.1 Greedy Approach
4.3.3 Stage i: POI-Tagging Pricing
4.4 Performance Evaluation
4.4.1 Performance Comparison
4.4.2 Performance Evaluation for Platform
4.4.3 Performance Evaluation for Participation Rate and App
4.5 Conclusion
References
5 Coopetition-Aware Incentive Mechanism for Mobile Crowdsensing
5.1 Introduction
5.1.1 Motivations and Challenges
5.1.2 Contributions
5.1.3 Related Works
5.2 Game Modeling
5.2.1 System Overview
5.2.2 Crowd Workers
5.2.3 Platforms and Negative Externalities
5.2.3.1 Competitive Platform Scenario
5.2.3.2 Negative Externalities Over Multiple Platforms
5.2.3.3 Cooperative Platform Scenario
5.2.4 Problem Definition
5.3 Detailed Design with Equilibrium Analysis: Competition Among Platforms
5.3.1 User's Decision
5.3.1.1 Strategizing in Single Platform
5.3.1.2 Strategizing with Multiple Platforms
5.3.2 Platforms' Competitive Pricing
5.4 Detailed Design with Equilibrium Analysis: Cooperation Among Platforms
5.4.1 Exact Bargaining
5.4.2 Heuristic Bargaining
5.4.3 Many-To-Many Bargaining
5.5 Performance Evaluation
5.5.1 Simulation Settings
5.5.2 Simulation Results
5.6 Conclusion
References
6 Summary
6.1 Summary of the Book
6.2 Future Directions