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Game Theory in Mobile CrowdSensing:A Comprehensive Survey
Venkat Surya Dasari1, Burak Kantarci1, Maryam Pouryazdan2
1School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada.
Sensors (Basel, Switzerland)
|April 10, 2020
Summary
Game theory enhances user participation and data quality in Mobile CrowdSensing (MCS) by optimizing incentives and costs. This review categorizes game theoretic solutions for MCS, ensuring participant satisfaction and platform efficiency.
Area of Science:
- Computer Science
- Artificial Intelligence
- Game Theory
Background:
- Mobile CrowdSensing (MCS) is crucial for collecting smart city and IoT data using mobile device sensors.
- Ensuring user participation and data trustworthiness is challenging due to individual user strategies and non-dedicated sensors.
- MCS platforms face a trade-off between acquiring dependable data and minimizing costs.
Purpose of the Study:
- To provide a comprehensive review of game theoretic solutions for Mobile CrowdSensing (MCS) challenges.
- To categorize existing game theory-based approaches for MCS platforms.
- To identify open research areas and future directions in game theoretic MCS.
Main Methods:
- Systematic review of game theoretic approaches applied to MCS.
- Categorization of solutions based on game types (Stackelberg, Bayesian, Evolutionary) and participant cooperation/information.
- Analysis of methods addressing sensing cost, data quality, pricing, and incentives.
Main Results:
- Game theory offers stable solutions for MCS by balancing user incentives and platform costs.
- A taxonomy is proposed, classifying MCS game theoretic solutions based on cooperation and information availability.
- Equilibrium is achieved where participants maximize utility without strategy changes.
Conclusions:
- Game theoretic frameworks are essential for optimizing user engagement and data reliability in MCS.
- The proposed taxonomy aids in understanding and developing advanced MCS solutions.
- Further research is needed to explore novel game theoretic applications in the evolving MCS landscape.
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