Efficient Path Planning and Truthful Incentive Mechanism Design for Mobile Crowdsensing

Area of Science:

• Computer Science
• Distributed Systems
• Artificial Intelligence

Background:

• Mobile crowdsensing (MCS) enables large-scale data collection via recruited users.
• Designing truthful incentive mechanisms that account for user mobility is a key challenge in MCS.

Purpose of the Study:

• To develop a technical framework for MCS incorporating a truthful incentive mechanism that considers worker mobility.
• To address path planning and incentive mechanism design problems within this framework.

Main Methods:

• A heuristic algorithm for independent worker path planning.
• A computationally efficient, individually rational, and truthful incentive mechanism for winner selection and payment determination.
• Comparison with baseline algorithms and the Vickrey-Clarke-Groves (VCG) mechanism.

Main Results:

• The proposed heuristic path planning algorithm outperforms baseline algorithms and approaches the optimal solution.
• The developed incentive mechanism achieves lower total payments than the VCG mechanism for equivalent performance.
• Simulations demonstrate the truthfulness of the proposed mechanism.

Conclusions:

• The proposed framework effectively integrates path planning and truthful incentive design for mobile crowdsensing.
• The heuristic algorithm and incentive mechanism offer efficient and cost-effective solutions for MCS platforms.
• The study validates the practical applicability and benefits of the proposed MCS framework.