Fairness-Aware Auction Mechanism for Sustainable Mobile Crowdsensing

Korn Sooksatra, Ruinian Li, Yingshu Li, Xin Guan, Wei Li

Abstract: With the proliferation of sensor-embedded mobile devices, mobile crowdsensing has become a paradigm of significant interest. Incentivizing sensory-data providers to keep sustainability in a mobile crowdsensing system is a critical issue nowadays, and auction-based mechanisms have been proposed to motivate providers via monetary rewards. In our work, this sustainability problem is formulated as an optimization problem maximizing providers’ proportionally fair utilities with respect to their multi-dimensional fairness factors, and a fairness-aware auction mechanism is designed accordingly. To the best of our knowledge, this is the first work that considers multi-dimensional fairness of providers as the objective in selecting providers for the mobile crowdsensing system. In addition, we present rigorous theoretical analysis proving that our mechanism meets budget feasibility, individual rationality and truthfulness. Finally, simulations are performed to demonstrate the performance of our proposed mechanism.