A Truthful Double Auction Mechanism for Crowdsensing Systems with Max-Min Fairness
He Huang, Yu Xin, Yu-E Sun, Wenjian Yang · 2017
Crowdsensing is regarded as an efficient way to collect a large number of sensing data by using sensor-equipped mobile phones. Most of the existing studies, which concentrate on the crowdsensing task assignment issue, often assume that there is only one data consumer in the system. Obviously, there may exist multiple data consumers in one real crowdsensing system, thus the previous works based on the single data consumer assumption may have many limitations. To tackle this challenge, we mainly focus on the crowdsensing task assignment problem with multiple data consumers, and propose an auction mechanism which can achieve max-min fairness. We first design an approximation transaction set construction mechanism, which can maximize the minimum utilities of data consumers. Then, a second- price-like winner determination and payment calculation mechanism is proposed to ensure the truthfulness. Finally, we prove that the proposed mechanism can achieve the essential economic properties, such as truthfulness, individual rationality and budget balance. The evaluation results corroborate our theoretical analysis, and further indicate that the proposed mechanism is efficient.