GuideMe: Routes coordination of participating agents in mobile crowd sensing platforms
Christine Bassem, Azer Bestavros · 2017
With the recent trend in Mobile Crowd Sensing (MCS), i.e., using the power of crowds to assist in completing spatio-temporal sensory tasks, the pool of resources suitable for sensor systems has expanded to include already roaming devices. In this work, we present a model of MCS, in which agents share their journey information and allow the platform to guide them through their journey, completing spatio-temporal tasks on their way, in return for monetary rewards. In this paper, we formulate the task allocation problem as a routes coordination problem for participating agents. We define an optimal routing algorithm for a single agent, with an objective to maximize the rewards collected from performing tasks, which is used to define a 1/2-approximation algorithm to coordinate the routes of multiple agents. The algorithm is accompanied with an incentive compatible, rational, and cash-positive payment mechanism, which guarantees that an agent's truthful participation is an ex-post Nash equilibrium strategy, in an optimal setting. Finally, we analyze the defined mechanisms theoretically, and evaluate their performance experimentally using real mobility traces from urban environments.