Dynamic Task Pricing in Multi-Requester Mobile Crowd Sensing with Markov Correlated Equilibrium

The recent proliferation of human-carried mobile devices has given rise to mobile crowd sensing (MCS) systems, where a myriad of data requesters outsource their sensing tasks to a crowd of workers via a cloud-based platform. In order to incentivize participation, requesters typically compensate workers with specific amount of payments. Clearly, setting an appropriate task price is critical for a requester to attract enough worker participation without unnecessary expenses. Therefore, we investigate the problem of task pricing in MCS systems with multi-requester price competition, and also dynamically arriving workers. Task pricing in such scenario is challenging, because of each requester's incomplete information about the others, uncertainty of future information, etc. So as to address these challenges, we use Markov game to model requesters' competitive task pricing, and Markov correlated equilibrium (MCE) as the solution concept. We propose that the platform uses the social cost-minimizing MCE to coordinate requesters' prices, which is self-enforcing, and optimizes the system-wide objective of social cost. Technically, we propose a computationally efficient algorithm to compute an approximately optimal MCE. Furthermore, through extensive performance evaluation, we show numerically that our algorithm yields close-to-minimum social cost in very short running time.