Towards high quality mobile crowdsensing: Incentive mechanism design based on fine-grained ability reputation

Abstract

Mobile crowdsensing has become an efficient paradigm for performing large-scale sensing tasks. Many quality-aware incentive mechanisms for mobile crowdsensing have been proposed. However, most of them measure the data quality by one single metric from a specific perspective. Moreover, they usually use the real-time quality, which cannot provide sufficient incentive for the workers with long-term high quality. In this paper, we refine the generalized data quality into the fine-grained ability requirement. We present a mobile crowdsensing system to achieve the fine-grained quality control, and formulate the problem of maximizing the social cost such that the fine-grained ability requirement of all sensing tasks can be satisfied. To stimulate the workers with long-term high quality, we design two ability reputation systems to assess workers’ fine-grained abilities online. The incentive mechanism based on the reverse auction and fine-grained ability reputation system is proposed. We design a greedy algorithm to select the winners and determine the payment based on the bids and fine-grained ability reputation of workers. Through both rigorous theoretical analysis and extensive simulations, we demonstrate that the proposed mechanisms achieve computational efficiency, individual rationality, truthfulness, whitewashing proof, and guaranteed approximation. Moreover, the designed mechanisms show prominent advantage in terms of social cost and average ability achievement ratio.