Online Incentive Mechanisms for Socially-Aware and Socially-Unaware Mobile Crowdsensing
Guoliang Ji, Baoxian Zhang, Guo Zhang, Cheng Li
Abstract
Mobile crowdsensing (MCS) has been a promising paradigm for gathering sensing data from surrounding environment by leveraging smart devices carried by mobile users and also their subjective initiatives. In this sensing paradigm, mobile users can make full use of such sensors-rich smart devices for task executions. Recently, social mobile crowdsensing (SMCS) has received a lot of attention and much work has been carried out. Many incentive mechanisms exploit the social relations among users/workers for improving the system performance. However, most existing work in this area focused on offline and socially-aware scenarios. In this paper, we study both online socially-aware and socially-unaware scenarios for maximizing the platform utility. We formulate the problem of worker selection for maximizing the platform utility and prove this problem is NP-hard. For the socially-aware scenario, we propose an incentive mechanism (called SA-WGRA), which adopts sociality and capability based clustering algorithm for Worker Group formation and uses Reverse Auction for worker selection. For the socially-unaware scenario, we propose an incentive mechanism (called SUA-CGRA), which adopts Coalitional Game combined with Reversed Auction for worker selection. We prove that both mechanisms achieve computational efficiency, individual rationality, and platform rationality. Moreover, for SUA-CGRA, we prove that its formed coalitions satisfy coalition rationality, and further each of its formed coalitions is convex and hence the Shapley value is in the core solutions for profit distribution in each formed coalition. Simulations results show that both SA-WGRA and SUA-CGRA can effectively improve the platform utility.