Data Offloading for Mobile Crowdsensing in Opportunistic Social Networks

Mobile crowdsensing is a novel paradigm by exploiting mobility, sensing, computation, and communication capability of smart devices. In this paper, we study data offloading problem for mobile crowdsensing in opportunistic social networks. In this scenario, mobile users can upload sensing data directly via cellular networks using various data plans. A mobile user can also resort to another user for data offloading by forwarding sensing data to that user using short-range communications (when they encounter). To minimize total data uploading cost while meeting given uploading deadlines, data plan assignment for users and data forwarding strategy when two users encounter should be elaborately designed. In this paper, we use Benders decomposition algorithm to solve offline data plan assignment problem. Then we propose two algorithms including progress- balanced algorithm and social-aware forwarding algorithm to solve online data forwarding problem. Simulation results show that data offloading between users can largely reduce the total data uploading cost. Simulation results also show that the performance of our proposed online algorithms is close to the offline optimal solution.