Online Task Assignment for Crowdsensing in Predictable Mobile Social Networks

Abstract

Mobile crowdsensing is a new paradigm in which a crowd of mobile users exploit their carried smart phones to conduct complex sensing tasks. In this paper, we focus on the makespan sensitive task assignment problems for the crowdsensing in mobile social networks, where the mobility model is predicable, and the time of sending tasks and recycling results is non-negligible. To solve the problems, we propose an Average makespan sensitive Online Task Assignment (AOTA) algorithm and a Largest makespan sensitive Online Task Assignment (LOTA) algorithm. In AOTA and LOTA, the online task assignments are viewed as multiple rounds of virtual offline task assignments. Moreover, a greedy strategy of small-task-first-assignment and earliest-idle-user-receive-task is adopted for each round of virtual offline task assignment in AOTA, while the greedy strategy of large-task-first-assignment and earliest-idle-user-receive-task is adopted for the virtual offline task assignments in LOTA. Based on the two greedy strategies, both AOTA and LOTA can achieve nearly optimal online decision performances. We prove this and give the competitive ratios of the two algorithms. In addition, we also demonstrate the significant performance of the two algorithms through extensive simulations, based on four real MSN traces and a synthetic MSN trace.