Worker recruitment with cost and time constraints in Mobile Crowd Sensing

With the proliferation of sensor-rich smart devices (smartphones, ipads, etc.), Mobile Crowd Sensing (MCS) has gradually attracted much attention in the research community recently. Worker recruitment is a crucial research issue in MCS system, in which platform recruits workers and assigns sensing tasks to them. While previous studies focus on either opportunistic-sensing-based worker recruitment or participatory-sensing-based worker recruitment separately, we proposed a two-phase hybrid worker recruitment framework named HySelector, which recruits workers in two phases. First, in the offline phase, borrowing the idea of influence propagation in communication and social network, we proposed algorithm to recruit opportunistic workers during their daily routines which can alleviate the cold start problem in traditional MCS system. Then, in the online phase, in order to reduce the computational complexity, we devised algorithm to incentivize participatory workers to move to specific subareas obtained by subareas clustering to fulfil sensing tasks. In both phases, we considered guaranteeing the incentive cost and time constraint. Experimental results on two open datasets demonstrated that compared with other methods, HySelector had better performance in terms of spatial coverage and running time under budget constraints.