An Efficient Online Task Assignment Algorithm for Hybrid Mobile Crowdsensing
Kun Liu, Guo Zhang, Baoxian Zhang, Chen Liu, Cheng Li · 2024
Mobile crowdsensing is a sensing paradigm using mobile users’ smart devices to perform sensing tasks, which has attracted much attention due to its low system cost, high flexibility, and wide coverage. In this paper, we study the hybrid sensing online task allocation problem for maximizing the total quality of completed tasks under given budget constraint. We formulate this problem as a 0-1 integer programming. To address this problem, we propose an efficient hybrid sensing based online task assignment algorithm (HSTA), which consists of two major components: Expected task completion quality based opportunistic user recruitment and participatory user recruiting and path planning. We present the detailed algorithm design of HSTA and deduce its computational complexity. Simulation results demonstrate the effectiveness of the proposed HSTA algorithm.