The proliferation of smartphones has enabled a novel paradigm, participatory sensing, which leverages the smartphones to collect and share data about their surrounding environment. Since the sensing tasks are location-dependent and have time features, it is crucial and challenging to find a proper allocation of sensing tasks to ensure the timeliness of tasks and the quality of sensing data. In this paper, we investigate the heterogeneous sensing task allocation problem aiming at minimizing the total penalty caused by the tardiness of tasks. We prove this problem is NP-hard and propose two hybrid algorithms which combine a heuristic algorithm and two meta-heuristic algorithms respectively. The extensive simulation results show that the proposed hybrid algorithms outperform the meta-heuristic algorithms.
['Fan Yang', 'Jia-Liang Lu', 'Yanmin Zhu', 'Jia Peng', 'Wei Shu', 'Min-You Wu']
Heterogeneous Task Allocation in Participatory Sensing