Abstract
We study the problem of heterogeneous task assignment in mobile crowdsensing (MCS) scenarios where the opportunistic mode and participatory mode coexist. Workers in opportunistic mode complete tasks during their daily routines while workers in participatory mode complete tasks by moving to designated locations. This problem can be simplified into a Knapsack problem which is NP-hard. Then, to solve this problem, we propose a two-phase task assignment algorithm MSHTA based on the workers’ mobility and historical information which leverage the advantages of two sensing modes in sensing quality and sensing cost of tasks. Specifically, a task is optimally assigned to workers who meet their sensing requirements (e.g., sensing time, sensing sensor) at each phase. Extensive simulation results show the effectiveness of our proposed algorithm in terms of tasks’ sensing quality and tasks’ sensing cost.
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Acknowledgment
This work is supported by the Natural Science Foundation of China (No. 61872104), the Natural Science Foundation of Heilongjiang Province in China (No. F2016009), the Fundamental Research Fund for the Central Universities in China (No. HEUCF180602) and the Tianjin Key Laboratory of Advanced Networking (TANK), College of Intelligence and Computing, Tianjin University, Tianjin China, 300350.
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Feng, G., Li, Q., Lin, J., Lv, H., Wang, H., Lv, S. (2019). A Near-Optimal Heterogeneous Task Allocation Scheme for Mobile Crowdsensing. In: Guo, S., Liu, K., Chen, C., Huang, H. (eds) Wireless Sensor Networks. CWSN 2019. Communications in Computer and Information Science, vol 1101. Springer, Singapore. https://doi.org/10.1007/978-981-15-1785-3_20
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DOI: https://doi.org/10.1007/978-981-15-1785-3_20
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