Abstract
With the advances of sensors in smart devices, Mobile Crowdsensing (MCS) is flourishing. In this paper, we focus on Cost-minimizing Task Allocation (CTA) problem with spatial-temporal constraints in MCS. The MCS platform gives a set of tasks with different locations and sensing durations. Meanwhile, the platform hopes to allocate the spatial-temporal tasks to a part of participants with the minimum cost. We prove the NP-hardness of this problem and solve it based on the classical primal-dual algorithm. Moreover, we demonstrate that the approximation ratio is a variable about the scale of the problem. Then, we design a heuristic algorithm based on the Tabu search to solve the CTA problem and point out that the lower bound is guaranteed by the initialization progress. Finally, we conduct extensive simulations to show the significant performance of our algorithm.
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Acknowledgment
This research was supported by the National Natural Science Foundation of China (NSFC) (Grant No. 61572457, 61379132, 61303206, 61572342, 61502261) and the Natural Science Foundation of Jiangsu Province in China (Grant No. BK20131174, BK2009150).
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Yu, J., Xiao, M., Gao, G., Hu, C. (2016). Minimum Cost Spatial-Temporal Task Allocation in Mobile Crowdsensing. In: Yang, Q., Yu, W., Challal, Y. (eds) Wireless Algorithms, Systems, and Applications. WASA 2016. Lecture Notes in Computer Science(), vol 9798. Springer, Cham. https://doi.org/10.1007/978-3-319-42836-9_24
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DOI: https://doi.org/10.1007/978-3-319-42836-9_24
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