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- W2989969218 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." @default.
- W2989969218 created "2019-12-05" @default.
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- W2989969218 date "2019-01-01" @default.
- W2989969218 modified "2023-09-26" @default.
- W2989969218 title "A Near-Optimal Heterogeneous Task Allocation Scheme for Mobile Crowdsensing" @default.
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- W2989969218 doi "https://doi.org/10.1007/978-981-15-1785-3_20" @default.
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