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Journal Articles IEEE Transactions on Mobile Computing Year : 2022

Compact scheduling for task graph oriented mobile crowdsourcing

Abstract

With the proliferation of increasingly powerful mobile devices and wireless networks, mobile crowdsourcing has emerged as a novel service paradigm. It enables crowd workers to take over outsourced location-dependent tasks, and has attracted much attention from both research communities and industries. In this paper, we consider a mobile crowdsourcing scenario, where a mobile crowdsourcing task is too complex (e.g., post-earthquake recovery, citywide package delivery) but can be divided into a number of easier subtasks, which have interdependency between them. Under this scenario, we investigate an important problem, namely task graph scheduling in mobile crowdsourcing (TGS-MC), which seeks to optimize a compact scheduling, such that the task completion time (i.e., makespan) and overall idle time are simultaneously minimized with the consideration of worker reliability. We analyze the complexity and NP-complete of the TGS-MC problem, and propose two heuristic approaches, including BFS-based dynamic priority scheduling BFSPriD algorithm, and an evolutionary multitasking-based EMTTSch algorithm, to solve our problem from local and global optimization perspective, respectively. We conduct extensive evaluation using two real-world data sets, and demonstrate superiority of our proposed approaches.
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Dates and versions

hal-03548431 , version 1 (30-01-2022)

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Liang Wang, Zhiwen Yu, Qi Han, Dingqi Yang, Shirui Pan, et al.. Compact scheduling for task graph oriented mobile crowdsourcing. IEEE Transactions on Mobile Computing, 2022, 21 (7), pp.2358 - 2371. ⟨10.1109/tmc.2020.3040007⟩. ⟨hal-03548431⟩
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