Compact scheduling for task graph oriented mobile crowdsourcing - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue IEEE Transactions on Mobile Computing Année : 2022

Compact scheduling for task graph oriented mobile crowdsourcing

Résumé

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.
Fichier principal
Vignette du fichier
Compact_Scheduling_for_Task_Graph_Oriented_Mobile_Crowdsourcing.pdf (2 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

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

Identifiants

Citer

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⟩
38 Consultations
25 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More