Compact scheduling for task graph oriented mobile crowdsourcing

Liang Wang, Zhiwen Yu, Qi Han, Dingqi Yang, Shirui Pan, Yuan Yao, Daqing Zhang

Research output: Contribution to journalArticleResearchpeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)2358-2371
Number of pages15
JournalIEEE Transactions on Mobile Computing
Issue number7
Publication statusPublished - 1 Jul 2022


  • Crowdsourcing
  • Directed Acyclic Graph(DAG)
  • Job shop scheduling
  • Makespan
  • Mobile computing
  • Mobile Crowdsourcing
  • Optimization
  • Processor scheduling
  • Task analysis
  • Task Schedule
  • Toy manufacturing industry

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