期刊
IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS
卷 47, 期 3, 页码 392-403出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/THMS.2016.2599489
关键词
Mobile crowd sensing (MCS); multiple tasks; platform; task allocation; ubiquitous computing
资金
- National Basic Research Program of China 973 [2015CB352400]
- National Natural Science Foundation of China [61332005, 61373119]
- Fundamental Research Funds for the Central Universities [3102015ZY095]
Worker selection is a key issue in mobile crowd sensing (MCS). While the previous worker selection approaches mainly focus on selecting a proper subset of workers for a single MCS task, a multitask-oriented worker selection is essential and useful for the efficiency of large-scale MCS platforms. This paper proposes ActiveCrowd, a worker selection framework for multitask MCS environments. We study the problem of multitask worker selection under two situations: worker selection based on workers' intentional movement for time-sensitive tasks and unintentional movement for delay-tolerant tasks. For time-sensitive tasks, workers are required to move to the task venue intentionally and the goal is to minimize the total distance moved. For delay-tolerant tasks, we select workers whose route is predicted to pass by the task venues and the goal is to minimize the total number of workers. Two greedy-enhanced genetic algorithms are proposed to solve them. Experiments verify that the proposed algorithms outperform baseline methods under different experiment settings (scale of task sets, available workers, varied task distributions, etc.).
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