MB-GVNS: Memetic Based Bidirectional General Variable Neighborhood Search for Time-Sensitive Task Allocation in Mobile Crowd Sensing
IEEE Transactions on Vehicular Technology(2020)
摘要
With the explosive growth of mobile devices, it is convenient for participants to perform mobile crowd sensing (MCS) tasks. It is a useful way to recruit participants to perform location-dependent tasks. We first investigate Min-Max Task Planning (MMTP) problem on time-sensitive MCS systems, considering time-sensitivity and heterogeneity of sensing tasks, and people-variability of the participants. Namely, how to design a cooperation method for the participants so that they spend as little time as possible. To address the MMTP problem, we propose a Memetic based Bidirectional General Variable Neighborhood Search (MB-GVNS) algorithm, in which all tasks are separated into groups and traveling path is planned for each participant. Moreover, we consider the task in both people-invariable and people-variable scenarios. Finally, extensive experiments are conducted to demonstrate the benefits of our method, outperforming other similar state-of-the-art algorithms.
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关键词
Task allocation,efficient cooperation,crowdsensing
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