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Open AccessArticle

A Real-World-Oriented Multi-Task Allocation Approach Based on Multi-Agent Reinforcement Learning in Mobile Crowd Sensing

by Junying Han 1,2, Zhenyu Zhang 1,2,* and Xiaohong Wu 1,2
1
College of Information Science and Engineering, Xinjiang University, Urumqi 830046, China
2
Xinjiang Multilingual Information Technology Laboratory, Xinjiang University, Urumqi 830046, China
*
Author to whom correspondence should be addressed.
Information 2020, 11(2), 101; https://doi.org/10.3390/info11020101
Received: 23 December 2019 / Revised: 7 February 2020 / Accepted: 7 February 2020 / Published: 12 February 2020
(This article belongs to the Section Information and Communications Technology)
Mobile crowd sensing is an innovative and promising paradigm in the construction and perception of smart cities. However, multi-task allocation in real-world scenarios is a huge challenge. There are many unexpected factors in the execution of mobile crowd sensing tasks, such as traffic jams or accidents, that make participants unable to reach the target area. In addition, participants may quit halfway due to equipment failure, network paralysis, dishonest behavior, etc. Previous task allocation approaches mainly ignored some of the heterogeneity of participants and tasks in the real-world scenarios. This paper proposes a real-world-oriented multi-task allocation approach based on multi-agent reinforcement learning. Firstly, under the premise of fully considering the heterogeneity of participants and tasks, the approach enables participants as agents to learn multiple solutions independently, based on modified soft Q-learning. Secondly, two cooperation mechanisms are proposed for obtaining the stable joint action, which can minimize the total sensing time while meeting the sensing quality constraint, which optimizes the sensing quality of mobile crowd sensing (MCS) tasks. Experiments verify that the approach can effectively reduce the impact of emergencies on the efficiency of large-scale MCS platform and outperform baselines based on a real-world dataset under different experiment settings.
Keywords: mobile crowd sensing; multi-task allocation; multi-agent reinforcement learning; real-world-oriented mobile crowd sensing; multi-task allocation; multi-agent reinforcement learning; real-world-oriented
MDPI and ACS Style

Han, J.; Zhang, Z.; Wu, X. A Real-World-Oriented Multi-Task Allocation Approach Based on Multi-Agent Reinforcement Learning in Mobile Crowd Sensing. Information 2020, 11, 101.

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