期刊
ACM TRANSACTIONS ON INTERNET TECHNOLOGY
卷 19, 期 2, 页码 -出版社
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3234463
关键词
Fog computing; mobile crowdsensing; deep reinforcement learning
资金
- JSPS KAKENHI [JP16K00117, JP15K15976, JP17K12669]
- KDDI Foundation
- Research Fund for Postdoctoral Program of Muroran Institute of Technology
Mobile crowdsensing becomes a promising technology for the emerging Internet of Things (IoT) applications in smart environments. Fog computing is enabling a new breed of IoT services, which is also a new opportunity for mobile crowdsensing. Thus, in this article, we introduce a framework enabling mobile crowdsensing in fog environments with a hierarchical scheduling strategy. We first introduce the crowdsensing framework that has a hierarchical structure to organize different resources. Since different positions and performance of fog nodes influence the quality of service (QoS) of IoT applications, we formulate a scheduling problem in the hierarchical fog structure and solve it by using a deep reinforcement learning-based strategy. From extensive simulation results, our solution outperforms other scheduling solutions for mobile crowdsensing in the given fog computing environment.
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