@article{oai:muroran-it.repo.nii.ac.jp:00010358,
 author = {LI, He and 李, 鶴 and OTA,  Kaoru and 太田, 香 and DONG,  Mianxiong and 董, 冕雄},
 issue = {2},
 journal = {ACM TRANSACTIONS ON INTERNET TECHNOLOGY},
 month = {},
 note = {application/pdf, 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.},
 title = {Deep Reinforcement Scheduling for Mobile Crowdsensing in Fog Computing},
 volume = {19},
 year = {2019},
 yomi = {リ, ホ and オオタ, カオル and トウ, メンユウ}
}