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• W2938135176 abstract "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." @default.
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• W2938135176 date "2019-04-24" @default.
• W2938135176 modified "2023-10-12" @default.
• W2938135176 title "Deep Reinforcement Scheduling for Mobile Crowdsensing in Fog Computing" @default.
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• W2938135176 doi "https://doi.org/10.1145/3234463" @default.
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