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- W3208790065 abstract "As edge computing has been widely used in IoT (Internet of Things) systems, the multi-service access control has become one of important issues for IoT. That is because current edge computing technologies fail to provide supports to fine-grained multiple services with delay sensitivity and real time demands. To solve this problem, this paper proposes a deep reinforcement learning based access control scheme of the edge computing device in IoT system, in which a novel edge computing network architecture is build. by making full use of deep reinforcement learning algorithm, this proposed strategy is also designed to perform flexibly resources allocation for fine-grained services. Testing results show the feasibility and reasonability of the proposed scheme with better resources utilization under limited conditions." @default.
- W3208790065 created "2021-11-08" @default.
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- W3208790065 date "2021-09-24" @default.
- W3208790065 modified "2023-10-02" @default.
- W3208790065 title "Deep Reinforcement Learning Based Access Control Strategy for Edge Computing in IoT System" @default.
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- W3208790065 doi "https://doi.org/10.1109/cei52496.2021.9574449" @default.
- W3208790065 hasPublicationYear "2021" @default.
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