In this paper, we consider the dynamic content caching issue in the cache-enabled Internet of Things (IoT) systems. For real-time applications in cache-enabled IoT systems, it is imperative to design dynamic content caching schemes to reduce the energy consumption of sensors and improve the freshness of information at users. We first design a dynamic content caching procedure for a cache-enabled IoT system with limited cache capacity and express the evolution of the Age of Information (AoI) at both the edge caching node and each user. Then, we formulate the dynamic content caching problem as a Markov Decision Process to minimize the expectation of a long-term accumulative cost, which jointly considers the average AoI of users and the energy consumption of sensors. To solve this problem, we propose an actor-critic based caching algorithm without prior knowledge of users’ content demands. The numerical results show that the proposed algorithm can achieve lower average AoI and energy consumption than other baselines.


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    Titel :

    Dynamic Content Caching Based on Actor-Critic Reinforcement Learning for IoT Systems


    Beteiligte:
    Lai, Lifeng (Autor:in) / Zheng, Fu-Chun (Autor:in) / Wen, Wanli (Autor:in) / Luo, Jingjing (Autor:in) / Li, Ge (Autor:in)


    Erscheinungsdatum :

    2022-09-01


    Format / Umfang :

    832205 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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