Ubiquitous Internet of Things (IoT) devices have fueled plenty of innovations in the emerging network paradigms. Among them, IoT edge caching has emerged as a promising technique to cope with the explosive growth in network data traffic, with Quality of Service (QoS) improved and energy saved. However, the intrinsic storage limitations of the edge servers poses a critical challenge for the IoT edge caching system. Enabling edge servers to cooperate with each other can provide a potential perspective to improve the edge storage utilization widely discussed. Nevertheless, it also incurs an additional communication overhead, eventually making the caching system more complex. As a result, how to perform an efficient cooperative caching becomes a critical issue. Thus, in this paper, we propose a deep reinforcement learning-based cooperative edge caching approach, which allows the distributed edge servers to learn to cooperate with each other. Specifically, edge servers determine their cache actions based on the local caching state. After that, the centralized remote server evaluates these actions and feeds back the evaluation results to edge servers for subsequent caching actions optimization. We show that, by designing an appropriate reward function, our approach promotes cooperation between edge servers as well as improving the system hit rate. On this basis, we consider a practical and reasonable scenario with inconsistent data item size and propose a novel multi-agent actor-critic caching algorithm. Extensive simulation results demonstrate the performance improvement using our proposed solution over three other caching algorithms.INDEX TERMS Cooperative edge caching, Internet of Things, multi-agent deep learning, actor-critic, multi-agent deep deterministic policy gradient.
As the continuous in-depth research of sixth generation (6G) technology, the satellite networks in the Space-Air-Ground Integrated Network (SAGIN) have received more and more attention. However, since satellite nodes have the characteristics of limited resources and dynamic link switching, it is important to effectively save energy in satellite networks. In this paper, we focus on how to make Software-Defined Satellite Networks (SDSN) more energy efficient. First, we propose an energy consumption model for satellite networks. Based on this model, we put forward an improved network topology generation algorithm, which can comprehensively consider the link switching energy consumption and the intersatellite link energy consumption. Then, considering the huge energy consumption caused by abnormal traffic in the satellite network, we propose a DDoS mitigation mechanism in the satellite network, aiming to reduce the extra energy consumption generated by processing abnormal traffic in the satellite node. Finally, through performance evaluation, the proposed network topology generation algorithm and DDoS attack mitigation strategy can effectively reduce network energy consumption.
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