Abstract
The topology control technology is of great importance in the Power Line Communications (PLC) networks for smart power grid. Due to the weak topology controlling ability of current large-scale PLC networks, the throughput of current PLC networks is limited seriously and fail to support more real-time services. To deal with this problem, this paper proposes a Q-Learning algorithm enabled topology control scheme of PLC networks. This robust topology control scheme introduces the Q-learning algorithm into the CSMA/CA protocol and adopts the Markov process to build the networking model of PLC system. Through period on-line learning by proposed algorithm, the robust topology can be established in PLC networks. Test results show that the proposed approach can improve topology control ability for real-time services by smart grid IoT (SG-IoT) systems, in terms of both packet loss rate and time delay performance with great feasibility and efficiency.
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