Wireless sensor network (WSN) can effectively solve the problems of weak coverage, blind coverage, and low survivability of smart substation communication networks by deploying multiple relay nodes and adopting multihop transmission. However, there are still some challenges in the traditional relay selection strategy of WSN in substation, including incomplete information and the selection conflicts among multisource nodes. In this paper, we propose a matching learning-based relay selection mechanism for WSN-based substation power Internet of things (SPIoT). Firstly, considering the electromagnetic interference caused by the operation of high-voltage equipment, a multihop transmission model of SPIoT is built. Furthermore, based on the upper confidence bound (UCB) algorithm and matching theory, a matching learning-based relay selection (MLRS) algorithm is proposed to minimize the energy consumption of SPIoT devices. Simulation results demonstrate that MLRS outperforms existing algorithms in terms of energy consumption and optimal selection probability.

Message queuing telemetry transport has emerged as a promising communication protocol for resource-constrained electric Internet of things due to high bandwidth utilization, simple implementation, and various quality of service levels. Enabled by message queuing telemetry transport, electric Internet of things gateways adopt dynamic protocol adaptation, conversion, and quality of service level selection to realize bidirectional communication with massive devices and platforms based on heterogeneous communication protocols. However, protocol adaptation and quality of service guarantee in message queuing telemetry transport-empowered electric Internet of things still faces several challenges, such as unified communication architecture, differentiated quality of service requirements, lack of quality of service metric models, and incomplete information. In this paper, we first establish a unified communication architecture for message queuing telemetry transport-empowered electric Internet of things for adaptation and conversion of heterogeneous protocols. Second, we formulate the quality of service level selection optimization problem to minimize the weighted sum of packet-loss ratio and delay. Then, a delay-reliability-aware message queuing telemetry transport quality of service level selection algorithm based on upper confidence bound is proposed to learn the optimal quality of service level through dynamically interacting with the environment. Compared with single and fixed quality of service level selection strategies, delay-reliability-aware message queuing telemetry transport quality of service level selection can effectively reduce the weighted sum of delay and packet-loss ratio and satisfy the differentiated quality of service requirements of electric Internet of things.