Artificial-Neural-Network-Assisted Sensor Clustering for Robust Communication Network in IoT-Based Electricity Transmission Line Monitoring

As an Internet of Things (IoT) application in the smart grid, dynamic thermal rating (DTR) requires real-time measurement of conductor temperature from field sensors that are installed at remote transmission lines. For such IoT-based transmission line monitoring, we need a robust communication network. This article proposes to group sensors with similar measurement characteristics into a cluster such that each member can accurately represent the entire cluster. Then, communication robustness can be achieved by establishing multiple routes to the control center, where each route may originate from a different member in the cluster. First, we formulate a global constrained optimization for sensor clustering. We use the optimization solutions to train a set of artificial neural networks (ANNs), where each ANN is deployed to make a local decision at one sensor. Specifically, we propose the novel usage of ANN as a tool to transfer knowledge from a global decision maker to a set of local decision makers. Evaluation results show that there remains a noticeable knowledge gap between global and local decision makers, where in average, the global decision maker and an ANN make an identical decision in only about 70% of the times. Despite this less than ideal success probability, the number of clusters produced by the ANN-assisted local scheme is only 1.9 more than that by the global scheme.