An Efficient Clustering Framework for Massive Sensor Networking in Industrial Internet of Things

Abstract

Massive machine-type Internet of Things (IoT) communication (mMTIC) has the potential for high impact in the anticipated future industry 4.0 sensor networking applications. However, the energy limitation and battery life of the IoT nodes have always been one of the long-standing problems. Clustering routing protocol (CRP) being the most efficient existing approach often suffers when nodes closer to the sink depletes their energy, thereby producing an unwanted energy hole, where packets in flight toward the sink often get interrupted. Considering mMTIC covering a large geographical area, such as monitoring bush fires, the multihop communication among the nodes often causes such an energy hole problem. In this article, we develop an artificial-intelligence-based CRP framework for incorporating a small periphery of a fixed shaped area to ameliorate such energy holes. Our proposed framework is not only energy-optimized but also acts as a robust approach for massive communication and informed data collection.