EACA: An Energy Aware Clustering Algorithm for Wireless IoT Sensors

The role of Wireless Sensor Networks in the ongoing industrial development is becoming crucial on daily basis. It is undoubtedly the skeleton of the current global digital transformation and the fourth industrial revolution. WSN has grown into an emerging field of research due to its tremendous opportunities and several challenges. Machine to Machine (M2M), energy consumption, and wireless transmission are the most challenging areas of research with a plethora of solid papers that have been published in the last decade. Unquestionably, Low-Energy Adaptive Clustering Hierarchy (LEACH) is the most famous clustering protocol in the literature that allows the creation of self-organizing networks. However, the protocol presents several drawbacks in terms of cluster balancing, random CH selection, and single-hop communication. In this paper, we propose a hybrid energy-aware multi-hop clustering algorithm for WSN's self-organization and energy efficiency. The approach is based on the combination of K-medoids and LEACH clustering approach with a trade-off philosophy for the network clustering and eventual energy enhancement. The technique applies different energy ranges for a self-aware gateways' selection, along with the application of K-medoids, and LEACH for the building of dynamic clusters. The results are compared to the LEACH and K-medoids algorithms. Extensive simulation runs have shown a very good improvement in the network's performance based on the first dead node, the network lifetime, and the energy dissipation metrics. The algorithm's results show an improvement of 158% comparing to LEACH and 834% comparing to K-medoids in terms of the first dead node, while the network performance is enhanced by 151% comparing to LEACH and 33% comparing to K-medoids.