DBDCC: Density based Distorted Circle Clustering for Energy Efficient Wireless Sensor Networks

With the rise of the Internet of Things and the need for sustainable large-area monitoring, energy-efficient wireless sensor networks (WSNs) have become a necessity. As WSNs rely on sensor nodes with fixed, non-rechargeable batteries, optimizing the energy consumption is of paramount importance. In this paper, we propose a novel energy efficient clustering algorithm named Density Based Distorted Circle Clustering (DBDCC) designed for circular topologies. DBDCC creates localized and uniform circular clusters which are adaptable in size. We demonstrate through a lemma that circular topology is the optimal energy efficient topology for data transmission in a WSN. The DBDCC algorithm is compared with the competing popular algorithms such as density based spatial clustering of applications with noise (DBSCAN), K-means clustering and Fuzzy C-means clustering with particle swarm optimization (PSO) initialization in terms of key performance metrics like network lifetime, exhausted energy, data transmission volume and average data throughput. Simulation results corroborate the advantages of DBDCC over the existing algorithms in terms of the aforementioned key metrics. DBDCC improves 50% energy decay iteration (which is a metric of energy efficiency) by 40%-45% and total data transmission volume by 24%-33% as compared to the competing algorithms.