Data Traffic Based Shape Independent Adaptive Unequal Clustering for Heterogeneous Wireless Sensor Networks

Abstract

Due to the technological advancements in wireless communication and their continuously increasing applications in collaborative and cooperative smart infrastructures, energy efficient data collection using wireless devices, has gained significant importance recently. Modern wireless sensor networks refer to network of low-powered and energy-constrained Internet of Things (IoT) devices. Although data collection using hierarchical routing with clustered network improves energy efficiency but introduces energy holes in the region closer to the data gathering center due to heavy relaying load on cluster heads. In this paper, first an improved data gathering center deployment technique for heterogeneous networks has been proposed. Technique for Order of Preference by Similarity to Ideal Solutions (TOPSIS), a multi-criteria decision-making technique, is used to determine the optimal location for the deployment of data gathering center. The proposed technique is adaptive to various shaped networks as required by IoT and increases energy efficiency. Secondly, an unequal clustering based on transmission distances has been proposed. Moreover, cubical, and spherical segmentation schemes for 3D heterogeneous networks have been proposed that assist the formation of unequal clusters. Finally, a shape independent data rate-based segmentation has been proposed that further extends the adaptability and scalability of the proposed unequal clustering. The results demonstrate that the proposed data traffic-based shape independent adaptive clustering scheme increases network lifetime up to 14.2% and 18.8% as compared to Fuzzy Logic based unequal clustering and IUCR respectively. It also reduces the overall network energy consumption up to 61.4% as compared to the state-of-the art unequal clustering methods.