A sustainable data gathering technique based on nature inspired optimization in WSNs

Abstract

Proficient clustering method helps in decreasing the battery consumption of the resources in wireless sensor networks (WSNs). Election of an appropriate sensor for Cluster Head (CH) can be an effective way to increase the efficiency of the clustering process. In the last two decades, the number of clustering methods have been proposed. However, most of the methods are suffering from uneven variation in the number of CH, irregular energy consumption by nodes, transmission of the redundant data, and an unequal load on the CHs. This paper resolves these problems by proposing a sustainable data gathering technique based on nature inspired optimization for both homogeneous and heterogeneous networks. It considers a fitness function by integrating four fitness parameters namely: energy efficiency, cluster node density, average distance of sensors to the CH, and distance from CH to Base Station (BS). This method considers a chain-based data gathering and transmission process for intra and inter-cluster communication. A data aggregation process is also introduced for removing the redundant data which helps in decreasing the transmission cost and overhead of networks. The performance of the proposed method is evaluated against the state-of-the-art protocols by considering the different performance matrices like network lifetime, stability period, total energy consumption, throughput, number of CHs etc. The experimental results show the network lifetime and throughput of GSA-DEEC, GSA-DEEC-CA, and GSA-DEEC-CA-DA are increased by 08.37%, 39.36%, & 44.72% and 18.77%, 49.53%, & 77.29% in respect of the DEEC for 100J network energy in case of tier-3 heterogeneity, respectively.