Ensembled Elbow and Bray-Curtis Fuzzy C-Means Clustering For Energy Efficient Data Aggregation in WSN

Abstract

Wireless sensor network (WSN) comprises distributed sensors for aggregating and organizing data. Data aggregation is a major concern in WSNs as it relies on several factors, namely energy constraints of sensors, network topology, and link conditions. The conventional approach does not perform efficient data aggregation due to the battery power of nodes, which degrades the network lifetime. To improve data aggregation and network lifetime, an Energy-Efficient Ensembled Elbow Fuzzy C-means Clustering-based Data Aggregation (EEEEFCC-DA) method is designed. Initially, the residual energy of each sensor node (SN) is calculated. The elbow method is used in the fuzzy c-means clustering algorithm to determine the number of clusters. Then, centroids values are calculated for every cluster to group SNs. The Bray-Curtis Similarity Index is used to compute the similarity between the SN and the centroids value of the cluster. SNs are grouped depending on the similarity value. The process is iterated until every SN is clustered into suitable clusters. After that, the SN with higher residual energy is selected as the cluster head (CH). CH gathers data from each SN and sends it to the sink node. This assists in enhancing data gathering accuracy and reducing energy consumption. Simulation of the EEEEFCC-DA method is carried out with various metrics, namely energy consumption, network lifetime, data aggregation accuracy (DAA), and data aggregation time with the number of SNs and the number of data packets (DP). Results show that the EEEEFCC-DA method provides better performance in terms of DAA, network lifetime, energy consumption, and data aggregation time than conventional methods.