An energy aware clustering and data gathering technique based on nature inspired optimization in WSNs

Abstract

An efficient energy-aware clustering method helps in reducing the battery depletion of the different resources in WSNs. The selection of suitable sensors for cluster head can be an effective way to increase the proficiency of the clustering process. In the past two decades, a number of clustering methods have been proposed. However, most of the methods are suffering from the uneven variation in the number of the Cluster Head (CH), irregular energy consumption by the nodes, transmission of the redundant data, and unequal load of the cluster heads. This paper resolves these problems by proposing an energy-aware 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 the networks. The performance of the proposed methods is evaluated against the state of the art protocols by considering the different performance matrices like network lifetime in terms of rounds, stability period in terms of first node dead, total energy consumption per round, throughput, number of CHs per round etc. The experimental results show the network lifetime and throughput of the proposed method are increased by 23.14%, 29.42%, 60.48%, & 80.16%, and 38.38%, 40.06%, 71.88%, & 95.58%, in respect of the Senthil and Kannapiran method (Wirel Pers Commun 94(4):2239–2258, 2017), ICSCA (Gupta, Procedia Comput Sci 125:234–240, 2018), Adnan et al. method (Lect Note Electric Eng 362: 621–634, 2016), DEEC (Qing et al., Comput Commun 29(12):2230–2237, 2016), respectively, for 100 J network energy in case of tier-3 heterogeneity, respectively.