Clustering algorithm based on nature-inspired approach for energy optimization in heterogeneous wireless sensor network

Abstract

In this paper, we present a clustering model for energy optimization based on the nature-inspired behaviour of animals. This clustering model finds the optimal distance to send data packets from one location to another, either long or short distances, so as to maintain the lifetime of the sensor network. The challenge with sensor networks is how to balance the energy load, which can be achieved by selecting a sensor node with an adequate amount of energy from a cluster to compensate for those sensor nodes with limited amount of energy. Generally, the clustering technique is one of the approaches to solve this challenge because it optimizes energy to increase the lifetime of the sensor network. We focus on nodes with different energy makeup, and based on the number of nodes that send packets, and evaluated the network performance in terms of the stability period, network lifetime and network throughput. Two nature-inspired algorithms (that is, kestrel-based search algorithm and wolf search algorithm with minus step previous) were compared to evaluate which one is energy-efficient when used as a clustering algorithm. It was found that, the Kestrel-based Search Algorithm Distributed Energy Efficient Clustering (KSA-DEEC) model has the optimal network run time (in seconds) to send a higher number of packets to base station successfully. Consequently, The KSA-DEEC model has an optimal network lifetime performance as compared to the Wolf Search Algorithm with Minus Step Previous (WSAMP)-DEEC model. It also has the highest network throughput in the simulation that was performed while the WSAMP-DEEC model showed prospects of better performance in some of the cases. © 2021