An Optimized Clustering Approach for Wireless Sensor Networks Using Improved Squirrel Search Algorithm (ISSA)

Abstract

Wireless Sensor Networks (WSNs) are experiencing rapid growth, reshaping human lifestyles. They consist of numerous spatially distributed sensor nodes dedicated to monitoring and storing environmental data. WSNs have been a vital component of the Internet of Things (IoT) since its inception, where all sensor nodes typically connect to the Internet for data exchange. However, WSNs lack a direct Internet connection for their nodes, necessitating the use of mediators to establish connectivity. Substantial scholarly research in recent years has focused on integrating WSNs into the IoT. WSN consist of a large number of sensors but face constraints due to limited energy capacity. These nodes can perform effectively in challenging environmental conditions, but their batteries cannot be recharged or replaced in harsh environments and also will lead to problems in basis of cost. Consequently, energy preservation is crucial for the network’s sustainability. In the field of networking, routing protocols play a significant role in impacting energy consumption. The primary focus during the design of routing protocols is the optimization of energy utilization. This paper aims to minimize overall energy consumption and enhance the network’s lifespan using Cluster-based routing algorithms. Clusters, which are essentially groups of sensors, feature a central entity called the Cluster Head (CH), along with associated sensor nodes (SN). CHs are high-energy nodes responsible for collecting and transmitting data from other nodes to the base station (BS). This research introduces a novel energy-efficient clustering approach based on the Nature Inspired Improved Squirrel Search Algorithm (ISSA) with specified objective functions. The selection of CHs considers factors like node residual energy, distance from neighbors and distance from the base station, node degree, and load based on packet processing. Significantly, this research enables energy efficient, scalable and reliable networks. Further, this research also helps in optimizing the CH selection to give better performance. The performance of ISSA is compared with other optimization algorithms like SSA, GWO and LEACH. The effectiveness of this proposed approach had given better results in terms of metrics such as the number of active nodes, dead nodes, energy consumption, and data packets received by the BS.

Keywords

• Wireless sensor networks
• energy efficiency
• clustering
• CH selection
• bio inspired optimization
• IoT