Abstract
Abstract The most famous wireless sensor networks is one of the cheapest and rapidly evolving networks in modern communication. It can be used to sense various substantial and environmental specifications by providing cost-effective sensor devices. The development of these sensor networks is exploited to provide an energy-efficient weighted clustering method to increase the lifespan of the network. We propose a novel energy-efficient method, which utilizes the brainstorm algorithm in order to adopt the ideal cluster head (CH) to reduce energy draining. Furthermore, the effectiveness of the BrainStorm Optimization (BSO) algorithm is enhanced with the incorporation of the modified teacher–learner optimized (MTLBO) algorithm with it. The modified BSO–MTLBO algorithm can be used to attain an improved throughput, network lifetime, and to reduce the energy consumption by nodes and CH, death of sensor nodes, routing overhead. The performance of our proposed work is analyzed with other existing approaches and inferred that our approach performs better than all the other approaches.
Highlights
The most common and popular evolving network is Wireless Sensor Network (WSN), which plays a vital role in communication fields because of cheaper sensor devices, low developmental complications, inexpensive with the capability to sense various kinds of physical and environmental considerations transmitting, processing and sensing information [1]
Evaluation of cluster-based routing is performed by considering different performance metrics such as FND, Half Nodes Dies (HND), Last Node Dies (LND), dead nodes, active nodes, Average energy consumption, network lifetime, First Node Dies (FND):FND determines the total aggregations of runs completed before the initial node of the simulated networks dies
The efficacy of the WSN can be estimated with optimal cluster head (CH) selection and routing
Summary
The most common and popular evolving network is Wireless Sensor Network (WSN), which plays a vital role in communication fields because of cheaper sensor devices, low developmental complications, inexpensive with the capability to sense various kinds of physical and environmental considerations transmitting, processing and sensing information [1]. The data processing and sensing required lower energy than data transmission These routing protocols may construct the communication path among both base stations as well as sensor nodes (SN). Different kinds of clustering methods such as fuzzy, k-means, LEACH,Aware ClusterBased Routing (ACBR) protocol, Gravitation Search Algorithm (GSA), Harmony search algorithm (HSA),Optics inspired optimization (OIO),Simulated Annealing (SA), etc to enhance the SN deficiency [4,5,6,7]. These methodologies have few shortcomings in terms of node energy level preservation, cost, packet delay, limited energy in sensor nodes, poor network performance, path selection, reliability, node selection, etc.
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