Energy Efficient Approaches for Dynamic Cluster Head Selection Using Optimized Genetic Algorithm in Cluster Networks of WSN

Abstract

Expanding the duration and reliability of wireless sensor networks (WSNs) via energy efficiency is challenging. Clustering has increased energy efficiency by selecting Cluster Head (CH), but its deployment is still tricky. The areas where cluster-heads are desired are initially explored in current cluster-head selection techniques. The cluster heads are then chosen from the nodes nearest to these sites. This location-based method has several drawbacks, including dynamic load, low selection precision, and repeated component selection. It proposes the weight-based Genetic Algorithm (GA) methods for dynamic cluster head selection (DCHS) in a cluster network to solve these problems. The current work proposed is based on multiple cluster generation for effective and lightweight data transmission from Cluster Member (CM) to Base Station (BS) via CH. The fundamental objective behind this research is to reduce the cut generation in-network and reduce communication cost and network overhead. When it generates the multiple clusters with n number of nodes, each node has some energy used for communication and data transmission. The optimized GA-based function is used for selection of best CH in specific cluster region. Moreover, we also define a logic backup cluster head when extra overhead is generated on the CH. It receives additional benefits such as eliminating data collision problems, data leakage, and single-point bottleneck attacks. In the current experimental analysis, this demonstrates the CH selection efficiency with different NS2 protocols such as DSR, DSDV, AODV, and LEACH, etc. It also determines that the experiment automatically extends the network lifetime. In comparative analysis our method has evolute with state-of-art such as ACO, LEACH and dynamic CH selection techniques. It required low computation for transmission and enhance the network lifetime due effective utilization of nodes and energy. The GA provides around 9% high throughput and reduce the 60% packet overhead with exiting methods during the communication.