HCPFRP: Heterogeneous cluster prediction and formation routing protocol for wireless sensor network

Kamini Maheshwar; S Veenadhari

doi:10.59796/jcst.V13N2.2023.1745

Authors

Kamini Maheshwar Department of CSE, Rabindranath Tagore University (RNTU), Bhopal (M.P), India, 462047
S Veenadhari Department of CSE, Rabindranath Tagore University (RNTU), Bhopal (M.P), India, 462047

DOI:

https://doi.org/10.59796/jcst.V13N2.2023.1745

Keywords:

cluster-based routing protocol, formation routing protocol, heterogeneous cluster prediction, lifetime prediction, optimization, wireless sensor network

Abstract

Wireless sensor network (WSN) is composed of multiple sensors that are connected through a communication channel and communicate with each other. As these sensor nodes are battery-operated, therefore, as a consequence, battery life or energy is always an issue of concern. Therefore, researchers focus their work on optimizing the routing strategies to save energy wastage in WSNs. Among all routing strategies, cluster-based techniques proved to be quite able to successfully manage propagation from sender to receiver. Because it must gather all data and send it to the base station, each cluster's elected head is responsible for bearing the complete load. A cluster-based routing mechanism is established under this paper and termed a Heterogeneous Cluster Prediction and Formation Routing Protocol (HCPFR) in which the algorithm first creates the cluster and predicts the energy utilization or network lifetime, and then provides energy-efficient optimized clustering. In this method, the proposed HCPFR model is compared with different methods; LEACH PSO, LEACH-GWO, LEACH-EEGWO, and FZR, and the performance is compared with different parameters mainly First Dead Node (FDN), Network Longevity (NL) and throughput (THP) in term of packet delivered and residual energy. The result shows that the HCPFR model outperforms better over these approaches. The FDN, NL, and THP of the proposed HCPFR are nearly 8000,10000, and 30000. Also, the suggested model shows that as the number of rounds increases the residual energy drops to 0.1 from 3.8 as the rounds increases to 10000 from 2000.

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