Optimized hybrid routing protocol for energy-aware cluster head selection in wireless sensor networks

The clustering mechanism in wireless sensor networks (WSNs) is the ideal strategy for constructing an energy efficient protocol for achieving extended network lifetime, energy efficiency, and scalability. The design of energy efficient protocols in WSNs is essential, as the sensor nodes are battery...

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Veröffentlicht in:Digital signal processing Jg. 130; S. 103737
Hauptverfasser: Roberts, Michaelraj Kingston, Ramasamy, Poonkodi
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier Inc 01.10.2022
Schlagworte:
Hybrid optimization algorithm
Network lifetime
Residual energy
Routing protocols
Wireless sensor networks
Residual energy
Wireless sensor networks
Hybrid optimization algorithm
Network lifetime
Routing protocols
ISSN:1051-2004, 1095-4333
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Zusammenfassung:The clustering mechanism in wireless sensor networks (WSNs) is the ideal strategy for constructing an energy efficient protocol for achieving extended network lifetime, energy efficiency, and scalability. The design of energy efficient protocols in WSNs is essential, as the sensor nodes are battery powered, and may be completely drained, resulting in deterioration in network lifetime. Energy efficient protocols aim to maximize network lifetime while minimizing overall energy consumption to avoid interruptions to the wireless sensor nodes deployed for monitoring and recording physical information from the environment. Moreover, hybrid metaheuristic-optimization based clustering and routing protocols are widely used for attaining energy stability and network lifetime. In this study, the golden eagle optimization algorithm (GEOA) and improved grasshopper optimization algorithm (IGHOA) based on the energy efficient cluster-based routing protocol (GEIGOA) are proposed for sustaining energy stability and augmenting network lifetime longevity by overcoming challenges in the cluster head (CH) selection process. In particular, by optimizing the node centrality, node degree, distance to the base station, distance to the neighbors, and residual node energy, GEOA selects an optimal CH from the deployed group of sensor nodes in the network. Furthermore, IGHOA is utilized to determine a reliable and optimal route between the CH and base station (BS) by assessing node degree, residual energy, and distance parameters. Moreover, the proposed GEIGOA was confirmed to be sufficiently capable in improving the probability of preventing the worst nodes from being selected as CHs with different number of sensor nodes, which was predominantly enhanced by 12.64%, 15.82%, 18.96%, and 20.98% compared with the competitive CH selection schemes. In addition, the computational cost incurred by the proposed GEIGOA with different number of sensor nodes was also confirmed to be minimized by 14.98%, 17.21%, 19.76%, and 21.62% compared with the competitive CH selection schemes.
ISSN:1051-2004
1095-4333
DOI:10.1016/j.dsp.2022.103737