Energy‐efficient optimal sink placement using extended pelican optimization‐based clustering with Voronoi‐based node deployment.
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| Title: | Energy‐efficient optimal sink placement using extended pelican optimization‐based clustering with Voronoi‐based node deployment. |
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| Authors: | Abdur Rahman, Narayanasami, Shankarlal, Balraj, Sivarajan, Sankarapandian, Sharmila, Pandian |
| Source: | International Journal of Communication Systems; 2/1/2025, Vol. 38 Issue 3, p1-26, 26p |
| Subject Terms: | WIRELESS sensor networks, SENSOR placement, ENERGY consumption, ENVIRONMENTAL monitoring, ACQUISITION of data |
| Abstract: | Summary: A wireless sensor network (WSN) is a network of spatially distributed autonomous sensor nodes that collaborate to monitor physical or environmental conditions, collect data, and transmit it to a sink node. WSNs have a wide range of applications across various domains due to their ability to provide real‐time data collection, remote monitoring, and data analysis. Still, in a WSN with a fixed sink, sensor nodes closer to the sink tend to have higher traffic loads because they forward data to nodes further away. This can lead to hotspots and uneven energy consumption. Introducing a mobile sink can distribute the traffic more evenly across the network, reducing congestion and balancing the energy consumption among nodes. Hence, this research proposes a novel WSN environment with a focus on energy‐efficient routing. The network is deployed using Voronoi‐based criteria to address network coverage issues. The clustering of nodes is employed using the proposed extended pelican optimization (ExPo) algorithm to improve network lifetime and energy efficiency, critical concerns in WSNs due to limited sensor node battery capacity. Cluster heads (CHs) aggregate and process data locally, reducing the energy needed for long‐range communication. Then, an energy‐efficient optimal sink placement (EEOSP) approach is used to optimize the placement of the mobile sink. The proposed system model is evaluated based on various metrics, including average residual energy, delay, network lifetime, packet delivery ratio, and throughput and acquired the values of 0.99 J, 3.68 ms, 99.55%, 99.55%, and 81 Mbps, respectively. [ABSTRACT FROM AUTHOR] |
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| Database: | Complementary Index |
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