Adaptive Weight Based Harris Hawks Optimization for Energy-Efficient Cluster Head Selection and Routing in Wireless Sensor Networks
Lecture Notes in Electrical Engineering, ISSN: 1876-1119, Vol: 1270, Page: 151-161
2025
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
Wireless Sensor Network (WSN) consists of number of miniature Sensor Nodes (SN) utilized in number of investigating applications to sense environmental conditions. WSN collects and organizes the sensed data and the transmits to the Base Station (BS) or sink through the Sensor Nodes (SN). However, the energy carried by the sensor nodes is limited. Energy efficiency is one of the critical challenges in wireless sensor networks (WSNs). In this research, the Adaptive Weight-based Harris Hawks Optimization (AWHHO) is proposed for obtaining an energy efficient Cluster Head (CH) selection and routing in WSN. The Sensor Nodes (SN) with a predetermined less energy level qualify for the procedure of selection of CH. The optimization-based clustering and routing approach is developed for designing an optimal transmission path by CH to destination. The proposed method achieves better results and it is evaluated by various performance metrices like FND, HND LND and no. of alive nodes of values about 6750, 7980, 8300 and 50 respectively when compared to the existing methods like Energy-Efficient Clustering Algorithm (EECA), Particle Swarm Optimization (PSO) and Energy-Efficient CH by Improved Grey Wolf Optimization (EECHIGWO) respectively.
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