Research Article
HHO-LPWSN: Harris Hawks Optimization Algorithm for Sensor Nodes Localization Problem in Wireless Sensor Networks
@ARTICLE{10.4108/eai.25-2-2021.168807, author={Ravi Sharma and Shiva Prakash}, title={HHO-LPWSN: Harris Hawks Optimization Algorithm for Sensor Nodes Localization Problem in Wireless Sensor Networks}, journal={EAI Endorsed Transactions on Scalable Information Systems}, volume={8}, number={31}, publisher={EAI}, journal_a={SIS}, year={2021}, month={2}, keywords={Wireless Sensor Networks, Sensor Nodes, Localization Error, Computational Intelligence, Anchor Nodes, Location Optimization}, doi={10.4108/eai.25-2-2021.168807} }- Ravi Sharma
Shiva Prakash
Year: 2021
HHO-LPWSN: Harris Hawks Optimization Algorithm for Sensor Nodes Localization Problem in Wireless Sensor Networks
SIS
EAI
DOI: 10.4108/eai.25-2-2021.168807
Abstract
Wireless sensor network (WSN) is a prominent technology for remote area monitoring with the assimilation of the Internet of Things (IoT). Over the past decades, sensor node localization has become an essential challenge of WSNs. The sensor indicates localization challenges related to NP-hard problems. Nature-inspired computational intelligence algorithms are used to solve NP-hard problems efficiently for sensor node localization. After the rigorous advanced search in reputable research journals, efficient newly designed Harris Hawks Optimization (HHO) algorithm has not been used to sensor nodes localization until now. Therefore, this paper does and compares the proposed work from the recently available well-known optimization algorithms such as the Salp Swarm Algorithm (SSA), Equilibrium Optimizer (EO), and Grey Wolf Optimizer (GWO). The simulation results of the proposed work showed that it can outperform in terms of average localization error, the number of localized sensor nodes, and computational cost compared to other computational intelligence algorithms.
Copyright © 2021 Ravi Sharma et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/3.0/), which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.
