| Title |
Optimizing Sensor Node Placement In Wireless Sensor Networks Using Hybrid Pso-Gwo Technique |
| ID_Doc |
40874 |
| Authors |
Kumar P.; Pandey S. |
| Year |
2024 |
| Published |
2024 IEEE 1st International Conference on Advances in Signal Processing, Power, Communication, and Computing, ASPCC 2024 |
| DOI |
http://dx.doi.org/10.1109/ASPCC62191.2024.10881562 |
| Abstract |
Accurate sensor node localization in wireless sensor networks is essential for many uses, such as smart cities, military operations, and environmental monitoring. In order to achieve precise and economical sensor node localization, this paper introduces a hybrid optimization approach that combines Particle Swarm Optimization (PSO) with Grey Wolf Optimizer (GWO). While GWO has better local search capabilities, PSO is renowned for its strong global search power. The suggested approach combines these two algorithms, utilizing their respective advantages to improve performance overall. To establish a good approximation of sensor node placements, PSO is first used. This provides a refined baseline for the GWO. GWO then refines these places even further in order to get greater accuracy. Simulation is used to validate the hybrid technique and show how well it works to precisely estimate sensor node locations within a constrained search space. When compared to PSO and GWO, the hybrid PSO-GWO algorithm enhances coverage accuracy by about 9.41% and 5.68%, respectively and positioning accuracy by about 9.76% compared to PSO and 5.88% compared to GWO. © 2024 IEEE. |
| Author Keywords |
Grey Wolf Optimizer; Particle Swarm Optimization; Wireless Sensor Network |
