Abstract
Node localization in a wireless sensor network (WSN) seeks to compute the positions of unknown nodes (regular node) using known nodes(anchor nodes). The accuracy with which a WSN is located can have a substantial influence on its performance. Most standard range-free localization techniques disregard anisotropic considerations in real-time wireless sensor applications, resulting in low localization accuracy. To increase localization accuracy in the target area, bio-inspired algorithms will aid in the optimization of expected placements of unknown sensor nodes. The reliable anchor pairing approach and the salps swarm algorithm (SSA) in traditional range-free localization methods like the DV-Hop method are used to mitigate anisotropic factors. Particle swarm optimization (PSO) and grey wolf optimization (GWO) and other well-known optimization methods are compared to the proposed algorithm under different WSN topologies.
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