Node Localization in Wireless Sensor Networks using Swarm Intelligence
DOI:
https://doi.org/10.58213/vidhyayana.v10isi3.2225Keywords:
Wireless Sensor Networks, Node Localization, Swarm Intelligence, Ant Colony Optimization, Particle Swarm Optimization (ISF Academy. n.d.),, (He, S., Prempain, E., & Wu, Q. H. (2004)Abstract
Wireless Sensor Networks (WSNs) are used in different areas such as the environment, health, and defense where the node location is of extreme importance to facilitate data flow (Medium. n.d.). Traditional approach for localization which includes RSSI and TOA become vulnerable to interferences from the environment leading to so many errors and low efficiency. Classical global optimization techniques, like Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO)(Khan, S., Pathan, A. K., & Alrajeh, N. A. (2016), represent efficient discretional approximations based on swarm behavior imitation. PSO copies the migrant birds and makes the node placement of the network iteratively best by adding or removing nodes while ACO is the representation of ants in searching the optimal path based on the pheromone traces. These bio-inspired methodologies improve the accuracy of the localization process and decrease power consumption which is decisive for the existence of WSNs. This paper presents a new combined model based on PSO and ACO to enhance the accuracy (Computer Science Department, University of Ioannina. n.d.), convergence rate, and stability. Computation analysis of the proposed hybrid model shows that the model achieved a higher accuracy and efficiency of the node localization than a single PSO and ACO models while consuming fewer iterations. Additionally, graphs of node deployment and optimization show that this is relevant to real-world usage of swarm intelligence in WSNs. Therefore, this paper demonstrates the use of machine learning to enhance WSN localization and supports the inclusion of swarm intelligence to optimize these results under dynamic environments.
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