Localization Optimization in WSNs Using Meta-Heuristics Optimization Algorithms: A Survey

In Wireless Sensor Networks, node localization is one of the most important system parameters. Determining the exact position of nodes in these networks is one of vital and tedious tasks. This paper presents a review of the most localization methods which optimize the localization error. It provides a new taxonomy of techniques used in this field, including Mobile Anchor, Machine Learning, Matematical Models and Meta-heuristics. In this later, we survey its different algorithms such as Genetic Algorithm, Particle Swarm optimization, Ant Colony Optimization, BAT optimization algorithm, Firefly Optimization Algorithm, Flower Pollination Algorithm, Grey Wolf Optimization algorithm, Artificial Bees Colony Optimization Algorithm, Fish Swarm Optimization Algorithm and others. Further, the comparison between these metaheuristics algorithms based localization optimization is done. Finally, a comprehensive discussion of the performance parameters such as accuracy, convergence rate, energy consumption and the number of localized nodes is given.