This work addresses the energy-based source localization problem in wireless sensors
networks. Instead of circumventing the maximum likelihood (ML) problem by applying convex
relaxations and approximations, we approach it directly by the use of metaheuristics. To the best
of our knowledge, this is the first time that metaheuristics are applied to this type of problem.
More specifically, an elephant herding optimization (EHO) algorithm is applied. Through extensive
simulations, the key parameters of the EHO algorithm are optimized such that they match
the energy decay model between two sensor nodes. A detailed analysis of the computational
complexity is presented, as well as a performance comparison between the proposed algorithm
and existing non-metaheuristic ones. Simulation results show that the new approach significantly
outperforms existing solutions in noisy environments, encouraging further improvement and testing
of metaheuristic methods.
Keywords: nature inspired algorithms; swarm optimization; elephant search algorithm; energy-based
localization; acoustic positioning; wireless sensor networks