Correia, S.D.; Beko, M.; da Silva Cruz, L.A.; Tomic, S. Elephant Herding Optimization for Energy-Based Localization. Sensors2018, 18, 2849.
Correia, S.D.; Beko, M.; da Silva Cruz, L.A.; Tomic, S. Elephant Herding Optimization for Energy-Based Localization. Sensors 2018, 18, 2849.
Correia, S.D.; Beko, M.; da Silva Cruz, L.A.; Tomic, S. Elephant Herding Optimization for Energy-Based Localization. Sensors2018, 18, 2849.
Correia, S.D.; Beko, M.; da Silva Cruz, L.A.; Tomic, S. Elephant Herding Optimization for Energy-Based Localization. Sensors 2018, 18, 2849.
Abstract
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 (like all existing approaches do), we here tackle it directly by the use of metaheuristics. To the best of our knowledge, this is the first time that metaheuristics is applied to this type of problems. 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 performance comparison between the proposed algorithm and existing non-metaheuristic ones. Simulation results show that the new approach significantly outperforms the existing solutions in noisy environments, encouraging further improvement and testing of metaheuristic methods.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
