Elephant Herding Optimization for Energy-Based Localization

• Published in: Sensors (Basel, Switzerland) Vol. 18; no. 9; p. 2849
• Main Authors: Correia, Sérgio D, Beko, Marko, da Silva Cruz, Luis A, Tomic, Slavisa
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 29-08-2018; MDPI
• Subjects: Accuracy; acoustic positioning; Acoustics; Computer simulation; elephant search algorithm; Elephants; Energy; energy-based localization; International conferences; Localization; Methods; nature inspired algorithms; Noise; Optimization algorithms; Sensors; Signal processing; swarm optimization; Test procedures; Wireless networks; Wireless sensor networks; United States > US; Portugal; wireless sensor networks; energy-based localization; nature inspired algorithms; swarm optimization; elephant search algorithm; acoustic positioning
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s18092849

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.