Preprint Article Version 1 This version is not peer-reviewed

Elephant Herding Optimization for Energy-Based Localization

Version 1 : Received: 2 July 2018 / Approved: 3 July 2018 / Online: 3 July 2018 (13:54:19 CEST)

A peer-reviewed article of this Preprint also exists.

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. Sensors 2018, 18, 2849.

Journal reference: Sensors 2018, 18, 2849
DOI: 10.3390/s18092849

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.

Subject Areas

Nature Inspired Algorithms; Swarm Optimization; Elephant Search Algorithm; Energy-based Localization; Acoustic Positioning; Wireless Sensor Networks.

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