Elephant Herding Optimization for Energy-Based Localization

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 t...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2018-08, Vol.18 (9), p.2849
Main Authors: Correia, Sérgio D, Beko, Marko, da Silva Cruz, Luis A, Tomic, Slavisa
Format: Article
Language:English
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
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Summary: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.
ISSN:1424-8220
1424-8220
DOI:10.3390/s18092849