TY - JOUR
T1 - Elephant herding optimization for energy-based localization
AU - Correia, Sérgio D.
AU - Beko, Marko
AU - Cruz, Luis A. da Silva
AU - Tomic, Slavisa
N1 - info:eu-repo/grantAgreement/FCT/5876/147324/PT#
Project foRESTER PCIF/SSI/0102/2017.
Grant IF/00325/2015.
PY - 2018/9/1
Y1 - 2018/9/1
N2 - 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.
AB - 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.
KW - Acoustic positioning
KW - Elephant search algorithm
KW - Energy-based localization
KW - Nature inspired algorithms
KW - Swarm optimization
KW - Wireless sensor networks
UR - http://www.scopus.com/inward/record.url?scp=85052644239&partnerID=8YFLogxK
U2 - 10.3390/s18092849
DO - 10.3390/s18092849
M3 - Article
C2 - 30158442
AN - SCOPUS:85052644239
VL - 18
JO - Sensors
JF - Sensors
SN - 1424-8220
IS - 9
M1 - 2849
ER -