TY - GEN
T1 - Elephant herding optimization algorithm for wireless sensor network localization problem
AU - Strumberger, Ivana
AU - Beko, Marko
AU - Tuba, Milan
AU - Minovic, Miroslav
AU - Bacanin, Nebojsa
N1 - info:eu-repo/grantAgreement/FCT/5876/135950/PT#
sem pdf.
Ministry of Education, Science and Technological Development of Republic of Serbia, Grant No. III-44006.
The work of M. Beko was supported in part by Fundação para a Ciência e a Tecnologia and Program Investigador FCT (IF/00325/2015).
PY - 2018/1/1
Y1 - 2018/1/1
N2 - This paper presents elephant herding optimization algorithm (EHO) adopted for solving localization problems in wireless sensor networks. EHO is a relatively new swarm intelligence metaheuristic that obtains promising results when dealing with NP hard problems. Node localization problem in wireless sensor networks, that belongs to the group of NP hard optimization, represents one of the most significant challenges in this domain. The goal of node localization is to set geographical co-ordinates for each sensor node with unknown position that is randomly deployed in the monitoring area. Node localization is required to report the origin of events, assist group querying of sensors, routing and network coverage. The implementation of the EHO algorithm for node localization problem was not found in the literature. In the experimental section of this paper, we show comparative analysis with other state-of-the-art algorithms tested on the same problem instance.
AB - This paper presents elephant herding optimization algorithm (EHO) adopted for solving localization problems in wireless sensor networks. EHO is a relatively new swarm intelligence metaheuristic that obtains promising results when dealing with NP hard problems. Node localization problem in wireless sensor networks, that belongs to the group of NP hard optimization, represents one of the most significant challenges in this domain. The goal of node localization is to set geographical co-ordinates for each sensor node with unknown position that is randomly deployed in the monitoring area. Node localization is required to report the origin of events, assist group querying of sensors, routing and network coverage. The implementation of the EHO algorithm for node localization problem was not found in the literature. In the experimental section of this paper, we show comparative analysis with other state-of-the-art algorithms tested on the same problem instance.
KW - Elephant herding optimization
KW - Metaheuristics
KW - Node localization problem
KW - Swarm intelligence
KW - Wireless sensor networks
UR - http://www.scopus.com/inward/record.url?scp=85046549172&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-78574-5_17
DO - 10.1007/978-3-319-78574-5_17
M3 - Conference contribution
AN - SCOPUS:85046549172
SN - 978-3-319-78573-8
T3 - IFIP Advances in Information and Communication Technology
SP - 175
EP - 184
BT - Technological Innovation for Resilient Systems - 9th IFIP WG 5.5/SOCOLNET Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2018, Proceedings
PB - Springer New York LLC
T2 - 9th Advanced Doctoral Conference on Computing, Electrical and Industrial Systems, DoCEIS 2018
Y2 - 2 May 2018 through 4 May 2018
ER -