Please use this identifier to cite or link to this item: https://rfos.fon.bg.ac.rs/handle/123456789/1794
Title: Elephant Herding Optimization Algorithm for Wireless Sensor Network Localization Problem
Authors: Strumberger, Ivana
Beko, Marko
Tuba, Milan
Minović, Miroslav 
Bačanin, Nebojša
Keywords: Wireless sensor networks;Swarm intelligence;Node localization problem;Metaheuristics;Elephant herding optimization
Issue Date: 2018
Publisher: IFIP-Int Federation Information Processing, Laxenburg
Abstract: 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.
URI: https://rfos.fon.bg.ac.rs/handle/123456789/1794
ISSN: 1868-4238
Appears in Collections:Radovi istraživača / Researchers’ publications

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