A New Distributed Localization Algorithm Using Social Learning Based Particle Swarm Optimization for Internet of Things

Forfatter
Rauniyar, Ashish
Engelstad, Paal E.
Moen, Hans Jonas Fossum
Publisert
2018
Emneord
Sensorer
Trådløs kommunikasjon
Svermteknologi
Permalenke
http://hdl.handle.net/123456789/78734
http://hdl.handle.net/20.500.12242/2523
DOI
10.1109/VTCSpring.2018.8417665
Samling
Articles
Description
Rauniyar, Ashish; Engelstad, Paal E.; Moen, Hans Jonas Fossum. A New Distributed Localization Algorithm Using Social Learning Based Particle Swarm Optimization for Internet of Things. IEEE Vehicular Technology Conference (VTC) Proceedings 2018 ;Volum 2018-June. s. 1-7
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Sammendrag
Emerging applications in the Internet of Things (IoT) will depend on the accurate location of thousands of deployed sensors. However, accurate localization of deployed sensors nodes is a classical optimization problem which falls under NP-hard class of problems. Therefore in this work, we propose a new distributed localization algorithm using social learning based particle swarm optimization (SL-PSO) for IoT. With SL-PSO algorithm, we aim to do precise localization of deployed sensor nodes and reduce the computational complexity which will further enhance the lifetime of these resource-constrained IoT sensor nodes. Extensive simulations are carried out to show the effective performance of the SL-PSO algorithm in accurate localization. Experimental results depict that SL-PSO algorithm can not only increase convergence rate but also significantly reduce average localization error compared to traditional particle swarm optimization (PSO) and its other variants.
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