Energy-Efficient Cluster-based Routing Protocol in Internet of Things Using Swarm Intelligence

Sankar, S; Ramasubbareddy, S; Chen, F; Gandomi, AH

Energy-Efficient Cluster-based Routing Protocol in Internet of Things Using Swarm Intelligence

Sankar, S Ramasubbareddy, S Chen, F

Gandomi, AH

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020, 2020, 00, pp. 219-224
Issue Date:: 2020-12-01

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Field	Value	Language
dc.contributor.author	Sankar, S
dc.contributor.author	Ramasubbareddy, S
dc.contributor.author	Chen, F https://orcid.org/0000-0003-4971-8729
dc.contributor.author	Gandomi, AH
dc.date	2020-12-01
dc.date.accessioned	2021-03-03T03:54:40Z
dc.date.available	2021-03-03T03:54:40Z
dc.date.issued	2020-12-01
dc.identifier.citation	2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020, 2020, 00, pp. 219-224
dc.identifier.isbn	9781728125473
dc.identifier.uri	http://hdl.handle.net/10453/146688
dc.description.abstract	© 2020 IEEE. Energy conservation is a difficult challenge, because the Internet of Things (IoT) connects limited resource devices. Clustering is an efficient method for energy saving in network nodes. The existing clustering algorithms have problems with the short lifespan of a network, an unbalanced load among the network nodes and increased end-to-end delays. This paper proposes a new Cluster Head (CH) selection and cluster formation algorithm to overcome these issues. The process has two phases. First, the CH is selected using a Swarm Intelligence Algorithm called Sailfish optimization Algorithm (SOA). Second, the cluster is formed by the Euclidean distance. The simulation is conducted using the NS2 simulator. The efficacy of the SOA is compared to Improved Ant Bee Colony optimization-based Clustering (IABCOCT), Enhanced Particle Swarm optimization Technique (EPSOCT) and Hierarchical Clustering-based CH Election (HCCHE). The final results of the simulation show that the proposed SOA improves network life and decreases node-to-sink delays.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2020 IEEE Symposium Series on Computational Intelligence, SSCI 2020
dc.relation.ispartof	2020 IEEE Symposium Series on Computational Intelligence (SSCI)
dc.relation.isbasedon	10.1109/SSCI47803.2020.9308609
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Energy-Efficient Cluster-based Routing Protocol in Internet of Things Using Swarm Intelligence
dc.type	Conference Proceeding
utslib.citation.volume	00
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/A/DRsch The Data Science Institute
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2021-03-03T03:54:39Z
pubs.finish-date	2020-12-04
pubs.publication-status	Published
pubs.start-date	2020-12-01
pubs.volume	00

Abstract:

© 2020 IEEE. Energy conservation is a difficult challenge, because the Internet of Things (IoT) connects limited resource devices. Clustering is an efficient method for energy saving in network nodes. The existing clustering algorithms have problems with the short lifespan of a network, an unbalanced load among the network nodes and increased end-to-end delays. This paper proposes a new Cluster Head (CH) selection and cluster formation algorithm to overcome these issues. The process has two phases. First, the CH is selected using a Swarm Intelligence Algorithm called Sailfish optimization Algorithm (SOA). Second, the cluster is formed by the Euclidean distance. The simulation is conducted using the NS2 simulator. The efficacy of the SOA is compared to Improved Ant Bee Colony optimization-based Clustering (IABCOCT), Enhanced Particle Swarm optimization Technique (EPSOCT) and Hierarchical Clustering-based CH Election (HCCHE). The final results of the simulation show that the proposed SOA improves network life and decreases node-to-sink delays.

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http://hdl.handle.net/10453/146688