Show simple item record

dc.contributor.authorZhou, Zhangbing
dc.contributor.authorZhao, Deng
dc.contributor.authorLiu, Lu
dc.contributor.authorHung, Patrick C. K.
dc.date.accessioned2018-01-22T15:01:49Z
dc.date.available2018-01-22T15:01:49Z
dc.date.issued2017-03-02
dc.identifier.citationZhou Zhangbing, et al (2018) 'Energy-aware composition for wireless sensor networks as a service, Future Generation Computer Systems, 80, pp.299-310.en
dc.identifier.issn0167739X
dc.identifier.doi10.1016/j.future.2017.02.050
dc.identifier.urihttp://hdl.handle.net/10545/622065
dc.description.abstractWith the wide-adoption of the Internet of Things, heterogeneous smart things, serving as sensor nodes, require to work in a collective fashion for achieving complex applications. To address this challenge, this article proposes a service-oriented wireless sensor networks (WSNs) framework, and the cooperation between sensor nodes is achieved through the functional integration of neighboring sensor nodes. Generally, sensor nodes are encapsulated and represented as WSN services, which are energy-aware, and typically have constraints on their spatial and temporal aspects. WSN services are categorized into service classes according to the limited number of types of their functionalities. Consequently, service classes chains are generated with respect to the requirement of domain applications, and the composition of WSN services is constructed through discovering and selecting appropriate WSN services as the instantiation of service classes contained in chains. This WSN services composition is reduced to a multi-objective and multi-constrained optimization problem, which can be solved through adopting particle swarm optimization (PSO) algorithm and genetic algorithm (GA). Experimental evaluation shows that PSO outperforms GA in finding approximately optimal WSN services compositions.
dc.description.sponsorshipN/Aen
dc.language.isoenen
dc.publisherElsevieren
dc.relation.urlhttp://linkinghub.elsevier.com/retrieve/pii/S0167739X17303266en
dc.rightsArchived with thanks to Future Generation Computer Systemsen
dc.subjectWSN servicesen
dc.subjectService classes chainen
dc.subjectEnergy-aware WSN service compositionen
dc.subjectSpatial and temporal constraintsen
dc.titleEnergy-aware composition for wireless sensor networks as a service.en
dc.typeArticleen
dc.contributor.departmentChina University of Geosciencesen
dc.contributor.departmentUniversity of Derbyen
dc.contributor.departmentUniversity of Ontarioen
dc.contributor.departmentTELECOM Sud Parisen
dc.identifier.journalFuture Generation Computer Systemsen
dcterms.dateAccepted2017-02-28
html.description.abstractWith the wide-adoption of the Internet of Things, heterogeneous smart things, serving as sensor nodes, require to work in a collective fashion for achieving complex applications. To address this challenge, this article proposes a service-oriented wireless sensor networks (WSNs) framework, and the cooperation between sensor nodes is achieved through the functional integration of neighboring sensor nodes. Generally, sensor nodes are encapsulated and represented as WSN services, which are energy-aware, and typically have constraints on their spatial and temporal aspects. WSN services are categorized into service classes according to the limited number of types of their functionalities. Consequently, service classes chains are generated with respect to the requirement of domain applications, and the composition of WSN services is constructed through discovering and selecting appropriate WSN services as the instantiation of service classes contained in chains. This WSN services composition is reduced to a multi-objective and multi-constrained optimization problem, which can be solved through adopting particle swarm optimization (PSO) algorithm and genetic algorithm (GA). Experimental evaluation shows that PSO outperforms GA in finding approximately optimal WSN services compositions.


Files in this item

Thumbnail
Name:
Publisher version

This item appears in the following Collection(s)

Show simple item record