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    Energy-aware composition for wireless sensor networks as a service.

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    Authors
    Zhou, Zhangbing
    Zhao, Deng
    Liu, Lu cc
    Hung, Patrick C. K.
    Affiliation
    China University of Geosciences
    University of Derby
    University of Ontario
    TELECOM Sud Paris
    Issue Date
    2017-03-02
    
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    Abstract
    With 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.
    Citation
    Zhou Zhangbing, et al (2018) 'Energy-aware composition for wireless sensor networks as a service, Future Generation Computer Systems, 80, pp.299-310.
    Publisher
    Elsevier
    Journal
    Future Generation Computer Systems
    URI
    http://hdl.handle.net/10545/622065
    DOI
    10.1016/j.future.2017.02.050
    Additional Links
    http://linkinghub.elsevier.com/retrieve/pii/S0167739X17303266
    Type
    Article
    Language
    en
    ISSN
    0167739X
    ae974a485f413a2113503eed53cd6c53
    10.1016/j.future.2017.02.050
    Scopus Count
    Collections
    Department of Electronics, Computing & Maths

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