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    Maximizing lifetime of a wireless sensor network via joint optimizing sink placement and sensor-to-sink routing

    Access Status
    Fulltext not available
    Authors
    Zhao, C.
    Wu, Changzhi
    Wang, X.
    Ling, B.
    Teo, Kok Lay
    Lee, J.
    Jung, K.
    Date
    2017
    Type
    Journal Article
    
    Metadata
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    Citation
    Zhao, C. and Wu, C. and Wang, X. and Ling, B. and Teo, K.L. and Lee, J. and Jung, K. 2017. Maximizing lifetime of a wireless sensor network via joint optimizing sink placement and sensor-to-sink routing. Applied Mathematical Modelling. 49: pp. 319-337.
    Source Title
    Applied Mathematical Modelling
    DOI
    10.1016/j.apm.2017.05.001
    ISSN
    0307-904X
    School
    Department of Construction Management
    Funding and Sponsorship
    http://purl.org/au-research/grants/arc/LP140100873
    URI
    http://hdl.handle.net/20.500.11937/54642
    Collection
    • Curtin Research Publications
    Abstract

    Wireless sensor networks typically contain hundreds of sensors. The sensors collect data and relay it to sinks through single hop or multiple hop paths. Sink deployment significantly influences the performance of a network. Since the energy capacity of each sensor is limited, optimizing sink deployment and sensor-to-sink routing is crucial. In this paper, this problem is modeled as a mixed integer optimization problem. Then, a novel layer-based diffusion particle swarm optimization method is proposed to solve this large-scaled optimization problem. In particular, two sensor-to-sink binding algorithms are combined as inner layer optimization to evaluate the fitness values of the solutions. Compared to existing methods that the sinks are selected from candidate positions, our method can achieve better performance since they can be placed freely within a geometrical plane. Several numerical examples are used to validate and demonstrate the performance of our method. The reported numerical results show that our method is superior to those existing. Furthermore, our method has good scalability which can be used to deploy a large-scaled sensor network.

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