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Subject: search.filters.subject.Wireless Sensor Networks

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Now showing 1 - 4 of 4
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    PublicationEmbargo
    An analytical framework for cluster distribution of EDCR class of algorithms in Wireless Sensor Networks
    (IEEE, 2009-12-28) Gamwarige, S; Kulasekere, E. C
    Energy Driven Cluster-Head Rotation (EDCR) class of algorithms are energy aware distributed clustering techniques for effective ad hoc deployed Wireless Sensor Network (WSN) organization. The application of this class of algorithms requires the setting of salient parameters at the design stage of the WSN to achieve desired results. Two such parameters which should be known in advance are the cluster density and the distance between neighbouring cluster heads (CHs). In this research we analyze the effect of algorithm design based on these two parameters. Simulation techniques are given to support and verify the analytical results.
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    PublicationEmbargo
    Energy expenditure of global reclustering and local delegation in Wireless Sensor Networks
    (IEEE, 2010-09-06) De Silva, H. J; Gamwarige, S; Kulasekere, E. C
    One of the most important aspects in a clustering algorithm in a wireless sensor network is the cluster head (CH) rotation mechanism. Global reclustering is the common and established method used for this purpose in many existing algorithms. This research proposes an analytical framework to investigate the average energy expenditure of the global reclustering method and a local delegation strategy in which the CH role is delegated to a member of the cluster without changing the initial cluster boundary. Further we investigate a strategy of partial local delegation within global reclustering rounds to achieve energy efficiency. Simulations are used to visualize the analytically derived results using the simulation environment MATLAB.
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    PublicationOpen Access
    Dempster-Shafer Information Filtering in Multi-Modality Wireless Sensor Networks
    (publications.waset.org, 2013-07-27) Weeraddana, D. M; Walgama, K. S; Kulasekere, E. C
    A framework to estimate the state of dynamically varying environment where data are generated from heterogeneous sources possessing partial knowledge about the environment is presented. This is entirely derived within Dempster-Shafer and Evidence Filtering frameworks. The belief about the current state is expressed as belief and plausibility functions. An addition to Single Input Single Output Evidence Filter, Multiple Input Single Output Evidence Filtering approach is introduced. Variety of applications such as situational estimation of an emergency environment can be developed within the framework successfully. Fire propagation scenario is used to justify the proposed framework, simulation results are presented.
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    PublicationEmbargo
    Optimization of cluster head rotation in energy constrained wireless sensor networks
    (IEEE, 2007-07-02) Gamwarige, S; Kulasekere, E. C
    The performance of energy driven cluster head (CH) rotation algorithms have been shown to be far more superior to time driven CH rotation algorithms when it comes to maximizing the sensor bed lifetime. The sensor bed life time is improved by selecting the proper points at which a CH role is relinquished to higher energy nodes via a CH rotation phase. However no formal analytical method is available to find the optimal point at which the CH rotation should be carried out. This research proposes an analytical iterative method to obtain the optimal points at which the CH rotation can be carried out. The method proposes an optimal value c opt for the energy threshold at which this rotation could occur. The values obtained via the analytical method is shown to be optimal via simulations using the EDCR and EDCR-MH algorithms. The analytical method proposed can be used in any energy driven algorithm to find the optimal point for CH rotations avoiding any ad hoc simulation based methods to maximize the lifetime of the sensor bed.