Publication:
Formulation Of 3D Euclidean Distance For Network Clustering In Wireless Sensor Network

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dc.contributor.authorKalid Abdlkader Marsalen_US
dc.contributor.authorA.H Aznien_US
dc.contributor.authorFarida Ridzuanen_US
dc.date.accessioned2024-05-27T14:51:28Z
dc.date.available2024-05-27T14:51:28Z
dc.date.issued2019
dc.date.submitted18/2/2020
dc.descriptionVolume :8 Issue :9en_US
dc.description.abstractIn wireless sensor networks, nodes operating under dynamic topology are often correlated with their behavior. Correlated behavior may pose devastating impact towards network connectivity. A node may change its behaviour from cooperative node to misbehave node which directly affects the network’s connectivity. Misbehaviour nodes tend to have correlated effect which creates partitioning within the network. To improve network connectivity in providing an efficient communication in the events of the correlated behaviors, a new formulation of correlated degree to perform network clustering is required. This paper proposes a formulation on correlated degree using 3D Euclidean distance to achieve higher network connectivity under correlated node behavior. The key idea behind the 3D Euclidean distance in network clustering is to identify a set of sensors whose sensed values present some data correlation referring to correlated degree. The correlated degree is formulated based on three-point distance within a correlation region to identify the level of node correlation within neighboring nodes. In addition, the correlated degree also be able to detect the same group of node behavior which is grouped in correlated regions. 3D Euclidean distance is shown in mathematical analysis and how the new formulation calculates correlated degree is also discussed. It is also expected that the new 3D Euclidean distance formulation may help correlation region to change it cluster formation dynamically to achieve the required network connectivity.en_US
dc.identifier.epage3373
dc.identifier.issn2320-0790
dc.identifier.issue9
dc.identifier.spage3368
dc.identifier.urihttps://www.ijact.in/index.php/ijact/article/view/1019
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/3868
dc.identifier.volume8
dc.language.isoen_USen_US
dc.publisherCOMPUSOFTen_US
dc.relation.ispartofCompusoft, An International Journal of Advanced Computer Technologyen_US
dc.subject3D Euclidean Distance, Network Clustering, Wireless Sensoren_US
dc.titleFormulation Of 3D Euclidean Distance For Network Clustering In Wireless Sensor Networken_US
dc.typeArticleen_US
dspace.entity.typePublication

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