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Social Network Analysis Based Localization Technique with Clustered Closeness Centrality for 3D Wireless Sensor Networks

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Social Network Analysis Based Localization Technique with Clustered Closeness Centrality for 3D Wireless Sensor Networks

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dc.contributor.author Ahmad, Tanveer es_ES
dc.contributor.author Li, Xue Jun es_ES
dc.contributor.author Seet, Boon-Chong es_ES
dc.contributor.author Cano, Juan-Carlos es_ES
dc.date.accessioned 2021-03-10T04:31:21Z
dc.date.available 2021-03-10T04:31:21Z
dc.date.issued 2020-05 es_ES
dc.identifier.uri http://hdl.handle.net/10251/163579
dc.description.abstract [EN] In this paper, we proposed a new wireless localization technique based on the ideology of social network analysis (SNA), to study the different properties of networks as a graph. Centrality is a main concept in SNA, so we propose using closeness centrality (CC) as a measurement to denote the importance of the node inside the network due to its geo-location to others. The node with highest degree of CC is chosen as a cluster heads, then each cluster head can form its trilateration process to collect data from its cluster. The selection of closest cluster based on CC values, and the unknown node's location can be estimated through the trilateration process. To form a perfect trilateration, the cluster head chooses three anchor nodes. The proposed algorithm provides high accuracy even in different network topologies like concave shape, O shape, and C shape as compared to existing received signal strength indicator (RSSI) techniques. Matlab simulation results based on practical radio propagation data sets showed a localization error of 0.32 m with standard deviation of 0.26 m. es_ES
dc.description.sponsorship This work was fully supported by the Vice Chancellor Doctoral Scholarship at Auckland University of Technology, New Zealand. es_ES
dc.language Inglés es_ES
dc.publisher MDPI AG es_ES
dc.relation.ispartof Electronics es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Wireless sensor network es_ES
dc.subject Social network analysis es_ES
dc.subject Closeness centrality es_ES
dc.subject Cluster es_ES
dc.subject Receive signal strength indicator es_ES
dc.subject.classification ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES es_ES
dc.title Social Network Analysis Based Localization Technique with Clustered Closeness Centrality for 3D Wireless Sensor Networks es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/electronics9050738 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Informática de Sistemas y Computadores - Departament d'Informàtica de Sistemes i Computadors es_ES
dc.description.bibliographicCitation Ahmad, T.; Li, XJ.; Seet, B.; Cano, J. (2020). Social Network Analysis Based Localization Technique with Clustered Closeness Centrality for 3D Wireless Sensor Networks. Electronics. 9(5):1-19. https://doi.org/10.3390/electronics9050738 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.3390/electronics9050738 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 19 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 9 es_ES
dc.description.issue 5 es_ES
dc.identifier.eissn 2079-9292 es_ES
dc.relation.pasarela S\409122 es_ES
dc.contributor.funder University of Auckland es_ES
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