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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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