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Social clustering of vehicles based on semi-Markov processes
dc.creator | Maglaras L.A., Katsaros D. | en |
dc.date.accessioned | 2023-01-31T08:55:40Z | |
dc.date.available | 2023-01-31T08:55:40Z | |
dc.date.issued | 2016 | |
dc.identifier | 10.1109/TVT.2015.2394367 | |
dc.identifier.issn | 00189545 | |
dc.identifier.uri | http://hdl.handle.net/11615/76067 | |
dc.description.abstract | Vehicle clustering is a crucial network management task for vehicular networks to address the broadcast storm problem and to cope with the rapidly changing network topology. Developing algorithms that create stable clusters is a very challenging procedure because of the highly dynamic moving patterns of vehicles and the dense topology. Previous approaches to vehicle clustering have been based on either topology-agnostic features, such as vehicle IDs or hard-to-set parameters, or have exploited very limited knowledge of vehicle trajectories. This paper develops a pair of algorithms, namely, sociological pattern clustering (SPC) and route stability clustering (RSC), the latter being a specialization of the former that exploits, for the first time in the relevant literature, the "social behavior" of vehicles, i.e., their tendency to share the same/similar routes. Both methods exploit the historic trajectories of vehicles gathered by roadside units located in each subnetwork of a city and use the recently introduced clustering primitive of virtual forces. The mobility, i.e., mobile patterns of each vehicle, is modeled as semi-Markov processes. To assess the performance of the proposed clustering algorithms, we performed a detailed experimentation by simulation to compare its behavior with that of high-performance state-of-the-art algorithms, namely, the Low-Id, DDVC, and MPBC protocols. The comparison involved the investigation of the impact of a range of parameters on the performance of the protocols, including vehicle speed and transmission range, as well as the existence and strength of social patterns, for both urban and highway-like environments. All of the received results attested to the superiority of the proposed algorithms for creating stable and meaningful clusters. © 2015 IEEE. | en |
dc.language.iso | en | en |
dc.source | IEEE Transactions on Vehicular Technology | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84959347188&doi=10.1109%2fTVT.2015.2394367&partnerID=40&md5=c170d1a6680d29a32b4ebed224ccc53e | |
dc.subject | Algorithms | en |
dc.subject | Carrier mobility | en |
dc.subject | Crashworthiness | en |
dc.subject | Markov processes | en |
dc.subject | Network management | en |
dc.subject | Topology | en |
dc.subject | Vehicle transmissions | en |
dc.subject | Vehicles | en |
dc.subject | Broadcast storm problem | en |
dc.subject | Clustering | en |
dc.subject | Pattern clustering | en |
dc.subject | Semi markov process | en |
dc.subject | Social behavior | en |
dc.subject | Transmission ranges | en |
dc.subject | Vehicle trajectories | en |
dc.subject | Vehicular networks | en |
dc.subject | Clustering algorithms | en |
dc.subject | Institute of Electrical and Electronics Engineers Inc. | en |
dc.title | Social clustering of vehicles based on semi-Markov processes | en |
dc.type | journalArticle | en |
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