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Towards Resilient and Secure Cooperative Behavior of Intelligent Transportation System Using Sensor Technologies

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Towards Resilient and Secure Cooperative Behavior of Intelligent Transportation System Using Sensor Technologies

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dc.contributor.author Rehman, Amjad es_ES
dc.contributor.author Haseeb, Khalid es_ES
dc.contributor.author Saba, Tanzila es_ES
dc.contributor.author Lloret, Jaime es_ES
dc.contributor.author Ahmed, Zara es_ES
dc.date.accessioned 2024-02-02T19:01:14Z
dc.date.available 2024-02-02T19:01:14Z
dc.date.issued 2022-04-01 es_ES
dc.identifier.issn 1530-437X es_ES
dc.identifier.uri http://hdl.handle.net/10251/202309
dc.description.abstract [EN] Internet of things (IoT) connects heterogeneous physical objects to collect the observing data and eases to development of smart transmission systems. Vehicular ad hoc network (VANET) offers many smart services for emerging vehicle-to-vehicle communication systems using sensors. Although, geographical routing solutions have been improved the process of neighbor finding and inter-related vehicle sensors. However, due to the high mobility and realistic environment, reliability and data continuity among data routing are essential for emerging transportation systems. Also, the vehicles are communicating on unrestrained and exposed wireless mediums, thus they are more visible to security threats and compromised data for unauthorized usages. In this paper, we proposed a resilient and secure cooperative intelligent transportation system (RS-ITS) using sensor technologies to optimize route discovery with minimum communication failures. RS-ITS makes use of the roadside unit (RSU) as an intelligent agent using machine learning techniques to predict the finest routes and maintain nominal overheads. It also secures the vehicular network against vulnerabilities and ensures reliable propagation of messages between vehicles and RSUs. The proposed model is tested using simulations for varying data sizes and vehicles, which indicates improved performance for delivery ratio by 17%, data delay by 40%, energy consumption by 22%, routes in-continuity by 17%, network overheads by 27%, and malicious attacks by 43% as compared to other schemes. es_ES
dc.description.sponsorship This work was supported by the Artificial Intelligence and Data Analytics Laboratory (AIDA), CCIS, Prince Sultan University, Riyadh, Saudi Arabia. The associate editor coordinating the review of this article and approving it for publication was Prof. Jari Nurmi. es_ES
dc.language Inglés es_ES
dc.publisher Institute of Electrical and Electronics Engineers es_ES
dc.relation.ispartof IEEE Sensors Journal es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Routing es_ES
dc.subject Vehicular ad hoc networks es_ES
dc.subject Transportation es_ES
dc.subject Routing protocols es_ES
dc.subject Intelligent sensors es_ES
dc.subject Wireless sensor networks es_ES
dc.subject Security es_ES
dc.subject Intelligent transportation system es_ES
dc.subject Resilient systems es_ES
dc.subject Machine learning es_ES
dc.subject Vehicle sensors es_ES
dc.subject Technological development es_ES
dc.subject.classification INGENIERÍA TELEMÁTICA es_ES
dc.title Towards Resilient and Secure Cooperative Behavior of Intelligent Transportation System Using Sensor Technologies es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1109/JSEN.2022.3152808 es_ES
dc.rights.accessRights Cerrado es_ES
dc.contributor.affiliation Universitat Politècnica de València. Escuela Politécnica Superior de Gandia - Escola Politècnica Superior de Gandia es_ES
dc.description.bibliographicCitation Rehman, A.; Haseeb, K.; Saba, T.; Lloret, J.; Ahmed, Z. (2022). Towards Resilient and Secure Cooperative Behavior of Intelligent Transportation System Using Sensor Technologies. IEEE Sensors Journal. 22(7):7352-7360. https://doi.org/10.1109/JSEN.2022.3152808 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1109/JSEN.2022.3152808 es_ES
dc.description.upvformatpinicio 7352 es_ES
dc.description.upvformatpfin 7360 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 22 es_ES
dc.description.issue 7 es_ES
dc.relation.pasarela S\506753 es_ES
dc.contributor.funder Artificial Intelligence and Data Analytics Lab, Prince Sultan University es_ES


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