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dc.contributor.author | Patra, Subhadeep | es_ES |
dc.contributor.author | Manzoni, Pietro | es_ES |
dc.contributor.author | Tavares De Araujo Cesariny Calafate, Carlos Miguel | es_ES |
dc.contributor.author | Zamora-Mero, Willian Jesus | es_ES |
dc.contributor.author | Cano, Juan-Carlos | es_ES |
dc.date.accessioned | 2020-11-20T04:31:14Z | |
dc.date.available | 2020-11-20T04:31:14Z | |
dc.date.issued | 2019-09-06 | es_ES |
dc.identifier.uri | http://hdl.handle.net/10251/155396 | |
dc.description.abstract | [EN] Fog computing, an extension of the Cloud Computing paradigm where routers themselves may provide the virtualisation infrastructure, aims at achieving fluidity when distributing in-network functions, in addition to allowing fast and scalable processing, and exchange of information. In this paper we present a fog computing architecture based on a content island which interconnects sets of things to exchange and process data among themselves or with other content islands. We then present a use case that focuses on a smartphone-based forward collision warning application for a connected vehicle scenario. This application makes use of the optical sensor of smartphones to estimate the distance between the device itself and other vehicles in its field of view. The vehicle travelling directly ahead is identified relying on the information from the GPS, camera, and inter-island communication. Warnings are generated at both content islands, if the driver does not maintain a predefined safe distance towards the vehicle ahead. Experiments performed with the application show that with the developed method, we are able to estimate the distance between vehicles, and the inter-island communication has a very low overhead, resulting in improved performance. On comparing our proposed solution based on edge/fog computing with a cloud-based api, it was observed that our solution outperformed the cloud-based api, thus making us optimistic of the utility of the proposed architecture | es_ES |
dc.description.sponsorship | This work was partially funding by the Ministerio de Ciencia, Innovación y Universidades, Programa Estatal de Investigación, Desarrollo e Innovación Orientada a los Retos de la Sociedad, Proyectos I+D+I 2018 , Spain, under Grant RTI2018-096384-B-I00 | es_ES |
dc.language | Inglés | es_ES |
dc.publisher | MDPI AG | es_ES |
dc.relation.ispartof | Sensors | es_ES |
dc.rights | Reconocimiento (by) | es_ES |
dc.subject | Fog computing | es_ES |
dc.subject | IoT | es_ES |
dc.subject | ITS | es_ES |
dc.subject | Vehicular network | es_ES |
dc.subject | MQTT | es_ES |
dc.subject | FCW | es_ES |
dc.subject | Android | es_ES |
dc.subject | Smartphones | es_ES |
dc.subject.classification | ARQUITECTURA Y TECNOLOGIA DE COMPUTADORES | es_ES |
dc.title | Leveraging a Publish/Subscribe Fog System to Provide Collision Warnings in Vehicular Networks | es_ES |
dc.type | Artículo | es_ES |
dc.identifier.doi | 10.3390/s19183852 | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/RTI2018-096384-B-I00/ES/SOLUCIONES PARA UNA GESTION EFICIENTE DEL TRAFICO VEHICULAR BASADAS EN SISTEMAS Y SERVICIOS EN RED/ | 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 | Patra, S.; Manzoni, P.; Tavares De Araujo Cesariny Calafate, CM.; Zamora-Mero, WJ.; Cano, J. (2019). Leveraging a Publish/Subscribe Fog System to Provide Collision Warnings in Vehicular Networks. Sensors. 19(18):1-22. https://doi.org/10.3390/s19183852 | es_ES |
dc.description.accrualMethod | S | es_ES |
dc.relation.publisherversion | https://doi.org/10.3390/s19183852 | es_ES |
dc.description.upvformatpinicio | 1 | es_ES |
dc.description.upvformatpfin | 22 | es_ES |
dc.type.version | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.volume | 19 | es_ES |
dc.description.issue | 18 | es_ES |
dc.identifier.eissn | 1424-8220 | es_ES |
dc.identifier.pmid | 31489937 | es_ES |
dc.identifier.pmcid | PMC6767351 | es_ES |
dc.relation.pasarela | S\392810 | es_ES |
dc.contributor.funder | Agencia Estatal de Investigación | es_ES |
dc.description.references | Vaquero, L. M., & Rodero-Merino, L. (2014). Finding your Way in the Fog. ACM SIGCOMM Computer Communication Review, 44(5), 27-32. doi:10.1145/2677046.2677052 | es_ES |
dc.description.references | MQTT Version 3.1.1 http://docs.oasis-open.org/mqtt/mqtt/v3.1.1/os/mqtt-v3.1.1-os.doc | es_ES |
dc.description.references | Sultana, T., & Wahid, K. A. (2019). Choice of Application Layer Protocols for Next Generation Video Surveillance Using Internet of Video Things. IEEE Access, 7, 41607-41624. doi:10.1109/access.2019.2907525 | es_ES |
dc.description.references | Mehmood, F., Ullah, I., Ahmad, S., & Kim, D. (2019). Object detection mechanism based on deep learning algorithm using embedded IoT devices for smart home appliances control in CoT. Journal of Ambient Intelligence and Humanized Computing. doi:10.1007/s12652-019-01272-8 | es_ES |
dc.description.references | https://tools.ietf.org/html/rfc2616 | es_ES |
dc.description.references | https://tools.ietf.org/html/rfc7252 | es_ES |
dc.description.references | Volvo Official Website https://www.volvocars.com/ | es_ES |
dc.description.references | Chang, B. R., Tsai, H. F., & Young, C.-P. (2010). Intelligent data fusion system for predicting vehicle collision warning using vision/GPS sensing. Expert Systems with Applications, 37(3), 2439-2450. doi:10.1016/j.eswa.2009.07.036 | es_ES |
dc.description.references | Tan, H.-S., & Huang, J. (2006). DGPS-Based Vehicle-to-Vehicle Cooperative Collision Warning: Engineering Feasibility Viewpoints. IEEE Transactions on Intelligent Transportation Systems, 7(4), 415-428. doi:10.1109/tits.2006.883938 | es_ES |
dc.description.references | Gelernter, D. (1985). Generative communication in Linda. ACM Transactions on Programming Languages and Systems, 7(1), 80-112. doi:10.1145/2363.2433 | es_ES |
dc.description.references | Raspberry Pi Official Website https://www.raspberrypi.org/ | es_ES |
dc.description.references | https://tools.ietf.org/html/rfc768 | es_ES |
dc.description.references | Wallace, G. K. (1991). The JPEG still picture compression standard. Communications of the ACM, 34(4), 30-44. doi:10.1145/103085.103089 | es_ES |
dc.description.references | Sauvola, J., & Pietikäinen, M. (2000). Adaptive document image binarization. Pattern Recognition, 33(2), 225-236. doi:10.1016/s0031-3203(99)00055-2 | es_ES |
dc.description.references | Road Safety Authority of Ireland Suggest the Use of Two Second Rule http://www.rotr.ie/Rules_of_the_road.pdf | es_ES |
dc.description.references | OpenALPR Cloud-API Website https://www.openalpr.com/cloud-api.html | es_ES |
dc.description.references | Patra, S., Calafate, C. T., Cano, J.-C., & Manzoni, P. (2015). An ITS solution providing real-time visual overtaking assistance using smartphones. 2015 IEEE 40th Conference on Local Computer Networks (LCN). doi:10.1109/lcn.2015.7366320 | es_ES |