Design of a network with VANET sporadic cloud computing applied to traffic accident prevention

The study analyzes the bandwidth available in a segment of route in the VANET network, since this value directly affects sporadic cloud computing. For this purpose, the bandwidth was tested on a highly complex urban scenario, where a number of mobile nodes were used with random conditions both in mo...

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Autores:
amelec, viloria
Pineda, Omar
Varela Izquierdo, Noel
Diaz Martínez, Jorge Luis
Tipo de recurso:
http://purl.org/coar/resource_type/c_816b
Fecha de publicación:
2020
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/8042
Acceso en línea:
https://hdl.handle.net/11323/8042
https://doi.org/10.1007/978-981-15-6648-6_17
https://repositorio.cuc.edu.co/
Palabra clave:
Machine learning
Proactive control
Traffic
Smart cities
Autonomous computing VANET
Rights
openAccess
License
CC0 1.0 Universal
id RCUC2_5809b357a1720d8ee30db9db0970423c
oai_identifier_str oai:repositorio.cuc.edu.co:11323/8042
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Design of a network with VANET sporadic cloud computing applied to traffic accident prevention
title Design of a network with VANET sporadic cloud computing applied to traffic accident prevention
spellingShingle Design of a network with VANET sporadic cloud computing applied to traffic accident prevention
Machine learning
Proactive control
Traffic
Smart cities
Autonomous computing VANET
title_short Design of a network with VANET sporadic cloud computing applied to traffic accident prevention
title_full Design of a network with VANET sporadic cloud computing applied to traffic accident prevention
title_fullStr Design of a network with VANET sporadic cloud computing applied to traffic accident prevention
title_full_unstemmed Design of a network with VANET sporadic cloud computing applied to traffic accident prevention
title_sort Design of a network with VANET sporadic cloud computing applied to traffic accident prevention
dc.creator.fl_str_mv amelec, viloria
Pineda, Omar
Varela Izquierdo, Noel
Diaz Martínez, Jorge Luis
dc.contributor.author.spa.fl_str_mv amelec, viloria
Pineda, Omar
Varela Izquierdo, Noel
Diaz Martínez, Jorge Luis
dc.subject.spa.fl_str_mv Machine learning
Proactive control
Traffic
Smart cities
Autonomous computing VANET
topic Machine learning
Proactive control
Traffic
Smart cities
Autonomous computing VANET
description The study analyzes the bandwidth available in a segment of route in the VANET network, since this value directly affects sporadic cloud computing. For this purpose, the bandwidth was tested on a highly complex urban scenario, where a number of mobile nodes were used with random conditions both in mobility and in resources of transmission. The results of the tests show that the stability of the bandwidth available in each region is proportional to the number of real mobile nodes in the region. However, a considerable bandwidth is also reached with a smaller number of mobile nodes, but there is no stability in the region, thus causing the network to collapse. The VANET network simulation tool was NS-3, since it is currently one of the most commonly used free software that allows configure the simulation parameters in a vehicular environment. The urban simulation scenario is the historic center of the City of Bogotá, Colombia, which was created with SUMO for obtaining the mobility traces
publishDate 2020
dc.date.issued.none.fl_str_mv 2020-07-19
dc.date.accessioned.none.fl_str_mv 2021-03-18T14:51:26Z
dc.date.available.none.fl_str_mv 2021-03-18T14:51:26Z
dc.type.spa.fl_str_mv Pre-Publicación
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_816b
dc.type.content.spa.fl_str_mv Text
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dc.identifier.issn.spa.fl_str_mv 18650929
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/8042
dc.identifier.doi.spa.fl_str_mv https://doi.org/10.1007/978-981-15-6648-6_17
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv 18650929
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/8042
https://doi.org/10.1007/978-981-15-6648-6_17
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv 1. Mittal, A., Ostojic, M., Mahmassani, H.S.: Active traffic signal control for mixed vehicular traffic in connected environments: self-identifying platoon strategy (No. 19-05931) (2019)
2. Cheikhrouhou, O., Koubaa, A., Zarrad, A.: A cloud based disaster management system. J. Sens. Actuator Netw. 9(1), 6 (2020)
3. Alam, K.M., El Saddik, A.: C2PS: a digital twin architecture reference model for the cloud-based cyber-physical systems. IEEE Access 5, 2050–2062 (2017)
4. Lum, C., Koper, C.S., Wu, X., Johnson, W., Stoltz, M.: Examining the empirical realities of proactive policing through systematic observations and computer-aided dispatch data. Police Q. (2020). https://doi.org/10.1177/1098611119896081
5. Ferenchak, N.N., Marshall, W.E.: Equity analysis of proactively-vs. reactively-identified traffic safety issues. Transp. Res. Rec. 2673(7), 596–606 (2019)
6. Tucker, C., Nelson, H.T., Sarbora, R.S.: U.S. Patent No. 10,534,337. Washington, DC: U.S. Patent and Trademark Office (2020)
7. Azari, A., Papapetrou, P., Denic, S., Peters, G.: User traffic prediction for proactive resource management: learning-powered approaches. arXiv preprint arXiv:1906.00951 (2019)
8. Gillani, R., Nasir, A.: Proactive control of hybrid electric vehicles for maximum fuel efficiency. In: 2019 16th International Bhurban Conference on Applied Sciences and Technology (IBCAST), pp. 396–401. IEEE (2019)
9. Rai, A., Kannan, R.J.: Co-simulation based finite state machine for telematic and data compression microservices in IoT. Wirel. Pers. Commun. 105(3), 1069–1082 (2019)
10. Batkovic, I., Zanon, M., Ali, M., Falcone, P.: Real-time constrained trajectory planning and vehicle control for proactive autonomous driving with road users. In: 2019 18th European Control Conference (ECC), pp. 256–262. IEEE (2019)
11. Zhao, W.: Performance optimization for state machine replication based on application semantics: a review. J. Syst. Softw. 112, 96–109 (2016)
12. Bortnikov, V., Cahana, Z., Ifergan-Shachor, S., Shnayderman, I.: U.S. Patent No. 10,083,217. Washington, DC: U.S. Patent and Trademark Office (2018)
13. Viloria, A., Robayo, P.V.: Virtual network level of application composed IP networks connected with systems-(NETS peer-to-peer). Indian J. Sci. Technol. 9, 46 (2016)
14. Hu, Y., Chen, C., He, T., He, J., Guan, X., Yang, B.: Proactive power management scheme for hybrid electric storage system in EVs: an MPC method. IEEE Trans. Intell. Transp. Syst. (2019)
15. Al Shehri, A., et al.: U.S. Patent No. 10,533,937. Washington, DC: U.S. Patent and Trademark Office (2020)
16. Formosa, N., Quddus, M., Ison, S., Abdel-Aty, M., Yuan, J.: Predicting real-time traffic conflicts using deep learning. Accid. Anal. Prev. 136, 105429 (2020)
17. Zahid, M., Chen, Y., Jamal, A., Memon, M.Q.: Short term traffic state prediction via hyperparameter optimization based classifiers. Sensors 20(3), 685 (2020)
18. Lu, Z., Xia, J., Wang, M., Nie, Q., Ou, J.: Short-term traffic flow forecasting via multi-regime modeling and ensemble learning. Appl. Sci. 10(1), 356 (2020)
19. Paranjothi, A., Khan, M.S., Patan, R., Parizi, R.M., Atiquzzaman, M.: VANETomo: a congestion identification and control scheme in connected vehicles using network tomography. Comput. Commun. 151, 275–289 (2020)
20. Ramanathan, R., et al.: U.S. Patent No. 10,268,467. Washington, DC: U.S. Patent and Trademark Office (2019)
21. Ma, C., Zhou, J., Xu, X.D., Xu, J.: Evolution regularity mining and gating control method of urban recurrent traffic congestion: a literature review. J. Adv. Transp. (2020)
22. Jha, S., et al.: Derecho: fast state machine replication for cloud services. ACM Trans. Comput. Syst. (TOCS) 36(2), 1–49 (2019)
23. Liu, J., Khattak, A.: Informed decision-making by integrating historical on-road driving performance data in high-resolution maps for connected and automated vehicles. J. Intell. Transp. Syst. 24(1), 11–23 (2020)
24. Martinov, G.M., Ljubimov, A.B., Martinova, L.I.: From classic CNC systems to cloud-based technology and back. Robot. Comput.-Integr. Manuf. 63, 101927 (2020)
25. Chen, X., Wang, H., Ma, Y., Zheng, X., Guo, L.: Self-adaptive resource allocation for cloud-based software services based on iterative QoS prediction model. Future Gener. Comput. Syst. 105, 287–296 (2020)
26. Viloria, A., Acuña, G.C., Franco, D.J.A., Hernández-Palma, H., Fuentes, J.P., Rambal, E.P.: Integration of data mining techniques to PostgreSQL database manager system. Procedia Comput. Sci. 155, 575–580 (2019)
27. Perez, R., Vásquez, C., Viloria, A.: An intelligent strategy for faults location in distribution networks with distributed generation. J. Intell. Fuzzy Syst. 36(2), 1627–1637 (2019)
28. Chaubey, N.: Security analysis of vehicular ad hoc networks (VANETs): a comprehensive study. Int. J. Secur. Appl. 10, 261–274 (2016)
29. Chaubey, N.K., Yadav, D.: A taxonomy of Sybil attacks in vehicular ad-hoc network (VANET). In: Rao, R., Jain, V., Kaiwartya, O., Singh, N. (eds.) IoT and Cloud Computing Advancements in Vehicular Ad-Hoc Networks, pp. 174–190. IGI Global, Hershey (2020). https://doi.org/10.4018/978-1-7998-2570-8.ch009
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spelling amelec, viloriaPineda, OmarVarela Izquierdo, NoelDiaz Martínez, Jorge Luis2021-03-18T14:51:26Z2021-03-18T14:51:26Z2020-07-1918650929https://hdl.handle.net/11323/8042https://doi.org/10.1007/978-981-15-6648-6_17Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The study analyzes the bandwidth available in a segment of route in the VANET network, since this value directly affects sporadic cloud computing. For this purpose, the bandwidth was tested on a highly complex urban scenario, where a number of mobile nodes were used with random conditions both in mobility and in resources of transmission. The results of the tests show that the stability of the bandwidth available in each region is proportional to the number of real mobile nodes in the region. However, a considerable bandwidth is also reached with a smaller number of mobile nodes, but there is no stability in the region, thus causing the network to collapse. The VANET network simulation tool was NS-3, since it is currently one of the most commonly used free software that allows configure the simulation parameters in a vehicular environment. The urban simulation scenario is the historic center of the City of Bogotá, Colombia, which was created with SUMO for obtaining the mobility tracesamelec, viloria-will be generated-orcid-0000-0003-2673-6350-600Pineda, Omar-will be generated-orcid-0000-0002-8239-3906-600Varela Izquierdo, Noel-will be generated-orcid-0000-0001-7036-4414-600Diaz Martínez, Jorge Luisapplication/pdfengCorporación Universidad de la CostaRetractedCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Communications in Computer and Information Sciencehttps://link.springer.com/chapter/10.1007/978-981-15-6648-6_17Machine learningProactive controlTrafficSmart citiesAutonomous computing VANETDesign of a network with VANET sporadic cloud computing applied to traffic accident preventionPre-Publicaciónhttp://purl.org/coar/resource_type/c_816bTextinfo:eu-repo/semantics/preprinthttp://purl.org/redcol/resource_type/ARTOTRinfo:eu-repo/semantics/acceptedVersion1. Mittal, A., Ostojic, M., Mahmassani, H.S.: Active traffic signal control for mixed vehicular traffic in connected environments: self-identifying platoon strategy (No. 19-05931) (2019)2. Cheikhrouhou, O., Koubaa, A., Zarrad, A.: A cloud based disaster management system. J. Sens. Actuator Netw. 9(1), 6 (2020)3. Alam, K.M., El Saddik, A.: C2PS: a digital twin architecture reference model for the cloud-based cyber-physical systems. IEEE Access 5, 2050–2062 (2017)4. Lum, C., Koper, C.S., Wu, X., Johnson, W., Stoltz, M.: Examining the empirical realities of proactive policing through systematic observations and computer-aided dispatch data. Police Q. (2020). https://doi.org/10.1177/10986111198960815. Ferenchak, N.N., Marshall, W.E.: Equity analysis of proactively-vs. reactively-identified traffic safety issues. Transp. Res. Rec. 2673(7), 596–606 (2019)6. Tucker, C., Nelson, H.T., Sarbora, R.S.: U.S. Patent No. 10,534,337. Washington, DC: U.S. Patent and Trademark Office (2020)7. Azari, A., Papapetrou, P., Denic, S., Peters, G.: User traffic prediction for proactive resource management: learning-powered approaches. arXiv preprint arXiv:1906.00951 (2019)8. Gillani, R., Nasir, A.: Proactive control of hybrid electric vehicles for maximum fuel efficiency. In: 2019 16th International Bhurban Conference on Applied Sciences and Technology (IBCAST), pp. 396–401. IEEE (2019)9. Rai, A., Kannan, R.J.: Co-simulation based finite state machine for telematic and data compression microservices in IoT. Wirel. Pers. Commun. 105(3), 1069–1082 (2019)10. Batkovic, I., Zanon, M., Ali, M., Falcone, P.: Real-time constrained trajectory planning and vehicle control for proactive autonomous driving with road users. In: 2019 18th European Control Conference (ECC), pp. 256–262. IEEE (2019)11. Zhao, W.: Performance optimization for state machine replication based on application semantics: a review. J. Syst. Softw. 112, 96–109 (2016)12. Bortnikov, V., Cahana, Z., Ifergan-Shachor, S., Shnayderman, I.: U.S. Patent No. 10,083,217. Washington, DC: U.S. Patent and Trademark Office (2018)13. Viloria, A., Robayo, P.V.: Virtual network level of application composed IP networks connected with systems-(NETS peer-to-peer). Indian J. Sci. Technol. 9, 46 (2016)14. Hu, Y., Chen, C., He, T., He, J., Guan, X., Yang, B.: Proactive power management scheme for hybrid electric storage system in EVs: an MPC method. IEEE Trans. Intell. Transp. Syst. (2019)15. Al Shehri, A., et al.: U.S. Patent No. 10,533,937. Washington, DC: U.S. Patent and Trademark Office (2020)16. Formosa, N., Quddus, M., Ison, S., Abdel-Aty, M., Yuan, J.: Predicting real-time traffic conflicts using deep learning. Accid. Anal. Prev. 136, 105429 (2020)17. Zahid, M., Chen, Y., Jamal, A., Memon, M.Q.: Short term traffic state prediction via hyperparameter optimization based classifiers. Sensors 20(3), 685 (2020)18. Lu, Z., Xia, J., Wang, M., Nie, Q., Ou, J.: Short-term traffic flow forecasting via multi-regime modeling and ensemble learning. Appl. Sci. 10(1), 356 (2020)19. Paranjothi, A., Khan, M.S., Patan, R., Parizi, R.M., Atiquzzaman, M.: VANETomo: a congestion identification and control scheme in connected vehicles using network tomography. Comput. Commun. 151, 275–289 (2020)20. Ramanathan, R., et al.: U.S. Patent No. 10,268,467. Washington, DC: U.S. Patent and Trademark Office (2019)21. Ma, C., Zhou, J., Xu, X.D., Xu, J.: Evolution regularity mining and gating control method of urban recurrent traffic congestion: a literature review. J. Adv. Transp. (2020)22. Jha, S., et al.: Derecho: fast state machine replication for cloud services. ACM Trans. Comput. Syst. (TOCS) 36(2), 1–49 (2019)23. Liu, J., Khattak, A.: Informed decision-making by integrating historical on-road driving performance data in high-resolution maps for connected and automated vehicles. J. Intell. Transp. Syst. 24(1), 11–23 (2020)24. Martinov, G.M., Ljubimov, A.B., Martinova, L.I.: From classic CNC systems to cloud-based technology and back. Robot. Comput.-Integr. Manuf. 63, 101927 (2020)25. Chen, X., Wang, H., Ma, Y., Zheng, X., Guo, L.: Self-adaptive resource allocation for cloud-based software services based on iterative QoS prediction model. Future Gener. Comput. Syst. 105, 287–296 (2020)26. Viloria, A., Acuña, G.C., Franco, D.J.A., Hernández-Palma, H., Fuentes, J.P., Rambal, E.P.: Integration of data mining techniques to PostgreSQL database manager system. Procedia Comput. Sci. 155, 575–580 (2019)27. Perez, R., Vásquez, C., Viloria, A.: An intelligent strategy for faults location in distribution networks with distributed generation. J. Intell. Fuzzy Syst. 36(2), 1627–1637 (2019)28. Chaubey, N.: Security analysis of vehicular ad hoc networks (VANETs): a comprehensive study. Int. J. Secur. Appl. 10, 261–274 (2016)29. Chaubey, N.K., Yadav, D.: A taxonomy of Sybil attacks in vehicular ad-hoc network (VANET). In: Rao, R., Jain, V., Kaiwartya, O., Singh, N. (eds.) IoT and Cloud Computing Advancements in Vehicular Ad-Hoc Networks, pp. 174–190. 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