A multimetric predictive ANN-based routing protocol for vehicular ad hoc networks
| Autor: | Juan Pablo Astudillo León, Pablo Barbecho Bautista, Mónica Aguilar Igartua, Ahmad Mohamad Mezher, Leticia Lemus Cardenas |
|---|---|
| Přispěvatelé: | Universitat Politècnica de Catalunya. Doctorat en Enginyeria Telemàtica, Universitat Politècnica de Catalunya. Departament d'Enginyeria Telemàtica, Universitat Politècnica de Catalunya. SISCOM - Smart Services for Information Systems and Communication Networks |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Routing protocol
Dynamic network analysis General Computer Science Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors [Àrees temàtiques de la UPC] Computer science Wireless ad hoc network Multimetric routing protocol Vehicular ad hoc networks (Computer networks) Encaminament (Gestió de xarxes d'ordinadors) 02 engineering and technology Protecció de dades 0203 mechanical engineering Xarxes vehiculars ad hoc (Xarxes d'ordinadors) 0202 electrical engineering electronic engineering information engineering vehicular networks General Materials Science Intelligent transportation system Data protection Vehicular ad hoc network Artificial neural networks Network packet business.industry Wireless network ComputerSystemsOrganization_COMPUTER-COMMUNICATIONNETWORKS General Engineering 020302 automobile design & engineering Routing (Computer network management) Enginyeria de la telecomunicació [Àrees temàtiques de la UPC] TK1-9971 020201 artificial intelligence & image processing Enginyeria de la telecomunicació::Telemàtica i xarxes d'ordinadors::Trànsit de dades [Àrees temàtiques de la UPC] Electrical engineering. Electronics. Nuclear engineering Routing (electronic design automation) business artificial neural networks Vehicular networks Computer network |
| Zdroj: | UPCommons. Portal del coneixement obert de la UPC Universitat Politècnica de Catalunya (UPC) IEEE Access, Vol 9, Pp 86037-86053 (2021) |
| Popis: | © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Vehicular networks support intelligent transportation system (ITS) to improve drivers’ safety and traffic efficiency on the road by exchanging traffic-related information between vehicles and also between vehicles and infrastructure. Routing protocols that are designed for vehicular networks should be flexible and able to adapt to the inherent dynamic network characteristics of these kind of networks. Therefore, there is a need to have effective vehicular communications, not only to make mobility more efficient but also to reduce collateral issues such as pollution and health problems. Nowadays, the use of machine learning (ML) algorithms in wireless networks are on the rise, including vehicle networks that can benefit from possible data-driven predictions. This work aims to contribute to the design of a smart ML-based routing protocol for vehicular ad hoc networks (VANETs) used to report traffic-related messages in urban environments. We propose a new ML-based forwarding algorithm to be used by the current vehicle holding a given packet to predict which vehicle within its transmission range is the best next-hop to forward that packet towards its destination. Our algorithm is based on a neural network designed from a dataset that contains data records that are captured during simulated urban scenarios. Simulation results show how our ML-based proposal improves the performance of our multimetric routing protocol for VANETs in urban scenarios in terms of packet delivery probability. The performance evaluation of MPANN shows packet losses lower than 20% (and average packet delays below 0.04 ms) for different vehicles’ densities, in completely new scenarios but of similar complexity than the Barcelona scenario used to train the model. Even for much more complex scenarios (with narrow curvy streets), our proposal is able to reduce the packet losses in 20% with respect to the multimetric routing protocol as well as the average packet delays in 0.04 ms. This work was supported by the Spanish Government under Research Project sMArt Grid using Open Source intelligence (MAGOS) under Grant TEC2017-84197-C4-3-R. The work of Pablo Andrés Barbecho Bautista was supported by the Secretaría Nacional de Educación Superior, Ciencia y Tecnología (SENESCYT). The work of Leticia Lemus Cárdenas was supported by the Academic Coordination of the University of Guadalajara, México. |
| Databáze: | OpenAIRE |
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