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JCR Impact Factor: 0.700
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Issues per year: 4
Current issue: Nov 2024
Next issue: Feb 2025
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PUBLISHER

Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229
ROMANIA

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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2024-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2023. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.700 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.600.

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Clarivate Analytics published the InCites Journal Citations Report for 2022. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.800 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 1.000.

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SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

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Clarivate Analytics published the InCites Journal Citations Report for 2021. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.825 (0.722 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.752.

2022-Jun-16
SCOPUS published the CiteScore for 2021, computed by using an improved methodology, counting the citations received in 2018-2021 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2021 is 2.5, the same as for 2020 but better than all our previous results.

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  4/2024 - 3

Enhanced QL-Based Dynamic Routing Protocol for Urban VANETs

BUGARCIC, P. See more information about BUGARCIC, P. on SCOPUS See more information about BUGARCIC, P. on IEEExplore See more information about BUGARCIC, P. on Web of Science, JEVTIC, N. See more information about  JEVTIC, N. on SCOPUS See more information about  JEVTIC, N. on SCOPUS See more information about JEVTIC, N. on Web of Science, MALNAR, M. See more information about  MALNAR, M. on SCOPUS See more information about  MALNAR, M. on SCOPUS See more information about MALNAR, M. on Web of Science, STOJANOVIC, M. See more information about STOJANOVIC, M. on SCOPUS See more information about STOJANOVIC, M. on SCOPUS See more information about STOJANOVIC, M. on Web of Science
 
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Download PDF pdficon (937 KB) | Citation | Downloads: 24 | Views: 38

Author keywords
computer simulation, intelligent transportation systems, machine learning, routing protocols, vehicular ad hoc networks.

References keywords
learning(18), reinforcement(17), routing(16), networks(15), vehicular(14), communications(9), adaptive(6), intelligent(5), access(5), vanet(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2024-11-30
Volume 24, Issue 4, Year 2024, On page(s): 27 - 36
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2024.04003

Abstract
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Choosing an optimal data forwarding route is crucial for improving network performance in mobile ad hoc networks (MANETs). This process becomes very complex if the network topology changes frequently and quickly, as is the case with vehicular ad hoc networks (VANETs). Under these conditions, the routing process can be significantly improved by including machine learning in optimal route selection. The type of learning that suits highly dynamic networks the best is reinforcement learning (RL). One of the most important types of RL for dynamic MANETs is Q-learning (QL). In this study, an enhanced QL-based dynamic routing algorithm for urban VANETs (Q-DRAV) is proposed, which manages to significantly improve the overall network performance of VANETs, by including relevant network parameters in the RL process. Simulation analysis and comparison with other routing protocols are performed in the NS-3 simulator, and the protocol implementation code is publicly available. Simulation results show that the proposed protocol reduces the packet loss ratio, average packet end-to-end delay and jitter, while it increases the achieved application throughput in the network.


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Faculty of Electrical Engineering and Computer Science
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