An Enhanced GPSR Protocol for Vehicular Ad hoc Networks

Benmir, Abdelkader; Korichi, Ahmed; Bourouis, Abdelhabib; Alreshoodi, Mohammed; Al-Jobouri, Laith

doi:10.1109/ceec47804.2019.8974321

Cited by 8 publications

(3 citation statements)

References 30 publications

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“…SUMO, OMNET++, and Veins were used in the experiment. As a result of simulations using the improved GPSR model, packet delivery is superior to the existing GPSR model [23]. The GPSR method he proposed is an optimized version of GPSR based on comparisons with GPSR and MM-GPSR.…”

Section: Literaturementioning

confidence: 99%

Design and Evaluation of Improvement of GPSR-Based RoutingTechniques for Intelligent Transport Systems using Vehicular AdHoc Networks

Thippa Reddy Gadekallu,

Issad Sabrina

2023

WJCMS

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As topology has become extremely fast, VANETs (vehicular ad-hoc networks) have become increasingly complex, creating new routing protocols for geographic routing. A GPSR-enhanced system (E-GPSR) is presented in this paper, which includes extended capabilities that select the best route and bypass previous vehicles delivering such packets. The E-GPSR protocol also prevents packets from being sent to neighbours similarly, thus eliminating packet routing loops. Road accidents and dead-end roads are unavoidable reasons for link breakage, which the E-GPSR protocol helps to resolve. Simulation of Urban Mobility (SUMO) and Network Simulator-version 3 (NS-3.33) platforms were used to compare E-GPSR with traditional GPSR and Maxduration-Minangle GPSR (MM-GPSR). GPSR and MM GPSR have better packet loss ratios (PLR) and packet delivery ratios (PDR) than the proposed E-GPSR protocol.,

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Section: Literaturementioning

confidence: 99%

Design and Evaluation of Improvement of GPSR-Based RoutingTechniques for Intelligent Transport Systems using Vehicular AdHoc Networks

Thippa Reddy Gadekallu,

Issad Sabrina

2023

WJCMS

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“…To reduce packet loss ratio in VANET, Benmir et al (2019) [13] explored the GPSR routing protocol. This paper proposes a second route for sending the same packet to reduce the ratio.…”

Section: Literature Reviewmentioning

confidence: 99%

A Performance Analysis of VANETs Propagation Models and Routing Protocols

et al. 2022

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Vehicular Ad hoc Networks (VANETs) thrive on providing a communication channel between vehicles and infrastructures that facilitate efficient and safe Vehicle-to-Vehicle (V2V) as well as Vehicle-to-Infrastructure (V2I) communications. Reliable and efficient transmission amongst vehicles and Road Side Units (RSUs) is a prime concern of Intelligent Transportation System (ITS). One of the primary challenges involved in vehicular communication is designing an efficient routing mechanism for data dissemination from node to node via a reliable route. The harsh vehicular environment with varied road conditions and obstacles in the signal propagation path induces another challenge. Therefore, this paper presents a comparative analysis of existing routing protocols with propagation models to assist researchers in gaining insight into the existing propagation model and routing protocols. The study also optimizes routing and propagation models for reliable packet dissemination. This work uses a realistic scenario from Open Street Map (OSM), and simulations are performed using SUMO. The trace files generated from SUMO are used for further simulation in NS-3. The simulation results are presented and studied in detail. The results show that the Two-Ray Ground and FRIIS propagation model outperforms the compared models, and the routing protocol OLSR outperforms AODV and DSDV.

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“…In each broadcast, the location of neighbor nodes is predicted by considering the node speed and direction, and then the next hop node is selected. A. Benmir et al [22] proposed to send the same data packet in two different paths to maximize its receiving probability. Simulation results show that the scheme is better than GPSR in some performance.…”

Section: Preliminary Knowledge 21 Gpsrmentioning

confidence: 99%

A Diversified Message Type Forwarding Strategy Based on Reinforcement Learning in VANET

2022

KSII TIIS

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The development of Vehicular Ad hoc Network (VANET) has greatly improved the efficiency and safety of social transportation, and the routing strategy for VANET has also received high attention from both academia and industry. However, studies on dynamic matching of routing policies with the message types of VANET are in short supply, which affects the operational efficiency and security of VANET to a certain extent. This paper studies the message types in VANET and fully considers the urgency and reliability requirements of message forwarding under various types. Based on the diversified types of messages to be transmitted, and taking the diversified message forwarding strategies suitable for VANET scenarios as behavioral candidates, an adaptive routing method for the VANET message types based on reinforcement learning (RL) is proposed. The key parameters of the method, such as state, action and reward, are reasonably designed. Simulation and analysis show that the proposed method could converge quickly, and the comprehensive performance of the proposed method is obviously better than the comparison methods in terms of timeliness and reliability.

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An Enhanced GPSR Protocol for Vehicular Ad hoc Networks

Cited by 8 publications

References 30 publications

Design and Evaluation of Improvement of GPSR-Based RoutingTechniques for Intelligent Transport Systems using Vehicular AdHoc Networks

Design and Evaluation of Improvement of GPSR-Based RoutingTechniques for Intelligent Transport Systems using Vehicular AdHoc Networks

A Performance Analysis of VANETs Propagation Models and Routing Protocols

A Diversified Message Type Forwarding Strategy Based on Reinforcement Learning in VANET

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