A Stable Routing Algorithm Based on Link Prediction Method for Clustered VANET

Dappuri, Bhasker; Amru, M.; Venkatanaga, Allam Mahesh

doi:10.1007/978-3-030-32644-9_10

Cited by 5 publications

(2 citation statements)

References 15 publications

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“…Because Gaussian probability circulations approximately correspond to their node degree distributions and the topology. While a connection is too low, even considering incorporating the small-world characteristic, low-density VANETs act similarly to small-world networks [5].…”

Section: Related Workmentioning

confidence: 99%

An Optimal Navigation Model for Realistic Traffic Network Scenarios in VANET

Kumari,

Raja

2023

Preprint

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Vehicle Ad-hoc Networks (VANETs) are a kind of Internet of Things system where groups of vehicles connect and vehicle tracking infrastructure to enhance driver and public security as well as lifestyles. In a VANET, vehicles oversee the communicating status to the controller and other drivers for processing, as well as the statuses of the road, traffic, and atmosphere around them. In these actual VANET network traffic scenarios, this research presents the mixing of enhanced genetic algorithms (EGA) based on ant colony optimization (ACO) techniques (EGAACO) to create an optimized routing algorithm. This research evaluates the conventional algorithm with metaheuristic possibilities and includes the experimental VANET simulation scenario. To verify the outcomes, the proposed approach is tested using open-source network and traffic simulation tools. On Simulation of Urban Mobility (SUMO), all three different traffic scenarios were simulated and examined using NS3.2. The results of these analyses were satisfactory, and it emerged EGAACO algorithm, performed better than the others in each of the three traffic scenarios. The four real-time traffic network scenarios include average throughput, packet delivery ratio, end-to-end delay, and packet loss in a network, which is collected from the city of Bangalore, and the performance metric measures utilized in traffic network conditions. The experimental result proved that the proposed EGAACO algorithm outperforms the Ad-hoc on- Demand Distance Vector Routing (AODV), Particle Swarm Intelligence (PSO), and ACO routing protocols in all settings.

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Section: Related Workmentioning

confidence: 99%

An Optimal Navigation Model for Realistic Traffic Network Scenarios in VANET

Kumari,

Raja

2023

Preprint

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“…Multiple routes from the source to the destination are established with uniform density of the vehicles using ACO. In [21], a new approach has been proposed and compared with the existing algorithms. The proposed algorithm showed improved performance in terms of delay time reduction, throughput, stability up gradation, and lifetime.…”

Section: Overview Of Previous Workmentioning

confidence: 99%

A Novel Routing Protocol for Realistic Traffic Network Scenarios in VANET

Singh

Kumar

Alshazly

et al. 2021

Wireless Communications and Mobile Computing

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The vehicular ad hoc network (VANET) has traditional routing protocols that evolved from mobile ad hoc networks (MANET). The standard routing protocols of VANET are geocast, topology, broadcast, geographic, and cluster-based routing protocols. They have their limitations and are not suitable for all types of VANET traffic scenarios. Hence, metaheuristics algorithms like evolutionary, trajectory, nature-inspired, and ancient-inspired algorithms can be integrated with standard routing algorithms of VANET to achieve optimized routing performance results in desired VANET traffic scenarios. This paper proposes integrating genetic algorithm (GA) in ant colony optimization (ACO) technique (GAACO) for an optimized routing algorithm in three different realistic VANET network traffic scenarios. The paper compares the traditional VANET routing algorithm along with the metaheuristics approaches and also discusses the VANET simulation scenario for experimental purposes. The implementation of the proposed approach is tested on the open-source network and traffic simulation tools to verify the results. The three different traffic scenarios were deployed on Simulation of Urban Mobility (SUMO) and tested using NS3.2. After comparing them, the results were satisfactory and it is found that the GAACO algorithm has performed better in all three different traffic scenarios. The realistic traffic network scenarios are taken from Dehradun City with four performance metric parameters including the average throughput, packet delivery ratio, end-to-end delay, and packet loss in a network. The experimental results conclude that the proposed GAACO algorithm outperforms particle swarm intelligence (PSO), ACO, and Ad-hoc on Demand Distance Vector Routing (AODV) routing protocols with an average significant value of 1.55%, 1.45%, and 1.23% in three different VANET network scenarios.

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QoS-based energy-efficient hybrid routing protocols in RSU assisted VANET using clustering mechanism

Sahu,

Tripathy

2024

International Journal of Electronics

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A Stable Routing Algorithm Based on Link Prediction Method for Clustered VANET

Cited by 5 publications

References 15 publications

An Optimal Navigation Model for Realistic Traffic Network Scenarios in VANET

An Optimal Navigation Model for Realistic Traffic Network Scenarios in VANET

A Novel Routing Protocol for Realistic Traffic Network Scenarios in VANET

QoS-based energy-efficient hybrid routing protocols in RSU assisted VANET using clustering mechanism

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