A Novel Routing Protocol for Realistic Traffic Network Scenarios in VANET

Abstract

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.

Cluster‐based context‐aware route service management for smart intelligent autonomous vehicles with industrial transport system

“…Industrial transport systems can have a significant impact on the environment, including emissions, noise pollution, and waste disposal. 33,34 Thus, context-aware routing is needed for industrial transport systems to optimize the performance and efficiency of transport routes in real time based on the context of the system. By taking into account the characteristics of the environment, such as traffic congestion, vehicle location, and delivery priority, context-aware routing can improve delivery times, reduce fuel consumption, and minimize vehicle wear and tear.…”

“…In order for NHRCS's information dissemination in VANETs to function effectively in both dense and sparse contexts, intelligent and dynamic context‐aware solutions are required engine flexibility can exchange information. Singh et al 33 have proposed that a genetic algorithm (GA) is incorporated into the ant colony optimization approach (GAACO) to enhance the directing calculation under three different sensible VANET network traffic situations. In three distinct scenarios involving VANET networks, the results confirm that the GAACO algorithm outperforms the average value of 1.55%, 1.45%, and 1.23%.…”

“…Maintenance issues with vehicles, equipment, or infrastructure can result in downtime, increased maintenance costs, and reduced productivity. Industrial transport systems can have a significant impact on the environment, including emissions, noise pollution, and waste disposal 33,34 . Thus, context‐aware routing is needed for industrial transport systems to optimize the performance and efficiency of transport routes in real time based on the context of the system.…”

Solving User Priority in Cloud Computing Using Enhanced Optimization Algorithm in Workflow Scheduling

“…In the pooling of the resources, the provider's computing resources can be pooled to serve multifarious clients by utilizing a multitenant architecture with diverse physical as well as virtual resources that are assigned animatedly and reassigned as per the customer request. The rapid elasticity provides ease to the users because they can buy services of cloud computing according to their requirements and the measured services spontaneously control and enhance resource employment by leveraging a metering ability that is suitable to the kind of facility, for instance, storing, processing, routing, and bandwidth [ 5 ].…”

“…e rapid elasticity provides ease to the users because they can buy services of cloud computing according to their requirements and the measured services spontaneously control and enhance resource employment by leveraging a metering ability that is suitable to the kind of facility, for instance, storing, processing, routing, and bandwidth [5].…”