Energy efficient clustering with Heuristic optimization based Ro/uting protocol for VANETs

Vehicle networks have been the subject of increasing amounts of study from both academia and industry as a way of enhancing traffic safety and providing real-time data to motorists and passengers. But beyond that, the need for a safe and secure network in moving vehicles is what's pushing the s...

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Vydáno v:Measurement. Sensors Ročník 27; s. 100745
Hlavní autoři: Giridhar, Koppisetti, Anbuananth, C., Krishnaraj, N.
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.06.2023
Elsevier
Témata:
Clustering
Energy efficiency
Fitness function
Metaheuristic algorithms
Routing
VANET
VANET
Routing
Energy efficiency
Fitness function
Clustering
Metaheuristic algorithms
ISSN:2665-9174, 2665-9174
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Shrnutí:Vehicle networks have been the subject of increasing amounts of study from both academia and industry as a way of enhancing traffic safety and providing real-time data to motorists and passengers. But beyond that, the need for a safe and secure network in moving vehicles is what's pushing the spread of VANETs. Intelligent, on-the-road vehicles that can exchange data with one another and with fixed roadside infrastructure make up what are known as ad hoc vehicular networks. It's anticipated that it will soon provide a wide variety of exciting new services. There are a lot of distinguishing features of ad hoc networks in autos, such as their adaptability to a wide range of situations. Vehicular Ad hoc Networks (VANET) use a network of moving automobiles as nodes to create a wireless network. Clustering and routing can be thought of as a multi-objective minimization issue that can be handled with metaheuristic optimization methods. With this in view, this work proposes the ANFC-QGSOR protocol for VANET, which combines adaptive neural fuzzy clustering (ANFC) and quantum glowworm swarm optimization-based routing (QGSOR). The presented ANFC-QGSOR technique initially allows the vehicles to communicate with one another. For effective cluster head (CH) selection and cluster assembly, the ANFC technique is used with three input parameters: residual energy, distance, and node degree. In addition, by developing a fitness function, the QGSOR approach is used to select the best routes to the destination. Network Simulator is used to simulate the proposed ANFC-QGSOR method (NS3 tool). The experimental results demonstrated that the ANFC-QGSOR technique surpassed earlier state-of-the-art technologies in a variety of evaluation variables.
ISSN:2665-9174
2665-9174
DOI:10.1016/j.measen.2023.100745