Clustering scheme and destination aware context based routing protocol for VANET

In vehicular ad-hoc network (VANET), every vehicle node indicates a mobile node and it acts as a transmitter, receiver and router for the delivery of the information. VANET is a subgroup of mobile ad-hoc network (MANET) and is related to the dynamic topology. Dynamic network scenarios are more challenging issues as compared to MANET topologies, so finding a suitable algorithm for all VANET applications is the major challenge for the researchers. Routing protocols in VANET are divided into six parts i.e., cluster-based, geocast-based, topologybased, position-based, and broadcast-based. Autonomous robots and unmanned military vehicles (UMVs) become part of the advanced warfare strategy to execute dangerous war field operations and military combat missions. The military vehicles (MVs) transfer information to each other in order to achieve required military tasks collectively. In the proposed work, rhombus shaped area is divided into multiple clusters using a weight-based clustering algorithm for transmitting the event information to the vehicles. Intersection clustering with rhombus shaped area which are very effective for clustering. To choose cluster head (CH), the proposed method has used two weighted metrics, one is real time average speed and the other parameter is degree. This work is useful for choosing right CH in the network. Each vehicle in the same cluster transmits the data to the CH instead of broadcasting it. The simulation has been done in the SUMO and NETSIM simulator, which shows the network performance for the different protocols like Ad-hoc on-demand distance vector (AODV), dynamic source routing (DSR) in terms of packet delivery ratio, throughput, delay, overhead transmission, mean and standard deviation.

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“…In Fig. 14, cluster-2 consists of vehicle ids-1, 7, 15,19,25,10,14,18,22,23,24,28, 30 and elects vehicle id-22 as CH. In Fig.…”

Section: Cluster Based Simulation Resultsmentioning

confidence: 99%

Weight-Based Clustering Algorithm for Military Vehicles Communication in VANET

2023

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“…1 and Fig. 3 report a detailed packet delivery ratio (PDR) examination of the EMCMHR-LM model with recent models under varying vehicle counts [22][23][24]. The experimental values implied that the EMCMHR-LM model has resulted in maximum PDR values.…”

Section: Experimental Validationmentioning

confidence: 97%

Metaheuristics-based Clustering with Routing Technique for Lifetime Maximization in Vehicular Networks

Muthukrishnan¹,

Kannan²

2023

Computers, Materials &Amp; Continua

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Recently, vehicular ad hoc networks (VANETs) finds applicability in different domains such as security, rescue operations, intelligent transportation systems (ITS), etc. VANET has unique features like high mobility, limited mobility patterns, adequate topology modifications, and wireless communication. Despite the benefits of VANET, scalability is a challenging issue which could be addressed by the use of cluster-based routing techniques. It enables the vehicles to perform intercluster communication via chosen CHs and optimal routes. The main drawback of VANET network is the network unsteadiness that results in minimum lifetime. In order to avoid reduced network lifetime in VANET, this paper presents an enhanced metaheuristics based clustering with multihop routing technique for lifetime maximization (EMCMHR-LM) in VANET. The presented EMCMHR-LM model involves the procedure of arranging clusters, cluster head (CH) selection, and route selection appropriate for VANETs. The presented EMCMHR-LM model uses slime mold optimization based clustering (SMO-C) technique to group the vehicles into clusters. Besides, an enhanced wild horse optimization based multihop routing (EWHO-MHR) protocol by the optimization of network parameters. The presented EMCMHR-LM model is simulated using Network Simulator (NS3) tool and the simulation outcomes reported the enhanced performance of the proposed EMCMHR-LM technique over the other models.

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“…VANET is a discrete type of mobile ad hoc network in that vehicles are considered nodes and an entire communication usually occurs between vehicles [1,2]. VANET doesn't require any fixed architecture, but the fixed network nodes i.e., Road Side Unit (RSU) are deployed in the network.…”

Section: Introductionmentioning

confidence: 99%

Traffic Aware Routing using Multi-Objective Delay Centric Enhanced Artificial Ecosystem-Based Optimization for VANET

2023

IJIES

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A vehicular Ad hoc Network (VANET) is generally a heterogeneous wireless network generated between vehicles. The vehicles of the VANET have wireless transceivers and computerized control for allowing the vehicles to operate as network nodes, hence the vehicles can communicate in a VANET environment. However, the communication of VANET is affected because of the higher network congestion and energy usage caused by the dynamic topology of VANET. Therefore, an effective Traffic-Aware Routing (TAR) approach is required to be developed to enhance communication. In this paper, the Multi-Objective Delay Centric Enhanced Artificial Ecosystem-based Optimization (MDCEAEO) is proposed to develop a TAR in VANET. The End to End Delay (EED) is considered as primary cost in the MDCEAEO to develop the TAR where a vehicle's average predicted speed is utilized for identifying the traffic in VANET. The developed MDCEAEO-TAR method is used to improve data transmission by avoiding collisions. The performances of MDCEAEO-TAR are evaluated using EED, energy consumption, Packet Delivery Ratio (PDR), and the routing overhead. The existing research such as LARgeoOPT, DREAMgeoOPT, ZRPgeoOPT, Improved Harmony Search (IHS) and Enhanced Distance, Residual energy based Congestion Aware Ant Colony Optimization (EDR-CAACO), Artificial Ecosystem-based Optimization (AEO), FixedStep Average and Subtraction Based Optimizer (FS-ASBO) and Three Influential Members Based Optimizer (TIMBO) are used to evaluate the MDCEAEO-TAR. The PDR of the MDCEAEO-TAR is 0.9836 at 1000s, which is high when compared to the IHS.

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Clustering scheme and destination aware context based routing protocol for VANET

Cited by 17 publications

References 21 publications

Weight-Based Clustering Algorithm for Military Vehicles Communication in VANET

Weight-Based Clustering Algorithm for Military Vehicles Communication in VANET

Metaheuristics-based Clustering with Routing Technique for Lifetime Maximization in Vehicular Networks

Traffic Aware Routing using Multi-Objective Delay Centric Enhanced Artificial Ecosystem-Based Optimization for VANET

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