A Novel Routing and Hybrid Based Clustering Scheme in vehicular adhoc networks

Folsom, Raphael Duval; Aravindhan, K.; Sikamani, K. Thirunadana

doi:10.1016/j.ijin.2021.08.002

Cited by 4 publications

(5 citation statements)

References 25 publications

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“…Subsequently, the consumer phase is performed as shown in Eqs. (7)to (9) whereas the position update of herbivore, carnivore and omnivore is determined according to the random number. After performing the consumer phase, the decomposer phase is enabled based on Eq.…”

Section: Iterative Processmentioning

confidence: 99%

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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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Section: Iterative Processmentioning

confidence: 99%

“…The important objective of this VANET is to develop an Intelligent Transport System (ITS). The ITS is a wireless technology used in transportation systems to route data between vehicles in an intelligent way to enhance road safety [6,7].…”

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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“…31 Imparting independent vehicle frameworks are educated about the timing regarding solicitations and capacity conditions to further develop information spread. The test results show a high resource utilization rate of 93.4%, 8.3 Mbps throughput, low response time of 288.53 ms, 1.9% probe loss, and an average latency of 60 ms. Folsom et al 32 have proposed an NRHCS for VANETs using various concepts like cluster formation, insertion, and deletion, the vehicle's reference is calculated, and the vehicle's displacement is used. 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.…”

Section: State-of-art Studiesmentioning

confidence: 99%

“…Industrial transport systems often involve heavy machinery and large vehicles, which can pose safety risks to workers and other vehicles on the road. 32 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.…”

Section: Research Gapsmentioning

confidence: 99%

“…The lack of real‐time visibility into the status and location of goods or materials can result in delays, lost items, and increased inventory costs. Industrial transport systems often involve heavy machinery and large vehicles, which can pose safety risks to workers and other vehicles on the road 32 . Maintenance issues with vehicles, equipment, or infrastructure can result in downtime, increased maintenance costs, and reduced productivity.…”

Section: Related Workmentioning

confidence: 99%

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

Nagappan,

Maheswari,

Siva

et al. 2023

Int J Communication

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SummaryIndustrial transport system refers to the movement of goods, raw materials, and finished products within and between industrial facilities such as factories, warehouses, and distribution centers. It includes a variety of modes of transport such as trucks, forklifts, conveyor belts, and automated guided vehicles (AGVs). Context‐aware route service management refers to system that can dynamically manage and optimize the routes taken by vehicles in transportation system based on contextual information. Our proposed framework for industrial transport systems and smart intelligent autonomous vehicles utilizes cluster‐based context‐aware routing (CCAR), which includes several significant contributions. Firstly, modified elephant herding optimization (MEHO) algorithm is used to efficiently group autonomous vehicles for routing. Secondly, an improved gravitational search (IGS) algorithm is used for cluster head (CH) selection responsible for transferring context information between vehicles (V2V). Thirdly, a deep hybrid multi‐graph neural network (DHMG‐NN) is designed for optimal neighboring roadside unit (RSU) node selection through different design constraints to ensure data dissemination between vehicles and infrastructure (V2I). Finally, we validate the effectiveness of our CCAR framework using various simulation scenarios. Our framework achieves a reduction of up to 78% in computation time and an improvement of up to 25% in customer satisfaction ratio and 15% higher than the overall performance ratio compared to the existing frameworks.

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