Reliability Improvement in Clustering-Based Vehicular Ad-Hoc Network

Singh, Rohit; Saluja, Deepak; Kumar, Suman

doi:10.1109/lcomm.2020.2980819

Cited by 23 publications

(7 citation statements)

References 13 publications

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“…Several earlier studies are developed in cluster-based VANETs and CR-VANETs as well some of them are discussed in this section. They are, clustering of the VANETs in a highway environment [8], clustering oriented rouing model and in gets enhanced with the help of modified K-means method [9], efficient cluster head selection (ECHS) [10], fuzzy logic-based clustering control technique [11], in recent works clustering concept is combined with the medium access control (MAC) layer protocols to improve its efficiency [12], to reduce the power utilization and control the vehicle speed the connectivity prediction based clustering model with dynamic connectivity is developed [13], dual-slot transmission with mobile edge computing (MEC) [14], cluster-based resource management system [15], to minimize the broadcast overhead during communication the novel scheme is developed in VANETs called emergency message dissemination [16], to improve vehicles reliability during data transmission in real roads anovel idea id developed namely diverted path approach [17], destination-aware context-centered routing architecture [18], to optimize the power utilization in VANETs the concept of multi-hop clustering is developed [19], clustering particle swarm optimization (PSO)-based V2V routing method [20], clustering approach based on self-adaptive multi-kernel clustering [21], to develop a noise free and adaptive network structure in VANETs both novel clustering and adapted ordering points are built [22], effective channel selection in CR-VANETs [23], fuzzy cluster head (CH) selection scheme in CR-VANETs [24], and later multiple user based several inputs and outputs are collaborated with clustering model with the novel idea of cooperative spectrum sensing in CR-VANETs [25]. Once after analyzing the earlier research, it is understood that the CR-VANETs suffer from communication link failure, high energy consumption, and packet loss.…”

Section: Related Workmentioning

confidence: 99%

Link stability based multipath routing and effective mobility prediction in cognitive radio enabled vehicular ad hoc network

Rashid,

Hamdi,

AbdulElah

et al. 2024

Bulletin EEI

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Vehicular ad hoc networks (VANETs) provide a robust infrastructure for intelligent transportation system (ITS) applications. VANET communication involves vehicle-to-vehicle and vehicle-to-infrastructure connections, primarily with roadside units (RSUs). Analyzing cognitive radio (CR)-VANET studies revealed two key performance issues: high energy consumption and latency. To address these challenges, we propose a novel approach: link stability and mobility prediction-based clustered CR-VANETs, known as LMCCR-VANET. LMCCR-VANET consists of four main components: CR-VANET construction, clustering model, speed-based mobility prediction, and link-based multipath routing. Initially, we establish cluster-based CR-VANETs to analyze and mitigate spectrum scarcity and power utilization problems in VANETs. Mobility prediction evaluates vehicle speed variations and predictions. Finally, employing link stability-based multipath routing (LSMR) in conjunction with the fuzzy interference model and ad hoc on-demand multipath distance vector (AOMDV) routing protocol ensures stable and efficient routing. Experimental results showcase the superiority of LMCCR-VANET. It exhibits enhanced energy efficiency, delivery rates, reduced energy consumption, end-to-end latency, and routing overhead when compared to recent works such as SCCR-VANET, CFCR-VANET, and MMCR-VANET.

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Section: Related Workmentioning

confidence: 99%

Link stability based multipath routing and effective mobility prediction in cognitive radio enabled vehicular ad hoc network

Rashid,

Hamdi,

AbdulElah

et al. 2024

Bulletin EEI

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“…Finally, each cluster selects the most reliable vehicle as the CH to connect with the gNB according to the stability of the AD mode, location, and communication status. This clustering mechanism can reduce the burden on the base station network and provide reliable communication [39]. Clustering is performed based on the result of AD pattern recognition, and we model the AD mode in the next subsection.…”

Section: Ciovs Architecture For Admentioning

confidence: 99%

An Intelligent Multimode Clustering Mechanism Using Driving Pattern Recognition in Cognitive Internet of Vehicles

Chang

Ning

2021

Sensors

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Connected autonomous vehicles can leverage communication and artificial intelligence technologies to effectively overcome the perceived limitations of individuals and enhance driving safety and stability. However, due to the high dynamics of the vehicular network and frequent interruptions and handovers, it is still challenging to provide stable communication connections between vehicles, which is likely to cause disasters. To address this issue, in this paper, we propose an intelligent clustering mechanism based on driving patterns in heterogeneous Cognitive Internet of Vehicles (CIoVs). In the proposed approach, we analyze the driving mode containing multiple feature parameters to accurately capture the driving characteristics. To ensure the accuracy of pattern recognition, a genetic algorithm-based neural network pattern recognition algorithm is proposed to support the reliable clustering of connected autonomous vehicles. The cognitive engines recognize the driving modes to group vehicles with a similar driving mode into a relatively stable cluster. In addition, we formulate the stability and survival time of clusters and analyze the communication performance of the clustering mechanism. Simulation results show that the proposed mechanism improves the reliable communication throughput and average cluster lifetime by approximately 14.4% and 11.5% respectively compared to the state-of-the-art approaches.

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“…By learning the feature expression of clustering algorithm, we can get the convolution parameter setting of convolution neural network and the feature expression of clustering algorithm. By combining the feature expression of clustering algorithm with the convolution result, the starting point of random optimization can be closer to the local optimal solution in the process of deep network training [15][16].…”

Section: Deep Network Improvementmentioning

confidence: 99%

Improvement of Diesel Engine Cooling System Based on Deep Learning Algorithm

2020

KME

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The core idea of deep learning is to process input vectors and take their outputs as training samples, and then use classifiers to predict and modify these input features. When improving the existing diesel engine cooling system, we can use deep learning to solve the problem. Therefore, the purpose of this paper is to improve the design of diesel engine cooling system and improve the working efficiency. In this paper, the experimental method and comparison method are mainly used to build a diesel engine test bench. By setting the inlet water temperature of the diesel engine, the variation rules and influencing factors of the cooling water temperature and flow rate under three temperatures are analyzed. The experimental results show that when the inlet water temperature of the diesel engine is 90 ℃, the cooling water flow always takes the first place, which indicates that the diesel engine has good performance under this condition.

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Reliability Improvement in Clustering-Based Vehicular Ad-Hoc Network

Cited by 23 publications

References 13 publications

Link stability based multipath routing and effective mobility prediction in cognitive radio enabled vehicular ad hoc network

Link stability based multipath routing and effective mobility prediction in cognitive radio enabled vehicular ad hoc network

An Intelligent Multimode Clustering Mechanism Using Driving Pattern Recognition in Cognitive Internet of Vehicles

Improvement of Diesel Engine Cooling System Based on Deep Learning Algorithm

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