Collision Avoidance Method Using Vector-Based Mobility Model in TDMA-Based Vehicular Ad Hoc Networks

Bang, Jung-Hyun; Lee, Jung-Ryun

doi:10.3390/app10124181

Cited by 16 publications

(11 citation statements)

References 11 publications

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“…Bang and Lee [25] redict the awaited position in stirrings and direction of each conveyance for avoiding amalgamate or access of collision between vehicles. The vector-based mobility prognosticate model in the TDMA-based VANET avoids the collision by apportioning the time slots and prognosticates the mobility of proximate vehicles through exploiting the habitation information of the control time slot, vehicle ID, direction of the vehicle stir, hop information, and latitude and longitude of a vehicle.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

A New Hybrid Deep Learning Algorithm for Prediction of Wide Traffic Congestion in Smart Cities

Kothai¹,

Poovammal²,

Dhiman³

et al. 2021

Wireless Communications and Mobile Computing

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The vehicular adhoc network (VANET) is an emerging research topic in the intelligent transportation system that furnishes essential information to the vehicles in the network. Nearly 150 thousand people are affected by the road accidents that must be minimized, and improving safety is required in VANET. The prediction of traffic congestions plays a momentous role in minimizing accidents in roads and improving traffic management for people. However, the dynamic behavior of the vehicles in the network degrades the rendition of deep learning models in predicting the traffic congestion on roads. To overcome the congestion problem, this paper proposes a new hybrid boosted long short-term memory ensemble (BLSTME) and convolutional neural network (CNN) model that ensemble the powerful features of CNN with BLSTME to negotiate the dynamic behavior of the vehicle and to predict the congestion in traffic effectively on roads. The CNN extracts the features from traffic images, and the proposed BLSTME trains and strengthens the weak classifiers for the prediction of congestion. The proposed model is developed using Tensor flow python libraries and are tested in real traffic scenario simulated using SUMO and OMNeT++. The extensive experimentations are carried out, and the model is measured with the performance metrics likely prediction accuracy, precision, and recall. Thus, the experimental result shows 98% of accuracy, 96% of precision, and 94% of recall. The results complies that the proposed model clobbers the other existing algorithms by furnishing 10% higher than deep learning models in terms of stability and performance.

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Section: Related Workmentioning

confidence: 99%

“…The congestion can be minimized by identifying traffic jams, attaining the estimation of congestion levels, relaying the information about prevailing traffic state, and proposing new routes [24,25]. Hence, to reduce the congestion level of traffic, the methodology mandatorily needed to predict the traffic jams.…”

Section: Introductionmentioning

confidence: 99%

A New Hybrid Deep Learning Algorithm for Prediction of Wide Traffic Congestion in Smart Cities

Kothai¹,

Poovammal²,

Dhiman³

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…J. Bang et al [17] suggest a method of accident avoidance supported by the model of node-mobility prediction in VANET using TDMA. Proposed algorithm distributes TDMA time slots to avoid access to reduce collisions by forecasting movement of neighbouring vehicles.…”

Section: Literature Surveymentioning

confidence: 99%

Enhancement of Link Stability and Connectivity in Vehicular Ad hoc Networks

Tayade¹

2021

TURCOMAT

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Vehicular Ad hoc Networks is a promising sub-group of MANET. VANET is deployed on the highways, where the vehicles are mobile nodes. Safety and intelligent transportation are important VANET applications that require appropriate communication among vehicles, in particular routing technology. VANETs generally inherit their common features from MANETs where vehicles operate in a collaborative and dispersed way for promoting contact among vehicles and with network infrastructure like the Road Side Units (RSU) for enhanced traffic experience. In view of the fast growth of Intelligent Transportation Systems (ITS), VANETs has attracted considerable interest in this decade. VANET suffer from a major problem of link failure due to dynamic mobility of vehicles. In this paper we proposed a position based routing algorithm to identify stable path, this will improve the routing by decreasing overhead and interrupting the number of links. Link Expiration Time (LET) is used to provide the stable link, the link with the longest LET is considered as the most stable link. The multicast Ad-hoc On-demand Distance Vector (MAODV) is proposed to avoid the link breakages by using a link with longest LET. Data loss is reduced by avoiding link breakages and enhance throughput by reducing the communication delay.

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“…By extracting user location information and service preference information contained in mobile communication data, a spatiotemporal mobile user behavior model can be established so that user behavior patterns can be predicted [ 3 , 4 ]. Effective mobility prediction enables service providers to predict user needs in advance, thereby optimizing network resources and reducing network congestion [ 5 , 6 ]. As a result, mobile users can obtain the information they need faster and enjoy a better service experience [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Mobility Prediction Using a Weighted Markov Model Based on Mobile User Classification

Yan

Chan

et al. 2021

Sensors

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The vast amounts of mobile communication data collected by mobile operators can provide important insights regarding epidemic transmission or traffic patterns. By analyzing historical data and extracting user location information, various methods can be used to predict the mobility of mobile users. However, existing prediction algorithms are mainly based on the historical data of all users at an aggregated level and ignore the heterogeneity of individual behavior patterns. To improve prediction accuracy, this paper proposes a weighted Markov prediction model based on mobile user classification. The trajectory information of a user is extracted first by analyzing real mobile communication data, where the complexity of a user’s trajectory is measured using the mobile trajectory entropy. Second, classification criteria are proposed based on different user behavior patterns, and all users are classified with machine learning algorithms. Finally, according to the characteristics of each user classification, the step threshold and the weighting coefficients of the weighted Markov prediction model are optimized, and mobility prediction is performed for each user classification. Our results show that the optimized weighting coefficients can improve the performance of the weighted Markov prediction model.

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Collision Avoidance Method Using Vector-Based Mobility Model in TDMA-Based Vehicular Ad Hoc Networks

Cited by 16 publications

References 11 publications

A New Hybrid Deep Learning Algorithm for Prediction of Wide Traffic Congestion in Smart Cities

A New Hybrid Deep Learning Algorithm for Prediction of Wide Traffic Congestion in Smart Cities

Enhancement of Link Stability and Connectivity in Vehicular Ad hoc Networks

Mobility Prediction Using a Weighted Markov Model Based on Mobile User Classification

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