Optimal resource utilisation algorithm for visible light communication‐based vehicular ad‐hoc networks

Msongaleli, Dawson Ladislaus; Küçük, Kerem

doi:10.1049/iet-its.2019.0224

Cited by 7 publications

(8 citation statements)

References 36 publications

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“…The source sends out an inquiry message (REQ) to its neighbors, stating its direction, destination, and other information and reserving all available time slots within itself [29]. Msongaleli et al (2019) proposed visible light communication-based VANET data transmission. VANET node sends a data packet to another node, and the self-organizing remote vector routing protocol on request relies on a mechanism connected with the on-demand method, which commences the path [30].…”

Section: Literature Surveymentioning

confidence: 99%

“…Msongaleli et al (2019) proposed visible light communication-based VANET data transmission. VANET node sends a data packet to another node, and the self-organizing remote vector routing protocol on request relies on a mechanism connected with the on-demand method, which commences the path [30]. E cient routing protocol for VANET presented by Chinnasamy et al (2016).…”

Section: Literature Surveymentioning

confidence: 99%

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An Efficient Distance and Connectivity Based Traffic Density Stable Routing Protocol (TDSRP-DC) for Vehicular Ad Hoc Networks

Selvaraj¹,

Ramalingam²,

Mohan

et al. 2023

Preprint

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Vehicle ad-hoc networks (VANETs) have become a prominent research topic in recent years due to rapid dynamic topology, high vehicle mobility, frequent link failures and significant delay constraints. The mobility of the vehicle nodes increases, and the overhead of control traffic due to the high dynamics of the VANET. Efficient routing algorithms are necessary for VANETs to ensure reliable transmission (VANETs). This work proposes a Traffic Density Stable Routing Protocol based on Connection- and Distance (TDSRP-DC) to avoid data packet collisions at intersections and an adaptive routing schedule based on the selection at every instant. Our approach is based on vehicle-to-vehicle communication. Ground vehicles identify the most appropriate next junction and transfer the data packet to the receiver to find the optimal multi-hop route. It relies on transferring the data between all vehicles to estimate real-time traffic fluctuations sporadically. Network formation, neighbor realization, fitness value prediction, and routing methodology are the conceptual process needed in this research. Valid parameters for finding the optimum path in the conceptual model include node distance, node speed, node azimuth, link stability, and link reliability. The suggested solution's simulation was performed in comparison to traditional algorithms. Compared to existing TFOR algorithms on complex traffic, the proposed TDSRP-DC provides tremendous improvements based on packet delivery rate (10%) and performance (35%). The demonstrated results and comparisons illustrate the proposed routing protocol's significance with enhanced quality of service performance.

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Section: Literature Surveymentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

An Efficient Distance and Connectivity Based Traffic Density Stable Routing Protocol (TDSRP-DC) for Vehicular Ad Hoc Networks

Selvaraj¹,

Ramalingam²,

Mohan

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Since the RPMA uplink received signal strength is generally close to the reception sensitivity, it is appropriate to select the downlink new received signal strength received by the terminal as an indicator to evaluate the signal quality. The process of establishing the prediction model is to find the mapping function f between the data set characteristics and the signal quality evaluation indicators, as shown in equation (1).…”

Section: B Base Station Signal Quality Prediction Model Based On Hybr...mentioning

confidence: 99%

“…The traditional short-range wireless technology and cellular network technology can no longer meet the needs of the IoT service [1]. For this reason, a new communication mode, i.e.…”

Section: Introductionmentioning

confidence: 99%

Traffic Processing Model of Big Data Base Station Based on Hybrid Improved CNN Algorithm and K-Centroids Clustering Algorithm

Xu¹,

Shen³

et al. 2023

IEEE Access

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Wireless communication network (WCN) is very important for providing convenient mobile network communication services. Random Phase Multiple Access (RPMA) WCN under heterogeneous network architecture is widely used in wireless network construction worldwide due to its low power consumption and high cell density. However, this kind of WCN cannot meet the application scenario of high communication quality. Therefore, this research builds an RPMA communication quality prediction model for big data wireless base stations, which combines the convolutional neural network (CNN) algorithm and the lifting regression tree algorithm. It can be used to find the elements that have obvious influence on the communication quality. The reason for choosing the convolutional neural network algorithm is that its nonlinear feature relationship search and processing ability is excellent, and its computational complexity is relatively small. However, it is a black box algorithm and cannot obtain the importance coefficients of each feature, which is not conducive to subsequent analysis. Therefore, it is also necessary to select a relatively simple modified regression tree algorithm to participate in the calculation. The model constructed by integrating convolutional neural network and lifting regression tree algorithm is the RPMA wireless big data base station communication quality prediction model. A base station planning and deployment model based on weighted K-centroids algorithm is designed to obtain a better base station deployment scheme. In the CNN-DT model, the importance coefficients of T_B_diff, P_La and P_Lo are the largest and significantly larger than those of other features, which are 0.352, 0.289 and 0.264 respectively. The weighted K-centroids clustering algorithm designed in this study has the best overall downlink reception signal (RSSI) value distribution. For RSSI bucket ''-140∼-130'', the number of test points of WK centroids model, K-means model, GMC model, Mean Shift model and spectral clustering model accounted for 1.95%, 6.25%, 4.25%, 8.22% and 7.13% respectively. The model constructed in this study based on CNN and improved regression tree algorithm can accurately predict the communication quality of wireless big data base stations. This study's main contribution is that it can be used in conjunction with the base station planning and deployment model based on weighted K-centroids algorithm to improve the accuracy and effectiveness of location selection for the RPMA wireless communication network base station.INDEX TERMS CNN, promote the regression tree, K-centroids, big data base station, WCN.

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“…In the V2V VLC system, headlights [13], taillights [14], or even brake lights [15] can be used as the transmitter, and the image sensor [16], high-speed camera [17], or PD [18] can be used as the receiver. VLC can be used as a supplement to RF communications [19] solving the problems such as insufficient frequency band, slow transmission speed, and Doppler effect.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis and Node Selection of Intelligent Reflecting Surface-Aided Visible Light Communication for Parallel Vehicles

Zhan

Zhao

Zhang

et al. 2022

Wireless Communications and Mobile Computing

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In the visible light communication (VLC) for parallel vehicles, the light emitted by the headlamps cannot reach the photodiode (PD), which is the receiver installed on another vehicle. To solve this problem, the mirror array-based intelligent reflecting surface- (IRS-) aided VLC system is designed for parallel vehicles, and the system performance with different number of mirrors and transmission distance is analyzed. The maximum distance between adjacent IRSs is calculated via the exhaustive method, and the nearest neighbor iterative search (NNIS) algorithm is proposed for IRS node selection. Numerical results show that the SNR increases with the number of mirrors increasing. If the number of mirrors is 6 × 6 in the IRS, the maximum distance between adjacent IRSs is 37 meters. When the interval between adjacent IRSs is 32 meters, the required BER can be satisfied with merely three IRSs working together according to the NNIS algorithm.

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Optimal resource utilisation algorithm for visible light communication‐based vehicular ad‐hoc networks

Cited by 7 publications

References 36 publications

An Efficient Distance and Connectivity Based Traffic Density Stable Routing Protocol (TDSRP-DC) for Vehicular Ad Hoc Networks

An Efficient Distance and Connectivity Based Traffic Density Stable Routing Protocol (TDSRP-DC) for Vehicular Ad Hoc Networks

Traffic Processing Model of Big Data Base Station Based on Hybrid Improved CNN Algorithm and K-Centroids Clustering Algorithm

Performance Analysis and Node Selection of Intelligent Reflecting Surface-Aided Visible Light Communication for Parallel Vehicles

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