RETRACTED ARTICLE: EE-FMDRP: energy efficient-fast message distribution routing protocol for vehicular ad-hoc networks

Satheshkumar, K.; Mangai, S.

doi:10.1007/s12652-020-01730-8

Cited by 26 publications

(9 citation statements)

References 18 publications

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“…The relay selection problem has been paid wide attention in designing energy-efficiency vehicular routing protocols [65], [66], [67], [68]. The factors related to relay selection [65] mainly include the vehicular categories information (i.e., public vehicle and private vehicle), the driving state of its neighboring vehicles, the distance between the current section and the next intersection.…”

Section: V2v Communicationsmentioning

confidence: 99%

“…The factors related to relay selection [65] mainly include the vehicular categories information (i.e., public vehicle and private vehicle), the driving state of its neighboring vehicles, the distance between the current section and the next intersection. In [67], the relaying vehicles are selected based on vehicle position, directional flow and message delivery time. The vehicle direction (VD)-based authorization selection model can decrease the unnecessary message broadcasting in vehicular routing protocol, thus reducing communication overhead and improving energy efficiency.…”

Section: V2v Communicationsmentioning

confidence: 99%

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Green Internet of Vehicles (IoV) in the 6G Era: Toward Sustainable Vehicular Communications and Networking

Wang¹,

Zhu²,

Hossain³

2021

Preprint

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As one of the most promising applications in future Internet of Things, Internet of Vehicles (IoV) has been acknowledged as a fundamental technology for developing the Intelligent Transportation Systems in smart cities. With the emergence of the sixth generation (6G) communications technologies, massive network infrastructures will be densely deployed and the number of network nodes will increase exponentially, leading to extremely high energy consumption. There has been an upsurge of interest to develop the green IoV towards sustainable vehicular communication and networking in the 6G era. However, as a special mobile ad-hoc network, the energy cost in an IoV system involves the communication and computation energy in addition to the fuel consumption and the electricity cost of moving vehicles. Moreover, the energy harvesting technology, which is likely to be adopted widely in 6G systems, will complicate the optimization of energy efficiency in the entire system. Current studies focus only on part of the energy issues in IoV systems without a comprehensive discussion of the state-of-the-art energy-efficient approaches and the influence of the development of 6G networks on green IoV. In this paper, we present the main considerations for green IoV from five different scenarios, including the communication, computation, traffic, Electric Vehicles (EVs), and energy harvesting management. The literatures relevant to each of the scenarios are compared from the perspective of energy optimization (e.g., with respect to resource allocation, workload scheduling, routing design, traffic control, charging management, energy harvesting and sharing, etc.) and the related factors affecting energy efficiency (e.g., resource limitation, channel state, network topology, traffic condition, etc.). In addition, we introduce the potential challenges and the emerging technologies in 6G for developing green IoV systems. Finally, we discuss the research trends in designing energy-efficient IoV systems.

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Section: V2v Communicationsmentioning

confidence: 99%

Section: V2v Communicationsmentioning

confidence: 99%

Green Internet of Vehicles (IoV) in the 6G Era: Toward Sustainable Vehicular Communications and Networking

Wang¹,

Zhu²,

Hossain³

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…erefore, in order to solve these two problems, the typical way is to select some vehicles for forwarding, instead of repeating the broadcasting of all vehicles. Broadcast schemes are divided into five main categories according to the criteria for selecting forwarding nodes: geographic location-based [4][5][6][7][8][9][10][11][12][13], probability-based [14], [15], [16], [17], [18], [19], and [20], delay-based [21][22][23][24][25], clustering-based [26][27][28], and encoding based [29,30]. In Dynamic Hybrid Broadcasting Protocol (DHBP) [9], to solve the broadcast storm problem, the relay node stops rebroadcasting depending on the value of decision-making function (DMF).…”

Section: Related Workmentioning

confidence: 99%

“…At the same time, the system determines the priority of the forwarding node according to the predicted distance, density, and relative speed. In energy efficient-fast message distribution routing protocol (EE-MDRP) [11], the source vehicle found vehicle neighbors positions through using a vector-angle oriented classification model. en, the optimal relay is the neighbor who responds to data with minimum message delivery time.…”

Section: Related Workmentioning

confidence: 99%

Design and Analysis of Cooperative Broadcast Scheme Based on Reliability in Mesh Network

Dou

Tian

Yang

et al. 2021

Mobile Information Systems

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In order to ensure reliable data transmission of sender, the tree-based network is extended to Mesh network by collaborative technology, and the SW-HBH-CARQ broadcast scheme is introduced for the first time. Then, in order to solve the problems of broadcast storm and energy saving, SW-HBH-oiCARQ, SW-HBH-ieCARQ, and SW-HBH-oieCARQ are further proposed. Then, the four broadcasting schemes are analyzed quantitatively, and the performance indicators such as system energy consumption and time delay are obtained. Finally, the relationships between the performance indexes and the parameters are discussed by numerical simulation. At the same time, the effectiveness of the proposed schemes in energy saving is verified by comparing with the corresponding noncollaborative broadcasting scheme. However, the latency of proposes scheme is greater than uncooperative scheme, which is the cost of saving energy and resolving broadcast storms.

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“…This challenge is caused by the fast movement of vehicular nodes and the instability of networks, which result in frequent changes in network topology [6]. Therefore, delays in message distribution and reliable communication for emergency messages are critical issues in VANETs [7]. The transmission of safety messages may become congested due to ooding, which reduces reliability [8].…”

Section: Introductionmentioning

confidence: 99%

A Deep Learning Method for Automatic Reliability Prediction of CR-VANETs

Bahramnejad

Movahhedinia

Naseri

2023

Preprint

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Reliability is a critical issue in vehicular networks. A Deep Learning (DL) method is proposed in this study to automatically predict the reliability of Cognitive Radio Vehicular Networks (CR-VANETs) ignored in the previous research. First, a dataset is generated based on a previously proposed method for the reliability assessment of CR-VANETs. Then, a model is proposed to predict the networks' reliability using the DL method and compared with other machine learning methods. While machine learning methods have been applied in vehicular networks, they have not been used for reliability prediction. The proposed DL model is utilized in this research to predict CR-VANETs' reliability. Based on the results, the DL model outperforms other machine learning methods for reliability prediction. The correlation coefficient and root mean square error of the test data for the DL model are 0.9862 and 0.0381. These results indicate the CR-VANETs' reliability prediction accurately using the proposed method.

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RETRACTED ARTICLE: EE-FMDRP: energy efficient-fast message distribution routing protocol for vehicular ad-hoc networks

Cited by 26 publications

References 18 publications

Green Internet of Vehicles (IoV) in the 6G Era: Toward Sustainable Vehicular Communications and Networking

Green Internet of Vehicles (IoV) in the 6G Era: Toward Sustainable Vehicular Communications and Networking

Design and Analysis of Cooperative Broadcast Scheme Based on Reliability in Mesh Network

A Deep Learning Method for Automatic Reliability Prediction of CR-VANETs

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