A study of adaptive beacon transmission on Vehicular Ad-Hoc Networks

Thaina, Chayanin; Nakorn, Kulit Na; Rojviboonchai, Kultida

doi:10.1109/icct.2011.6157946

Cited by 16 publications

(11 citation statements)

References 8 publications

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“…In paper [13], the authors proposed two methods to adapt beacon rate with frequent topology changing of vehicular networks. The first method is linear regression analysis.…”

Section: Beacon Rate Controlmentioning

confidence: 99%

A Self-decision-making Beacon Interval Adjusting Method for VANETs

Zhang¹,

Zhang²,

Yao³

et al. 2018

dtcse

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Abstract. In Vehicular Ad Hoc Network (VANET), rapid movement of vehicles leads to the neighbor information fast outdated. Fast exchange of beacons in high traffic density causes wireless channel congested and overload of VANET. In this article, a Self-Decision-Making Beaconing (SDMB) method is proposed to solve the above issues. In this method, the beaconing interval is adjusted based on node activity and the change rate of its neighbors. Results show that, the successful beaconing rate is closed to or larger than 90% and the normalized channel load is less than 20% in different traffic density by using SDMB.

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“…In paper [13], the authors proposed two methods to adapt beacon rate with frequent topology changing of vehicular networks. The first method is linear regression analysis.…”

Section: Beacon Rate Controlmentioning

confidence: 99%

A Self-decision-making Beacon Interval Adjusting Method for VANETs

Zhang¹,

Zhang²,

Yao³

et al. 2018

dtcse

View full text Add to dashboard Cite

show abstract

“…It is evident that small beacon rate will alleviate link congestion at the cost of information accuracy. There are many schemes in literature that use different parameters to adapt beacon rate [16][17][18][19][20][21][22][23][24][25][26][27][28][29]. The following is the short summary of adaptive beacon rate control algorithms.…”

Section: Adaptive Transmission Rate Based Beaconing Schemesmentioning

confidence: 99%

“…More specifically, they consider movement (e.g., acceleration and velocity) of the vehicle itself and the movement of the neighbor vehicles to estimate beacon rate. The adaptive beacon transmission rate scheme in [19] uses statistical and machine learning technique to compute beacon rate based on number of neighbors or node density and number of buffered messages. PULSAR (periodically updated load sensitive adaptive rate control) [20] optimizes beacon rate by considering the target channel busy ratio (CBR) for the given transmission range.…”

Section: Adaptive Transmission Rate Based Beaconing Schemesmentioning

confidence: 99%

Hybrid Adaptive Beaconing in Vehicular Ad Hoc Networks: A Survey

Bouk

Kim

2015

International Journal of Distributed Sensor Networks

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Beacon or safety messages are broadcasted in vehicular ad hoc networks (VANETs) to disseminate network state or emergency incident information to other vehicles in the network. The freshness of information depends upon the frequent transmission of beacons. Similarly, to increase the awareness area or communicating with the distant nodes, beacons are disseminated with a high transmission power. However, increasing the beacon transmission power or rate has a negative effect on information communication efficiency because of the finite bandwidth of the wireless link. Therefore, different schemes have been proposed to individually control beacon's transmission power, transmission rate, or contention window at the MAC layer, or any combination of those, to achieve quality beacon communication in VANETs. The latter case is called hybrid adaptive beaconing schemes. In literature, there are many hybrid adaptive beaconing schemes that control multiple communication parameters to efficiently broadcast beacon messages in VANETs. In this paper, we explicitly survey and summarized various aspects of those schemes. The open and challenging issues are also highlighted in this paper.

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“…In [19,20], broadcast routing uses machine learning to predict whether a packet needs to be rebroadcast. In [21], the authors dynamically adjust the beacon interval through machine learning, which decreases the control overhead and maintains the reliability of transmissions.…”

Section: International Journal Of Distributed Sensor Networkmentioning

confidence: 99%

A Machine Learning System for Routing Decision-Making in Urban Vehicular Ad Hoc Networks

Lai

Lin

Wang

2015

International Journal of Distributed Sensor Networks

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In vehicular ad hoc networks (VANETs), network topology and communication links frequently change due to the high mobility of vehicles. Key challenges include how to shorten transmission delays and increase the stability of transmissions. When establishing routing paths, most research focuses on detecting traffic and selecting roads with higher vehicle densities in order to transmit packets, thus avoiding carry-and-forward scenarios and decreasing transmission delays; however, such approaches may not obtain accurate real-time traffic densities by periodically monitoring each road because vehicle densities change so rapidly. In this paper, we propose a novel routing information system called the machine learning-assisted route selection (MARS) system to estimate necessary information for routing protocols. In MARS, road information is maintained in roadside units with the help of machine learning. We use machine learning to predict the moves of vehicles and then choose some suitable routing paths with better transmission capacity to transmit packets. Further, MARS can help to decide the forwarding direction between two RSUs according to the predicted location of the destination and the estimated transmission delays in both forwarding directions. Our proposed system can provide in-time routing information for VANETs and greatly enhance network performance.

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A study of adaptive beacon transmission on Vehicular Ad-Hoc Networks

Cited by 16 publications

References 8 publications

A Self-decision-making Beacon Interval Adjusting Method for VANETs

A Self-decision-making Beacon Interval Adjusting Method for VANETs

Hybrid Adaptive Beaconing in Vehicular Ad Hoc Networks: A Survey

A Machine Learning System for Routing Decision-Making in Urban Vehicular Ad Hoc Networks

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