Direction based Hazard Routing Protocol (DHRP) for disseminating road hazard information using road side infrastructures in VANETs

Berlin, M.A.; Anand, S.

doi:10.1186/2193-1801-3-173

Cited by 9 publications

(6 citation statements)

References 8 publications

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“…to manage the movement of following vehicles in the group, and helps them to take the earliest action to avoid the vehicular hazards. M. Berlin et al [15] have worked on the development of dynamic rerouting to avoid the various hazards & hurdles across the roads. The direction based hazard routing protocol (DHRP) model is developed with a distributed information network among the vehicles travelling in particular direction or destination.…”

Section: Related Workmentioning

confidence: 99%

MRGM: An Adaptive Mechanism for Congestion Control in Smart Vehicular Network

Shahi

Batth

Egerton

2022

Int. j. commun. netw. inf. secur.

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Traffic flow on roads has increased manifolds from past few decades due to increase in number of vehicles and rise in population. With fixed road infrastructure and more vehicles on traffic routes lead to traffic congestion conditions especially in urban areas of developing nations. Traffic jams are normal in major cities which ultimately cause delay in travel time, more fuel consumption and more pollution. This manuscript propose a Multi-metric road guidance mechanism(MRGM) which considers multiple metrics to analyze the traffic congestion conditions and based on the conditions effective optimal routes are suggested to the vehicles. The Simulation of the proposed mechanism is performed with the SUMO by using the python script and the results show that proposed mechanism i.e MRGM outperforms other mechanism in terms of traffic efficiency, travel time, fuel consumption and pollution levels in the smart vehicular network.

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Section: Related Workmentioning

confidence: 99%

MRGM: An Adaptive Mechanism for Congestion Control in Smart Vehicular Network

Shahi

Batth

Egerton

2022

Int. j. commun. netw. inf. secur.

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“…Various obstacles and risks are identified as occurrences, like flash floods, tree breaking, land slipping, and collisions. Correct measures are performed for the cars, which are rerouted to escape the current scenario [33]. The current study presents an enhanced automobile ID-based statistical model in light of the preceding.…”

Section: Background To the Vehicular Communication Environmentsmentioning

confidence: 99%

A Traffic Density-Based Congestion Control Method for VANETs

Subramaniam

Rambabu

Chandrasekaran

et al. 2022

Wireless Communications and Mobile Computing

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This research presents a vehicle ID-based congestion aware message (CAM) for beacon signals on the vehicle environment. At the MAC protocol of the vehicle environment, enhanced vehicle ID-based analysis model is given first. With the automobile ID embedded in their separate CAMs, the model weights the randomized back-off numbers chosen by cars engaging in the back-off procedure. This leads to identifying a car ID-based randomized back-off code, which reduces the likelihood of a collision due to the identical back-off number. A traffic density based-congestion control algorithm (TDCCA) is suggested in this research. The revised mathematical approach surpasses previous work’s overall packet latency because just one-fourth of the congestion window is employed during the experiment. The research includes a congestion management method that adjusts the rate of CAM transmitted over the host controller to improve the efficiency of the model parameters. The method considers various circumstances, from nonsaturated to substantially saturated networks (in terms of congestion probability) and sparsely dispersed and teemed networks (in the form of vehicular intensity). The technique is run across various automobile ID-based back-off values for high-standard results analysis. The simulation outcomes in terms of packet delivery ratio, energy consumption, delay, success rate, and collision ensure the effectiveness of the TDCCA method. Even at high traffic densities, the automobile ID-based CAM following information method outperforms the typical fixed CAM frequency IEEE 802.11p, according to simulation findings for all back-off figures.

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“…Various obstacles and risks are identified as occurrences, like flash floods, tree breaking, land slipping, collisions, etc. Correct measures are performed for the cars, which are rerouted to escape the current scenario [33]. The recent study presents an enhanced automobile ID-based statistical model in light of the preceding.…”

Section: Rajput Et Al Have Worked On Developing Dynamic Redirection T...mentioning

confidence: 99%

A Traffic Density-based Congestion Control Method for Vehicular Communication Environment

Subramaniam¹,

Krishnan²

2022

Preprint

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This research presents a Vehicles ID-based Congestion Aware Message (CAM) for beacon signals in the vehicle environment. The MAC protocol of the vehicle environment and enhanced vehicle ID-based analysis model is given. With the automobile ID embedded in their separate CAMs, the model weights the randomized back-off numbers chosen by cars engaging in the back-off procedure. That leads to identifying a vehicle ID-based randomized back-off code, which reduces the likelihood of a collision due to the identical back-off number. A Traffic Density-Based Congestion Control Algorithm (TDCCA) is suggested in this research. The revised mathematical approach surpasses the previous work's overall packet latency because the congestion control is based on the network traffic. The study includes a congestion management method that adjusts the rate of CAM transmitted over the host controller to improve the efficiency of the model parameters. The technique considers various circumstances, from nonsaturated to substantially saturated networks (in terms of congestion probability) and sparsely dispersed and teemed networks (in the form of vehicular intensity). The technique is run across various automobile ID-based back-off values for results analysis. The simulation outcomes in terms of packet delivery ratio, energy consumption, delay, success rate, and collision ensure the effectiveness of the TDCCA method. Even at high traffic densities, the automobile ID-based CAM method outperforms the typical fixed CAM with IEEE 802.11p, according to simulation findings for all back-off figures.

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Direction based Hazard Routing Protocol (DHRP) for disseminating road hazard information using road side infrastructures in VANETs

Cited by 9 publications

References 8 publications

MRGM: An Adaptive Mechanism for Congestion Control in Smart Vehicular Network

MRGM: An Adaptive Mechanism for Congestion Control in Smart Vehicular Network

A Traffic Density-Based Congestion Control Method for VANETs

A Traffic Density-based Congestion Control Method for Vehicular Communication Environment

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