Opportunistic fleets for road event detection in vehicular sensor networks

Zeng, Yuanyuan; Li, Deshi; Vasilakos, Athanasios V.

doi:10.1007/s11276-015-0976-2

Cited by 11 publications

(6 citation statements)

References 27 publications

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“…Y. Zeng et al [24] investigated the administration of automobile fleets, which included the monitoring of road events. This concept is based on data gathered by vehicle detectors, which are also utilised to create an impulsive workforce, that is a localised automotive grid (or partial mesh).…”

Section: Related Workmentioning

confidence: 99%

Intelligent Urban Cities: Optimal Path Selection Based on Ad Hoc Network

2023

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Fusion of internet of things (IoT) and artificial intelligence for smart cities has allowed structured policy making.In particular, traffic jam is one of the serious concern in metropolitan cities. In advanced countries, specific devices are installed to assemble flow of traffic. The state of traffic is streamed to commuters through network. This dependency on internet connection results in inappropriate solution in absence of stable physical infrastructure. The developing countries lack availability of internet on roadway and urban zones face even worst condition. This study proposes a smart framework to enable optimal path selection by communicating with neighbouring devices. The android smart phones serve the purpose of embedded sensors. A temporary network is created for smart transportation system. Data processing and cleaning were conducted on different routes of two urban cities. The proposed system is evaluated on conventional and proposed optimal path selection techniques. Computed results depict that travel times have been decreased to around 23.3% in comparison to conventional methods using MDSP algorithm.

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Section: Related Workmentioning

confidence: 99%

Intelligent Urban Cities: Optimal Path Selection Based on Ad Hoc Network

2023

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“…The proposed algorithm is known as "DIVERT" and it's made capable of detecting & localizing the areas with heavy traffic which may lead to congestion, and computes the decongestion solution at earlier stage to avoid the congestion in the target area. Y. Zeng et al [14] have worked on the vehicular fleet management, which also associated with the road event detection. This model relies upon the data collected through the vehicular sensors, which are also used to establish the opportunistic fleet, which is a localized vehicular mesh (or partial mesh).…”

Section: Related Workmentioning

confidence: 99%

“…RSU will calculate the PWC (Path weight calculation) of road network. The PWC is calculated as (PWC) = Wij(∝,∅,θ)(14)…”

mentioning

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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“…Louazani et al (2014) proposed a clustering mechanism with the help of dynamic virtual backbone in VANETs. Zeng et al (2016) investigated the behaviour-aware fleet construction schemes for VANETs. Vehicles with similar behaviour and stability are clustered in an opportunistic manner.…”

Section: Related Workmentioning

confidence: 99%

Prediction-based next-hop forwarding strategy in vehicular ad-hoc networks

Bhatt

Sharma

2019

IJVICS

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Vehicular ad-hoc network (VANET) is an emerging technology where inter-vehicle communication occurs in a highly dynamic environment. Vehicles moving with varying speed communicate with the help of a suitable data forwarding strategy in vehicular ad-hoc networks. This paper presents next-hop selection of the candidate node for a highway scenario in vehicular ad-hoc networks. The next hop selection of candidate node is based on current position of the vehicle, predicted next-hop position of the vehicle, and transmission range of the vehicle. Further, the vehicles are heterogeneous in terms of transmission range and speed of the vehicles. We propose NHPS and NHPA prediction algorithms based on exponential smoothing and averaging methods, respectively. We then perform simulations for realistic vehicle distributions scenario in vehicular ad-hoc networks. Results show the effectiveness of our proposed approach.

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Opportunistic fleets for road event detection in vehicular sensor networks

Cited by 11 publications

References 27 publications

Intelligent Urban Cities: Optimal Path Selection Based on Ad Hoc Network

Intelligent Urban Cities: Optimal Path Selection Based on Ad Hoc Network

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

Prediction-based next-hop forwarding strategy in vehicular ad-hoc networks

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