Real-Time Routing for ITS Enabled Fog Oriented VANET

Paul, Ananya

doi:10.1109/indicon49873.2020.9342463

Cited by 6 publications

(2 citation statements)

References 8 publications

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“…To address this problem, the author proposed a Clique Searching Scheduling (CSS) algorithm, which caters to the heterogeneous nature of interfaces and vehicle mobility in scheduling and enables effective and timely transmission among the fog nodes, cloud, and vehicles. Paul et al [75] studied the traffic congestion issue with the exponential rise in road traffic. The author has proposed a Real-time routing algorithm designed for ITS-enabled fog-oriented VANET.…”

Section: B Fog Computing For Vanetsmentioning

confidence: 99%

A Review of Fog Computing Concept, Architecture, Application, Parameters and Challenges

Ghani,

Bakar Sajak,

Qureshi

et al. 2024

JOIV : Int. J. Inform. Visualization

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The Internet of Things (IoT) has become an integral part of our daily lives, growing exponentially from a facility to a necessity. IoT has been utilized extensively through cloud computing and has proven an excellent technology for deploying in various fields. The data generated by the IoT devices gets transmitted to the cloud for processing and storage. However, with this approach, there are specific issues like latency, energy, computation resources availability, bandwidth, heterogeneity, storage, and network failure. To overcome these obstacles, fog computing is utilized as a middle tier. Fog computing gathers and processes the generated data closer to the user end before transmitting it to the cloud. This paper aims to conduct a structured review of the current state of fog computing and its architectures deployed across multiple industries. This paper also focuses on the implementation and critical parameters for introducing fog computing in IoT-cloud architecture. A detailed comparative analysis has been carried out for 5 different architectures considering various crucial parameters to identify how the quality of service and quality of experience for end users can be optimized. Finally, this paper looks at the multiple challenges that fog computing faces in a structured six-level approach. These challenges will also lead the way for future research in resource management, green computing, and security.

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Section: B Fog Computing For Vanetsmentioning

confidence: 99%

A Review of Fog Computing Concept, Architecture, Application, Parameters and Challenges

Ghani,

Bakar Sajak,

Qureshi

et al. 2024

JOIV : Int. J. Inform. Visualization

View full text Add to dashboard Cite

show abstract

“…With an ever-increasing population and urbanization growth, cities experience rising traffic congestion, which makes it challenging to provide smooth and stable traffic mobility. 1,2 Congestion costs citizens significant time and leads to over-consumption of fuel and environmental pollution. Because of the random dynamics of traffic, it is complicated to formulate a detailed traffic model.…”

Section: Introductionmentioning

confidence: 99%

Deep reinforcement learning based cooperative control of traffic signal for multi‐intersection network in intelligent transportation system using edge computing

Paul

2022

Trans Emerging Tel Tech

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In the current era, the coordination of traffic flow is hindered by the discrepancy between road infrastructure and the number of vehicles which leads to traffic congestion. One of the widely used strategies to mitigate traffic congestion is to control traffic signals with the help of deep reinforcement learning (DRL) in edge computing based intelligent transportation system. This article provides a comprehensive analysis of the most recent DRL algorithms, advantage actor‐critic and proximal policy optimization in multiple deep neural networks (DNNs), including a state‐of‐the‐art transformer model for effective traffic signal management. Here, a single DRL agent is used, which obtains the spatio‐temporal information of the traffic to identify traffic patterns from complex intersection environments. The agent uses this information as the input to the DNNs and then applies the algorithms to retrieve the essential parameters of DNN to seek an optimal action selection policy to mitigate congestion. Different real‐time maps and small city networks are explored here to determine which DNN is best suited for traffic congestion management. The simulation study reveals that both the algorithms significantly outperform the baseline. The transformer model gives the best result when compared to other DNNs. The transformer model decreases average waiting time by 96.16%, implying that it has a higher capability of dealing with congested environments.

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An Intelligent Traffic Signal Management Strategy to Reduce Vehicles CO2 Emissions in Fog Oriented VANET

Paul

Haricharan

2021

Wireless Pers Commun

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Real-Time Routing for ITS Enabled Fog Oriented VANET

Cited by 6 publications

References 8 publications

A Review of Fog Computing Concept, Architecture, Application, Parameters and Challenges

A Review of Fog Computing Concept, Architecture, Application, Parameters and Challenges

Deep reinforcement learning based cooperative control of traffic signal for multi‐intersection network in intelligent transportation system using edge computing

An Intelligent Traffic Signal Management Strategy to Reduce Vehicles CO2 Emissions in Fog Oriented VANET

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