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Modeling and Optimization of Vehicular Fog Network Towards Minimizing Latency

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Abstract

Vehicular Fog Computing (VFC) makes computing power available much closer to the vehicles and end-user thereby, reducing latency. The latency is of critical importance for ensuring safety and quality of service (QoS) in today’s Intelligent Transportation System (ITS). However, achieving low latency, while meeting the workload demand from vehicular nodes in VFC is challenging due to dynamic changes in resource availability and demand. In this research work, an investigation is conducted on characteristics of optimal Vehicular Fog Node (VFN), and a multi-variable constrained non-linear multi-objective optimization (MOO) model-based technique is proposed, for minimizing latency in vehicular fog network. Then proposed MOO technique is evaluated for optimum latency under varying parameters like radio bandwidth, the number of vehicles, request arrival rate, and workload sizes. Statistical validation of simulated data is offered using a one-way ANOVA test, with 95 percentile confidence. The effect of vehicle fleet size, workload size, request arrival rate, and probability of processing at VFN is analyzed for the latency and QoS satisfaction for real-time, latency-sensitive requests in VFC. The proposed technique is compared with generic fog-cloud, ECOS, and ETEFN techniques for latency and QoS satisfaction. Simulation results depict on average 350 times higher latency for workload sizes from 300 to 1000, for the generic fog-cloud system as compared to the proposed VFC model. The QoS satisfaction ratio is maintained between 96 and 93% for the proposed technique as compared to 93 to 84% for ECOS, and 94 to 78% for ETEFN for the request arrival rate from 0.5 to 5.0. Whereas, the QoS satisfaction ratio declines very sharply from 88 to 10% for generic fog-cloud system. The proposed MOO technique for VFC in ITS offers quantitative QoS enhancement as compared to the generic fog-cloud, and the state-of-the-art ECOS, and ETEFN techniques, which makes it suitable for adaptation for VFC in real-time applications.

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The authors declare that the data supporting the findings of this study are available within the article.

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Acknowledgements

We would like to thank the anonymous reviewers for providing their valuable time and efforts in suggesting very relevant comments, that helped us improve this manuscript to bring it to its present form.

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Authors and Affiliations

  1. University of Petroleum and Energy Studies, Dehradun, India

    Deep Chandra Binwal & Rajeev Tiwari

  2. Indian Air Force, New Delhi, India

    Deep Chandra Binwal

  3. School of Computer Science and Engineering, IILM University, Greater Noida, Uttar Pradesh, India

    Rajeev Tiwari

  4. Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India

    Monit Kapoor

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  1. Deep Chandra Binwal
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Correspondence to Rajeev Tiwari.

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Binwal, D.C., Tiwari, R. & Kapoor, M. Modeling and Optimization of Vehicular Fog Network Towards Minimizing Latency. Mobile Netw Appl (2023). https://doi.org/10.1007/s11036-023-02197-5

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