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Optimal RSU deployment using complex network analysis for traffic prediction in VANET

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Abstract

Road Side Units (RSUs) are an integral component of Vehicular ad hoc Networks (VANET) along with connected and autonomous vehicles. RSUs have been used to host numerous traffic sensing and control mechanisms to enhance transportation throughput in terms of safety, congestion avoidance, route planning, etc. In order to reduce installation and maintenance costs and associated network and security overhead, it is highly desirable to deploy these RSUs optimally, particularly in strategic and influential positions. While too many RSUs may increase overhead, too few RSUs may fail to map the entire region properly, resulting in erroneous computations. This paper aims to address this trade-off by incorporating a novel scheme called Intersection Influence Analysis System for Optimal RSU Deployment (IIA-ORD). The primary objective of IIA-ORD is achieved through modelling the transportation network as connected graphs and executing a modified K-shell and TOPSIS-based framework. Specifically, the network vertices are mapped with road intersections, and live traffic data is used to analyze various statistical measures, leading to the identification of influential junctions. Extensive performance analysis in an open-source simulation platform backed by real-time data justifies the performance superiority of the IIA-ORD system over existing RSU deployment strategies in terms of an overall number of deployed RSUs, average coverage, coverage time ratio, packet delivery ratio, and delay. The system is validated by a traffic forecasting application. The RSU is equipped with the Stacked Bidirectional Long Short-Term Memory (SBi-LSTM) based traffic prediction model, under which the RSU of a particular junction predicts the traffic congestion of the entire region without the deployment of additional RSUs. Comparative analysis records high accuracy with low loss values for the proposed model in relation to the vanilla LSTM model.

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Data availability

Data is generated in open source simulation platform and also taken from Google’s crowd sourcing. Previous researcher’s data that are used for comparison is properly cited.

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Acknowledgements

I wish to show my gratitude towards CSIR-HRDG for providing their support.

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Authors and Affiliations

  1. Electronics and Telecommunication Engineering, Jadavpur University, Kolkata, WB, India

    Sreya Ghosh & Iti Saha Misra

  2. Computer Science and Engineering, Future Institute of Engineering and Management, Kolkata, WB, India

    Tamal Chakraborty

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  1. Sreya Ghosh
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  2. Iti Saha Misra
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  3. Tamal Chakraborty
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Sreya Ghosh carried out the background study, collected and analyzed the data and wrote the manuscript. Tamal Chakraborty contributed to build the idea and corrected the manuscript writing. Iti Saha Misra guided and finalized it.

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Correspondence to Sreya Ghosh.

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Ghosh, S., Saha Misra, I. & Chakraborty, T. Optimal RSU deployment using complex network analysis for traffic prediction in VANET. Peer-to-Peer Netw. Appl. 16, 1135–1154 (2023). https://doi.org/10.1007/s12083-023-01453-5

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