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Referenced Blockchain Approach for Road Traffic Monitoring in a Smart City using Internet of Drones | IEEE Conference Publication | IEEE Xplore

Referenced Blockchain Approach for Road Traffic Monitoring in a Smart City using Internet of Drones


Abstract:

The global escalation in the road traffic density alleviates the harmful emissions and fuel bills due to congestion and misaligned traffic control. The conventional traff...Show More

Abstract:

The global escalation in the road traffic density alleviates the harmful emissions and fuel bills due to congestion and misaligned traffic control. The conventional traffic monitoring schemes (camera or sensor-based) are not able to cover every nook and corner and thus miss various vital traffic parameters that can otherwise be very useful for traffic density and pattern analysis. Internet of Drones (IoD) has been widely adopted to resolve various related challenges and has strong potential in traffic monitoring even in the areas where scarcity of fixed infrastructure is witnessed. Thus, in this paper, we have proposed an IoD-based traffic monitoring system to avoid the congestion on the roads within the available infrastructure. Moreover, to deal with the dynamic network typologies, an software-defined networking (SDN)-based centralized controller is configured to generate the flow rules for end to end data transmission. However, the drones communicate with each other through an open channel (now controlled through a programmable SDN architecture). Thus, the integrity of data collected by drones must be protected through a robust security mechanism. So, we have adopted a blockchain technology to secure the proposed system against unauthorised access and maintain data integrity. However, maintaining the entire blockchain on the drones can lead to several resource bottlenecks. Thus, we have used a referenced blockchain architecture that decouples the data from the blockchain part and stores it in the off-chain. The proposed scheme has been validated using simulated environments that validates its the superiority in contrast to the existing variants.
Date of Conference: 16-20 May 2022
Date Added to IEEE Xplore: 11 August 2022
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Conference Location: Seoul, Korea, Republic of
Citations are not available for this document.

I. Introduction

Traffic congestion is one of the major concerns looking into the ever increasing vehicle demand across the globe. According to world economic forum [1], the number of vehicles running on the roads worldwide will double by 2040. The increase in the road traffic directly increases the harmful emissions and fuel consumption due to traffic jams, that further has adverse affect on the health and economy of the global population. To overcome this issue, many countries has expanded their treasury shares to improve the road and traffic infrastructure. For example, in 2018, the US Government invested $187 billion to construct the new highways and road infrastructure [2]. However, the surging road traffic and unmatched traffic monitoring and control capabilities often end up in large number of casualties on the roads. According to the World Health Organization report, there were around 1.35 million road moralities across the globe in 2016 [3].

Cites in Papers - |

Cites in Papers - IEEE (4)

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1.
Hailong Zhu, Umit Demirbaga, Gagangeet Singh Aujla, Lei Shi, Peiying Zhang, "Explainable Edge AI Framework for IoD-Assisted Aerial Surveillance in Extreme Scenarios", IEEE Internet of Things Journal, vol.12, no.5, pp.4570-4578, 2025.
2.
Anakhi Hazarika, Nikumani Choudhury, Moustafa M. Nasralla, Sohaib Bin Altaf Khattak, Ikram Ur Rehman, "Edge ML Technique for Smart Traffic Management in Intelligent Transportation Systems", IEEE Access, vol.12, pp.25443-25458, 2024.
3.
Marco Fiore, Marina Mongiello, "Blockchain for Smart Cities Improvement: an Architecture Proposal", 2023 7th International Conference on Computer, Software and Modeling (ICCSM), pp.1-5, 2023.
4.
Wu Wen, Umit Demirbaga, Amritpal Singh, Anish Jindal, Ranbir Singh Batth, Peiying Zhang, Gagangeet Singh Aujla, "Health Monitoring and Diagnosis for Geo-Distributed Edge Ecosystem in Smart City", IEEE Internet of Things Journal, vol.10, no.21, pp.18571-18578, 2023.

Cites in Papers - Other Publishers (1)

1.
Xiaonan Wang, Yang Guo, Yuan Gao, "Unmanned Autonomous Intelligent System in 6G Non-Terrestrial Network", Information, vol.15, no.1, pp.38, 2024.
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References

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