IOT based Smart Traffic Signal Violation Monitoring System using Edge Computing

Meghana, V.; Anisha, B S; Kumar, Piyush

doi:10.1109/gcat52182.2021.9587585

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…Common traffic violation detection systems, such as the IoT-based smart traffic signal violation monitoring system using edge computing [20], predominantly rely on vision systems utilising cameras. However, in mining operations, camera-based detection is suboptimal in challenging conditions, notably dust.…”

Section: Traffic Violations Detection Methodsmentioning

confidence: 99%

A digital twin framework to support vehicle interaction risk management in the mining industry

Verster,

Roux,

Magweregwede

et al. 2023

MATEC Web Conf.

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In recent years, transport-related accidents, notably those involving trackless mobile machinery (TMM), have consistently ranked among the top three causes of fatalities and injuries in the South African mining industry (SAMI) [1]. These accidents arise from a combination of mechanical and technical malfunctions, environmental factors, and human or machine operator errors. Remarkably, these incidents persist despite the existence of specific regulations, standards, and codes of practice for transportation and machinery. This paper introduces a digital twin framework for TMM, which employs a systems engineering approach combined with software tools and computational analysis. This framework aims to enhance the current regulations by offering a continuous, quantitative risk assessment. By modelling and detecting non-conformance and adverse vehicle interaction events, the framework provides a quantitative risk analysis that complements the prevailing qualitative methods reliant on historical data and operational experience. A case study conducted at the CSIR main campus in Pretoria showcases the potential of the TMM Digital Twin.

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Section: Traffic Violations Detection Methodsmentioning

confidence: 99%

A digital twin framework to support vehicle interaction risk management in the mining industry

Verster,

Roux,

Magweregwede

et al. 2023

MATEC Web Conf.

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“…To keep their functionality working properly without any security breaches to avoid traffic jams, the general architecture shown before is where computer vision can run directly at the junction without transferring the data to the nearby data centre. Meghana et al [39] developed a method in which aRaspberry Pi along with a Pi Camera is used to capture events at a traffic junction and image processing of the data happens on the device. This research showcases that these kind of jobs are viable but the problem comes down to the overwhelming of the Pi devices.…”

Section: Real-world Applicationsmentioning

confidence: 99%

Cyber Security on the Edge: Efficient Enabling of Machine Learning on IoT Devices

Kumari,

Tulshyan,

Tewari

2024

Information

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Due to rising cyber threats, IoT devices’ security vulnerabilities are expanding. However, these devices cannot run complicated security algorithms locally due to hardware restrictions. Data must be transferred to cloud nodes for processing, giving attackers an entry point. This research investigates distributed computing on the edge, using AI-enabled IoT devices and container orchestration tools to process data in real time at the network edge. The purpose is to identify and mitigate DDoS assaults while minimizing CPU usage to improve security. It compares typical IoT devices with and without AI-enabled chips, container orchestration, and assesses their performance in running machine learning models with different cluster settings. The proposed architecture aims to empower IoT devices to process data locally, minimizing the reliance on cloud transmission and bolstering security in IoT environments. The results correlate with the update in the architecture. With the addition of AI-enabled IoT device and container orchestration, there is a difference of 60% between the new architecture and traditional architecture where only Raspberry Pi were being used.

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“…However, this field still faces challenges, including the amount of data, cooperation among vehicles, and privacy, and these challenges can be tackled by different methods, such as federated learning [21]. Edge computing has been used for traffic management (e.g., [22,23]), but with little focus on traffic violation monitoring (e.g., [12,24,25]). Similarly, fog computing has been used for traffic management (e.g., [19,26]) but with little focus on monitoring violations (e.g., [27]).…”

Section: Related Workmentioning

confidence: 99%

IoT-Based System for Improving Vehicular Safety by Continuous Traffic Violation Monitoring

Daraghmi

Helou²,

Daraghmi

et al. 2022

Future Internet

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The violation traffic laws by driving at high speeds, the overloading of passengers, and the unfastening of seatbelts are of high risk and can be fatal in the event of any accident. Several systems have been proposed to improve passenger safety, and the systems either use the sensor-based approach or the computer-vision-based approach. However, the accuracy of these systems still needs enhancement because the entire road network is not covered; the approaches utilize complex estimation techniques, and they are significantly influenced by the surrounding environment, such as the weather and physical obstacles. Therefore, this paper proposes a novel IoT-based traffic violation monitoring system that accurately estimates the vehicle speed, counts the number of passengers, and detects the seatbelt status on the entire road network. The system also utilizes edge computing, fog computing, and cloud computing technologies to achieve high accuracy. The system is evaluated using real-life experiments and compared with another system where the edge and cloud layers are used without the fog layer. The results show that adding a fog layer improves the monitoring accuracy as the accuracy of passenger counting rises from 94% to 97%, the accuracy of seatbelt detection rises from 95% to 99%, and the root mean square error of speed estimation is reduced from 2.64 to 1.87.

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IOT based Smart Traffic Signal Violation Monitoring System using Edge Computing

Cited by 4 publications

References 7 publications

A digital twin framework to support vehicle interaction risk management in the mining industry

A digital twin framework to support vehicle interaction risk management in the mining industry

Cyber Security on the Edge: Efficient Enabling of Machine Learning on IoT Devices

IoT-Based System for Improving Vehicular Safety by Continuous Traffic Violation Monitoring

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