EcRD: Edge-Cloud Computing Framework for Smart Road Damage Detection and Warning

Yuan, Yachao; Islam, Md. Saiful; Wang, Shengjin; Baker, Thar; Kolbe, Lutz M.

doi:10.1109/jiot.2020.3024885

Cited by 26 publications

(35 citation statements)

References 36 publications

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“…In FedRD, a new map construction approach is introduced. It provides drivers/users a hazardous road damage warning map, which has hundreds or even thousands of times wider coverage than EcRD [7]. Additionally, the FL strategy [52] is utilized to collaboratively learn hazardous road damage information from decentralized edges without direct data sharing between edges and the cloud.…”

Section: Discussionmentioning

confidence: 99%

“…and FedMRD, are introduced to answer these research questions. The EcRD [7] and FedRD [8] frameworks…”

Section: Motivationmentioning

confidence: 99%

“…The contributions of this chapter, which were published in the IEEE Internet of Things Journal in 2020 [7]), include: The image-label generator automatically produces road damage danger images and their corresponding labels for MDD.…”

Section: Contributionsmentioning

confidence: 99%

“…Kawano et al [26] used edge computing for road damaged lane markings detection. Following its success, the authors' previous work [7] proposed an edge-cloud computing framework (EcRD) for intelligent road damage detection and warning. EcRD exploits the fast-responding benefit of edge and the high computational power and enormous storage space advantages of the cloud.…”

Section: Road Damage Classification Algorithmsmentioning

confidence: 99%

“…Despite the success of edge/cloud computing for road damage inspection, the privacy issue is still not addressed in many existing schemes [50,71,72]. Only a few systems considered the privacy issues, e.g., [7,50,51]. The authors of [50] preserved privacy against clouds by receiving data in ciphertext format.…”

Section: Privacy-preserving Techniquesmentioning

confidence: 99%

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Smart Road Danger Detection and Warning

Yuan¹

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Road dangers have caused numerous accidents, thus detecting them and warning users are critical to improving traffic safety. However, it is challenging to recognize road dangers from numerous normal data and warn road users due to cluttered real-world backgrounds, ever-changing road danger appearances, high intra-class differences, limited data for one party, and high privacy leakage risk of sensitive information.To address these challenges, in this thesis, three novel road danger detection and warning frameworks are proposed to improve the performance of real-time road danger prediction and notification in challenging real-world environments in four main aspects, i.e., accuracy, latency, communication efficiency, and privacy.Firstly, many existing road danger detection systems mainly process data on clouds. However, they cannot warn users timely about road dangers due to long distances. Meanwhile, supervised machine learning algorithms are usually used in these systems requiring large and precisely labeled datasets to perform well. I also would like to extend my sincere thanks to Prof. Dr. Xiaoming Fu, Prof. Dr. Marcus Baum, and Prof. Dr. Shengjin Wang for their precious time, guidance, and help during my Ph.D. study. I am very grateful to Prof. Dr. Thar Baker since he never hesitates to provide me feedback, guidance, and help.

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Section: Discussionmentioning

confidence: 99%

“…and FedMRD, are introduced to answer these research questions. The EcRD [7] and FedRD [8] frameworks…”

Section: Motivationmentioning

confidence: 99%

Section: Contributionsmentioning

confidence: 99%

Section: Road Damage Classification Algorithmsmentioning

confidence: 99%

Section: Privacy-preserving Techniquesmentioning

confidence: 99%

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Smart Road Danger Detection and Warning

Yuan¹

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Reinforcement Learning-Based Routing in Underwater Acoustic Sensor Networks

Halakarnimath¹,

Sutagundar²

2021

Wireless Pers Commun

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Artificial Intelligence for road quality assessment in smart cities: a machine learning approach to acoustic data analysis

Jagatheesaperumal,

Bibri,

Ganesan

et al. 2023

Comput.Urban Sci.

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In smart cities, ensuring road safety and optimizing transportation efficiency heavily relies on streamlined road condition monitoring. The application of Artificial Intelligence (AI) has notably enhanced the capability to detect road surfaces effectively. This study presents a novel approach to road condition monitoring in smart cities through the development of an acoustic data processing and analysis module. It focuses on four types of road conditions: smooth, slippery, grassy, and rough roads. To assess road conditions, a microphone integrated road surface detector unit is designed to collect audio signals, and an ultrasonic module is used to observe the road depth information. The whole hardware unit is installed in the wheel rim of the vehicles. The data collected from the road surfaces are then analyzed using machine learning algorithms, such as Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), and Random Forest (RF). The results demonstrate the effectiveness of the proposed method in accurately identifying different road conditions. From these results, it was observed that the MLP provides better accuracy of 98.98% in assessing road conditions. The study provides valuable insights into the development of a more efficient and reliable road condition monitoring system for delivering secure transportation services in smart cities.

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EcRD: Edge-Cloud Computing Framework for Smart Road Damage Detection and Warning

Cited by 26 publications

References 36 publications

Smart Road Danger Detection and Warning

Smart Road Danger Detection and Warning

Reinforcement Learning-Based Routing in Underwater Acoustic Sensor Networks

Artificial Intelligence for road quality assessment in smart cities: a machine learning approach to acoustic data analysis

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