IoV-Fog-Assisted Framework for Accident Detection and Classification

IF 2.8 3区 计算机科学 Q2 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE ACM Transactions on Embedded Computing Systems Pub Date : 2023-11-24 DOI:10.1145/3633805
Navin Kumar, Sandeep Kumar Sood, Munish Saini
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Abstract

The evolution of vehicular research into an effectuating area like the Internet of Vehicles (IoV) was verified by technical developments in hardware. The integration of the Internet of Things (IoT) and Vehicular Ad-hoc Networks (VANET) has significantly impacted addressing various problems, from dangerous situations to finding practical solutions. During a catastrophic collision, the vehicle experiences extreme turbulence, which may be captured using Micro-Electromechanical systems (MEMS) to yield signatures characterizing the severity of the accident. This study presents a three-layer design, with the data collecting layer relying on a low-power IoT configuration that includes GPS and an MPU 6050 placed on an Arduino Mega. The fog layer oversees data pre-processing and other low-level computing operations. With its extensive computing capabilities, the farthest cloud layer carries out Multidimensional Dynamic Time Warping (MDTW) to identify accidents and maintains the information repository by updating it. The experimentation compared the state-of-the-art algorithms such as Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Random Forest Tree (RFT) using threshold-based detection with the proposed MDTW clustering approach. Data collection involves simulating accidents via VirtualCrash for training and testing, whereas the IoV circuitry would be utilized in actual real-life scenarios. The proposed approach achieved an F1-Score of 0.8921 and 0.8184 for rear and head-on collisions.

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基于iov - fog的事故检测与分类框架
硬件技术的发展验证了车辆研究向车联网(IoV)等实施领域的演变。物联网(IoT)和车载自组织网络(VANET)的集成对解决各种问题(从危险情况到寻找实际解决方案)产生了重大影响。在灾难性碰撞中,车辆会经历极端的湍流,这些湍流可以通过微机电系统(MEMS)捕捉到,从而产生表征事故严重程度的特征。本研究提出了一个三层设计,其中数据收集层依赖于低功耗物联网配置,包括GPS和放置在Arduino Mega上的MPU 6050。雾层监督数据预处理和其他低级计算操作。由于其广泛的计算能力,最远的云层执行多维动态时间扭曲(MDTW)来识别事故,并通过更新来维护信息存储库。实验比较了最先进的算法,如支持向量机(SVM)、k近邻(KNN)和随机森林树(RFT)使用基于阈值的检测与提出的MDTW聚类方法。数据收集包括通过VirtualCrash模拟事故进行培训和测试,而车联网电路将用于实际生活场景。该方法在后碰撞和正面碰撞的F1-Score分别为0.8921和0.8184。
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来源期刊
ACM Transactions on Embedded Computing Systems
ACM Transactions on Embedded Computing Systems 工程技术-计算机:软件工程
CiteScore
3.70
自引率
0.00%
发文量
138
审稿时长
6 months
期刊介绍: The design of embedded computing systems, both the software and hardware, increasingly relies on sophisticated algorithms, analytical models, and methodologies. ACM Transactions on Embedded Computing Systems (TECS) aims to present the leading work relating to the analysis, design, behavior, and experience with embedded computing systems.
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