scholarly journals Data Anomaly Detection for Internet of Vehicles Based on Traffic Cellular Automata and Driving Style

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4926 ◽  
Author(s):  
Nan Ding ◽  
Haoxuan Ma ◽  
Chuanguo Zhao ◽  
Yanhua Ma ◽  
Hongwei Ge
Keyword(s):  
Cellular Automata ◽  
Anomaly Detection ◽  
Real Time ◽  
Safety Data ◽  
Gaussian Mixture ◽  
The Internet ◽  
Data Sets ◽  
Driving Style ◽  
Safe Driving ◽  
Internet Of Vehicles

The data validity of safe driving in the Internet of Vehicles (IoV) is the basis of improving the safety of vehicles. Different from a traditional information systems, the data anomaly analysis of vehicle safety driving faces the diversity of data anomaly and the randomness and subjectivity of the driver’s driving behavior. How to combine the characteristics of the IOV data with the driving style analysis to provide effective real-time anomaly data detection has become an important issue in the IOV applications. This paper aims at the critical safety data analysis, considering the large computing cost generated by the real-time anomaly detection of all data in the data package. We preprocess it through the traffic cellular automata model which is built to achieve the ideal abnormal detection effect with limited computing resources. On the basis of this model, the Anomaly Detection based on Driving style (ADD) algorithm is proposed to realize real-time and online detection of anomaly data related to safe driving. Firstly, this paper designs the driving coefficient and proposes a driving style quantization model to represent the driving style of the driver. Then, based on driving style quantization model and vehicle driving state information, a data anomaly detection algorithm is developed by using Gaussian mixture model (GMM). Finally, combining with the application scenarios of multi-vehicle collaboration in the Internet of Vehicles, this paper uses real data sets and simulation data sets to analyze the effectiveness of the proposed ADD algorithm.

scholarly journals Stopping Accidents before They Happen: Perceiving Lane-Level Moving Vehicle Danger Regions to Warn Surrounding Drivers and Pedestrians

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Chi Guo ◽  
Guangyi Cao ◽  
Jieru Zeng ◽  
Jinsong Cui ◽  
Rong Peng
Keyword(s):  
Real Time ◽  
Large Scale ◽  
The Internet ◽  
Active Safety ◽  
Internet Of Vehicles ◽  
Moving Vehicle ◽  
Moving Vehicles ◽  
Traditional Service ◽  
New Type ◽  
New System

Perceiving the location of dangerous moving vehicles and broadcasting this information to vehicles nearby are essential to achieve active safety in the Internet of Vehicles (IOV). To address this issue, we implement a real-time high-precision lane-level danger region service for moving vehicles. A traditional service depends on static geofencing and fails to deal with dynamic vehicles. To overcome this defect, we devised a new type of IOV service that manages to track dangerous moving vehicles in real time and recognize their danger regions quickly and accurately. Next, we designed algorithms to distinguish the vehicles in danger regions and broadcast the information to these vehicles. Our system can simultaneously manipulate a mass of danger regions for various dangerous vehicles and broadcast this information to surrounding vehicles at a large scale. This new system was tested in Shanghai, Guangzhou, Wuhan, and other cities; the data analysis is presented in this paper as well.

scholarly journals Real-Time Performance Evaluation of IEEE 802.11p EDCA Mechanism for IoV in a Highway Environment

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Hong Li ◽  
Qiong Wu ◽  
Jing Fan ◽  
Qiang Fan ◽  
Bo Chang ◽  
...  
Keyword(s):  
Real Time ◽  
Network Performance ◽  
Dynamic Network ◽  
Ieee 802.11P ◽  
The Internet ◽  
Public Infrastructure ◽  
Internet Of Vehicles ◽  
Time Performance ◽  
Time Requirements ◽  
The Internet Of Things

With the development of 5G, the Internet of Vehicles (IoV) evolves to be one important component of the Internet of Things (IoT), where vehicles and public infrastructure communicate with each other through a IEEE 802.11p EDCA mechanism to support four access categories (ACs) to access a channel. Due to the mobility of the vehicles, the network topology is time varying and thus incurs a dynamic network performance. There are many works on the stationary performance of 802.11p EDCA and some on real-time performance, but existing work does not consider real-time performance under extreme highway scenario. In this paper, we consider four ACs defined in the 802.11p EDCA mechanism to evaluate the limit of the real-time network performance in an extreme highway scenario, i.e., all vehicles keep the minimum safety distance between each other. The performance of the model has been demonstrated through simulations. It is found that some ACs can meet real-time requirements while others cannot in the extreme scenario.

scholarly journals Enabling real-time road anomaly detection via mobile edge computing

2019 ◽  
Vol 15 (11) ◽  
pp. 155014771989131 ◽  
Author(s):  
Zengwei Zheng ◽  
Mingxuan Zhou ◽  
Yuanyi Chen ◽  
Meimei Huo ◽  
Dan Chen
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Detection System ◽  
Edge Computing ◽  
Detection Methods ◽  
Data Sets ◽  
Mobile Edge Computing ◽  
Real World Data ◽  
Subsequent Work ◽  
The Road

To discover road anomalies, a large number of detection methods have been proposed. Most of them apply classification techniques by extracting time and frequency features from the acceleration data. Existing methods are time-consuming since these methods perform on the whole datasets. In addition, few of them pay attention to the similarity of the data itself when vehicle passes over the road anomalies. In this article, we propose QF-COTE, a real-time road anomaly detection system via mobile edge computing. Specifically, QF-COTE consists of two phases: (1) Quick filter. This phase is designed to roughly extract road anomaly segments by applying random forest filter and can be performed on the edge node. (2) Road anomaly detection. In this phase, we utilize collective of transformation-based ensembles to detect road anomalies and can be performed on the cloud node. We show that our method performs clearly beyond some existing methods in both detection performance and running time. To support this conclusion, experiments are conducted based on two real-world data sets and the results are statistically analyzed. We also conduct two experiments to explore the influence of velocity and sample rate. We expect to lay the first step to some new thoughts to the field of real-time road anomalies detection in subsequent work.

scholarly journals An Efficient Anomaly Detection Based on Optimal Deep Belief Network in Big Data

2019 ◽  
Vol 9 (1) ◽  
pp. 708-716
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Anomaly Detection ◽  
Computer Network ◽  
Large Datasets ◽  
Support Vector ◽  
The Internet ◽  
Data Sets ◽  
Data Generation ◽  
Detection Techniques

Nowadays, the internet and network service user’s counts are increasing and the data generation speed also very high. Then again, we see greater security dangers on the internet, enterprise network, websites and the network. Anomaly has been known as one of the effective cyber threats over the internet which increasing exponentially and thus overcomes the commonly used approaches for anomaly detection and classification. Anomaly detection is used in big data analytics to recognize the unexpected behaviour. The most commonly used characteristics in network environment are size and dimensionality, which are big datasets and also impose problems in recognizing useful patterns, For example, to identify the network traffic anomalies from the large datasets. Due to the enormous increase of computer network based facilities it is a challenge to perform fast and efficient anomaly detection. The anomaly recognition in big data sets is more useful to discover fraud and abnormal action. Here, we mainly focus on the problems regarding anomaly detection, so we introduce a novel machine learning based anomaly detection technique. Machine learning approach is used to enhance the anomaly detection speed which is very much useful to detect the anomaly from the large datasets. We evaluate the proposed framework by performing experiments with larger data sets and compare to several existing techniques such as fuzzy, SVM (Support Vector Machine) and PSO (Particle swarm optimization). It has shown 98% percentage of accuracy and the false rate of 0.002 % on proposed classifier. The experimental results illuminate that better performance than existing anomaly detection techniques in big data environment.

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