scholarly journals Anomaly Detection of Highway Vehicle Trajectory under the Internet of Things Converged with 5G Technology

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Ketao Deng
Keyword(s):  
Internet Of Things ◽  
Anomaly Detection ◽  
Communication Technology ◽  
Traffic Monitoring ◽  
The Internet ◽  
Moving Target ◽  
Continuous Increase ◽  
5G Communication ◽  
Vehicle Trajectory ◽  
The Internet Of Things

The gradual increase in the density of highway vehicles and traffic flow makes the abnormal driving state of vehicles an indispensable tool for assisting traffic dispatch. Intelligent transportation systems can detect and track vehicles in real time, acquire characteristics such as vehicle traffic, vehicle speed, vehicle flow density, and vehicle trajectory, and further perform advanced tasks such as vehicle trajectory. The detection of abnormal vehicle trajectory is an important content of vehicle trajectory understanding. And the development of the Internet of Things (IoT) and 5G technology has led to a continuous increase in the rate of data information circulation. The “Internet of Vehicles” generated based on the practice of 5G communication technology constitutes a vehicle abnormal trajectory detection system, which has very high feasibility and safety and stability. Therefore, this research is aimed at the needs of preventing major accidents and forensic analysis during highway vehicles. Based on the integration of the Internet of Things 5G communication technology, a trajectorial anomaly detection of highway vehicle trajectory based on the integration of the Internet of Things 5G is proposed. By accurately sensing unsafe events at the perception layer, network layer, and application layer, the vehicle driving trajectory state is divided into several simple semantic representations. The semantic representation is analyzed, and then the moving target detection and moving target tracking algorithms needed to extract the vehicle trajectory are introduced. Through video detection and tracking of moving vehicle targets, the driving trajectory of the vehicle is obtained, and the movement characteristics of the vehicle in each frame of image are extracted. According to the relationship between the trajectory of the vehicle and the lane line, the vehicle trajectory analysis is realized, and then it is judged whether the vehicle has abnormal trajectory. Compared with the traditional method of manually detecting the driving condition of the vehicle, the abnormal trajectory detection of the vehicle based on the integration of the Internet of Things and 5G can quickly detect the abnormal trajectory of the vehicle in the traffic monitoring video.

Construction of e-commerce system based on 5G and Internet of Things technology

2022 ◽  
Vol 15 (2) ◽  
pp. 0-0
Keyword(s):  
Internet Of Things ◽  
Experimental Research ◽  
Communication Technology ◽  
The Internet ◽  
Logistics System ◽  
Smart Logistics ◽  
Client Data ◽  
5G Communication ◽  
Internet Of Things Technology ◽  
The Internet Of Things

In order to improve the comprehensive performance of the e-commerce system, this paper combines 5G communication technology and the Internet of Things technology to improve the e-commerce system, and conduct end-point analysis on the e-commerce client data analysis system and smart logistics system. Moreover, this paper uses 5G technology to improve machine learning algorithms to process e-commerce back-end data and improve the efficiency of e-commerce client data processing. In addition, this paper combines the Internet of Things to build an e-commerce smart logistics system model to improve the overall efficiency of the logistics system. Finally, this paper combines the demand analysis to construct the functional module structure of the e-commerce system, and verifies the practical functions of the system through experimental research. From the experimental research results, it can be seen that the e-commerce system based on 5G communication technology and Internet of Things technology constructed in this paper is very reliable.

A Novel HTTP Anomaly Detection Framework Based on Edge Intelligence for the Internet of Things (IoT)

2020 ◽  
pp. 1-7
Author(s):  
Yufei An ◽  
Jianqiang Li ◽  
F. Richard Yu ◽  
Jianyong Chen ◽  
Victor C. M. Leung
Keyword(s):  
Internet Of Things ◽  
Anomaly Detection ◽  
The Internet ◽  
The Internet Of Things

The Internet of Things: Making Cities — AndThe Way They Use Technology — Smarter

2016 ◽  
Author(s):  
Wendy W. Fok ◽  
Keyword(s):  
New York ◽  
Internet Of Things ◽  
Energy Use ◽  
Smart Cities ◽  
Traffic Monitoring ◽  
Urban Life ◽  
The Internet ◽  
Time Data ◽  
Heating And Cooling ◽  
The Internet Of Things

Minerva Tantoco was named New York City’s first chief technology officer last year, charged with developing a coordinated citywide strategy on technology and innovation. We’re likely to see more of that as cities around the country, and around the world, consider how best to use innovation and technology to operate as “smart cities.”The work has major implications for energy use and sustainability, as cities take advantage of available, real-time data – from ‘smart’ phones, computers, traffic monitoring, and even weather patterns — to shift the way in which heating and cooling systems, landscaping, flow of people through cities, and other pieces of urban life are controlled. But harnessing Open Innovation and the Internet of Things can promote sustainability on a much broader and deeper scale. The question is, how do you use all the available data to create a more environmentally sound future? The term “Internet of Things” was coined in 1999 by Kevin Ashton, who at the time was a brand manager trying to find a better way to track inventory. His idea? Put a microchip on the packaging to let stores know what was on the shelves.

scholarly journals A Novel Sensor Data Pre-Processing Methodology for the Internet of Things Using Anomaly Detection and Transfer-By-Subspace-Similarity Transformation

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4536 ◽  
Author(s):  
Yan Zhong ◽  
Simon Fong ◽  
Shimin Hu ◽  
Raymond Wong ◽  
Weiwei Lin
Keyword(s):  
Data Mining ◽  
Internet Of Things ◽  
Anomaly Detection ◽  
Sensor Data ◽  
Detection Methods ◽  
The Internet ◽  
Quality Of Data ◽  
Data Mining Algorithms ◽  
Sensing Applications ◽  
The Internet Of Things

The Internet of Things (IoT) and sensors are becoming increasingly popular, especially in monitoring large and ambient environments. Applications that embrace IoT and sensors often require mining the data feeds that are collected at frequent intervals for intelligence. Despite the fact that such sensor data are massive, most of the data contents are identical and repetitive; for example, human traffic in a park at night. Most of the traditional classification algorithms were originally formulated decades ago, and they were not designed to handle such sensor data effectively. Hence, the performance of the learned model is often poor because of the small granularity in classification and the sporadic patterns in the data. To improve the quality of data mining from the IoT data, a new pre-processing methodology based on subspace similarity detection is proposed. Our method can be well integrated with traditional data mining algorithms and anomaly detection methods. The pre-processing method is flexible for handling similar kinds of sensor data that are sporadic in nature that exist in many ambient sensing applications. The proposed methodology is evaluated by extensive experiment with a collection of classical data mining models. An improvement over the precision rate is shown by using the proposed method.

Changing the Game: A Micro Moving Target IPv6 Defense for the Internet of Things

2018 ◽  
Vol 7 (4) ◽  
pp. 578-581 ◽  
Author(s):  
Kimberly Zeitz ◽  
Michael Cantrell ◽  
Randy Marchany ◽  
Joseph Tront
Keyword(s):  
Internet Of Things ◽  
The Internet ◽  
Moving Target ◽  
The Internet Of Things

scholarly journals A Triband Slot Patch Antenna for Conformal and Wearable Applications

Electronics ◽  
2021 ◽  
Vol 10 (24) ◽  
pp. 3155
Author(s):  
Erfeng Li ◽  
Xue Jun Li ◽  
Boon-Chong Seet
Keyword(s):  
Internet Of Things ◽  
Communication Technology ◽  
Patch Antenna ◽  
Absorption Rate ◽  
Specific Absorption Rate ◽  
Rapid Development ◽  
The Internet ◽  
Body Area ◽  
The Internet Of Things ◽  
Pet Substrate

With the rapid development of wireless communication technology and the Internet of Things (IoT), wireless body area networks (WBAN) have been thriving. This paper presents a triband patch antenna with multiple slots for conformal and wearable applications. The proposed antenna operates at 5.8, 6.2, and 8.4 GHz. The antenna was designed with a flexible polyethylene terephthalate (PET) substrate, and the corresponding conformal tests and on-body performance were conducted via simulation. The antenna demonstrated promising gain and acceptable fluctuations when applied on curvature surfaces. The specific absorption rate (SAR) for on-body simulation also suggests that this antenna is suitable for wearable applications.

scholarly journals System Log Detection Model Based on Conformal Prediction

Electronics ◽  
2020 ◽  
Vol 9 (2) ◽  
pp. 232 ◽  
Author(s):  
Yitong Ren ◽  
Zhaojun Gu ◽  
Zhi Wang ◽  
Zhihong Tian ◽  
Chunbo Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Anomaly Detection ◽  
Statistical Learning ◽  
The Internet ◽  
Learning Method ◽  
Log Data ◽  
Detection Model ◽  
Statistical Learning Method ◽  
The Internet Of Things

With the rapid development of the Internet of Things, the combination of the Internet of Things with machine learning, Hadoop and other fields are current development trends. Hadoop Distributed File System (HDFS) is one of the core components of Hadoop, which is used to process files that are divided into data blocks distributed in the cluster. Once the distributed log data are abnormal, it will cause serious losses. When using machine learning algorithms for system log anomaly detection, the output of threshold-based classification models are only normal or abnormal simple predictions. This paper used the statistical learning method of conformity measure to calculate the similarity between test data and past experience. Compared with detection methods based on static threshold, the statistical learning method of the conformity measure can dynamically adapt to the changing log data. By adjusting the maximum fault tolerance, a system administrator can better manage and monitor the system logs. In addition, the computational efficiency of the statistical learning method for conformity measurement was improved. This paper implemented an intranet anomaly detection model based on log analysis, and conducted trial detection on HDFS data sets quickly and efficiently.

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