scholarly journals Unmanned Driving Infringement Judgment Based on Wireless Sensor Network Data Mining

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Menglu Yang
Keyword(s):  
Data Mining ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Monitoring Network ◽  
System Model ◽  
Wireless Sensor ◽  
Display System ◽  
Mining Technology ◽  
Sensory Data ◽  
Communication Module

Based on the wireless sensor network unmanned driving infringement identification system, this paper focuses on the application of data mining technology and state machine technology and designs and implements a set of practical and effective. Self-driving cars can reduce the frequency of traffic accidents, alleviate urban traffic congestion, improve people’s travel efficiency, and lower the threshold of driving and other social values. The data processing program and a number of algorithms are given, and a complete set of data processing procedures and algorithms are proposed, including the collection of raw sensor data, the preprocessing of the collected data, and the feature extraction of the processed data. In the experiment, the unmanned driving infringement monitoring network was first designed to conduct real-time monitoring of unmanned driving infringements during transportation and application. Aiming at the characteristics of unmanned driving infringements, a monitoring network platform was designed for remote control and large-scale monitoring. Secondly, according to the characteristics of the unmanned driving infringement monitoring sensor network, the unmanned driving infringement node monitoring terminal is designed. The monitoring terminal part mainly designs the sensor module, the wireless communication module, the display warning module power module, and the data mining processing module. The sensor modules, respectively, include temperature, humidity, and concentration sensors, and the communication mode in the communication module mainly adopts Wi-Fi. At the same time, the research is based on wireless sensor network, combined with data mining technology, puts forward a sensory data display system model based on data mining technology, and conducts an in-depth analysis of the sensory data display system model, including the logical level of the system, system architecture, and functional modules. Finally, it focuses on the specific application of data mining technology in environmental information analysis and prediction, uses JAVA programming and realizes a data analysis and display system based on wireless sensor network, and verifies the accuracy of the data mining algorithm. The experimental results analyze the application of data mining technology in the driverless infringement determination system and use a large number of unmanned driving infringements to analyze the determination rules, so as to realize the interaction between active people and driverless cars.

scholarly journals A Remote Monitoring System of Logistics Carrier Based on Wireless Sensor Network

2018 ◽  
Vol 14 (01) ◽  
pp. 4
Author(s):  
Wang Weidong
Keyword(s):  
Wireless Sensor Network ◽  
Real Time ◽  
Sensor Network ◽  
Monitoring System ◽  
Remote Monitoring ◽  
Monitoring Network ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Remote Monitoring System ◽  
Monitoring Center

To improve the efficiency of the remote monitoring system for logistics transportation, we proposed a remote monitoring system based on wireless sensor network and GPRS communication. The system can collect information from the wireless sensor network and transmit the information to the ZigBee interpreter. The monitoring system mainly includes the following parts: Car terminal, GPRS transmission network and monitoring center. Car terminal mainly consists by the Zigbee microcontroller and peripherals, wireless sensor nodes, RFID reader, GPRS wireless communication module composed of a micro-wireless monitoring network. The information collected by the sensor communicates through the GPRS and the monitoring center on the network coordinator, sends the collected information to the monitoring center, and the monitoring center realizes the information of the logistics vehicle in real time. The system has high applicability, meets the design requirements in the real-time acquisition and information transmission of the information of the logistics transport vehicles and goods, and realizes the function of remote monitoring.

A Simple Monitoring Network System of Wireless Sensor Network

2013 ◽  
Vol 278-280 ◽  
pp. 689-692 ◽  
Author(s):  
Jin Jin Xu ◽  
Sheng Jun Su ◽  
Ming Hui Yuan
Keyword(s):  
Wireless Sensor Networks ◽  
Wireless Sensor Network ◽  
Sensor Network ◽  
Ieee 802.15.4 ◽  
Time Synchronization ◽  
Dynamic Change ◽  
Monitoring Network ◽  
Wireless Sensor ◽  
Network System ◽  
Analysis Program

A SSNS (simple sensor network sniffer) is used to analyze and evaluate the Wireless Sensor Networks (WSN) effectively. SSNS is designed to monitor IEEE 802.15.4 protocol frame, which based on the Ethernet. Unlike the existed monitoring system, our design is much simpler and needs less resource. It is analyzed in this paper that the monitor network framework, time synchronization, and analysis program design. The results show that SSNS works stably, and can real-time display the frame monitored and reflect the dynamic change of WSN.

Design of a Wireless Sensor Network Node Based on STM32

2013 ◽  
Vol 347-350 ◽  
pp. 1920-1923
Author(s):  
Yu Jia Sun ◽  
Xiao Ming Wang ◽  
Fang Xiu Jia ◽  
Ji Yan Yu
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Sensor Node ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Network Node ◽  
Wireless Sensor Node ◽  
Wireless Sensor Nodes ◽  
Communication Module ◽  
Design Factors

The characteristics and the design factors of wireless sensor network node are talked in this article. According to the design factors of wireless sensor network, this article will mainly point out the design of wireless sensor nodes based a Cortex-M3 Microcontroller STM32F103RE chip. And the wireless communication module is designed with a CC2430 chip. Our wireless sensor node has good performance in our test.

Application of Wireless Sensor Network in Embedded Smart Home System

2015 ◽  
Vol 738-739 ◽  
pp. 74-78
Author(s):  
Peng Ju Zhang ◽  
Gai Zhi Guo ◽  
Zong Zuo Yu
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Smart Home ◽  
Electrical Equipment ◽  
Wireless Sensor ◽  
Protocol Stack ◽  
System Solution ◽  
Control Centre ◽  
Communication Module ◽  
Smart Control

This paper presents an Embedded Smart Home system solution using wireless sensor network (WSN). The Smart Home system can be sectioned into four parts: wireless sensor network, embedded smart control centre, Server and Client. The major technical of the wireless sensor network is ZigBee. The wireless sensor network includes coordinator node and Sensor node. It is developed based on the Z-Stack protocol stack and the wireless chip CC2530. It is mainly responsible for collecting the environmental parameter of the house and controlling the electrical equipment in the house. It can also support the RFID access control and camera monitor. The control centre communicates with the wireless sensor network by the serial port. It communicates with Server by the TCP socket and transmits data to each client, or communicates with the client by using the wireless communication module directly. Partial hardware electric diagram and software flowchart were provided. Field using indicates that this system is economical and flexible.

Wireless Sensor Network for Performance Monitoring of Electrical Machine

2015 ◽  
Vol 4 (1) ◽  
pp. 1
Author(s):  
Priyanka Ranaware ◽  
N.D. Dhoot
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Low Cost ◽  
Monitoring Network ◽  
Base Station ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Electrical Machine ◽  
Prototype System ◽  
Monitoring And Control

<p class="Default">This paper proposes a novel industrial wireless sensor network for industrial machine condition monitoring. To avoid unexpected equipment failures and obtain higher accuracy in diagnostic and prognostic for the health condition of a motor, efficient and comprehensive data collecting, monitoring, and control play an important role to improve the system more reliable and effective. A novel wireless data collection for health monitoring system of electric machine based on wireless sensor network is proposed and developed in this paper. The unique characteristics of ZigBee networks such as low power, low cost, and high flexibility make them ideal for this application. The proposed system consists of wireless sensor nodes which are organized into a monitoring network by ZigBee protocols. A base station and wireless nodes have been developed to form a prototype system. Various sensors have the capability to monitor physiological as well as environmental conditions. Therefore proposed system provides a flexible solution that makes our living spaces more intelligent.</p>

scholarly journals Mining Wireless Sensor Network Data: an adaptive approach based on artificial neuralnetworks algorithm

2011 ◽  
pp. 129-135
Author(s):  
Lambodar Jena ◽  
Ramakrushna Swain ◽  
N.K. kamila
Keyword(s):  
Data Mining ◽  
Wireless Sensor Network ◽  
Sensor Networks ◽  
Dimensionality Reduction ◽  
Sensor Network ◽  
Power Supply ◽  
Sensor Data ◽  
Wireless Sensor ◽  
Limited Power ◽  
Limited Power Supply

This paper proposes a layered modular architecture to adaptively perform data mining tasks in large sensor networks. The architecture consists in a lower layer which performs data aggregation in a modular fashion and in an upper layer which employs an adaptive local learning technique to extract a prediction model from the aggregated information. The rationale of the approach is that a modular aggregation of sensor data can serve jointly two purposes: first, the organization of sensors in clusters, then reducing the communication effort, second, the dimensionality reduction of the data mining task, then improving the accuracy of the sensing task . Here we show that some of the algorithms developed within the artificial neuralnetworks tradition can be easily adopted to wireless sensor-network platforms and will meet several aspects of the constraints for data mining in sensor networks like: limited communication bandwidth, limited computing resources, limited power supply, and the need for fault-tolerance. The analysis of the dimensionality reduction obtained from the outputs of the neural-networks clustering algorithms shows that the communication costs of the proposed approach are significantly smaller, which is an important consideration in sensor-networks due to limited power supply. In this paper we will present two possible implementations of the ART and FuzzyART neuralnetworks algorithms, which are unsupervised learning methods for categorization of the sensory inputs. They are tested on a data obtained from a set of several nodes, equipped with several sensors each.

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