Study on WSN Localization Algorithm and Simulation Model for Intelligent Transportation System

2014 ◽  
Vol 539 ◽  
pp. 867-873 ◽  
Author(s):  
Lian Zhou Gao

This paper conducts research on the algorithm to improve the location of Wireless Sensor Network (WSN) in Intelligent Transportation System (ITS). The localization algorithm introduced an improved RSSI vehicle localization algorithm based on multi-path effect and Gaussian white noise. The localization results under different values of Gaussian white noise and different density of beacon nodes are analyzes, and Kalman filtering algorithm is introduced to reduce the influence of signal noise. Finally, a simulation model of ITS is developed to test the algorithm based on mixed noise and Kalman filtering algorithm, which is used to simulate the localization of real vehicles. The simulation shows the algorithm has effect to improve location accuracy and to application

2014 ◽  
Vol 548-549 ◽  
pp. 1407-1414
Author(s):  
Zheng Feng Li ◽  
Lian Zhou Gao

This paper conducts research on the algorithm to improve the location of Wireless Sensor Network (WSN) in Intelligent Transportation System (ITS). The localization algorithm introduced an improved RSSI vehicle localization algorithm based on multi-path effect and Gaussian white noise. The localization results under different values of Gaussian white noise and different density of beacon nodes are analyzes, and Kalman filtering algorithm is introduced to reduce the influence of signal noise. Finally, a simulation model of ITS is developed to test the algorithm based on mixed noise and Kalman filtering algorithm, which is used to simulate the localization of real vehicles. The simulation shows the algorithm has effect to improve location accuracy and to application


2014 ◽  
Vol 945-949 ◽  
pp. 2380-2385
Author(s):  
Lian Zhou Gao

This paper studies on the algorithm to improve the location of Wireless Sensor Network (WSN) in Intelligent Transportation System (ITS). Considering multi-path effect in the localization, an improved RSSI algorithm is introduced in the localization algorithm. The localization results are analyzed under different density of beacon nodes, and Kalman filtering algorithm is introduced to reduce the influence of signal noise. Finally, to test the algorithm based on Kalman filtering algorithm, a simulation model of ITS is developed, which is used to simulate the localization of real vehicles. The simulation shows the algorithm has effect to improve location accuracy and to application.


2014 ◽  
Vol 538 ◽  
pp. 465-469
Author(s):  
Shan Wang ◽  
Cheng Gu

In traditional Kalman filtering algorithm, the system noise and observation noise should be assumed as zero-mean Gaussian white noise, meanwhile need the state-space model and relevant references be given and accurate. However, the white noise is just an ideal noise model that doesnt exist in real environment. This paper analyzed the effect to filtering result from the statistical estimation in traditional Kalman filtering algorithm and brought interval calculation into traditional Kalman filtering algorithm, which based on the concept of interval and could improve the robustness of the system, decrease the error caused by the statistical estimation of noise model.


2001 ◽  
Vol 1779 (1) ◽  
pp. 173-181 ◽  
Author(s):  
Anthony A. Saka ◽  
Richard A. Glassco

A microscopic simulation model was developed to capture the traffic safety benefits of using intelligent transportation system (ITS) technologies, including weigh-in-motion scales with variable message signs, at truck inspection facilities. The development of the simulation model was motivated by prevalent safety concerns at congested truck inspection facilities nationwide. Three primary safety components (roadway, driver, and vehicle) were considered in the model. The roadway component focuses on the varying size of truck queues at inspection facilities and safety implications. The driver component captures key human factor elements and their variability, including distributions for perception-reaction time, speed, gap acceptance, headway, and braking characteristics. The vehicular component incorporates the size distribution of vehicles (trucks and nontrucks), proportion of trucks with defective braking systems, and their safety implications with respect to stopping distance. The primary objective for the model is to depict variations in traffic pattern for baseline (pre-ITS) and post-ITS situations. Measures of effectiveness used for evaluating traffic benefits of using ITS technologies include percent reduction in sudden deceleration of vehicles resulting from shock wave phenomena and percent reduction in duration of truck-queue overflow resulting from a high traffic intensity. Results from simulation runs support the hypothesis that the use of ITS technologies at truck inspection facilities significantly reduces the frequency of experiencing the high-risk traffic phenomena (e.g., hard braking and truck-queue overflow). The postulation is made that the reduction in the frequency of high-risk phenomena will translate into a decrease in the likelihood of experiencing crash-related incidents in the vicinity of truck inspection facilities.


Author(s):  
Bingya Zhao ◽  
Ya Zhang

This paper studies the distributed secure estimation problem of sensor networks (SNs) in the presence of eavesdroppers. In an SN, sensors communicate with each other through digital communication channels, and the eavesdropper overhears the messages transmitted by the sensors over fading wiretap channels. The increasing transmission rate plays a positive role in the detectability of the network while playing a negative role in the secrecy. Two types of SNs under two cooperative filtering algorithms are considered. For networks with collectively observable nodes and the Kalman filtering algorithm, by studying the topological entropy of sensing measurements, a sufficient condition of distributed detectability and secrecy, under which there exists a code–decode strategy such that the sensors’ estimation errors are bounded while the eavesdropper’s error grows unbounded, is given. For collectively observable SNs under the consensus Kalman filtering algorithm, by studying the topological entropy of the sensors’ covariance matrices, a necessary condition of distributed detectability and secrecy is provided. A simulation example is given to illustrate the results.


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