Localization at wireless sensor networks by time difference of arrival method

2010 ◽  
Author(s):  
Osman Ceylan ◽  
Deniz Sumbullu ◽  
H. Bulent Yagci
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Time Difference ◽  
Wireless Sensor ◽  
Time Difference Of Arrival

scholarly journals A Time Difference of Arrival-based Localization Algorithm for Wireless Sensor Networks

2016 ◽  
Vol 12 (11) ◽  
pp. 80 ◽  
Author(s):  
Songbo Ji
Keyword(s):  
Wireless Sensor Networks ◽  
Electromagnetic Wave ◽  
Sensor Networks ◽  
Large Scale ◽  
Time Difference ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Time Difference Of Arrival ◽  
Positioning Accuracy ◽  
Simulation Results

<p class="Abstract"><span lang="EN-US">Aimed at solving the problem of local divergence and low data accuracy, this paper introduces a new Time Difference of Arrival(TDOA)-based localization algorithm (TBL) for the large-scale, high-density wireless sensor networks which are designed for real-time surveillance and unexpected incidents management. In particular, several means to improve the accuracy of distance measurement are investigated, and the TDOA method, based on the sound wave and electromagnetic wave to locate in the large-scale WSN, is discussed. Also, the well-designed circular location process has the advantage of better positioning accuracy and coverage percentage. Simulation results have confirmed the effectiveness of the formed TBL algorithm.</span></p>

Detection and Tracking for Anomalous Motion Targets Using Wireless Sensor Networks

2014 ◽  
Vol 599-601 ◽  
pp. 1478-1483
Author(s):  
Jing Xiong ◽  
Zhi Jing Liu ◽  
Guo Liang Tang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Template Matching ◽  
Gaussian Mixture ◽  
Time Difference ◽  
Wireless Sensor ◽  
Feature Points ◽  
Swarm Optimization ◽  
Detection And Tracking ◽  
Surveillance Area

In this paper, we focus on the detection and relay tracking for anomalous motion targets, and present an automatic solution which combined the advantage of improved wireless sensor networks with single intelligent monitor. Particle Swarm Optimization (PSO) is used to deploy legitimately wireless sensor networks that consist of many static intelligent monitors and dynamic intelligent monitors, moving targets are extracted using Gaussian Mixture Model and the time difference method by each intelligent monitor, then each monitor judges the behavior of the motion target is legal or not by the new method couple with behavior template matching and feature points tracking. Monitors relay track the anomalous target considering the associated information and time difference between in and out to the surveillance area. The experiment results show that the solution has a better performance and less energy lost than other analogous methods.

Sign in / Sign up

Export Citation Format

Share Document