scholarly journals An Enhanced-Time Difference of Arrival Technique for Estimating Mobile Station Position in Wireless Sensor Networks

2020 ◽  
Vol 175 (22) ◽  
pp. 5-11
Author(s):  
Adekunle A. Adeyelu ◽  
Onaji J. Onah ◽  
Iwuese J. Orban
2016 ◽  
Vol 12 (11) ◽  
pp. 80 ◽  
Author(s):  
Songbo Ji

<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>


2014 ◽  
Vol 599-601 ◽  
pp. 1478-1483
Author(s):  
Jing Xiong ◽  
Zhi Jing Liu ◽  
Guo Liang Tang

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.


2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Anwen Wang ◽  
Xianjia Meng ◽  
Lvju Wang ◽  
Xiang Ji ◽  
Hao Chen ◽  
...  

Wireless sensor networks as the base support for the Internet of things have been a large number of popularity and application. Such as intelligent agriculture, we have to use the sensor network to obtain the growing environment data of crops and others. However, the difficulty of power supply of wireless nodes has seriously hindered the application and development of Internet of things. In order to solve this problem, people use low-power sleep scheduling and other energy-saving methods on the nodes. Although these methods can prolong the working time of nodes, they will eventually become invalid because of the exhaustion of energy. The use of solar energy, wind energy, and wireless signals in the environment to obtain energy is another way to solve the energy problem of nodes. However, these methods are affected by weather, environment, and other factors, and they are unstable. Thus, the discontinuity work of the node is caused. In recent years, the development of wireless power transfer (WPT) has brought another solution to this problem. In this paper, a three-layer framework is proposed for mobile station data collection in rechargeable wireless sensor networks to keep the node running forever, named TLFW which includes the sensor layer, cluster head layer, and mobile station layer. And the framework can minimize the total energy consumption of the system. The simulation results show that the scheme can reduce the energy consumption of the entire system, compared with a Mobile Station in a Rechargeable Sensor Network (MSiRSN).


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