A Distributed and Maximum-Likelihood Sensor Network Localization Algorithm Based Upon a Nonconvex Problem Formulation

2015 ◽  
Vol 1 (4) ◽  
pp. 247-258 ◽  
Author(s):  
Tomaso Erseghe
Keyword(s):  
Maximum Likelihood ◽  
Sensor Network ◽  
Problem Formulation ◽  
Localization Algorithm ◽  
Sensor Network Localization ◽  
Network Localization ◽  
Nonconvex Problem

scholarly journals An RSSI-based Wireless Sensor Network Localization Algorithm with Error Checking and Correction

2017 ◽  
Vol 13 (12) ◽  
pp. 52 ◽  
Author(s):  
Bo Guan ◽  
Xin Li
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Sensor Network Localization ◽  
Anchor Node ◽  
Network Localization ◽  
Error Checking

<p style="margin: 1em 0px;"><span style="font-family: Times New Roman; font-size: medium;">This paper studies the wireless sensor network localization algorithm based on the received signal strength indicator (RSSI) in detail. Considering the large errors in ranging and localization of nodes made by the algorithm, this paper corrects and compensates the errors of the algorithm to improve the coordinate accuracy of the node. The improved node localization algorithm performs error checking and correction on the anchor node and the node to be measured, respectively so as to make the received signal strength value of the node to be measured closer to the real value. It corrects the weighting factor by using the measured distance between communication nodes to make the coordinate of the node to be measured more accurate. Then, it calculates the mean deviation of localization based on the anchor node close to the node to be measured and compensates the coordinate error. Through the simulation experiment, it is found that the new localization algorithm with error checking and correction proposed in this paper improves the localization accuracy by 5%-6% compared with the weighted centroid algorithm based on RSSI.</span></p>

scholarly journals Wireless Sensor Network Localization Based On A Mobile Beacon And Tsvm

2014 ◽  
Vol 14 (5) ◽  
pp. 98-107 ◽  
Author(s):  
Jiang Xu ◽  
Huanyan Qian ◽  
Huan Dai ◽  
Jianxin Zhu
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Sensor Nodes ◽  
Wireless Sensor ◽  
Support Vector ◽  
Localization Algorithm ◽  
Sensor Network Localization ◽  
Network Localization ◽  
Gps Devices ◽  
Mobile Beacon

Abstract In this paper a new wireless sensor network localization algorithm, based on a mobile beacon and TSVM (Transductive Support Vector Machines) is proposed, which is referred to as MTSVM. The new algorithm takes advantage of a mobile beacon to generate virtual beacon nodes and then utilizes the beacon vector produced by the communication between the nodes to transform the problem of localization into one of classification. TSVM helps to minimize the error of classification of unknown fixed nodes (unlabeled samples). An auxiliary mobile beacon is designed to save the large volumes of expensive sensor nodes with GPS devices. As shown by the simulation test, the algorithm achieves good localization performance.

Wireless Sensor Network Localization Algorithm Based on Hop-Count and Distributed Learning

2014 ◽  
Vol 940 ◽  
pp. 457-460
Author(s):  
Ying Zhang ◽  
Yi Wang ◽  
Ying Ze Ye
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Distributed Learning ◽  
Training Data ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Hop Count ◽  
Sensor Network Localization ◽  
Network Localization ◽  
Positioning Method

The wireless sensor network localization algorithm in this paper combines hop-count information and distributed learning. The network is classified into many classes based on sensors’ location, and then the class that each sensor falls into is specified. There are a certain number of beacon nodes with position coordinate in network, and they use their own locations as training data in performing above classification. This positioning method merely uses the partial hop-count information between target sensor and reference node in specifying the class of each node. The final simulation experiment will analyze the excellent performance of this method under different system parameters.

A Research of TOA Wireless Sensor Network Localization Algorithm

2013 ◽  
Vol 791-793 ◽  
pp. 1601-1604
Author(s):  
Yan Yan Li ◽  
Zhi Hong Qian ◽  
Shi Qiang Zhao ◽  
Tian Ping Li ◽  
Shuang Zhu
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Sensor Network Localization ◽  
Network Localization ◽  
Locating Error ◽  
Positioning Algorithm ◽  
Research Hotspots

Wireless sensor network positioning technology has become one of research hotspots today. This paper mainly studies the TOA positioning algorithm based on WSN. The number of beacon nodes used to locate unknown nodes is discussed. There are four situations for the number of beacon nodes n=3, 4, 5, 7 that are analyzed via simulation, also locating error. We get the optimum value of n via simulations.

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