New Decentralized Positioning Schemes for Wireless Sensor Networks Based on Recursive Least-Squares Optimization

2014 ◽  
Vol 3 (1) ◽  
pp. 78-81 ◽  
Author(s):  
Chin-Liang Wang ◽  
Dong-Shing Wu ◽  
Kai-Jie Yang
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Least Squares ◽  
Recursive Least Squares ◽  
Wireless Sensor ◽  
Least Squares Optimization

scholarly journals A Low-Complexity Geometric Bilateration Method for Localization in Wireless Sensor Networks and Its Comparison with Least-Squares Methods

Sensors ◽  
2012 ◽  
Vol 12 (1) ◽  
pp. 839-862 ◽  
Author(s):  
Juan Cota-Ruiz ◽  
Jose-Gerardo Rosiles ◽  
Ernesto Sifuentes ◽  
Pablo Rivas-Perea
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Least Squares ◽  
Low Complexity ◽  
Wireless Sensor

A Distributed Least-Squares Algorithm in Wireless Sensor Networks With Unknown and Limited Communications

2017 ◽  
Vol 8 (3) ◽  
pp. 15-36 ◽  
Author(s):  
Jing Wang ◽  
In Soo Ahn ◽  
Yufeng Lu ◽  
Tianyu Yang ◽  
Gennady Staskevich
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Least Squares ◽  
Multiagent Systems ◽  
Information Exchange ◽  
Large Scale ◽  
Wireless Sensor ◽  
Left Eigenvector ◽  
Mild Assumption ◽  
Least Squares Algorithm

In this article, the authors propose a new distributed least-squares algorithm to address the sensor fusion problem in using wireless sensor networks (WSN) to monitor the behaviors of large-scale multiagent systems. Under a mild assumption on network observability, that is, each sensor can take the measurements of a limited number of agents but the complete multiagent systems are covered under the union of all sensors in the network, the proposed algorithm achieves the estimation consensus if local information exchange can be performed among sensors. The proposed distributed least-squares algorithm can handle the directed communication network by explicitly estimating the left eigenvector corresponding to the largest eigenvalue of the sensing/communication matrix. The convergence of the proposed algorithm is analyzed, and simulation results are provided to further illustrate its effectiveness.

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