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Evaluating positioning estimation using LSE, gradient descent, and particle swarm optimization with dynamic inertia weight in wireless sensor network.

Authors :
Sagala, Albert
Lubis, Ramot
Turnip, Mardi
Source :
AIP Conference Proceedings; 2023, Vol. 2865 Issue 1, p1-11, 11p
Publication Year :
2023

Abstract

Knowing the position of the interesting object has gotten much attention from many researchers due to the opportunity to develop myriad applications. In this paper, we will integrate all the processes required as a process that can be used to estimate the unknown object's position. First, we measured a real-time signal strength (SS) received by the unknown node simultaneously from three anchor nodes. We use the Tmote sky node as transceivers. Second, we estimate the distance of an unknown node from the anchor nodes based on the path loss model; third, we use the linear least square estimation, gradient descent (GD), and particle swarm optimization (PSO) to estimate four different unknown node position (P1, P2, P3, and P4). We found that all algorithms are sufficient to estimate the node position even with some error in the distance estimation obtained except that linear least square got the worst position estimation. A linear LSE does not minimize the error of unknown node position estimation from all anchor nodes. We found that the PSO algorithm is better than GD algorithms regarding time execution and gives an opportunity to be implemented in real-time hardware execution. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
0094243X
Volume :
2865
Issue :
1
Database :
Complementary Index
Journal :
AIP Conference Proceedings
Publication Type :
Conference
Accession number :
174492422
Full Text :
https://doi.org/10.1063/5.0183305