Color filtering localization for three-dimensional underwater acoustic sensor networks

Accurate localization of mobile nodes has been an important and fundamental problem in underwater acoustic sensor networks (UASNs). The detection information returned from a mobile node is meaningful only if its location is known. In this paper, we propose two localization algorithms based on color...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2015-03, Vol.15 (3), p.6009-6032
Main Authors: Liu, Zhihua, Gao, Han, Wang, Wuling, Chang, Shuai, Chen, Jiaxing
Format: Article
Language:English
Subjects:
Acoustics
Algorithms
Anchors
color filtering
Communication
Filtering
Global positioning systems
GPS
hierarchical structure
Localization
Networks
Position (location)
Red Green Blue (RGB)
self-localization
Sensors
Tasks
underwater acoustic sensor networks (UASNs)
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Summary:Accurate localization of mobile nodes has been an important and fundamental problem in underwater acoustic sensor networks (UASNs). The detection information returned from a mobile node is meaningful only if its location is known. In this paper, we propose two localization algorithms based on color filtering technology called PCFL and ACFL. PCFL and ACFL aim at collaboratively accomplishing accurate localization of underwater mobile nodes with minimum energy expenditure. They both adopt the overlapping signal region of task anchors which can communicate with the mobile node directly as the current sampling area. PCFL employs the projected distances between each of the task projections and the mobile node, while ACFL adopts the direct distance between each of the task anchors and the mobile node. The proportion factor of distance is also proposed to weight the RGB values. By comparing the nearness degrees of the RGB sequences between the samples and the mobile node, samples can be filtered out. The normalized nearness degrees are considered as the weighted standards to calculate the coordinates of the mobile nodes. The simulation results show that the proposed methods have excellent localization performance and can localize the mobile node in a timely way. The average localization error of PCFL is decreased by about 30.4% compared to the AFLA method.
ISSN:1424-8220
1424-8220
DOI:10.3390/s150306009