300 research outputs found
Color Filtering Localization for Three-Dimensional Underwater Acoustic Sensor Networks
Accurate localization for 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. Also the proportion factor of
distance is 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. And the normalized nearness degrees are considered as the
weighted standards to calculate coordinates of the mobile nodes. The simulation
results show that the proposed methods have excellent localization performance
and can timely localize the mobile node. The average localization error of PCFL
can decline by about 30.4% than the AFLA method.Comment: 18 pages, 11 figures, 2 table
Toward Collinearity-Avoidable Localization for Wireless Sensor Network
In accordance with the collinearity problem during computation caused by the beacon nodes used for location estimation which are close to be in the same line or same plane, two solutions are proposed in this paper: the geometric analytical localization algorithm based on positioning units and the localization algorithm based on the multivariate analysis method. The geometric analytical localization algorithm based on positioning units analyzes the topology quality of positioning units used to estimate location and provides quantitative criteria based on that; the localization algorithm based on the multivariate analysis method uses the multivariate analysis method to filter and integrate the beacon nodes coordinate matrixes during the process of location estimation. Both methods can avoid low estimation accuracy and instability caused by multicollinearity
Two-range connectivity-based sensor network localization
This paper presents a new connectivity-based sensor network localization method. The novelty is on a two-level range/indication of connectivity between each pair of nodes. The method relies on the information of the connectivity which is either strong, weak or zero. Research results in this paper have shown that such sensor node information can give better accuracy in localization. We have also obtained a suitable setting for the two-level ranges. Modified algorithms based on MDS, DV-hop and SDP have also been developed with the 2-level range. The simulation results show that the 2-level range-based MDS, DV-hop and SDP are more accurate than the usual 1-level range connectivity-based method. © 2011 IEEE.published_or_final_versionThe 2011 IEEE Pacific Rim Conference on Communications, Computers and Signal Processing (PacRim), Victoria, B.C., 23-26 August 2011. In IEEE PacRim Conference Proceedings, 2011, p. 220-22
Underwater acoustic sensor networks node localization based on compressive sensing in water hydrology
Peer reviewedPublisher PD
- …