Precise Distributed Localization Algorithms for Wireless Networks

Biaz, Saad; Ji, Yiming

doi:10.1109/wowmom.2005.80

Cited by 26 publications

(27 citation statements)

References 32 publications

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“…Table 1 defines the common parameters that we use in all four experiments. For convenience of demonstration, the values α (2) and α (3) are equal to α (1) . In this section, the normal nodes are updated in a random order to show the distributed ability of PPE.…”

Section: Experiments Results and Discussionmentioning

confidence: 99%

“…In fact, MDS is such a novel tool that many of its variations have been developed for node localization in WSNs. Iterative MDS (IT-MDS) and simulated annealing MDS (SA-MDS) discussed in [1] are two of these variations in which simulated annealing is a famous method imitating the metal cooling process to find the optimal state. Fastmap and MDS are combined in [4] to give a two-stage algorithm in which Fastmap provides the initial coarse input and MDS does the distributed gradient descent for more accuracy like in [2].…”

Section: Related Workmentioning

confidence: 99%

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Distributed Push–pull Estimation for node localization in wireless sensor networks

Dang

Lee

et al. 2011

Journal of Parallel and Distributed Computing

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a b s t r a c tA great deal of research achievements on localization in wireless sensor networks (WSNs) has been obtained in recent years. Nevertheless, its interesting challenges in terms of cost-reduction, accuracy improvement, scalability, and distributed ability design have led to the development of a new algorithm, the Push-pull Estimation (PPE). In this algorithm, the differences between measurements and current calculated distances are modeled into forces, dragging the nodes close to their actual positions. Based on very few known-location sensors or beacons, PPE can pervasively estimate the coordinates of many unknown-location sensors. Each unknown-location sensor, with given pair-wise distances, could independently estimate its own position through remarkably uncomplicated calculations. Characteristics of the algorithm are examined through analyses and simulations to demonstrate that it has advantages over those of previous works in dealing with the above challenges.

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Section: Experiments Results and Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Distributed Push–pull Estimation for node localization in wireless sensor networks

Dang

Lee

et al. 2011

Journal of Parallel and Distributed Computing

View full text Add to dashboard Cite

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“…It has its origins in psychometrics, where it was proposed to help understand people's judgments about the similarity between elements of a set of objects [3]. However, it has become a general data analysis technique used in a wide variety of fields such as marketing, sociology, physics, political science, biology and biomedical [7,6,20,27,20] and recently in wireless network sensors [16,28,19].…”

Section: Multidimensional Scaling Representation Of Complex Datamentioning

confidence: 99%

Analysis of financial data series using fractional Fourier transform and multidimensional scaling

Machado

Duarte

2010

Nonlinear Dyn

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The goal of this study is the analysis of the dynamical properties of financial data series from worldwide stock market indexes during the period 2000 − 2009. We analyze, under a regional criterium, ten main indexes at a daily time horizon. The methods and algorithms that have been explored for the description of dynamical phenomena become an effective background in the analysis of economical data. We start by applying the classical concepts of signal analysis, fractional Fourier transform, and methods of fractional calculus. In a second phase we adopt the multidimensional scaling approach. Stock market indexes are examples of complex interacting systems for which a huge amount of data exists. Therefore, these indexes, viewed from a different perspectives, lead to new classification patterns.

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“…These classic MDS approaches based on principal component analysis may not scale well with network size as its complexity is cubic in the number of sensors. A popular alternative to principal component analysis is the use of gradient descent or other numerical optimizations [8][9][10]. The weighted version of MDS in [11] utilized the weighted cost function to improve positioning accuracy.…”

Section: Introductionmentioning

confidence: 99%

“…It should be noted that the above studies [5][6][7][8][9][10][11][12][13][14][15][16][17] are based on line-of-sight (LOS) assumption which may lead to severe degradations since non-line-of-sight (NLOS) propagation is a main problem for accurate localization in actual WSNs system. In the cellular location system (CLS) and local positioning system (LPS), some localization methods and performance analyses for NLOS environment have been addressed in the literature [18][19][20][21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

CRLBs for WSNs localization in NLOS environment

Huang

Wang

Wan

2011

J Wireless Com Network

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Determination of Cramer-Rao lower bound (CRLB) as an optimality criterion for the problem of localization in wireless sensor networks (WSNs) is a very important issue. Currently, CRLBs have been derived for line-of-sight (LOS) situation in WSNs. However, one of major problems for accurate localization in WSNs is non-line-of-sight (NLOS) propagation. This article proposes two CRLBs for WSNs localization in NLOS environment. The proposed CRLBs consider both the cases that positions of reference devices (RDs) are perfectly or imperfectly known. Since non-parametric kernel method is used to build probability density function of NLOS errors, the proposed CRLBs are suitable for various distributions of NLOS errors. Moreover, the proposed CRLBs provide a unified presentation for both LOS and NLOS environments. Theoretical analysis also proves that the proposed CRLB for NLOS situation becomes the CRLB for LOS situation when NLOS errors go to 0, which gives a robust check for the proposed CRLB.

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Precise Distributed Localization Algorithms for Wireless Networks

Abstract: We propose in this paper reliable and precise distributed localization algorithms for wireless networks: iterative multidimensional scaling (IT-MDS)

Cited by 26 publications

References 32 publications

Distributed Push–pull Estimation for node localization in wireless sensor networks

Distributed Push–pull Estimation for node localization in wireless sensor networks

Analysis of financial data series using fractional Fourier transform and multidimensional scaling

CRLBs for WSNs localization in NLOS environment

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