Semidefinite programming based algorithms for sensor network localization

By Pratik K. Biswas, Tzu-Chen Liang, Ta-Chung Wang, Yinyu Ye. TOSN, 2006

601 citations recorded by the source corpus.

Subfields: Computational Mechanics, Electrical and Electronic Engineering, Numerical Analysis

View Paper: https://www.semanticscholar.org/paper/f8fdae5e2e778314d602cd45dfb73862d983910b

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In Computational Mechanics

In Electrical and Electronic Engineering

In Numerical Analysis

Citations in the corpus

Matrix estimation by Universal Singular Value Thresholding (2012, background): “Completing a distance matrix with missing entries has been a popular problem in the engineering and social sciences for a long time; see, for example, [6, 11, 18, 89, 90, 92].”
The Power of Convex Relaxation: Near-Optimal Matrix Completion (2009, background): “, of sensors in a network—from partial distance information [5], [22], [23], remote sensing applications in signal processing where we would like to infer a full covariance matrix from partially observed correlations [26], and many statistical problems involving succinct factor models.”
Matrix Completion With Noise (2009, background): “Finding the global positioning of points in Euclidean space from a local or partial set of pairwise distances is a problem in geometry that emerges naturally in sensor networks [6], [28], [29].”
1-Bit Matrix Completion (2012, background): “Matrix completion arises in a wide variety of practical contexts, including collaborative filtering [28], system identification [45], sensor localization [7,59,60], rank aggregation [26] and many more.”
A survey on sensor localization (2010, background): “problem is formulated in [ 31 ] to improve the localization performance by relaxing the constraint from equality to inequality.”
Semidefinite Programming Approaches for Sensor Network Localization With Noisy Distance Measurements (2006, background): “The results for the SDP model (1) and the results from [17] where detailed results of different SDP models without regularization are discussed will serve as comparisons against which the effectiveness of the regularization technique can be gauged.”
A Theoretical Foundation of Network Localization and Navigation (2018, background): “Such a process is highly complex and inefficient especially in medium- to large-scale networks, because joint estimation requires high-dimensional inference and incurs large communication overhead [165]–[167].”
Convex Optimization & Euclidean Distance Geometry (2004, background): “[33] [32] Their heuristics far outperform Biswas’ localizations both in execution-time and proximity to the desired result.”
FedLoc: Federated Learning Framework for Data-Driven Cooperative Localization and Location Data Processing (2020, background): “Representative works include [24, 25, 26, 27, 28, 29], to mention a few.”
Weiszfeld’s Method: Old and New Results (2014, background): “A possible modelling of the problem (see e.g., Biswas et al [51]) is via the minimization problem: min x1,x2,...,xn f (x1,x2, . . . ,xn) ≡ ∑ (j,t)∈M (‖xj − xt‖ − djt)2 + ∑ (j,i)∈N (‖xj − ai‖ − wji)2 .”
An alternating direction algorithm for matrix completion with nonnegative factors (2011, background): “Netflix to infer individual preference from an incomplete set of user preferences [13], global positioning, which discovers the positions of nodes in a network from incomplete pair-wise distances [3], system identification and order reduction, which recovers or reduces the dimension of the state vec”
An Overview of Low-Rank Matrix Recovery From Incomplete Observations (2016, background): “To name just a few examples, we commonly encounter low-rank matrices in contexts as varied as: • ensembles of signals: the output of a sensor array or network, a collection of video frames, or a sequence of segments of a longer signal can often be highly correlated and represented using a low-rank m”
Approximation accuracy, gradient methods, and error bound for structured convex optimization (2010, background): “How well does (10) approximate (9)? A fourth problem of much recent interest is that of ad hoc wireless sensor network localization [4,18,19,21,22,31,38,39,49,73,75,128].”
Euclidean Distance Geometry and Applications (2012, background): “Biswas and Ye proposed in [27] an SDP formulation of the WSNL problem which then gave rise to a series of papers [23, 28, 24, 22, 25, 26] focusing on algorithmic exploitations of their formulation.”