Semidefinite programming for ad hoc wireless sensor network localization

By Pratik K. Biswas, Yinyu Ye. Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004, 2004

664 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/213d383684f3544b405afda353866702e9b86e97

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Citations in the corpus

Parameter Estimation of Statistical Models Using Convex Optimization (2010, methodology): “Following [3] and [24], to transform this into an SDP problem, we relax the rank-one condition to a positive semi definite condition for all Zk as follows:”
Euclidean Distance Geometry and Applications (2012, methodology): “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.”
A Theory of Network Localization (2006, methodology): “This worstcase result for sparse graphs stands in contrast to results t hat show that localization is possible for dense graphs [7] or with high probability for random geom etric graphs.”
Visualization of wormholes in sensor networks (2004, methodology): “Another approach [2] for sensor network localization applies semidefinite programming relaxation to minimize the errors for fitting the distance measurements.”
The non-convex geometry of low-rank matrix optimization (2016, background): “These expensive operations form the major computational bottleneck and prevent them from scaling to scenarios with millions of variables, a typical situation in a diverse range of applications, including quantum state tomography [27], user preferences prediction [20], and pairwise distances estimati”
Semidefinite programming based algorithms for sensor network localization (2006, methodology): “anchor nodes [Doherty et al. 2001; Niculescu and Nath 2001; Savarese et al. 2002; Savvides et al. 2001, 2002; Shang et al. 2004]; or employ a grid of beacon nodes with known positions [Bulusu et al. 2000; Howard et al. 2001]. Also see Moré and Wu [1997] for solving another distance geometry problem.”
Theory of semidefinite programming for Sensor Network Localization (2005, methodology): “√ The 2-norm of a vector x , denoted by (cid:2) x (cid:2) , is given by x • x .”
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.”
Semidefinite Programming Approaches for Sensor Network Localization With Noisy Distance Measurements (2006, background): “One way is to ignore the augmented dimensions and use the projection as a suboptimal solution, which is the case in [18].”
Wireless sensor network localization techniques (2007, methodology): “In [91] the distance-based sensor network localization problem is formulated in a quadratic form and solved using SDP; and in [92] the result in [91] is improved using a gradient search procedure to fine-tune the initial solution obtained using SDP.”
Towards IP geolocation using delay and topology measurements (2006, background): “Other systems such as Place Lab [13], Cricket [20] and RADAR [1] locate mobile hosts using 802.11 and GSM beacons with known locations.”
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].”
Scalable Localization with Mobility Prediction for Underwater Sensor Networks (2008, neutral): “been proposed [ 6 ]‐[9], [11], [13], [15], [18], [20]‐[24], [31], [33], [34].”