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distributed sequential algorithms for regional source localization

AUTOMATICA 2013年第1期49卷 178-185页

作者： Dandach, Sandra H. Bullo, Francesco United Technol Res Ctr E Hartford CT 06108 USA Univ Calif Santa Barbara Ctr Control Dynam Syst & Computat Santa Barbara CA 93106 USA

We study the problem of source localization as a multiple hypothesis testing problem, where each hypothesis corresponds to the event that the source belongs to a particular region. We use sequential hypothesis tests based on posterior computations to solve for the correct hypothesis. Measurements corrupted with noise are used to calculate conditional posteriors. We prove that the regional localization problem has asymptotic properties that allow correct detection almost surely in the limit of a large number of measurements. We present the Sense, Transmit & Test distributed algorithm that allows sequential sensing, communication and testing and we analyze the accuracy of this distributed algorithm and show that the test ends in a finite time. We also present numerical results illustrating properties of the suggested algorithm. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Regional localization distributed localization algorithms Sequential decision making Cooperative detection Sequential localization

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Monte Carlo localization for mobile wireless sensor networks

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AD HOC NETWORKS 2008年第5期6卷 718-733页

作者： Baggio, Aline Langendoen, Koen Delft Univ Technol NL-2600 GA Delft Netherlands

localization is crucial to many applications in wireless sensor networks. In this article, we propose a range-free anchor-based localization algorithm for mobile wireless sensor networks that builds upon the Monte Carlo localization algorithm. We concentrate on improving the localization accuracy and efficiency by making better use of the information a sensor node gathers and by drawing the necessary location samples faster. To do so, we constrain the area from which samples are drawn by building a box that covers the region where anchors' radio ranges overlap. This box is the region of the deployment area where the sensor node is localized. Simulation results show that localization accuracy is improved by a minimum of 4% and by a maximum of 73% (average 30%), for varying node speeds when considering nodes with knowledge of at least three anchors. The coverage is also strongly affected by speed and its improvement ranges from 3% to 55% (average 22%). Finally, the processing time is reduced by 93% for a similar localization accuracy. (C) 2007 Elsevier B.V. All rights reserved.

关键词： distributed localization algorithms Wireless sensor networks Mobility Monte Carlo localization Simulations

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An Asynchronous Consensus-Based Algorithm for Estimation From Noisy Relative Measurements

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IEEE TRANSACTIONS ON CONTROL OF NETWORK SYSTEMS 2014年第3期1卷 283-295页

作者： Carron, Andrea Todescato, Marco Carli, Ruggero Schenato, Luca Univ Padua Dept Informat Engn I-35131 Padua Italy

In this paper, we address the problem of optimal estimating the position of each agent in a network from relative noisy vectorial distances with its neighbors by means of only local communication and bounded complexity, independent of network size and topology. We propose a consensus-based algorithm with the use of local memory variables which allows asynchronous implementation, has guaranteed exponential convergence to the optimal solution under simple deterministic and randomized communication protocols, and requires minimal packet transmission. In the randomized scenario, we then study the rate of convergence in expectation of the estimation error and we argue that it can be used to obtain upper and lower bound for the rate of converge in mean square. In particular, we show that for regular graphs, such as Cayley, Ramanujan, and complete graphs, the convergence rate in expectation has the same asymptotic degradation of memoryless asynchronous consensus algorithms in terms of network size. In addition, we show that the asynchronous implementation is also robust to delays and communication failures. We finally complement the analytical results with some numerical simulations, comparing the proposed strategy with other algorithms which have been recently proposed in the literature.

关键词： Wireless-sensor networks (WSNs) distributed localization algorithms consensus algorithms

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Monte-carlo localization for mobile wireless sensor networks

Monte-carlo localization for mobile wireless sensor networks

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2nd International Conference on Mobile Ad-hoc and Sensor Networks

作者： Baggio, Aline Langendoen, Koen Delft Univ Technol NL-2600 AA Delft Netherlands

ISBN: (纸本)9783540499329

localization is crucial to many applications in wireless sensor networks. This article presents a range-free anchor-based localization algorithm for mobile wireless sensor networks that builds upon the Monte Carlo localization algorithm. We improve the localization accuracy and efficiency by making better use of the information a sensor node gathers and by drawing the necessary location samples faster. Namely, we constrain the area from which samples are drawn by building a box that covers the region where anchors' radio ranges overlap. Simulation results show that localization accuracy is improved by a minimum of 4% and by a maximum of 73%, on average 30%, for varying node speeds when considering nodes with knowledge of at least three anchors. The coverage is also strongly affected by speed and its improvement ranges from 3% to 55%, on average 22%. Finally, the processing time is reduced by 93% for a similar localization accuracy.

关键词： distributed localization algorithms wireless sensor networks mobility Monte Carlo localization simulations

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STRONG: Synchronous and asynchronous robust network localization, under non-Gaussian noise

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SIGNAL PROCESSING 2021年 185卷 108066-108066页

作者： Soares, Claudia Gomes, Joao NOVA Sch Sci & Technol Comp Sci Dept NOVA LINCS Caparica Portugal Univ Lisbon Inst Syst & Robot ISRIST Inst Super Tecn LARSyS Lisbon Portugal

Real-world network applications must cope with failing nodes, malicious attacks, or nodes facing corrupted data - data classified as outliers. Our work addresses these concerns in the scope of the sensor network localization problem where, despite the abundance of technical literature, prior research seldom considered outlier data. We propose robust, fast, and distributed network localization algorithms, resilient to high-power noise, but also precise under regular Gaussian noise. We use a Huber M-estimator, thus obtaining a robust (but nonconvex) optimization problem. We convexify and change the problem representation, to allow for distributed robust localization algorithms: a synchronous distributed method that has optimal convergence rate and an asynchronous one with proven convergence guarantees. A major highlight of our contribution lies on the fact that we pay no price for provable distributed computation neither in accuracy, nor in communication cost or convergence speed. Simulations showcase the superior performance of our algorithms, both in the presence of outliers and under regular Gaussian noise: our method exceeds the accuracy of alternative approaches, distributed and centralized, even under heavy additive and multiplicative outlier noise. (c) 2021 Elsevier B.V. All rights reserved.

关键词： distributed localization algorithms Robust estimation Huber function Convex relaxation Nonconvex optimization distributed iterative network localization Sensor networks

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