22,111 research outputs found

    DECENTRALIZED AUTONOMOUS FAULT DETECTION IN WIRELESS STRUCTURAL HEALTH MONITORING SYSTEMS USING STRUCTURAL RESPONSE DATA

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    Sensor faults can affect the dependability and the accuracy of structural health monitoring (SHM) systems. Recent studies demonstrate that artificial neural networks can be used to detect sensor faults. In this paper, decentralized artificial neural networks (ANNs) are applied for autonomous sensor fault detection. On each sensor node of a wireless SHM system, an ANN is implemented to measure and to process structural response data. Structural response data is predicted by each sensor node based on correlations between adjacent sensor nodes and on redundancies inherent in the SHM system. Evaluating the deviations (or residuals) between measured and predicted data, sensor faults are autonomously detected by the wireless sensor nodes in a fully decentralized manner. A prototype SHM system implemented in this study, which is capable of decentralized autonomous sensor fault detection, is validated in laboratory experiments through simulated sensor faults. Several topologies and modes of operation of the embedded ANNs are investigated with respect to the dependability and the accuracy of the fault detection approach. In conclusion, the prototype SHM system is able to accurately detect sensor faults, demonstrating that neural networks, processing decentralized structural response data, facilitate autonomous fault detection, thus increasing the dependability and the accuracy of structural health monitoring systems

    Decentralized Detection in Realistic Sensor Networks

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    Tämä työ käsittelee kohteen ilmaisua sensoriverkolla, joka koostuu äänisensoreista. Työn pääpaino on epäideaalisen tilanteen käsittelyllä, jossa monet hajautettua ilmaisua käsittelevät oletukset, joita alan kirjallisuudessa tehdään, eivät enää päde. Sensoriverkko koostuu mielivaltaiseen verkkotopologiaan asetetuista sensoreista ja fuusiokeskuksesta, ja tavoite on ilmaista verkkoa lähestyvä kohde, joka tuottaa äänisignaalia. Tiedon käsittelyyn sensoreilla ja fuusiokeskuksella esitetään kaksi erilaista algoritmia. Toinen algoritmeista perustuu suurimman uskottavuuden menetelmään ja toinen on heuristinen, klassiseen ilmaisuteoriaan perustuva, lähestymistapa ongelmaan. Algoritmien suorituskykyä tutkitaan simulaatioiden avulla. Heuristisen algoritmin suorituskyky on huomattavasti parempi kaikissa simuloiduissa tilanteissa. Algoritmien johdossa taustakohina oletettiin normaalijakautuneeksi, mutta simulaatioiden perusteella algoritmit toimivat kohtuullisen hyvin myös pidempihäntäisen taustakohinajakauman tapauksessa. Heuristinen algoritmi tarjoaa paremman suorituskyvyn lisäksi myös helpomman tavan asettaa kynnysarvoparametrit niin, että sensoreilla ja fuusiokeskuksella on haluttu väärän hälytyksen todennäköisyys.This thesis discusses the detection of a target using a network of acoustic sensors. The focus of the work is on considering what to do in a non-ideal situation, where many of the assumptions often made in decentralized detection literature are no longer valid. The sensors and a fusion center are grouped in an arbitrary formation, and the object is to detect an approaching target which emits a sound signal. Two different schemes are considered for processing the data at sensors and the fusion center. One of the schemes is based on maximum likelihood estimation and the other one is a heuristic approach based on classical detection theory. The performances of the two schemes are studied in simulations. The heuristic scheme has a better detection performance for a given false alarm rate with all different sets of settings for the simulation. In derivation of the schemes, the background acoustic noise is assumed to be normal distributed, but, according to the simulations, the schemes still work relatively well under a long tailed noise distribution. In addition to better performance, the heuristic scheme offers easier setup of threshold values and approximation of false alarm rates for given thresholds using simple equations

    Rate Allocation for Decentralized Detection in Wireless Sensor Networks

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    We consider the problem of decentralized detection where peripheral nodes make noisy observations of a phenomenon and send quantized information about the phenomenon towards a fusion center over a sum-rate constrained multiple access channel. The fusion center then makes a decision about the state of the phenomenon based on the aggregate received data. Using the Chernoff information as a performance metric, Chamberland and Veeravalli previously studied the structure of optimal rate allocation strategies for this scenario under the assumption of an unlimited number of sensors. Our key contribution is to extend these result to the case where there is a constraint on the maximum number of active sensors. In particular, we find sufficient conditions under which the uniform rate allocation is an optimal strategy, and then numerically verify that these conditions are satisfied for some relevant sensor design rules under a Gaussian observation model.Comment: Accepted at SPAWC 201

    Decentralized Detection in Clustered Sensor Networks

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    DECENTRALIZED AUTONOMOUS FAULT DETECTION IN WIRELESS STRUCTURAL HEALTH MONITORING SYSTEMS USING STRUCTURAL RESPONSE DATA

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    Sensor faults can affect the dependability and the accuracy of structural health monitoring (SHM) systems. Recent studies demonstrate that artificial neural networks can be used to detect sensor faults. In this paper, decentralized artificial neural networks (ANNs) are applied for autonomous sensor fault detection. On each sensor node of a wireless SHM system, an ANN is implemented to measure and to process structural response data. Structural response data is predicted by each sensor node based on correlations between adjacent sensor nodes and on redundancies inherent in the SHM system. Evaluating the deviations (or residuals) between measured and predicted data, sensor faults are autonomously detected by the wireless sensor nodes in a fully decentralized manner. A prototype SHM system implemented in this study, which is capable of decentralized autonomous sensor fault detection, is validated in laboratory experiments through simulated sensor faults. Several topologies and modes of operation of the embedded ANNs are investigated with respect to the dependability and the accuracy of the fault detection approach. In conclusion, the prototype SHM system is able to accurately detect sensor faults, demonstrating that neural networks, processing decentralized structural response data, facilitate autonomous fault detection, thus increasing the dependability and the accuracy of structural health monitoring systems

    Decentralized detection in IEEE 802.15.4 wireless sensor networks

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    We present a mathematical model to study decentralized detection in clustered wireless sensor networks (WSNs). Sensors and fusion centers (FCs) are distributed with the aim of detecting an event of interest. Sensors are organized in clusters, with FCs acting as cluster heads, and are supposed to observe the same common binary phenomenon. A query-based application is accounted for; FCs periodically send queries and wait for replies coming from sensors. After reception of data, FCs perform data fusion with a majority-like fusion rule and send their decisions to an access point (AP), where a final data fusion is carried out and an estimate of the phenomenon is obtained. We assume that sensors are IEEE 802.15.4-compliant devices and use the medium access control (MAC) protocol defined by the standard, based on carrier-sense multiple access with collision avoidance. Decentralized detection and MAC issues are jointly investigated through analytical modelling. The proposed framework allows the derivation of the probability of decision error at the AP, when accounting for packets' losses due to possible collisions. Our results show that MAC losses strongly affect system performance. The impact of different clustering configurations and of noisy communications is also investigated

    Distributed Learning in Wireless Sensor Networks

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    The problem of distributed or decentralized detection and estimation in applications such as wireless sensor networks has often been considered in the framework of parametric models, in which strong assumptions are made about a statistical description of nature. In certain applications, such assumptions are warranted and systems designed from these models show promise. However, in other scenarios, prior knowledge is at best vague and translating such knowledge into a statistical model is undesirable. Applications such as these pave the way for a nonparametric study of distributed detection and estimation. In this paper, we review recent work of the authors in which some elementary models for distributed learning are considered. These models are in the spirit of classical work in nonparametric statistics and are applicable to wireless sensor networks.Comment: Published in the Proceedings of the 42nd Annual Allerton Conference on Communication, Control and Computing, University of Illinois, 200
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