1,720,980 research outputs found
RSS-Based Localization of Multiple Radio Transmitters via Blind Source Separation
Full text linkThis letter proposes a methodology for counting and locating the nodes of an uncooperative wireless network using power measurements collected by sensors. The approach is blind, allowing the detection and localization of the nodes without knowing the network’s specific features (i.e., the number of nodes, modulation type, and medium access control (MAC)). Because the signals captured by the radio-frequency (RF) sensors are additively mixed, blind source separation (BSS) is used to separate transmitted power profiles. Then, received signal strength (RSS) is extracted from the reconstructed signals and localization is performed through conventional least square (LS) and maximum likelihood (ML) techniques. Numerical results reveal that the BSS-ML approach reaches a rather low localization error in mild shadowing regimes, even when the ratio between the number of RF sensors and nodes, ρ , is close to 1. Finally, it is shown how the performance degradation introduced by the imperfect BSS is slight and that the root mean square error (RMSE) approaches the Cramér-Rao lower bound (CRLB) when increasing ρ
One Class Classifier Neural Network for Anomaly Detection in Low Dimensional Feature Spaces
No full textIn the last decade, many approaches have been developed to solve one-class classification (OCC) problems for anomaly detection. Many of them rely on estimating the statistical distribution of the data, find hidden patterns, or remap the data in advantageous feature spaces. This kind of techniques usually needs some a priori knowledge of the data distribution (i.e., Gaussian) or the setting of some parameters to achieve good classification performance, making their use less effective when the data distribution is unknown. In this paper, we propose a novel blind anomaly detection for low dimensional feature spaces, that exploits the flexibility of the neural network (NN) structure to find the class boundaries without any information about the shape of the data distribution. To prove the generality of the solution, we tested many different class shapes, and we applied it to a structural health monitoring (SHM) case study. Without requiring the tuning of hyperparameters, the performance of the proposed algorithm overcomes that of some known approaches like principal component analysis (PCA), kernel principal component analysis (KPCA), Gaussian mixture model (GMM), and autoassociative neural network (ANN) in many cases, and performs well in the specific SHM setting
The impact of sensing parameters on data management and anomaly detection in structural health monitoring
Full text linkThe massive and autonomous structural health monitoring (SHM) of bridges is a problem that is of growing interest due to its importance and topicality. However, a considerable amount of data must be elaborated and managed in such an application. This paper proposes a set of machine learning (ML) tools to detect anomalies in a bridge from vibrational measurements using the minimum amount of data. The proposed framework starts from the fundamental frequencies extracted through operational modal analysis (OMA) and clustering, followed by a density-based time-domain tracking algorithm. The funda- mental frequencies extracted are then fed to one-class classification (OCC) algorithms that perform anomaly detection. Then, to reduce the amount of data, we analyze the effect of the number of sensors, the number of bits per sample, the observation time, and the measurement noise on damage detection performance. As a case study, the Z-24 bridge is considered because of the extensive database of accelerometric measurements in both standard and damaged conditions. A comparison of OCC algorithms, such as principal component analysis (PCA), kernel principal component analysis (KPCA), Gaussian mixture model (GMM) and one-class classifier neural network (OCCNN)2 is performed, and their robustness to data shrinking is evaluated. In many cases, OCCNN2 increases the performance with respect to classical anomaly detection techniques in terms of accuracy
Machine Learning for User Traffic Classification in Wireless Systems
Full text linkThe ability to answer all important questions about the radio-frequency (RF) scene is essential for cognitive radios (CRs) to be effective. In this paper, we propose a RF -based automatic traffic recognizer that, observing the radio spectrum emitted by a communication link and exploiting machine learning (ML) techniques, is able to distinguish between two types of data streams. Numerical results based on real waveforms collected by a RF sensor, demonstrate that over-the-air user traffic classification is possible with an accuracy of 97% at high signal-to-noise ratios (SNRs). Moreover, we show that using a neural network (NN) very good classification performance can be achieved also at low SNRs (around 2 dB). Finally, the impact of the observed RF bandwidth and the acquisition time window on the classification accuracy are analyzed in detail
Human activities classification using biaxial seismic sensors
No full textIn this letter, we propose a method for passive human activity classification exploiting ground vibrations observed by a biaxial geophone. The solution is grounded on the idea that some activities can be better analyzed by the horizontal channel (bicycle and car) and others by the vertical one (walk and run). Thus, the following two solutions are proposed: first, joint processing of the vertical and horizontal data by a single classifier and, second, cascade processing by two classifiers that analyze the two channels separately. Numerical results based on real data show that while a parametric method such as a support vector machine performs well in both cases, a nonparametric method such as the k-nearest neighbors reaches a higher accuracy in cascade processing. Besides, the results are compared with those obtained using a monoaxial geophone only
Anomaly Detection Using WiFi Signals of Opportunity
No full textDetection of changes in indoor areas and controlled environments is getting increasing interest in ambient intelligence and security. In this paper, we propose a radio-frequency (RF)- based anomaly detector that, observing the spectrum received from signals of opportunity (SoOp) and exploiting machine learning (ML) techniques, is capable of revealing changes in an indoor environment. Based on real waveforms emitted by a WiFi access point (AP) and collected by a RF sensor, we demonstrate that anomaly detection, e.g., represented by the presence of a person in the monitored area, is possible. The proposed methodology, tested in a typical office environment when the AP- sensor link is in non-line-of-sight (NLOS), achieves an accuracy greater than 95 % just by collecting few beacon packets, i.e., in dozens of milliseconds. Moreover, results demonstrate that the proposed approach outperforms a well-known received signal strength (RSS)-based solution in terms of accuracy, even using just a single sensor
Machine Learning for Wireless Network Topology Inference
No full textIn this work, we propose a new framework for blind wireless network topology inference and present a novel solution based on machine learning (ML) techniques. In particular, we seek to identify a causal relationship between the patterns of the radio-frequency (RF) transmissions of the nodes in the network from over-the-air signals observed by a cloud of sensors randomly deployed in the network landscape. The proposed framework is based on simple RF sensors that measure the received power at a rate sufficient to extract traffic patterns. Numerical results based on simulated data show how, despite the propagation impairments and noise may affect the performance of the algorithms, the neural network (NN)-based solution reaches 93% of accuracy even with a relatively low number of sensors
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