1,721,134 research outputs found
Blind wireless network topology inference
Full text linkThis work proposes a framework to discover the topology of a non-collaborative packet-based wireless network using radio-frequency (RF) sensors. The methodology developed is blind, allowing topology sensing of a network whose key features (i.e., number of nodes, physical layer signals, and medium access control (MAC) and routing protocols) are unknown. Because of the wireless medium, over-the-air signals captured by the sensors are mixed; therefore, blind source separation (BSS) and measurement association are used to separate traffic patterns. Then, to infer the topology, we detect directed data flows among nodes by identifying causal relationships between the separated transmitted patterns. We propose causal inference methods such as Granger causality (GC), transfer entropy (TE), and conditional transfer entropy (CTE) that use the times series of traffic profiles, and a solution based on a neural network (NN) that exploits distilled time-based features. The framework is validated on an ad-hoc wireless network accounting for MAC protocol, packet collisions, nodes mobility, the spatial density of sensors, and channel impairments, such as path-loss, shadowing, and noise. Numerical results reveal that the proposed approach reaches a high probability of link detection and a moderate false alarm rate in mild shadowing regimes and low to moderate network nodes mobility
Machine learning for automatic processing of modal analysis in damage detection of bridges
Full text linkAutonomous structural health monitoring (SHM) of a large number of bridges became a topic of paramount importance for maintenance purposes and safety reasons. This article proposes a set of machine learning (ML) tools to perform automatic detection of anomalies in a bridge structure from vibrational data. As a case study, we considered the Z-24 bridge for which an extensive database of accelerometric data is available. The proposed framework starts from the stabilization diagram obtained through operational modal analysis (OMA) to perform the clustering of modal frequencies and their tracking by density-based time-domain filtering. The features extracted are then fed to a one-class classification (OCC) algorithm to perform anomaly detection. In particular, we propose two new anomaly detectors, namely, one-class classifier neural network (OCCNN) and OCCNN 2 , that find the normal class (the boundary of the features space in normal operating conditions) through a two-step approach: coarse and fine boundary estimate. The detection algorithms are then compared with known methods based on the principal component analysis (PCA), the kernel PCA (KPCA), the Gaussian mixture model (GMM), and the autoassociative neural network (ANN). The proposed OCCNN solution presents increased accuracy and F 1 score over conventional algorithms, without the need to set critical parameters, while OCCNN 2 provides the best performance in terms of F 1 score, accuracy, and responsiveness
Multiple radio transmitter localization via UAV-based mapping
Full text linkThe widespread use of unmanned aerial vehicles (UAVs) has opened a novel perspective on spectrum awareness and localization because of their ability to overcome spatial limits and the burden of dedicated infrastructure. In such scenarios, an underinvestigated problem is the simultaneous localization of multiple non-collaborative primary users (PUs) of the spectrum, whose number, transmit power, activity pattern, and signal structure are unknown. This work proposes a framework for multiple PU localization based on the received power measured by an antenna array mounted on a UAV. A score map is firstly constructed based on the measured power. Then two algorithms, k-means clustering and weighted centroid (KCWC), and Gaussian mixture model fitting (GMMF) are applied to the score map to estimate the number and the positions of the PUs. The performance is evaluated in terms of optimal subpattern assignment (OSPA) distance, compared with a genie-aided (GA) localization algorithm capable of separating the signals emitted by the PUs. Despite the blind nature of the method proposed, numerical results exhibit very good localization accuracy, with an OSPA distance below 6 m in large, line-of-sight (LOS)-dominated, outdoor scenarios with five PUs, even in the presence of channel impairments and UAV position and heading uncertainties
Limits on Sparse Data Acquisition: RIC Analysis of Finite Gaussian Matrices
Full text linkOne of the key issues in the acquisition of sparse data by means of compressed sensing (CS) is the design of the measurement matrix. Gaussian matrices have been proven to be information-theoretically optimal in terms of minimizing the required number of measurements for sparse recovery. In this paper we provide a new approach for the analysis of the restricted isometry constant (RIC) of finite dimensional Gaussian measurement matrices. The proposed method relies on the exact distributions of the extreme eigenvalues for Wishart matrices. First, we derive the probability that the restricted isometry property is satisfied for a given sufficient recovery condition on the RIC, and propose a probabilistic framework to study both the symmetric and asymmetric RICs. Then, we analyze the recovery of compressible signals in noise through the statistical characterization of stability and robustness. The presented framework determines limits on various sparse recovery algorithms for finite size problems. In particular, it provides a tight lower bound on the maximum sparsity order of the acquired data allowing signal recovery with a given target probability. Also, we derive simple approximations for the RICs based on the Tracy-Widom distribution
Dimensionality reduction in modal analysis for structural health monitoring
No full textAutonomous structural health monitoring (SHM) of many structures and bridges became a topic of paramount importance for maintenance purposes and safety reasons. This paper proposes a set of machine learning (ML) tools to perform automatic feature selection and detection of anomalies in a bridge from vibrational data and compare different feature extraction schemes to increase the accuracy and reduce the amount of data collected. As a case study, the Z-24 bridge is considered because of the extensive database of accelerometric data in both standard and damaged conditions. The proposed framework starts from the first four fundamental frequencies extracted through operational modal analysis (OMA) and clustering, followed by time-domain filtering (tracking). The fundamental frequencies extracted are then fed to a dimensionality reduction block implemented through two different approaches: feature selection (intelligent multiplexer) that tries to estimate the most reliable frequencies based on the evaluation of some statistical features (i.e., entropy, variance, kurtosis), and feature extraction (auto-associative neural network (ANN)) that combine the fundamental frequencies to extract new damage sensitive features in a low dimensional feature space. Finally, one-class classification (OCC) algorithms perform anomaly detection, trained with standard condition points, and tested with normal and anomaly ones. In particular, principal component analysis (PCA), kernel principal component analysis (KPCA), and autoassociative neural network (ANN) are presented and their performance are compared. It is also shown that, by evaluating the correct features, the anomaly can be detected with accuracy and an F1 score greater than 95%
Detection of Jamming Attacks via Source Separation and Causal Inference
Full text linkJamming attacks to hinder communication capabilities are becoming a critical aspect of wireless networks. A challenging issue is the detection of reactive jammers that perform spectrum sensing and attack the network only when legitimate communication is in progress. In this scenario, we introduce a novel framework for reactive jamming detection using a patrol of radio-frequency (RF) sensors external to the network to be protected. The solution relies on two key components: i) a novel underdetermined blind source separation (UBSS) method that, starting from the signal mixtures observed by the RF patrollers, is capable of separating the jamming temporal profile from the network nodes’ transmission profiles; ii) a new jamming detection based on causal inference called all-versus-one transfer entropy (AvOTE). The framework is then applied to a case study where the victim network is a Long Range (LoRa)-based internet of things (IoT) system with star topology. The solution outperforms a state-of-the-art method and an approach that attempts to find the causal relationship via time series correlation, exhibiting very good performance in the presence of shadowing. Indeed, a detection probability of 90% is achieved with a false alarm probability of 6% in the presence of nuisances such as collisions and severe shadowing
Distributed ‘ring-around’ sequential spectrum sensing for cognitive radio networks
No full textIn this paper we present a distributed spectrum
sensing technique based on a ring-formulation of the cognitive
radio (CR) nodes in a network. The CR nodes in a network form
a logical ring based on a particular criteria and distributes the
local spectrum sensing decisions along the ring in a sequential
manner to the successive CR nodes. Considering this method, we
eliminate the requirement for all the CR nodes to send/broadcast
its local decisions to all the other CR nodes as in the traditional
distributed detection method. Moreover, in our method all the CR
nodes in the ring will have the spectrum sensing information from
all the other nodes in the ring unlike the traditional sequential
distributed-sensing technique (without the ring formation). We
also consider the temporal behavior of the primary user modeled
as a Poisson-Pareto burst process, and present two distributed
sensing techniques based on the ‘ring-around’ strategy for the
energy based local detection method. We provide closed-form
solutions for the detection and false alarm probabilities for the
ring-around detection methods and present numerical results in
a scenario with Rayleigh fading and AWGN
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