This work proposes a framework to discover the topology of non-collaborative packet-based wireless network using radio-frequency (RF) sensors. The methodology developed is blind, allowing sensing whose key features (i.e., number nodes, physical layer signals, and medium access control (MAC) routing protocols) are unknown. Because medium, over-the-air signals captured by sensors mixed; therefore, blind source separation (BSS) measurement association used separate traffic patterns. Then, infer...

The ability to answer all important questions about the radio-frequency (RF) scene is essential for cognitive radios (CRs) be effective. In this paper, we propose a RF -based automatic traffic recognizer that, observing radio spectrum emitted by communication link and exploiting machine learning (ML) techniques, able distinguish between two types of data streams. Numerical results based on real waveforms collected sensor, demonstrate that over-the-air user classification possible with an...

In this letter, we propose a method for passive human activity classification exploiting ground vibrations observed by biaxial geophone. The solution is grounded on the idea that some activities can be better analyzed horizontal channel (bicycle and car) others vertical one (walk run). Thus, following two solutions are proposed: first, joint processing of data single classifier and, second, cascade classifiers analyze channels separately. Numerical results based real show while parametric...

In precision farming, a very promising scenario is represented by connected and autonomous vehicle (CAV) moving in cultivated field collecting high-resolution videos hyperspectral images, requiring both localization broadband communication. An effective approach to provide wideband communication exploits unmanned aerial vehicles (UAVs) that may act as relays ensure seamless connectivity with base station (BS). this paper, we propose reinforcement learning (RL)-based algorithm find the best...

In this work, we propose a new framework for blind wireless network topology inference and present novel solution based on machine learning (ML) techniques. particular, seek to identify causal relationship between the patterns of radio-frequency (RF) transmissions nodes in from over-the-air signals observed by cloud sensors randomly deployed landscape. The proposed is simple RF that measure received power at rate sufficient extract traffic patterns. Numerical results simulated data show how,...

Detection of changes in indoor areas and controlled environments is getting increasing interest ambient intelligence security. In this paper, we propose a radio-frequency (RF)based anomaly detector that, observing the spectrum received from signals opportunity (SoOp) exploiting machine learning (ML) techniques, capable revealing an environment. Based on real waveforms emitted by WiFi access point (AP) collected RF sensor, demonstrate that detection, e.g., represented presence person...

Jamming attacks to hinder communication capabilities are becoming a critical aspect of wireless networks. A challenging issue is the detection reactive jammers that perform spectrum sensing and attack network only when legitimate in progress. In this scenario, we introduce novel framework for jamming using patrol radio-frequency (RF) sensors external be protected. The solution relies on two key components: i) underdetermined blind source separation (UBSS) method that, starting from signal...

In the last decade, many approaches have been developed to solve one-class classification (OCC) problems for anomaly detection. Many of them rely on estimating statistical distribution data, find hidden patterns, or remap data in advantageous feature spaces. This kind techniques usually needs some a priori knowledge (i.e., Gaussian) setting parameters achieve good performance, making their use less effective when is unknown. this paper, we propose novel blind detection low dimensional...

Abstract The massive and autonomous structural health monitoring (SHM) of bridges is a problem that growing interest due to its importance topicality. However, considerable amount data must be elaborated managed in such an application. This paper proposes set machine learning (ML) tools detect anomalies bridge from vibrational measurements using the minimum data. proposed framework starts fundamental frequencies extracted through operational modal analysis (OMA) clustering, followed by...

The development of novel tools to detect, classify and counteract the new generation smart jammers in Internet Things (IoT) is paramount importance. Detection classification have be performed a short time, with high reliability, preserving privacy network users. In this work, we propose machine learning (ML)-based jamming detection algorithm which can implemented gateway (GW). proposed method based on energy detector (ED), extraction specific problem-tailored features, dimensionality...

This 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 detection localization without knowing network’s specific features (i.e., number nodes, modulation type, medium access control (MAC)). Because signals captured radio-frequency (RF) sensors are additively mixed, blind source separation (BSS) used to separate transmitted profiles. Then, received signal...

Grant-free random access protocols are among the enabling techniques for mMTC, where a large number of devices activate sporadically and transmit short packets, typically containing preamble (or pilot sequence), without any resource allocation from BS. One critical tasks to be accomplished by BS is thus preamble-based detection transmitted packets. This letter proposes DL-based solution detecting preambles in an asynchronous grant-free uplink scenario, assuming multiple antennas at The...

In upcoming 6G networks, unmanned aerial vehicles (UAVs) are expected to play a fundamental role by acting as mobile base stations, particularly for demanding vehicle-to-everything (V2X) applications. this scenario, one of the most challenging problems is design trajectories multiple UAVs, cooperatively serving same area. Such joint trajectory can be performed using multi-agent deep reinforcement learning (MADRL) algorithms, but ensuring collision-free paths among UAVs becomes critical...

This letter introduces two innovative solutions for cooperative wideband spectrum sensing (WSS) that obviate the requirement prior knowledge of noise power at sensors and primary users (PUs) signals. The first method employs an information theoretic criteria (ITC)-based approach, presenting a threshold-free solution. second harnesses sensor cooperation through novel mixture detector based on meta-analysis, statistical combines results from multiple independent tests. To evaluate efficacy...

In upcoming 6G networks, unmanned aerial vehicles (UAVs) are expected to play a fundamental role by acting as mobile base stations, particularly for demanding vehicle-to-everything (V2X) applications. this scenario, one of the most challenging problems is design trajectories multiple UAVs, cooperatively serving same area. Such joint trajectory can be performed using multi-agent deep reinforcement learning (MADRL) algorithms, but ensuring collision-free paths among UAVs becomes critical...

In this paper, grant-free uplink communication from a large number of machine-type devices in CF-mMIMO networks is explored. A novel approach that leverages coded random access, on the device side, with combining signals received at properly selected AP and cooperative successive interference cancellation, network presented. Initially, an analytical framework based stochastic geometry developed to investigate performance cooperation through signal under diverse cluster compositions. The...

Cell-free massive MIMO (CF-mMIMO) networks have recently emerged as a promising solution to tackle the challenges arising from next-generation machine-type communications. In this paper, fully grant-free deep learning (DL)-based method for user activity detection in CF-mMIMO is proposed. Initially, known non-orthogonal pilot sequences are used estimate channel coefficients between each and access points. Then, convolutional neural network status of users. The proposed "blind", i.e., it...

This paper introduces a novel unsupervised jamming detection framework designed specifically for monostatic multiple-input multiple-output (MIMO)-orthogonal frequency-division multiplexing (OFDM) radar systems. The leverages echo signals captured at the base station (BS) and employs latent data representation learning capability of variational autoencoders (VAEs). VAE-based detector is trained on received from real target in absence jamming, enabling it to learn an optimal normal network...

The Industrial Internet of Things (IIoT) paradigm has emerged as a transformative force, revolutionizing industrial processes by integrating advanced wireless technologies into traditional procedures to enhance their efficiency. importance this shift produced massive, yet heterogeneous, proliferation scientific contributions. However, these works lack standardized and cohesive characterization the IIoT framework coming from different entities, like 3rd Generation Partnership Project (3GPP)...