In this paper we consider authentication at the physical layer, in which the authenticator aims at distinguishing a legitimate supplicant from an attacker on the basis of the characteristics of a set of parallel wireless channels, which are affected by time-varying fading. Moreover, the attacker’s channel has a spatial correlation with the supplicant’s one. In this setting, we assess and compare the performance achieved by different approaches under different channel conditions. We first consider the use of two different statistical decision methods, and we prove that using a large number of references (in the form of channel estimates) affected by different levels of time-varying fading is not beneficial from a security point of view. We then consider classification methods based on machine learning. In order to face the worst case scenario of an authenticator provided with no forged messages during training, we consider one-class classifiers. When instead the training set includes some forged messages, we resort to more conventional binary classifiers, considering the cases in which such messages are either labelled or not. For the latter case, we exploit clustering algorithms to label the training set. The performance of both nearest neighbor (NN) and support vector machine (SVM) classification techniques is evaluated. Through numerical examples, we show that under the same probability of false alarm, one-class classification (OCC) algorithms achieve the lowest probability of missed detection when a small spatial correlation exists between the main channel and the adversary one, while statistical methods are advantageous when the spatial correlation between the two channels is large.

The Human Activity Recognition is a focal point for Ambient Assisted Living, and may be implemented in several ways usually involving the use of different technologies, as wearable, video, environmental or radio frequency sensors, which can be used alone or in combination among them. Recently, the approaches based on machine learning have attracted a lot of interest, especially in order to create recognition systems that do not require a high detection capacity by the single sensor, as they base their decision on the processing of the information acquired from multiple sensors simultaneously. The aim of the present work is to derive information about the activities that are carried out inside the house on the basis of the data acquired by a set of sensors analyzing the air components. The Human Activity Recognition is then the result of a machine learning classification of the output of an array of low cost “commercial off-the-shelf” air quality sensors. The considered recognition system exploits electrochemical sensing, Wi-Fi technology, cloud computing, machine learning and application services. The obtained results evidence that a good accuracy in the recognition of “activities of daily living” is reached, even if a not calibrated sensing was performed.

Automotive radars are able to guarantee high performances at the expenses of a relatively low cost, and recently their application has been extended to several fields in addition to the original one. In this paper we consider the use of this kind of radars to discriminate different types of people’s movements in a real context. To this end, we exploit two different maps obtained from radar, that is, a spectrogram and a range-Doppler map. Through the application of dimensionality reduction methods, such as principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) algorithm, and the use of machine learning techniques we prove that is possible to classify with a very good precision people’s way of walking even employing commercial devices specifically designed for other purposes.

We consider the problem of downloading content from a cellular network that is cached at the wireless edge while achieving privacy. In particular, we consider private information retrieval (PIR) of content from a library of ?les, i.e., the user wishes to download a ?le and does not want the network to learn any information about which ?le she is interested in. To reduce the backhaul usage, content is cached at the wireless edge in a number of small-cell base stations (SBSs) using maximum distance separable codes. We propose a PIR scheme based on generalized Reed-Solomon codes for this scenario that achieves privacy against a number of spy SBSs that collaborate. The proposed PIR scheme is an extension of a scheme by Kumar et al. to the case of multiple code rates, suitable for the scenario where ?les have different popularities. We derive the backhaul rate and optimize the content placement to minimize it. We prove that uniform content placement is optimal, i.e., all ?les that are cached should be stored using the same code rate. This is in contrast to the case where no PIR is required. Furthermore, we show numerically that popular content placement is optimal for some scenarios.

The growing number of civil applications in which Unmanned Aerial Vehicles (UAVs) are involved can create many concerns for airspace security and surveillance. Gathering as much information as possible about a drone can be crucial to apply proper countermeasures if a potentially dangerous situation is detected. Of course, the presence of a UAV can be detected by radar, but it is possible to extend the system capabilities to obtain additional information. For example, in the case in which the UAV is equipped with propellers, the radar-measured rotational speed could be important information to classify the type of UAV or to reveal if it is carrying some possibly harmful payload. In addition, the rotational speed measured through radar could be used for different purposes, such as to detect a drone manumission, to estimate its maximum payload, or for predictive maintenance of the drone. Measuring the propellers’ rotational speed with radar systems is a critical task, as the Doppler generated by the rotation can be very high, and it is very difficult to find commercial radar systems in the market able to handle such a high Doppler. Another problem is caused by the typically very small Radar Cross-Section (RCS) of the propellers, which makes their detection even more difficult. In the literature, common detection techniques are based on the measurement of the Doppler effect produced by the propellers to derive their rotational speed, but due to the very limited capabilities of commercial sensors, this approach can be applied only at very low values of the rotational speed. In this work, a different approach based on a Frequency-Modulated Continuous Wave (FMCW) radar is proposed, which exploits the vibration of the UAV generated by the rotation of the propellers. The phenomenon and how the sensor can detect it will be presented, which is joined with a performance analysis comparing different estimation techniques for the indirect measurement of the propellers’ speed to evaluate the potential benefits of the proposed approach.

Radar interferometry is a technique able to measure the displacement of a target through an electromagnetic signal. Such a possibility can be of interest in different application fields, from civil infrastructure monitoring to human vital signals extraction. In recent years, Frequency Modulated Continuous Wave (FMCW) Radars have raised huge interest and become popular in the automotive industry, as key sensing elements for detection and tracking in Advanced Driver Assistance Systems (ADAS). As they get more widespread and less expensive, it is interesting to study the performance attainable by FMCW automotive Radars in measuring the vibration displacement of a target, through interferometry. A powerful feature to exploit in these sensors is the Multiple Input Multiple Output (MIMO), which gives the opportunity to reveal not only the distance or the velocity of a target but also its Angle of Arrival (AoA). This can be also exploited to measure the displacement of many targets at different angles. In this work, the performance of a mmWave automotive MIMO FMCW Radar in estimating the displacement of a vibrating target is experimentally evaluated by comparing the results provided by the Radar with a laser Doppler Vibrometer. Experiments are performed at different frequency and amplitude values of the target vibration, and with different Radar configurations. The angle and the distance of the target are changed to obtain a deeper understanding of how these parameters influence the results. The proposed performance evaluation procedure is inspired by ISO 16063-11 used as a reference standard, and the results obtained show that the Radar bandwidth does not affect the displacement estimation results, unlike target range distance and angle of arrival.