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Remotely piloted vehicles have become more and more popular in recent years due to their increased availability on the market. In general, drones can represent a threat for public safety due to their potential misuse. This motivation gives rise to the need of devising specific methods and techniques to monitor sensitive areas, in order to deploy effective drone surveillance systems. These systems are designed to detect nearby undesired targets, and to neutralize impending threats. One of the main tasks involved in this scenarios is target localization, which can be performed with different approaches. In particular, in this article we carry out a study on the main literature works related to the framework of wireless target localization based on RF and WiFi techniques, providing a thorough analysis on the perspective of their deployment in drone surveillance applications.

The great availability of commercial drones has raised growing interest among people, since remotely piloted vehicles can be employed in numerous applications. The pervasive use of these devices has created many privacy and safety concerns that need to be addressed by means of proper surveillance systems able to cope with such threats. In this paper, we propose a WiFi statistical fingerprint-based drone detection approach, which is capable of identifying nearby drone threats, even in the presence of malicious attacks. We present a performance analysis carried out through experimental tests, where our solution is able to achieve very good results in terms of correct recognitions in many real-life scenarios, with a peak true positive rate of ${\text{96}}$ %.

Amateur drones are enjoying great popularity in recent years due to the wide commercial diffusion of small, rather low-cost devices. More and more user-friendly, easy-to-pilot aerial and terrestrial drones are available off the shelf, and people can even remotely pilot them using their smartphones. This situation brings up the problem of keeping unauthorized drones away from private or sensitive areas, where they can represent a personal or public threat. With this motivation, after a survey of the existing solutions, we propose a WiFi-based approach aimed at detecting nearby aerial or terrestrial devices by performing statistical fingerprint analysis on wireless traffic. This novel detection technique, tested in a variety of real-life scenarios, proved able to efficiently detect and identify intruder drones in all the considered experimental setups, making it a promising unmanned aerial vehicle detection approach in the framework of amateur drone surveillance.

The paper describes an original architecture aimed at tracking assets within construction sites. The main components are Radio Frequency IDentification (RFID), Bluetooth Low Energy (BLE) tags and smartphones. The core functions are performed by two Android applications, which implement asset tracking and searching. The main merits of the architecture are its ability to maximize smartphone battery lifetime, that can reach an entire working shift, very satisfactory accuracy of BLE tag-smartphone distance estimation (with a mean error around 2 m), high probability of detecting all the tags present in the construction site, as well as a suitably short Aging time.

In recent years, the emerging framework of the Internet of Things has been leading the technological landscape in a number of different fields and applications, from autonomous and connected vehicles to wearable devices. The healthcare system is benefiting from this continuously evolving environment since it leverages the opportunities offered by the ubiquitous and pervasive presence of connected objects and smart services. This attitude has given rise to the concept of eHealth, thus enabling new approaches and solutions for healthcare. In this framework we propose SmartPants, an IoT-based wireless system specifically designed for the remote rehabilitation of lower limbs in poststroke patients. The platform consists of multiple nodes used to monitor physical therapy and a software platform that provides real-time feedback on the execution by recognizing the type of exercise currently being performed by the patient. Our experimental results, evaluated through appropriate metrics, show that the proposed movement recognition algorithm provides very good results in terms of classification performance, independent of the considered classifier, with an average true positive rate of about 91 percent and an overall accuracy of around 96.5 percent.

In the latest years, the popularity of commercial drones has grown rapidly due to their cheaper costs and great availability on the market. The great diffusion of remotely piloted devices unfortunately leads to several security and safety concerns that need to be tackled. In this paper, we consider a fingerprint-based drone detection approach relying on the analysis of WiFi traffic features to identify the presence of unauthorized devices. In particular, we study the statistical distribution of the features composing the fingerprint vector, and we propose an adversarial approach to drone detection in order to invalidate the reliability of the surveillance system, by introducing fake ad-hoc traffic features. Results show that our novel approach is able to deceive the drone detection system through the introduction of flows belonging to arbitrary traffic classes. Also, the proposed adversarial method provides the expected significant impact on the performance of the system, actually reducing the recognition accuracy to about 50%.

The increasing popularity of low-cost aerial vehicles that can be remotely piloted by amateurs, is giving rise to a number of issues related to public safety and privacy. At the same time, the need for surveillance methods able to detect the presence of unauthorized drones meets the ubiquitous connectivity and pervasive technology typical of the big data era. This dualism drives the research community towards the design of monitoring systems able to integrate machine learning methods and data mining techniques in this evolving environment. In this framework, we consider a WiFi based approach aimed at detecting nearby unmanned aerial vehicles, by performing statistical fingerprint analysis on wireless traffic. We study the inherent vulnerabilities of the considered method through real-life experimental tests by setting up specific attack scenarios, and we devise and test a solution in order to improve the efficiency of the proposed technique in the presence of malicious countermeasures. Results show that the proposed improved detection technique is indeed robust to stealth attacks, and it is able to achieve very good recognition performance in different real-life testing scenarios.

This paper proposes a system that allows recognizing a person's emotional state starting from audio signal registrations. The provided solution is aimed at improving the interaction among humans and computers, thus allowing effective human-computer intelligent interaction. The system is able to recognize six emotions(anger, boredom, disgust, fear, happiness, and sadness) and the neutral state. This set of emotional states is widely used for emotion recognition purposes. It also distinguishes a single emotion versus all the other possible ones, as proven in the proposed numerical results. The system is composed of two subsystems: 1) gender recognition(GR) and 2) emotion recognition(ER). The experimental analysis shows the performance in terms of accuracy of the proposed ER system. The results highlight that the a priori knowledge of the speaker's gender allows a performance increase. The obtained results show also that the features selection adoption assures a satisfying recognition rate and allows reducing the employed features. Future developments of the proposed solution may include the implementation of this system over mobile devices such as smartphones.

Nowadays, the widespread use of Mobile Devices (MDs) open the door to exploit the presence of multiple nodes to accomplish collaborative tasks. In this paper, a speaker recognition system for MDs based on a multiple-observations approach is presented. We propose different fusion and clustering algorithms aimed at efficiently exploiting signals coming from multiple sensors. Numerical results show that in most cases our multiple-observations approach is able to significantly improve the performance of a single- receiver approach.

This paper presents a new asset tracking system that integrates a classical tracking solution with the Bluetooth Low Energy (BLE) technology and the opportunities offered by smartphones. The proposed platform has been explicitly designed and implemented to be employed in construction sites and was tested in a real environment. The solution aims at guaranteeing a two-fold requirement: i) a good level of precision in terms of accuracy of the position of the asset location and completeness of the acquired information (i.e., detection of all the tagged assets) and, at the same time, ii) saving smartphones' resources such as CPU, memory and, in particular, energy.

This paper focuses on countering data hiding in images. Specifically, we refer to the active warden problem, a process which aims at disrupting the covert communications that are possibly taking place within host media. Although this has been extensively studied in the literature, typical state of the art approaches treat the hidden message as a noise-like component, proposing de- noising techniques, lossy compression or noise addition as a countermeasure. In this paper we present novel approach called Lesser Components Distortion (LCD) that aims at disrupting the covert communication while minimizing distortion on the host medium. Although stemming from insights gained from notable works in the spread spectrum steganography field, we prove through extensive testing that it shows remarkable effectiveness against steganographic techniques in general, compared to common attacks.

The paper describes an original architecture aimed at tracking assets within construction sites or similar contexts. The main components are: i) RFID tags, ii) Bluetooth Low Energy (BLE) tags and iii) smartphones. The core functions of the architecture are performed by two Android applications: the Asset Proximity Locator (APL) and the Wandering Objects Location Finder (WOLF). The key feature of the asset tracking function is the ability to maximize smartphone battery lifetime by switching on and off the GPS unit, thus guaranteeing that the smartphone can be used for an entire working shift. A detailed energy consumption analysis is carried out for each component of the architecture. The results achieved by simulations, concerning BLE tag detection probability, are presented and discussed.

Telemedicine applications provide healthcare services through communications technologies overcoming the geographical separation between patients and caregivers. These services can be provided via wireless devices, such as smart-phones with dedicated applications. An interesting application concerns the so-called situation awareness algorithms and, in particular, the Activity Recognition (AR) aimed at tracking the physical activity (or movements) of patients that need a constant monitoring of their medical conditions. This work takes as reference an architecture applicable, but not limited to, patients suffering from Heart Failure (HF) and presents a performance comparison between AR approaches based on the accelerometer signal captured through the patients' smartphones. In more detail, the considered AR techniques apply two different classifiers used to decide the patients movements: a J48 decision tree and a Support Vector Machine (SVM). For each classifier, three different features sets, characterizing the accelerometer signal, have been employed. The performance are evaluated both in terms of accuracy-related metrics and time needed by each classifiers to perform the decision. The results show that SVM provides the best accuracy while the J48 requires less classification time.

Recently, patients suffering from a set of physical and mental limitations, called co-morbidities, are often treated at home. In this environment, modern communication systems represent a great support to implement Ambient Assisted Living platforms aimed at monitoring patients at home because they enable the seamless integration of heterogeneous sensing units, medical devices, and ubiquitous access to data. This article describes a specific smartphone-centric architecture where smartphones are employed not only as hubs of the health information but also as sensing, processing, and transmitting devices. Smartphones have both short-range (Bluetooth and WiFi employed for local information exchange) and long-range (GPRS, 3G/4G, and WiFi employed as Internet access) communication capabilities; information processing capabilities offered by modern platforms often equipped with different CPUs and with flexible and efficient software; and sensing capabilities implemented through sensors embedded into smartphones such as GPS receivers, accelerometers, microphones, and radio interfaces or through external sensors added to smartphones by cables or connected through local radio interfaces. The specific case of co-morbidities considered in this article implies the necessity to acquire a heterogeneous set of data from patients and from their environment. For this reason this article highlights the information processing capabilities of the introduced smartphone-centric platform. Audio, localization, and movement information processing have been evidenced as well as the specific implementations of these capabilities and their performance.