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The leading trends in the framework of the Internet of Things are driving the research community to provide smart systems and solutions aimed at revolutionizing medical sciences and healthcare. One of the major opportunities offered by IoT lies in the ubiquitous connectivity, thus enabling smart services such as remote patient monitoring, in-home therapy/rehabilitation, and assisted living platforms. In this paper we present a prototype of wearable smart glasses able to monitor the Eye Blinks (EBs) through ElectroOculoGram (EOG) signal in a transparent way with respect to the final user. We propose a novel pre-filtering scheme to reduce EOG noise along with an analytical derivation of a matched filter to detect and count EBs. We have carried out an in-depth experimental campaign in order to validate the robustness of our approach with respect to the main solutions available in the literature. Furthermore, we have compared the performances obtained with out wearable prototype versus the results achievable with professional medical equipments. Results show that our solution is able to achieve very high accuracy in EB detection, obtaining comparable performance with respect to professional medical desktop equipment, with the additional benefit of portability, comfort and easiness of use for the patients.

The use of microwave techniques for biomedical sensing and imaging is in continuous evolution, and the realization of accurate and efficient system prototypes is fundamental in bridging the gap between the algorithm development and its practical application. In this paper, a prototype of multistatic imaging system, mainly designed for the quantitative reconstruction of the dielectric properties of the human head for brain stroke detection, is presented and some experimental results, obtained by processing the acquired data with a nonlinear inverse scattering technique, are shown.

A conjugate-gradient-based regularization technique in $L^{p}$ Banach spaces, in conjunction with an inexact-Newton solving scheme, is presented in this paper. The proposed method is applied to obtain the reconstruction of the dielectric properties of human tissues in hemorrhagic brain stroke microwave imaging. The dielectric reconstruction is obtained by solving a nonlinear inverse problem, starting from a set of scattered-field measurements. The presented numerical simulations with 2-D and 3-D anatomically realistic phantoms, as well as the preliminary experimental results, show that this method can be useful for retrieving the dielectric properties of the human head with a reduced over-smoothing effect in the final images.

This article surveys the concept of Industry 4.0 (I4.0), which has become more and more pervasive in recent years thanks to the great effort that factories, researchers, and organizations are putting into its definition and development. We present the IoT as the key I4.0 technology since it enables faster and more efficient production and management processes, leveraging the flexibility of smart, ubiquitous, connected devices. In particular, we discuss the role of the I4.0 revolution in driving the diffusion of smart products and services, by focusing on ambient intelligence and context awareness in IoT and on the so-called DIKW hierarchy. Finally, to demonstrate the practical impact of this emerging framework, we show, as practical examples, three typical I4.0 applications in the smart factory, smart home, and smart health scenarios.

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.

In the Internet of Things (IoT) era with the wide employment of Mobile Devices (MDs), the distribution of video streams is continuously increasing and specific solutions for transmissions between mobiles are needed. In this paper, we provide the performance comparison, through emulations, among Application Layer Joint Coding approaches for videos over channels whose conditions change over time. The proposed analysis highlights that the video streaming over such critical channels is practicable, from the Quality of Experience (QoE) viewpoint, only if source and channel coding are both adaptively applied to the video transmissions.

In this paper, a numerical study devoted to evaluate the application of a microwave imaging method for brain stroke detection is described. First of all, suitable operating conditions for the imaging system are defined by solving the forward electromagnetic scattering problem with respect to simplified configurations and analyzing the interactions between an illuminating electromagnetic wave at microwave frequencies and the biological tissues inside the head. Then, preliminary inversion results are obtained by applying an imaging procedure based on an iterative Gauss-Newton scheme to a realistic model of the human head. The proposed imaging algorithm is able to deal with the nonlinear and ill-posed problem associated to the integral equations describing the inverse scattering problem. The aim of the inversion procedure is related to the determination of the presence of a hemorrhagic brain stroke by retrieving the distributions of the dielectric parameters of the human tissues inside a slice of the head model.

This article describes a microwave tomographic approach for the quantitative imaging of brain stroke inside the human head. For the acquisition of the scattered-field information, a prototype of multistatic system is adopted. An array of custom antennas is placed in contact with the head, and a switching matrix is used to measure the scattering parameters for each pair of probes. The collected data are processed by an inversion method based on a variable-exponent Lebesgue-space regularization technique, whose outcome is a map of dielectric properties of a slice of the head. With respect to previous approaches, this kind of inversion procedure performs an adaptive update of the Lebesgue-space exponents on the basis of the results at each inexact-Newton iteration and exploits stepped frequency data. This allows for an automatic setting of the regularization level, which becomes variable and target-dependent inside the whole investigation domain. The proposed approach is validated by means of FDTD synthetic simulations with a realistic 3-D forward scattering model of the human head, as well as by using real experimental cylindrical test phantoms filled with saline and glycerin/water mixtures.

Recently the problem of interconnecting Internet of Things nodes when they are dispersed over wide geographical areas has been highlighted: this is the Internet of Remote Things (IoRT). It could be able to play a key role in disaster recovery scenarios (e.g., earthquakes, flash floods, terrorist attacks, etc.), where the presence of a communication infrastructure enabling video transmissions from the emergency areas is crucial. Local communications can be provided by incident area networks, while satellite communication systems can bridge the gap toward external networks. In this context, we propose a cooperative resource allocation mechanism, which exploits feedback from links conditions and knowledge of the adaptive coding, allowing a higher quality and more fair experience for all nodes of the IoRT transmitting videos. We implemented each element of the mentioned scenario over an emulation platform to prove the concept and show the obtained benefits.

Different positioning schemes are based on the probability p ( o | l ) to have an observation vector o at a Reference Point (RP) l , based on Gaussian probabilities. This paper presents an approach to speed-up the p ( o | l ) computation without any approximation. The consequent positioning scheme is called Smart P-FP. The comparison between Traditional (without any p ( o | l ) acceleration) and Smart P-FP is performed over different smartphones. The saved energy is about 90% for a large number of Access Points (APs) but is significant even with few APs: more than 86% with 3 APs. The proposed p ( o | l ) computation is beneficial to any p ( o | l ) -based positioning scheme.

The growth of Internet traffic especially with respect to mobile broadband services is one of the main motivations towards the integration of satellite and 5G networks. A key building block for the overall network orchestration will be the efficient definition and management of network slices, which eventually translates into efficient allocation of physical and computing resources. This paper explores the resource allocation problem in the case of railway communications building on satellite and 5G connectivity. Some exemplary candidates solutions have been elaborated, whose validation through simulation campaigns has shed some lights about the main factors impacting on the overall system performance and the most effective resource allocation solutions.

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%.

Indoor positioning systems have gained a lot of attention in the last few years. With the introduction of the Internet of Things (IoT) paradigm, the knowledge of user's location has become crucial information to deliver the efficient and tailored Location Based Services (LBS), especially in indoor environments. In this paper we propose an AP (Access Point)-centred indoor positioning system that overcomes common limitations presented in conventional positioning systems, such as an excessive involvement of Mobile Devices (MDs). Our work merges ideas originally proposed in [1] and [2] to build an efficient, accurate and smart Probabilistic-FingerPrint (P-FP) algorithm that avoids the MD involvement and considers the signal strength measurements as a random variable in the positioning process. Numerical results, obtained in a real-world deployment, show better performance on positioning accuracy, energy consumption and latency with respect to the MD-based architecture.

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 presents a study on the performance of a speaker identification system in challenging environmental conditions, such as in the presence of noise and at a distance. We propose a robust speaker recognition algorithm, which includes a smart pre-processing method based on Voice Activity Detection (VAD), capable to boost the system accuracy. Results show that our solution is able to improve the correct classification rate of traditional speaker recognition systems, even in case of distant audio acquisition and noisy environments.

A microwave imaging technique for the detection and monitoring of brain stroke is presented in this paper. The imaging algorithm, which operates in the framework of Lp Banach spaces, iteratively solves a nonlinear inverse scattering problem. This allows to reconstruct the differential dielectric properties of a horizontal slice of the patient’s head. The proposed approach is validated by means of a set of numerical simulations with three-dimensional anatomically-realistic phantoms of the human head, in presence of different measurement configurations.

The treatments for brain stroke are strongly time- dependent. The medical literature highlights the need of a quick diagnosis in order to guarantee the most effective therapy. An important target for strokes is trying to achieve a Door-to-Needle (DTN) time of less than 60 minutes, which is called Golden Hour [1]. This paper proposes a mobile Smart Helmet (SH) thought to be worn by a patient when the first aid medical team arrives and the aim is to efficiently recognize and detect a brain stroke, on site. While similar solutions in the literature employ the (usually computationally heavy) electromagnetic field inversion problem and image analysis, the proposal of this paper is an NN-based SH. It uses signal analysis to recognize the presence of a stroke with a limited computational burden. In the reported preliminary experiments, carried out via simulations, we have employed a MultiLayer Perceptron (MLP) model that implements a 4-layer NN. Numerical results show that proposed signal analysis, applied to a single brain model, is able to efficiently detect the stroke presence with an accuracy around 90%.

This paper concerns the problem of data inversion in microwave imaging for biomedical applications. In particular, the integral equations of the inverse scattering problem are solved — in a regularized sense — by a new iterative procedure based on a conjugate gradient method. The approach is developed in Lp Banach spaces. Transverse magnetic illumination conditions are assumed in a tomographic configuration. The detection of hemorrhagic brain strokes is considered in the reported numerical simulations.

Location Fingerprinting (LF) is a promising localization technique that enables many commercial and emergency Location-Based Services (LBS). The idea of this paper is two- folded. First, a Gaussian Process (GP) is used during the training (offline) phase of an indoor positioning algorithm to generate the fingerprint database, reducing the expensive labor of acquiring and maintaining the fingerprint database significantly. Furthermore, during the positioning (online) phase, a Smart algorithm (already proposed in [1]) is used for reducing the computation effort for positioning calculation. We call our idea Smart2 since it enhances the advantages of the base Smart approach. Specifically, Smart2 reduces the labor during the offline phase by trading it with a small positioning error and, at the same time, it limits the energy consumption in the online phase without incurring in any additional accuracy detriment.

This paper presents a tomographic inverse scattering algorithm based on an iterative regularization approach developed in Lp Banach spaces. The proposed technique has been applied to the microwave biomedical imaging of the human head, in order to detect and locate hemorrhagic brain strokes. Preliminary numerical results are provided in order to show the capabilities of the inversion method.

An approach for microwave imaging of brain strokes is presented. Numerical simulations show the potentialities of this technique in retrieving the dielectric properties of biological tissues inside the head. An efficient iterative two-loop method is used to face the ill-posedness of the considered inverse scattering problem, which is formulated in terms of integral equations.

Rehabilitation and elderly monitoring for active aging can benefit from Internet of Things (IoT) capabilities in particular for in-home treatments. In this paper, we consider two functions useful for such treatments: 1) activity recognition (AR) and 2) movement recognition (MR). The former is aimed at detecting if a patient is idle, still, walking, running, going up/down the stairs, or cycling; the latter individuates specific movements often required for physical rehabilitation, such as arm circles, arm presses, arm twist, curls, seaweed, and shoulder rolls. Smartphones are the reference platforms being equipped with an accelerometer sensor and elements of the IoT. The work surveys and compares accelerometer signals classification methods to enable IoT for the aforementioned functions. The considered methods are support vector machines (SVMs), decision trees, and dynamic time warping. A comparison of the methods has been proposed to highlight their performance: all the techniques have good recognition accuracies and, among them, the SVM-based approaches show an accuracy above 90% in the case of AR and above 99% in the case of MR.

Brain stroke detection is becoming an important topic for the microwave imaging research community. In this work, we propose a quantitative method for detecting hemorrhagic brain strokes through the reconstruction of the dielectric permittivity distribution inside head. The associated nonlinear inverse scattering problem is iteratively solved by means of an inexact-Newton method. Preliminary numerical results, produced by applying the proposed method without a previous knowledge of the internal structure of the head, are presented and briefly discussed.

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.

In-home physical therapy is one of the best options for many individuals and families thanks to its convenience and because it makes possible to receive professional care in the comfort of your own home. To enable this therapeutic approach, this paper proposes the employment of a smartphone-centric platform for in-home rehabilitation. The platform helps physicians to monitor the patients remotely so avoiding hospitalization therapies that can be stressful. In more detail, the work is focused on the Movement Recognition (MR) functionality of the aforementioned platform. It compares algorithms, which process the signal provided by the embedded accelerometer sensor of the smartphone, able to recognize if a patient had performed the movements requested by the physicians. The provided performance comparison of different MR techniques shows that Support Vector Machine-based approaches have very good accuracy (up to 99.3%), thus making the in-home physical therapy reliable.

This paper presents the Heuristic Application Layer Joint Coding (Heuristic-ALJC) scheme for video transmissions aimed at adaptively and jointly varying both applied video compression and source encoding at the application layer used to protect video streams. Heuristic-ALJC includes also a simple acknowledgement based adaptation of the transmission rate and acts on the basis of feedback information about the overall network status estimated in terms of maximum allowable network throughput and link quality (lossiness). Heuristic-ALJC is implemented through two smartphone Apps (transmitter and receiver) and is suitable to be employed to transmit video streams over networks based on time varying and possibly lossy channels. A performance investigation, carried out through a real implementation of the Apps over Android smartphones, compares Heuristic-ALJC with static schemes.

In this paper, a microwave imaging method is proposed for brain stroke detection. In particular, the developed imaging procedure is based on an iterative Gauss-Newton scheme and it is aimed at determining the presence of a hemorrhagic brain stroke. Interrogating microwaves are used in a multistatic and multiview arrangement. Preliminary numerical results concerning the reconstruction of a simulated stroke inside a two-dimensional slice of the human head are reported. A numerical model is used to obtain the synthetic data used in the inversion process through the solution of a forward electromagnetic scattering problem, which is performed under transverse magnetic conditions.

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 exponential increase in the number and types of mobile devices, along with their ever-growing sets of capabilities, have enabled the development of new architectures that aim to harness such heterogeneity. Transient clouds are examples of mobile clouds, which are created on-the-fly by the devices present in an environment to share their physical resources (e.g. CPU, memory and network) and would disappear as the nodes leave the network. They enable a device to go beyond its own physical limitations through utilising the capabilities offered by nearby devices over an ad hoc network. This idea exploits the device-to-device (D2D) communications paradigm, which allows two nearby devices to communicate with each other in the licenced cellular bandwidth without a base station involved. In this paper, we present a transient context-aware cloud (TCAC) paradigm based on the assumption that the nodes of the network care more about providing/learning higher level functionalities rather than lower level capabilities in D2D scenario. The proposed architecture, realised by using a WiFi Direct, can be portable through any paradigm, which exploits the D2D communications, so opening the doors to forthcoming 5G scenarios. We present a prototype implementation of our architecture over Android smartphones connected via WiFi Direct along with the performance metrics (power/energy consumption and accuracy) to show the benefits of TCAC. A theoretical and analytical model for the energy consumption related to a device within the TCAC is provided as well. Copyright © 2015 John Wiley & Sons, Ltd.