The integration of the Social Internet of Things (SIoT) with Wireless Sensor Networks (WSNs) significantly enhances the efficiency, scalability, and intelligence of distributed sensing systems. However, these networks often encounter severe resource constraints, limiting the control and management traffic that can be introduced. WSNs, typically composed of battery-powered edge sensor nodes with limited computational capabilities, memory, and communication bandwidth, face challenges in optimizing performance. In this work, we address the multi-objective optimization problem within the context of resource-constrained SIoT, aiming to reduce the energy consumption of edge nodes while simultaneously enhancing the quality of the received data based on channel conditions. We propose a Pareto Optimization framework to jointly optimize the compression factor and coding rate in a WSN scenario utilizing LoRa technology for communication. This framework explores the trade-offs between energy consumption and data reconstruction quality, leveraging Compressive Sensing (CS) for efficient data compression to alleviate the transmission load on edge nodes. Furthermore, we present a distributed optimization solution to minimize energy consumption while maximizing data quality, thereby reducing signaling and control overhead. This study contributes to the development of energy-efficient, scalable, and sustainable SIoT systems by providing a foundation for optimizing data transmission in LoRa networks.

Low-Power Wide-Area Network (LPWAN) technologies such as LoRa are key enablers of large-scale IoT monitoring systems, where long communication range and low energy consumption are essential. These advantages, however, come at the cost of strict throughput limitations, which significantly shape IoT system design. Compressive Sensing (CS) can mitigate this constraint by reducing transmitted data volumes, effectively trading communication load for additional processing at the sensing node and receiver. From an IoT perspective, this shift impacts node lifetime, hardware requirements, and overall network scalability. In this paper, we propose a data-driven optimization framework for LoRa-based IoT sensing systems employing CS. The approach jointly analyzes reconstruction quality and energy consumption through surrogate regression models that capture the interaction between physical-layer parameters and compression levels. This enables efficient multi-objective optimization via Pareto-front analysis and utopia-based selection. Results show that CS does not always dominate the quality-energy trade-off and that unified and stratified surrogate strategies identify closely aligned optimal operating points. Overall, the framework provides a practical and interpretable tool for the design of energy-efficient IoT sensing deployments.

The healthcare sector is experiencing a profound transformation, fueled by the rapid evolution of sixth-generation (6G) cellular networks and Internet of Things (IoT) technologies. At the heart of this shift lies the concept of medical digital twins (MDTs), which serve as dynamic virtual representations of physical systems or biological processes. MDTs offer a secure environment to simulate and evaluate therapeutic strategies, leading to reduced costs and more informed clinical decision-making. They also enable real-time support and in-depth data analysis, setting new standards for patient care. Nonetheless, realizing the full capabilities of MDTs remains challenging due to the inherent complexity of human life cycles. Crucial aspects include selecting appropriate data sources and defining robust communication protocols between the physical and digital realms. In particular, integrating wearable technologies with edge computing and WiFi-based Channel State Information (CSI) can significantly enhance health monitoring and activity recognition for elderly individuals within indoor settings. The synergy of IoT advancements and 6G networks paves the way for improved data exchange and continuous synchronization between digital and physical counterparts. This paper, part of the HIPPOCRATES project, presents an IoT-driven architecture for MDTs that incorporates wearable sensors and CSI data to strengthen health monitoring and early intervention strategies, with a focus on elderly care.

The integration of Social Internet of Things (SIoT) paradigms with Wireless Sensor Networks (WSNs) offers significant improvements in the efficiency, scalability, and intelligence of distributed sensing systems. However, these networks are often subject to severe resource constraints, particularly at the edge, where sensor nodes are typically battery-powered and limited in computational power, memory, and communication bandwidth. Consequently, the introduction of control and management traffic must be carefully limited to avoid compromising network performance. In this work, we tackle the problem of multi-objective optimization in resource-constrained SIoT environments. Specifically, we aim to reduce the energy consumption of edge sensor nodes while improving the quality of the received data, taking into account the underlying channel conditions. To this end, we propose a distributed optimization strategy that minimizes energy consumption and maximizes data quality, while implicitly reducing the overhead associated with signaling and control messages. This framework explores the trade-offs between energy efficiency and data reconstruction accuracy, leveraging both Compressive Sensing (CS) to effectively reduce the transmission burden, and channel coding techniques to enhance data protection in LoRa-based WSNs.

The Widespread adoption of randomization in modern operating systems has introduced significant challenges for passive monitoring and user tracking in wireless environments. These challenges are further increased in large-scale environments covered by multiple access points, where associating transmission across zones becomes more complicated. This paper presents a frame association-based approach that enables cross-zone tracking and trajectory reconstruction using Wi-Fi probe request frames. The proposed method correlates transmissions across multiple access points by analyzing a combination of fingerprints. Results show that our approach effectively associates frames transmitted from the same origin, tracks devices across multiple zones, and provides insights into user movement and behavior, such as the type of transitions between zones and the reconstructed trajectory, all while preserving user privacy.

Accurate assessment of postural stability is crucial for monitoring patients with movement disorders, as it helps detect early signs of instability and prevent falls. Traditional methods, such as force platforms, are expensive, bulky, and limited to specialized laboratory settings, making them unsuitable for regular clinical screening or continuous home-based monitoring. In this work, we propose a deep learning approach to predict the Center of Pressure (CoP) trajectory using Inertial Measurement Unit (IMU) data from wearable smart glasses, offering a portable and cost-effective alternative. We use synchronized data from a force platform and a 9-axis IMU sensor to model the relationship between raw IMU signals and CoP force data (Force X and Y). The method involves windowing the IMU data, preprocessing it with low-pass filtering, and applying normalization. The dataset includes sequences from three standing tasks (eyes open, eyes closed, and free stance), captured at a frequency of 100 Hz. Experimental results show that the LSTM and BiLSTM models accurately predict CoP trajectories, achieving low Mean Absolute Error (MAE), Mean Squared Error (MSE), and high R2 values. While the TCN and GRU models face certain challenges in achieving the same level of performance as LSTM and BiLSTM, they present valuable insights and potential for future refinement. This approach has the potential to enable real-time, portable balance monitoring and early detection of postural instability, offering a scalable solution for clinical settings and home-based monitoring.

Enabled by the integration of AI and the Internet of Medical Things (IoMT), smart and remote monitoring systems are poised to play a pivotal role in the future of healthcare. Specifically, monitoring respiration is a critical component of this evolution, offering a straightforward yet effective method for assessing an individual's health status. In this paper, we introduce an innovative contactless approach to respiration monitoring that leverages Channel State Information (CSI) from Wi-Fi channels. By analyzing variations in the amplitude and phase of CSI data, we infer human respiration patterns. To validate our method, we conducted experiments with multiple subjects in indoor environments, assessing the system's capability to track the respiration cycle and determine breathing frequency. We also compared the performance of various AI algorithms in identifying accurate breath rates. Our findings indicate that the CSI-based system is a promising solution for respiration monitoring, achieving an average accuracy of approximately 84 % in estimating breathing frequency, thus paving the way for future studies to enhance the robustness of the proposed approach.

The Internet of Things (IoT) has garnered significant attention in recent years, with the integration of AI solutions and wireless sensing technologies enabling innovative approaches to context awareness and user location. Additionally, Channel State Information (CSI) from WiFi channels is emerging as a key component in next-generation wireless systems. In this work, we conduct a comprehensive analysis of the sensing capabilities of state-of-the-art CSI tools, namely the Intel 5300 and the ESP32 CSI tools, through extensive experimental tests in a dedicated testbed. The results offer valuable insights into CSI-based techniques, demonstrating their strong potential for activity detection and context aware applications.

This paper presents the design, development, and implementation of an Industrial IoT (IIoT) node aimed at monitoring vibrations on various types of structures, such as bridges, lightning rods, and large industrial machinery. The IoT node leverages a ESP32 microcontroller, an Inertial Measurement Unit (IMU), and radio communication technology for data transmission. The proposed solution is capable of gathering and transmitting real-time data from accelerometers, gyroscopes, and magnetometers via a LoRa interface. In this work we carry out a fine system calibration procedure, to assess the actual features of the IIoT node and provide a thorough experimental analysis of the performance of LoRa technology in complex urban environments performing extensive field tests. This work lays the foundation for the utilization of the IIoT node equipped with LoRa technology in both urban environments and industrial IoT frameworks, highlighting its adaptability and potential for wide-scale applications in these settings.

Multi-agent collaborative perception can mitigate individual blind spots by sharing and integrating sensory data, thereby providing a more comprehensive understanding of the surrounding environment. However, challenges remain, particularly in feature fusion, where issues such as redundancy, localization errors, and the trade-off between perceptual accuracy and real-time performance must be addressed. In this paper, we propose the Triple-Attention Collaborative Perception (TA-CoPe) framework, which employs hybrid channel attention, frequency attention, and spatial attention to enhance feature representation across these dimensions, optimizing the fusion and selection of multi-dimensional features. Additionally, a dynamic gating mechanism with adaptive weight learning assigns varying weights to each module, dynamically optimizing their contributions to the final output, thus improving both the accuracy and robustness of feature fusion. Experimental results demonstrate that the proposed method significantly outperforms existing state-of-the-art methods on two public datasets in multi-agent perception tasks.

Mobile devices with enabled Wi-Fi continuously discover nearby access points by broadcasting management frames known as probe requests. This broadcasting leaks fingerprints that can be used to identify the presence and movement of flow in a given area. To protect users' privacy and location, probe requests use randomized MAC addresses generated according to the randomization protocols. In this paper, we analyze the behavior and characteristics of probe request frames across different devices from various vendors, operating systems, and features, as well as the influence of user-device interaction in different phases. In particular, we provide a detailed examination of the adoption of MAC address randomization techniques to highlight the strengths and weaknesses of recent randomization policies and to demonstrate that, despite the OS implementation, there is still potential to utilize open Wi-Fi in different services and development.

The convergence of Symbiotic IoT, AI foundational models, and 6G is ushering in a new era of intelligent connectivity, where networks, devices, and algorithms operate in close coordination to enable real-time, adaptive, and efficient systems. In the context of Structural Health Monitoring (SHM), this integrated vision provides a powerful framework to tackle challenges such as limited resources, harsh environments, and the need for timely, high-fidelity data. By enabling intelligent, collaborative processing across edge and network layers, it supports efficient data compression, transmission, and decision-making—ensuring robust and adaptive monitoring even in complex structural settings. In this work, we investigate the use of AI for data compression in an IoT-based SHM scenario. Specifically, we evaluate and compare the performance of four different Convolutional Autoencoders (CAEs) in compressing and reconstructing inertial signals collected from various structural systems, aiming to enable adaptive and context-aware processing directly at the edge. By testing across heterogeneous sources, we assess the generalizability and robustness of each CAE model, providing insights into the potential of deep learning-based compression techniques for SHM applications.

Integrated sensing and communication (ISAC) enables several emerging applications while suffering severe security issues. In this article, the novel illegal sensing suppression (ISS) is proposed to protect the privacy of the specific legitimate user in the presence of an adversary with the sensing capacity in the ISAC system. The target detection and the direction of arrival estimation as common sensing tasks are investigated to deteriorate the sensing performance at the adversary side. To this end, we propose the missed detection probability maximization and the Cramér-Rao bound (CRB) maximization-based ISS model. This model also ensures the achievable rate and the legitimate sensing CRB toward the adversary at the ISAC base station side. The beamforming optimization algorithms are developed based on the alternating optimization and semidefinite relaxation techniques in order to address the nonconvex issue in the formulated ISS models and explore the rate-ISS tradeoff in the ISAC system. We further propose the closed-form solution for the single user case in the target detection suppression scenario. Numerical results demonstrate the necessity of the ISS and the ISS gain of proposed algorithms compared with other baseline schemes.

The use of multiple access protocols information in Internet of Things (IoT) environments has gained significant interest over the past decade, particularly for crowd behavior monitoring. Due to its high data rates and low infrastructure requirements, WiFi is considered one of the most promising wireless technologies for leveraging the explosive growth of transmitted data from mobile devices. However, with the introduction of MAC address randomization and the application of new randomization policies on assigning randomized sequence numbers (SNs) to transmitted frames in mobile devices to enhance privacy, traditional approaches to device identification face significant challenges. To tackle this issue, we conduct a comprehensive analysis at the multiple access level and propose CrowdWatch which is an innovative framework designed to enhance the understanding and utilization of MAC randomization dynamics. Additionally, we introduce a novel approach that leverages multiple device-specific features to accurately associate frames with randomized MAC addresses to their corresponding devices. By integrating multimodal fingerprints, the framework can effectively identify mobile devices and track their behavior. The presented approach ensures reliable detection even under the latest randomization policies. We examined the introduced framework through real-world experiments, and the findings prove that it is an effective solution for smart building management and occupancy estimation in dynamic environments.

In recent years, the increase in the elderly population has placed significant burdens on post-rehabilitation schemes, resulting in high logistical costs and considerable social impacts due to hospitalization or frequent visits. These challenges call for a transformation in the traditional approach to physical patient care, which can be achieved by leveraging the Internet of Medical Things (IoMT), particularly through the use of pervasive wearable sensors. When attached to patients during treatment or therapy, these sensors can provide valuable supplementary information to healthcare professionals. When it comes to adopting IoMT technologies, cost efficiency, portability, and generalization are key factors. Specifically, this study aims to enhance the cost-effectiveness and versatility of wearable eHealth monitoring architectures that utilize foot pressure-sensing hardware for the motor assessment of post-stroke and neurologically impaired patients. It leverages lower limb inertial measurement unit sensory information and machine learning to mitigate the reliance on foot pressure-sensing hardware. We demonstrate the potential of artificial intelligence (AI) in predicting fine-scale foot pressure using only inexpensive, off-the-shelf motion sensors. We propose a self-supervised, exercise-agnostic asynchronous foot pressure decoding model that does not require human annotation. The algorithm is thoroughly evaluated using appropriate performance metrics, and our experimental tests show promising results.

Drones are integral to various applications, out of which traffic surveillance is an important application. However, their operational efficiency is limited by battery life, which restricts their capacity for extended critical missions. Additionally, in remote or high-interference areas, the bandwidth for drone communication is often limited, leading to a decrease in the quality of images transmitted to the base station. This paper aims to address such challenges by having drones transmit video data in real-time at lower resolutions for traffic monitoring. This approach conserves energy and optimizes transmission. However, it adversely affects object detection accuracy at the base station due to compromised data quality. To address this issue, we incorporate Generative Adversarial Networks (GANs) to improve LR images, restoring their quality for precise object detection. Results indicate that the accuracy of traffic analytics achieved with GAN-enhanced images is comparable to that obtained with high-resolution data transmission. Consequently, our approach allows a fundamental trade-off among drone energy consumption, transmission time, flight time, and object detection accuracy, enabling robust detection performance while conserving energy and enhancing operational capabilities.

Contactless patient monitoring is one of the most trending topics in eHealth, due to the utmost importance of non-invasive tele-enabled biomedical systems in next-generation healthcare. In this connection, this work investigates Human Activity Recognition (HAR) using commodity 5GHz WiFi devices, exploiting Channel State Information (CSI) to distinguish among a set of actions performed by different people. The position and movements of the human body affect wireless signal reflections and, consequently, CSI. Data collected from wireless packets are organized into the CSI matrix, which describes the status of the link at each time instant. In this work we employ amplitude and phase information, related to the variations in CSI values, to classify human activities leveraging simple machine learning techniques. Different radio link modes are also compared to evaluate their impact on the classification performance. Experimental results show that CSI data is capable of providing very accurate results in classifying activities performed by different people, especially when considering phase-related information in a multiple-input-multiple-output (MIMO) configuration, thus making CSI-based HAR a promising solution for contactless patient monitoring.

Human balance is essential for everyday activities, from basic functions like standing and walking to more complex movements required in sports and work tasks. Optimal balance reduces the risk of falls, a major cause of injuries among the elderly and individuals with certain medical conditions. However, traditional force platforms can be prohibitively expensive for smaller clinics and individual practitioners, and patients with mobility issues may find it difficult to access locations equipped with such platforms. In light of these challenges, this work explores the effectiveness and versatility of using simpler hardware for pressure sensing. As an alternative to conventional force platforms, low-cost wearable sensors, such as Inertial Measurement Units (IMUs), have been explored. This research focuses on developing a simple yet effective balance evaluation system using smart-glasses embedded with an IMU sensor to replace traditional feet pressure sensing machines. Furthermore, we developed a custom dataset for the estimation of sway parameters by simultaneously collecting the IMU data and labels from the force platform for a set of 20 participants. We experimented with various Deep Learning (DL) models, leveraging the latest advancements in Machine Learning (ML), to estimate sway parameters such as sway path, sway area, and their ratio, typically measured by the gold standard force platform. When evaluated with appropriate performance criteria, the experimental results indicate that our proposed methodology performs robustly using only accelerometer data.

Structural Health Monitoring (SHM) is a process aimed at studying variations in the expected behavior of a structure in order to locate damage, material deterioration and other abnormalities. To this aim, SHM is usually performed continuously, thus generating large amounts of data, often by employing wired, expensive and proprietary systems. Introducing low-cost, low-energy consumption and low-rate IoT devices allows for cheaper and easier installations also in scenarios where computation and transmission resources are limited. Since many structural signals (e.g., vibrations) are sparse in the frequency domain, it is possible to apply well-known Compressive Sensing (CS) techniques to limit the amount of information to be transmitted. CS allows recovering a vector using a reduced amount of entries, thus being able to perform sub-Nyquist sampling. This paper shows the results obtained by applying CS to inertial signals coming from wireless IoT devices, developed as laboratory prototypes, applied to real structures (specifically, a bridge). Such findings are further expanded by discussing the efficiency of CS with respect to the number of used samples and its feasibility for IoT applications, from the transmission burden and energy consumption standpoints.

Compressive Sensing (CS) is a sampling technique which, provided the sparsity of the arrival domain and specific properties of the reconstruction matrix, allows rebuilding a vector starting from a significantly small subset of measures. This paves the way to a plethora of applications, ranging from specialized frameworks, such as medical imaging, to more general purposes, such as data compression. Among these, Structural Health Monitoring (SHM) is a primary and current topic, focused on analyzing structures to determine their residual lifespan and their health conditions (material degradation, damage localization, disaster prevention, etc.), In this regard, CS is able to provide accurate results, at the same time limiting the amount of data needed to propagate information between different end-points. Indeed, SHM usually deals with continuous flows of information originating from heterogeneous sensors and locations, often characterized by diverse computational power and signal coverage. In this paper we apply CS to signals coming from two different structures, i.e., a laboratory model and a bridge. Results show that CS is a viable way of reconstructing the considered signals by exploiting a subset of samples while still maintaining a high degree of precision, achieving an average normalized RMSE of 0.12.

Compressive Sensing (CS) is a sampling technique that challenges the traditional sampling scheme introduced by the Whittaker-Shannon theorem. Under certain conditions, a signal can be sampled at rates lower than the Nyquist rate, introducing a different kind of approach to signal handling, both in the acquisition and in the reconstruction phases. CS relies on the property of sparsity, the idea that a signal possesses an amount of information which is smaller than the amount of data required to store it. This paper employs the CS approach to inertial signals sensed by innovative IoT devices by showing applications of real-world infrastructure monitoring. Numerical results show that our approach is able to efficiently estimate the infrastructures modal frequencies with an innovative inertial IoT prototype by achieving a compression level around 20 times below the Nyquist rate.

Alongside many traditional as well as novel applications, in the latest years, drones have been widely adopted as remote sensing platforms for road traffic monitoring in urban areas and on highways. The problem of traffic monitoring on Region of Interest (RoI) based on drone imagery is a challenging task, especially when the surveillance drone is constantly moving. In this work, two specific subtasks have been addressed. The goal of the first stage is to predict the RoI in drone imagery of traffic scenes using deep-learning (DL)-based approaches instead of traditional image processing; in this connection, the goal of the second task is to perform vehicle detection on the selected RoI. To ensure diversity and robustness, drone images with different altitudes, positions, and view points have been considered. To achieve these goals, two custom aerial data sets for RoI extraction and detection were built by collecting aerial sequences from flying unmanned aerial vehicles (UAVs) and by transmitting them to the base station leveraging 5G technology. Two different ad hoc DL-based architectures have been designed for the RoI extraction task to maximize the accuracy and inference speed, respectively, and have been evaluated on two different data sets: 1) a custom-built data set and 2) a Massachusetts roads data set. Finally, the models providing the best performance have been combined to further improve the overall results. Experimental tests show that the proposed framework represents a promising solution for drone-based road traffic monitoring in critical areas, exploiting imagery from a variety of viewing angles and altitudes.

Drones and video processing have become a vital and integrated aspect of smart city development in several applications such as search and rescue, surveillance and delivery. Newly, camera-equipped drones are flown, and high-resolution photos and video data are relayed via a communication link to the base station. Apart from tackling the video processing issues in applications, such as object identification and tracking, energy consumption that may present a stumbling block in completing a successful drone flight for data collecting must be highlighted and considered. Drones have a limited amount of energy storage, which must be used to power the drone's movement, hovering, data collection, and communication. This study aims at building and testing a drone energy profile that estimates the total energy consumed and a maximum flying time of a drone in a traffic monitoring scenario. Additionally, the relationship between the video processing task and the drone energy profile is explored to determine the optimal strategy for flying the drones while maintaining the video processing task's performance. The conclusion of this evaluation and investigation can be conceived of as the test-case scenario to fly a drone for surveillance and monitoring applications to attain the optimum results.

Unmanned aerial vehicles (UAVs) are increasingly utilized in smart cities to perform traffic monitoring tasks such as multiple object detection and tracking. The task's criticality is dependent on the drones' dynamic altitude, movable camera, and various viewing angles. These challenges are addressed once the UAVs' collected data is received. However, parameters affecting drone data collecting flight operations must also be explored, including drone actual flight time, data collection time, and energy consumption profile. The drone flight time would depend upon the battery capacity and energy consumption profile, which comprises drone movement, data collection, and communication energies. Besides, data collection energy consumption is subjected to video quality, frame rates, and data compression. The installed battery in UAVs is of limited capacity and determining actual flight time, data collection time, and energy consumption profile based on the factors mentioned above is critical. This paper develops and examines a drone energy consumption profile and proposes a drone flight strategy in a surveillance scenario to correctly estimate the drone's actual flight time, data collection time, and the distance the drone could travel from its original location. The results of this analysis are presented as a test case for flying a drone to collect the traffic monitoring data.

From smart cities development perspective, road vehicle detection exploiting drone-based aerial imagery is a crucial part of traffic surveillance and monitoring systems where effective results are of utmost demand. A recent boom in the field of deep learning (DL) has provided remarkable development in the problem of vehicle detection. Aerial views pose more complexity with respect to the ground view but the rapid advancement in the field of DL, the volume of data, and hardware configuration has facilitated the realization of these intelligent detection systems effectively. In this article, a detailed performance evaluation of some of the main state-of-the-art DL-based object detection techniques has been carried out along with an experimental analysis of vehicle detection using the RetinaNet framework on the VisDrone-benchmark data set. The performance of the RetinaNet framework has been validated together with the results provided by the VisDrone team. Further experiments are then conducted to investigate the impact of various parameters. Finally, the selection of suitable models that can be practically implemented is also discussed based both on a qualitative and quantitative analysis.

According to data traffic forecast reported in Cisco (2018), the volume of data transported by Internet in 2021 will exceed the threshold of 3.0 ZB per year, generated by roughly 30 billions of devices. SATCOM’s capability to bring Internet connectivity in parts of the world that until few years ago was impossible to reach (e.g., in the middle of the ocean), the use of satellite platforms for M2M services has become more and more widespread, with an unprecedented increase over the last five years (see Herrero and De Gaudenzi, 2012 and Scaliseet al., 2013) and expected further diffusion in the near future. One of the most interesting applications, enabled by the Internet of Things (IoT), that has attracted significant attention in the M2M field is the personal vocal assistant (PVA). It is a software agent that can perform tasks or services for an individual based on commands or questions given through voice instructions. The main contribution of this article is a performance analysis, both theoretical and simulated, of satellite networks for PVA services over cruise ships. Namely, a PVA must provide feedback to the user in a short amount of time to guarantee a high Quality of Experience (QoE) Zhanget al., 2013. For this analysis, we tested different satellite providers and we come to a conclusion regarding their feasibility as a reference network for the PVS employment on a cruise ship. Future studies will investigate the proposed scenario by broadening the performance analysis through actual measurements over a real-world satellite channel to practically demonstrate the validity of our approach.

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.

Given the extreme diffusion of Alzheimer's disease (AD) and Parkinson's disease (PD), the necessity for a solution to early detect neurological symptoms of such diseases strongly arose. According to the medical literature, such early detection can be achieved by exploiting the correlation between PD and AD and some external symptoms: the Essential Tremor (ET) and the number of Eye Blinks (EBs). In this paper we present a prototype of sensored glasses able to detect the presence of ET of the head and to count the number of EBs at the same time. To the best of authors' knowledge this is the first attempt to monitor such external symptoms with a transparent and wearable device without any a-priori training. Numerical results prove the reliability of the proposed approach: the proposed algorithms are able to i) correctly recognize the ET with an overall accuracy above 97% and ii) count the number of EBs with a Root Mean Square Error (RMSE) around 0.4.

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%.

In the latest years we have been witnessing an evolution of the technological framework thanks to the Internet of Things paradigm, which is enabling innovative smart services for many different applications. The healthcare system is among the main scenarios that are benefiting from this new trend, thus giving rise to the concept of eHealth. In this framework we propose SmartPANTS, an IoT-based wireless system specifically designed for the rehabilitation of lower limbs. Our system is conceived as a prototype of a medical platform to be employed during the physical therapy for patients recovering from a brain stroke condition. SmartPANTS includes a signal processing and machine learning algorithm able to automatically recognize the type of exercise the patient is performing, and to provide real-time feedback on the execution. Moreover, experimental tests show that our platform is able to estimate the execution time of the different exercises, providing values very close to real ones. Performance results show that the SmartPANTS system is able to correctly identify the type of exercise currently being performed with an accuracy of about 99%.

Information related to recognizing the place in which a user is has become crucial to deliver efficient and tailored Location-Based Services (LBSs). Though plenty of solutions exist for recognizing indoor places, almost no idea is present in the literature for recognizing big outdoor places without the GPS employment. Though these solutions assure great accuracy they also have a strong request in terms of energy necessary to achieve such result. This paper proposes a Place of Interest (POI) recognition algorithm called Enhanced-LRACI. It is an evolution of LRACI (Location Recognition Algorithm for automatic CheckIn applications), a former work reported in [1]. E-LRACI aims at recognizing big outdoor places only by using WiFi Access Points (APs) over mobile devices (smartphones). The main contributions of this work are: i) it solves the problem of big outdoor POI recognition without using GPS by leveraging on the concept of spot, ii) it proposes a novel fingerprint (FP) algorithm and iii) it outperforms the results obtained by other reference works in terms of recognition accuracy. Performance investigation reported in this paper, carried out on real data acquired over mobile devices (Android smartphones), shows that our proposal is able to correctly recognize big outdoor POIs in 95% of the cases whereas other state of the art papers do not exceed 89%.

The telecommunication infrastructure in emergency scenarios is necessarily composed of heterogeneous radio/mobile portions. Mobile Nodes (MNs) equipped with multiple network interfaces can assure continuous communications when different Radio Access Networks (RANs) that employ different Radio Access Technologies (RATs) are available. In this context, the paper proposes the definition of a Decision Maker (DM), within the protocol stack of the MN, in charge of performing network selections and handover decisions. The DM has been designed to optimize one or more performance metrics and it is based on Multiattribute Decision Making (MADM) methods. Among several MADM techniques considered, taken from the literature, the work is then focused on the TOPSIS approach, which allows introducing some improvements aimed at reducing the computational burden needed to select the RAT to be employed. The enhanced method is called Dynamic-TOPSIS (D-TOPSIS). Finally, the numerical results, obtained through a large simulative campaign and aimed at comparing the performance and the running time of the D-TOPSIS, the TOPSIS, and the algorithms found in the literature, are reported and discussed.

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.

Nowadays, Location Based Services (LBS) are fore- seen as a fundamental building block of modern mobile applications and services. Important examples of LBS concerns indoor environments in which GPS technology cannot be used. On the other hand, the great diffusion of pervasive Mobile Devices (MDs) as smartphones and tablets has enabled many positioning techniques, such as WiFi FingerPrinting (FP), that exploits all the MD's embedded sensors. This paper proposes and investigates the performance of a method exploiting a WiFi FP algorithm for indoor localization fed with information from the barometer to estimate the floor in which the MD is located. Our results, carried out in indoor areas at the University of Genoa (UniGE) and at the University of Bologna (UniBO), show that when more than 5 Access Points (APs) are used the proposed 3D positioning system is able to accurately localize the user with an error below 2 and 1.2 and meters for the UniBO and UniGE case, respectively.

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.

Nowadays, Internet of Things (IoT) devices can collect a large amount of data and infer the context they are operating in. One of the most pervasive IoT device is the smartphone. Among the plethora of sensors that such device has, microphone is probably the most versatile. It can be used to infer information about the context by acquiring the environmental sound. In this paper, we propose an active ultrasonic-based method able to sense context information. The approach is based on the emission of periodic ultrasonic impulses, called pings, whose echoes are continuously acquired to extract a proper set of features. Using a classifier the context information is then retrieved. Two applications are presented: 1) an indoor/outdoor detector and 2) an earphone wearing state detector. In both cases a smartphone is the employed device. The former senses if the smartphone is in an indoor or outdoor environment while the latter detects if a user is wearing or not his earphones. The obtained results are encouraging for both the solutions that have been stressed in different working conditions and employed within proper application frameworks.

Mobile satellite services are extremely important when continuity of service is requested while the user is moving over a wide area. A notable example is represented by live TV services, or more in general by the Multimedia Broadcast/Multicast Services (MBMSs) and its evolved version enhanced MBMS. In this paper, we present the combined use of multicast resource allocation schemes based on subgrouping and application layer joint coding to enhance the performance of live video streaming in mobile satellite systems. The reference architecture foresees a satellite-based long term evolution transmission using orthogonal frequency division multiple access in the forward link. The results show that the combined use of multicast subgrouping resource allocation and application layer joint coding allows high-throughput transmissions with satisfactory user quality of experience of the received video over different satellite channel propagation environments.

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%.

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 Smart2since it enhances the advantages of the base Smart approach. Specifically, Smart2reduces 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.

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.

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 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.

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 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.

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 (TCs) 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, network) and would disappear as the nodes leave the network. They enable a device to go beyond its own physical limitations through utilizing the capabilities offered by nearby devices over an ad-hoc network. In this paper we present a Transient Context-Aware Cloud (TCAC) in which the nodes of the network care more about providing/learning higher level functionalities rather than lower level capabilities. We make the case for such an architecture in scenarios where it is not feasible for all the nodes to compute the context due to privacy, energy, and delay constraints rather than an unreachable network.We present a prototype implementation of our architecture over Android smartphones connected via WiFi along with the performance metrics (power/energy consumption and accuracy)to show the benefits of context awareness in TCs.

In this paper, an Android SPEech proCessing plaTform as smaRtphone Application (SPECTRA) is presented. Such application, developed by the authors, has three main functions: i) Gender Recognition (GR), ii) Speaker Recognition (SR) and iii) Language Recognition (LR). All these recognition functions are performed simultaneously by using unsupervised Support Vector Machine (SVM) classifiers. An innovative point of this paper lies in the automatic re-training of the employed SVMs which are able to dynamically update themselves when a (new) audio from a (new) speaker is provided. This allow to build more robust classifiers, which results in better recognition performances. In terms of accuracy, the GR reaches about 98% of correct classifications, SR performs around 80% while LR shows an accuracy of about 74%.

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.

This paper proposes a new location recognition algorithm for automatic check-in applications (LRACI), suited to be implemented within Smartphones, integrated in the Cloud platform and representing a service for Cloud end users. The algorithm, the performance of which is independent of the employed device, uses both global and hybrid positioning systems (GPS/HPS) and, in an opportunistic way, the presence of Wi-Fi access points (APs), through a new definition of Wi-Fi FingerPrint (FP), which is proposed in this paper. This FP definition considers the order relation among the received signal strength (RSS) rather than the absolute values. This is one of the main contributions of this paper. LRACI is designed to be employed where traditional approaches, usually based only on GPS/HPS, fail, and is aimed at finding user location, with a room-level resolution, in order to estimate the overall time spent in the location, called Permanence, instead of the simple presence. LRACI allows automatic check-in in a given location only if the users' Permanence is larger than a minimum amount of time, called Stay Length (SL), and may be exploited in the Cloud. For example, if many people check-in in a particular location (e.g., a supermarket or a post office), it means that the location is crowded. Using LRACI-based data, collected by smartphones in the Cloud and made available in the Cloud itself, end users can manage their daily activities (e.g., buying food or paying a bill) in a more efficient way. The proposal, practically implemented over Android operating system-based Smartphones, has been extensively tested. Experimental results have shown a location recognition accuracy of about 90%, opening the door to real LRACI employments. In this sense, a preliminary study of its application in the Cloud, obtained through simulation, has been provided to highlight the advantages of the LRACI features.

Recent multimedia and location-based services (LBSs) employ information about location, orientation and context of a mobile device. Moreover, the wide spread adoption of Smartphones, usually equipped with powerful processors, accelerometers, compasses and Global and Hybrid Positioning Systems (GPS/HPSs) receivers, has favored the increasing of location- and context-based services over the last years. In this work a novel Location Recognition approach aimed at supporting location aware services, in particular Check-In services, is presented. The proposed method uses both the GPS/HPS positioning information and, in an opportunistic way, the presence of Wi-Fi Access Points (APs). The method is based on the concept of radio FingerPrint (FP) whose definition proposed in this paper, to the best of authors' knowledge, has never been applied previously. The method is suited to be employed where traditional approaches - usually based only on GPS/HPS - fail, as in case of indoor or dense urban environments. Finally, the proposal, practically realized over different Android OS Smartphones, has been tested in terms of performance. The experimental results are very satisfying and open the doors to a wide employment of the method.