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 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.

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.

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.

The eHealth systems are in great demand, particularly during times of outbreak like COVID-19, when there is a shortage of caregivers. The technological advancements, such as wearable wireless devices, the Internet of Things, and improved machine learning methods have made these systems more reliable. In modern times, these systems can play a vital role in post-rehabilitation journeys, which have significant social impact and high costs in traditional settings. Cost efficiency, portability, and generalization are key factors in adopting new technology. In this study, we investigate the potential for optimizing and simplifying hardware in order to increase the cost-effectiveness and versatility of post-stroke eHealth rehabilitation systems. It leverages the rich information available from Inertial Measurement Unit (IMU) sensors to compensate the need for foot pressure sensing. We present the first attempt to demonstrate the potential of machine learning, aided by affordable off the shelf motion sensing devices, for foot pressure analysis. Our proposed foot pressure decoding model is trained in an exercise-agnostic, self-supervised manner that eliminates the need for human annotation. The algorithm is evaluated using appropriate performance metrics, and our experimental results show very promising performance.

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.

The objective of the FRACTAL project is to create a novel approach to reliable edge computing. The FRACTAL computing node will be the building block of scalable Internet of Things (from Low Computing to High Computing Edge Nodes). The node will also have the capability of learning how to improve its performance against the uncertainty of the environment. In such a context, this paper presents in detail one of the key use cases: an Internet-of-Things solution, represented by intelligent totems for advertisement and wayfinding services, within advanced ICT-based shopping malls conceived as a sentient space. The paper outlines the reference scenario and provides an overview of the architecture and the functionality of the demonstrator, as well as a roadmap for its development and evaluation.

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 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, 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.