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.

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.

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.

The deployment of terrestrial/satellite networks, plays a crucial role for for risk and emergency management. In this context, efficient solutions for heterogeneous and mobile networks, including satellite portions that allows wide coverages, represents a key issue. In the mentioned scenario, transmitting video with portable devices (such as smartphone), over terrestrial/satellite networks, to a Remote Monitoring Host (RMH) may support emergency and rescue operations after crisis situations. Unfortunately heterogeneity often implies impairments such as packet losses, due to errors and congestion, which negatively affect the video quality. We present an application layer joint coding algorithm for video transmission, that adaptively applies video compression and channel coding at the application layer, on the basis of the overall network condition estimated in terms of maximum allowable throughput of the network and quality (packet cancellations or lossiness). A deep performance investigation, carried out with real implementation of the algorithm, compares the joint coding against fixed schemes and shows that the joint approach adapt the video transmission to terrestrial/satellite emergency networks so allowing a more efficient resource exploitation.