The widespread adoption of Internet of Things (IoT) devices in home, industrial, and business environments has made available the deployment of innovative distributed measurement systems (DMS). This paper takes into account constrained hardware and a security-oriented virtual local area network (VLAN) approach that utilizes local message queuing telemetry transport (MQTT) brokers, transport layer security (TLS) tunnels for local sensor data, and secure socket layer (SSL) tunnels to transmit TLS-encrypted data to a cloud-based central broker. On the other hand, the recent literature has shown a correlated exponential increase in cyber attacks, mainly devoted to destroying critical infrastructure and creating hazards or retrieving sensitive data about individuals, industrial or business companies, and many other entities. Much progress has been made to develop security protocols and guarantee quality of service (QoS), but they are prone to reducing the network throughput. From a measurement science perspective, lower throughput can lead to a reduced frequency with which the phenomena can be observed, generating, again, misevaluation. This paper does not give a new approach to protect measurement data but tests the network performance of the typically used ones that can run on constrained hardware. This is a more general scenario typical for IoT-based DMS. The proposal takes into account a security-oriented VLAN approach for hardware-constrained solutions. Since it is a worst-case scenario, this permits the generalization of the achieved results. In particular, in the paper, all OpenSSL cipher suites are considered for compatibility with the Mosquitto server. The most used key metrics are evaluated for each cipher suite and QoS level, such as the total ratio, total runtime, average runtime, message time, average bandwidth, and total bandwidth. Numerical and experimental results confirm the proposal’s effectiveness in foreseeing the minimum network throughput concerning the selected QoS and security. Operating systems yield diverse performance metric values based on various configurations. The primary objective is identifying algorithms to ensure suitable data transmission and encryption ratios. Another aim is to explore algorithms that ensure wider compatibility with existing infrastructures supporting MQTT technology, facilitating secure connections for geographically dispersed DMS IoT networks, particularly in challenging environments like suburban or rural areas. Additionally, leveraging open firmware on constrained devices compatible with various MQTT protocols enables the customization of the software components, a crucial necessity for DMS.

Virtual private network (VPN) represents an HW/SW infrastructure that implements private and confidential communication channels that usually travel through the Internet. VPN is currently one of the most reliable technologies to achieve this goal, also because being a consolidated technology, it is possible to apply appropriate patches to remedy any security holes. In this paper we analyze the performances of open source firmware OpenWrt 21.x compared with a server-side operating system (Debian 11 x64) and Mikrotik 7.x, also virtualized, and different types of clients (Windows 10/11, iOS 15, Android 11, OpenWrt 21.x, Debian 11 x64 and Mikrotik 7.x), observing the performance of the network according to the current implementation of the various protocols and algorithms of VPN tunnel examined on what are the most recent HW and SW for deployment in outdoor locations with poor network connectivity. Specifically, operating systems provide different performance metric values for various combinations of configuration variables. The first pursued goal is to find the algorithms to guarantee a data transmission/encryption ratio as efficiently as possible. The second goal is to research the algorithms capable of guaranteeing the widest spectrum of compatibility with the current infrastructures that support VPN technology, to obtain a connection system secure for geographically scattered IoT networks spread over difficult-to-manage areas such as suburban or rural environments. The third goal is to be able to use open firmware on constrained routers that provide compatibility with different VPN protocols.

In the context of the internet of things, and particularly within distributed measurement systems that are subject to high privacy risks, it is essential to emphasize the need for increasingly effective privacy protections. The idea presented in this work involves managing critical traffic through an architectural proposal aimed at solving the problem of communications between nodes by optimizing both the confidentiality to be guaranteed to the payload and the transmission speed. Specifically, data such as a typical sensor on/off signal could be sent via a standard encrypted channel, while a sensitive aggregate could be transmitted through a dedicated private channel. Additionally, this work emphasizes the critical importance of optimizing message sizes to 5 k-bytes (small payload messages) for transmission over the reserve channel, enhancing both privacy and system responsiveness, a mandatory requirement in distributed measurement systems. By focusing on small, encrypted payloads, the study facilitates secure, timely updates and summaries of network conditions, maintaining the integrity and privacy of communications in even the most challenging and privacy-sensitive environments. This study provides a comprehensive performance analysis of IoT networks using Dark Net technologies and MQTT protocols, with a focus on privacy and anonymity. It highlights the trade-offs between enhanced security and performance, noting increased latency, reduced bandwidth, and network instability when using TOR, particularly with cipher suites like AES256-GCM-SHA384 and DHE-RSA-CHACHA20-POLY1305. The research emphasizes the need for further exploration of alternative protocols like LWM2M in secure IoT environments and calls for optimization to balance privacy with performance in Dark-Net-based IoT deployments.

Nowadays, some of the most well-deployed infrastructures are Virtual Private Networks (VPNs) and Overlay Networks (ONs). They consist of hardware and software components designed to build private/secure channels, typically over the Internet. They are currently among the most reliable technologies for achieving this objective. VPNs are well-established and can be patched to address security vulnerabilities, while overlay networks represent the next-generation solution for secure communication. In this paper, for both VPNs and ONs, we analyze some important network performance components (RTT and bandwidth) while varying the type of overlay networks utilized for interconnecting traffic between two or more hosts (in the same data center, in different data centers in the same building, or over the Internet). These networks establish connections between KVM (Kernel-based Virtual Machine) instances rather than the typical Docker/LXC/Podman containers. The first analysis aims to assess network performance as it is, without any overlay channels. Meanwhile, the second establishes various channels without encryption and the final analysis encapsulates overlay traffic via IPsec (Transport mode), where encrypted channels like VTI are not already available for use. A deep set of traffic simulation campaigns shows the obtained performance.

Some of the most deployed infrastructures nowadays are Overlay Networks (ONs). They consist of hardware and software components designed to establish private and secure communication channels, typically over the Internet. ONs are among the most reliable technologies for achieving this objective and represent the next-generation solution for secure communication. In this paper, we analyze important network performance metrics (RTT, bandwidth) while varying the type of Overlay Network used for interconnecting traffic between two or more hosts (within the same data center, in different data centers in the same building, or over the Internet). These networks establish connections between KVM (Kernel-based Virtual Machine) instances rather than the typical Docker/LXC/Podman containers. The first analysis will assess network performance as it is, without any overlay channels. The second will establish various types of channels without encryption, and the final one will encapsulate overlay traffic via IPsec (Transport mode), where encrypted channels like VTI are not already available for use. The obtained performance is demonstrated through a comprehensive set of traffic-simulation campaigns.

Chronic pain is a pervasive healthcare challenge with profound implications for patient well-being, clinical decision-making, and resource allocation. Traditional detection methods often rely on subjective assessments and manual documentation review, which can be time-consuming and unpredictable. Integrating Artificial Intelligence (AI) into healthcare offers a promising approach to enhance chronic pain management through automated and standardized text analysis. This study examines the use of AI in detecting chronic pain from Italian clinical notes. We leverage machine learning (ML) and natural language processing (NLP) techniques to better understand how chronic pain is documented, thereby enabling efficient, data-driven solutions in nursing and medical practice. We trained XGBoost, Gradient Boosting (GBM), and BERT-based models (BioBit, bert-base-italian-xxl) on 1,008 annotated Italian clinical notes. Input texts were encoded using TF-IDF, Word2Vec, or FastText for tree-based models and tokenized for transformers. While models were trained on full notes, evaluation was performed on fragmented text to simulate realistic usage. Bayesian optimization and stratified cross-validation over 30 trials ensured robust hyperparameter tuning and performance estimates. Our AI-based approach achieved high overall accuracy. In particular, XGBoost with TF-IDF embeddings yielded the best performance, reaching an F1-score of 0.92 ± 0.01, with precision at 94%, sensitivity at 91%, and specificity at 93%. The chronic pain notes contained fewer total words (73.91 vs. 119.86, p = 0.0021) and unique words (57.27 vs. 92.78, p = 0.0006) than non-chronic pain notes, underscoring the significance of concise, keyword-rich clinical documentation. Our findings demonstrate the effectiveness of AI in identifying chronic pain cases from fragmentary clinical notes. By focusing on concise, keyword-oriented text, this work establishes a solid baseline for domain-specific NLP approaches in healthcare. The proposed method reduces the burden of manual review, facilitates real-time decision support, and may standardize chronic pain assessment processes. Furthermore, we plan to explore new embedding techniques specifically designed for short, context-limited clinical notes, where dynamic contextual models (e.g., BERT) often encounter challenges due to insufficient extended textual context.