Location-Based Services (LBS) are increasingly important across different applications, particularly within the growing Internet of Things (IoT) domain, as they provide accurate context-aware information, i.e., position about the devices moving across a network. One of the most used LBS technologies exploits the distance between pairs of network nodes to determine the position of the target. Although several methods exist to compute such distances, the Received Signal Strength Indicator (RSSI) is the one with the lowest computational complexity, which is desirable in IoT-based applications. High-quality experimental data is crucial to effectively researching new strategies for positioning and navigation in interference-prone environments such as industrial ones. This work presents an experimental methodology comprising multiple wireless technologies, WiFi and Bluetooth Low Energy (BLE), to generate a dataset of RSSI measurements in an indoor industrial research laboratory. This dataset includes detailed ground truth data on target positions, orientations, and velocities, enabling a thorough evaluation of different positioning methods and advancing the development of accurate and robust solutions. In particular, this paper validates the dataset through experimental tests employing a fingerprinting-based approach.

The growing integration of IoT devices into Edge and Fog infrastructures, alongside the increasing adoption of low-latency QUIC-based protocols like HTTP/3, has intensified the need for lightweight, resource-efficient security mechanisms to counter emerging threats such as request smuggling. Within this context, proxy-based architectures offer an optimal trade-off to strengthen network security while accommodating the limited computational capacity of IoT devices. In this direction, this paper presents a comprehensive experimental evaluation of the impact of different proxies for HTTP/3 services on resource usage when deployed on platforms such as the Raspberry Pi (RPi), considering diverse traffic patterns, operational conditions, and device configurations. The results highlight that proxies can achieve a promising balance between security and resource overhead, confirming their viability for integration into distributed IoT-based Edge and Fog networks.

This paper examines the challenges posed by constructive-destructive interference and Denial of Service (DoS) jamming attacks on multilateration-based localization algorithms, particularly concerning Least Square (LS)-based approaches. Despite the increasing interest in localization technologies in a wide range of applications, the vulnerabilities of these systems to such attacks have been insufficiently explored in existing literature. To evaluate the impact of jamming, scenarios with varying numbers of malicious nodes are considered in this paper to assess their effects on localization accuracy. The results reveal a significant degradation in accuracy as the number of compromised anchors increases. However, the implementation of mitigation strategies leads to a substantial performance improvement, effectively reducing the impact of interference and maintaining lower position estimation errors.

This paper analyzes the impact of specular signal reflections on the accuracy of Received Signal Strength (RSS)-based localization for Internet of Things (IoT) devices using the weighted least squares (WLS) regression algorithm within a two-ray propagation channel. Simulations with realistic WiFi/BLE settings, considering distance, antenna heights, and carrier frequency, reveal that localization accuracy is significantly influenced by deep fades caused by surface reflections, which depend on the geometry of anchor-target positions. A pseudo-outlier elimination approach based on feasible localization distances effectively mitigates this issue, significantly reducing localization error. These findings offer practical insights into the performance of WLS-based IoT localization in two-ray environments and lay the groundwork for GPS-free or GPS-denied localization systems in challenging scenarios, such as overwater environments, where two-ray propagation is predominant.

Human Activity Recognition (HAR) techniques play a key role in identifying and categorizing human activities based on environmental information. More recently, the use of Channel State Information (CSI) has gained momentum because this information can be extracted in a non-intrusive manner. CSI-based algorithms leverage the correlation between CSI dynamics of wireless transmissions and human body movements. However, privacy concerns may arise, as this approach may inadvertently disclose sensitive information about individuals’ movements, habits, and behaviors. In this context, this study investigates the challenge of preserving user privacy in CSI-based HAR for eHealth Ambient Assisted Living (AAL) applications. More specifically, the impact of simple filter-based CSI obfuscation is evaluated on the accuracy performance of a HAR model that makes use of a Long-Short-Term Memory (LSTM) algorithm. Using a publicly available dataset, the accuracy between the original and the obfuscated versions of the dataset generated by simple filtering techniques is compared. The results show significant performance degradation when data is obfuscated, with a HAR accuracy degradation compared to the original results ranging from a minimum of 17.9% to a maximum of nearly 90%. Such results prove that, even with simple obfuscation techniques, the privacy of CSI-enabled HAR-based systems can be sufficiently preserved.

This paper addresses the problem of data privacy in Human Activity Recognition (HAR) applications for eHealth. Cryptography, a proven privacy safeguard on the Internet, remains underutilized in the HAR context, as observed in the existing literature. This study highlights the importance of cryptographic practices by conducting a performance analysis, focusing on the accuracy of HAR with and without data en-cryption. This paper proves that even by introducing a very simple cryptographic mechanism, a potential eavesdropper would experience a reduction of more than 20% of accuracy in the activity recognition task as compared to the case where no encryption is used, at the price of a limited increase in the energy consumption for the involved sensors. Such preliminary results demonstrate the effectiveness of encryption for applications of this type, encouraging further exploration and refinement in this direction.

The last years have been characterized by strong market exploitation of the Internet of Things (IoT) technologies in different application domains, such as Industry 4.0, smart cities, and eHealth. All the relevant solutions should properly address the security issues to ensure that sensor data and actuators are not under the control of malicious entities. Additionally, many applications should at the same time provide low-latency communications, as in the case for instance of remote control of industrial robots. Low latency and security are two of the most important challenges to be addressed for the successful deployment of IoT applications. These issues have been analyzed by several scientific papers and surveys that appeared in the last decade. However, few of them consider the two challenges jointly. Moreover, the security aspects are primarily investigated only in specific application domains or protocol levels and the latency issues are typically investigated only at low layers (e.g., physical, access). This paper addresses this shortcoming and provides a systematic review of state-of-the-art solutions for providing fast and secure IoT communications. Although the two requirements may appear to be in contrast to each other, we investigate possible integrated solutions that minimize device connection and service provisioning. We follow an approach where the proposals are reviewed by grouping them based on the reference architectural layer, i.e., access, network, and application layers. We also review the works that propose promising solutions that rely on the exploitation of the QUIC protocol at the higher levels of the protocol stack.

This work addresses the problem of security and low latency in communications typical of several Internet of Things (IoT) scenarios, such as those in Industry 4.0 applications. In particular, we propose a WebSocket over QUIC (WS-QUIC) protocol for intra-network communications between the IoT devices and the gateway. In particular, low latency is achieved by combining the connection persistence of WebSocket (WS) with the reduced connection establishment time required by QUIC. Moreover, the use of QUIC implicitly exploit the security extensions of WS provided by the Transport Layer Security (TLS) protocol. We experimentally analyzed the performance of the proposed system and compare it with that provided by other Web-based secure protocols, such as HyperText Transfer Protocol Secure (HTTPS) and WebSocket Secure (WSS). Our results show that WS-QUIC outperforms HTTPS and WSS for medium-large file sizes. Moreover, the use of the so-called TLS ticket resumption makes WS-QUIC suitable also for medium-small file sizes. Finally, we also discuss the potential use of a single shared session ticket between different IoT devices in the same cluster to further decrease the latency.