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.

In this paper, we propose an effective target localization strategy for Internet of Things (IoT) scenarios, where positioning is performed by resource-constrained devices. Target-anchor links may be impaired by Non-Line-Of-Sight (NLOS) communication conditions. In order to derive a feasible IoT-oriented positioning strategy, we rely on the acquisition, at the target, of a sequence of consecutive measurements of the Received Signal Strength Indicator (RSSI) of the wireless signals transmitted by the anchors. We then consider a pragmatic approach according to which the NLOS channels are pre-mitigated and “transformed” into equivalent Line-Of-Sight (LOS) channels to estimate more accurately each target-anchor distance. The estimated distances feed “agnostic” localization algorithms, operating as if all links were LOS. We experimentally assess the performance of our approach in indoor (IEEE 802.11-based) and outdoor (Long Term Evolution, LTE-based) scenarios, considering both geometric and Particle Swarm Optimization (PSO)-based localization algorithms. Even if NLOS mitigation per single communication link is very effective, our results show that, in a given environment, it is possible to derive an “average” NLOS mitigation strategy regardless of the specific position of the target in the given environment. This is crucial to limit the computational complexity at IoT nodes performing localization, yet guaranteeing a relatively high (for IoT scenarios) localization accuracy, especially in an IEEE 802.11-based indoor case (with six anchors). The obtained performance compares favorably (in relative terms) with that obtained with more sophisticated wireless technologies (e.g., Ultra-WideBand, UWB).

In this paper, hybrid radio/inertial mobile target tracking for accurate and smooth path estimation is considered. The proposed tracking approach builds upon an Ultra WideBand (UWB)-based positioning algorithm, based on the Linear Hyperbolic Positioning System (LinHPS), with Time Difference of Arrival (TDoA) processing and anchors concentrated on a single hotspot at the center of the environment where the target moves. First, we design an Adaptive Radio-based Extended Kalman Filter (AREKF), which does not require a priori statistical knowledge of the noise in the target movement model and estimates the measurement noise covariance, at each sampling time, according to a proper LookUp Table (LUT). In order to improve the performance of AREKF, we incorporate inertial data collected from the target and propose three “hybrid” radio/inertial algorithms, denoted as Hybrid Inertial Measurement Unit (IMU)-aided Radio-based EKF (HIREKF), Hybrid Noisy Control EKF (HNCEKF), and Hybrid Control EKF (HCEKF). Our results on experimentally acquired paths show that the proposed algorithms achieve an average instantaneous position estimation error on the order of a few centimeters. Moreover, the minimum target path length estimation error, obtained with HCEKF, is on the order of 6% and 1% for two paths with lengths equal to approximately 17 m and 46 m, respectively.

In this paper, a performance analysis on the estimation of the so-called observation filter for the Virtual Microphone Technique (VMT) in a realistic automotive environment is presented. A performance comparison between adaptive and fixed observation filter estimation methods, namely Least Mean Square (LMS) and Minimum Mean Square Error (MMSE), respectively, was carried on. Two different experimental setups were implemented on a popular B-segment car. Eight microphones were placed at the monitoring and virtual positions in order to sense environmental acoustic noise propagating within the cabin of the car running at variable speed on a smooth asphalt. Our experimental results show that a large spectral coherence between monitoring and virtual microphone signals indicates a potentially effective and relatively wide-band virtual microphone signal reconstruction. The fixed observation filter estimation method achieves better performance than the adaptive one, guaranteeing remarkable broadband estimation accuracy. Moreover, for each considered setup, design guidelines are proposed to obtain a good trade-off between estimation accuracy and material costs.

The goal of this article is to investigate ultra wideband localization with time difference of arrival processing at the anchors. We consider scenarios where the anchors are placed very close to each other and the target to be localized is around the group of anchors. All target–anchor communications are assumed to be in line-of-sight. Since our analysis shows that symmetries in anchors’ placement, with respect to the target position, degrade the positioning accuracy of standard algorithms, we propose to use a subset selection strategy, where position estimates obtained with properly selected subsets of asymmetric anchors are fused together to get the final localization output. Our results show improved localization accuracy with respect to the use of all anchors, especially in estimating the angle of arrival. Finally, we analyze the impact of an inaccurate time synchronization among the anchors, deriving guidelines for hardware implementation.

We consider downlink communications between a Base Station (BS) and various mobile stations, equipped with multiple antennas, based on Orthogonal Frequency Division Multiplexing (OFDM). Transmission is compliant with the Long Term Evolution (LTE) standard operating in Frequency Division Duplex (FDD) mode. Since ideal feedback of channel state information to the BS may be cumbersome, we consider two suboptimal channel estimation algorithms, denoted as Resource Block (RB) and Resource Block Group (RBG). Both approaches approximate the channel as constant over multiples of the fundamental LTE block, known as Physical Resource Block (PRB). Our results show that RB and RBG incur a limited performance loss, yet guaranteeing significant saving in the amount of feedback information.

In this paper, we investigate a wireless communication scenario, where polarization multiplexing is exploited to increase the spectral efficiency. Independent modems over each polarization are considered, with communication links affected by phase noise and cross-polarization interference (XPI). We devise a novel per-polarization soft decision-directed iterative receiver with separatea posterioriprobability-based synchronization and decoding. The synchronization algorithm relies on a minimum mean square error-based master–slave phase estimation followed by the cancellation of the XPI on the polarization of interest and requires no statistical knowledge of the phase noise process. The performance of the proposed iterative receiver is investigated for a pilot symbol-assisted low-density parity-check-coded quadrature amplitude modulation scheme.

In this paper, we analyze a cognitive wireless scenario, where a primary wireless network (PWN) coexists with a cognitive (or secondary) wireless network (CWN). The PWN uses licensed spectrum and the nodes of the CWN cooperate to detect idle subchannels (not used by the PWN's nodes), possibly taking into account the knowledge of their positions'. On the basis of this scenario, we present a simple, yet effective, framework to analyze the tradeoff between the CWN detection capabilities, i.e., the probability of detecting an unused lincensed subchannel, and the energy consumption needed to detect this subchannel. To this end, we introduce a novel performance indicator, denoted as detection energy efficiency. Our results show that there is an optimal working point, i.e., an optimal number of collaborating CWN nodes that allows to achieve the highest detection energy efficiency.

This study discusses synchronisation in phase noise‐impaired spectrally efficient communication systems employing high‐order modulations. In particular, an iterative receiver, where demodulation and decoding are separate from maximum a posteriori probability (MAP) synchronisation, is presented. The authors’ separate approach is tailored to the design of pragmatic iterative receiver schemes employing ‘off‐the‐shelf’ demodulation and decoding blocks. This allows full compatibility with already existing systems, which is attractive from the implementation viewpoint. The proposed MAP synchronisation algorithm also requires very limited knowledge of the phase noise process and achieves near coherent performance with moderate computational complexity. Although the approach is very general, the authors discuss its performance for low‐density parity‐check‐coded pilot symbol‐aided quadrature amplitude modulation schemes, demonstrating that a significantly lower computational complexity can be achieved with respect to benchmark joint receivers.

This paper focuses on phase noise-impaired communications. An efficient Maximum A-posteriori Probability (MAP) iterative synchronization algorithm, where detection and decoding are performed separately from phase estimation, is proposed. This approach has the following key advantages: (i) its computational complexity is relatively low and its performance is near optimal; (ii) it requires very limited statistical knowledge of the phase noise process; and (iii) it enables the direct use of “off-the-shelf” demodulation and decoding blocks. These features are particularly attractive from the implementation viewpoint, as they lead to the design of effective pragmatic high-order coded modulated schemes. The proposed iterative synchronization and decoding algorithm, evaluated for Low-Density Parity-Check (LDPC)-coded pilot symbol-assisted Quadrature Amplitude Modulation (QAM) schemes, entails a negligible energy efficiency loss with respect to optimized joint decoding and phase estimation approaches, with significantly lower computational complexity.

Summary The widespread and ubiquitous availability of Internet access enables the collective sharing of huge amount of data generated by heterogeneous sources. For example, the information, which will be exchanged among entities (sensors, people, and services) of future smart cities to enhance the security and lifestyle of their citizens, poses the challenging question of how this information can be efficiently and effectively maintained across the city. In this article, we propose a decentralized approach, based on the distributed geographic table (DGT) overlay scheme, which exploits geo‐referenced information about nodes to achieve efficient data management. After recalling DGT main concepts, we illustrate the possible node types and how information can be published and retrieved within the network. To cope with the unavoidable node failures and disconnections, our approach leverages upon randomized network coding to increase the robustness of publish/retrieval operations. Evaluation is carried out through an extensive simulation analysis for a realistic urban scenario using the metrics of efficiency in data publication/search, resource availability, and storage occupancy requirements. Results show the approach effectiveness for large‐scale sharing of geo‐referenced information and tradeoffs between redundancy overhead and resource availability. A few results obtained with a preliminary DGT implementation are also presented in the paper. Copyright © 2014 John Wiley & Sons, Ltd.

In this paper, we consider orthogonal multiple access coding schemes, where correlated sources are encoded in a distributed fashion and transmitted through additive white Gaussian noise (AWGN) channels to an access point (AP). At the AP, component decoders, which are associated with the source encoders, iteratively exchange soft information by taking into account the source correlation. The first goal of this paper is to investigate the ultimate achievable performance limits in terms of a multi-dimensional feasible region in the space of channel parameters, deriving insights on the impact of the number of sources. The second goal is the design of pragmatic schemes, where the sources use “off-the-shelf” channel codes. In order to analyze the performance of given coding schemes, we propose an extrinsic information transfer-based approach, which allows to determine the corresponding multi-dimensional feasible regions. On the basis of the proposed analytical framework, the performance of pragmatic coded schemes, based on serially concatenated convolutional codes, is discussed.

In this paper, some novel results on the encoding complexity of network coding and its relation with the network topology are reported. The encoding complexity in network coding is defined as the number of nodes which have to perform coding operations in order to achieve the multicast capacity. These nodes are referred to as coding points. Known results state that the number of coding points is cubic in the mincut and quadratic in the number of receivers. In this paper, we show, through extensive simulations and through analysis of these results, that the number of coding points tends to increase linearly in the min-cut and the number of receivers in random graphs. We show that this is correlated to the length of path from the source to the receivers. To verify this, we also analyze pseudo-random graphs with a larger path length.

In this paper, we present a mathematical framework to study decentralized detection in IEEE 802.15.4-compliant clustered wireless sensor networks (WSNs). Sensors are organized in clusters, with fusion centers (FCs) acting as cluster heads, and are supposed to observe the same common binary phenomenon. The FCs periodically send queries to sensors and wait for replies. Once data are received, the FCs perform majority fusion. Decisions are sent to an access point (AP), where a final data fusion is carried out and an estimate of the phenomenon is obtained. A data aggregation strategy at sensors is also investigated. The goal of this paper is to jointly investigate decentralized detection, medium access control (MAC), and data aggregation issues. Our results show that an optimum WSN clustering configuration exists and it depends on the data aggregation strategy used and the MAC parameters.

In this paper, we study the performance of non-cooperative multiple access systems with noisy separated channels, where correlated sources communicate to an access point (AP) through block-faded links. In the considered scenario, perfect channel state information (CSI) is assumed at the receiver while no CSI is available at the transmitters. We first consider uncoded transmissions from the sources to the AP, which exploits the source correlation to carry out joint channel detection (JCD). In this scenario, we propose an analytical approach to evaluate the achievable performance in terms of average bit error rate (BER). We then investigate the impact of coding, considering the same fixed coding scheme at each source. Then, we consider an extrinsic information transfer (EXIT) chart-based framework to optimize the design of concatenated and low-density parity-check (LDPC) codes for JCD schemes.

In this paper, we analyze the lifetime of clustered sensor networks with decentralized binary detection under a physical layer quality of service (QoS) constraint, given by the maximum tolerable probability of decision error at the access point (AP). In order to properly model the network behavior, we consider four different distributions (exponential, uniform, Rayleigh, and lognormal) for the single sensors' lifetime. We show the benefits, in terms of longer network lifetime, of adaptive reclustering. On the other hand, absence of reclustering leads to a shorter network lifetime, and we show the impact of various clustering configurations under different QoS conditions. Our results show that the organization of sensors in a few big clusters is the winning strategy to maximize the network lifetime