Secure indoor ranging is a critical component of modern industrial systems, with applications in access control, contactless payments, and industrial automation. This article presents the first comprehensive survey focusing exclusively on secure-ranging protocols for Bluetooth, ultrawideband (UWB), and Wi-Fi. Unlike localization, which relies on multiple anchors, ranging involves only two devices; therefore, popular attack detection methods based on multianchor data do not generalize to secure ranging. This article begins by detailing industry-standard protocols such as IEEE 802.15.4a for UWB, Bluetooth, and IEEE 802.11 for Wi-Fi, followed by an analysis of their vulnerabilities and distance manipulation attacks. Common attack strategies, including overshadow, early path injection, and relay attacks, are explored. To mitigate these threats, we review countermeasures grouped into two main categories: randomization and channel integrity verification, which incorporate techniques from machine learning, threshold-based anomaly detection, and cryptography. Finally, we highlight cross-technology insights, discuss lessons learned, and propose future research directions to address unresolved challenges in indoor-ranging security.

The increasing commercial viability of body area network (BAN) devices has expanded their applications beyond smartwatches and smartphones to include wearables for industrial safety, healthcare monitoring, and augmented reality systems. Secure communication between these devices remains a challenge, as traditional cryptographic key exchange protocols impose significant computational and power constraints. Physically Derived Symmetric Key (PDSK) generation offers a lightweight alternative by leveraging the shared wireless channel characteristics to establish encryption keys without prior trust. Recent studies have demonstrated the effectiveness of PDSK in ultra-wideband (UWB) systems, as UWB’s high-resolution channel measurements capture high-entropy yet highly correlated signal variations at different devices. In this work, we evaluate the feasibility of UWB-based PDSK in BAN wearable devices. Our results show that secret keys can be reliably generated, achieving a bit agreement rate (BAR) of 84.14% and a key success rate (KSR) of 92.7% while generating 240-bit keys in 0.35s. We further analyse key generation performance across different wearable placements and movement conditions, demonstrating that UWB-based PDSK is a viable and practical solution for secure communication in BAN applications.

NLOS and multipath propagation are the main hurdles for accurate localization with time-based localization systems. Over the years, researchers have proposed myriad approaches to mitigate the effects of Non-Line-of-Sight (NLOS) and multipath propagation for accurate indoor localization. Residual Weighting (Rwgh) is a widely used technique for NLOS mitigation in literature. This is because it does not require expensive site surveys or statistical information of the channel. Also, position estimation is possible even if all the nodes (anchors) are in NLOS. We observed that with Ultra-Wideband (UWB), the ranging results in Line-Of-Sight conditions are stable compared to NLOS and multipath ranging scenarios. Based on this, we propose a Rwgh approach by exploring the stability of the range measurements obtained during the localization phase. The stability of the range measurement gives an indication of the veracity of the introduced residuals, which is then weighted to mitigate the NLOS effects directly during localization. We test the performance of the proposed approach with data collected from two distinct real-world environments with heavy NLOS and multipath presence instead of simulated data as used in the preponderance of Rwgh-based approaches.

A major challenge to Ultra-Wideband (UWB) positioning, especially in indoor environments, is the prevalent presence of Non-Line-Of-Sight (NLOS) and multipath propagations, degrading localization performance. Moreover, indoor settings tend to constantly change, making it difficult to characterize NLOS signals each time these changes occur. Promising approaches have been proposed to identify NLOS signals before localization; however, their performance is limited to the specific environment where measurements were carried out and cannot be extended to new or different environments. This can be attributed to feature discrepancies causing domain shifts and distribution divergence, owing to differences in environments captured by the Channel Impulse Response (CIR) waveforms. In this paper, we propose a domain Adaptive NLOS (AdaLOS) identification framework for UWB positioning systems. First, adaptation knowledge is obtained to align the source and target domains’ CIRs to mitigate the domain shift problem via cross-domain mappings. The distribution gap of the CIRs is further reduced by minimizing the marginal and conditional distribution divergence between the two domains (environments) by employing the Maximum Mean Discrepancy (MMD) criterion. A joint optimization procedure is then formulated to minimize the domain shift, marginal, and conditional distribution differences between CIRs from the two domains simultaneously. After minimizing the domain difference, Transformed Representative Features (TRF) of the source and target domains are obtained to train a domain-invariant classifier. We perform extensive simulations with CIR information collected from four distinct indoor environments characterized by different relative permittivity, signal distortions, noise, etc. AdaLOS is effective in adapting to new, distinct environments and significantly outperforms state-of-the-art works for NLOS identification.

Physical layer key sharing has become a popular topic in today's literature, as it could provide an alternative to computationally expensive key-sharing protocols. Its importance is emphasized by several resource-constrained, battery-powered autonomous robots, and personal devices that must communicate within modern-day industrial complexes. Physical layer key sharing has been explored using temporally and spatially variant characteristics of signals to produce the same secret keys at different devices. In this paper, we propose a novel method for ultra-wideband (UWB)-based physical layer key sharing, leveraging an off-the-shelf plug-and-play UWB module and utilizing the frequency domain of the Channel Impulse Response (CIR) magnitude. The frequency domain effectively aligns and denoises CIR samples, increasing the spatial and temporal uniqueness of channel characteristics. In turn, our approach offers the advantage of creating high-entropy keys with a 92% success rate. Our paper is the first to examine employing an UWB module for key sharing under different dynamic scenarios. Finally, our system maintains a higher level of security against several types of attackers compared to standard CIR methods.

Positioning with ultrawideband (UWB) is prominent among industrial localization systems, due to its high-range resolution attributes and lower cost. However, one notable challenge with UWB positioning in industrial environments is the prevalent presence of nonline-of-sight (NLOS) components or signals that degrade localization performance drastically. Coupled with this, industrial settings tend to constantly change making NLOS signals challenging to characterize each time these changes occur. Recently, promising approaches have been proposed to identify NLOS components, however their performances are limited to the specific environment where measurements were performed. Their performance cannot be extended to other unknown environments due to the distribution divergence problem, owing to differences in environments captured by the channel impulse response (CIR) waveforms. This therefore requires laborious processes of data collection and training environment-specific models for NLOS identification. In this article, we propose a robust transfer learning-based NLOS identification approach, which harnesses transition information via cross-domain mappings from both source and target domains, to construct representative homogeneous features of both domains. The representative homogeneous features capture discriminative information of both domains, while reducing the distribution divergence between the domains, making it easy to classify LOS and NLOS components from both environments together. To test the robustness of our approach, we perform extensive simulations with CIR data collected from two distinct environments—“hard NLOS” (characterized by high relative permittivity of surrounding objects, e.g., thick concrete walls, metallic objects, etc.) and “soft NLOS” (characterized by low relative permittivity of surrounding objects, e.g., plasterboard walls). Our proposed approach is not just effective in transferring knowledge between distinct environments, but significantly outperforms state-of-the-art works to NLOS identification in UWB positioning networks, while reducing the laborious process of data collection in the target domain.

With 2-factor authentication practices becoming ever more popular, the need for developing new authentication procedures is gaining ever more traction. Rhythm-based gesture authentication appears to be a promising area of research as it encompasses two of the three main authenticator factors: something you know(the chosen rhythm) and something you are(how you enter that rhythm). Rhythm-based authentication approaches have mostly been achieved using touchscreen functions. But accelerometers and gyroscope sensors have the unique ability to capture the small differences in how users enter a set rhythm and how they hold their phone. Additionally, these sensors do not limit our approach to mobile devices with mobile screens but can be expanded to headsets, smart glasses, and screen-less fitness trackers. It also circumvents issues like wearing face masks and gloves. All 12 of our participants were asked to input the same tapping rhythm consisting of 7 taps, 50 times, totaling 600 samples to be used for trial and testing. If our system is able to perform well under these conditions it proves that even an attacker who knows your rhythm, wouldn't be able to access your device. This could be equated to you telling someone your password, but them still not being able to gain access to your phone. Our models were able to achieve an authentication accuracy of 99.65% only using 10 valid samples for training and an identification accuracy of 99.17 %.

In the fourth industrial revolution (Industry 4.0), robotics and autonomous navigational systems are essential for smart agriculture. Industrial smart agriculture relies heavily on autonomous navigational systems, which have important uses in unmanned farms, industrial supply chains, etc. For effective navigation of autonomous robotic systems in industrial settings, indoor-based positioning and navigation technologies play key roles. However, positioning performance is severely impacted by the abundance of Non-Line-Of-Sight (NLOS) components due to the dynamic nature of industrial environments. Different approaches have been proposed in literature for identifying and mitigating NLOS components in UWB positioning systems. However, the performance of these proposed approaches on par in multiple distinct environments is unknown. In this paper, we experimentally investigate and compare the performance of recent UWB-based state-of-the-art approaches to NLOS identification and mitigation on par, in distinct and dynamic obstructed settings to ascertain two key insights: i) how the performance of existing approaches change based on the environment type; ii) the impact of the environment type on NLOS identification and mitigation.

It is notable that localization accuracy using received signal strength (RSS) fingerprints solely is very vulnerable to dynamic environments. Utilizing multiple fingerprints gleaned from RSS for localization is a propitious strategy to overcome the RSS susceptibility. Brimful utilization via fusing multiple fingerprint functions which supplement each other are not harnessed by existing fusion-based techniques, resulting in low localization accuracy. This paper presents a novel and robust WiFi localization modus operandi by fusing DerIvative Fingerprints of RSS with MultIple Classifiers (DIFMIC). DIFMIC first constructs a multiple fingerprints group by gleaning hyperbolic location fingerprint (HLF) and signal strength differences fingerprint (DIFF) from RSS fingerprints. Then, it obtains Multiple Fingerprints Trained Classifiers (MFTCs) via training each basic classifier with each fingerprint. To fully leverage the inherent supplementation among fingerprints and classifiers, a two-layer fusion profile (weights) joint optimization algorithm with multiple constraints is proposed. We also propose a Fusion Profile Selection (FPS) algorithm to intelligently choose fusion weights from the two-layer fusion profile for a more accurate localization. DIFMIC shows more leverage in combining multiple information, thus exhibiting better robustness in WiFi positioning. Results from our experiments reflect that DIFMIC performs better than other existing methods in real environments.