The growing proliferation of Wi-Fi 6E devices, which operate at a high transmission power in the same frequency band used by ultra-wideband (UWB) technology, poses significant challenges to the reliability of UWB-based systems. Prior work has explored the impact of Wi-Fi 6E traffic on UWB performance experimentally using real-world testbeds, but lacks a theoretical framework capturing how Wi-Fi 6E’s physical-layer (PHY) settings, signal strength, and traffic profile affect UWB performance. We address this gap by introducing a probabilistic error model that accurately estimates the packet error rate (PER) of UWB systems operating alongside Wi-Fi 6E traffic. Using a conducted test setup, we analyze coexisting transmissions to characterize real-world Wi-Fi 6E traffic (e.g., airtime, inter-arrival times, and channel occupancy) and quantify the sensitivity of UWB transmissions to Wi-Fi 6E interference for different PHY settings, traffic profiles, and signal strengths. We leverage this characterization to parametrize the model using different approaches, ranging from a trace-based estimation of the collision probability to an analytical derivation of Wi-Fi 6E’s channel occupancy. This allows us to explore different trade-offs w.r.t. the model’s accuracy and generality. Our experimental evaluation demonstrates that our model accurately captures the UWB PER, with average deviations as low as 3.6% on conducted measurements and below 9.7% on real-world experiments in a university building.

Indoor localization systems based on UltraWideBand (UWB) technology can typically achieve cm-level accuracy, but their performance degrades in Non-Line-of-Sight (NLoS) conditions. To cope with this problem, Machine Learning (ML) techniques have been applied to detect such NLoS conditions and adapt the localization algorithm accordingly. However, such ML techniques are typically optimized for accuracy, resulting in computationally-complex models that cannot be run on resource-constrained UWB devices. In this paper, we study and propose methods to reduce the computational complexity of NLoS classification models by applying ML-based feature selection and by reducing the window length of the channel impulse response for feature extraction. Specifically, we consider 29 features and study the effect of feature selection across five different datasets to obtain generalizable results. We show that we can extract two sets of only 3 and 8 features, which result in tiny ML models (smaller than 1kB), and low computation times ($3.6 \mathrm{~ms}$ and $27.7 \mathrm{~ms}$ on a 80MHz ESP8266 microcontroller, respectively). This allows a reduction of the runtime by more than $90 \%$ compared to the state of the art, while still maintaining an average classification accuracy above $85 \%$ across all five datasets.

The introduction of the Wi-Fi 6E standard operating in the 6 GHz frequency band is a serious threat for IoT systems based on ultra-wideband technology, as they share portions of the same spectrum. Wi-Fi 6E devices can in fact support channel bandwidths up to 160 MHz and operate at a much higher transmission power compared to ultra-wideband devices, which may lead to severe coexitence issues and degraded performance. However, whether and to which extent the performance of ultra-wide band systems worsens due to Wi-Fi 6E interference has not been investigated in detail yet. In this paper, we fill this gap and study how Wi-Fi 6E traffic affects ultra-wideband performance. Our experiments on a large-scale testbed demonstrate that Wi-Fi 6E transmissions may largely disrupt ultra-wideband communications and decrease the accuracy as well as the precision of ranging measurements, with significant consequences on the efficiency of localization systems. We investigate in detail the root causes for the degraded performance and derive empirical observations that can be used to design countermeasures mitigating the impact of Wi-Fi 6E interference. These include, among others, an optimal selection of physical layer settings, as well as the use of a tight synchronization to prevent a false detection of Wi-Fi 6E traffic as ultra-wide band frames and an overshooting of the radio's automatic gain control. We further devise a technique to detect the presence of Wi-Fi 6E traffic and postpone ultra-wideband transmissions accordingly. Our experiments demonstrate that these countermeasures effectively mitigate the impact of Wi-Fi 6E interference on the performance of ultra-wide band systems.

Positioning systems based on ultra-wideband (UWB) technology are becoming ubiquitous and enable a plethora of attractive Internet of Things applications, ranging from smart access and asset tracking to the navigation of autonomous vehicles. As these positioning systems are often deployed over large areas, the focus of UWB-based research has recently shifted to the development of scalable solutions that can offer a high positioning accuracy for countless tags while maximizing energyefficiency. At the same time, as positioning systems are increasingly used in safety-critical settings, several academic efforts and the standardization activities of the IEEE 802. 15.4 z working group have laid the foundations for a secure distance estimation using UWB technology. Unfortunately, these two endeavours have followed independent tracks that do not blend together. In this paper, we highlight this issue and describe the challenge of securing modern UWB-based positioning systems that are designed with scalability in mind. We first illustrate how the use of unidirectional communications, the need for synchronized anchors, and the use of quasi-simultaneous responses, which are common features of recent scalable UWB systems based on timedifference-of-arrival, make these solutions vulnerable to several attacks, despite the use of IEEE 802. 15.4 z. After carrying out a security analysis and describing how scalable UWB systems are exposed to several attacks, we devise a number of design concepts to counteract the identified attacks and secure these systems.

A large body of work has shown that ultra-wideband (UWB) technology enables accurate indoor localization and tracking thanks to its high time-domain resolution. Existing systems, however, are typically designed to localize only a limited number of tags, and involve the exchange of several messages following a given schedule. As a result, the scalability of current solutions in terms of tag density is limited, as well as their efficiency and responsiveness. In this paper, we present SnapLoc, a UWB-based indoor localization system that allows an unlimited number of tags to self-localize at a theoretical upper bound of 2.3 kHz. In SnapLoc, a tag obtains the responses from multiple anchors simultaneously. Based on these signals, the tag derives the time difference of arrival between anchors and estimates its position. Therefore, SnapLoc does not require tags to actively transmit packets, but to receive only a single message. This allows tags to passively localize themselves and ensures that the performance of SnapLoc does not degrade with high node densities. Moreover, due to the (quasi-)simultaneous responses, a tight clock synchronization between anchors is not needed. We have implemented SnapLoc on a low-cost platform based on the Decawave DW1000 radio and solved limitations in the transceiver's timestamp resolution to sustain a high localization accuracy. An experimental evaluation shows that SnapLoc exhibits a 90% error and median error of 33 cm and 18 cm, respectively, hence enabling decimeter-level accuracy at fast update rates for countless tags.

In this demo, we present SnapLoc, a UWB-based indoor localization system that allows decimeter-accurate self-localization of mobile tags by listening to only a single message. To this end, SnapLoc leverages (quasi-)simultaneous responses of multiple anchors. Based on these responses, the tag derives the time difference of arrival between anchors and unambiguously estimates its position. Thanks to this principle, SnapLoc carries out passive localization and supports an unlimited number of tags at high update rates. Our SnapLoc implementation runs on the off-the-shelf Decawave DW1000 UWB transceiver. The latter suffers from a limited transmit timestamp resolution that affects the achievable localization accuracy when used in conjunction with concurrent anchor responses. Therefore, in this demo we also showcase techniques to overcome these limitations and achieve nevertheless a high localization accuracy.