Ultra-wideband technology is applied for indoor positioning systems and achieves a position accuracy in the order of decimeters. A trending approach in this context relies on combined angle-of-arrival and time-of-flight measurements, and enables localization using a single anchor, thereby reducing infrastructure overhead. Although analytical models already exist for Ultra-wideband-based distance estimation using time-of-flight, no model has been proposed for its angle-of-arrival counterpart. In this paper we cover this gap by investigating the use of 4 different machine learning regressors to generate such models. The models were trained with data from real-world experiments performed with commercial off-the-shelf Ultra-wideband modules. The models can be easily integrated in simulators, facilitating and even enabling the evaluation of scalable positioning systems using this technology. Among the tested regressors, the random forest regressor presented the best fit to the experimental data, with MAE of the “mean” parameter of 8°.

Hybrid time-of-flight and phase-difference-of-arrival ultra-wideband-based localization systems enable indoor position estimation of mo-bile nodes at a low infrastructure overhead. A known drawback in such systems is the decreasing localization accuracy over distance from node(s) with known location. In this paper we evaluated 3 ML algorithms: Random Forest Regression, LightGBM Regression, and Linear Regression to mitigate this issue. We evaluate the ben-efit of using those methods on experimental data obtained with commercial off-the-shelf transceivers. Our results show that the Random Forest Regression outperforms a typical linear regression correction by 0.49±0.01 (mean±std) meters in positioning accuracy at an computing overhead of nearly 0.34±0.003 [s].

Distance bounding has gained attention in the last decades due to the increasing need for security in applications, such as controlling drones and keyless access control. In such applications, it is important to verify if the two entities participating in a transaction are geographically close to each other. A distance fraud is a known class of attacks in this context, and it has been shown that particular attacks within this class can be successfully applied to any distance estimation approach relying on round-trip time-of-flight (ToF) measurements. In this paper, we show that by combining ToF-based with Received Signal Strength (RSS)-based distance estimations, a system comprising at least two anchors can detect distance reduction and enlargement frauds. The latter so far had no general solution. Providing such a solution constitutes the main contribution of this paper. The proposed solution is based on the fact that the apparent position of the prover is constrained to two different feasibility regions: one for RSS and another for ToF-based measurements. These feasibility regions overlap only at the true position of the prover and get inconsistent with an increasing magnitude of the attack. The proposed hybrid method adds virtually no overhead to a ToF-based system, and not only achieves a tighter bound on distance reduction frauds than the pure ToF method, but (tightly) bounds the distance enlargement fraud, which is currently unbounded when using the pure ToF method. Additionally, it requires only two verifiers, outperforming a well-known solution, which requires at least three verifiers. Simulation results show that when using verifiers separated by just 10 cm, suitable for implementation in a single device, the system is able to detect enlargement frauds as small as 120 cm with a high probability.

Recovering causality from copious time series data beyond mere correlations has been an important contributing factor in numerous scientific fields. Most existing works assume linearity in the data that may not comply with many real-world scenarios. Moreover, it is usually not sufficient to solely infer the causal relationships. Identifying the correct time delay of cause-effect is extremely vital for further insight and effective policies in inter-disciplinary domains. To bridge this gap, we propose KOMPOS, a novel algorithmic framework that combines a powerful concept from causal discovery of additive noise models with graphical ones. We primarily build our structural causal model from multivariate adaptive regression splines with inherent additive local nonlinearities, which render the underlying causal structure more easily identifiable. In contrast to other methods, our approach is not restricted to Gaussian or non-Gaussian noise due to the non-parametric attribute of the regression method. We conduct extensive experiments on both synthetic and real-world datasets, demonstrating the superiority of the proposed algorithm over existing causal discovery methods, especially for the challenging cases of autocorrelated and non-stationary time series.

With silicon technology decreasing in size, memories get more susceptible to external influences, which can lead to soft errors. Although temporary, these errors constitute a challenge for safety-critical systems. Redundancy-based error detection is commonly used in industry to increase safety and mitigate these errors. When an error is detected, safety-critical systems are usually switched to a safe state. While this prevents failures, it negatively affects the system's availability. In this work, we propose Just-in-Time fault detection, a novel method which enables a system to be switched to the safe state only in case a detected error would affect the system's behavior. A software tool enabling the deployment of this method on an off-the-shelf processor is implemented, and the method is validated and compared with a state-of-the-art alternative approach using mixed-critical memories. Our results show an availability gain between 25.2% and 100% compared with the state-of-the-art approach while executing two different standard algorithms.

Using Angle-of-Arrival based on Phase-Difference-of-Arrival is a recent trend in indoor positioning. Although the basic techniques for direction estimation have been known and used for a long time in different application scenarios (e.g., RADAR), the complexity of Phase-Difference-of-Arrival transceivers discouraged its use for indoor applications. This scenario changed with the recent development of modern chips at competitive prices. Specifically, two ubiquitous technologies in the context of indoor positioning, namely Bluetooth Low Energy (BLE) and Ultra-Wideband (UWB), currently feature Angle-of-Arrival estimation. In this paper we compare these two technologies regarding their potential for indoor positioning based on a fair evaluation of their performance for estimating Angle-of-Arrival in five different realistic setups. The results obtained are compared with existing studies when available. Our results show that Ultra-Wideband is, in general, more accurate and precise, especially when subject to multipath interference. Additionally, we discuss possible approaches to improve the accuracy and precision of Angle-of-Arrival estimation using BLE, such as the impact of increasing the number of antenna elements. By using UWB, we achieved an angular accuracy of up to 5° even under obstructed LOS and multipath, while an overall mean error of nearly 25° was obtained with BLE in an outdoor scenario without obstacles.

Knowing whether a room is occupied or not is crucial for improving electrical energy efficiency. For instance, if a given room is empty there is no need for the lights to be turned on. Usually in small rooms such as elevator halls, a PIR sensor is used together with the lighting, but as it lacks of precision, people often are left in the dark after a few minutes. In the present work a device capable of counting people in a room is implemented and analyzed. It is also compared with other solutions.