arXiv subjects

Andreas Springer

Publications and source records attributed to Andreas Springer.

12 recordsLinked to original sources

arXiv2026

Automated Testbed for Repeatable Evaluation of Ultra-Wideband Localization Performance

Testing Ultra-Wideband (UWB) systems is challenging, as multiple devices need to coordinate over lossy links and the systems' behavior is influenced by timing, synchronization, and environmental factors. Traditional testing is often insufficient to capture these complex interactions, highlighting the need for an overarching testbed infrastructure that can manage devices, control the environment, and make measurements and test scenarios repeatable. In this work, we present a highly automated testbed architecture built on Robot Operating System Version 2, integrating device management with environmental control and measurement systems. It includes an optical reference system, a controllable Autonomous Guided Vehicle to position devices within the environment, and time synchronization via Network Time Protocol (NTP). The testbed achieves a Root Mean Squared Error of 4.8 mm for positioning repeatability and 0.493$\deg$ for the orientation, and our NTP-based synchronization approach achieves a timing accuracy of below 1 ms. All testbed functionality can be controlled remotely through simple Python scripts to allow automated orchestration tasks such as conducting complex measurement scenarios. We demonstrate this with a measurement campaign on UWB localization, showing how it enables repeatable, observable, and fully controlled wireless experiments.

eess.SP↗

arXiv2024

Trustworthiness for an Ultra-Wideband Localization Service

Trustworthiness assessment is an essential step to assure that interdependent systems perform critical functions as anticipated, even under adverse conditions. In this paper, a holistic trustworthiness assessment framework for ultra-wideband self-localization is proposed, including attributes of reliability, security, privacy, and resilience. Our goal is to provide guidance for evaluating a system's trustworthiness based on objective evidence, so-called trustworthiness indicators. These indicators are carefully selected through the threat analysis of the particular system. Our approach guarantees that the resulting trustworthiness indicators correspond to chosen real-world threats. Moreover, experimental evaluations are conducted to demonstrate the effectiveness of the proposed method. While the framework is tailored for this specific use case, the process itself serves as a versatile template, which can be used in other applications in the domains of the Internet of Things or cyber-physical systems.

eess.SP↗

arXiv2023

Predicting the Channel Access of Bluetooth Low Energy

Bluetooth Low Energy (BLE) is one of the key enablers for low-power and low-cost applications in consumer electronics and the Internet of Things. The latest features such as audio and direction finding will introduce more and more devices that rely on BLE for communication. However, like many other wireless standards, BLE relies on the unlicensed 2.4 GHz frequency band where the spectrum is already very crowded and a channel access without collisions with other devices is difficult to guarantee. For applications with high reliability requirements, it will be beneficial to actively consider channel access from other devices or standards. In this work, we present an approach to estimate the connection parameters of multiple BLE connections outside our control and knowledge by passively listening to the channel. With this, we are able to predict future channel access of these BLE connections that can be used by other wireless networks to avoid collisions. We show the applicability of our algorithm with measurements from which we are able to identify unknown BLE connections, reconstruct their specific connection parameters, and predict their future channel access.

cs.IT↗

arXiv2022

Joint Communication and Sensing: 5G NR Compliant Ranging Using the Sounding Reference Signal

In this work, a proof of concept for 5G-compliant user-equipment side sensing is presented. It is based on orthogonal frequency division multiplexing radar-based ranging which is realized in this work by using the sounding reference signal from the 5G New Radio standard. The signal configuration and thus the corresponding waveform is generated in compliance with the existing 3rd Generation Partnership Project standard for 5G. It is an uplink physical signal and is originally intended, amongst others, for channel estimation. The used model is introduced, followed by the sounding reference signal. This leads to a first proof of concept by presenting simulation and measurement results. We show, that a range estimation error in the order of centimeters is achievable.

eess.SP↗

arXiv2021

RSSI-Based Machine Learning with Pre- and Post-Processing for Cell-Localization in IWSNs

Industrial wireless sensor networks are becoming crucial for modern manufacturing. If the sensors in those networks are mobile, the position information, besides the sensor data itself, can be of high relevance. E.g. this position information can increase the trustability of a wireless sensor measurement by assuring that the sensor is not physically removed, off track, or otherwise compromised. In certain applications, localization information at cell-level, whether the sensor is inside or outside a room or cell, is sufficient. For this, localization using Received Signal Strength Indicator (RSSI) measurements is very popular since RSSI values are available in almost all existing technologies and no direct interaction with the mobile sensor node and its communication in the network is needed. For this scenario, we propose methods to improve the robustness and accuracy of common machine learning classifiers, by using features based on short-term moments and a second classification stage using Hidden Markov Models. With the data from an extensive measurement campaign, we show the applicability of our method and achieve a cell-level localization accuracy of 93.5\%.

cs.IT↗

arXiv2020

Using Channel State Information for Physical Tamper Attack Detection in OFDM Systems: A Deep Learning Approach

This letter proposes a deep learning approach to detect a change in the antenna orientation of transmitter or receiver as a physical tamper attack in OFDM systems using channel state information. We treat the physical tamper attack problem as a semi-supervised anomaly detection problem and utilize a deep convolutional autoencoder (DCAE) to tackle it. The past observations of the estimated channel state information (CSI) are used to train the DCAE. Then, a post-processing is deployed on the trained DCAE output to perform the physical tamper detection. Our experimental results show that the proposed approach, deployed in an office and a hall environment, is able to detect on average 99.6% of tamper events (TPR = 99.6%) while creating zero false alarms (FPR = 0%).

eess.SP↗

arXiv2018

Numerical simulation of transport in porous media: some problems from micro to macro scale

This paper deals with simulation of flow and transport in porous media such as transport of groundwater contaminants. We first discuss how macro scale equations are derived and which terms have to be closed by models. The transport of tracers is strongly influenced by pore scale velocity structure and large scale inhomogeneities in the permeability field. The velocity structure on the pore scale is investigated by direct numerical simulations of the 3D velocity field in a random sphere pack. The velocity probability density functions are strongly skewed, including some negative velocities. The large probability for very small velocities might be the reason for non-Fickian dispersion in the initial phase of contaminant transport. We present a method to determine large scale distributions of the permeability field from point-wise velocity measurements. The adjoint-based optimisation algorithm delivers fully satisfying agreement between input and estimated permeability fields. Finally numerical methods for convection dominated tracer transports are investigated from a theoretical point of view. It is shown that high order Finite Element Methods can reduce or even eliminate non-physical oscillations in the solution without introducing additional numerical diffusivity.

math.NA↗

arXiv2017

On the Impact of Transposition Errors in Diffusion-Based Channels

In this work, we consider diffusion-based molecular communication with and without drift between two static nano-machines. We employ type-based information encoding, releasing a single molecule per information bit. At the receiver, we consider an asynchronous detection algorithm which exploits the arrival order of the molecules. In such systems, transposition errors fundamentally undermine reliability and capacity. Thus, in this work we study the impact of transpositions on the system performance. Towards this, we present an analytical expression for the exact bit error probability (BEP) caused by transpositions and derive computationally tractable approximations of the BEP for diffusion-based channels with and without drift. Based on these results, we analyze the BEP when background is not negligible and derive the optimal bit interval that minimizes the BEP. Simulation results confirm the theoretical results and show the error and goodput performance for different parameters such as block size or noise generation rate.

cs.IT↗

arXiv2016

Cooperative Simultaneous Localization and Synchronization in Mobile Agent Networks

Cooperative localization in agent networks based on interagent time-of-flight measurements is closely related to synchronization. To leverage this relation, we propose a Bayesian factor graph framework for cooperative simultaneous localization and synchronization (CoSLAS). This framework is suited to mobile agents and time-varying local clock parameters. Building on the CoSLAS factor graph, we develop a distributed (decentralized) belief propagation algorithm for CoSLAS in the practically important case of an affine clock model and asymmetric time stamping. Our algorithm allows for real-time operation and is suitable for a time-varying network connectivity. To achieve high accuracy at reduced complexity and communication cost, the algorithm combines particle implementations with parametric message representations and takes advantage of a conditional independence property. Simulation results demonstrate the good performance of the proposed algorithm in a challenging scenario with time-varying network connectivity.

cs.DC↗

arXiv2015

MiXiM, PAWiS, and STEAM-Sim Integration - Combining Channel Models, Energy Awareness, and Real-life Application Code

After a decade of research in the field of wireless sensor networks (WSNs) there are still open issues. WSNs impose several severe requirements regarding energy consumption, processing capabilities, mobility, and robustness of wireless transmissions. Simulation has shown to be the most cost-efficient approach for evaluation of WSNs, thus a number of simulators are available. Unfortunately, these simulation environments typically consider WSNs from a special point of view. In this work we present the integration of three such specialized frameworks, namely MiXiM, PAWiS, and STEAM-Sim. This integration combines the strengths of the single frameworks such as realistic channel models, mobility patterns, accurate energy models, and inclusion of real-life application code. The result is a new simulation environment which enables a more general consideration of WSNs. We implemented and verified our proposed concept by means of static and mobile scenarios. As the presented results show, the combined framework gives the same results regarding the functionality and energy consumption as our "golden model". Therefore the system integration was successful and the framework is ready to be used by the community.

cs.NI↗

arXiv2014

Energy Efficient and Reliable Wireless Sensor Networks - An Extension to IEEE 802.15.4e

Collecting sensor data in industrial environments from up to some tenth of battery powered sensor nodes with sampling rates up to 100Hz requires energy aware protocols, which avoid collisions and long listening phases. The IEEE 802.15.4 standard focuses on energy aware wireless sensor networks (WSNs) and the Task Group 4e has published an amendment to fulfill up to 100 sensor value transmissions per second per sensor node (Low Latency Deterministic Network (LLDN) mode) to satisfy demands of factory automation. To improve the reliability of the data collection in the star topology of the LLDN mode, we propose a relay strategy, which can be performed within the LLDN schedule. Furthermore we propose an extension of the star topology to collect data from two-hop sensor nodes. The proposed Retransmission Mode enables power savings in the sensor node of more than 33%, while reducing the packet loss by up to 50%. To reach this performance, an optimum spatial distribution is necessary, which is discussed in detail.

cs.NI↗

arXiv2013

Cooperative Synchronization in Wireless Networks

Synchronization is a key functionality in wireless network, enabling a wide variety of services. We consider a Bayesian inference framework whereby network nodes can achieve phase and skew synchronization in a fully distributed way. In particular, under the assumption of Gaussian measurement noise, we derive two message passing methods (belief propagation and mean field), analyze their convergence behavior, and perform a qualitative and quantitative comparison with a number of competing algorithms. We also show that both methods can be applied in networks with and without master nodes. Our performance results are complemented by, and compared with, the relevant Bayesian Cram\'er-Rao bounds.

cs.DC↗