Trustworthiness for an Ultra-Wideband Localization Service

TL;DR

This work tackles the problem of quantifying and maintaining trustworthiness for UWB self-localization in IoT contexts. It proposes a threat-driven framework that offline-develops metrics and online-estimates trust indicators and indices by mapping threats to measurable signals, then aggregating through a composite index I = min{I_ rel, I_res, I_sec, I_priv}. Key innovations include the systematization of metrics across node, link, and system views, a sigmoid-based normalization of real-valued metrics, and a sequential anchor-selection scheme that uses trustworthy links to improve resilience. Experimental evaluation under improper anchor configurations and active jamming demonstrates that trustworthiness indicators can provide early warnings and that sequential anchor selection can substantially improve localization performance (e.g., RMSE reductions from ~81 cm to ~17 cm) while preserving availability. The framework serves as a general blueprint for holistic trustworthiness assessment in IoT and cyber-physical systems, enabling proactive countermeasures and extensibility to other localization and communication scenarios.

Abstract

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.

Figures (10)

• Figure S1: Taxonomy -- attributes and sub-attributes of trustworthiness.
• Figure S2: Methodology -- the metric development (left) is carried out once during the offline phase, while trustworthiness assessment (right) is used during the online phase.
• Figure S3: (a) The double-sided two-way ranging message exchange using four packets with channel measurements; the superscripts are omitted for simplicity, e.g., $t_\mathrm{a}$ is used instead of $t_\mathrm{a}^{(A)}$. (b) UWB packet configuration possibilities and the position of timestamping within the packet according to the IEEE 802.15.4 specification.
• Figure S4: Mapping of trustworthiness attributes to metrics through threats.
• Figure S5: (a) The sigmoid function is used to map metrics to a trustworthiness indicator. (b) The RSSI is used to account for anchors with low signal strength that might not respond.