Blockchain-Enabled Uncertainty-Aware Passive Wi-Fi Localization for Secure Critical Infrastructure Sensor Networks

Passive Wi-Fi localization for critical-infrastructure security operations centers (SOCs) faces three interconnected limitations. First, many existing methods produce single-point coordinate estimates without calibrated uncertainty, making them unsuitable for automated SOC response. Second, localization pipelines often lack an evidentiary chain of custody, limiting reliable post-incident auditability. Third, SOC automation cannot safely rely on uncalibrated confidence values because erroneous high-impact actions and missed escalations carry asymmetric operational costs. This study presents a Blockchain-Enabled Uncertainty-Aware Passive Wi-Fi Localization framework for heterogeneous sensor networks composed of stationary sensors, mobile receivers, and UAV-assisted collection nodes. Instead of producing a single coordinate estimate, the method derives a posterior spatial distribution with calibrated uncertainty from monitor-mode observations, including RSSI aggregates, management/control frame features, channel occupancy indicators, and receiver logs. The framework combines three tightly coupled components: (i) Bayesian coordinate estimation with robust loss functions and range-dependent error modeling; (ii) uncertainty calibration that converts posterior confidence into operational SOC response modes (AUTO, VERIFY, and OBSERVE) via empirical coverage metrics and reliability diagrams; and (iii) a permissioned evidentiary logging layer that anchors integrity-relevant metadata and policy labels on-chain while keeping raw telemetry off-chain for tamper-evident auditability and scalability. The coupling between layers is explicit: calibrated confidence scores govern smart-contract gating conditions, and smart-contract policy thresholds feed back into the calibration stage. Field validation shows that localization performance degrades markedly beyond approximately 40 m, indicating a practical boundary for confident automated action. The proposed framework integrates passive sensing, uncertainty-aware localization, and blockchain-based evidentiary trust for secure critical-infrastructure sensor networks. Its key contributions are: (1) a posterior-distribution-based passive localization pipeline; (2) empirical coverage metrics for calibrating SOC response thresholds; (3) a hybrid on-chain/off-chain architecture linking localization outputs to a permissioned ledger; and (4) field validation establishing the 40 m operational validity boundary.

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Journal
Sensors
Published
2026-04-30
DOI
https://doi.org/10.3390/s26092797
Primary Topic
Indoor and Outdoor Localization Technologies
Type
article
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article

Blockchain-Enabled Uncertainty-Aware Passive Wi-Fi Localization for Secure Critical Infrastructure Sensor Networks

Салтанат Адилжанова, Edison Pignaton de Freitas, Volodymyr Zvieriev, Oleksandr Galushchenko et al.
Sensors
Indoor and Outdoor Localization Technologies
article

Blockchain-Enabled Uncertainty-Aware Passive Wi-Fi Localization for Secure Critical Infrastructure Sensor Networks

Салтанат Адилжанова, Edison Pignaton de Freitas, Volodymyr Zvieriev, Oleksandr Galushchenko, Ольга Торстенссон, Dmytro Prokopovych-Tkachenko
article en

Abstract

Passive Wi-Fi localization for critical-infrastructure security operations centers (SOCs) faces three interconnected limitations. First, many existing methods produce single-point coordinate estimates without calibrated uncertainty, making them unsuitable for automated SOC response. Second, localization pipelines often lack an evidentiary chain of custody, limiting reliable post-incident auditability. Third, SOC automation cannot safely rely on uncalibrated confidence values because erroneous high-impact actions and missed escalations carry asymmetric operational costs. This study presents a Blockchain-Enabled Uncertainty-Aware Passive Wi-Fi Localization framework for heterogeneous sensor networks composed of stationary sensors, mobile receivers, and UAV-assisted collection nodes. Instead of producing a single coordinate estimate, the method derives a posterior spatial distribution with calibrated uncertainty from monitor-mode observations, including RSSI aggregates, management/control frame features, channel occupancy indicators, and receiver logs. The framework combines three tightly coupled components: (i) Bayesian coordinate estimation with robust loss functions and range-dependent error modeling; (ii) uncertainty calibration that converts posterior confidence into operational SOC response modes (AUTO, VERIFY, and OBSERVE) via empirical coverage metrics and reliability diagrams; and (iii) a permissioned evidentiary logging layer that anchors integrity-relevant metadata and policy labels on-chain while keeping raw telemetry off-chain for tamper-evident auditability and scalability. The coupling between layers is explicit: calibrated confidence scores govern smart-contract gating conditions, and smart-contract policy thresholds feed back into the calibration stage. Field validation shows that localization performance degrades markedly beyond approximately 40 m, indicating a practical boundary for confident automated action. The proposed framework integrates passive sensing, uncertainty-aware localization, and blockchain-based evidentiary trust for secure critical-infrastructure sensor networks. Its key contributions are: (1) a posterior-distribution-based passive localization pipeline; (2) empirical coverage metrics for calibrating SOC response thresholds; (3) a hybrid on-chain/off-chain architecture linking localization outputs to a permissioned ledger; and (4) field validation establishing the 40 m operational validity boundary.

SensorsVol. 26(9)
Universidade Federal do Rio Grande do Sul (BR), State University of Trade and Economics (UA), Vinnytsia National Technical University (UA), Almaty Technological University (KZ), University of Customs and Finance (UA), Halmstad University (SE)
Industry, innovation and infrastructure
Openalex Percentile: Top 10%
Indoor and Outdoor Localization Technologies
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