Saltanat Adilzhanova

Dr. Saltanat Adilzhanova


Research Focus & Metrics

Academic Career Length

1Year (2026-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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30 April 2026 in Sensors
Verified
Journal Article
Open access
PDF file
DPDmytro Igorovych Prokopovych-Tkachenko
Dmytro Igorovych Prokopovych-Tkachenko
OGOleksandr Galushchenko
Oleksandr Galushchenko
OTOlga Olha Torstensson
Olga Olha Torstensson
VZVolodymyr Zvieriev
Volodymyr Zvieriev
SASaltanat Adilzhanova
Saltanat Adilzhanova
Edison  Pignaton de FreitasEdison Pignaton de Freitas
Edison Pignaton de Freitas
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.