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Open AccessArticle

Securing Cyber–Physical Water Infrastructures: A Hybrid Intrusion Detection System for IoT Telemetry and Industrial Protocols

by César López Rodríguez, Miguel Ángel Ortega Velázquez and Antonio J. Jara

Sensors 2026, 26(10), 3160; https://doi.org/10.3390/s26103160 - 16 May 2026

Viewed by 466

Abstract

Historically, critical water infrastructures have operated with limited digitalization, relying on legacy protocols designed without intrinsic security. The rapid integration of advanced IoT telemetry into Operational Technology (OT) networks has dissolved traditional air gaps, exposing these facilities to severe cyber–physical threats. Concurrently, regulatory [...] Read more.

Historically, critical water infrastructures have operated with limited digitalization, relying on legacy protocols designed without intrinsic security. The rapid integration of advanced IoT telemetry into Operational Technology (OT) networks has dissolved traditional air gaps, exposing these facilities to severe cyber–physical threats. Concurrently, regulatory frameworks such as the European NIS2 Directive and the Cyber Resilience Act (CRA) now strictly mandate robust risk monitoring for essential entities. To address these challenges, this study develops a non-intrusive, hybrid Intrusion Detection System (IDS) tailored for converged IT/OT environments. Engineered upon the Snort 3 multi-threaded engine, the architecture captures both North–South and East–West traffic. A defense-in-depth rule set was constructed using threat intelligence (MITRE ATT&CK, CISA KEV) to perform Deep Packet Inspection (DPI) across legacy industrial protocols (Modbus, S7Comm, CIP) and IoT application layers (MQTT, HTTP). Experimental validation against high-volume synthetic packet captures (exceeding 170,000 packets) replicating specific manufacturer vulnerabilities (CVEs) demonstrated an improvement in the detection rate from a 0% baseline to 100%. Crucially, the system demonstrated high scalability and minimal computational overhead, processing high-volume traffic streams with zero dropped packets. This contextualized signature approach provides the deterministic security required to ensure operational continuity and regulatory compliance in modern water infrastructures. Full article

(This article belongs to the Special Issue Sensors in 2026)

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16 pages, 1359 KB

Open AccessArticle

Spatiotemporal Locality-Aware Adaptive Hybrid Optoelectronic Interconnect for Reconfigurable Array Processors

by Bowen Yang, Yong Li, Rui Shan, Junyong Deng and Yu Feng

Sensors 2026, 26(9), 2871; https://doi.org/10.3390/s26092871 - 4 May 2026

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Abstract

As data-intensive applications continue to scale reconfigurable array processors (RAPs), electrical networks-on-chip (NoCs) are increasingly constrained by energy-delay bottlenecks due to RC-delay constraints. Hybrid optoelectronic NoCs (HONoCs) suffer from a fundamental medium-selection dilemma: optical circuit switching incurs microsecond-scale setup overheads for long flows, [...] Read more.

As data-intensive applications continue to scale reconfigurable array processors (RAPs), electrical networks-on-chip (NoCs) are increasingly constrained by energy-delay bottlenecks due to RC-delay constraints. Hybrid optoelectronic NoCs (HONoCs) suffer from a fundamental medium-selection dilemma: optical circuit switching incurs microsecond-scale setup overheads for long flows, whereas static distance thresholds fail to capture the spatiotemporal heterogeneity of traffic, causing wavelength waste for bursty flows and congestion diffusion under non-stationary loads. This paper presents an adaptive switching framework that is aware of spatiotemporal locality. We introduce the Temporal-Spatial Locality Index (TSLI) to classify flows into Electrophilic (EF), Photophilic (PF), and Hybrid-sensitive (HF) categories, and propose Cross-layer Congestion Entropy (CCE) to unify electrical and optical resource states. Based on these metrics, an Adaptive Medium Selection State Machine (AMSSM) dynamically switches among Electro-Dominant (EDM), Electro-Optical Synergistic (EOSM), and Optical-Dominant (ODM) modes, while a Weighted Multi-dimensional Medium Matching (WMMM) algorithm performs fine-grained channel selection. A Predictive Optical Path Provisioning (POPP) mechanism further amortizes setup latencies via trend-aware pre-establishment. Evaluation on an 8 × 8 mesh HONoCs demonstrates 22% higher saturation throughput, 38% lower energy-delay product (EDP), and 57% reduction in average latency under non-stationary traffic, compared to static thresholds. The proposed mechanisms provide a theoretical foundation and engineering paradigm for efficient on-chip interconnects. Full article

(This article belongs to the Special Issue Sensors in 2026)

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15 pages, 6524 KB

Open AccessArticle

Fourier Ambiguity Validation for Carrier-Phase GNSS

by Peter J. G. Teunissen

Sensors 2026, 26(7), 2201; https://doi.org/10.3390/s26072201 - 2 Apr 2026

Viewed by 664

Abstract

Carrier-phase ambiguity validation is essential to ensure the reliability of integer ambiguity resolution in high-precision GNSS positioning. Although integer equivariant (IE) estimators provide optimal integer candidates within their class, noise and model limitations may lead to incorrect fixing. Validation procedures are therefore crucial [...] Read more.

Carrier-phase ambiguity validation is essential to ensure the reliability of integer ambiguity resolution in high-precision GNSS positioning. Although integer equivariant (IE) estimators provide optimal integer candidates within their class, noise and model limitations may lead to incorrect fixing. Validation procedures are therefore crucial for safeguarding the transition from float to fixed solutions, particularly in high-precision and safety-critical applications. In this contribution we introduce the concept of Fourier ambiguity validation and show how it is rooted in the principles of integer aperture (IA) estimation and its periodic representation. Unlike classical integer estimators that always return an integer solution, IA estimators introduce adjustable acceptance regions in the float ambiguity domain and fix ambiguities only when sufficient statistical evidence is present. As a result we present a general Fourier representation of IA estimators and provide an analytical description of the probabilistic properties of integer-aperture bootstrapping. We also present a hybrid description and show how the spatial and frequency representations can be mixed so as to do justice to the practical situation when carrier-phase ambiguities have a wide range of varying precision. Full article

(This article belongs to the Special Issue Sensors in 2026)

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17 pages, 22047 KB

Open AccessFeature PaperArticle

Urban Water Leakage Detection System over Dark Fiber Networks Based on Distributed Acoustic Sensing and Sparse Autoencoders

by Vahid Sharif, Yuanyuan Yao, Alayn Loayssa and Mikel Sagues

Sensors 2026, 26(7), 2152; https://doi.org/10.3390/s26072152 - 31 Mar 2026

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Abstract

We propose and experimentally validate an automatic urban water leakage detection architecture that leverages dark fiber links already deployed in telecommunication networks in underground conduits in the vicinity of water pipelines. The sensing stage relies on a differential-phase coherent optical time-domain reflectometry interrogator [...] Read more.

We propose and experimentally validate an automatic urban water leakage detection architecture that leverages dark fiber links already deployed in telecommunication networks in underground conduits in the vicinity of water pipelines. The sensing stage relies on a differential-phase coherent optical time-domain reflectometry interrogator enhanced with optical pulse compression to improve sensitivity. Building on this vibration acquisition stage, automatic leakage detection algorithms are implemented by searching for leak-induced activity in the frequency domain, which is well suited to revealing leakage-related features. After acquiring a baseline calibration to characterize normal-condition vibrations at each sensing position, leakage candidates are identified by comparing distribution-based metrics computed over multiple measurements against the corresponding baseline statistics. Two automatic leakage detection strategies are developed. First, low-complexity feature-based metrics are implemented, enabling continuous monitoring with minimal computational requirements. Second, an autoencoder-based anomaly detection technique is introduced, which also relies on location-specific normal-condition calibration but reduces the dependence on prior knowledge of the expected leakage vibration signatures. A real-world field trial on an urban network demonstrates reliable detection and localization using controlled leak events generated in the field, with measurements performed over a 17 km sensing fiber and an effective spatial resolution of 2.6 m. Benchmarking against a commercial punctual electro-acoustic leak detector yields consistent trends. Overall, the proposed system could complement existing technologies by enabling automated, continuous city-scale monitoring over already deployed dark fiber infrastructure. Full article

(This article belongs to the Special Issue Sensors in 2026)

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16 pages, 3358 KB

Open AccessArticle

The Volatile Signature: Tracking Ripening Dynamics to Ensure Goat Cheese Quality

by Giovanni Ferrara, Cristina Matarazzo, Maria Staiano, Sabato D’Auria and Rosaria Cozzolino

Sensors 2026, 26(5), 1583; https://doi.org/10.3390/s26051583 - 3 Mar 2026

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Abstract

Cheese ripening involves a series of biochemical and microbiological transformations that directly affect the texture, aroma, flavor, and quality of the final product. This study aimed to characterize the volatile organic compounds (VOCs) produced during the ripening of goat cheese to find suitable [...] Read more.

Cheese ripening involves a series of biochemical and microbiological transformations that directly affect the texture, aroma, flavor, and quality of the final product. This study aimed to characterize the volatile organic compounds (VOCs) produced during the ripening of goat cheese to find suitable molecular markers for monitoring the maturation process. Headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry (HS-SPME/GC–MS) was applied to samples collected at different ripening times (0, 30, 60, 90, 120, and 150 days). Overall, sixty-eight different VOCs were identified, including alcohols, esters, ketones, carboxylic acids, aldehydes, terpenes, sulfur compounds, and others. The total volatile content progressively increased up to 120 days and slightly decreased thereafter. This dynamic evolution reflected the interplay of proteolysis, lipolysis, and microbial metabolism occurring during the ripening process. Among the compounds, 2-butanone and 2-butanol appeared as promising volatile markers of the advanced ripening stages. These results offer new insights into goat cheese flavor development and support the design of a sensing approach for a first warning of the end of the cheese maturation process. Full article

(This article belongs to the Special Issue Sensors in 2026)

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23 pages, 17521 KB

Open AccessArticle

Extreme-Aware Time-Series Forecasting via Weak-Label-Guided Mixture of Experts

by Jialou Wang, Jacob Sanderson and Wai Lok Woo

Sensors 2026, 26(5), 1571; https://doi.org/10.3390/s26051571 - 2 Mar 2026

Viewed by 663

Abstract

Deep time-series forecasting models can achieve strong average accuracy under normal conditions, yet they often struggle with rare, high-impact extremes, where severe class imbalance biases learning toward majority dynamics. Although infrequent, these extremes frequently correspond to critical events such as natural disasters or [...] Read more.

Deep time-series forecasting models can achieve strong average accuracy under normal conditions, yet they often struggle with rare, high-impact extremes, where severe class imbalance biases learning toward majority dynamics. Although infrequent, these extremes frequently correspond to critical events such as natural disasters or power outages. We address this challenge with a weak-label-guided mixture of experts (WL-MoE) that routes each input window to lightweight specialists designed to capture distinct temporal regimes. To prevent routing collapse during early optimisation, WL-MoE follows a two-stage training curriculum. In Stage I, cluster-derived weak labels encourage diverse expert utilisation and promote specialisation under imbalance. In Stage II, guidance is removed and training proceeds solely with the forecasting objective, ensuring that inferences remain fully data-driven. The expert-based structure also supports interpretable routing via expert-usage profiling, enabling regime-level auditing of model behaviour in high-stakes settings. Across seven benchmark datasets, WL-MoE reduces the average MSE by approximately

7.9 %

and the extreme-case MSE by approximately

23.58 %

relative to the best baseline. In a UK flood forecasting study, it reduces the all-water MSE by

31.6 %

and the high-water MSE by approximately

35.0 %

. These results indicate that weak-label guidance can stabilise specialisation and improve reliability under rare extremes while keeping model behaviour auditable for real-world deployment. Full article

(This article belongs to the Special Issue Sensors in 2026)

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Other

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15 pages, 2184 KB

Open AccessPerspective

Electrochemical Stripping Analysis at Paper-Based (Bio)Sensors: Current State-of-the-Art and Prospects

by Christos Kokkinos and Anastasios Economou

Sensors 2026, 26(9), 2819; https://doi.org/10.3390/s26092819 - 30 Apr 2026

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Abstract

Paper-based devices (PADs) have gained increasing attention over the last few years as portable, low-cost and disposable (bio)sensors for point-of-care and on-site analysis. Electrochemistry is a particularly attractive detection mode in PAD assays thanks to its sensitivity and compatibility with portable instrumentation. In [...] Read more.

Paper-based devices (PADs) have gained increasing attention over the last few years as portable, low-cost and disposable (bio)sensors for point-of-care and on-site analysis. Electrochemistry is a particularly attractive detection mode in PAD assays thanks to its sensitivity and compatibility with portable instrumentation. In particular, electrochemical stripping analysis (ESA) is one of the most sensitive electroanalytical techniques, and, therefore, is suitable for trace assays required in environmental monitoring, clinical diagnostics and food control. Coupling paper as a functional platform with the exceptional sensitivity of ESA creates a powerful analytical tool for trace metals and (bio)sensing. This perspective briefly outlines the current state-of-the art in the field of paper-based (bio)sensors using ESA. It describes the principle of ESA, illustrates different strategies for on-paper electrode fabrication and modification and demonstrates representative applications to trace metal analysis and biosensing. Finally, limitations are identified and future prospects are discussed. Full article

(This article belongs to the Special Issue Sensors in 2026)

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24 pages, 1460 KB

Open AccessPerspective

From Sensing to Sense-Making: A Framework for On-Person Intelligence with Wearable Biosensors and Edge LLMs

by Tad T. Brunyé, Mitchell V. Petrimoulx and Julie A. Cantelon

Sensors 2026, 26(7), 2034; https://doi.org/10.3390/s26072034 - 25 Mar 2026

Viewed by 977

Abstract

Wearable biosensors increasingly stream multi-channel physiological and behavioral data outside the laboratory, yet most deployments still end in dashboards or threshold alarms that leave interpretation open to the user. In high-stakes domains, such as military, emergency response, aviation, industry, and elite sport, the [...] Read more.

Wearable biosensors increasingly stream multi-channel physiological and behavioral data outside the laboratory, yet most deployments still end in dashboards or threshold alarms that leave interpretation open to the user. In high-stakes domains, such as military, emergency response, aviation, industry, and elite sport, the constraint is rarely data availability but the cognitive effort required to convert noisy signals into timely, actionable decisions. We argue for on-person cognitive co-pilots: systems that integrate multimodal sensing, compute probabilistic state estimates on devices, synthesize those states with task and environmental context using locally hosted large language models (LLMs), and deliver recommendations through attention-appropriate cues that preserve autonomy. Enabling conditions include mature wearable sensing, edge artificial intelligence (AI) accelerators, tiny machine learning (TinyML) pipelines, privacy-preserving learning, and open-weight LLMs capable of local deployment with retrieval and guardrails. However, critical research gaps remain across layers: sensor validity under real-world conditions, uncertainty calibration and fusion under distribution shift, verification of LLM-mediated reasoning, interaction design that avoids alarm fatigue and automation bias, and governance models that protect privacy and consent in constrained settings. We propose a layered technical framework and research agenda grounded in cognitive engineering and human–automation interaction. Our core claim is that local, uncertainty-aware reasoning is an architectural prerequisite for trustworthy, low-latency augmentation in isolated, confined, and extreme environments. Full article

(This article belongs to the Special Issue Sensors in 2026)

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