Journal of Sensor and Actuator Networks

39 pages, 5377 KB

Open AccessArticle

STGen: A Lightweight Process-Based Testbed for Scalable IoT Protocol Evaluation with Physically Validated Synthetic Sensor and Anomaly Generation

by Hasan M. A. Islam, Md. M. R. Maharaz, M. Georgiades, S. M. N. Shahriar, P. Akibuzzaman, N. R. Aurna, Md. Masum and Riadul Islam

J. Sens. Actuator Netw. 2026, 15(4), 63; https://doi.org/10.3390/jsan15040063 - 3 Aug 2026

Abstract

This paper introduces the Sensor Traffic Generator (STGen), a lightweight, pure-software testbed for evaluating IoT application- and transport-layer protocols at scale. Relative to existing software-based IoT evaluation platforms, STGen combines three design decisions that, to the best of our knowledge, no prior testbed [...] Read more.

This paper introduces the Sensor Traffic Generator (STGen), a lightweight, pure-software testbed for evaluating IoT application- and transport-layer protocols at scale. Relative to existing software-based IoT evaluation platforms, STGen combines three design decisions that, to the best of our knowledge, no prior testbed offers together. Every emulated sensor node runs as an independent operating-system process using a real transport stack rather than a discrete-event model or a container. Sensor workloads are generated using physically grounded stochastic models calibrated against real deployment data. Experiments are specified in three independent tiers, IoT Protocols (N), Scenarios (M), and Networks (L), reducing configuration effort from a combinatorial

N \times M \times L

problem to a linear

N + M + L

workflow, with new protocols integrated by overriding a four-method abstract interface. STGen operates above OSI Layer 4 and therefore does not model PHY- or MAC-layer behavior, such as RF interference, CSMA/CA collision avoidance, or duty cycling. The sensor models are calibrated using 1,826,223 real-world readings from the Intel Berkeley Research Laboratory; for temperature, the synthetic stream matches the 37-day measurements of 54 Mica2Dot motes with a Kolmogorov–Smirnov D of 0.071 and a Jensen–Shannon divergence of 0.018, showing that STGen reproduces the statistical structure of real sensor data rather than only plausible values. By inverting these calibrated models, STGen also synthesizes labeled false-data-injection anomalies that are separable from normal traffic, with a receiver operating characteristic AUC of 0.898 for stealthy drift and 1.0 for hard physical range violations. In our experiments, STGen instantiates 6000 concurrently emulated sensor nodes on a commodity workstation in 1.02 s using 0.62 GB of memory (approximately 99 KB per node), which is more than two orders of magnitude below the per-node memory costs of container- and VM-based testbeds. STGen also exposes deployment-relevant behavior that controlled emulation alone may hide. Under live wide-area jitter, MQTT and CoAP exhibit different loss and latency patterns than those observed under uniformly degraded NetEm conditions, including MQTT reconnection storms. These results show that STGen provides a scalable and reproducible bridge between lightweight protocol emulation and practical deployment-oriented IoT protocol evaluation. Full article

(This article belongs to the Topic Challenges and Future Trends of Wireless Networks)

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Graphical abstract

14 pages, 468 KB

Open AccessArticle

Longitudinal Behavioural Analysis of Industrial IoT Network Traffic Using Passive Monitoring

by Henrique Santos and Pedro Magalhães

J. Sens. Actuator Netw. 2026, 15(4), 62; https://doi.org/10.3390/jsan15040062 - 2 Aug 2026

Abstract

Industrial Internet of Things (IIoT) production environments rely on automated communication between control systems and embedded devices while operating under strict availability constraints that limit the deployment of conventional IT security controls. Despite extensive research on intrusion detection systems, empirical studies based on [...] Read more.

Industrial Internet of Things (IIoT) production environments rely on automated communication between control systems and embedded devices while operating under strict availability constraints that limit the deployment of conventional IT security controls. Despite extensive research on intrusion detection systems, empirical studies based on long-term observations of real industrial networks remain scarce. This paper presents a longitudinal 92-day passive monitoring study of a production-line IIoT network comprising 22 monitored devices. A containerised instance of Zeek was deployed in promiscuous mode to collect flow-level and application-layer telemetry without interfering with operations. The resulting dataset contains more than 41.5 million network flows and 520.5 million packets, represented by 48.48 GB of structured Zeek logs. The results reveal highly deterministic communication patterns dominated by periodic HTTP polling between a central server and distributed devices. In particular, the hourly mean HTTP response size remained highly stable at 132.76 bytes, with a standard deviation of 1.37 bytes and a coefficient of variation of 1.0%. Although no confirmed malicious activity was observed, transient deviations were identified and attributed to planned production stoppages restart periods, which caused temporary traffic reductions and short-lived packet bursts. These findings demonstrate that production-line IIoT networks can exhibit predictable behaviour regimes suitable for statistical anomaly detection. The study contributes a longitudinal empirical characterisation of a real operational IIoT network, a reproducible methodology for behavioural baseline extraction using passive telemetry, and practical insights for safe monitoring deployment. Full article

(This article belongs to the Topic Application of IOT on Manufacturing, Communication and Engineering, 2nd Volume)

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32 pages, 2754 KB

Open AccessSystematic Review

Security Challenges and Mitigation Strategies in IoT-Enabled Video Surveillance Systems: A Systematic Review

by Josphat Moyo, Brett Van Niekerk, Richard C. Millham and Halleluyah Oluwatobi Aworinde

J. Sens. Actuator Netw. 2026, 15(4), 61; https://doi.org/10.3390/jsan15040061 - 31 Jul 2026

Abstract

The rapid deployment of Internet of Things (IoT)-enabled video surveillance systems has expanded the capabilities of real-time monitoring in smart cities, healthcare facilities, industrial environments and critical infrastructure. However, integrating resource-constrained cameras, heterogeneous communication protocols, edge/cloud analytics, and sensitive video data creates a [...] Read more.

The rapid deployment of Internet of Things (IoT)-enabled video surveillance systems has expanded the capabilities of real-time monitoring in smart cities, healthcare facilities, industrial environments and critical infrastructure. However, integrating resource-constrained cameras, heterogeneous communication protocols, edge/cloud analytics, and sensitive video data creates a complex cybersecurity landscape. This systematic review synthesizes recent evidence on security challenges and mitigation strategies in IoT-enabled video surveillance systems. Following the PRISMA 2020 guidelines, four bibliographic databases (Scopus, IEEE Xplore, Web of Science, and Google Scholar) were searched for peer-reviewed journal articles and conference papers published between January 2021 and July 2025. After duplicate removal, title/abstract screening, full-text assessment, and quality appraisal, 21 studies were included for qualitative synthesis. The findings show that vulnerabilities occur across three interdependent architectural layers: device/perception, network/communication, and application/cloud. The frequently reported weaknesses were default credentials, insecure firmware, unencrypted video streams, weak protocol configuration, metadata leakage, and inadequate cloud access control. Existing mitigation strategies, including multi-factor authentication, role-based access control, TLS/DTLS, lightweight encryption, intrusion detection systems, and secure boot, provide partial protection but remain constrained by latency, computational overhead, energy consumption, scalability, cost and legacy device compatibility. This review further identifies a persistent research–practice gap: only a small subset of studies provides evidence of real-world deployments, while most solutions remain evaluated in simulations, testbeds, or conceptual frameworks. This review contributes a domain-specific taxonomy of IoT video surveillance security, a comparative evaluation of mitigation strategies using technical, operational, and economic criteria, and deployment-oriented recommendations for smart city, industrial, healthcare, residential, and critical infrastructure settings. The study highlights the need for cross-layer security architectures, lightweight and post-quantum-ready cryptography, privacy preservation, edge AI, federated learning, zero-trust access control, and standardized security baselines. Full article

(This article belongs to the Special Issue IoT and Networking Technologies for Smart Mobile Systems)

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44 pages, 4439 KB

Open AccessArticle

An Edge-Deployable Spectral QoS Controller for Periodic Traffic Aggregation in High-Speed 5G/6G Mobile Platforms

by Anton A. Esin and Elmira Yu. Kalimulina

J. Sens. Actuator Netw. 2026, 15(4), 60; https://doi.org/10.3390/jsan15040060 - 24 Jul 2026

Abstract

Mobile platforms such as high-speed trains and unmanned aerial vehicles (UAVs) experience quasi-periodic variation in link quality as they move through a cellular base-station lattice, so the service rate of their on-board uplink buffer is itself time-periodic. We model this buffer as a [...] Read more.

Mobile platforms such as high-speed trains and unmanned aerial vehicles (UAVs) experience quasi-periodic variation in link quality as they move through a cellular base-station lattice, so the service rate of their on-board uplink buffer is itself time-periodic. We model this buffer as a periodic

M / M (t) / 1

queue whose service rate follows from a signal-to-noise-ratio (SNR)-to-rate map and construct an edge-resident controller that exploits this periodic structure for real-time quality-of-service (QoS) control. From a harmonic-balance (Fourier–Galerkin) solution of the periodic regime, the controller derives backlog and tail-probability indicators and uses them to drive admission, redundancy and handover decisions on the device. The method rests on a stability criterion and a quantitative error bound for the spectral truncation, under stated regularity and stability conditions, and is validated against Monte Carlo simulation along a ∼650 km geo-anchored corridor: on the periodic backbone, the solver matches simulation to within about

1.6 %

, and a coefficient-driven admission rule lowers the 99th-percentile delay by about

28 %

relative to a reactive baseline at high load. On the full map-derived profile with aperiodic coverage gaps, the proposed proactive controller—spectral backbone admission combined with a radio-map look-ahead—attains the lowest mean and tail delay, about

27 %

and

21 %

below the reactive baseline and

54 %

and

42 %

below uncontrolled DropTail, with buffer overflow cut from

2.2 %

to

0.1 %

, at a deliberate admitted-load cost (goodput ≈0.84 vs.

0.94

). An operation-count analysis indicates compatibility with sub-

100

ms control deadlines on a Cortex-A55-class system-on-chip. The controller runs on the device itself, without cloud or GPU, and the architecture is realised in a granted patent; end-to-end hardware benchmarking and an extension to non-Poisson traffic are left for future work. Full article

(This article belongs to the Special Issue IoT and Networking Technologies for Smart Mobile Systems)

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5 pages, 180 KB

Open AccessEditorial

AI-Assisted Machine–Environment Interaction

by Manolo Dulva Hina and Amar Ramdane-Cherif

J. Sens. Actuator Netw. 2026, 15(4), 59; https://doi.org/10.3390/jsan15040059 - 24 Jul 2026

Abstract

AI-assisted machine–environment interaction has emerged as an important research direction at the intersection of artificial intelligence (AI), sensor and actuator networks, and the Internet of Things (IoT) [...] Full article

(This article belongs to the Special Issue AI-Assisted Machine-Environment Interaction)

26 pages, 2395 KB

Open AccessArticle

PhySec-Edge: A Hybrid Physics-Informed and Edge AI Framework for Anomaly Detection in Industrial IoT Sensor Networks

by Dalibor Radovanovic, Nikola Savanovic, Petar Kresoja, Jelena Janackovic and Teodor Petrovic

J. Sens. Actuator Netw. 2026, 15(4), 58; https://doi.org/10.3390/jsan15040058 - 17 Jul 2026

Abstract

Industrial Internet of Things (IIoT) deployments face a security challenge that neither physics-based nor AI-based anomaly detection addresses alone: physics models are adversarially robust but miss behavioral attacks that remain within physical bounds, while AI models detect behavioral anomalies but are vulnerable to [...] Read more.

Industrial Internet of Things (IIoT) deployments face a security challenge that neither physics-based nor AI-based anomaly detection addresses alone: physics models are adversarially robust but miss behavioral attacks that remain within physical bounds, while AI models detect behavioral anomalies but are vulnerable to adversarial evasion and blind to physical sensor spoofing. This paper proposes PhySec-Edge, a hybrid framework integrating a Physics Validation Engine (PVE) with a multi-model Edge AI Detection Engine (EADE) in a layered, residual-sharing architecture. The PVE applies process model residuals, Kalman filter state estimation, cross-sensor consistency checks, and temporal gradient validation to generate physics-grounded anomaly signals. The EADE is designed around LSTM temporal detection, variational autoencoder reconstruction analysis, and graph neural network process monitoring augmented with PVE residuals; the current evaluation uses computationally tractable proxy implementations to provide a conservative lower bound on the benefits of residual sharing. Randomized smoothing is applied under bounded perturbation assumptions to improve adversarial robustness. PhySec-Edge is evaluated in a controlled synthetic IIoT setting parameterized using SWaT-inspired structural and statistical assumptions, comprising 9875 samples across seven attack classes. Across five random seeds, the hybrid framework achieves mean precision = 0.789 ± 0.004, recall = 0.808 ± 0.003, F1 = 0.798 ± 0.003, and FPR = 5.0% ± 0.0%, compared to F1 = 0.654 ± 0.006/FPR = 24.0% for the physics-only baseline and F1 = 0.774 ± 0.003/FPR = 5.0% for the AI-only baseline. An ablation study identifies residual augmentation as the primary individual improvement mechanism (ΔF1 = +0.017), while the full hybrid configuration achieves a combined gain of ΔF1 = +0.025 over the AI-only baseline. Critical hybrid advantages appear on adversarial evasion (+0.15 F1) and firmware implant (+0.17 F1), the two attack classes where neither layer alone is sufficient. A preliminary feasibility check on an Edge-IIoTset-inspired benchmark confirms that the architectural advantage pattern generalizes across dataset structures. Gateway latency analysis confirms compatibility with soft real-time industrial monitoring constraints. Full article

(This article belongs to the Special Issue Industrial Networks of the Future Across the Edge-to-Cloud Continuum)

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21 pages, 2956 KB

Open AccessArticle

Radio-Quality-Aware Model Predictive Control for Industrial Wireless Control Networks

by Anar Khabay, Akerke Baurzhan, Amandyk Tuleshov, Moldir Kuatova, Nurgul Smailova, Bauyrzhan Bazarbay, Ainur Ormanbekova, Zhazira Julayeva and Yerkebulan Tuleshov

J. Sens. Actuator Netw. 2026, 15(4), 57; https://doi.org/10.3390/jsan15040057 - 17 Jul 2026

Abstract

Industrial wireless sensor and actuator networks are increasingly used in closed-loop control systems. In such systems, packet dropout, channel degradation, and communication delay can deteriorate tracking performance. This paper proposes a Radio-Quality-Aware Model Predictive Control (MPC) strategy for an industrial conveyor drive system [...] Read more.

Industrial wireless sensor and actuator networks are increasingly used in closed-loop control systems. In such systems, packet dropout, channel degradation, and communication delay can deteriorate tracking performance. This paper proposes a Radio-Quality-Aware Model Predictive Control (MPC) strategy for an industrial conveyor drive system operating under time-varying wireless communication conditions. Unlike classical MPC, the proposed controller incorporates radio-channel information, including the signal-to-interference-plus-noise ratio (SINR), packet error rate (PER), and network-induced delay, into the predictive control formulation, thereby adapting the control action to degraded communication conditions. In the MATLAB simulations, an industrial load torque profile based on sensor-measured signals was used, and the wireless communication scenario was divided into three regions representing favorable, moderately degraded, and severely degraded conditions. The results demonstrate that the proposed MPC maintains the angular speed closer to its reference value than the classical MPC, particularly under severe degradation when the SINR decreases to 5 dB. Compared with the classical MPC, the proposed Radio-Quality-Aware MPC reduces the integral of absolute error (IAE) by approximately 90.6%, the root mean square error (RMSE) by 91.4%, the maximum absolute tracking error by 92.7%, and the input variation index by 67.2%, while maintaining a comparable level of control energy. Overall, these results demonstrate that the proposed Radio-Quality-Aware Model Predictive Control framework improves tracking accuracy, actuator smoothness, and system robustness in industrial wireless control applications. Full article

(This article belongs to the Section Wireless Control Networks)

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33 pages, 16829 KB

Open AccessArticle

JCCO: Jointly Optimizing the Computational and Communication Costs for Resource Allocation in Energy-Efficient Swarm Robotics

by Amir Ijaz, Hashem Haghbayan, Ethiopia Nigussie, Abdul Malik and Juha Plosila

J. Sens. Actuator Netw. 2026, 15(4), 56; https://doi.org/10.3390/jsan15040056 - 13 Jul 2026

Abstract

This paper presents a joint communication–computation optimization (JCCO) framework for energy-efficient resource allocation in edge-assisted swarm robotics. The proposed framework jointly optimizes task offloading, bandwidth allocation, transmission power, and computational workload under latency and energy constraints. A distributed optimization strategy combined with a [...] Read more.

This paper presents a joint communication–computation optimization (JCCO) framework for energy-efficient resource allocation in edge-assisted swarm robotics. The proposed framework jointly optimizes task offloading, bandwidth allocation, transmission power, and computational workload under latency and energy constraints. A distributed optimization strategy combined with a lightweight deep reinforcement learning controller enables adaptive and scalable decision making for resource-constrained robotic swarms. The simulation results demonstrate that the proposed method reduces the total swarm energy consumption by up to 41% while maintaining more than 99% deadline satisfaction across varying swarm sizes and communication conditions. The framework further achieves improved fairness, lower communication overhead, and efficient embedded deployment suitability for TinyML-enabled robotic platforms. Full article

(This article belongs to the Special Issue Research on Robot Systems for Embodied Intelligence Applications)

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42 pages, 3934 KB

Open AccessArticle

Distributed Intelligent IoT System for High Reliability and Scalability in Vertical Farming Systems

by Doan Perdana, Pascal Lorenz, Ongko Cahyono and Sri Hartati

J. Sens. Actuator Netw. 2026, 15(4), 55; https://doi.org/10.3390/jsan15040055 - 13 Jul 2026

Abstract

The paper suggests a distributed cross-layer IoT architecture that combines LoRaWAN (Long Range Wide Area Network) with federated learning (FL) to improve reliability, scalability, and fault tolerance in multi-layer vertical farming systems in dense and dynamic environments. Unlike the traditional frameworks that rely [...] Read more.

The paper suggests a distributed cross-layer IoT architecture that combines LoRaWAN (Long Range Wide Area Network) with federated learning (FL) to improve reliability, scalability, and fault tolerance in multi-layer vertical farming systems in dense and dynamic environments. Unlike the traditional frameworks that rely on independent measures of QoS (Quality of Service), the proposed framework directly represents the inter-layer relationships, such as heterogeneity of latencies, robustness of connectivity, and propagation of faults. One of the contributions is the development of a cohesive cross-layer evaluation framework with six strictly defined metrics: MLDC (Multi-Layer Deployment Capacity), C-LCRI (Cross-Layer Connectivity Robustness Index), C-LFCI (Cross-Layer Fault Containment Index), SART (Smart Adaptive Recovery Time), and AIRSM (AI Resilience Score Metric), which allows for quantitatively characterizing latency differences, network resilience, fault containment, recovery efficiency, AI robustness, and energy-performance trade-offs. The experimental results show that the proposed Smart Distributed LoRaWAN–Federated Learning architecture operates reliably in high-density and multi-layer vertical farming environments, and is scalable to handle larger amounts of data. The proposed system guarantees a packet delivery ratio (PDR) of around 95% under a large-scale deployment with up to 1050 IoT nodes spread across seven cultivation layers, with a latency reduction of nearly 60%, less than 1.6 J/msg on average energy consumption, and a fault recovery time of less than 0.3 s in case of network disruptions. The proposed framework was validated using large-scale simulation scenarios developed based on experimentally reported LoRaWAN communication characteristics and agricultural IoT deployments, and operational conditions at the edge intelligence. This evaluation included up to 1050 sensing nodes in 7 vertical farming layers to approximate a realistic deployment of smart farming in a large-scale environment while keeping consistency with the recorded communication and reliability profile. Full article

(This article belongs to the Section Communications and Networking)

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25 pages, 5005 KB

Open AccessArticle

Multi-Domain Feature Engineering for Noise-Tolerant Fault Classification in Analog Filter Circuits

by Archana Dhamotharan, Balakumar Muniandi, Vennila Anandaraj Umapathy, Neya Subramanian and Sowmiya Balamurugan

J. Sens. Actuator Netw. 2026, 15(4), 54; https://doi.org/10.3390/jsan15040054 - 13 Jul 2026

Abstract

This paper proposes a method of fault detection in analog circuits which involves various steps, including selection of benchmark circuits, dataset preparation, signal decomposition, model training, and performance analysis. The main aim of this work is to provide solid performance even in noisy [...] Read more.

This paper proposes a method of fault detection in analog circuits which involves various steps, including selection of benchmark circuits, dataset preparation, signal decomposition, model training, and performance analysis. The main aim of this work is to provide solid performance even in noisy environments. Monte Carlo analysis is used to generate a synthetic dataset with 200 runs per fault class by introducing component tolerances and realistic faults. A multi-stage pipeline is proposed; it begins with resampling the signals and normalizing them, and then noise is added at different levels: 5 dB, 10 dB and 20 dB. Feature fusion is performed by combining time-, frequency-, and statistical-domain features. Statistical-domain features are extracted by applying Variational Mode Decomposition (VMD) to split them into four IMF levels, followed by the application of Continuous Wavelet Transform (CWT) for time–frequency-domain analysis. Support Vector Machine (SVM), Random Forest, and Gradient Boosting are used as base-level classification models. A stacking ensemble model is developed which uses Random Forest, Gradient Boosting, and Extra Trees as base learners and Logistic Regression as the meta-learner. Full article

(This article belongs to the Topic Fault Diagnosis and System Health Intelligent Management)

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21 pages, 509 KB

Open AccessArticle

J-DEAS: A Jamming-Driven Exponential Adaptive Sleeping Technique for Energy-Aware Mitigation in LoRa Networks

by Carolina Del-Valle-Soto, Carlos Mex-Perera, Eduard Velazquez, José Varela-Aldás, Leonardo J. Valdivia and Orlando Montoya-Márquez

J. Sens. Actuator Netw. 2026, 15(4), 53; https://doi.org/10.3390/jsan15040053 - 2 Jul 2026

Abstract

Low-Power Wide-Area Networks based on LoRa are widely deployed in smart city, agricultural, and environmental monitoring, where their constrained energy budget makes them vulnerable to radio-frequency jamming. (1) Background: a node that keeps transmitting into a jammed channel wastes energy on undeliverable packets, [...] Read more.

Low-Power Wide-Area Networks based on LoRa are widely deployed in smart city, agricultural, and environmental monitoring, where their constrained energy budget makes them vulnerable to radio-frequency jamming. (1) Background: a node that keeps transmitting into a jammed channel wastes energy on undeliverable packets, yet detection and energy management are usually treated separately. (2) Methods: we present J-DEAS, a Jamming-Driven Exponential Adaptive Sleeping technique that couples a lightweight, threshold-based detector with an exponential sleep back-off scheduler. The detector uses only the RSSI and SNR reported by commodity transceivers, and a single exponentially weighted confidence variable drives the sleep interval; we analyze the decision boundary, confidence dynamics, steady-state duty cycle, and latency–energy trade-off in closed form. (3) Results: on a measurement dataset the detector reaches an AUC of 0.985 and an F1 of 0.969; under sustained jamming, J-DEAS cuts the duty cycle from 100% to 5.5% and wasted transmissions from 94.3% to 8.3%, with a single-slot median latency and a sub-2% false-sleep rate on clean channels. (4) Conclusions: the technique needs no training and no extra hardware, making it suitable for resource-constrained end devices. Full article

(This article belongs to the Section Network Security and Privacy)

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34 pages, 14558 KB

Open AccessArticle

Citywide Air Quality Forecasting over Sparse Sensor Networks: Cross-Location Generalization and Deep Learning Reliability Under Missing Data

by Francisco-Jose Alvarado-Alcon, Rafael Asorey-Cacheda, Joan Garcia-Haro, Laura García and Antonio-Javier Garcia-Sanchez

J. Sens. Actuator Netw. 2026, 15(4), 52; https://doi.org/10.3390/jsan15040052 - 29 Jun 2026

Abstract

Smart city environmental monitoring depends on sparse air quality sensor networks and analytics services that remain reliable under node additions, outages, and missing streams. We propose an operational deep learning framework for citywide cross-location forecasting from a limited set of sensors, delivering low-latency, [...] Read more.

Smart city environmental monitoring depends on sparse air quality sensor networks and analytics services that remain reliable under node additions, outages, and missing streams. We propose an operational deep learning framework for citywide cross-location forecasting from a limited set of sensors, delivering low-latency, real-time concentration heatmaps at unsensed locations by combining temporal prediction with spatial regression. We formulate single-stage spatiotemporal forecasting and benchmark nine recurrent, convolutional, and multilayer architectures against classical baselines. The framework forecasts

O_{3}

,

{NO}_{2}

,

{PM}_{2.5}

, and

{PM}_{10}

over horizons from 1 h to 10 days. Using open monitoring data from Madrid (Spain) and Cali (Colombia), we evaluate generalization by holding out stations, reflecting deployment to new sensor nodes and sparse coverage regimes. We further compare missing data handling strategies and show that common imputation can substantially degrade accuracy, increasing RMSE by up to 74% in some settings. Beyond prediction, the framework provides a basis for guiding sensor network densification; confidence estimates can highlight locations where additional sensors may be most beneficial. These results provide actionable guidance for deploying AI-enabled sensing services with robust performance under realistic sensor reliability constraints while supporting real-time citywide mapping. Full article

(This article belongs to the Section Network Services and Applications)

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38 pages, 9214 KB

Open AccessArticle

Networked Predictive Control and Intelligent Diagnostics for Automated Mechatronic Manufacturing and Intralogistics Systems

by Sholpan Bekmukhanbetova, Elmira Zhatkanbayeva, Akmaral Sagybekova, Daniyar Mukashev, Meirambay Toilybayev, Tatyana Baratova, Gulbarshyn Smailova, Ayaulym Rakhmatulina and Kalmukhamed Tazhen

J. Sens. Actuator Netw. 2026, 15(4), 51; https://doi.org/10.3390/jsan15040051 - 29 Jun 2026

Abstract

As automation increases, mechatronic manufacturing systems require supervisory solutions that combine precise control, intelligent diagnostics, and intralogistics awareness. This paper presents a networked sensor–actuator–information architecture integrating model predictive control (MPC), Random Forest (RF)-based diagnostics, and logistics-aware coordination for automated mechatronic manufacturing systems. The [...] Read more.

As automation increases, mechatronic manufacturing systems require supervisory solutions that combine precise control, intelligent diagnostics, and intralogistics awareness. This paper presents a networked sensor–actuator–information architecture integrating model predictive control (MPC), Random Forest (RF)-based diagnostics, and logistics-aware coordination for automated mechatronic manufacturing systems. The main contribution is the explicit coupling of logistics-related supervisory variables with the predictive control problem and the diagnostic feature space. Buffer occupancy, transport delay, and logistics-induced waiting state are incorporated into an augmented reduced-order model to support constrained control and health-state interpretation. The framework is evaluated through a comparative simulation-based feasibility study using a low-order model of a robotic production axis affected by disturbances, degradation, and logistics-related constraints. The proposed approach is compared with classical feedback control, predictive control without diagnostics, and predictive control with diagnostics but without explicit intralogistics coupling. In the reduced-order simulation scenario, the proposed method achieved the lowest mean RMSE of 0.330 ± 0.015 and the lowest mean constraint violation rate of 3.133 ± 0.280% across 40 repeated simulation runs. However, the improvement in nominal tracking accuracy over the strongest diagnostic-assisted MPC baseline was marginal. Adding logistics-related diagnostic features improved mean accuracy from 0.848 ± 0.014 to 0.874 ± 0.012 and mean F1-score from 0.844 ± 0.016 to 0.872 ± 0.013. The main advantage of the proposed architecture was observed in reliability- and continuity-oriented indicators, including reduced downtime, lower final damage accumulation, fewer cooling cycles, and improved differentiation between machine-related and logistics-induced abnormal conditions. Full article

(This article belongs to the Section Big Data, Computing and Artificial Intelligence)

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27 pages, 14867 KB

Open AccessArticle

Discrete Event Modeling, Supervisor Control, and Fault Diagnosis of the Chlorinated Water Station of Delfino Based on the Sensor and Actuator Interaction

by Dimitrios G. Fragkoulis, Fotis N. Koumboulis, Maria P. Tzamtzi, Nikolaos D. Kouvakas, Konstantinos S. Katsiavrias and Klimis K. Katsiavrias

J. Sens. Actuator Netw. 2026, 15(4), 50; https://doi.org/10.3390/jsan15040050 - 29 Jun 2026

Abstract

A chlorinated water station in Delfino, Greece, was studied from the control and fault diagnosis point of view, using the interaction of the devices installed to the station as well as rules resulting from the physical characteristics of the station. The DES models [...] Read more.

A chlorinated water station in Delfino, Greece, was studied from the control and fault diagnosis point of view, using the interaction of the devices installed to the station as well as rules resulting from the physical characteristics of the station. The DES models of the station’s devices (pumps, level sensors, flow sensors, and pressure sensors) are presented. The models of the pumps include both the activation/deactivation functionality and the regulation of the output flow of the pump. The models of the devices were validated using field data extracted from the monitoring system of the station. Towards protecting the pump from dry running and the tanks from overflow, a set of safety requirements were realized in the form of supervisor automata. Using field data, the effect of the supervisors in the activation/deactivation of the pumps was tested. A modular fault diagnosis system, where the number of fault diagnosers is equal to the number of pumps, was implemented to diagnose the faulty case of pump having stuck open despite deactivation command. A fault diagnosis system for a flow sensor of the station was developed and tested using the field data of the sensors and the pumping system. Supervisors and diagnosers were tested using one-week field data. The structured language code for PLC implementation of the diagnosers is presented. Full article

(This article belongs to the Topic Fault Diagnosis and System Health Intelligent Management)

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16 pages, 2550 KB

Open AccessArticle

Enhancing Robustness to Device Heterogeneity in WiFi-Based Indoor Localization

by Adrián García, Jorge Beltrán, Noelia Hernández, Ignacio Parra and Euntai Kim

J. Sens. Actuator Netw. 2026, 15(4), 49; https://doi.org/10.3390/jsan15040049 - 27 Jun 2026

Abstract

Indoor localization systems based on WiFi are gaining popularity due to their low implementation cost and the widespread availability of WiFi infrastructure. However, the wide variety of existing hardware poses a significant challenge in developing systems that maintain robust and consistent performance regardless [...] Read more.

Indoor localization systems based on WiFi are gaining popularity due to their low implementation cost and the widespread availability of WiFi infrastructure. However, the wide variety of existing hardware poses a significant challenge in developing systems that maintain robust and consistent performance regardless of the device used. Recent research has addressed this issue of device heterogeneity by building datasets that include data from a diverse set of devices. In this paper, we tackle this challenge by presenting a novel, multi-device, WiFi Received Signal Strength dataset collected along unconstrained trajectories using nine Android devices over a three-month period with precise ground truth positions obtained using Simultaneous Localization And Mapping. We then study the effect of heterogeneity in the localization performance using an LSTM-based neural network that leverages the temporal nature of sequential WiFi scans, and introduce two mitigation strategies: per-device Received Signal Strength normalization and the incorporation of temporal features as additional input. Our results show that these methods significantly improve cross-device performance with a mean average localization error reduction of 56% and enable generalization to previously unseen hardware with a mean average localization error 8% higher for the unseen devices. Full article

(This article belongs to the Special Issue Collaborative Integrated Sensing and Localization in Autonomous Systems)

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40 pages, 27259 KB

Open AccessArticle

Monocular 3D Position Estimation of a Moving Vehicle Based on a Kalman-Goldschmidt Adaptive Filter

by Diana Kalita, Pavel Lyakhov, Valery Andreev and Denis Butusov

J. Sens. Actuator Netw. 2026, 15(3), 48; https://doi.org/10.3390/jsan15030048 - 18 Jun 2026

Abstract

Determining the 3D position of a vehicle from a 2D image plays a key role in video surveillance, autonomous driving, and spatial localization. However, localization accuracy can significantly degrade in conditions of incomplete or synthetic measurement noise and keypoint jitter. In this paper, [...] Read more.

Determining the 3D position of a vehicle from a 2D image plays a key role in video surveillance, autonomous driving, and spatial localization. However, localization accuracy can significantly degrade in conditions of incomplete or synthetic measurement noise and keypoint jitter. In this paper, we propose a new iterative 3D position estimation algorithm (KGA). This algorithm includes geometric correction and calibration steps for converting from 2D to 3D coordinates; trajectory prediction and correction using a Kalman filter; and adaptive tuning of the filter parameters using the Goldschmidt algorithm. Experiments confirm that KGA outperforms the standard (FK) and modified (MFK) Kalman filters in accuracy and convergence speed, demonstrating robustness to various camera angles and noise levels. The novelty of this approach lies in the integration of the Goldschmidt algorithm into the Kalman filter to create an adaptation mechanism that dynamically adjusts the measurement noise covariance based on instantaneous innovation magnitude. Unlike end-to-end deep learning trackers or nonlinear filters (EKF/UKF), KGA is designed as a lightweight post-processing stage that can be seamlessly integrated into existing detection pipelines while maintaining the low computational footprint required for UAV-based edge deployment. The algorithm is of practical value for computer vision systems requiring accurate and robust tracking under varying observational conditions, with current implementation suitable for offline or buffered processing, and clear pathways to real-time deployment through code optimization. The algorithm is of practical value for computer vision systems requiring accurate and robust tracking under varying observational conditions. Full article

(This article belongs to the Section Big Data, Computing and Artificial Intelligence)

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22 pages, 1357 KB

Open AccessArticle

A Closed-Form Cooperative Avoidance Control for Multiple m-DOF Manipulators

by Wenxue Zhang, Ziyi Ma, Ning Zong and Dušan M. Stipanović

J. Sens. Actuator Netw. 2026, 15(3), 47; https://doi.org/10.3390/jsan15030047 - 18 Jun 2026

Abstract

Multi-manipulator cooperative systems are widely deployed in industrial assembly, intelligent manufacturing and other fields, but collision safety and efficient motion coordination during coordinated operation remain key challenges. In this paper, a novel cooperative control strategy based on relative velocity information is derived to [...] Read more.

Multi-manipulator cooperative systems are widely deployed in industrial assembly, intelligent manufacturing and other fields, but collision safety and efficient motion coordination during coordinated operation remain key challenges. In this paper, a novel cooperative control strategy based on relative velocity information is derived to guarantee collision-free maneuvers for multiple m-degree-of-freedom (m-DOF) manipulator systems with general Lagrangian dynamics. One key advantage is that it ensures reliable safety while achieving smoother avoidance maneuvers, reduced interference with objective tasks, lower energy consumption, and improved task efficiency; notably, the avoidance control depends not only on the relative distance between manipulators but also on their relative motion, making it less conservative as manipulators avoid unnecessary spreading during collision avoidance. Another is that it integrates collision avoidance, disturbance attenuation, and deadlock elimination into a unified closed-form control law, which yields a closed-form solution and is easy to implement in engineering practice. Theoretically, this paper adopts the generalized Lyapunov stability theory to rigorously prove the asymptotic convergence and persistent collision-free property. Finally, simulation results on a dual two-DOF manipulator system further verify the effectiveness and reliability of the proposed control strategy. Full article

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25 pages, 1799 KB

Open AccessArticle

Self-Supervised Transfer Learning for IMU-Based Upper-Limb Action Detection and Motion Quality Analysis in an Immersive VR Functional Task

by Zhao Liu, Daniele Soria, Chee Siang Ang and Sukhi Shergill

J. Sens. Actuator Netw. 2026, 15(3), 46; https://doi.org/10.3390/jsan15030046 - 12 Jun 2026

Abstract

Wearable inertial sensing has considerable potential for process-level analysis of upper-limb function, but further evidence is needed to understand how it can be applied within ecologically structured immersive virtual reality (VR) tasks. Most VR-based functional assessments rely primarily on outcome-level indicators, such as [...] Read more.

Wearable inertial sensing has considerable potential for process-level analysis of upper-limb function, but further evidence is needed to understand how it can be applied within ecologically structured immersive virtual reality (VR) tasks. Most VR-based functional assessments rely primarily on outcome-level indicators, such as task completion time, success rate, or error count, which may not fully capture how a task is executed. This exploratory study investigated whether wearable IMU signals collected during an immersive VR sushi-making task could support binary detection of a core upper-limb manipulation phase and provide additional information about task execution beyond global performance outcomes. A total of 45 participants contributed usable motion recordings for this study, with five Xsens DOT sensors placed on the hands, forearms, and waist. Three signal modalities were analysed, including acceleration (ACC), gyroscope angular velocity (GYR), and Euler angles. The downstream recognition problem was formulated as a binary classification task (Placing vs. Non-Placing), and a self-supervised learning (SSL) pretrain–fine-tune strategy was evaluated against conventional machine learning and from-scratch deep learning baselines using five subject-wise validation splits. The strongest overall performance was achieved with hand-mounted accelerometer signals, with LeftHand–ACC achieving a Macro-F1 of

0.712 \pm 0.128

and RightHand–ACC achieving

0.679 \pm 0.118

. Under both hand-ACC settings, SSL fine-tuning showed higher mean Macro-F1 than the Balanced Random Forest baseline and the same deep architecture trained from scratch. Recognition performance varied substantially across sensor locations, signal modalities, and task segments, with distal upper-limb sensors generally outperforming waist-based configurations. Cross-age analyses further showed that within-cohort and cross-cohort performance did not fully align, indicating sensitivity to age-related distribution shift. Beyond classification, Log Dimensionless Jerk (LDLJ) derived from the Placing action showed a significant positive association with Cognitron motor control time cost (

r = 0.636

,

p < 0.001

). These findings suggest that wearable IMU sensing can provide preliminary process-level information during immersive VR functional tasks, including task-phase detection, sensing-configuration comparison, cross-cohort generalisation assessment, and exploratory motion-quality analysis. The results should be interpreted as evidence of feasibility rather than as a mature biomechanical or clinical assessment model. Full article

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23 pages, 7293 KB

Open AccessArticle

Multi-Modal Data Processing in Digital Twins: Connecting Sensors and Actuators for Health Optimisation

by Alexandru-George Berciu, Dan Doru Micu and Eva-Henrietta Dulf

J. Sens. Actuator Netw. 2026, 15(3), 45; https://doi.org/10.3390/jsan15030045 - 10 Jun 2026

Abstract

The continuous monitoring of population health is a major focus in scientific literature, with numerous studies highlighting the critical role of sleep. However, to the best of the authors’ knowledge, the multi-modal data processing required to fully map the tripartite relationship between environmental [...] Read more.

The continuous monitoring of population health is a major focus in scientific literature, with numerous studies highlighting the critical role of sleep. However, to the best of the authors’ knowledge, the multi-modal data processing required to fully map the tripartite relationship between environmental stimuli, sleep, and health has not been achieved. This paper proposes a comprehensive data fusion strategy, integrating public databases to extract common features from historical sensor data. The present paper proposes a robust processing architecture by training four classes of algorithms (mathematical, machine learning, artificial intelligence, and ensemble models) to analyse how environmental inputs impact sleep quality and, consequently, physiological health. The resulting state-of-the-art model, a multi-modal architecture comprising 10 integrated models, was tested on a massive combined dataset of 139,950 rows and 8249 columns. The model achieved an R-squared of 0.958, demonstrating superior data processing and predictive accuracy. Alongside the integrated dataset, this research establishes the computational groundwork for human-centric Digital Twins, paving the way for closed-loop IoT environments where sensor-driven analytics inform automated actuator interventions to improve sleep and health. Full article

(This article belongs to the Section Big Data, Computing and Artificial Intelligence)

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Journal of Sensor and Actuator Networks

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