ArticleSensors (Basel, Switzerland)2025
A LoRa-Based Multi-Node System for Laboratory Safety Monitoring and Intelligent Early-Warning: Towards Multi-Source Sensing and Heterogeneous Networks.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Real-Time Remote Monitoring of Environmental Conditions and Actuator Status in Smart Greenhouses Using a Smartphone Application.Sensors (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Laboratories are complex and dynamic environments where diverse hazards-including toxic gas leakage, volatile solvent combustion, and unexpected fire ignition-pose serious threats to personnel safety and property. Traditional monitoring systems relying on single-type sensors or manual inspections often fail to provide timely warnings or comprehensive hazard perception, resulting in delayed response and potential escalation of incidents. To address these limitations, this study proposes a multi-node laboratory safety monitoring and early warning system integrating multi-source sensing, heterogeneous communication, and cloud-edge collaboration. The system employs a LoRa-based star-topology network to connect distributed sensing and actuation nodes, ensuring long-range, low-power communication. A Raspberry Pi-based module performs real-time facial recognition for intelligent access control, while an OpenMV module conducts lightweight flame detection using color-space blob analysis for early fire identification. These edge-intelligent components are optimized for embedded operation under resource constraints. The cloud-edge-app collaborative architecture supports real-time data visualization, remote control, and adaptive threshold configuration, forming a closed-loop safety management cycle from perception to decision and execution. Experimental results show that the facial recognition module achieves 95.2% accuracy at the optimal threshold, and the flame detection algorithm attains the best balance of precision, recall, and F1-score at an area threshold of around 60. The LoRa network maintains stable communication up to 0.8 km, and the system's emergency actuation latency ranges from 0.3 s to 5.5 s, meeting real-time safety requirements. Overall, the proposed system significantly enhances early fire warning, multi-source environmental monitoring, and rapid hazard response, demonstrating strong applicability and scalability in modern laboratory safety management.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.