ReviewSensors (Basel, Switzerland)2024
Internet of Things Long-Range-Wide-Area-Network-Based Wireless Sensors Network for Underground Mine Monitoring: Planning an Efficient, Safe, and Sustainable Labor Environment.
Review in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
3 citing papers in PubMed.
- Integrating Artificial Intelligence into Ventilation on Demand: Current Practice and Future Promises.Sensors (Basel, Switzerland) · 2026Review
- Development of an Advanced Multi-Layer Digital Twin Conceptual Framework for Underground Mining.Sensors (Basel, Switzerland) · 2025Review
- Experimental Study on Multi-Directional Hybrid Energy Harvesting of a Two-Degree-of-Freedom Cantilever Beam.Sensors (Basel, Switzerland) · 2025Article
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
No grant is acknowledged in the PubMed record.
Abstract
Underground mines are considered one of the riskiest facilities for human activities due to numerous accidents and geotechnical failures recorded worldwide over the last century, which have resulted in unsafe labor conditions, poor health outcomes, injuries, and fatalities. One significant cause of these accidents is the inadequate or nonexistent capacity for the real-time monitoring of safety conditions in underground mines. In this context, new emerging technologies linked to the Industry 4.0 paradigm, such as sensors, the Internet of Things (IoT), and LoRaWAN (Long Range Wide Area Network) wireless connectivity, are being implemented for planning the efficient, safe, and sustainable performance of underground mine labor environments. This paper studies the implementation of an ecosystem composed of IoT sensors and LoRa wireless connectivity in a data-acquisition system, which eliminates the need for expensive cabling and manual monitoring in mining operations. Laying cables in an underground mine necessitates cable support and protection against issues, such as machinery operations, vehicle movements, mine operator activities, and groundwater intrusion. As the underground mine expands, additional sensors typically require costly cable installations unless wireless connectivity is employed. The results of this review indicate that an IoT LoRaWAN-based wireless sensor network (WSN) provides real-time data under complex conditions, effectively transmitting data through physical barriers. This network presents an attractive low-cost solution with reliable, simple, scalable, secure, and competitive characteristics compared to cable installations and manually collected readings, which are more sporadic and prone to human error. Reliable data on the behavior of the underground mine enhances productivity by improving key performance indicators (KPIs), minimizing accident risks, and promoting sustainable environmental conditions for mine operators. Finally, the adoption of IoT sensors and LoRaWAN wireless connectivity technologies provides information of the underground mine in real-time, which supports better decisions by the mining industry managers, by ensuring compliance with safety regulations, improving the productive performance, and fostering a roadmap towards more environmentally friendly labor conditions.
Indexed as
Identifiers
What OpenQuestion holds
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.