Deploying IIoT Systems for Long-Term Planning in Underground Mining: A Focus on the Monitoring of Explosive Atmospheres

被引:1
|
作者
Medina, Fabian [1 ]
Ruiz, Hugo [2 ]
Espindola, Jorge [1 ]
Avendano, Eduardo [3 ]
机构
[1] Univ Pedag & Tecnol Colombia, Dept Comp Engn, Tunja 150003, Colombia
[2] Univ Pedag & Tecnol Colombia, Dept Ind Engn, Tunja 150003, Colombia
[3] Univ Pedag & Tecnol Colombia, Dept Elect Engn, Tunja 150003, Colombia
来源
APPLIED SCIENCES-BASEL | 2024年 / 14卷 / 03期
关键词
explosive atmospheres; underground coal mining; WSN; IIoT; node deployment; WIRELESS SENSOR NETWORKS; DEPLOYMENT; COVERAGE; INTERNET; IOT; CONNECTIVITY; PLATFORM;
D O I
10.3390/app14031116
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
This paper presents a novel methodology for deploying wireless sensor nodes in the Industrial Internet of Things (IIoT) to address the safety and efficiency challenges in underground coal mining. The methodology is intended to support long-term planning on mitigating the risks in occupational health and safety policies. To ensure realistic and accurate deployment, we propose a software tool that generates mine models based on geolocation data or blueprints in image format, allowing precise adaptation to the specific conditions of each mine. Furthermore, the process is based on sensing and communication range values obtained through simulations and on-site experiments. The deployment strategy is articulated in two complementary steps: a deterministic deployment, where nodes are strategically placed according to the structure of the tunnels, followed by a random stage to include additional nodes that ensure optimal coverage and connectivity inside the mine by comparing different methodologies for deploying sensor networks using coverage density as a performance metric. We analyze coverage and connectivity based on the three probability density functions (PDFs) for the random deployment of nodes: uniform, normal, and exponential, evaluating both the degree of coverage (k-coverage) and the degree of connectivity (k-connectivity). The results show that our proposed methodology stands out for its lower density of sensors per square meter, which translates into a reduction of between 20.81% and 23.46% for uniform and exponential PDFs, respectively, concerning the number of sensors compared to the analyzed methodologies. In this way, it is possible to determine which distribution is suitable to cover the elongated area with the smallest number of nodes, considering the coverage and connectivity requirements, to reduce the deployment cost. The uniform PDF minimizes the number of sensors needed by 44.70% in small mines and 46.27% in medium ones compared to the exponential PDF. These findings provide valuable information to optimize node deployment regarding cost and efficiency; a uniform function is a good option depending on prices. The exponential distribution reached the highest values of k-coverage and k-connectivity for small and medium-sized mines; in addition, it has greater robustness and tolerance to faults like signal network intermittence. This methodology not only improves the collection of critical information for the mining operation but also plays a vital role in reducing the risks to the health and safety of workers by providing a more robust and adaptive monitoring system. The approach can be used to plan IIoT systems based on Wireless Sensor Networks (WSN) for underground mining exploitation, offering a more reliable and adaptable strategy for monitoring and managing complex work environments.
引用
收藏
页数:25
相关论文
共 50 条
  • [21] LONG-TERM STABILITY MONITORING OF THE MINING BLOCKS IN ESTONIAN OIL SHALE MINES
    Pastarus, J. -R.
    Environment, Technology, Resources, Proceedings, 2003, : 206 - 211
  • [22] Long-term monitoring of mediterranean socio-ecological systems
    Calvache, Marta F.
    Santos, Rui
    Antunes, Paula
    Santos-Reis, Margarida
    AGROFORESTRY SYSTEMS, 2021, 95 (03) : 459 - 473
  • [23] Experiences in long-term evaluation of mercury emission monitoring systems
    Cheng, Chin-Min
    Lin, Hung-Ta
    Wang, Qiang
    Chen, Chien-Wei
    Wang, Chia-Wei
    Liu, Ming-Chung
    Chen, Chi-Kuan
    Pan, Wei-Ping
    ENERGY & FUELS, 2008, 22 (05) : 3040 - 3049
  • [24] Long-term monitoring of mediterranean socio-ecological systems
    Marta F. Calvache
    Rui Santos
    Paula Antunes
    Margarida Santos-Reis
    Agroforestry Systems, 2021, 95 : 459 - 473
  • [25] Long-term phonocardiographic fetal home monitoring for telemedicine systems
    Kovacs, F.
    Horvath, Cs.
    Torok, M.
    Hosszu, G.
    2005 27TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY, VOLS 1-7, 2005, : 3946 - 3949
  • [26] Long-term planning and the sustainable power system: a focus on flexibility needs and network reliability
    Maizi, Nadia
    Mazauric, Vincent
    Assoumou, Edi
    Drouineau, Mathilde
    2009 IEEE/PES POWER SYSTEMS CONFERENCE AND EXPOSITION, VOLS 1-3, 2009, : 384 - +
  • [27] Long-Term Urban Epidemic and Disaster Resilience: The Planning and Assessment of a Comprehensive Underground Resilience Core
    Qiu, Tong
    Chen, Xiangsheng
    Su, Dong
    Lin, Xingtao
    BUILDINGS, 2023, 13 (05)
  • [28] Linear Programming Model Applied to Long-Term Mine Planning in Strip Mining Operations
    Bassani, Marcel Antonio Arcari
    Guimaraes, Octavio R. A.
    Tavares, Flavio H.
    Cantadori, Breno
    Vicenzi, Ricardo
    Alves, Joao Lucas de Oliveira
    Mariz, Jorge Luiz Valenca
    Peroni, Rodrigo de Lemos
    MINING METALLURGY & EXPLORATION, 2025, : 737 - 750
  • [29] The development of adaptation pathways for the long-term planning of urban drainage systems
    Babovic, Filip
    Mijic, Ana
    JOURNAL OF FLOOD RISK MANAGEMENT, 2019, 12
  • [30] Smart Distribution Systems Stakeholders Analysis and The Effects on Long-Term Planning
    Sindi, Hatem
    El-Saadany, Ehab
    Shaaban, Mostafa
    2017 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL TECHNOLOGY (ICIT), 2017, : 621 - 625