Web of Things-Based Remote Monitoring System for Coal Mine Safety Using Wireless Sensor Network

Cheng, Bo; Cheng, Xin; Zhai, Zhongyi; Zhang, Chengwen; Chen, Junliang

doi:10.1155/2014/323127

Cited by 25 publications

(15 citation statements)

References 11 publications

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“…Monitoring and controlling technologies [32][33][34][35][36] Employing internet of things (IOT) and cloud computing (CC) to monitor mine safety based on prealarm system; using wireless sensor network (WSN) to monitor the temperature, humidity, gas, and status of smoke in underground mine; establishing a Web of Things-based remote monitoring system for coal mine safety; employing cable monitoring system (CMS) and the WSN to build an integrated environment monitoring system for underground coal mine; using iris identification and radio frequency identification (RFID) technique to improve safety management system. studies have been carried out by worldwide researchers to improve the safety performance.…”

Section: Themementioning

confidence: 99%

“…[33] designed a wireless sensor network (WSN) based on MSP430 controller for monitoring smoke, gas, temperature, and humidity in coal mine. Based on wireless sensor network and controller area network (CAN), Bo et al [34] proposed a remote monitoring system, which was tested in different remote monitoring scenarios. Zhang et al [35] proposed an integrated environment monitoring system that takes full advantage of cable monitoring system (CMS) in combination with wireless sensor network (WSN).…”

Section: Themementioning

confidence: 99%

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An Approach for Understanding and Promoting Coal Mine Safety by Exploring Coal Mine Risk Network

et al. 2017

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Capturing the interrelations among risks is essential to thoroughly understand and promote coal mining safety. From this standpoint, 105 risks and 135 interrelations among risks had been identified from 126 typical accidents, which were also the foundation of constructing coal mine risk network (CMRN). Based on the complex network theory and Pajek, six parameters (i.e., network diameter, network density, average path length, degree, betweenness, and clustering coefficient) were employed to reveal the topological properties of CMRN. As indicated by the results, CMRN possesses scale-free network property because its cumulative degree distribution obeys power-law distribution. This means that CMRN is robust to random hazard and vulnerable to deliberate attack. CMRN is also a small-world network due to its relatively small average path length as well as high clustering coefficient, implying that accident propagation in CMRN is faster than regular network. Furthermore, the effect of risk control is explored. According to the result, it shows that roof collapse, fire, and gas concentration exceeding limit refer to three most valuable targets for risk control among all the risks. This study will help offer recommendations and proposals for making beforehand strategies that can restrain original risks and reduce accidents.

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Section: Themementioning

confidence: 99%

Section: Themementioning

confidence: 99%

An Approach for Understanding and Promoting Coal Mine Safety by Exploring Coal Mine Risk Network

et al. 2017

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“…In recent years, researchers have used a variety of techniques for different types of geological hazard monitoring and early warning to conduct relevant research and have achieved fruitful results. Bo [5] introduced the wireless sensor network in a remote-monitoring system for coal mine safety based on Internet of Things. Hack [6] researched environmental 60 monitoring technologies and methods with respect to geological hazards in the Slovak Republic.…”

Section: Reviewmentioning

confidence: 99%

Implementation of a Geological Disaster Monitoring and Early Warning System Based on Multi-source Spatial Data: A Case Study of Deqin County, Yunnan Province

Chen

Zhao

Yuan

et al. 2017

Preprint

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New technologies, such as three-dimensional laser scanning, interferometric synthetic aperture radar (InSAR), global navigation satellite systems (GNSSs), unmanned aerial vehicles (UAVs), and the Internet of Things, will provide greater volumes of data for surveying and monitoring as well as for the development of early warning systems (EWS). This research proposes solutions for the design and implementation of a geological hazard monitoring and early warning system (GHMEWS) for 15 landslides and debris-flow hazards based on data multi-sourced from the aforementioned technologies. We describe the complex and changeable characteristics of the GHMEWS and analyze the architecture of the system, the composition of the multi-source database, the development mode and service logic, and the methods and key technologies of the system development. To illustrate the implementation process of the GHMEWS, we selected Deqin County as the case study area due to its unique terrain and diverse types of typical landslides and debris flows. First, we discuss the system's functional requirements and the monitoring and 20 forecasting models of the system. Second, we examine the logic relations of the overall disaster process, including pre-disaster, disaster rescue, and post-disaster reconstruction, and develop a support tool for disaster prevention, disaster reduction, and geological disaster management. Third, we describe the methods for multi-source monitoring data integration and the generation and simulation of the mechanism model of geological disasters. Finally, we construct the GHMEWS for application to the dynamic and real-time management, monitoring, and forecasting of the entire hazard process in Deqin County. 25

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“…In this regard, standardized solutions provided by the rapid evolution of the Internet have laid the foundation of the web of things (WoT) [40]. Although the latest developments on the IoT field have definitely contributed to the physical connection of such an overwhelming amount of smart devices [40], several issues have arisen when attempting to provide a common management and monitoring interface for the whole smart grid [41,42].…”

Section: A Web Of Energymentioning

confidence: 99%

Prototyping a Software Defined Utility

et al. 2017

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Abstract:The smart grid can be seen as a hybrid system composed by many systems. From a large scale point of view, it combines the electric power system itself and a heterogeneous information and communication technology (ICT) infrastructure. Additionally, these systems are composed by many building blocks that are designed and managed as separated systems which are hard to fully integrate between each other. Relying on the experiences arisen and the knowledge gathered from the partners during the development of the FP7 European projects INTEGRIS (intelligent electrical grid sensor communications) and FINESCE (future internet smart utility services), this paper presents the software defined utility (SDU) concept for the management of the smart grid and its security, which advocates for the migration of the utility infrastructure to software systems instead of relying on complex and rigid hardware based systems. Following this approach, SDU proposes the evolution of power systems' ICT and the usage of programmable commodity hardware, low-cost sensors, and reliable high-speed IP-based communications underneath. More concretely, this paper proposes some building blocks for the deployment of the SDU (flexible data management infrastructure, context-aware security and web of things interface) and evaluates their functionalities and benefits for the smart grids of the future.

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Web of Things-Based Remote Monitoring System for Coal Mine Safety Using Wireless Sensor Network

Cited by 25 publications

References 11 publications

An Approach for Understanding and Promoting Coal Mine Safety by Exploring Coal Mine Risk Network

An Approach for Understanding and Promoting Coal Mine Safety by Exploring Coal Mine Risk Network

Implementation of a Geological Disaster Monitoring and Early Warning System Based on Multi-source Spatial Data: A Case Study of Deqin County, Yunnan Province

Prototyping a Software Defined Utility

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