Modeling and Application of the Underground Emergency Hedging System Based on Internet of Things Technology

Wang, Jinhua; Guo, Yunfei; Jia, Yuna; Ya-jing, Zhang; Li, Mengqian

doi:10.1109/access.2019.2917263

Cited by 19 publications

(14 citation statements)

References 9 publications

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“…The applications of IoT also extend to the underground such as mining of mineral resources. In [ 84 ], the paper introduced an underground emergency hedging subsystem that aids the use of IoT technology to improve its underground application. In the agricultural field, IoT has found application in arable farming as well, which is detailed in [ 85 ].…”

Section: State-of-the-artmentioning

confidence: 99%

Suitability of NB-IoT for Indoor Industrial Environment: A Survey and Insights

Dangana

Ansari

Abbasi

et al. 2021

Sensors

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The Internet of Things (IoT) and its applications in industrial settings are set to bring in the fourth industrial revolution. The industrial environment consisting of high profile manufacturing plants and a variety of equipment is inherently characterized by high reflectiveness, causing significant multi-path components that affect the propagation of wireless communications—a challenge among others that needs to be resolved. This paper provides a detailed insight into Narrow-Band IoT (NB-IoT), Industrial IoT (IIoT), and Wireless Sensor Networks (WSN) within the context of indoor industrial environments. It presents the applications of NB-IoT for industrial settings, such as the challenges associated with these applications. Furthermore, future research directions were put forth in the areas of NB-IoT network management using self-organizing network (SON) technology, edge computing for scalability enhancement, security in NB-IoT generated data, and proposing a suitable propagation model for reliable wireless communications.

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Section: State-of-the-artmentioning

confidence: 99%

Suitability of NB-IoT for Indoor Industrial Environment: A Survey and Insights

Dangana

Ansari

Abbasi

et al. 2021

Sensors

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“…In recent years, with the continuous progress of Internet video technology and the continuous update of mobile video devices, browsing video has increasingly become an important way for people to understand the world and obtain information [1][2]. The information content carried by video data is much larger than that of text and image data.…”

Section: Introductionmentioning

confidence: 99%

Video Key Frame Monitoring Algorithm and Virtual Reality Display Based on Motion Vector

Wang

Zhu

2020

IEEE Access

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In this paper, a motion vector-based video key frame detection algorithm is proposed to solve the problem of miss election and missing selection caused by the difficulty in detecting the moving target characteristics of the video key frame. Firstly, the entropy of adjacent frame difference and the twodimensional entropy of image are introduced, and the combination of the two is taken as the measurement of the difference between video frames. Secondly, outliers are detected by statistical tools to obtain the lens boundary, thus realizing the adaptive lens detection of video content. Then, ViBe algorithm is used to detect the foreground object in the video sequence and extract the scale-invariant feature transformation features of the foreground moving object. Finally, the motion vector is introduced, and the sum of the block matching results is motion vector by partitioning the two adjacent frames and performing block matching. The magnitude of motion vector reflects the intensity of motion in the video, so active and inactive motion regions are obtained, and the similarity of video frames is calculated according to the defined formula, and key frames are extracted in these regions respectively. The experimental results show that the detection algorithm proposed in this paper improves the video results with rich motion information obviously, and the objective indexes and subjective scores are improved to some extent, which improves the universality of the algorithm. In addition, this paper also studies the display mode of key frame extraction results in virtual reality environment. The key frame display mode of video in virtual reality is optimized mainly by changing the display mode of information and changing the scene and testing the result of user task execution.INDEX TERMS Video; key Frame; monitoring algorithm; virtual reality display; motion vector.

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“…It also helps to reduce data retransmissions and enhance link reliability. However, the radio signals in coal mines do not propagate well due to the bounding of the tunnel walls [1]- [5].…”

Section: Introductionmentioning

confidence: 99%

Optimal Deployment of Antenna for Field Coverage in Coal Mine Tunnels

Huo

Zhao

et al. 2020

IEEE Access

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A deployment strategy for antennas is proposed to enhance the field coverage of wireless devices in underground coal mines, which uses the multimodal approach. Based on the attenuation coefficient of each mode, the propagation loss of the vertically polarized modes with n = 1, 2, and 3 and that of the horizontally polarized modes with m = 1, 2, and 3 are less. For the signal propagation in the near-field region of the source, these modes play significant role. According to the coupling of the antenna to the modes, the significant modes can be effectively excited by mounting horizontally polarized antenna on the ceiling or the vertically polarized antenna on the sidewall. For the signal propagation in the far-field region, the (1, 1) mode is significant, and the horizontally polarized excitation is preferred due to smaller attenuation. By comparing the total coupling efficiency of the antenna-radiated power, stronger excitation power can be obtained by mounting the antenna near the center of the ceiling or that of the sidewall. The field strength in the near-field and far-field region coverage can be maximized by optimizing the antenna polarization and position. For verification, a grid-based test system was designed and a series of measurements inside an underground coal mine tunnel were conducted. The measured results have a good agreement with the theoretical ones. In addition, the EM interference caused by the infrastructure in real mines on the signal received by the antennas at different positions was also measured and compared. Results show that the influences of cables with long-term stable loads, such as power cables and communication cables, can be neglected. INDEX TERMS Antenna deployment, field coverage, underground mines, wireless networks.

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Modeling and Application of the Underground Emergency Hedging System Based on Internet of Things Technology

Cited by 19 publications

References 9 publications

Suitability of NB-IoT for Indoor Industrial Environment: A Survey and Insights

Suitability of NB-IoT for Indoor Industrial Environment: A Survey and Insights

Video Key Frame Monitoring Algorithm and Virtual Reality Display Based on Motion Vector

Optimal Deployment of Antenna for Field Coverage in Coal Mine Tunnels

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