Edge Computing Resource Allocation Method for Mining 5G Communication System

Yang, Zhifang; Yang, Wei; Hao, Bonan; Yang, Zhaohui; Zhao, Qi

doi:10.1109/access.2023.3244242

Cited by 7 publications

(8 citation statements)

References 23 publications

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“…It provides a solid definition of the reliability of communication systems. In all the publications reviewed by the authors of this paper, only the authors of [14] defined reliability, which was while discussing private 5G networks; thus, it is imperative that we add to this definition to cover communication reliability conclusively; 3.…”

Section: Summary Of Contributionsmentioning

confidence: 99%

Reliability of LoRaWAN Communications in Mining Environments: A Survey on Challenges and Design Requirements

Musonda,

Ndiaye,

Libati

et al. 2024

JSAN

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While a robust and reliable communication network for monitoring the mining environment in a timely manner to take care of people, the planet Earth and profits is key, the mining environment is very challenging in terms of achieving reliable wireless transmission. This survey therefore investigates the reliability of LoRaWAN communication in the mining environment, identifying the challenges and design requirements. Bearing in mind that LoRaWAN is an IoT communication technology that has not yet been fully deployed in mining, the survey incorporates an investigation of LoRaWAN and other mining IoT communication technologies to determine their records of reliability, strengths and weaknesses and applications in mining. This aspect of the survey gives insight into the requirements of future mining IoT communication technologies and where LoRaWAN can be deployed in both underground and surface mining. Specific questions that the survey addresses are: (1) What is the record of reliability of LoRaWAN in mining environments? (2) What contributions have been made with regard to LoRa/LoRaWAN communication in general towards improving reliability? (3) What are the challenges and design requirements of LoRaWAN reliability in mining environments? (4) What research opportunities exist for achieving LoRaWAN communication in mining environments? In addition to recommending open research opportunities, the lessons learnt from the survey are also outlined.

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Section: Summary Of Contributionsmentioning

confidence: 99%

Reliability of LoRaWAN Communications in Mining Environments: A Survey on Challenges and Design Requirements

Musonda,

Ndiaye,

Libati

et al. 2024

JSAN

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“…This subsection gives an outline of the 5G design, its key parts, and the fundamental innovations that empower its high-level abilities. The 5G architecture can be divided into three main components: the radio access network (RAN), the core network (CN), and the user equipment (UE) as illustrated in Figure 2 [36]. The wireless communication between the UE and the CN is the responsibility of the RAN.…”

Section: G Architecturementioning

confidence: 99%

“…The smartphones, tablets, and IoT devices that are connected to the 5G network are referred to as the UE. To fully utilize the capabilities of the network, these devices must support the advanced features of 5G, such as new waveform designs and higher frequency bands [36].…”

Section: G Architecturementioning

confidence: 99%

“…Figure 5 illustrates the levels of vehicle automation from conventional vehicles (CVs) to connected autonomous vehicles (CAVs) whereas Figure 6 shows the levels of automation in autonomous vehicles. However, to enable fully autonomous driving and high-speed networks, fifth-generation (5G) or beyond 5G (B5G) technologies, is the key to a successful implementation of the levels of autonomy required by such vehicles [36]. The switch from conventional vehicles to fully autonomous vehicles is a slow but sure process that 5G will spearhead as illustrated in Figure 6.…”

Section: G and Transportationmentioning

confidence: 99%

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Perspective Chapter: 5G Enabling Technologies – Revolutionizing Transport, Environment, and Health

Sarpong Adu-Manu,

Amponsa Koranteng,

Nii Adotei Brown

2023

Edge Computing - Technology, Management and Integration

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The latest cellular technology, known as 5G, is anticipated to significantly improve the way systems in the physical and social environment (PSE) interact with technology. 5G technologies allow for the creation of a wide range of novel automation and applications. Recently, the Internet of Things (IoT), virtual and augmented reality (VAR), telemedicine, and autonomous vehicles have increased the growth of applications in the PSEs and can further benefit from 5G’s fast data transfer speeds (ranging from 1 to 10 Gbps) and low latency. The introduction of 5G may cause a paradigm shift in the operations of some industries, offer new economic opportunities, and impact our daily lives and relationships with the PSE. In this chapter, we examine how 5G revolutionize transport, the environment, and health. The chapter focuses on recent technologies related to virtual and augmented reality, autonomous vehicles, telemedicine, and edge computing among others.

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“…Some deep learning algorithms may face speed challenges due to computational resource limitations. Additionally, coal mine working faces are often constrained by computational resources, necessitating lightweight algorithms for rapid image processing and recognition in resource-constrained environments [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

CM-YOLOv8: Lightweight YOLO for Coal Mine Fully Mechanized Mining Face

Fan,

Mao,

et al. 2024

Sensors

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With the continuous development of deep learning, the application of object detection based on deep neural networks in the coal mine has been expanding. Simultaneously, as the production applications demand higher recognition accuracy, most research chooses to enlarge the depth and parameters of the network to improve accuracy. However, due to the limited computing resources in the coal mining face, it is challenging to meet the computation demands of a large number of hardware resources. Therefore, this paper proposes a lightweight object detection algorithm designed specifically for the coal mining face, referred to as CM-YOLOv8. The algorithm introduces adaptive predefined anchor boxes tailored to the coal mining face dataset to enhance the detection performance of various targets. Simultaneously, a pruning method based on the L1 norm is designed, significantly compressing the model’s computation and parameter volume without compromising accuracy. The proposed algorithm is validated on the coal mining dataset DsLMF+, achieving a compression rate of 40% on the model volume with less than a 1% drop in accuracy. Comparative analysis with other existing algorithms demonstrates its efficiency and practicality in coal mining scenarios. The experiments confirm that CM-YOLOv8 significantly reduces the model’s computational requirements and volume while maintaining high accuracy.

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Edge Computing Resource Allocation Method for Mining 5G Communication System

Cited by 7 publications

References 23 publications

Reliability of LoRaWAN Communications in Mining Environments: A Survey on Challenges and Design Requirements

Reliability of LoRaWAN Communications in Mining Environments: A Survey on Challenges and Design Requirements

Perspective Chapter: 5G Enabling Technologies – Revolutionizing Transport, Environment, and Health

CM-YOLOv8: Lightweight YOLO for Coal Mine Fully Mechanized Mining Face

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