Belts are widely applied in mine production for conveying ores. Understanding ore granularity, which is a crucial factor in determining the effectiveness of crushers, is vital for optimising production efficiency throughout the crushing process and ensuring the success of subsequent operations. Based on edge computing technology, an online detection method is investigated to rapidly and accurately obtain ore granularity information on high-speed conveyor belts. The detection system utilising machine vision technology is designed in this paper. The high-speed camera set above the belt is used to collect the image of the ore flow, and the collected image is input into the edge computing device. After binary, grey morphology and convex hull algorithm processing, the particle size distribution of ore is obtained by statistical analysis. Finally, a 5G router is used to output the settlement result to a cloud platform. In the GUANBAOSHAN mine of Ansteel Group, the deviation between manual screening and image particle size analysis was studied. Experimental results show that the proposed method can detect the ore granularity, ore flow width and ore flow terminal in real-time. It can provide a reference for the staff to adjust the parameters of the crushing equipment, reduce the mechanical loss and the energy consumption of the equipment, improve the efficiency of crushing operation and reduce the failure rate of the crusher.

The dynamically changing environment forces companies to introduce changes in production processes and the need for employees to adapt quickly to new tasks. Therefore, it is expected to implement solutions to support employees. The system that will manage the work on a manufacturing line should work in real time to support the ongoing activities and, to be implemented in SMEs, must not be expensive. The authors identified important system components and expected functionalities. The methodology of the work is based on humancentered design. A concept of a cyber-physical system is proposed. The aim of the proposed edge computing-based system is to manage the work on the manufacturing line in which certain elements communicate with each other to achieve common goals. The paper presents what the system can consist of, how information and knowledge are managed in the system, and what can be the benefits for enterprises from its implementation.

The future Internet of Things (IoT) era is anticipated to support computation-intensive and time-critical applications using edge computing for mobile (MEC), which is regarded as promising technique. However, the transmitting uplink performance will be highly impacted by the hostile wireless channel, the low bandwidth, and the low transmission power of IoT devices. Using edge computing for mobile (MEC) to offload tasks becomes a crucial technology to reduce service latency for computation-intensive applications and reduce the computational workloads of mobile devices. Under the restrictions of computation latency and cloud computing capacity, our goal is to reduce the overall energy consumption of all users, including transmission energy and local computation energy. In this article, the Deep Q Network Algorithm (DQNA) to deal with the data rates with respect to the user base in different time slots of 5G NOMA network. The DQNA is optimized by considering more number of cell structures like 2, 4, 6 and 8. Therefore, the DQNA provides the optimal distribution of power among all 3 users in the 5G network, which gives the increased data rates. The existing various power distribution algorithms like frequent pattern (FP), weighted least squares mean error weighted least squares mean error (WLSME), and Random Power and Maximal Power allocation are used to justify the proposed DQNA technique. The proposed technique which gives 81.6% more the data rates when increased the cell structure to 8. Thus 25% more in comparison to other algorithms like FP, WLSME Random Power and Maximal Power allocation.

IP scheduled throughput defined according to 3GPP TS 36.314 reflects user throughput regardless of traffic characteristics, and therefore has become one of the most important indicators for monitoring Quality of Service (QoS) of the end user in Evolved Universal Terrestrial Radio Access Network (E-UTRAN). However, networks built on a distributed architecture make the above definition impossible to be applied directly due to the implementation challenges. This paper gives an overview of the classical Long Term Evolution (LTE) architecture as opposed to Dual Connectivity (DC) topology and focuses on a novel method of solving the calculation issue with the IP scheduled throughput measurement in edge computing environment. Experimental results show a good agreement with the real end user perception.