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Related Experiment Videos

Task offloading and resource allocation for cooperative communication and sensing in edge computing for mine.

Wanbo Zheng^1,2, Qiuping Yang³, Kerong Chen³

¹The Faculty of Science, Kunming University of Science and Technology, Kunming, 650500, China. zwanbo2001@163.com.

Scientific Reports

|May 11, 2026

Summary

This study introduces a new task offloading scheme for intelligent mining, optimizing edge computing to reduce latency and energy use. The Improved Gray Wolf Optimization algorithm effectively minimizes overall utility value for better system performance.

Related Experiment Videos

Area of Science:

Intelligent Mining
Edge Computing
Task Offloading

Background:

Terminal devices in intelligent mining face inadequate computing power for demanding tasks.
Existing task offloading methods often lead to suboptimal resource allocation, causing high latency and energy consumption.
A new metric, Overall Utility Value (OUV), is needed to balance system delay and energy usage.

Purpose of the Study:

To develop an edge computing task-offloading framework for intelligent mining scenarios.
To minimize system latency (SL) and system energy consumption (SEC) by optimizing task allocation and wireless bandwidth.
To introduce a Cooperative Communication and Sensing Task Offloading Scheme (CCTS).

Main Methods:

A novel edge computing framework with a central control unit, distributed service nodes, and terminal devices.
Leveraging environmental awareness of service nodes for optimized offloading decisions.
Developing an Improved Gray Wolf Optimization algorithm integrated with a Feasibility Checking Algorithm (IGWO-FCA).

Main Results:

The proposed Cooperative Communication and Sensing Task Offloading Scheme (CCTS) effectively minimizes system latency and energy consumption.
The Improved Gray Wolf Optimization algorithm with Feasibility Checking Algorithm (IGWO-FCA) achieved the lowest Overall Utility Value (OUV).
Simulation results validated the effectiveness of the IGWO-FCA in optimizing task offloading.

Conclusions:

The developed edge computing framework and CCTS scheme significantly improve performance in intelligent mining.
The IGWO-FCA is an effective optimization algorithm for task offloading in edge computing environments.
Optimized task allocation and bandwidth ratios are crucial for reducing latency and energy consumption in mining applications.