Cluster-based computing offloading system and the operating method

The cluster-based computing offloading system addresses energy and latency issues in IIoT devices by using reinforcement learning and D2D communication to optimize task distribution, achieving reduced energy consumption and latency through strategic offloading.

KR102999724B1Active Publication Date: 2026-08-03IND ACADEMIC COOP FOUND SOOKMYUNG WOMENS UNIV
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Patent Information

Application Number
KR1020220031942
Authority / Receiving Office
KR · KR
Patent Type
Patents
Current Assignee / Owner
Filing Date
2022-03-15
Publication Date
2026-08-03
Estimated Expiration
2042-03-15

AI Technical Summary

Technical Problem

Industrial Internet of Things (IIoT) devices face challenges with limited battery life and computing power, leading to high energy consumption and task execution delays, especially when processing multiple tasks within latency constraints in Mobile Edge Computing (MEC) environments.

Method used

A cluster-based computing offloading system utilizing reinforcement learning and D2D communication to determine an offloading strategy by comparing predicted values of IIoT devices and a MEC server, minimizing energy consumption and task execution delay through task distribution among devices and the server.

Benefits of technology

The system reduces energy consumption and execution latency by optimizing task distribution among IIoT devices and the MEC server, while reducing complexity in managing multiple devices by grouping them into clusters.

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Abstract

The present invention provides a computing offloading system comprising a cluster head that, when a new task is assigned from either of the first or second IIoT devices, applies Q-learning reinforcement learning to each of the first and second IIoT devices and the MEC server to the first and second IIoT devices to calculate a first specific predicted value of the first IIoT device and a second specific predicted value of the second IIoT device, and compares the calculated values ​​with a set reference value, wherein the first and second specific predicted values ​​are calculated by applying Q-learning reinforcement learning to each of the first and second IIoT devices and the MEC information provided by the MEC server, respectively, and offloads the new task to either of the first and second IIoT devices and the MEC server.
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