Towards a Partial Computation offloading in In-networking Computing-Assisted MEC: A Digital Twin Approach

TL;DR

This paper introduces a digital twin-based scheme for partial task offloading in in-network computing-assisted MEC, leveraging game theory and deep reinforcement learning to minimize latency in industrial IoT systems.

Contribution

It proposes a novel digital twin framework combined with a game-theoretic distributed offloading scheme using DDQN for resource optimization.

Findings

Reduces latency in IoT systems through partial offloading.

Enhances robustness and reliability of MEC with digital twin and game theory.

Addresses centralized control issues and resource contention.

Abstract

This paper addresses the problem of minimizing latency with partial computation offloading within Industrial Internet-of-Things (IoT) systems in in-network computing (COIN)-assisted Multiaccess Edge Computing (C-MEC) via ultra-reliable and low latency communications (URLLC) links. We propose a digital twin (DT) scheme for a multiuser scenario, allowing collaborative partial task offloading from user equipment (UE) to COIN-aided nodes or MEC. Specifically, we formulate the problem as joint task offloading decision, ratio and resource allocation. We employ game theory to create a low-complexity distributed offloading scheme in which the task offloading decision problem is modelled as an exact potential game. Double Deep Q-Network (DDQN) is utilized within the game to proactively predict optimal offloading ratio and resource allocation. This approach optimizes resource allocation across the whole system and enhances the robustness of the computing framework, ensuring efficient execution of computation-intensive services. Additionally, it addresses centralized approaches and UE resource contention issues, thus ensuring faster and more reliable communication.