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arXiv · 2504.16729

MEC Task Offloading in AIoT: A User-Centric DRL Model Splitting Inference Scheme

Weixi Li·Rongzuo Guo·Yuning Wang·Fangying Chen
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Abstract

With the rapid development of the Artificial Intelligence of Things (AIoT), mobile edge computing (MEC) becomes an essential technology underpinning AIoT applications. However, multi-angle resource constraints, multi-user task competition, and the complexity of task offloading decisions in dynamic MEC environments present new technical challenges. Therefore, a user-centric deep reinforcement learning (DRL) model splitting inference scheme is proposed to address the problem. This scheme combines model splitting inference technology and designs a UCMS_MADDPG-based offloading algorithm to realize efficient model splitting inference responses in the dynamic MEC environment with multi-angle resource constraints. Specifically, we formulate a joint optimization problem that integrates resource allocation, server selection, and task offloading, aiming to minimize the weighted sum of task execution delay and energy consumption. We also introduce a user-server co-selection algorithm to address the selection issue between users and servers. Furthermore, we design an algorithm centered on user pre-decision to coordinate the outputs of continuous and discrete hybrid decisions, and introduce a priority sampling mechanism based on reward-error trade-off to optimize the experience replay mechanism of the network. Simulation results show that the proposed UCMS_MADDPG-based offloading algorithm demonstrates superior overall performance compared with other benchmark algorithms in dynamic environments.

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Weixi Li, Rongzuo Guo, Yuning Wang, Fangying Chen. 2026-03-05. MEC Task Offloading in AIoT: A User-Centric DRL Model Splitting Inference Scheme. https://arxiv.org/abs/2504.16729

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