AISC deployment in dynamic UAV-assisted MEC network: a reinforcement learning method based on heterogeneous graph attention neural network

2026-06-04

AI Summary

This study addresses the challenge of deploying AI service chains (AISCs) in dynamic unmanned aerial vehicle–assisted mobile edge computing (UMEC) networks, where high topological dynamics, complex interdependencies among virtual network functions (VNFs), and trade-offs between energy consumption and load balancing hinder minimization of service completion time. To tackle this, the work proposes a dual deep attention Q-network method that integrates heterogeneous graph attention into a deep reinforcement learning framework. By modeling heterogeneous nodes and links in the drone network and leveraging attention mechanisms to adaptively focus on critical resources, the approach enables end-to-end optimized AISC deployment. Experimental results demonstrate that the proposed method significantly outperforms existing baselines in terms of service completion time, success rate, load balancing, and energy efficiency.

Abstract

Unmanned aerial vehicles-assisted mobile edge computing (UMEC) can execute compute-intensive and latency-critical artificial intelligence (AI) services, which can be provided by multiple UAVs collaborating in the air to perform inference tasks. Completing an AI service requires multiple inferences, each of which is implemented by an AI service chain consisting of multiple virtual network functions (VNFs). The application of AISC relies on an efficient AISC deployment strategy to determine which UAV to deploy VNF on. However, the UMEC network topology is highly dynamic due to the high-speed movement of UAVs or their departure/arrival, which makes the AISC deployment in the UMEC network challenging. In addition, the intricate relationships between UMEC environment and AISC, as well as between individual VNFs in an AISC, can also affect the effectiveness of AISC deployment strategy. Moreover, under the constraints of energy consumption and load balancing, it is also difficult to optimize the AISC strategy to minimize AISC completion time for enhancing the quality of AI service. To address the above challenges, this paper proposes a double deep attention Q-network based on heterogeneous graph neural networks, which incorporates heterogeneous graph to capture diverse relationships in UMEC and utilizes attention mechanisms to adaptively focus on critical nodes and links for intelligent AISC deployment. The experimental results demonstrate that the proposed algorithm performs excellently in AISC completion time, AISC completion rate, load balancing and energy consumption.

Problem

• AISC deployment
• UAV-assisted MEC
• dynamic network topology
• virtual network functions
• AI service chain

Innovation

• heterogeneous graph attention network
• reinforcement learning
• AI service chain deployment
• UAV-assisted MEC
• dynamic network topology