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Joint Task Allocation and Trajectory Optimization for Multi-UAV Collaborative Air-Ground Edge Computing
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AI
DOI：10.1109/TNSE.2024.3481061.png)
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Abstract
En 中文
With the proliferation of Internet of Things (IoT), compute-intensive and latency-critical applications continue to emerge. However, IoT devices in isolated locations have insufficient energy storage as well as computing resources and may fall outside the service range of ground communication networks. To overcome the constraints of communication coverage and terminal resource, this paper proposes a multiple Unmanned Aerial Vehicle (UAV)-assisted air-ground collaborative edge computing network model, which comprises associated UAVs, auxiliary UAVs, ground user devices (GDs), and base stations (BSs), intending to minimize the overall system energy consumption. It delves into task offloading, UAV trajectory planning and edge resource allocation, which thus is classified as a Mixed-Integer Nonlinear Programming (MINLP) problem. Worse still, the coupling of long-term task queuing delay and short-term offloading decision makes it challenging to address the original issue directly. Therefore, we employ Lyapunov optimization to transform it into two sub-problems. The first involves task offloading for GDs, trajectory optimization for associated UAVs as well as auxiliary UAVs, which is tackled using Deep Reinforcement Learning (DRL), while the second deals with task partitioning and computing resource allocation, which we address via convex optimization. Through numerical simulations, we verify that the proposed approach outperforms other benchmark methods regarding overall system energy consumption.
Keywords:
Autonomous aerial vehicles
Resource management
Gold
Air to ground communication
Collaboration
Energy consumption
Computational modeling
Internet of Things
Edge computing
Delays
Multi-UAV collaborative air-ground edge computing
trajectory optimization
resource allocation
Lyapunov optimization
Multi-Agent Deep Deterministic Policy Gradients (MADDPG)
Journal
I
IF:
7.9
Papers:
2.5K
Citations:
10.0K