Robust priority aware multi-criterion offloading in digital twin UAVs networks

Abstract

Unmanned Aerial Vehicles (UAVs) play a critical role in replenishing the energy of power-constrained Internet of Things (IoT) devices, particularly in public safety operations, thereby maintaining continuous system functionality. Integrating Mobile Edge Computing (MEC) into UAV platforms enables offloading computational tasks to aerial nodes, optimizing resource utilization. Efficient orchestration of communication, computation, caching, and energy resources is imperative to maximize the benefits of UAV-assisted MEC networks. Additionally, ensuring high situational awareness is essential for supporting priority-based latency-sensitive applications. Digital twin technology can be instrumental in minimizing latency by generating a real-time digital representation of the physical infrastructure, enabling enhanced system monitoring and optimization. Accordingly, we have formulated an optimization problem to maximize the number of IoT devices UAVs can support while adhering to predefined constraints. The formulated problem is a mixed integer non-linear programming model. Additionally, the dynamic management of tasks with varying priorities and computational demands introduces a significant resource allocation and scheduling challenge. Our proposed approach entails an efficient task offloading and priority-based scheduling strategy that prioritizes tasks, allocating computational resources to those with higher priority. The approach encompasses a multi-stage offloading strategy combining an interior-point method with a learning algorithm to address the inherent complexity and provide a viable solution. Simulation results validate the effectiveness of the proposed approach, outperforming conventional methods. Specifically, the Penalty Function Method Heuristic combined with the Interior Point Method achieves superior user connectivity compared to the Simple Relaxation Heuristic strategy.