# Joint Task Offloading and Resource Allocation in Aerial-Terrestrial UAV Networks with Edge and Fog Computing for Post-Disaster Rescue

arXiv: https://arxiv.org/abs/2309.16709 · HTML: https://mlsanity.com/paper/2309.16709/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2023.

## Built on by (most cited first, 14 of 14)

- [Diffusion Models as Network Optimizers: Explorations and Analysis](https://mlsanity.com/paper/2411.00453/) (2024)
- [Generative Artificial Intelligence Assisted Wireless Sensing: Human Flow Detection in Practical Communication Environments](https://mlsanity.com/paper/2404.14140/) (2024)
- [Hierarchical Federated Learning in Multi-hop Cluster-Based VANETs](https://mlsanity.com/paper/2401.10361/) (2024)
- [Digital Twin-Empowered Task Assignment in Aerial MEC Network: A Resource Coalition Cooperation Approach with Generative Model](https://mlsanity.com/paper/2405.01555/) (2024)
- [Task Assignment and Exploration Optimization for Low Altitude UAV Rescue via Generative AI Enhanced Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2504.13554/) (2025)
- [Joint Resource Management for Energy-efficient UAV-assisted SWIPT-MEC: A Deep Reinforcement Learning Approach](https://mlsanity.com/paper/2505.03230/) (2025)
- [U2UData+: A Scalable Swarm UAVs Autonomous Flight Dataset for Embodied Long-horizon Tasks](https://mlsanity.com/paper/2509.00055/) (2025)
- [Enhancing Disaster Resilience with UAV-Assisted Edge Computing: A Reinforcement Learning Approach to Managing Heterogeneous Edge Devices](https://mlsanity.com/paper/2501.15305/) (2025)
- [UAV-assisted Joint Mobile Edge Computing and Data Collection via Matching-enabled Deep Reinforcement Learning](https://mlsanity.com/paper/2502.07388/) (2025)
- [Joint Task Offloading and Resource Allocation in Low-Altitude MEC via Graph Attention Diffusion](https://mlsanity.com/paper/2506.21933/) (2025)
- [Joint AoI and Handover Optimization in Space-Air-Ground Integrated Network](https://mlsanity.com/paper/2509.12716/) (2025)
- [Enhanced Evolutionary Multi-Objective Deep Reinforcement Learning for Reliable and Efficient Wireless Rechargeable Sensor Networks](https://mlsanity.com/paper/2510.21127/) (2025)
- [When UAV Swarm Meets IRS: Collaborative Secure Communications in Low-altitude Wireless Networks](https://mlsanity.com/paper/2510.22117/) (2025)
- [Joint Trajectory, RIS, and Computation Offloading Optimization via Decentralized Model-Based PPO in Urban Multi-UAV Mobile Edge Computing](https://mlsanity.com/paper/2603.20238/) (2026)

## Abstract (arXiv)

Unmanned aerial vehicles (UAVs) play an increasingly important role in assisting fast-response post-disaster rescue due to their fast deployment, flexible mobility, and low cost. However, UAVs face the challenges of limited battery capacity and computing resources, which could shorten the expected flight endurance of UAVs and increase the rescue response delay during performing mission-critical tasks. To address this challenge, we first present a three-layer post-disaster rescue computing architecture by leveraging the aerial-terrestrial edge capabilities of mobile edge computing (MEC) and vehicle fog computing (VFC), which consists of a vehicle fog layer, a UAV client layer, and a UAV edge layer. Moreover, we formulate a joint task offloading and resource allocation optimization problem (JTRAOP) with the aim of maximizing the time-average system utility. Since the formulated JTRAOP is proved to be NP-hard, we propose an MEC-VFC-aided task offloading and resource allocation (MVTORA) approach, which consists of a game theoretic algorithm for task offloading decision, a convex optimization-based algorithm for MEC resource allocation, and an evolutionary computation-based hybrid algorithm for VFC resource allocation. Simulation results validate that the proposed approach can achieve superior system performance compared to the other benchmark schemes, especially under heavy system workloads.