# Deep Reinforcement Learning for Task Offloading in UAV-Aided Smart Farm Networks

arXiv: https://arxiv.org/abs/2209.07367 · HTML: https://mlsanity.com/paper/2209.07367/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 20 references that are in the map.
- First posted on arXiv 15 Sep 2022.

## Builds on (most cited first, 9 of 9)

- [Accessing From The Sky: A Tutorial on UAV Communications for 5G and Beyond](https://mlsanity.com/paper/1903.05289/) (2019)
- [UAV Communications for 5G and Beyond: Recent Advances and Future Trends](https://mlsanity.com/paper/1901.06637/) (2019)
- [AI-enabled Future Wireless Networks: Challenges, Opportunities and Open Issues](https://mlsanity.com/paper/2103.04536/) (2021)
- [Age of Information Aware VNF Scheduling in Industrial IoT Using Deep Reinforcement Learning](https://mlsanity.com/paper/2105.04207/) (2021)
- [Mobile Edge Computing in Unmanned Aerial Vehicle Networks](https://mlsanity.com/paper/1910.10523/) (2019)
- [Reinforcement Learning-Based Deadline and Battery-Aware Offloading in Smart Farm IoT-UAV Networks](https://mlsanity.com/paper/2201.10361/) (2022)
- [Beyond D2D: Full Dimension UAV-to-Everything Communications in 6G](https://mlsanity.com/paper/2004.01920/) (2020)
- [Energy-Efficient Multi-UAV Data Collection for IoT Networks with Time Deadlines](https://mlsanity.com/paper/2009.06838/) (2020)
- [Mobility for Cellular-Connected UAVs: challenges for the network provider](https://mlsanity.com/paper/2102.12899/) (2021)

## Built on by (most cited first, 2 of 2)

- [Current applications and potential future directions of reinforcement learning-based Digital Twins in agriculture](https://mlsanity.com/paper/2406.08854/) (2024)
- [To Risk or Not to Risk: Learning with Risk Quantification for IoT Task Offloading in UAVs](https://mlsanity.com/paper/2302.07399/) (2023)

## Abstract (arXiv)

The fifth and sixth generations of wireless communication networks are enabling tools such as internet of things devices, unmanned aerial vehicles (UAVs), and artificial intelligence, to improve the agricultural landscape using a network of devices to automatically monitor farmlands. Surveying a large area requires performing a lot of image classification tasks within a specific period of time in order to prevent damage to the farm in case of an incident, such as fire or flood. UAVs have limited energy and computing power, and may not be able to perform all of the intense image classification tasks locally and within an appropriate amount of time. Hence, it is assumed that the UAVs are able to partially offload their workload to nearby multi-access edge computing devices. The UAVs need a decision-making algorithm that will decide where the tasks will be performed, while also considering the time constraints and energy level of the other UAVs in the network. In this paper, we introduce a Deep Q-Learning (DQL) approach to solve this multi-objective problem. The proposed method is compared with Q-Learning and three heuristic baselines, and the simulation results show that our proposed DQL-based method achieves comparable results when it comes to the UAVs' remaining battery levels and percentage of deadline violations. In addition, our method is able to reach convergence 13 times faster than Q-Learning.