# Reinforcement Learning-Based Deadline and Battery-Aware Offloading in Smart Farm IoT-UAV Networks

arXiv: https://arxiv.org/abs/2201.10361 · HTML: https://mlsanity.com/paper/2201.10361/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2022.

## Built on by (most cited first, 4 of 4)

- [Deep Reinforcement Learning for Task Offloading in UAV-Aided Smart Farm Networks](https://mlsanity.com/paper/2209.07367/) (2022)
- [IoT-Aerial Base Station Task Offloading with Risk-Sensitive Reinforcement Learning for Smart Agriculture](https://mlsanity.com/paper/2209.07382/) (2022)
- [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)

Unmanned aerial vehicles (UAVs) with mounted base stations are a promising technology for monitoring smart farms. They can provide communication and computation services to extensive agricultural regions. With the assistance of a Multi-Access Edge Computing infrastructure, an aerial base station (ABS) network can provide an energy-efficient solution for smart farms that need to process deadline critical tasks fed by IoT devices deployed on the field. In this paper, we introduce a multi-objective maximization problem and a Q-Learning based method which aim to process these tasks before their deadline while considering the UAVs' hover time. We also present three heuristic baselines to evaluate the performance of our approaches. In addition, we introduce an integer linear programming (ILP) model to define the upper bound of our objective function. The results show that Q-Learning outperforms the baselines in terms of remaining energy levels and percentage of delay violations.