# To Risk or Not to Risk: Learning with Risk Quantification for IoT Task Offloading in UAVs

arXiv: https://arxiv.org/abs/2302.07399 · HTML: https://mlsanity.com/paper/2302.07399/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 20 references that are in the map.
- First posted on arXiv 14 Feb 2023.

## Builds on (most cited first, 5 of 5)

- [Reinforcement Learning Based Dynamic Function Splitting in Disaggregated Green Open RANs](https://mlsanity.com/paper/2012.03213/) (2020)
- [Risk-Aware Optimization of Age of Information in the Internet of Things](https://mlsanity.com/paper/2002.09805/) (2020)
- [Reinforcement Learning-Based Deadline and Battery-Aware Offloading in Smart Farm IoT-UAV Networks](https://mlsanity.com/paper/2201.10361/) (2022)
- [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)

## Abstract (arXiv)

A deep reinforcement learning technique is presented for task offloading decision-making algorithms for a multi-access edge computing (MEC) assisted unmanned aerial vehicle (UAV) network in a smart farm Internet of Things (IoT) environment. The task offloading technique uses financial concepts such as cost functions and conditional variable at risk (CVaR) in order to quantify the damage that may be caused by each risky action. The approach was able to quantify potential risks to train the reinforcement learning agent to avoid risky behaviors that will lead to irreversible consequences for the farm. Such consequences include an undetected fire, pest infestation, or a UAV being unusable. The proposed CVaR-based technique was compared to other deep reinforcement learning techniques and two fixed rule-based techniques. The simulation results show that the CVaR-based risk quantifying method eliminated the most dangerous risk, which was exceeding the deadline for a fire detection task. As a result, it reduced the total number of deadline violations with a negligible increase in energy consumption.