# IoT-Aerial Base Station Task Offloading with Risk-Sensitive Reinforcement Learning for Smart Agriculture

arXiv: https://arxiv.org/abs/2209.07382 · HTML: https://mlsanity.com/paper/2209.07382/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 31 references that are in the map.
- First posted on arXiv 15 Sep 2022.

## Builds on (most cited first, 11 of 11)

- [Is Q-learning Provably Efficient?](https://mlsanity.com/paper/1807.03765/) (2018)
- [Intelligence and Learning in O-RAN for Data-driven NextG Cellular Networks](https://mlsanity.com/paper/2012.01263/) (2020)
- [Constrained Deep Reinforcement Learning for Energy Sustainable Multi-UAV based Random Access IoT Networks with NOMA](https://mlsanity.com/paper/2002.00073/) (2020)
- [Age of Information Aware VNF Scheduling in Industrial IoT Using Deep Reinforcement Learning](https://mlsanity.com/paper/2105.04207/) (2021)
- [Reinforcement Learning Based Dynamic Function Splitting in Disaggregated Green Open RANs](https://mlsanity.com/paper/2012.03213/) (2020)
- [eMBB-URLLC Resource Slicing: A Risk-Sensitive Approach](https://mlsanity.com/paper/1902.01648/) (2019)
- [Deep Reinforcement Learning for Delay-Oriented IoT Task Scheduling in Space-Air-Ground Integrated Network](https://mlsanity.com/paper/2010.01471/) (2020)
- [Reinforcement Learning-Based Deadline and Battery-Aware Offloading in Smart Farm IoT-UAV Networks](https://mlsanity.com/paper/2201.10361/) (2022)
- [Energy-Efficient Multi-UAV Data Collection for IoT Networks with Time Deadlines](https://mlsanity.com/paper/2009.06838/) (2020)
- [Risk-Sensitive Reinforcement Learning for URLLC Traffic in Wireless Networks](https://mlsanity.com/paper/1811.02341/) (2018)
- [GROVE: A Cost-Efficient Green Radio over Ethernet Architecture for Next Generation Radio Access Network](https://mlsanity.com/paper/2005.09081/) (2020)

## 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)

Aerial base stations (ABSs) allow smart farms to offload processing responsibility of complex tasks from internet of things (IoT) devices to ABSs. IoT devices have limited energy and computing resources, thus it is required to provide an advanced solution for a system that requires the support of ABSs. This paper introduces a novel multi-actor-based risk-sensitive reinforcement learning approach for ABS task scheduling for smart agriculture. The problem is defined as task offloading with a strict condition on completing the IoT tasks before their deadlines. Moreover, the algorithm must also consider the limited energy capacity of the ABSs. The results show that our proposed approach outperforms several heuristics and the classic Q-Learning approach. Furthermore, we provide a mixed integer linear programming solution to determine a lower bound on the performance, and clarify the gap between our risk-sensitive solution and the optimal solution, as well. The comparison proves our extensive simulation results demonstrate that our method is a promising approach for providing a guaranteed task processing services for the IoT tasks in a smart farm, while increasing the hovering time of the ABSs in this farm.