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

Abstract

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.

Accepted Paper. Please check footnote in Page 1 for copyright

References in corpus (1)

• Vehicle Routing Problems for Drone Delivery

Cited by in corpus (3)

• IoT-Aerial Base Station Task Offloading with Risk-Sensitive Reinforcement Learning for Smart Agriculture
• Heterogeneous GNN-RL Based Task Offloading for UAV-aided Smart Agriculture
• A Survey on Intelligent Computation Offloading and Pricing Strategy in UAV-Enabled MEC Network: Challenges and Research Directions