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

TL;DR

This paper proposes a reinforcement learning approach for deadline and battery-aware task offloading in UAV-assisted smart farm IoT networks, improving energy efficiency and deadline adherence.

Contribution

It introduces a multi-objective Q-Learning method for UAV task offloading considering energy and deadline constraints, with an ILP model for performance bounds.

Findings

Q-Learning outperforms heuristic baselines in energy retention

The approach reduces deadline violations

Provides an energy-efficient offloading solution

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.