Sustainable Task Offloading in Secure UAV-assisted Smart Farm Networks: A Multi-Agent DRL with Action Mask Approach

TL;DR

This paper presents a multi-agent DRL approach with action masking for secure UAV-assisted smart farm networks, optimizing task offloading to reduce delay and energy use while ensuring data security.

Contribution

It introduces a novel multi-agent DRL method with action masking for dynamic, secure task offloading in UAV-assisted smart farms, improving efficiency and sustainability.

Findings

Significant reduction in delay and energy consumption.

Enhanced security in data communications.

Superior performance over existing methods.

Abstract

The integration of unmanned aerial vehicles (UAVs) with mobile edge computing (MEC) and Internet of Things (IoT) technology in smart farms is pivotal for efficient resource management and enhanced agricultural productivity sustainably. This paper addresses the critical need for optimizing task offloading in secure UAV-assisted smart farm networks, aiming to reduce total delay and energy consumption while maintaining robust security in data communications. We propose a multi-agent deep reinforcement learning (DRL)-based approach using a deep double Q-network (DDQN) with an action mask (AM), designed to manage task offloading dynamically and efficiently. The simulation results demonstrate the superior performance of our method in managing task offloading, highlighting significant improvements in operational efficiency by reducing delay and energy consumption. This aligns with the goal of developing sustainable and energy-efficient solutions for next-generation network infrastructures, making our approach an advanced solution for achieving both performance and sustainability in smart farming applications.