# Proximal Policy Optimization-based Task Offloading Framework for Smart Disaster Monitoring using UAV-assisted WSNs

## TL;DR

This paper introduces a new framework using AI to improve energy efficiency and task success in disaster monitoring systems that use drones and sensors.

## Contribution

The novel ETORL-UAV framework uses PPO-based reinforcement learning for energy-efficient task offloading in UAV-assisted WSNs.

## Key findings

- ETORL-UAV achieves 9.3% higher task offloading success compared to existing methods.
- The framework improves network lifetime by 18.75% and reduces energy consumption by 27%.
- It demonstrates scalability and reliability for real-world disaster-response scenarios.

## Abstract

Unmanned Aerial Vehicles (UAVs) are increasingly employed in Wireless Sensor Networks (WSNs) to enhance communication, coverage, and energy efficiency, particularly in disaster monitoring and remote surveillance scenarios. However, challenges such as limited energy resources, dynamic task allocation, and UAV trajectory optimization remain critical. This paper presents Energy-efficient Task Offloading using Reinforcement Learning for UAV-assisted WSNs (ETORL-UAV), a novel framework that integrates Proximal Policy Optimization (PPO) based reinforcement learning to intelligently manage UAV-assisted operations in edge-enabled WSNs. The proposed approach utilizes a multi-objective reward model to adaptively balance energy consumption, task success rate, and network lifetime. Extensive simulation results demonstrate that ETORL-UAV outperforms five state-of-the-art methods Meta-RL, g-MAPPO, Backscatter Optimization, Hierarchical Optimization, and Game Theory based Pricing achieving up to 9.3 % higher task offloading success, 18.75 % improvement in network lifetime, and 27 % reduction in energy consumption. These results validate the framework's scalability, reliability, and practical applicability for real-world disaster-response WSN deployments.•Proposes ETORL-UAV: Energy-efficient Task Offloading using Reinforcement Learning for UAV-assisted WSNs•Leverages PPO-based reinforcement learning and a multi-objective reward model•Demonstrates superior performance over five benchmark approaches in disaster-response simulations

Proposes ETORL-UAV: Energy-efficient Task Offloading using Reinforcement Learning for UAV-assisted WSNs

Leverages PPO-based reinforcement learning and a multi-objective reward model

Demonstrates superior performance over five benchmark approaches in disaster-response simulations

Image, graphical abstract