Diffusion-Model-enhanced Multiobjective Optimization for Improving Forest Monitoring Efficiency in UAV-enabled Internet-of-Things

TL;DR

This paper introduces a diffusion-model-enhanced multi-objective optimization framework for UAV-enabled IoT forest monitoring, balancing energy, delay, and resource constraints effectively.

Contribution

It proposes a novel IMOGWO algorithm combining diffusion models with multi-objective grey wolf optimization for hybrid solution spaces.

Findings

IMOGWO outperforms benchmarks in reducing energy and resource consumption.

Significant energy savings of over 50% in small networks.

Maintains comparable monitoring delay while optimizing other objectives.

Abstract

The Internet-of-Things (IoT) is widely applied for forest monitoring, since the sensor nodes (SNs) in IoT network are low-cost and have computing ability to process the monitoring data. To further improve the performance of forest monitoring, uncrewed aerial vehicles (UAVs) are employed as the data processors to enhance computing capability. However, efficient forest monitoring with limited energy budget and computing resource presents a significant challenge. For this purpose, this paper formulates a multi-objective optimization framework to simultaneously consider three optimization objectives, which are minimizing the maximum computing delay, minimizing the total motion energy consumption, and minimizing the maximum computing resource, corresponding to efficient forest monitoring, energy consumption reduction, and computing resource control, respectively. Due to the hybrid solution space that consists of continuous and discrete solutions, we propose a diffusion model-enhanced improved multi-objective grey wolf optimizer (IMOGWO) to solve the formulated framework. The simulation results show that the proposed IMOGWO outperforms other benchmarks for solving the formulated framework. Specifically, for a small-scale network with $6$ UAVs and $50$ SNs, compared to the suboptimal benchmark, IMOGWO reduces the motion energy consumption and the computing resource by $53.32\%$ and $9.83\%$, respectively, while maintaining computing delay at the same level. Similarly, for a large-scale network with $8$ UAVs and $100$ SNs, IMOGWO achieves reductions of $41.81\%$ in motion energy consumption and $7.93\%$ in computing resource, with the computing delay also remaining comparable.