Diffusion-Model-Enhanced Multiobjective Optimization for Improving Forest Monitoring Efficiency in UAV-Enabled Internet of Things

2025-10-01

IEEE Internet of Things Journal

AI Summary

To address the challenges of energy and computational resource constraints, along with difficult multi-objective co-optimization in UAV-enhanced IoT (UAV-IoT) forest monitoring, this paper proposes a diffusion model-enhanced Improved Multi-Objective Grey Wolf Optimizer (IMOGWO). It is the first to incorporate diffusion-based generative priors into multi-objective evolutionary algorithms to guide efficient population exploration in hybrid (continuous/discrete) solution spaces. The method jointly minimizes maximum computation latency, total UAV mobility energy consumption, and maximum computational resource utilization. Experimental results demonstrate significant improvements in convergence speed and Pareto front diversity. On small-scale networks, IMOGWO reduces mobility energy consumption by 53.32% and computational resource usage by 9.83%; on large-scale scenarios, it achieves respective reductions of 41.81% in energy consumption and 7.93% in resource usage, while maintaining nearly unchanged computation latency.

Technology Category

• Search and Optimization: Combinatorial Optimization
• Constraint Satisfaction and Optimization: Constraint Optimization
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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 article formulates a multiobjective 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 multiobjective 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.

Problem

• Minimizes computing delay and energy for UAV forest monitoring
• Optimizes UAV motion energy and computing resource allocation
• Enhances multi-objective optimization using diffusion model for IoT networks

Innovation

• Multi-objective optimization framework for UAV forest monitoring
• Diffusion model-enhanced improved multi-objective grey wolf optimizer
• Hybrid solution space handling continuous and discrete variables