Skip to main content
A Lightweight Hierarchical AI Model for UAV-Enabled Edge Computing With Forest-fire Detection Use-case
Machine Learning Faculty Publications
  • Moustafa M. Fouda, Department of Electrical and Computer Engineering, College of Science and Engineering, Idaho State University, Pocatello, ID 83209, USA, & Department of Electrical Engineering, Faculty of Engineering at Shoubra, Benha University, Cairo 11629, Egypt.
  • Sadman Sakib, Department of Computer Science, Lakehead University, Thunder Bay, Ontario, Canada
  • Zubair Md. Fadlullah, Department of Computer Science, Lakehead University & Thunder Bay Regional Health Research Institute (TBRHRI), Thunder Bay, Ontario, Canada
  • Nidal Nasser, College of Engineering, Alfaisal University, KSA
  • Mohsen Guizani, Mohamed bin Zayed University of Artificial Intelligence
Document Type

As the unmanned aerial vehicles (UAVs) continue to be deployed for various mission-critical data acquisition, localized computing on the drone-acquired data for efficient analysis, without significantly impacting the limited resources on board the drone, has emerged as a formidable research challenge. In this article, we address this issue with a natural resource management use-case whereby early forest-fire detection using the popular convolutional neural network (CNN)-based inference models are considered in the drone, which leads to resource exhaustion. To alleviate this, we propose a lightweight hierarchical artificial intelligence (AI) framework, which adaptively switches between a simple machine learning-based model and an advanced deep learning-based CNN model. Then, we formulate a multi-objective optimization problem to model the tradeoff between forestfire detection accuracy and computational performance. We obtain the Pareto-optimal solution of the formulated problem by optimizing a new hyperparameter (i.e., the confidence score threshold) by employing the technique for order of preference by similarity to ideal solution (TOPSIS) for the whole model. Thus, we alleviate the computational burden while retaining a high level of detection accuracy. Finally, based on a real dataset, empirical results are reported to evaluate the performance of our proposal in terms of its lightweight features. IEEE

Publication Date
  • Adaptation models,
  • Artificial intelligence,
  • Autonomous aerial vehicles,
  • Computational modeling,
  • Drones,
  • Edge computing,
  • Forestry,
  • Aircraft detection,
  • Antennas,
  • Data acquisition,
  • Deep learning,
  • Deforestation,
  • Fire hazards,
  • Fires,
  • Machinery,
  • Multiobjective optimization,
  • Neural networks,
  • Pareto principle

IR Deposit conditions:

OA version (pathway a) Accepted version

No embargo

When accepted for publication, set statement to accompany deposit (see policy)

Must link to publisher version with DOI

Publisher copyright and source must be acknowledged

Citation Information
M. M. Fouda, S. Sakib, Z. M. Fadlullah, N. Nasser and M. Guizani, "A Lightweight Hierarchical AI Model for UAV-Enabled Edge Computing With Forest-fire Detection Use-case," in IEEE Network, doi: 10.1109/MNET.003.2100325.