MetaEdge: A Meta-Learning-Based Auto-Selective Tool for Hardware-Aware Anomaly Detection on Edge Devices

The deployment of anomaly detection systems across heterogeneous edge computing environments faces significant challenges due to varying computational constraints and resource limitations. Existing approaches typically employ static model selection strategies that fail to adapt to diverse hardware capabilities, resulting in suboptimal detection performance and inefficient resource utilization. To address this, we propose MetaEdge, a novel hardware-aware framework that intelligently selects and deploys anomaly detection models based on specific device characteristics and hardware constraints. The MetaEdge framework introduces a systematic methodology that leverages meta-learning in the first stage to train a machine learning model to predict the top-k anomaly detectors by considering dataset characteristics. These candidates are then put through hardware-aware optimization that incorporates the hardware constraints of edge devices to ensure deployment feasibility. The framework evaluates 11 candidate anomaly detection algorithms spanning traditional machine learning and deep learning methods across four representative computing architectures ranging from ultra-constrained edge devices to GPU-accelerated cloud instances. Model conversion through ONNX standardization enables cross-platform deployment while maintaining detection capabilities. Experimental evaluation demonstrates the framework's effectiveness in achieving superior anomaly detection performance across diverse hardware configurations. The hardware-aware stage successfully identifies optimal model-hardware pairings, with the deployed models achieving up to 96.6% accuracy and 90.4% precision on edge devices. The framework demonstrates high accuracy in model selection decisions, with confidence scores providing meaningful hardware compatibility assessments that guide deployment. MetaEdge introduces a novel paradigm for hardware-aware anomaly detection in edge computing, demonstrating that meta-learning–driven model selection can deliver superior detection performance while adhering to stringent hardware constraints. By integrating automatic model selection with hardware-aware optimization, the proposed approach enables anomaly detection systems to intelligently adapt to diverse computing environments and maximize performance under resource constraints.

@article{Rashid2025,
  title     = {MetaEdge: A Meta-Learning-Based Auto-Selective Tool for Hardware-Aware Anomaly Detection on Edge Devices},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {12},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Nadia Rashid and Rashid Mehmood and Fahad Alqurashi and Turki Alghamdi},
  doi       = {10.14569/IJACSA.2025.0161240},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161240}
}