Research Information System
APPLIED SCIENCES-BASEL, no.3, 2025 (SCI-Expanded)
This study proposes a method for selecting suitable edge hardware and Artificial Intelligence (AI) models to be deployed on these edge devices. Edge AI, which enables devices at the network periphery to perform intelligent tasks locally, is rapidly expanding across various domains. However, selecting appropriate edge hardware and AI models is a multi-faceted challenge due to the wide range of available options, diverse application requirements, and the unique constraints of edge environments, such as limited computational power, strict energy constraints, and the need for real-time processing. Ad hoc approaches often lead to non-optimal solutions and inefficiency problems. Considering these issues, we propose a method based on the ISO/IEC 25010:2011 quality standard, integrating Multi-Criteria Decision Analysis (MCDA) techniques to assess both the hardware and software aspects of Edge AI applications systematically. For the proposed method, we conducted an experiment consisting of two stages: In the first stage of the experiment, to show the applicability of the method across different use cases, we tested the method with four scenarios on UAVs, each presenting distinct edge requirements. In the second stage of the experiment, guided by the method's recommendations for Scenario I, where the STM32H7 series microcontrollers were identified as the suitable hardware and the object detection model with Single Shot Multi-Box Detector (SSD) architecture and MobileNet backbone as the suitable AI model, we developed a TensorFlow Lite model from scratch to enhance the efficiency and versatility of the model for object detection tasks across various categories. This additional TensorFlow Lite model is aimed to show how the proposed method can guide the further development of optimized AI models tailored to the constraints and requirements of specific edge hardware.