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The Edge Computing environments enable the development of pervasive applications distributed across extensive geographical areas, overcoming specific issues associated with centralized information processing, such as network bandwidth saturation and the need for large computing infrastructures. This work presents two case studies of deploying applications for environmental monitoring on low-energy and high-performance edge computing devices, employing accelerated artificial intelligence techniques based on GPUs. The first problem entails the classification of various materials within hyperspectral images, while the second problem focuses on identifying floating plastic debris. The applications were validated on a Nvidia Jetson Nano sensor board, demonstrating good accuracy, effectiveness, and energy consumption results.

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Deploying AI-Based Environmental Monitoring Applications at the Edge: Two Case Studies

  • 会议论文

  • 发表日期：

    2025年

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  • Gianluca de Lucia,
  • Giuliano Laccetti,
  • Marco Lapegna,
  • Raffaele Montella,
  • Diego Romano

摘要

The Edge Computing environments enable the development of pervasive applications distributed across extensive geographical areas, overcoming specific issues associated with centralized information processing, such as network bandwidth saturation and the need for large computing infrastructures. This work presents two case studies of deploying applications for environmental monitoring on low-energy and high-performance edge computing devices, employing accelerated artificial intelligence techniques based on GPUs. The first problem entails the classification of various materials within hyperspectral images, while the second problem focuses on identifying floating plastic debris. The applications were validated on a Nvidia Jetson Nano sensor board, demonstrating good accuracy, effectiveness, and energy consumption results.