Next-Gen IoT Architecture: Long-Range, Low-Bandwidth Video Transmission Using GenAI

This paper introduces a groundbreaking IoT frame-work combining the Luckfox Pico Max RV1106 with LoRa (RYLR998) for low-bandwidth, long-range video transmission. The system integrates YOLOv5 Tiny for efficient edge processing, enabling real-time object detection and metadata generation directly on the Luckfox Pico Max RV1106. Detected metadata, including object, bounding box coordinates, confidence scores, is transmitted via LoRa to a central server. At the server, a large language model (LLM) processes the metadata to reconstruct a simulated visualization of the detected scene, offering a low-power alternative to full video transmission. The architecture demonstrates significant scalability, low energy consumption, and high reliability, making it ideal for remote surveillance, smart cities, and autonomous systems. By offloading computation to edge devices and utilizing LLM-based reconstruction, this approach showcases a transformative vision for future IoT networks emphasizing efficiency, scalability, and innovative data processing.

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Publication Details

Published
2025-01-28
DOI
https://doi.org/10.1109/isnib64820.2025.10983192
Primary Topic
CCD and CMOS Imaging Sensors
Type
article
Field-Weighted Citation Impact
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article

Next-Gen IoT Architecture: Long-Range, Low-Bandwidth Video Transmission Using GenAI

Salim Bitam, A Mellouk, Abdelhak Heroucha
CCD and CMOS Imaging Sensors
article

Next-Gen IoT Architecture: Long-Range, Low-Bandwidth Video Transmission Using GenAI

Salim Bitam, A Mellouk, Abdelhak Heroucha
article en

Abstract

This paper introduces a groundbreaking IoT frame-work combining the Luckfox Pico Max RV1106 with LoRa (RYLR998) for low-bandwidth, long-range video transmission. The system integrates YOLOv5 Tiny for efficient edge processing, enabling real-time object detection and metadata generation directly on the Luckfox Pico Max RV1106. Detected metadata, including object, bounding box coordinates, confidence scores, is transmitted via LoRa to a central server. At the server, a large language model (LLM) processes the metadata to reconstruct a simulated visualization of the detected scene, offering a low-power alternative to full video transmission. The architecture demonstrates significant scalability, low energy consumption, and high reliability, making it ideal for remote surveillance, smart cities, and autonomous systems. By offloading computation to edge devices and utilizing LLM-based reconstruction, this approach showcases a transformative vision for future IoT networks emphasizing efficiency, scalability, and innovative data processing.

Université Paris-Est Créteil (FR), University of Biskra (DZ), Paris-Est Sup (FR)
Affordable and clean energy
Openalex Percentile: Top 92%
CCD and CMOS Imaging Sensors
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