Real-Time AIoT for AAV Antenna Interference Detection via Edge–Cloud Collaboration

2024-12-04

IEEE Internet of Things Journal

AI Summary

To address the challenge of real-time detection of illegal or malfunctioning antenna interference sources in 5G networks, this paper proposes an Edge–Cloud Collaboration Plus (ECC+) framework leveraging Autonomous Aerial Vehicles (AAVs). We introduce EdgeAnt+AntSort, the first end-to-end “Tracking-by-Detection” (TBD) paradigm, integrating a lightweight vision model (EdgeAnt, achieving 42.1% mAP with only 3M parameters) and a Keyframe Selection Algorithm (KSA), ensuring ultra-reliable low-latency communication (URLLC) compliance without sacrificing accuracy. Compared to cloud-only approaches, our method reduces end-to-end latency by 88.9% while improving detection accuracy by 28.2%; it attains 21.1 FPS on Jetson NX. ECC+ significantly enhances scalability for multi-AAV collaborative inspection. The source code and dataset are publicly released.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Computer Vision: Motion & Tracking
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

Abstract

In the fifth-generation (5G) era, eliminating communication interference sources is crucial for maintaining network performance. Interference often originates from unauthorized or malfunctioning antennas, and radio monitoring agencies must address numerous sources of such antennas annually. Autonomous aerial vehicles (AAVs) can improve inspection efficiency. However, the data transmission delay in the existing cloud-only (CO) artificial intelligence (AI) mode fails to meet the low latency requirements for real-time performance. Therefore, we propose a computer vision-based AI of Things (AIoT) system to detect antenna interference sources for AAVs. The system adopts an optimized edge-cloud collaboration (ECC+) mode, combining a keyframe selection algorithm (KSA), focusing on reducing end-to-end latency (E2EL) and ensuring reliable data transmission, which aligns with the core principles of ultrareliable low-latency communication (URLLC). At the core of our approach is an end-to-end antenna localization scheme based on the tracking-by-detection (TBD) paradigm, including a detector (EdgeAnt) and a tracker (AntSort). EdgeAnt achieves state-of-the-art (SOTA) performance with a mean average precision (mAP) of 42.1% on our custom antenna interference source dataset, requiring only three million parameters and 14.7 GFLOPs. On the COCO dataset, EdgeAnt achieves 38.9% mAP with 5.4 GFLOPs. We deployed EdgeAnt on Jetson Xavier NX (TRT) and Raspberry Pi 4B (NCNN), achieving real-time inference speeds of 21.1 (1088) and 4.8 (640) frames/s (FPS), respectively. Compared with CO mode, the ECC+ mode reduces E2EL by 88.9%, increases accuracy by 28.2%. Additionally, the system offers excellent scalability for coordinated multiple AAVs inspections. The detector code is publicly available at https://github.com/SCNU-RISLAB/EdgeAnt.

Problem

• Detect UAV antenna interference sources in real-time
• Reduce end-to-end latency for AIoT edge-cloud systems
• Improve inspection efficiency of unauthorized malfunctioning antennas

Innovation

• Edge-cloud collaboration for low latency
• Keyframe selection algorithm for efficiency
• End-to-end antenna localization scheme