Real-Time AIoT for AAV Antenna Interference Detection via Edge-Cloud Collaboration
Jun Dong, Jintao Cheng, Jin Wu, Chengxi Zhang, Shunyi Zhao, Xiaoyu Tang

TL;DR
This paper presents a real-time AIoT system combining edge-cloud collaboration for UAV-based antenna interference detection, significantly reducing latency and improving accuracy over cloud-only methods.
Contribution
It introduces an optimized edge-cloud collaboration mode and a novel antenna localization scheme with a lightweight detector, achieving state-of-the-art performance and real-time inference on embedded devices.
Findings
Edge-cloud collaboration reduces end-to-end latency by 88.9%.
The proposed detector achieves 42.1% mAP with only 3 million parameters.
Real-time inference speeds of 21.1 FPS on Jetson Xavier NX and 4.8 FPS on Raspberry Pi 4B.
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. Unmanned aerial vehicles (UAVs) 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 UAVs. 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…
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Taxonomy
TopicsUAV Applications and Optimization · Antenna Design and Optimization · Millimeter-Wave Propagation and Modeling
