Abstract
Underwater communication faces challenges like high attenuation, limited bandwidth, and energy constraints. This paper presents an underwater communication system using ultrasonic sensors for image transmission and TinyML for efficient object detection. The architecture comprises a Raspberry Pi-based transmitter node, intermediate repeater nodes and a receiver node. Images are processed using Discrete Cosine Transform (DCT), transmitted as text files, reconstructed via Inverse DCT (IDCT), and analyzed using a lightweight MobileNetV2 model for real-time object detection. The integration of TinyML enables energy-efficient on-device inference, addressing resource constraints in edge devices. The system demonstrates effective data transmission and accurate detection, with applications in underwater surveillance, marine monitoring, and aquaculture. This work underscores TinyML’s role in advancing AI-driven Internet of Underwater Things (IoUT) technologies.
