Low-Cost AIS Vessel Tracking System for Real-Time Web Visualization Using Software Defined Radio and Leaflet.js

Inka Trisna Dewi; Tri Budi Santoso; Rini Satiti; Fabria Alieftya Yusvianti; Nafilah Aqilatuz Zuhroh

doi:10.33019/

Authors

Inka Trisna Dewi Department of Electrical Engineering, Politeknik Elektronika Negeri Surabaya
Tri Budi Santoso Department of Electrical Engineering, Politeknik Elektronika Negeri Surabaya
Rini Satiti Department of Electrical Engineering, Politeknik Elektronika Negeri Surabaya
Fabria Alieftya Yusvianti Department of Electrical Engineering, Politeknik Elektronika Negeri Surabaya
Nafilah Aqilatuz Zuhroh Department of Electrical Engineering, Politeknik Elektronika Negeri Surabaya

DOI:

https://doi.org/10.33019/

Keywords:

Automatic Identification System, Leaflet.js, RTL-SDR, Vessel Tracking, Web-Based Monitoring

Abstract

A low-cost edge-based Automatic Identification System (AIS) monitoring platform was developed for real-time vessel surveillance, integrating AIS signal acquisition, decoding, data management, and geospatial visualization within a unified web-based architecture. The proposed platform was implemented using commercially available hardware components with a total hardware cost of approximately USD 189, providing a cost-effective alternative for real-time AIS monitoring. The system employs RTL-SDR for signal reception and a lightweight C-based processing pipeline incorporating GMSK demodulation, NRZI decoding, and CRC16 validation. Experimental deployment at Tanjung Perak Port under dense maritime traffic conditions demonstrated continuous end-to-end operation, achieving an average throughput of 3.71 messages per second and an end-to-end latency of 0–2 seconds. Decoded AIS records were stored in MariaDB and visualized through a Leaflet.js-based interface supporting real-time vessel tracking, historical trajectory reconstruction, and traffic analytics. Existing AIS studies commonly address signal reception, decoding, and visualization as separate processing stages. In contrast, the proposed platform consolidates the complete processing chain from radio-frequency AIS acquisition to web-based maritime information delivery within a single edge-computing environment. The results demonstrate the feasibility of employing resource-constrained edge devices for continuous AIS processing and provide an integrated framework for real-time maritime monitoring without reliance on high-performance computing infrastructure.

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