Environmental Remote Sensing with Lightweight Edge Server: A Low-Cost River Depth Monitoring System via Embedded Web Interface

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Karisma Trinanda Putra, Puji Harsanto, Sarwo Pranoto, Jazaul Ikhsan, Arief Rahayu Agusttya, Wily Nasrullah

2025 ICE3IS 2025 - Conference Proceedings: 5th International Conference on Electronic and Electrical Engineering and Intelligent System Conference paper Cited by 0 Quartile

Abstract

River depth monitoring is a critical component of water resource management and flood risk mitigation. Traditional methods, i.e., relying on manual observation, are constrained by low temporal resolution, limited spatial coverage, and the absence of real-time data acquisition. While previous studies have explored Internet of Things (IoT)-based monitoring, many existing solutions are either costprohibitive, overly complex for remote deployment, or dependent on cloud infrastructure, which may not be reliable in rural or disaster-prone areas. This study presents the development of a low-cost, lightweight edge server system for river depth monitoring using the AJ-SR04M ultrasonic sensor integrated with an ESP32 microcontroller. Unlike clouddependent systems, this architecture embeds a standalone web server based on the Microdot framework, capable of storing real-time sensory data locally in Comma Separated Values (CSV) format using a Unix-like Virtual File System (VFS) layer. This design simplifies deployment and enables autonomous operation without constant internet connectivity. The system was developed and tested using a quantitative approach complemented by qualitative evaluation in a swimming pool, calm river, and fast-flowing river. It consistently provided real-time depth data with minimal deviation, such as 0.05 meters in a controlled test. Real-world experiments recorded water depth values from 1.8 to 3.88 meters. The findings confirm the system's reliability, accuracy, and scalability for edge-based environmental sensing, with a web interface that supports real-time monitoring in low-infrastructure and flood-prone areas. © 2025 IEEE.

Affiliations

Universitas Muhammadiyah Yogyakarta, Departement of Electrical Engineering, Center of Artificial Intelligence and Robotics Studies, Bantul, Indonesia; Universitas Muhammadiyah Yogyakarta, Faculty of Engineering, Department of Civil Engineering, Bantul, Indonesia; Yogyakarta State University, Faculty of Engineering, Departement of Electrical Engineering, Yogyakarta, Indonesia; Universitas Muhammadiyah Yogyakarta, Faculty of Engineering, Departement of Electrical Engineering, Bantul, Indonesia