Prototype of an ESP32-Based Internet of Things Trash Vessel with Integrated Solar Cell Charging - Handoko Rusiana Iskandar; Hendriyana; Fadillah Arianto; Firman Ramdhani Putra; Lisman Khosiban; Ibnu Supriatna; M. Ansel Wardana (Jurnal Teknologi, 2026)
The global issue of increasing amounts of waste in water areas has been very detrimental to the environment and has become a serious concern due to its impacts, especially on water quality, which can disrupt aquatic ecosystems and cause public health problems. Current conventional cleaning methods still rely on manual labor, which is time-consuming and inefficient. This study proposes a solar cell-based and Internet of Things (IoT)-enabled waste transport vessel and utilizes sensors in its design. This prototype was built using an ESP32 microcontroller as the main control unit, two HC-SR04 ultrasonic sensors for waste detection and container level monitoring, servo-based waste pickup, and a DC motor-driven conveyor. Remote monitoring and supervision are implemented through the Blynk IoT platform, enabling actual and real-time system observation. The prototype was built and evaluated, showing an average response time of 4.0–5.2 seconds across various types of waste samples; sensor calibration yielded good linearity (R² = 0.9987) and an average distance error of only 2.08 mm. Stable buoyancy testing where the prototype was able to hold a total mass of 1.73 kg. The solar cell successfully charged a parallel Li-ion 18650 battery bank from 3.40 V to 3.75 V over a period of 5 hours, resulting in a near-neutral daily energy output (~3.43 Wh consumption vs. ~3.38 Wh generation). IoT telemetry successfully monitors and stores data in the cloud efficiently. These results enhance operational reliability, energy efficiency, and control systems that benefit the environment, making this technology a promising solution for sustainability and addressing environmental issues, particularly in aquatic environments. Further research is expected to focus on real-world, dynamic field applications by integrating multi-sensor data and waste classification.