SMART TRASH BIN WITH FILL LEVEL DETECTION
Abstract
ABSTRACT
Background: Traditional urban waste management systems heavily rely on static schedules and manual inspections, leading to significant inefficiencies, overflowing bins, high operational expenses, environmental degradation, and preventable public health risks.
Methodology: This study adopts a quantitative and design-oriented paradigm using Design Science Research (DSR). A simulated environment was engineered via Autodesk Tinkercad and Firebase Realtime Database to model an embedded system driven by an ESP32 microcontroller and an HC-SR04 ultrasonic sensor. Event-driven firmware logic classifications categorize waste volumes into structured thresholds—Empty (80%).
Results & Implementation: The simulation confirmed deterministic accuracy and real-time processing stability across gradual and sudden overflow test scenarios. Status transitions execute seamlessly with minimal processing latency, illuminating local multi-tier LED visual alerts and logging data values successfully onto a central platform database for localized optimization.
Conclusion: The investigation validates that a low-cost, sensor-driven framework offers an adaptive, scalable infrastructure option tailored specifically to resource-constrained public municipal domains, establishing a sustainable blueprint for real-world IoT deployment integration.
Full-Text Access Notice
In accordance with the NERD Policy on promoting peer-reviewed publication, public access to the full text of a project, thesis or dissertation is restricted for three years, allowing the author and supervisors sufficient time to pursue peer-reviewed publication.
During this period, researchers with legitimate academic or research purposes may request authorisation directly from the author to enable NERD to release the indexed full texts of the work using the form below.