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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM68324
Volume:
10 |
10 |
Oct 2026
Pages:
1-4
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING
AMRUTA INSTITUTE OF ENGINEERING AND MANAGEMENT SCIENCE , BIDADI , BENGALURU
DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING
AMRUTA INSTITUTE OF ENGINEERING AND MANAGEMENT SCIENCE , BIDADI , BENGALURU
DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING
AMRUTA INSTITUTE OF ENGINEERING AND MANAGEMENT SCIENCE , BIDADI , BENGALURU
DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING
AMRUTA INSTITUTE OF ENGINEERING AND MANAGEMENT SCIENCE , BIDADI , BENGALURU
DEPARTMENT OF ELECTRONICS AND COMMUNICATION ENGINEERING
AMRUTA INSTITUTE OF ENGINEERING AND MANAGEMENT SCIENCE , BIDADI , BENGALURU
This paper proposes an Internet of Things (IoT)-based smart waste management prototype integrating multi-point bin fill-level sensing, weight measurement, camera-assisted waste classification, automated sorting, and data analytics. An ESP32-S3 controller gathers measurements from four VL53L0X time-of-flight distance sensors through a TCA9548A I²C multiplexer and from a load cell through an HX711 interface. A compatible AI camera supplies images to a trained classifier, while a stepper motor, driver, servo, and limit switch support controlled sorting experiments. Time-stamped readings can be sent over Wi-Fi to a dashboard for status monitoring, threshold alerts, and historical analysis. The paper presents the proposed architecture, hardware interfaces, processing logic, and an experimental protocol for evaluating sensing accuracy, classification performance, communication reliability, and sorting success. No experimental results are claimed because validated prototype measurements have not yet been provided. The work therefore serves as an implementation and evaluation framework that should be completed with measured data before performance conclusions are drawn.
KEYWORDS
This paper presented a proposed IoT-based smart waste management system combining multi-point fill-level sensing,weight measurement, camera-assisted waste classification, motorized sorting, and data analytics. The architecture uses an ESP32-S3 controller and TCA9548A multiplexer for multiple VL53L0X sensors, an HX711 interface for load-cell readings, and a motor/servo arrangement for sorting experiments. A dashboard can present current status and historical trends, while future records may support fill-level forecasting and collection prioritization. The design is a prototype framework, not proof of achieved performance. Sensor calibration, model evaluation, actuator safety, and baseline comparisons are required before claims of accuracy or operational improvement can be supported. A final submission should include measured results, test conditions, implementation details, and limitations.
[1] I. Sosunova and J. Porras, “IoT-Enabled Smart Waste Management Systems for Smart Cities: A Systematic Review,” IEEE Access, vol. 10, pp. 73326–73363, 2022, doi: 10.1109/ACCESS.2022.3188308. [2] D. V. Yevle and P. S. Mann, “Artificial intelligence based classification for waste management: A survey based on taxonomy, classification & future direction,” Computer Science Review, vol. 56, Art. no. 100723, 2025, doi: 10.1016/j.cosrev.2024.100723. [3] Espressif Systems, “ESP32-S3 Series Datasheet” and official ESP-IDF/Arduino documentation. Consult the documentation for the exact board revision and software framework used. [4] STMicroelectronics, “VL53L0X Time-of-Flight Ranging Sensor” datasheet. Consult the current official datasheet and the documentation for the breakout board used. [5] Texas Instruments, “DRV8825 Stepper Motor Controller IC” datasheet. Set current limit and cooling according to the driver board and motor specifications. [6] DFRobot, “ESP32-S3 AI Camera Module” product and software documentation. Confirm supported firmware, model format, and inference workflow for the exact module revision.
Publication Date
10/27/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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