IoT-Enabled Real-Time Monitoring of Urban Garbage Levels Using Time-of-Flight Sensing Technology

This manuscript presents a real-time monitoring system for urban garbage levels using Time-of-Flight (ToF) sensing technology. The experiment employs the VL53L8CX sensor, which accurately measures distances, along with an ESP32-S3 microcontroller that enables IoT connectivity. The ToF-Node IoT system, consisting of the VL53L8CX sensor connected to the ESP32-S3, communicates with an IoT gateway (Raspberry Pi 3) via Wi-Fi, which then connects to an IoT cloud. The ToF-Node communicates with the IoT gateway using Wi-Fi, and after with the IoT cloud, also using Wi-Fi. This setup provides real-time data on waste container capacities, facilitating efficient waste collection management. By integrating sensor data and network communication, the system supports informed decision-making for optimizing collection logistics, contributing to cleaner and more sustainable cities. The ToF-Node was tested in four scenarios, with a PCB measuring 40 × 18 × 4 mm and an enclosure of 65 × 40 × 30 mm. We used an office trash box with a height of 250 mm (25 cm), and the ToF-Node was located on the top. Results demonstrate that the effectiveness of ToF technology in environmental monitoring and the potential of IoT to enhance urban services. For detailed monitoring, additional ToF sensors may be required. Data collected are displayed in the IoT cloud for better monitoring and can be viewed by level and volume. The ToF-Node and the IoT gateway have a combined power consumption of 153.8 mAh.

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Publication Details

Journal
Sensors
Published
2025-03-28
DOI
https://doi.org/10.3390/s25072152
Citations
6
Primary Topic
Air Quality Monitoring and Forecasting
Type
article
Field-Weighted Citation Impact
4.79
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article

IoT-Enabled Real-Time Monitoring of Urban Garbage Levels Using Time-of-Flight Sensing Technology

Luis Miguel Rego Pires, João Figueiredo, R. Vieira Martins, J. Vanderlei Martins
6 citations
Sensors
Air Quality Monitoring and Forecasting
4.79
article

IoT-Enabled Real-Time Monitoring of Urban Garbage Levels Using Time-of-Flight Sensing Technology

Luis Miguel Rego Pires, João Figueiredo, R. Vieira Martins, J. Vanderlei Martins
article en
6 citations

Abstract

This manuscript presents a real-time monitoring system for urban garbage levels using Time-of-Flight (ToF) sensing technology. The experiment employs the VL53L8CX sensor, which accurately measures distances, along with an ESP32-S3 microcontroller that enables IoT connectivity. The ToF-Node IoT system, consisting of the VL53L8CX sensor connected to the ESP32-S3, communicates with an IoT gateway (Raspberry Pi 3) via Wi-Fi, which then connects to an IoT cloud. The ToF-Node communicates with the IoT gateway using Wi-Fi, and after with the IoT cloud, also using Wi-Fi. This setup provides real-time data on waste container capacities, facilitating efficient waste collection management. By integrating sensor data and network communication, the system supports informed decision-making for optimizing collection logistics, contributing to cleaner and more sustainable cities. The ToF-Node was tested in four scenarios, with a PCB measuring 40 × 18 × 4 mm and an enclosure of 65 × 40 × 30 mm. We used an office trash box with a height of 250 mm (25 cm), and the ToF-Node was located on the top. Results demonstrate that the effectiveness of ToF technology in environmental monitoring and the potential of IoT to enhance urban services. For detailed monitoring, additional ToF sensors may be required. Data collected are displayed in the IoT cloud for better monitoring and can be viewed by level and volume. The ToF-Node and the IoT gateway have a combined power consumption of 153.8 mAh.

SensorsVol. 25(7)
Universidade Politécnica de Lisboa (PT), Universidade Politécnica de Setúbal (PT), Instituto Politécnico da Lusofonia (PT), Universidade Lusófona (PT)
Openalex Percentile: Top 6%
Air Quality Monitoring and Forecasting
4.79
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IoT-Enabled Real-Time Monitoring of Urban Garbage Levels Using Time-of-Flight Sensing Technology — Luis Miguel Rego Pires, João Figueiredo, et al. · Sensors (2025) | TGRS Research Map | TGRS