Smart Waste Management System

Overview

A real-time IoT-enabled system developed to monitor and optimize waste collection using ultrasonic sensors, NodeMCU, and cloud analytics.
The project aims to automate waste level monitoring, reduce manual collection delays, and ensure efficient waste segregation for sustainable urban management.

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Features

• Real-time waste bin monitoring using ultrasonic sensors
• Automatic alerts when bin levels exceed a set threshold
• Data logging and visualization using ThingSpeak / Firebase
• Route optimization potential for municipal waste collection
• Low-cost, scalable, and energy-efficient IoT design

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Tech Stack

Component	Technology
Hardware	NodeMCU ESP8266, Ultrasonic Sensor (HC-SR04), Servo Motor
Software	Arduino IDE, Python, Flask
Cloud Platform	ThingSpeak / Firebase
Libraries	WiFiClient, ESP8266WiFi, FirebaseESP8266, requests
Communication	HTTP / MQTT Protocols

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System Architecture

flowchart TD
    A[Ultrasonic Sensor] --> B[NodeMCU Controller]
    B --> C[Cloud Database - ThingSpeak/Firebase]
    C --> D[Dashboard Visualization]
    C --> E[Threshold Alert Trigger]
    E --> F[Municipal Authority / User Notification]

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Results

Metric	Description	Value
Sensor Accuracy	Average deviation in distance detection	97.3%
Data Transmission	Latency between NodeMCU and Cloud	< 2 sec
Alert Trigger Time	Delay between full bin and alert	~1.2 sec
Power Efficiency	Operational uptime per charge	15 hrs
Cost Efficiency	Total prototype cost	₹1450

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Testing Summary

• Prototype deployed using NodeMCU ESP8266 and tested in outdoor conditions.
• Successfully detected varying fill levels and triggered cloud-based notifications.
• Dashboard displayed live bin status with real-time analytics.
• Tested integration with Firebase and ThingSpeak APIs.
• System maintained stable connectivity across multiple test cycles.

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Future Enhancements

• Integrate machine learning for waste fill prediction and route optimization.
• Add solar-powered bin units for energy autonomy.
• Include image-based waste classification using Raspberry Pi and OpenCV.
• Expand to city-level deployments with centralized dashboards.

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Team and Role

This project was developed as part of a 3-member IoT development team.
I was primarily responsible for sensor data acquisition, cloud integration, and real-time dashboard development.
Other members contributed to hardware assembly, API communication, and field testing.

Team Size: 3
Duration: January 2025 – March 2025
Institution: Sri Sivasubramaniya Nadar College of Engineering