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Smart Electricity,Water and Air Quality Monitoring System

The project comprises two main parts: an IoT system with sensors for monitoring air, water quality, and electricity usage, and a Machine Learning system for forecasting these parameters. Data is transmitted to a web app via APIs, and users can access this information through a dashboard. The system's scope can be expanded to track additional resources if required.

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Project Overview

This project combines IoT and ML technologies to create an Environmental Monitoring and Prediction System with a user-friendly React web app dashboard. It tracks real-time data on electricity usage, water quality, and air quality, providing predictive insights for resource management and sustainability.

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The Product

This product is an IoT and ML-based Environmental Monitoring and Prediction System. It uses sensors to collect data on electricity consumption, water quality, and air quality, processed by machine learning. The user-centric React web app dashboard offers a visually appealing interface, empowering users to make informed decisions for sustainable resource management.

Project Duration

2 years

The Problem

The project addresses the challenge of inefficient environmental monitoring and resource management. Existing methods lack real-time data and predictive capabilities, hindering informed decision-making. Complex interfaces also limit accessibility to critical data.

The Goal

Our goal is to create an Environmental Monitoring and Prediction System with IoT and ML capabilities. Objectives include setting up IoT infrastructure, developing accurate ML models, creating an intuitive React dashboard, ensuring security, scalability, and fostering data-driven decision-making for sustainability.

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User research: summary

Key Takeaways:

  1. High Environmental Concern: Users are highly concerned about environmental issues and are actively seeking solutions to address them.

  2. Interest in Technology: Participants expressed interest in technology-driven solutions to tackle environmental challenges, indicating a willingness to embrace innovative approaches.

  3. Recognition of Resource Management Importance: Users acknowledged the significance of efficient resource management, particularly in the context of air and water quality and energy utilisation

User research: pain points

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User Personas

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User Journey Maps

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Circuit Diagram

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(A) Air Quality Sensor
(B) DHT11 Temperature Humidity Sensor
(C) OLED display
(D) ACS712 Electricity sensor
(E) Arduino Uno
(F) Turbidity Sensor
(G) pH Sensor
(H) Wifi Module (or Node MCU)

High Level Backend Flow

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Technologies Used

  1. Arduino:

    • Microcontroller Platform: Arduino serves as the microcontroller platform for the project. It enables the integration of various sensors and actuators to collect data and control hardware components.

    • Sensor Integration: Arduino facilitates the connection of sensors such as temperature sensors for climate data, humidity sensors for moisture information, and turbidity sensors to measure water quality. It collects data from these sensors and can process it for further analysis.

  2. React JS:

    • User Interface Development: React JS is used for building the user interface (UI) of the application. It provides a modular, component-based approach to UI development, making it easier to create interactive and visually appealing frontends.

    • Real-Time Data Display: React's ability to handle real-time updates and dynamic content makes it ideal for displaying data collected from sensors. Users can view live data and analytics through the web interface.

  3. MongoDB:

    • Database Management: MongoDB, a NoSQL database, is chosen for efficient data storage and retrieval. It accommodates large volumes of unstructured or semi-structured data, which is essential for storing environmental data from various sensors.

    • Scalability and Flexibility: MongoDB's flexible schema allows for easy adaptation as new sensor types or data points are introduced to the system. This scalability is crucial for accommodating future expansion and enhancements.

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Paper wireframes

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Digital Wireframes

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Low fidelity prototype



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Usability study: findings

Search Functionality: Users found the search function somewhat limited. They requested advanced search options, filters, and sorting capabilities to help them quickly locate specific content or users within the application.

Content Accessibility: Some participants with disabilities mentioned challenges related to accessibility. They recommended improvements in screen reader compatibility, keyboard navigation, and alternative text for images and multimedia content.

User Documentation: Participants recommended the creation of user documentation, including FAQs and guides, to assist users in understanding the application's features and functionalities.

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High-fidelity mockups


Accessibility Considerations

Keyboard Navigation: Ensure that all functionality within your application can be accessed and operated using a keyboard alone, without requiring a mouse. Users with mobility impairments often rely on keyboard navigation.
User Testing with Diverse Audiences: Conducted usability testing with individuals who have disabilities to gather feedback and insights on their experiences and challenges while using your application.

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Environmental Awareness: The project emphasizes the importance of raising awareness about global environmental challenges and the role that technology can play in addressing them. Users become more informed about issues like climate change, resource depletion, and pollution.
Advocacy and Education: The project not only offers tools but also encourages users to advocate for change and educate themselves and their communities about sustainable practices.

To learn more about this project, please visit its publication:

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