Temperature and Humidity IoT Module

GitHub Repository

An IoT module designed for a rural environment, capable of detecting humidity, lighting, and temperature through three different sensors. These measurements are read and handled by a FEZ Spider II board.

Project Overview

The FEZ microcontroller collects data from connected sensors and stores it on an SD card. These data are transmitted using the HTTP protocol in JSON format to an ASP.NET Web Service API, which stores them in a Microsoft SQL Server database.

Using the MQTT protocol, the Web Service publishes data to two different topics:

• To configure AWS
• To send measurement data

A Python AWS Lambda function deserializes the received data and stores it in DynamoDB tables.

System Components

Sensor

The FEZ Spider II microcontroller connects to sensors for:

• Humidity
• Light
• Temperature

These sensors capture environmental conditions and send readings to the microcontroller.

ACK/NACK Mechanism

A Python Lambda function sends ACK or NACK messages back to the Web Service:

• ACK: Confirms successful storage; the Web Service informs the microcontroller to delete the data from the SD card.

• NACK: Indicates failure; the microcontroller resends the data.

  

Interface

A Web App built using Node-RED visualizes the stored data in real-time charts via AWS API and DynamoDB.

Technical Workflow

• Microcontroller logic developed in C#:

  • Reads sensor data.
  • Stores it on an SD card.
  • Maintains an internal log of operations.
  • Waits for an ACK from the Web Service before deleting stored data.

• ASP.NET Web Service:

  • Exposes APIs to receive/send microcontroller data.
  • Stores data and ACK/NACK statuses in Microsoft SQL Server.
  • Acts as an MQTT broker, forwarding data to AWS.

• AWS Integration:

  • Lambda Function: Deserializes JSON payloads and stores data in DynamoDB.
  • Sends ACK or NACK based on data processing outcome.

• Frontend:

  • Developed in Node-RED.
  • Fetches data using DynamoDB API.
  • Displays real-time charts for monitoring.

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This project simulates a complete IoT data acquisition and processing pipeline—from edge sensor readings to cloud storage and visualization—with robust feedback mechanisms for data integrity.