IoT Workplace Safety and Security Monitoring with Cloud Processing

Introduction

This project demonstrates the integration of cloud processing and monitoring capabilities to manage sensor data effectively. It highlights a system designed to collect sensor data, process it through cloud infrastructure, and visualize the results via a web interface.

Table of Contents

1. Project Overview
2. System Overview
3. Flow Chart
4. Hardware Components
5. Technologies and Services
6. Circuitry

Project Overview

Key Features:

• Cloud Integration: Efficiently collects and transmits sensor data to the cloud for processing and storage.
• Cloud Services Utilization: Utilizes various cloud services for data processing and storage.
• Visualization through Website Interface: Showcases stored data visually via a website interface, enabling insights and informed decision-making.
• Analytics Tools Integration: Incorporates analytics tools for data analysis, providing deeper insights.
• Raspberry Pi Control: Demonstrates the system's capability to control the Raspberry Pi, triggering specific actions, highlighting its versatility and control over the IoT environment.

Contributors:

• AWS Design/Code and Raspberry Pi Hardware/Code: Zion
• Website Development: Kenichi, Gustavo, Thien

System Overview

Hardware/System

• User Check-In: Collects temperature data and evaluates user suitability.
• Work Time: Monitors environmental factors during work hours.
• Off Work Turn On Security: Activates sensors for security when off work.
• Web Control: Responds to MQTT messages to control system functions.
• Turn Off System: Closes all connections and terminates the program.

Cloud Processing & Monitoring (AWS Services)

1. AWS IoT Core

   • Uses MQTT for sending/receiving messages.
   • IoT Rules direct data to specific services (Lambda, IoT Analytics).

2. Lambda Function

   • Processes IoT data, sends it to DynamoDB, and triggers events (AWS SNS).

3. AWS Analytics

   • Utilizes IoT Analytics Channel, Pipeline, and Data Store to store raw data in an S3 bucket.
   • SageMaker enables data analysis through a Jupyter Notebook instance.

Website (PHP-based)

1. Login and Sign-up Pages

   • User authentication for system access.

2. Main Page

   • Displays processed data stored in DynamoDB.
   • Provides buttons for users to control work mode.

Flow Chart:

Hardware Components:

• Raspberry Pi: Microcontroller acting as the system's brain.

• DHT11 Temperature / Humidity Sensor: Detects surrounding temperature and humidity.

• Flame Sensor: Detects the presence of a flame in front of the sensor.

• MQ-2 Gas Sensor: Detects various gases like LPG, i-butane, propane, methane, alcohol, Hydrogen, and smoke in the surroundings.

• Collision Sensor: Detects collisions with the sensor.

• LED: Light emitting diode.

• ADC (Analog to Digital Converter): Converts analog signals to digital.

Technologies and Services:

• MQTT (MQ Telemetry Transport) Protocol: Lightweight, publish-subscribe, machine-to-machine network protocol, primarily used in IoT.

• AWS IoT Core: Managed cloud service facilitating secure communication between IoT devices and the AWS Cloud through MQTT.

• AWS IoT Analytics: Processes and analyzes IoT data at scale, enabling data collection, storage, processing, querying, and integration with other AWS services for comprehensive analytics.

• AWS DynamoDB: Fully managed NoSQL database service providing seamless scalability, high availability, and low-latency data storage and retrieval.

• AWS SageMaker: Platform for building, training, and deploying machine learning models at scale.

• Amazon S3 (Simple Storage Service): Scalable, secure, highly available object storage service for storing and retrieving any amount of data.

• AWS Lambda: Serverless computing service allowing code execution without server management.

• AWS SNS (Simple Notification Service): Fully managed messaging service for publishing and delivering messages to endpoints or distributed systems.

• IAM (Identity and Access Management) User: Represents a person or application in the AWS environment, granting specific permissions based on policies assigned by an AWS account administrator.

• User-end Website: Monitors and allows admin users to view employee health and workplace conditions. Implements security features like Strong Password Rule, Hashing, and Session Control.

  • Strong Password Rule: Requires users to input a strong password with uppercase, numbers, and symbols for signup.

  • Hashing: Converts plaintext passwords into irreversible hashed values before saving in the database, enhancing security.

  • Session Control: Authenticates, authorizes users, manages session timeouts, implements secure protocols (like HTTPS), and monitors for suspicious activities or unauthorized access within user sessions.

Circuitry: