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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:

IOT Project

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:

IMG_0087