Industrial Training IoT (Internet of Things) with Raspberry PI and AWS IoT Core Training Course

Module 0: Foundations and the AWS IoT Ecosystem

• Introduction to IoT
  • Defining IoT in 2024: Moving Beyond 'Things' (Edge Intelligence, AI/ML at the Edge, Cyber-Physical Systems).
  • Drivers of IoT Growth (Industries, Use Cases).
  • Key IoT Trends (Edge Computing, Sustainability, AI/ML Integration, Enhanced Security).
  • AWS IoT within the wider AWS ecosystem (AWS Partner Network - APN resources).
• Overview of AWS IoT Services
  • AWS IoT Core (MQTT/Bridge, Jobs, Device Defender).
  • AWS IoT Device Management (Device Onboarding, Configuration Management, OTA Updates).
  • AWS IoT Analytics (Data processing, enrichment, modeling).
  • AWS IoT Greengrass (Edge compute, local execution, secure connectivity).
  • AWS IoT Button (Conceptual overview for simple devices).
  • Integration: AWS IoT Core -> Lambda/DynamoDB/OpenSearch/Step Functions/SageMaker.

Module 1: IoT Architecture, Components, and Security

• IoT Architecture
  • Device Layer (Sensors, Actuators, Edge Devices like RP2013/Raspberry Pi/ESP32).
  • Connectivity Layer (MQTT, CoAP, HTTP, LPWAN - LoRaWAN, NB-IoT, Sigfox, Cellular IoT).
  • Cloud Integration Layer (AWS IoT Core, API Gateway, Lambda, Step Functions).
  • Data Processing and Analytics Layer (DynamoDB, Timestream, OpenSearch, S3, Athena, SageMaker).
  • Application Layer (Mobile, Web Apps using AWS Amplify, Custom Business Apps).
  • Importance: Understanding the why behind distributed architectures (latency, bandwidth, compute power, security).
• Deep Dive into Essential IoT Components
  • Hardware: Selection criteria (MCU, connectivity, sensors), Security elements (Trusted Execution Environments - TEEs).
  • Edge Computing (AWS Greengrass): Benefits (low latency, reduced cloud traffic, local decision-making).
  • Device Management: Onboarding (Over-the-Air - OTA, Pre-provisioning), Configuration, Monitoring, Remote Debugging.
  • Security Deep Dive: Device Identity, Authentication and Authorization (X.509 Certs, JSON Web Tokens - JWTs), Data Encryption (at rest and in transit), AWS IoT Device Defender (Service and Device Defender).
  • Security Standardization: Introduction to standards (e.g., IEEE P2145, Open Connectivity Foundation - OCF) and compliance (ISO/IEC 27001, SOC 2).
• AWS-Specific PaaS Functions for IoT
  • AWS IoT Core (Secure MQTT/Bridge, Jobs for firmware updates, Device Defender).
  • AWS Lambda (Serverless compute for data preprocessing, triggering actions).
  • AWS Step Functions (Stateful workflows for complex device interactions).
  • Amazon DynamoDB (NoSQL DB for fast IoT data ingestion).
  • Amazon OpenSearch Service (Search and Analytics, Time Series data handling).
  • Amazon Timestream (Specialized time-series database).
  • Amazon S3 (Raw data lake storage).
  • AWS IoT Device Defender (Monitoring and security assessment).
  • AWS IoT Wireless (Connecting remote LPWAN devices).

Module 2: IoT Device Communication Protocols

• MQTT (MQTT v5 and WebSockets)
  • MQTT 5.0 Features (Retain, Clean Session flags, User Properties, Wildcard topics).
  • MQTT over WebSockets (Standardization).
  • Quality of Service (QoS) Levels explained.
  • Protocol Best Practices.
• Alternative Protocols
  • CoAP (Constrained Application Protocol) for constrained devices.
  • AMQP / MQTT over AMQP (Standard data interchange formats).
  • HTTP (For simpler, less frequent updates).
  • WebSockets (Full-duplex communication).

Module 3: Building Robust IoT Applications with AWS

• Device Onboarding and Secure Connectivity
  • AWS IoT Device Defender Pre-Provisioning.
  • Secure Over-The-Air (OTA) Onboarding (e.g., using AWS IoT Button concepts).
  • Managing Device Certificates (ACM/PKI).
  • Implementing MQTT with TLS.
• Data Ingestion, Storage, and Processing
  • Efficiently sending data from devices to AWS IoT Core.
  • Choosing the right target: Lambda (event-driven), Step Functions (orchestration), Timestream (time-series), OpenSearch (search and analytics), S3 (raw data).
  • Using AWS IoT Analytics for data enrichment and cleansing before storage.
  • Handling high-throughput scenarios (Kinesis/Firehose).
• Device Management and Operations
  • Using AWS IoT Device Management for fleet management.
  • Implementing and managing OTA Updates (using AWS IoT Jobs).
  • Remote Monitoring and Configuration.
• Building the IoT Backend
  • API Gateway for creating REST/GraphQL APIs to interact with devices and data.
  • AWS Lambda for business logic.
  • AWS Step Functions for coordinating distributed components.
  • Amazon SQS/SNS for asynchronous messaging and event triggering.

Module 4: Edge Computing and Advanced Integration

• AWS IoT Greengrass
  • Concepts (Core, Device, Connector).
  • Running Lambda functions locally on the device.
  • Executing code directly on the device (C++, Python).
  • Secure communication between Greengrass Core and AWS/IoT devices.
  • Use Case: Local data filtering, preprocessing, or AI inference at the edge.
• Integration with AI/ML
  • Using SageMaker for complex ML models in the cloud.
  • Running ML inference on the edge with Greengrass ML Accelerator (GMA).
• Data Visualization and User Interfaces
  • Using AWS IoT SiteWise for industrial data visualization.
  • Building Web Apps with AWS Amplify (API, UI, Authentication).
  • Dashboards using Amazon QuickSight or OpenSearch Dashboards.

Module 5: Security, Governance, and Best Practices

• IoT Security Lifecycle
  • Secure Design Principles (Defense-in-Depth).
  • Secure Development Practices (OWASP IoT Top 10).
  • Vulnerability Management.
  • Threat Modeling for IoT.
• AWS Security Services for IoT
  • AWS IoT Device Defender (Service & Device Defender).
  • AWS Shield, AWS Identity and Access Management (IAM).
  • AWS Config for compliance checks.
  • Hardware Security Modules (HSMs) integration.
• Data Privacy and Governance
  • Handling sensitive data (PII).
  • Data Retention and Deletion policies.
  • Compliance considerations.

Module 6: Hands-on Projects and Capstone

• Guided Hands-on Labs
  • Device Onboarding and MQTT Communication.
  • Implementing Secure Data Ingestion to AWS.
  • Building a Simple IoT Dashboard.
  • OTA Update Simulation.
  • Introduction to AWS IoT Greengrass.
• Capstone Project
  • Build a complete IoT solution addressing a real-world problem (e.g., Smart Home Automation, Environmental Monitoring, Industrial Sensor Hub).
  • Requirements: Secure device, data ingestion, processing, visualization, and optional edge component.
  • Use AWS services covered throughout the course.