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

Module 0: Foundations & AWS IoT Ecosystem

• Introduction to IoT
  • Defining IoT in 2024: Moving Beyond "Things" (Edge Intelligence, Edge AI/ML, Cyber-Physical Systems).
  • Drivers behind IoT adoption across various industries and use cases.
  • Emerging IoT Trends (Edge Computing, Sustainability, AI/ML Integration, Enhanced Security).
  • The position of AWS IoT within the wider AWS ecosystem (including AWS Partner Network - APN resources).
• Overview of the AWS IoT Service Landscape
  • AWS IoT Core (MQTT/Bridge, Jobs, Device Defender).
  • AWS IoT Device Management (Onboarding, Configuration Management, OTA Updates).
  • AWS IoT Analytics (Data processing, enrichment, and modeling).
  • AWS IoT Greengrass (Edge compute, local execution, secure connectivity).
  • AWS IoT Button (Conceptual overview for simple device interactions).
  • Integration: AWS IoT Core connecting to Lambda, DynamoDB, OpenSearch, Step Functions, and SageMaker.

Module 1: IoT Architecture, Components & Security

• IoT Architecture
  • Device Layer (Sensors, Actuators, Edge Devices like Raspberry Pi, ESP32).
  • Connectivity Layer (MQTT, CoAP, HTTP, LPWAN - including LoRaWAN, NB-IoT, Sigfox, Cellular IoT).
  • Cloud Integration Layer (AWS IoT Core, API Gateway, Lambda, Step Functions).
  • Data Processing & Analytics Layer (DynamoDB, Timestream, OpenSearch, S3, Athena, SageMaker).
  • Application Layer (Mobile and Web Apps via AWS Amplify, Custom Business Applications).
  • Significance: Understanding the rationale behind distributed architectures (latency, bandwidth, compute power, security).
• In-Depth Look at Essential IoT Components
  • Hardware: Selection criteria (MCU, connectivity, sensors) and Security elements (Trusted Execution Environments - TEEs).
  • Edge Computing (AWS Greengrass): Advantages (low latency, reduced cloud traffic, local decision making).
  • Device Management: Onboarding (Over-the-Air - OTA, Pre-provisioning), Configuration, Monitoring, and Remote Debugging.
  • Security Analysis: Device Identity, Authentication & Authorization (X.509 Certificates, JSON Web Tokens - JWTs), Data Encryption (at rest and in transit), and AWS IoT Device Defender.
  • Security Standards: Introduction to standards (e.g., IEEE P2145, Open Connectivity Foundation - OCF) and compliance frameworks (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 and triggering actions).
  • AWS Step Functions (Stateful workflows for complex device interactions).
  • Amazon DynamoDB (NoSQL database for rapid IoT data ingestion).
  • Amazon OpenSearch Service (Search, Analytics, and 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 & WebSockets)
  • Key Features of MQTT 5.0 (Retain, Clean Session flags, User Properties, Wildcard topics).
  • MQTT over WebSockets (Standardization aspects).
  • Explanation of Quality of Service (QoS) Levels.
  • Best Practices for Protocol implementation.
• Alternative Protocols
  • CoAP (Constrained Application Protocol) for resource-constrained devices.
  • AMQP / MQTT over AMQP (Standard data interchange formats).
  • HTTP (Suitable for simpler, less frequent updates).
  • WebSockets (Full-duplex communication).

Module 3: Building Robust IoT Applications with AWS

• Device Onboarding & Secure Connectivity
  • Pre-Provisioning with AWS IoT Device Defender.
  • Secure Over-The-Air (OTA) Onboarding (e.g., leveraging AWS IoT Button concepts).
  • Management of Device Certificates (ACM/PKI).
  • Implementation of MQTT with TLS.
• Data Ingestion, Storage & Processing
  • Efficient transmission of data from devices to AWS IoT Core.
  • Selecting the appropriate target: Lambda (event-driven), Step Functions (orchestration), Timestream (time-series), OpenSearch (search & analytics), or S3 (raw data).
  • Utilizing AWS IoT Analytics for data enrichment and cleansing prior to storage.
  • Managing high-throughput scenarios (using Kinesis/Firehose).
• Device Management & Operations
  • Leveraging AWS IoT Device Management for fleet oversight.
  • Implementation and management of OTA Updates (using AWS IoT Jobs).
  • Remote Monitoring and Configuration.
• Constructing the IoT Backend
  • API Gateway for creating REST/GraphQL APIs to interact with devices and data.
  • AWS Lambda for executing business logic.
  • AWS Step Functions for coordinating distributed components.
  • Amazon SQS/SNS for asynchronous messaging and event triggering.

Module 4: Edge Computing & Advanced Integration

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

Module 5: Security, Governance & Best Practices

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

Module 6: Hands-on Projects & Capstone

• Guided Hands-on Labs
  • Device Onboarding and MQTT Communication.
  • Implementing Secure Data Ingestion to AWS.
  • Creating a Simple IoT Dashboard.
  • Simulating OTA Updates.
  • Introduction to AWS IoT Greengrass.
• Capstone Project
  • Develop a comprehensive IoT solution addressing a real-world issue (e.g., Smart Home Automation, Environmental Monitoring, or an Industrial Sensor Hub).
  • Requirements: Secure device setup, data ingestion, processing, visualization, and an optional edge component.
  • Application of AWS services covered throughout the course.