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

Module 0: Foundations & the AWS IoT Ecosystem

• Introduction to IoT
  • Defining IoT in 2024: Extending beyond "Things" to include Edge Intelligence, AI/ML at the Edge, and Cyber-Physical Systems.
  • Key drivers behind IoT adoption across industries and specific use cases.
  • Emerging IoT trends such as Edge Computing, sustainability, AI/ML integration, and enhanced security measures.
  • Positioning AWS IoT within the wider AWS ecosystem, including resources from the AWS Partner Network (APN).
• Overview of the AWS IoT Service Landscape
  • AWS IoT Core (covering MQTT/Bridge, Jobs, and Device Defender).
  • AWS IoT Device Management (handling Device Onboarding, Configuration Management, and OTA Updates).
  • AWS IoT Analytics (focusing on data processing, enrichment, and modeling).
  • AWS IoT Greengrass (enabling Edge compute, local execution, and secure connectivity).
  • AWS IoT Button (a conceptual look at simple device implementations).
  • Integration point: Connecting AWS IoT Core with Lambda/DynamoDB/OpenSearch/Step Functions/SageMaker.

Module 1: IoT Architecture, Components & Security

• IoT Architecture
  • Device Layer (including Sensors, Actuators, and Edge Devices like Raspberry Pi and ESP32).
  • Connectivity Layer (covering MQTT, CoAP, HTTP, and LPWAN technologies such as LoRaWAN, NB-IoT, Sigfox, and Cellular IoT).
  • Cloud Integration Layer (utilizing AWS IoT Core, API Gateway, Lambda, and Step Functions).
  • Data Processing & Analytics Layer (involving DynamoDB, Timestream, OpenSearch, S3, Athena, and SageMaker).
  • Application Layer (comprising Mobile and Web Apps using AWS Amplify, as well as Custom Business Apps).
  • Rationale: Understanding the "why" behind distributed architectures regarding latency, bandwidth, compute power, and 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 such as low latency, reduced cloud traffic, and local decision-making.
  • Device Management: Onboarding (Over-the-Air - OTA, Pre-provisioning), Configuration, Monitoring, and Remote Debugging.
  • Security Deep Dive: Device Identity, Authentication & Authorization (X.509 Certs, JSON Web Tokens - JWTs), Data Encryption (at rest and in transit), and AWS IoT 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, and Device Defender).
  • AWS Lambda (Serverless compute for data preprocessing and triggering actions).
  • AWS Step Functions (Stateful workflows for managing complex device interactions).
  • Amazon DynamoDB (NoSQL database optimized for fast IoT data ingestion).
  • Amazon OpenSearch Service (For Search & Analytics and Time Series data handling).
  • Amazon Timestream (A specialized time-series database).
  • Amazon S3 (Storage for raw data lakes).
  • AWS IoT Device Defender (For monitoring and security assessment).
  • AWS IoT Wireless (For connecting remote LPWAN devices).

Module 2: IoT Device Communication Protocols

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

Module 3: Building Robust IoT Applications with AWS

• Device Onboarding & Secure Connectivity
  • Pre-Provisioning via 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
  • Efficiently transmitting 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).
  • Leveraging AWS IoT Analytics for data enrichment and cleansing prior to storage.
  • Managing high-throughput scenarios (using Kinesis/Firehose).
• Device Management & Operations
  • Utilizing AWS IoT Device Management for fleet oversight.
  • Implementing and managing OTA Updates (via AWS IoT Jobs).
  • Remote Monitoring and Configuration management.
• Constructing the IoT Backend
  • Using API Gateway to create REST/GraphQL APIs for interacting with devices and data.
  • Employing AWS Lambda for business logic.
  • Using AWS Step Functions to coordinate 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).
  • Executing Lambda functions locally on the device.
  • Running code directly on the device (C++, Python).
  • Securing communication between Greengrass Core and AWS/IoT devices.
  • Use Case: Local data filtering, preprocessing, or AI inference at the edge.
• Integration with AI/ML
  • Utilizing SageMaker for complex ML models in the cloud.
  • Performing ML inference on the edge using the Greengrass ML Accelerator (GMA).
• Data Visualization & User Interfaces
  • Applying AWS IoT SiteWise for industrial data visualization.
  • Building Web Apps with AWS Amplify (covering API, UI, and Authentication).
  • Creating Dashboards using Amazon QuickSight or OpenSearch Dashboards.

Module 5: Security, Governance & Best Practices

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

Module 6: Hands-on Projects & Capstone

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