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 the IoT landscape in 2024: Extending beyond "Things" to include Edge Intelligence, AI/ML at the Edge, and Cyber-Physical Systems.
  • Factors driving IoT adoption across various industries and use cases.
  • Prominent IoT trends such as Edge Computing, Sustainability, AI/ML integration, and enhanced security measures.
  • The role of AWS IoT within the expansive 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 (focused on data processing, enrichment, and modeling).
  • AWS IoT Greengrass (emphasizing edge compute, local execution, and secure connectivity).
  • AWS IoT Button (providing a conceptual overview for simple device implementations).
  • Integration path: Connecting AWS IoT Core to Lambda/DynamoDB/OpenSearch/Step Functions/SageMaker.

Module 1: IoT Architecture, Components & Security

• IoT Architecture
  • Device Layer (including Sensors, Actuators, and Edge Devices such as Raspberry Pi and ESP32).
  • Connectivity Layer (covering MQTT, CoAP, HTTP, and LPWAN technologies like 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 (encompassing Mobile and Web Apps via AWS Amplify, as well as Custom Business Apps).
  • Context: Explaining the rationale behind distributed architectures regarding latency, bandwidth, compute power, and security.
• In-Depth Look at Essential IoT Components
  • Hardware: Criteria for selection (MCU, connectivity, sensors) and security elements (Trusted Execution Environments - TEEs).
  • Edge Computing (via 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 (using X.509 Certs and JSON Web Tokens - JWTs), Data Encryption (both at rest and in transit), and AWS IoT Device Defender.
  • Security Standardization: An introduction to relevant 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 (providing Secure MQTT/Bridge, Jobs for firmware updates, and Device Defender).
  • AWS Lambda (offering serverless compute for data preprocessing and action triggering).
  • AWS Step Functions (managing stateful workflows for complex device interactions).
  • Amazon DynamoDB (serving as a NoSQL database for rapid IoT data ingestion).
  • Amazon OpenSearch Service (handling Search & Analytics and Time Series data).
  • Amazon Timestream (a specialized time-series database).
  • Amazon S3 (for raw data lake storage).
  • 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 (including Retain, Clean Session flags, User Properties, and Wildcard topics).
  • Standardization of MQTT over WebSockets.
  • Explanation of Quality of Service (QoS) Levels.
  • Best practices for protocol implementation.
• Alternative Protocols
  • CoAP (Constrained Application Protocol) tailored for constrained devices.
  • AMQP / MQTT over AMQP (establishing 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 using AWS IoT Device Defender.
  • Secure Over-The-Air (OTA) Onboarding (for example, applying concepts from AWS IoT Button).
  • Management of Device Certificates (via ACM/PKI).
  • Implementation of MQTT secured with TLS.
• Data Ingestion, Storage & Processing
  • Efficient transmission of data from devices to AWS IoT Core.
  • Selecting appropriate targets: Lambda (for event-driven tasks), Step Functions (for orchestration), Timestream (for time-series data), OpenSearch (for search & analytics), or S3 (for 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).
  • Performing Remote Monitoring and Configuration.
• Constructing the IoT Backend
  • Using API Gateway to create REST/GraphQL APIs for interacting with devices and data.
  • Employing AWS Lambda for business logic implementation.
  • Utilizing AWS Step Functions to coordinate distributed components.
  • Using Amazon SQS/SNS for asynchronous messaging and event triggering.

Module 4: Edge Computing & Advanced Integration

• AWS IoT Greengrass
  • Core concepts (Core, Device, and Connector).
  • Executing Lambda functions locally on the device.
  • Running code directly on the device (in C++ or Python).
  • Ensuring secure communication between the Greengrass Core and AWS/IoT devices.
  • Application examples: Local data filtering, preprocessing, or AI inference at the edge.
• Integration with AI/ML
  • Using SageMaker for complex ML models hosted in the cloud.
  • Performing ML inference on the edge using Greengrass ML Accelerator (GMA).
• Data Visualization & User Interfaces
  • Leveraging AWS IoT SiteWise for visualizing industrial data.
  • Developing 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).
  • Management of vulnerabilities.
  • Threat modeling specific to IoT environments.
• AWS Security Services for IoT
  • AWS IoT Device Defender (encompassing Service & Device Defender components).
  • Utilizing AWS Shield and AWS Identity and Access Management (IAM).
  • Employing AWS Config for compliance verification.
  • Integration of Hardware Security Modules (HSMs).
• Data Privacy & Governance
  • Handling of sensitive data (such as PII).
  • Policies regarding Data Retention and Deletion.
  • Considerations for regulatory compliance.

Module 6: Hands-on Projects & Capstone

• Guided Hands-on Labs
  • Device Onboarding and MQTT Communication.
  • Implementing Secure Data Ingestion to AWS.
  • Constructing a Simple IoT Dashboard.
  • Simulating OTA Updates.
  • Fundamentals of AWS IoT Greengrass.
• Capstone Project
  • Developing a comprehensive IoT solution that addresses a real-world challenge (e.g., Smart Home Automation, Environmental Monitoring, or an Industrial Sensor Hub).
  • Requirements: A secure device, data ingestion pipeline, processing layer, visualization interface, and an optional edge component.
  • Utilization of the AWS services explored throughout the course.