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 the IoT in 2024: Moving beyond simple "Things" to encompass Edge Intelligence, AI/ML at the Edge, and Cyber-Physical Systems.
  • Key drivers of IoT adoption across various industries and use cases.
  • Prominent IoT trends, including Edge Computing, sustainability initiatives, AI/ML integration, and enhanced security measures.
  • The position of AWS IoT within the broader AWS ecosystem, including resources from the AWS Partner Network (APN).
• Overview of the AWS IoT Service Landscape
  • AWS IoT Core (MQTT/Bridge, Jobs, Device Defender).
  • AWS IoT Device Management (covering Device Onboarding, Configuration Management, and OTA Updates).
  • AWS IoT Analytics (for data processing, enrichment, and modeling).
  • AWS IoT Greengrass (focused on edge compute, local execution, and secure connectivity).
  • AWS IoT Button (a conceptual overview suitable for simple devices).
  • Integration Point: AWS IoT Core connecting to Lambda, DynamoDB, OpenSearch, Step Functions, and SageMaker >.

Module 1: IoT Architecture, Components & Security

• IoT Architecture
  • Device Layer (comprising Sensors, Actuators, and Edge Devices such as Raspberry Pi and ESP32).
  • Connectivity Layer (utilizing MQTT, CoAP, HTTP, and LPWAN standards like LoRaWAN, NB-IoT, Sigfox, and Cellular IoT).
  • Cloud Integration Layer (involving AWS IoT Core, API Gateway, Lambda, and Step Functions).
  • Data Processing & Analytics Layer (featuring DynamoDB, Timestream, OpenSearch, S3, Athena, and SageMaker).
  • Application Layer (encompassing Mobile and Web Apps built with AWS Amplify, as well as Custom Business Apps).
  • Strategic Importance: Explaining the rationale behind distributed architectures regarding latency, bandwidth, compute power, and security.
• In-Depth Look at Essential IoT Components
  • Hardware: Evaluation criteria for MCU, connectivity, and sensors, along with security elements such as Trusted Execution Environments (TEEs).
  • Edge Computing (via AWS Greengrass): Highlighting benefits like low latency, reduced cloud traffic, and local decision-making capabilities.
  • Device Management: Covering Onboarding (including Over-the-Air/OTA and Pre-provisioning), Configuration, Monitoring, and Remote Debugging.
  • Advanced Security: Addressing Device Identity, Authentication & Authorization (using X.509 Certs and JSON Web Tokens/JWTs), Data Encryption (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 (a NoSQL DB optimized for rapid IoT data ingestion).
  • Amazon OpenSearch Service (facilitating Search & Analytics and Time Series data handling).
  • Amazon Timestream (a specialized time-series database).
  • Amazon S3 (serving as raw data lake storage).
  • AWS IoT Device Defender (for monitoring and security assessment).
  • AWS IoT Wireless (enabling the connection of 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.
  • The standardization of MQTT over WebSockets.
  • An explanation of Quality of Service (QoS) Levels.
  • Best Practices for protocol implementation.
• Alternative Protocols
  • CoAP (Constrained Application Protocol) suited for constrained devices.
  • AMQP / MQTT over AMQP (for standard data interchange formats).
  • HTTP (ideal 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, leveraging concepts similar to the AWS IoT Button.
  • Managing Device Certificates through ACM/PKI.
  • Implementing MQTT with TLS for secure transmission.
• Data Ingestion, Storage & Processing
  • Efficiently transmitting data from devices to AWS IoT Core.
  • Selecting the appropriate target: 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 comprehensive fleet management.
  • Implementing and managing OTA Updates via AWS IoT Jobs.
  • Executing Remote Monitoring and Configuration tasks.
• Constructing the IoT Backend
  • Using API Gateway to create REST/GraphQL APIs for interacting with devices and data.
  • Employing AWS Lambda for core business logic.
  • Leveraging 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 components.
  • Executing Lambda functions locally on the device.
  • Running code directly on the device using C++ or Python.
  • Ensuring secure communication between Greengrass Core and AWS/IoT devices.
  • Application Use Cases: Local data filtering, preprocessing, or AI inference at the edge.
• Integration with AI/ML
  • Deploying complex ML models in the cloud using SageMaker.
  • Performing ML inference on the edge with the Greengrass ML Accelerator (GMA).
• Data Visualization & User Interfaces
  • Employing AWS IoT SiteWise for industrial data visualization.
  • Developing Web Apps with AWS Amplify, including API, UI, and Authentication modules.
  • Creating Dashboards using Amazon QuickSight or OpenSearch Dashboards.

Module 5: Security, Governance & Best Practices

• IoT Security Lifecycle
  • Adhering to Secure Design Principles, specifically Defense-in-Depth.
  • Following Secure Development Practices aligned with the OWASP IoT Top 10.
  • Implementing effective Vulnerability Management.
  • Conducting Threat Modeling specific to IoT environments.
• AWS Security Services for IoT
  • Utilizing AWS IoT Device Defender (both Service and Device levels).
  • Integrating AWS Shield and AWS Identity and Access Management (IAM).
  • Performing compliance checks with AWS Config.
  • Integrating Hardware Security Modules (HSMs).
• Data Privacy & Governance
  • Managing sensitive data, including PII.
  • Establishing Data Retention and Deletion policies.
  • Addressing relevant compliance considerations.

Module 6: Hands-on Projects & Capstone

• Guided Hands-on Labs
  • Device Onboarding and MQTT Communication exercises.
  • Implementing Secure Data Ingestion to AWS.
  • Constructing a Simple IoT Dashboard.
  • Simulating an OTA Update process.
  • Introduction to AWS IoT Greengrass functionalities.
• Capstone Project
  • Designing a complete IoT solution to address a real-world problem, such as Smart Home Automation, Environmental Monitoring, or an Industrial Sensor Hub.
  • Requirements: A secure device, data ingestion pipeline, processing logic, visualization, and an optional edge component.
  • Utilizing the AWS services covered throughout the course curriculum.