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 simple "Things" to include Edge Intelligence, AI/ML at the Edge, and Cyber-Physical Systems.
  • Key drivers behind IoT adoption across various industries and use cases.
  • Current trends in IoT, such as Edge Computing, sustainability initiatives, AI/ML integration, and enhanced security measures.
  • Positioning 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 features, including MQTT/Bridge, Jobs, and Device Defender.
  • AWS IoT Device Management capabilities, such as device onboarding, configuration management, and OTA updates.
  • AWS IoT Analytics for data processing, enrichment, and modeling.
  • AWS IoT Greengrass for edge computing, local execution, and secure connectivity.
  • Conceptual overview of the AWS IoT Button for simple device interactions.
  • Integration Point: Connecting AWS IoT Core with Lambda, DynamoDB, OpenSearch, Step Functions, and SageMaker >.

Module 1: IoT Architecture, Components & Security

• IoT Architecture
  • Device Layer: Includes sensors, actuators, and edge devices such as Raspberry Pi and ESP32.
  • Connectivity Layer: Protocols like MQTT, CoAP, HTTP, and LPWAN standards (LoRaWAN, NB-IoT, Sigfox, Cellular IoT).
  • Cloud Integration Layer: Components including AWS IoT Core, API Gateway, Lambda, and Step Functions.
  • Data Processing & Analytics Layer: Services such as DynamoDB, Timestream, OpenSearch, S3, Athena, and SageMaker.
  • Application Layer: Mobile and web applications built with AWS Amplify or custom business apps.
  • Strategic Importance: Understanding the rationale for distributed architectures regarding latency, bandwidth, compute power, and security.
• In-Depth Analysis of Essential IoT Components
  • Hardware Selection: Criteria for MCU, connectivity, and sensors, along with security elements like Trusted Execution Environments (TEEs).
  • Edge Computing (AWS Greengrass): Advantages including low latency, reduced cloud traffic, and local decision-making capabilities.
  • Device Management: Processes for onboarding (OTA, Pre-provisioning), configuration, monitoring, and remote debugging.
  • Security Deep Dive: Device identity, authentication, and authorization (X.509 Certs, JWTs), data encryption (at rest and in transit), and AWS IoT Device Defender.
  • Security Standardization: Introduction to industry standards (e.g., IEEE P2145, OCF) and compliance frameworks (ISO/IEC 27001, SOC 2).
• AWS-Specific PaaS Functions for IoT
  • AWS IoT Core: Secure MQTT/Bridge support, Jobs for firmware updates, and Device Defender.
  • AWS Lambda: Serverless computing for data preprocessing and action triggering.
  • AWS Step Functions: Managing stateful workflows for complex device interactions.
  • Amazon DynamoDB: NoSQL database optimized for rapid IoT data ingestion.
  • Amazon OpenSearch Service: Capabilities for search, analytics, and time-series data handling.
  • Amazon Timestream: Specialized database for time-series data.
  • Amazon S3: Storage solution for raw data lakes.
  • AWS IoT Device Defender: Tools for continuous monitoring and security assessment.
  • AWS IoT Wireless: Facilitating connections for 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.
  • Implementation of MQTT over WebSockets and its standardization.
  • Detailed explanation of Quality of Service (QoS) levels.
  • Best practices for protocol implementation.
• Alternative Communication Protocols
  • CoAP (Constrained Application Protocol) tailored for constrained devices.
  • AMQP and MQTT over AMQP for standard data interchange formats.
  • HTTP usage for simpler, less frequent update scenarios.
  • WebSockets for full-duplex communication channels.

Module 3: Building Robust IoT Applications with AWS

• Device Onboarding & Secure Connectivity
  • Implementing AWS IoT Device Defender Pre-Provisioning.
  • Establishing secure Over-The-Air (OTA) onboarding, drawing concepts from the AWS IoT Button.
  • Management of device certificates via ACM/PKI.
  • Implementation of MQTT with TLS encryption.
• Data Ingestion, Storage & Processing
  • Efficient strategies for transmitting 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 analytics, and S3 for raw storage.
  • Leveraging AWS IoT Analytics for data enrichment and cleansing prior to storage.
  • Managing high-throughput data streams using Kinesis/Firehose.
• Device Management & Operations
  • Utilizing AWS IoT Device Management for comprehensive fleet oversight.
  • Implementing and managing OTA Updates through AWS IoT Jobs.
  • Performing remote monitoring and device configuration adjustments.
• Constructing the IoT Backend
  • Using API Gateway to create REST/GraphQL APIs for interacting with devices and data.
  • Implementing business logic using AWS Lambda.
  • Coordinating distributed components with AWS Step Functions.
  • Handling asynchronous messaging and event triggering via Amazon SQS/SNS.

Module 4: Edge Computing & Advanced Integration

• AWS IoT Greengrass
  • Core concepts: Core, Device, and Connector roles.
  • Execution of 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.
  • Practical Use Case: Local data filtering, preprocessing, or AI inference at the edge.
• Integration with AI/ML
  • Utilizing SageMaker for deploying complex ML models in the cloud.
  • Performing ML inference on the edge using the Greengrass ML Accelerator (GMA).
• Data Visualization & User Interfaces
  • Employing AWS IoT SiteWise for industrial data visualization.
  • Developing web applications using AWS Amplify (API, UI, Authentication).
  • Creating dashboards with 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 strategies.
  • Conducting threat modeling specifically for IoT environments.
• AWS Security Services for IoT
  • Deployment of AWS IoT Device Defender (both Service and Device components).
  • Integration with AWS Shield and AWS Identity and Access Management (IAM).
  • Using AWS Config for automated compliance checks.
  • Incorporating Hardware Security Modules (HSMs) for enhanced security.
• Data Privacy & Governance
  • Protocols for handling sensitive data, including Personally Identifiable Information (PII).
  • Establishing data retention and deletion policies.
  • Addressing relevant compliance considerations.

Module 6: Hands-on Projects & Capstone

• Guided Hands-on Labs
  • Practical exercises in Device Onboarding and MQTT Communication.
  • Implementing secure data ingestion pipelines to AWS.
  • Constructing a basic IoT dashboard for monitoring.
  • Simulating OTA update processes.
  • Introductory session on AWS IoT Greengrass implementation.
• Capstone Project
  • Development of a complete IoT solution addressing a real-world challenge, such as Smart Home Automation, Environmental Monitoring, or an Industrial Sensor Hub.
  • Project requirements include secure device setup, data ingestion, processing, visualization, and optional edge computing components.
  • Utilization of the AWS services covered throughout the course to build the solution.