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: Moving beyond "Things" (Edge Intelligence, AI/ML at the Edge, Cyber-Physical Systems).
  • Key drivers behind IoT adoption across various industries and use cases.
  • Major IoT trends, including Edge Computing, sustainability, AI/ML integration, and enhanced security.
  • The role 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 (covering MQTT/Bridge, Jobs, and Device Defender).
  • AWS IoT Device Management (focusing on onboarding, configuration management, and OTA updates).
  • AWS IoT Analytics (for data processing, enrichment, and modeling).
  • AWS IoT Greengrass (enabling edge compute, local execution, and secure connectivity).
  • AWS IoT Button (a conceptual overview for simple device interactions).
  • Integration Path: AWS IoT Core connected to Lambda/DynamoDB/OpenSearch/Step Functions/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: Covers protocols like MQTT, CoAP, HTTP, and LPWAN standards (LoRaWAN, NB-IoT, Sigfox, Cellular IoT).
  • Cloud Integration Layer: Utilizes AWS IoT Core, API Gateway, Lambda, and Step Functions.
  • Data Processing & Analytics Layer: Leverages DynamoDB, Timestream, OpenSearch, S3, Athena, and SageMaker.
  • Application Layer: Supports Mobile and Web Apps (using AWS Amplify) and custom business applications.
  • Architectural Rationale: Explaining 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: Covers onboarding (OTA, pre-provisioning), configuration, monitoring, and remote debugging.
  • Security Details: Device identity, authentication & authorization (X.509 Certs, JWTs), data encryption (at rest and in transit), and AWS IoT Device Defender.
  • Security Standards: 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, firmware update Jobs, and Device Defender.
  • AWS Lambda: Serverless compute for data preprocessing and action triggering.
  • AWS Step Functions: Stateful workflows for complex device interactions.
  • Amazon DynamoDB: NoSQL database for rapid 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 continuous monitoring and security assessment.
  • AWS IoT Wireless: Connecting remote LPWAN devices.

Module 2: IoT Device Communication Protocols

• MQTT (Version 5 & WebSockets)
  • MQTT 5.0 features: Retain flags, Clean Session, User Properties, and wildcard topics.
  • Standardization of MQTT over WebSockets.
  • Explanation of Quality of Service (QoS) levels.
  • Protocol best practices for implementation.
• Alternative Communication Protocols
  • CoAP (Constrained Application Protocol) tailored for constrained devices.
  • AMQP / MQTT over AMQP for standard data interchange formats.
  • HTTP: Suitable for simpler, less frequent updates.
  • WebSockets: Enabling full-duplex communication.

Module 3: Developing Robust IoT Applications with AWS

• Device Onboarding & Secure Connectivity
  • Pre-provisioning via AWS IoT Device Defender.
  • Secure Over-The-Air (OTA) onboarding (leveraging concepts like AWS IoT Button).
  • Management of device certificates (ACM/PKI).
  • Implementation of MQTT with TLS encryption.
• Data Ingestion, Storage & Processing
  • Efficient methods for 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).
  • 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 capabilities.
• 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, and Connector.
  • Executing Lambda functions locally on the device.
  • Running code directly on the device (C++, Python).
  • Secure communication between Greengrass Core and AWS/IoT devices.
  • 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 using the Greengrass ML Accelerator (GMA).
• Data Visualization & User Interfaces
  • Utilizing AWS IoT SiteWise for industrial data visualization.
  • Developing Web Apps with AWS Amplify (API, UI, Authentication).
  • Creating dashboards using Amazon QuickSight or OpenSearch Dashboards.

Module 5: Security, Governance & Best Practices

• The IoT Security Lifecycle
  • Secure design principles (Defense-in-Depth).
  • Secure development practices (referencing the OWASP IoT Top 10).
  • Vulnerability management strategies.
  • Threat modeling specific to IoT systems.
• AWS Security Services for IoT
  • AWS IoT Device Defender (both Service and Device levels).
  • AWS Shield and AWS Identity and Access Management (IAM).
  • AWS Config for ongoing compliance checks.
  • Integration of Hardware Security Modules (HSMs).
• Data Privacy & Governance
  • Handling sensitive data (PII).
  • Policies for data retention and deletion.
  • Key compliance considerations.

Module 6: Hands-On Projects & Capstone

• Guided Practical Labs
  • Device onboarding and MQTT communication exercises.
  • Implementing secure data ingestion into AWS.
  • Building a basic IoT dashboard.
  • Simulating OTA updates.
  • Introduction to AWS IoT Greengrass.
• Capstone Project
  • Developing a complete IoT solution to address a real-world problem (e.g., Smart Home Automation, Environmental Monitoring, or an Industrial Sensor Hub).
  • Requirements: Secure device, data ingestion, processing, visualization, and optional edge components.
  • Application of AWS services covered throughout the course.