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: Extending beyond "Things" to include Edge Intelligence, AI/ML at the Edge, and Cyber-Physical Systems.
  • Factors driving IoT growth across industries and use cases.
  • Major IoT trends such as Edge Computing, sustainability, AI/ML integration, and enhanced security measures.
  • The role of 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 features like MQTT/Bridge, Jobs, and Device Defender.
  • AWS IoT Device Management capabilities for onboarding, configuration, and OTA updates.
  • AWS IoT Analytics for data processing, enrichment, and modeling.
  • AWS IoT Greengrass for edge computing, local execution, and secure connectivity.
  • A conceptual overview of AWS IoT Button for simple device applications.
  • Connection: Linking AWS IoT Core with > Lambda, DynamoDB, OpenSearch, Step Functions, and SageMaker.

Module 1: IoT Architecture, Components & Security

• IoT Architecture
  • The Device Layer, encompassing sensors, actuators, and edge devices such as Raspberry Pi and ESP32.
  • The Connectivity Layer, covering protocols like MQTT, CoAP, HTTP, and LPWAN options including LoRaWAN, NB-IoT, Sigfox, and Cellular IoT.
  • The Cloud Integration Layer, involving AWS IoT Core, API Gateway, Lambda, and Step Functions.
  • The Data Processing & Analytics Layer, utilizing DynamoDB, Timestream, OpenSearch, S3, Athena, and SageMaker.
  • The Application Layer, supporting mobile and web apps via AWS Amplify, as well as custom business applications.
  • Explaining the rationale behind distributed architectures, focusing on latency, bandwidth, compute power, and security.
• In-Depth Analysis of Essential IoT Components
  • Hardware selection criteria (MCU, connectivity, sensors) and security elements like Trusted Execution Environments (TEEs).
  • Edge Computing with AWS Greengrass, highlighting benefits such as low latency, reduced cloud traffic, and local decision-making.
  • Device management processes including onboarding (Over-the-Air - OTA, Pre-provisioning), configuration, monitoring, and remote debugging.
  • A deep dive into security covering device identity, authentication and authorization (X.509 Certs, JSON Web Tokens - JWTs), data encryption (at rest and in transit), and AWS IoT Device Defender.
  • Security standardization, introducing standards like IEEE P2145 and Open Connectivity Foundation (OCF), along with compliance frameworks such as ISO/IEC 27001 and SOC 2.
• AWS-Specific PaaS Functions for IoT
  • AWS IoT Core for secure MQTT/Bridge communication, firmware update jobs, and Device Defender.
  • AWS Lambda for serverless compute tasks, including data preprocessing and action triggering.
  • AWS Step Functions for managing stateful workflows in complex device interactions.
  • Amazon DynamoDB as a NoSQL database for rapid IoT data ingestion.
  • Amazon OpenSearch Service for search, analytics, and time-series data handling.
  • Amazon Timestream as a specialized time-series database.
  • Amazon S3 for raw data lake storage.
  • AWS IoT Device Defender for continuous 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.
  • Detailed explanation of Quality of Service (QoS) levels.
  • Best practices for protocol usage.
• Alternative Protocols
  • CoAP (Constrained Application Protocol) tailored for constrained devices.
  • AMQP and MQTT over AMQP for standardized data interchange formats.
  • HTTP for simpler, less frequent data updates.
  • WebSockets for full-duplex communication channels.

Module 3: Building Robust IoT Applications with AWS

• Device Onboarding & Secure Connectivity
  • Pre-provisioning using AWS IoT Device Defender.
  • Implementing secure Over-The-Air (OTA) onboarding, drawing on concepts from AWS IoT Button.
  • Managing device certificates using ACM/PKI.
  • Establishing MQTT connections with TLS encryption.
• Data Ingestion, Storage & Processing
  • Efficiently transmitting data from devices to AWS IoT Core.
  • Selecting the appropriate target service: Lambda for event-driven tasks, Step Functions for orchestration, Timestream for time-series data, OpenSearch for search and analytics, or S3 for raw data storage.
  • Utilizing AWS IoT Analytics to enrich and clean data prior to storage.
  • Managing high-throughput scenarios using Kinesis and Firehose.
• Device Management & Operations
  • Leveraging AWS IoT Device Management for efficient fleet oversight.
  • Implementing and managing OTA Updates via AWS IoT Jobs.
  • Performing remote monitoring and configuration adjustments.
• Developing the IoT Backend
  • Creating REST/GraphQL APIs with API Gateway to interact with devices and data.
  • Implementing business logic using AWS Lambda.
  • Coordinating distributed components with AWS Step Functions.
  • Facilitating asynchronous messaging and event triggering with Amazon SQS and SNS.

Module 4: Edge Computing & Advanced Integration

• AWS IoT Greengrass
  • Understanding core concepts, devices, and connectors.
  • Executing Lambda functions locally on the device.
  • Running code directly on the device using C++ or Python.
  • Ensuring secure communication between the Greengrass Core and AWS/IoT devices.
  • Applying use cases for local data filtering, preprocessing, and AI inference at the edge.
• Integration with AI/ML
  • Employing SageMaker for complex ML models within the cloud.
  • 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 applications 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
  • Applying secure design principles based on Defense-in-Depth.
  • Adhering to secure development practices, including the OWASP IoT Top 10.
  • Implementing effective vulnerability management strategies.
  • Conducting threat modeling specifically for IoT environments.
• AWS Security Services for IoT
  • Using AWS IoT Device Defender (both Service and Device Defender variants).
  • Integrating AWS Shield and AWS Identity and Access Management (IAM).
  • Performing compliance checks with AWS Config.
  • Incorporating Hardware Security Modules (HSMs).
• Data Privacy & Governance
  • Managing sensitive data, including Personally Identifiable Information (PII).
  • Establishing data retention and deletion policies.
  • Addressing various compliance considerations.

Module 6: Hands-on Projects & Capstone

• Guided Hands-on Labs
  • Practicing device onboarding and MQTT communication.
  • Implementing secure data ingestion into AWS.
  • Constructing a basic IoT dashboard.
  • Simulating OTA update processes.
  • Getting started with AWS IoT Greengrass.
• Capstone Project
  • Developing a complete IoT solution to address a real-world challenge, such as Smart Home Automation, Environmental Monitoring, or an Industrial Sensor Hub.
  • Fulfilling requirements for a secure device, data ingestion, processing, visualization, and optional edge components.
  • Utilizing the AWS services covered throughout the course.