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 "Things" to include Edge Intelligence, AI/ML at the Edge, and Cyber-Physical Systems.
  • Key drivers of IoT adoption across various industries and use cases.
  • Emerging trends such as Edge Computing, sustainability, AI/ML integration, and enhanced security measures.
  • The position 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 (covering MQTT/Bridge, Jobs, and Device Defender).
  • AWS IoT Device Management (handling Device Onboarding, Configuration Management, and OTA Updates).
  • AWS IoT Analytics (focusing on data processing, enrichment, and modelling).
  • 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 via >.

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 standards like LoRaWAN, NB-IoT, Sigfox, and Cellular IoT).
  • Cloud Integration Layer (utilising AWS IoT Core, API Gateway, Lambda, and Step Functions).
  • Data Processing & Analytics Layer (employing DynamoDB, Timestream, OpenSearch, S3, Athena, and SageMaker).
  • Application Layer (Mobile and Web Apps using AWS Amplify, plus Custom Business Apps).
  • Context: Explaining the rationale behind distributed architectures, focusing on latency, bandwidth, compute power, and security.
• In-depth Look at Essential IoT Components
  • Hardware: Selection criteria (MCU, connectivity, sensors) and 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.
  • Device Management: Covering Onboarding (Over-the-Air - OTA, Pre-provisioning), Configuration, Monitoring, and Remote Debugging.
  • Security Deep Dive: Focusing on Device Identity, Authentication & Authorization (X.509 Certs, JSON Web Tokens - JWTs), Data Encryption (at rest and in transit), and AWS IoT Device Defender.
  • Security Standardisation: 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 triggering actions).
  • AWS Step Functions (managing Stateful workflows for complex device interactions).
  • Amazon DynamoDB (a NoSQL DB designed for fast IoT data ingestion).
  • Amazon OpenSearch Service (for Search & Analytics and Time Series data handling).
  • Amazon Timestream (a specialised time-series database).
  • Amazon S3 (for Raw data lake storage).
  • AWS IoT Device Defender (for monitoring and security assessment).
  • AWS IoT Wireless (facilitating connection for remote LPWAN devices).

Module 2: IoT Device Communication Protocols

• MQTT (MQTT v5 & WebSockets)
  • Key features of MQTT 5.0 (including Retain, Clean Session flags, User Properties, and Wildcard topics).
  • Implementing MQTT over WebSockets (Standardisation aspects).
  • Explanation of Quality of Service (QoS) Levels.
  • Best practices for protocol implementation.
• Alternative Protocols
  • CoAP (Constrained Application Protocol) suitable for constrained devices.
  • AMQP / MQTT over AMQP (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 (drawing on AWS IoT Button concepts).
  • Managing Device Certificates (via ACM/PKI).
  • Implementing MQTT with TLS encryption.
• Data Ingestion, Storage & Processing
  • Efficiently 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).
  • Utilising 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 operations.
  • Implementing and managing OTA Updates (using AWS IoT Jobs).
  • Conducting Remote Monitoring and Configuration.
• Constructing the IoT Backend
  • Using API Gateway to create REST/GraphQL APIs for interacting with devices and data.
  • Implementing business logic with AWS Lambda.
  • Coordinating distributed components with AWS Step Functions.
  • Utilising 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 (using C++ or Python).
  • Ensuring 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 at the edge using the Greengrass ML Accelerator (GMA).
• Data Visualization & User Interfaces
  • Employing AWS IoT SiteWise for industrial data visualisation.
  • 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 (focusing on Defense-in-Depth).
  • Secure Development Practices (referencing the OWASP IoT Top 10).
  • Strategies for Vulnerability Management.
  • Threat Modelling specifically for IoT environments.
• AWS Security Services for IoT
  • AWS IoT Device Defender (both Service & Device Defender components).
  • Utilising AWS Shield and AWS Identity and Access Management (IAM).
  • Performing compliance checks with AWS Config.
  • Integration with Hardware Security Modules (HSMs).
• Data Privacy & Governance
  • Handling sensitive data (including PII).
  • Establishing Data Retention and Deletion policies.
  • Addressing Compliance considerations.

Module 6: Hands-on Projects & Capstone

• Guided Hands-on Labs
  • Device Onboarding and MQTT Communication.
  • Implementing Secure Data Ingestion to AWS.
  • Creating a Simple IoT Dashboard.
  • Simulating OTA Updates.
  • Introduction to AWS IoT Greengrass.
• Capstone Project
  • Developing a complete IoT solution addressing a real-world issue (e.g., Smart Home Automation, Environmental Monitoring, or Industrial Sensor Hub).
  • Requirements: A secure device, data ingestion, processing, visualisation, and an optional edge component.
  • Applying AWS services covered throughout the course.