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: Beyond "Things" (Edge Intelligence, AI/ML at the Edge, Cyber-Physical Systems).
  • Key drivers of IoT growth across industries and use cases.
  • Major IoT trends, including edge computing, sustainability, AI/ML integration, and enhanced security.
  • Positioning AWS IoT within the broader AWS ecosystem (referencing AWS Partner Network - APN resources).
• AWS IoT Service Landscape Overview
  • AWS IoT Core (covering MQTT/Bridge, Jobs, and Device Defender).
  • AWS IoT Device Management (encompassing device onboarding, configuration management, and OTA updates).
  • AWS IoT Analytics (focusing on data processing, enrichment, and modelling).
  • AWS IoT Greengrass (providing edge compute, local execution, and secure connectivity).
  • AWS IoT Button (a conceptual overview for simple devices).
  • Connections: Linking AWS IoT Core with > Lambda/DynamoDB/OpenSearch/Step Functions/SageMaker.

Module 1: IoT Architecture, Components & Security

• IoT Architecture
  • Device Layer (involving sensors, actuators, and edge devices such as Raspberry Pi and ESP32).
  • Connectivity Layer (utilising MQTT, CoAP, HTTP, and LPWAN standards like LoRaWAN, NB-IoT, Sigfox, and Cellular IoT).
  • Cloud Integration Layer (leveraging AWS IoT Core, API Gateway, Lambda, and Step Functions).
  • Data Processing & Analytics Layer (using DynamoDB, Timestream, OpenSearch, S3, Athena, and SageMaker).
  • Application Layer (developing mobile and web apps via AWS Amplify, as well as custom business applications).
  • Key Rationale: Understanding the "why" behind distributed architectures regarding latency, bandwidth, compute power, and security.
• Essential IoT Components Deep Dive
  • 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: Covering onboarding (Over-the-Air - OTA, pre-provisioning), configuration, monitoring, and remote debugging.
  • Security Deep Dive: Addressing device identity, authentication, and authorisation (X.509 Certs, JSON Web Tokens - JWTs), data encryption (at rest and in transit), and AWS IoT Device Defender.
  • Security Standardisation: An introduction to 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 (offering secure MQTT/Bridge, jobs for firmware updates, and Device Defender).
  • AWS Lambda (providing serverless compute for data preprocessing and action triggering).
  • AWS Step Functions (enabling stateful workflows for complex device interactions).
  • Amazon DynamoDB (serving as a NoSQL DB for rapid IoT data ingestion).
  • Amazon OpenSearch Service (handling search, analytics, and time-series data).
  • Amazon Timestream (functioning as a specialised time-series database).
  • Amazon S3 (providing raw data lake storage).
  • AWS IoT Device Defender (facilitating monitoring and security assessments).
  • AWS IoT Wireless (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).
  • Standardisation of MQTT over WebSockets.
  • Explanation of Quality of Service (QoS) levels.
  • Best practices for protocol implementation.
• Alternative Protocols
  • CoAP (Constrained Application Protocol) tailored for constrained devices.
  • AMQP and MQTT over AMQP (standard data interchange formats).
  • HTTP (suitable for simpler, less frequent updates).
  • WebSockets (enabling full-duplex communication).

Module 3: Building Robust IoT Applications with AWS

• Device Onboarding & Secure Connectivity
  • Pre-provisioning via AWS IoT Device Defender.
  • Secure Over-The-Air (OTA) onboarding (e.g., applying AWS IoT Button concepts).
  • Managing device certificates (ACM/PKI).
  • Implementing MQTT with TLS.
• Data Ingestion, Storage & Processing
  • Efficient transmission of 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 administration.
  • Implementing and managing OTA updates (using AWS IoT Jobs).
  • Conducting remote monitoring and configuration.
• Building the IoT Backend
  • Using API Gateway to create REST/GraphQL APIs for interacting with devices and data.
  • Employing AWS Lambda for business logic.
  • Using AWS Step Functions to coordinate distributed components.
  • Utilising Amazon SQS/SNS for asynchronous messaging and event triggering.

Module 4: Edge Computing & Advanced Integration

• AWS IoT Greengrass
  • Core concepts (Core, Device, Connector).
  • Executing Lambda functions locally on the device.
  • Running code directly on the device (in C++ or Python).
  • Ensuring secure communication between Greengrass Core and AWS/IoT devices.
  • Application case: Local data filtering, preprocessing, or AI inference at the edge.
• Integration with AI/ML
  • Using SageMaker for complex ML models in the cloud.
  • Performing ML inference on the edge via the Greengrass ML Accelerator (GMA).
• Data Visualisation & User Interfaces
  • Utilising 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
  • Secure design principles (applying Defence-in-Depth).
  • Secure development practices (addressing OWASP IoT Top 10).
  • Vulnerability management strategies.
  • Threat modelling for IoT systems.
• AWS Security Services for IoT
  • AWS IoT Device Defender (covering both Service and Device Defender).
  • Integration with AWS Shield and AWS Identity and Access Management (IAM).
  • Using AWS Config for compliance checks.
  • Integration of Hardware Security Modules (HSMs).
• Data Privacy & Governance
  • Handling sensitive data (PII).
  • Establishing data retention and deletion policies.
  • Considering compliance requirements.

Module 6: Hands-on Projects & Capstone

• Guided Hands-on Labs
  • Device onboarding and MQTT communication.
  • Implementing secure data ingestion to AWS.
  • Constructing a basic IoT dashboard.
  • Simulating OTA updates.
  • Introduction to AWS IoT Greengrass.
• Capstone Project
  • Developing a comprehensive IoT solution to address a real-world issue (e.g., Smart Home Automation, Environmental Monitoring, or an Industrial Sensor Hub).
  • Requirements: Ensuring secure devices, data ingestion, processing, visualisation, and optional edge components.
  • Utilising the AWS services covered throughout the course.