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.
  • Factors driving IoT adoption across industries and use cases.
  • Major IoT trends such as Edge Computing, Sustainability, AI/ML integration, and enhanced security.
  • Positioning AWS IoT within the wider AWS ecosystem, including AWS Partner Network (APN) resources.
• Overview of the AWS IoT Service Landscape
  • AWS IoT Core (MQTT/Bridge, Jobs, Device Defender).
  • AWS IoT Device Management (covering 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 (providing a conceptual overview for simple devices).
  • Connectivity: Integration of AWS IoT Core with Lambda/DynamoDB/OpenSearch/Step Functions/SageMaker >.

Module 1: IoT Architecture, Components & Security

• IoT Architecture
  • Device Layer (comprising Sensors, Actuators, and Edge Devices like Raspberry Pi and ESP32).
  • Connectivity Layer (utilizing MQTT, CoAP, HTTP, and LPWAN technologies such as LoRaWAN, NB-IoT, Sigfox, and Cellular IoT).
  • Cloud Integration Layer (involving 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 using AWS Amplify, or Custom Business Apps).
  • Significance: Understanding the rationale 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 aspects (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 Analysis: 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 Standardization: Introducing 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 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 (facilitating Search & Analytics and Time Series data handling).
  • Amazon Timestream (functioning as a specialized time-series database).
  • Amazon S3 (providing Raw data lake storage).
  • AWS IoT Device Defender (performing Monitoring and security assessment).
  • AWS IoT Wireless (supporting the connection of remote LPWAN devices).

Module 2: IoT Device Communication Protocols

• MQTT (MQTT v5 & WebSockets)
  • Features of MQTT 5.0 (Retain, Clean Session flags, User Properties, and Wildcard topics).
  • Standardization of MQTT over WebSockets.
  • Explanation of Quality of Service (QoS) levels.
  • Best practices for protocol implementation.
• Alternative Protocols
  • CoAP (Constrained Application Protocol) suited for constrained devices.
  • AMQP / MQTT over AMQP (standardizing 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 with AWS IoT Device Defender.
  • Secure Over-The-Air (OTA) Onboarding (leveraging concepts from AWS IoT Button).
  • Management of Device Certificates (using ACM/PKI).
  • Implementation of MQTT with TLS.
• Data Ingestion, Storage & Processing
  • Efficiently transmitting data from devices to AWS IoT Core.
  • Selecting appropriate targets: 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.
  • Implementing and managing OTA Updates (via AWS IoT Jobs).
  • Conducting Remote Monitoring and Configuration.
• Constructing the IoT Backend
  • Using API Gateway to create REST/GraphQL APIs for device and data interaction.
  • Employing AWS Lambda for business logic execution.
  • Utilizing AWS Step Functions to coordinate distributed components.
  • Using Amazon SQS/SNS for asynchronous messaging and event triggering.

Module 4: Edge Computing & Advanced Integration

• AWS IoT Greengrass
  • Core concepts (Core, Device, Connector).
  • Execution of Lambda functions locally on the device.
  • Direct code execution on the device (using C++, Python).
  • Secure communication between Greengrass Core and AWS/IoT devices.
  • Use Cases: Performing local data filtering, preprocessing, or AI inference at the edge.
• Integration with AI/ML
  • Using SageMaker for complex ML models in the cloud.
  • Running ML inference on the edge using Greengrass ML Accelerator (GMA).
• Data Visualization & User Interfaces
  • Utilizing AWS IoT SiteWise for industrial data visualization.
  • 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
  • Applying Secure Design Principles (such as Defense-in-Depth).
  • Adopting Secure Development Practices (referencing OWASP IoT Top 10).
  • Managing Vulnerabilities.
  • Conducting Threat Modeling for IoT systems.
• AWS Security Services for IoT
  • Deploying AWS IoT Device Defender (including Service & Device Defender components).
  • Implementing AWS Shield and AWS Identity and Access Management (IAM).
  • Using AWS Config for compliance verification.
  • Integrating Hardware Security Modules (HSMs).
• Data Privacy & Governance
  • Handling sensitive data (PII) appropriately.
  • Defining Data Retention and Deletion policies.
  • Addressing Compliance considerations.

Module 6: Hands-on Projects & Capstone

• Guided Hands-on Labs
  • Performing Device Onboarding and MQTT Communication.
  • Implementing Secure Data Ingestion to AWS.
  • Constructing a Basic IoT Dashboard.
  • Simulating OTA Updates.
  • Getting started with AWS IoT Greengrass.
• Capstone Project
  • Developing a complete IoT solution that addresses a real-world problem (e.g., Smart Home Automation, Environmental Monitoring, or an Industrial Sensor Hub).
  • Requirements include a secure device, data ingestion, processing, visualization, and an optional edge component.
  • Utilizing AWS services covered throughout the course.