Industrial Training IoT (Internet of Things) with Raspberry PI and AWS IoT Core Training Course

Module 0: Fundamentals & The 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 growth across various industries and use cases.
  • Major IoT trends such as Edge Computing, sustainability, AI/ML integration, and enhanced security.
  • The role of AWS IoT within the broader 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 (Onboarding, Configuration Management, OTA Updates).
  • AWS IoT Analytics (Data processing, enrichment, and modeling).
  • AWS IoT Greengrass (Edge compute, local execution, and secure connectivity).
  • AWS IoT Button (Conceptual overview for simple devices).
  • Integration: Connecting AWS IoT Core with Lambda/DynamoDB/OpenSearch/Step Functions/SageMaker.

Module 1: IoT Architecture, Components & Security

• IoT Architecture
  • Device Layer (Sensors, Actuators, Edge Devices like Raspberry Pi, ESP32).
  • Connectivity Layer (MQTT, CoAP, HTTP, LPWAN - LoRaWAN, NB-IoT, Sigfox, Cellular IoT).
  • Cloud Integration Layer (AWS IoT Core, API Gateway, Lambda, Step Functions).
  • Data Processing & Analytics Layer (DynamoDB, Timestream, OpenSearch, S3, Athena, SageMaker).
  • Application Layer (Mobile and Web Apps using AWS Amplify, Custom Business Apps).
  • Rationale for distributed architectures (latency, bandwidth, compute power, 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 including low latency, reduced cloud traffic, and local decision-making.
  • Device Management: Onboarding (Over-the-Air - OTA, Pre-provisioning), Configuration, Monitoring, and Remote Debugging.
  • Security Deep Dive: 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: Introduction to standards (e.g., IEEE P2145, Open Connectivity Foundation - OCF) and compliance (ISO/IEC 27001, SOC 2).
• AWS-Specific PaaS Functions for IoT
  • AWS IoT Core (Secure MQTT/Bridge, Jobs for firmware updates, Device Defender).
  • AWS Lambda (Serverless compute for data preprocessing and action triggering).
  • AWS Step Functions (Stateful workflows for complex device interactions).
  • Amazon DynamoDB (NoSQL DB for rapid IoT data ingestion).
  • Amazon OpenSearch Service (Search & Analytics, Time Series data handling).
  • Amazon Timestream (Specialized time-series database).
  • Amazon S3 (Raw data lake storage).
  • AWS IoT Device Defender (Monitoring and security assessment).
  • AWS IoT Wireless (Connecting remote LPWAN devices).

Module 2: IoT Device Communication Protocols

• MQTT (MQTT v5 & WebSockets)
  • MQTT 5.0 Features (Retain, Clean Session flags, User Properties, Wildcard topics).
  • MQTT over WebSockets (Standardization).
  • Explanation of Quality of Service (QoS) Levels.
  • Protocol Best Practices.
• Alternative Protocols
  • CoAP (Constrained Application Protocol) for constrained devices.
  • AMQP / MQTT over AMQP (Standard data interchange formats).
  • HTTP (For simpler, less frequent updates).
  • WebSockets (Full-duplex communication).

Module 3: Constructing Robust IoT Applications with AWS

• Device Onboarding & Secure Connectivity
  • AWS IoT Device Defender Pre-Provisioning.
  • Secure Over-The-Air (OTA) Onboarding (e.g., leveraging AWS IoT Button concepts).
  • Managing Device Certificates (ACM/PKI).
  • Implementing 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), S3 (raw data).
  • Utilizing AWS IoT Analytics for data enrichment and cleansing prior to storage.
  • Managing high-throughput scenarios (Kinesis/Firehose).
• Device Management & Operations
  • Leveraging AWS IoT Device Management for fleet management.
  • Implementing and managing OTA Updates (using AWS IoT Jobs).
  • Remote Monitoring and Configuration.
• Building the IoT Backend
  • Using API Gateway to create REST/GraphQL APIs for device and data interaction.
  • Implementing business logic with AWS Lambda.
  • Coordinating distributed components with AWS Step Functions.
  • Handling asynchronous messaging and event triggering with Amazon SQS/SNS.

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 (C++, Python).
  • Securing communication between Greengrass Core and AWS/IoT devices.
  • Use Case: Local data filtering, preprocessing, or AI inference at the edge.
• Integration with AI/ML
  • Utilizing SageMaker for complex ML models in the cloud.
  • Performing ML inference on the edge with Greengrass ML Accelerator (GMA).
• Data Visualization & User Interfaces
  • Visualizing industrial data with AWS IoT SiteWise.
  • Developing Web Apps using AWS Amplify (API, UI, Authentication).
  • Creating dashboards using Amazon QuickSight or OpenSearch Dashboards.

Module 5: Security, Governance & Best Practices

• IoT Security Lifecycle
  • Secure Design Principles (Defense-in-Depth).
  • Secure Development Practices (OWASP IoT Top 10).
  • Vulnerability Management.
  • Threat Modeling for IoT.
• AWS Security Services for IoT
  • AWS IoT Device Defender (Service & Device Defender).
  • AWS Shield, AWS Identity and Access Management (IAM).
  • AWS Config for compliance checks.
  • Integration of Hardware Security Modules (HSMs).
• Data Privacy & Governance
  • Managing sensitive data (PII).
  • Data Retention and Deletion policies.
  • Compliance considerations.

Module 6: Practical Projects & Capstone

• Guided Hands-on Labs
  • Device Onboarding & MQTT Communication.
  • Implementing Secure Data Ingestion to AWS.
  • Building a Simple IoT Dashboard.
  • OTA Update Simulation.
  • Introduction to AWS IoT Greengrass.
• Capstone Project
  • Develop a complete IoT solution addressing a real-world problem (e.g., Smart Home Automation, Environmental Monitoring, Industrial Sensor Hub).
  • Requirements: Secure device, data ingestion, processing, visualization, and optional edge component.
  • Utilize the AWS services covered throughout the course.