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

Module 0: Foundational Principles and the AWS IoT Ecosystem

• Introduction to the Internet of Things
  • Defining the 2024 IoT Landscape: Expanding Beyond Static Objects (Edge Intelligence, On-Device AI/ML, Cyber-Physical Systems).
  • Catalysts for IoT Adoption (Sector-Specific Applications and Operational Use Cases).
  • Emerging IoT Trends (Edge Computing, Sustainability Initiatives, AI/ML Integration, Advanced Security Postures).
  • Positioning AWS IoT within the broader AWS Service Portfolio (Leveraging AWS Partner Network - APN resources).
• Comprehensive Overview of the AWS IoT Service Portfolio
  • AWS IoT Core (MQTT/Bridge Connectivity, Job Scheduling, Device Defender).
  • AWS IoT Device Management (Fleet Onboarding, Configuration Control, Over-the-Air Updates).
  • AWS IoT Analytics (Data Processing, Enrichment, and Modeling Capabilities).
  • AWS IoT Greengrass (Edge Computing, Local Execution, Secure Connectivity).
  • AWS IoT Button (Conceptual Framework for Simple Device Integration).
  • Integration Pathway: AWS IoT Core → Lambda/DynamoDB/OpenSearch/Step Functions/SageMaker.

Module 1: IoT Architecture, Core Components, and Security Frameworks

• IoT Architectural Frameworks
  • Device Layer (Sensors, Actuators, Edge Hardware such as Raspberry Pi and 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 and Analytics Layer (DynamoDB, Timestream, OpenSearch, S3, Athena, SageMaker).
  • Application Layer (Mobile and Web Applications utilizing AWS Amplify, Custom Business Systems).
  • Rationale: Analyzing the drivers for distributed architectures (Latency Optimization, Bandwidth Efficiency, Compute Distribution, Security Posture).
• Deep Dive into Essential IoT Components
  • Hardware: Selection Criteria (MCU Capabilities, Connectivity Options, Sensor Types), Security Elements (Trusted Execution Environments - TEEs).
  • Edge Computing (AWS Greengrass): Strategic Benefits (Reduced Latency, Minimized Cloud Traffic, Local Decision-Making).
  • Device Management: Onboarding (Over-the-Air - OTA, Pre-Provisioning), Configuration Management, Monitoring, Remote Debugging.
  • Security Analysis: Device Identity, Authentication and Authorization Mechanisms (X.509 Certificates, JSON Web Tokens - JWTs), Data Encryption (at Rest and In Transit), AWS IoT Device Defender.
  • Security Standardization: Overview of Industry Standards (e.g., IEEE P2145, Open Connectivity Foundation - OCF) and Compliance Frameworks (ISO/IEC 27001, SOC 2).
• AWS-Specific PaaS Capabilities for IoT
  • AWS IoT Core (Secure MQTT/Bridge, Firmware Update Jobs, Device Defender).
  • AWS Lambda (Serverless Compute for Data Preprocessing and Action Triggering).
  • AWS Step Functions (Stateful Workflows for Complex Device Interactions).
  • Amazon DynamoDB (NoSQL Database for High-Speed IoT Data Ingestion).
  • Amazon OpenSearch Service (Search and Analytics, Time Series Data Management).
  • Amazon Timestream (Specialized Time-Series Database).
  • Amazon S3 (Raw Data Lake Storage).
  • AWS IoT Device Defender (Security Monitoring and Assessment).
  • AWS IoT Wireless (Connectivity for Remote LPWAN Devices).

Module 2: IoT Device Communication Protocols

• MQTT (MQTT v5 and WebSockets)
  • MQTT 5.0 Features (Retain Mechanism, Clean Session Flags, User Properties, Wildcard Topics).
  • MQTT over WebSockets (Standardization Protocols).
  • Quality of Service (QoS) Levels: Detailed Explanation.
  • Protocol Implementation Best Practices.
• Alternative Communication Protocols
  • CoAP (Constrained Application Protocol) for Resource-Constrained Devices.
  • AMQP and MQTT over AMQP (Standard Data Interchange Formats).
  • HTTP (Suitable for Simpler, Less Frequent Updates).
  • WebSockets (Full-Duplex Communication Capabilities).

Module 3: Constructing Robust IoT Applications with AWS

• Device Onboarding and Secure Connectivity
  • AWS IoT Device Defender Pre-Provisioning.
  • Secure Over-The-Air (OTA) Onboarding (e.g., Applying AWS IoT Button Concepts).
  • Device Certificate Management (ACM/PKI Integration).
  • Implementing MQTT with TLS Security.
• Data Ingestion, Storage, and Processing
  • Optimizing Data Transmission from Devices to AWS IoT Core.
  • Selecting Appropriate Targets: Lambda (Event-Driven), Step Functions (Orchestration), Timestream (Time-Series), OpenSearch (Search and Analytics), S3 (Raw Data).
  • Leveraging AWS IoT Analytics for Data Enrichment and Cleansing Prior to Storage.
  • Managing High-Throughput Scenarios (Kinesis/Firehose Integration).
• Device Management and Operational Oversight
  • Utilizing AWS IoT Device Management for Fleet Operations.
  • Implementing and Managing OTA Updates (via AWS IoT Jobs).
  • Remote Monitoring and Configuration Management.
• Constructing the IoT Backend Infrastructure
  • API Gateway for Creating REST/GraphQL APIs to Interact with Devices and Data.
  • AWS Lambda for Business Logic Execution.
  • AWS Step Functions for Coordinating Distributed Components.
  • Amazon SQS/SNS for Asynchronous Messaging and Event Triggering.

Module 4: Edge Computing and Advanced Integration

• AWS IoT Greengrass
  • Core Concepts (Core, Device, Connector).
  • Executing Lambda Functions Locally on Edge Devices.
  • Running Code Directly on Devices (C++, Python).
  • Secure Communication Between Greengrass Core and AWS/IoT Devices.
  • Application Scenarios: Local Data Filtering, Preprocessing, and AI Inference at the Edge.
• Integration with AI/ML Capabilities
  • Utilizing SageMaker for Complex ML Models in the Cloud.
  • Executing ML Inference on the Edge using Greengrass ML Accelerator (GMA).
• Data Visualization and User Interface Design
  • Leveraging AWS IoT SiteWise for Industrial Data Visualization.
  • Constructing Web Applications with AWS Amplify (API, UI, Authentication).
  • Dashboard Creation using Amazon QuickSight or OpenSearch Dashboards.

Module 5: Security, Governance, and Best Practices

• IoT Security Lifecycle Management
  • Secure Design Principles (Defense-in-Depth Strategies).
  • Secure Development Practices (OWASP IoT Top 10 Guidelines).
  • Vulnerability Management Protocols.
  • Threat Modeling Specific to IoT Environments.
• AWS Security Services for IoT
  • AWS IoT Device Defender (Service and Device-Level Protection).
  • AWS Shield, AWS Identity and Access Management (IAM).
  • AWS Config for Compliance Verification.
  • Hardware Security Module (HSM) Integration.
• Data Privacy and Governance
  • Handling Sensitive Data (PII Protection).
  • Data Retention and Deletion Policies.
  • Regulatory Compliance Considerations.

Module 6: Practical Projects and Capstone Exercise

• Guided Practical Labs
  • Device Onboarding and MQTT Communication Setup.
  • Implementing Secure Data Ingestion to AWS.
  • Constructing a Basic IoT Dashboard.
  • OTA Update Simulation.
  • Introduction to AWS IoT Greengrass Deployment.
• Capstone Project
  • Developing a Comprehensive IoT Solution Addressing Real-World Challenges (e.g., Smart Home Automation, Environmental Monitoring, Industrial Sensor Hub).
  • Requirements: Secure Device Implementation, Data Ingestion, Processing, Visualization, and Optional Edge Component.
  • Utilizing AWS Services Covered Throughout the Course.