Industrial Training IoT (Internet of Things) with Raspberry PI and AWS IoT Core 「8 Hours Remote」 Training Course

Module 0: Foundations & AWS IoT Ecosystem

• Introduction to IoT
  • Defining IoT in 2024: Moving beyond connected devices to include edge intelligence, AI/ML at the edge, and cyber-physical systems.
  • Drivers of IoT Adoption: Industry applications and specific use cases.
  • Emerging Trends: Edge computing, sustainability initiatives, AI/ML integration, and enhanced security protocols.
  • AWS IoT Integration: Leveraging the AWS Partner Network (APN) and broader AWS ecosystem resources.
• AWS IoT Service Landscape Overview
  • AWS IoT Core: MQTT broker, Jobs, and Device Defender services.
  • AWS IoT Device Management: Streamlined device onboarding, configuration management, and over-the-air (OTA) updates.
  • AWS IoT Analytics: Comprehensive data processing, enrichment, and modeling capabilities.
  • AWS IoT Greengrass: Edge computing, local execution environments, and secure connectivity.
  • AWS IoT Button: Conceptual overview for basic device interactions.
  • Integration Pathways: AWS IoT Core connections to Lambda, DynamoDB, OpenSearch, Step Functions, and SageMaker.

Module 1: IoT Architecture, Components & Security

• IoT Architecture
  • Device Layer: Components including sensors, actuators, and edge devices such as RP2013, Raspberry Pi, and ESP32.
  • Connectivity Layer: Protocols and technologies including MQTT, CoAP, HTTP, LPWAN (LoRaWAN, NB-IoT, Sigfox), and Cellular IoT.
  • Cloud Integration Layer: AWS IoT Core, API Gateway, Lambda, and Step Functions.
  • Data Processing & Analytics Layer: DynamoDB, Timestream, OpenSearch, S3, Athena, and SageMaker.
  • Application Layer: Mobile and web applications utilizing AWS Amplify and custom business solutions.
  • Architectural Rationale: Justification for distributed architectures based on latency, bandwidth, compute resources, and security requirements.
• Essential IoT Components Deep Dive
  • Hardware: Selection criteria for MCUs, connectivity modules, and sensors; integration of security elements such as Trusted Execution Environments (TEEs).
  • Edge Computing (AWS Greengrass): Advantages including low latency, reduced cloud dependency, and localized decision-making.
  • Device Management: Processes for onboarding (OTA, pre-provisioning), configuration, monitoring, and remote debugging.
  • Security Framework: Device identity management, authentication and authorization via X.509 certificates and JWTs, data encryption (at rest and in transit), and AWS IoT Device Defender.
  • Security Standards: Overview of industry standards (IEEE P2145, OCF) and compliance frameworks (ISO/IEC 27001, SOC 2).
• AWS-Specific PaaS Functions for IoT
  • AWS IoT Core: Secure MQTT broker, Jobs for firmware management, and Device Defender.
  • AWS Lambda: Serverless compute for data preprocessing and action triggering.
  • AWS Step Functions: Stateful workflow orchestration for complex device interactions.
  • Amazon DynamoDB: NoSQL database optimized for high-throughput IoT data ingestion.
  • Amazon OpenSearch Service: Search and analytics capabilities for time-series data.
  • Amazon Timestream: Specialized time-series database solution.
  • Amazon S3: Scalable storage for raw data lakes.
  • AWS IoT Device Defender: Continuous monitoring and security assessment.
  • AWS IoT Wireless: Connectivity solutions for remote LPWAN devices.

Module 2: IoT Device Communication Protocols

• MQTT (MQTT v5 & WebSockets)
  • MQTT 5.0 Features: Retain flags, clean session settings, user properties, and wildcard topics.
  • MQTT over WebSockets: Standardization of real-time communication.
  • Quality of Service (QoS): Explanation of delivery guarantees.
  • Protocol Best Practices: Guidelines for efficient implementation.
• Alternative Protocols
  • CoAP: Constrained Application Protocol for resource-constrained devices.
  • AMQP / MQTT over AMQP: Standardized data interchange formats.
  • HTTP: Suitable for simpler, infrequent data updates.
  • WebSockets: Enabling full-duplex communication channels.

Module 3: Building Robust IoT Applications with AWS

• Device Onboarding & Secure Connectivity
  • AWS IoT Device Defender Pre-Provisioning strategies.
  • Secure Over-The-Air (OTA) Onboarding processes.
  • Device Certificate Management via ACM/PKI.
  • Implementation of MQTT with TLS encryption.
• Data Ingestion, Storage & Processing
  • Efficient data transmission from devices to AWS IoT Core.
  • Service Selection: Lambda (event-driven), Step Functions (orchestration), Timestream (time-series), OpenSearch (analytics), S3 (storage).
  • Data enrichment and cleansing using AWS IoT Analytics.
  • Strategies for high-throughput scenarios using Kinesis and Firehose.
• Device Management & Operations
  • Fleet management via AWS IoT Device Management.
  • OTA Update implementation and management using AWS IoT Jobs.
  • Remote monitoring and configuration capabilities.
• Building the IoT Backend
  • API Gateway for REST/GraphQL APIs to interface with devices and data.
  • AWS Lambda for business logic execution.
  • AWS Step Functions for coordinating distributed components.
  • AWS SQS/SNS for asynchronous messaging and event-driven triggers.

Module 4: Edge Computing & Advanced Integration

• AWS IoT Greengrass
  • Core Concepts: Core, Device, and Connector architectures.
  • Local Execution: Running Lambda functions and code (C++, Python) directly on devices.
  • Secure Communication: Establishing trusted links between Greengrass Cores, AWS, and IoT devices.
  • Use Cases: Local data filtering, preprocessing, and AI inference at the edge.
• Integration with AI/ML
  • Complex model training and deployment using SageMaker.
  • Edge ML inference execution via Greengrass ML Accelerator (GMA).
• Data Visualization & User Interfaces
  • Industrial data visualization using AWS IoT SiteWise.
  • Web application development with AWS Amplify (API, UI, Authentication).
  • Dashboard creation using Amazon QuickSight or OpenSearch Dashboards.

Module 5: Security, Governance & Best Practices

• IoT Security Lifecycle
  • Secure Design Principles: Implementation of defense-in-depth strategies.
  • Secure Development Practices: Adherence to OWASP IoT Top 10 guidelines.
  • Vulnerability Management: Continuous identification and remediation.
  • Threat Modeling: Proactive analysis of potential IoT risks.
• AWS Security Services for IoT
  • AWS IoT Device Defender: Service and device-level monitoring.
  • AWS Shield and IAM: DDoS protection and identity management.
  • AWS Config: Automated compliance checks.
  • Hardware Security Modules (HSMs): Secure key storage and management.
• Data Privacy & Governance
  • Handling sensitive data, including Personally Identifiable Information (PII).
  • Data retention and deletion policies.
  • Compliance requirements and regulatory considerations.

Module 6: Hands-on Projects & Capstone

• Guided Hands-on Labs
  • Device onboarding and MQTT communication setup.
  • Secure data ingestion implementation into AWS.
  • Construction of a basic IoT dashboard.
  • OTA update simulation exercises.
  • Introduction to AWS IoT Greengrass deployment.
• Capstone Project
  • Development of a comprehensive IoT solution addressing a real-world requirement (e.g., Smart Home Automation, Environmental Monitoring, Industrial Sensor Hub).
  • Requirements: Secure device integration, data ingestion, processing, visualization, and optional edge components.
  • Utilization of AWS services covered throughout the course curriculum.