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Course Outline
Module 0: Foundations & AWS IoT Ecosystem
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Introduction to IoT
- Defining IoT in 2024: Beyond "Things" (Edge Intelligence, AI/ML at the Edge, Cyber-Physical Systems).
- Drivers of IoT Growth (Industries, Use Cases).
- Key IoT Trends (Edge Computing, Sustainability, AI/ML integration, Enhanced Security).
- AWS IoT within the broader AWS ecosystem (AWS Partner Network - APN resources).
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AWS IoT Service Landscape Overview
- AWS IoT Core (MQTT/Bridge, Jobs, Device Defender).
- AWS IoT Device Management (Device Onboarding, Configuration Management, OTA Updates).
- AWS IoT Analytics (Data processing, enrichment, modeling).
- AWS IoT Greengrass (Edge compute, local execution, secure connectivity).
- AWS IoT Button (Conceptual overview for simple devices).
- Connection: AWS IoT Core -> Lambda/DynamoDB/OpenSearch/Step Functions/SageMaker.
Module 1: IoT Architecture, Components & Security
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IoT Architecture
- Device Layer (Sensors, Actuators, Edge Devices like RP2013/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, Web Apps using AWS Amplify, Custom Buisness Apps).
- Importance: Explain the why behind distributed architectures (latency, bandwidth, compute power, security).
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Essential IoT Components Deep Dive
- Hardware: Selection criteria (MCU, connectivity, sensors), Security elements (Trusted Execution Environments - TEEs).
- Edge Computing (AWS Greengrass): Benefits (low latency, reduced cloud traffic, local decision making).
- Device Management: Onboarding (Over-the-Air - OTA, Pre-provisioning), Configuration, Monitoring, Remote Debugging.
- Security Deep Dive: Device Identity, Authentication & Authorization (X.509 Certs, JSON Web Tokens - JWTs), Data Encryption (at rest and in transit), AWS IoT Device Defender (Service and Device Defender).
- Security Standardization: Introduction to standards (e.g., IEEE P2145, Open Connectivity Foundation - OCF) and compliance (ISO/IEC 27001, SOC 2).
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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, triggering actions).
- AWS Step Functions (Stateful workflows for complex device interactions).
- Amazon DynamoDB (NoSQL DB for fast 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
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MQTT (MQTT v5 & WebSockets)
- MQTT 5.0 Features (Retain, Clean Session flags, User Properties, Wildcard topics).
- MQTT over WebSockets (Standardization).
- Quality of Service (QoS) Levels explained.
- Protocol Best Practices.
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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: Building Robust IoT Applications with AWS
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Device Onboarding & Secure Connectivity
- AWS IoT Device Defender Pre-Provisioning.
- Secure Over-The-Air (OTA) Onboarding (e.g., using AWS IoT Button concepts).
- Managing Device Certificates (ACM/PKI).
- Implementing MQTT with TLS.
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Data Ingestion, Storage & Processing
- Efficiently sending data from devices to AWS IoT Core.
- Choosing the right target: Lambda (event-driven), Step Functions (orchestration), Timestream (time-series), OpenSearch (search & analytics), S3 (raw data).
- Using AWS IoT Analytics for data enrichment and cleansing before storage.
- Handling high-throughput scenarios (Kinesis/ Firehose).
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Device Management & Operations
- Using AWS IoT Device Management for fleet management.
- Implementing and managing OTA Updates (using AWS IoT Jobs).
- Remote Monitoring and Configuration.
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Building the IoT Backend
- API Gateway for creating REST/GraphQL APIs to interact with devices and data.
- AWS Lambda for business logic.
- AWS Step Functions for coordinating distributed components.
- Amazon SQS/SNS for asynchronous messaging and event triggering.
Module 4: Edge Computing & Advanced Integration
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AWS IoT Greengrass
- Concepts (Core, Device, Connector).
- Running Lambda functions locally on the device.
- Executing code directly on the device (C++, Python).
- Secure communication between Greengrass Core and AWS/IoT devices.
- Use Case: Local data filtering, preprocessing, or AI inference at the edge.
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Integration with AI/ML
- Using SageMaker for complex ML models in the cloud.
- Running ML inference on the edge with Greengrass ML Accelerator (GMA).
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Data Visualization & User Interfaces
- Using AWS IoT SiteWise for industrial data visualization.
- Building Web Apps with AWS Amplify (API, UI, Authentication).
- Dashboards using Amazon QuickSight or OpenSearch Dashboards.
Module 5: Security, Governance & Best Practices
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IoT Security Lifecycle
- Secure Design Principles (Defense-in-Depth).
- Secure Development Practices (OWASP IoT Top 10).
- Vulnerability Management.
- Threat Modeling for IoT.
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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.
- Hardware Security Modules (HSMs) integration.
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Data Privacy & Governance
- Handling sensitive data (PII).
- Data Retention and Deletion policies.
- Compliance considerations.
Module 6: Hands-on Projects & Capstone
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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.
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Capstone Project
- Build 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.
- Use AWS services covered throughout the course.
Requirements
Purpose:
Currently any new IoT development must be done on PaaS (Platform as a service) IoT infrastructure. Leading PaaS IoT systems include, Microsoft Azure, AWS IoT (Amazon), Google IoT cloud and Siemens Mindsphere etc. It’s also important for the developers to know associated PaaS functions necessary to connect IoT data to other ecosystem. In this course a customer will be trained hands on with a Raspberry Pi, a multi-sensor TI sensor Tag chip (which has 10 sensors built in – motion, ambient temperature, humidity, pressure, light meter etc.). A trainee will learn basics of all IoT functions and how to implement them in AWS IoT PaaS cloud using Lambda functions.
8 Hours
