Master Study AI

AI and IoT Integration

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Table of Contents

Course Modules:

Module 1: Introduction to AIoT (AI + IoT)

What is AIoT and why it matters

Key components of an AIoT system

Use cases in smart homes, cities, health, and industry

Module 2: IoT Devices and Sensor Technologies

Types of IoT sensors and actuators

Data collection and signal processing

Device communication protocols (MQTT, HTTP, CoAP)

Module 3: AI Fundamentals for IoT Applications

Supervised vs. unsupervised learning in IoT

Real-time vs. batch analytics

AI algorithms suited for IoT (SVM, KNN, anomaly detection)

 Module 4: Edge AI and On-Device Intelligence

What is edge computing?

Deploying AI models on microcontrollers and edge gateways

Tools: TensorFlow Lite, NVIDIA Jetson, Edge Impulse

Module 5: Cloud Platforms and Data Pipelines

IoT cloud architecture (AWS IoT, Azure IoT, GCP)

Stream processing with Apache Kafka and IoT Core

Building scalable pipelines for sensor data

Module 6: Predictive Maintenance and Anomaly Detection

Using ML to detect failures in machinery and systems

Time-series modeling for sensor data

Implementing threshold-based and AI-based alerts

Module 7: Smart Automation & Decision Making

Rule-based vs AI-based automation

Reinforcement learning for autonomous actions

Examples: Smart thermostats, irrigation systems, robotic arms

Module 8: Security & Privacy in AIoT Systems

Securing device communications

Data encryption, identity, and access control

AI for intrusion detection in connected environments

Module 9: Tools & Frameworks for AIoT Development

Arduino, Raspberry Pi, ESP32

Open-source platforms: Node-RED, ThingsBoard, TensorFlow Lite

End-to-end project building

Module 10: Capstone Project

Choose from:

Building a smart home AI system

Industrial predictive maintenance solution

Real-time object detection on an edge device

Deliver working prototype, documentation, and demo

 

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