AI for Internet of Things (IoT)

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Mentor Based

Program Syllabus

Module 1

About This Course

The program explores the application of AI in IoT systems, covering AI-driven data analytics, predictive maintenance, smart devices, and automation. Participants will learn how AI can process vast amounts of sensor data, making IoT systems more efficient, scalable, and intelligent.

Module 2

Aim

This program focuses on how AI enhances IoT systems by enabling smart decision-making and automating processes in real-time. It emphasizes the integration of AI in IoT for optimizing operations, improving data analytics, and supporting intelligent systems.

Module 3

Program Objectives

Learn to integrate AI into IoT systems for real-time decision-making.
Implement AI-driven data analytics for processing sensor data.
Gain proficiency in predictive maintenance with AI-powered IoT solutions.
Understand the challenges of security and privacy in AI-enabled IoT.
Build and deploy an AI-driven IoT system.

Module 4

Program Structure

Introduction to IoT and AI
- Overview of IoT: Concepts and Applications
- Key Components of IoT Systems (Sensors, Actuators, Connectivity)
- Role of AI in Enhancing IoT Systems
IoT Architectures and Frameworks
- Layered IoT Architecture (Perception, Network, and Application Layers)
- IoT Communication Protocols (MQTT, CoAP, HTTP)
- Edge, Fog, and Cloud Computing in IoT
AI Fundamentals for IoT Systems
- Overview of Machine Learning and Deep Learning in IoT
- AI for Real-Time Data Processing in IoT
- Lightweight AI Models for Low-Power IoT Devices
Data Collection and Management in IoT
- IoT Data Streams and Sensor Data Processing
- Data Preprocessing for AI (Filtering, Normalization)
- Real-Time Data Ingestion with Apache Kafka and MQTT
Edge AI for IoT
- Deploying AI Models on Edge Devices (Raspberry Pi, NVIDIA Jetson)
- Edge Computing vs. Cloud Computing for IoT AI Applications
- Real-Time Decision Making at the Edge
AI-Driven Predictive Maintenance in IoT
- Using AI for Predictive Maintenance in Industrial IoT (IIoT)
- Time-Series Analysis and Anomaly Detection
- AI Techniques for Fault Detection and Diagnosis
Computer Vision in IoT
- Image and Video Processing for IoT Devices
- Real-Time Object Detection and Tracking
- Use Cases: Smart Surveillance, Autonomous Vehicles
Natural Language Processing (NLP) in IoT
- Voice Assistants and Speech Recognition in Smart Devices
- NLP for Home Automation and Wearables
- Sentiment Analysis and Voice Control in IoT Systems
AI for IoT Security and Privacy
- AI for Intrusion Detection and Network Security
- Privacy Concerns in IoT Data Collection and Processing
- Federated Learning and Privacy-Preserving AI
Energy Efficiency in AI-Driven IoT
- Power Management in IoT Systems
- Optimizing AI Inference for Low-Energy Devices
- AI Techniques for Extending Battery Life in IoT Devices
Real-Time Analytics and Decision Making in IoT
- AI for Real-Time Monitoring and Control
- Stream Processing with AI in IoT Systems
- Real-Time AI Applications: Smart Cities, Healthcare, Smart Grids

Lead Instructor

Dr. Sarah Chen

PhD in Computational Mechanics from MIT with 15+ years of experience in Industrial AI. Former Lead Data Scientist at Tesla and current advisor to Fortune 500 manufacturing firms.