AI for Internet of Things (IoT)

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.

• 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.

1. 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
2. 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
3. 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
4. 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
5. 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
6. 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
7. Computer Vision in IoT
   • Image and Video Processing for IoT Devices
   • Real-Time Object Detection and Tracking
   • Use Cases: Smart Surveillance, Autonomous Vehicles
8. 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
9. 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
10. 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
11. 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