Aim

This program focuses on how AI enhances IoT systems by enabling real-time smart decision-making and automating processes. Participants will learn to integrate AI with IoT to optimize operations, improve data analytics, and build intelligent systems that support industries like smart cities, healthcare, and manufacturing.

Program Objectives

Integrate AI into IoT Systems: Learn to combine AI with IoT for real-time decision-making.
AI-Driven Data Analytics: Master AI techniques for analyzing and processing sensor data.
Predictive Maintenance: Gain proficiency in developing AI-powered predictive maintenance systems.
Address Security and Privacy: Understand the challenges and solutions for security and privacy in AI-enabled IoT.
Build an AI-Driven IoT System: Develop and deploy an AI-based IoT solution.

Program Structure

Module 1: Introduction to IoT and AI

Overview of IoT concepts and real-world applications.
Key components of IoT: Sensors, actuators, connectivity.
Role of AI in enhancing IoT systems for smart decision-making.

Module 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 for data processing.

Module 3: AI Fundamentals for IoT Systems

Overview of machine learning and deep learning in IoT.
AI for real-time data processing.
Lightweight AI models for low-power IoT devices.

Module 4: Data Collection and Management in IoT

Processing IoT data streams and sensor data.
Data preprocessing for AI: Filtering, normalization, and feature engineering.
Real-time data ingestion using Apache Kafka and MQTT.

Module 5: Edge AI for IoT

Deploying AI models on edge devices like Raspberry Pi and NVIDIA Jetson.
Comparing edge vs. cloud computing for IoT AI applications.
Real-time decision-making and processing at the edge.

Module 6: AI-Driven Predictive Maintenance in IoT

AI for predictive maintenance in industrial IoT (IIoT).
Time-series analysis and anomaly detection techniques.
Fault detection and diagnosis using AI.

Module 7: Computer Vision in IoT

Image and video processing for IoT devices.
Real-time object detection and tracking using AI.
Use cases: Smart surveillance, autonomous vehicles, quality control.

Module 8: Natural Language Processing (NLP) in IoT

AI-powered voice assistants and speech recognition for smart devices.
NLP applications in home automation, wearables, and sentiment analysis.

Module 9: AI for IoT Security and Privacy

AI for intrusion detection and securing IoT networks.
Privacy concerns in IoT data collection and processing.
Federated learning and privacy-preserving AI for IoT.

Module 10: Energy Efficiency in AI-Driven IoT

Managing power consumption in IoT systems.
Optimizing AI inference for low-energy IoT devices.
AI techniques to extend battery life in IoT devices.

Module 11: Real-Time Analytics and Decision Making in IoT

AI for real-time monitoring, control, and decision-making.
Stream processing with AI in IoT systems.
Real-time AI applications: Smart cities, healthcare, smart grids.

Final Project

Develop and deploy an AI-driven IoT system for a specific application.
Example: Build a predictive maintenance solution for industrial equipment or an AI-powered smart home automation system.

Participant Eligibility

IoT Engineers: Interested in adding AI capabilities to IoT systems.
Data Scientists: Focused on analyzing IoT data streams with AI techniques.
AI Engineers: Looking to apply AI in IoT applications.
Embedded Systems Developers: Working on smart, connected devices that leverage AI.

Program Outcomes

AI-Enhanced IoT Systems: Master integrating AI with IoT to create intelligent, real-time systems.
AI-Driven Automation: Learn to build IoT solutions for predictive maintenance and automation.
Real-Time Data Analytics: Gain hands-on experience with AI models for real-time IoT data processing.
Security and Privacy: Understand how to manage security and privacy in AI-powered IoT environments.

Program Deliverables

Access to e-LMS: Full access to online learning materials and resources.
Real-Time Projects: Develop and implement an AI-powered IoT project.
Project Guidance: Mentorship to help you through the development process.
Research Paper Opportunity: Support for publishing your work in the AI-IoT domain.
Final Examination: Certification based on assignments, project submission, and exams.
e-Certification: Provided upon successful course completion.

Future Career Prospects

IoT AI Engineer: Design and develop IoT systems enhanced by AI.
Embedded AI Developer: Implement AI on embedded systems and edge devices.
AI-Powered IoT Specialist: Focus on smart decision-making and automation with IoT data.
Predictive Maintenance Engineer: Build AI-based predictive maintenance solutions for industrial IoT.
Smart City Solution Architect: Develop AI-driven IoT solutions for urban planning and smart cities.
Edge AI Developer: Create AI solutions optimized for low-latency, low-power edge computing.

Job Opportunities

AI-Powered IoT Product Development: Focus on sectors like smart homes, smart cities, healthcare, and manufacturing.
IoT-Based Automation: Companies leveraging AI for IoT-driven automation and predictive maintenance.
Edge Computing Startups: Innovating with AI-enhanced IoT systems for real-time processing.

MODE	Online/ e-LMS
TYPE	Self Paced
LEVEL	Moderate
DURATION	3 Weeks

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AI for Internet of Things (IoT) Course – 3 Weeks

Aim

Program Objectives