AED
UAD Dirham
Code: AED
Mentor Based
AI for Internet of Things (IoT)
Revolutionize IoT with AI: Smarter, Faster, and More Efficient Connected Systems
Early access to the e-LMS platform is included
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.
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.
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.
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
Who Should Enrol?
IoT engineers, data scientists, AI engineers, and embedded systems developers focusing on IoT solutions.
Program Outcomes
- Master the integration of AI into IoT systems for smarter decision-making.
- Proficiency in building AI-driven automation and predictive maintenance systems.
- Hands-on experience in deploying AI models for real-time IoT data analytics.
- Knowledge of security and privacy challenges in AI-enhanced IoT systems.
Fee Structure
Discounted: ₹8499 | $112
We accept 20+ global currencies. View list →
What You’ll Gain
- Full access to e-LMS
- Real-world dry lab projects
- One-on-one project guidance
- Publication opportunity
- Self-assessment & final exam
- e-Certificate & e-Marksheet
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