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Course Detail

Course Name Artificial Intelligence for IoT
Course Code 24AI746
Program M. Tech. in Artificial Intelligence
Credits 3
Campus Amritapuri ,Coimbatore

Syllabus

Introduction to IoT, Architectural Overview and Design Principles, Elements of IoT (Arduino, Rasp- berry Pi, NodeMCU, Sensors & Actuators), IoT Applications, Sensing, Actuation, Networking Basics, Embedded OS, IoT and Cloud, Security aspects in IoT.

IoT Application Development, Introduction to Raspberry Pi, Integrating Sensors and Actuators with Raspberry Pi, Pushing and Managing Data in IoT Clouds, Programming APIs (Python/Node.js/Arduino) for communication protocols (MQTT, ZigBee, Bluetooth, UDP, TCP), Implementation of IoT with Raspberry Pi (lab – sensor, MQTT, visualization)

 

Introduction to ML and Deep learning models for IoT (challenges, opportunities, solutions), Sensor data classification using ML in Raspberry Pi (lab), Introduction to TensorFlowLite, Image classification on Raspberry Pi (lab), building scalable ML pipeline using Flask, Python, uWSGI, TensorFlow

Objectives and Outcomes

Preamble

This course introduces the architectural overview and design principles of IoT, how to develop a machine learning application using Raspberry Pi and building Machine learning models for edge devices using Raspberry Pi. Deep learning models using TensorFlowLite is also discussed in this course.

 

Course Objectives

  • Understand the general concepts in IoT and get familiar with the various hardware and software components of it
  • Understand how to build real-life IoT based projects for different application domains
  • Hands-on training to implement IoT with Raspberry Pi

 

Course Outcomes

 

COs

Description

CO1

Understand the architecture, the design principles and elements of IoT.

CO2

Gain the necessary skills needed to build Machine learning models for edge devices

CO3

Be able to design, deploy and evaluate scalable real-life IoT systems for different

application domains

CO4

Understand and build scalable ML pipeline using Flask, Python, uWSGI, TensorFlow

 

Prerequisites

  • Basic knowledge on Python Programming
  • Basic knowledge on Machine Learning

CO-PO Mapping

 

COs

Description

PO1

PO2

PO3

PO4

PO5

CO1

Understand the architecture, the design principles and elements of IoT.

1

2

3

1

1

CO2

Gain the necessary skills needed to build Machine learning models for edge devices

3

2

2

–

1

CO3

Be able to design, deploy and evaluate scalable real-life IoT systems for different

application domains

3

3

3

1

3

CO4

Understand and build scalable ML pipeline using Flask, Python, uWSGI, TensorFlow

1

3

3

1

3

Evaluation Pattern

Evaluation Pattern – 70:30

 

  • Midterm Exam – 20%
  • Lab Assignments – 25%
  • Project – 25%
  • End Semester Exam – 30%

Text Books / References

Text Book / References

  1. Vijay Madisetti, Arshdeep Bahga, ¨Internet of Things, “A Hands on Approach”, University Press
  2. Raj Kamal, “Internet of Things: Architecture and Design”, McGraw Hill
  3. Stuart Russell and Peter Norvig, “Artificial Intelligence: A Modern Approach”, 3rd Edition, Prentice Hall
  4. Sudip Misra, Anandarup Mukherjee, Arijit Roy, “Introduction to IoT”, Cambridge University Press, 2020
  5. Elaine Rich and Kevin Knight, “Artificial Intelligence”, Tata McGraw Hill
  6. Amita Kapoor, Hands-On Artificial Intelligence for IoT, 2019, Packt Publishing
  7. https://www.tensorflow.org/lite/tutorials

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