AP4092 Edge Analytics and Internet of Things Syllabus:
AP4092 Edge Analytics and Internet of Things Syllabus – Anna University PG Syllabus Regulation 2021
COURSE OBJECTIVES:
To Understand the basis for intersection of IOT and Edge Analytics
To Understand the IOT protocols and standards
To comprehend the use of Machine Learning in Edge Analytics
To gain understanding on the use of Deep Learning techniques for analytics
To gain insight into edge analytics models and deployment
UNIT I INTRODUCTION TO IOT
Importance and Need for IoT – Application and Use cases of IoT – Overview of Industrial IoT – Intersection of IoT and Edge Analytics.
UNIT II IOT PROTOCOLS AND SYSTEMS
IoT protocols and standards – Cloud IoT Infrastructure – Setup and program IoT device- Data Collection from IoT device.
UNIT III MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE
Introduction to Machine Learning and Artificial Intelligence – Overview of Deep Learning and Neural Networks- Introduction to Convolution Neural Networks.
UNIT IV AUTO ENCODERS AND ITS PROGRAMMING
Introduction to Recurrent Neural Networks- Introduction to Auto Encoders Programming Practice: Build Image Classifier, Build Anomaly Detector
UNIT V EDGE ANALYTICS
Challenges with Edge Devices and Deployment – Need for Model Quantization Quantization Aware Training- Post Model Quantization- Programming Practice: Model quantization, Deploying model on Edge Devices
TOTAL: 45 PERIODS
COURSE OUTCOMES:
Upon completion of this course, student will be able to
CO 1: Use the foundational concepts in Edge Analytics for application design and development
CO 2: Use IOT protocols in cloud environments.
CO 3: Implement and use Machine Learning and Artificial Intelligence algorithms and tools
CO 4: implement and use Deep Learning techniques for applications
CO 5: Analyze Edge devices analytics models and and its challenges
REFERENCES:
1. Honbo Zhou, “The Internet of Things in the Cloud: A Middleware Perspective”, CRC Press, 2012.
2. P. Flach, “Machine learning: The art and science of algorithms that make sense of data‖, Cambridge University Press, 2012.
3. Anirudh Koul, Siddha Ganju, Meher Kasam, “Practical Deep Learning for Cloud, Mobile, and Edge” O’Reilly Media, 2019.
4. Dieter Uckelmann, Mark Harrison, Florian Michahelles, “Architecting the Internet of Things”, Springer, 2011.