IoT Module 2 PPT 

 IoT Module 2 Notes

Quiz 1 on Module1 of IoT 17CS81

 Before you start solving quiz, make sure that you have gone through the notes of module 1. You have prepared a scanned copy of case study on IoT. 

click the following link to solve quiz

  Link for quiz

 or copy and paste the URL in your browser

https://docs.google.com/forms/d/19G8RXQz7OdkDekFB6QUsqkRSGG5Wp4A_C3rwyt-iZA8/edit?usp=sharing

Case study: IoT impact

Prepare a Case study using Internet Source. The case study shall reflect how IoT has an Impact on the process/operational management and control. Keep it ready befor you attempt Quiz for IOT.

According to a Covid-19 impact in Indian IoT research by Goldstein Market Intelligence, the Indian IoT market is expected to reach USD 1 Trillion in the next 5-7 years, growing at the Compound Annual Growth Rate (CARG) of 17.1%.

With the help of IoT, India has been converting the nation into a digitized state-of-the-art nation, assisting it in improving efficiencies and economies of scale.

Organizations are taking on this investment of smart video surveillance systems to keep a constant check on their enterprise, even if it involves a high price.

Source: https://www.iotforall.com

Few area where they are being used successfully are mentioned below:

• Inventory management
• Tracking
• Internal and external security
• Efficient environment
• Monitor multiple branches and ATM units
• Crime deterrence
• Asset management
• Data analytic
• Remote Access Contro
  

 

 

IoT 17CS71 Module 1 Question Bank

Module 1

1. Define IOT. Explain evolution phase of IOT

2. Compare IT and OT ? How IoT can play vital role in bridging the gap.

3. How IoT can impact the society. Explain using a case study of India.

4. What aspects are important to build an IoT based network architecture.

5. Explain Architecture of IOT Network with its comparison.

6. Difference Between IT and IOT also list the challenges of IOT.

7. With neat diagram explain simplified IOT Architecture.

8. Explain Concept of IOT data management with diagram.

9. What is the role of things and Internet in IoT?

10. What is IoT? Explain components in IoT.

11. What are the differences between machines in M2M and Things in IoT?

12. Explain evolutionary phases of the internet.

13. Illustrate the challenges of IoT and their impact, with any example.

14. List and explain the challenges and problems that IoT is currently facing.

15. Explain oneM2M IoT Standardized Architecture with a neat diagram.

16. Explain the IoT architectural drivers.

17. With a neat diagram explain the IoTWF standardized architecture.

18. Explain in detail the expanded simplified IoT architecture.

19. Explain in detail the communication network layer. Illustrate the various access technologies with respect to distances.

20. Explain the 2 types of gateway and backhaul sublayers.

21. With a neat diagram explain the fog computing model along with its defining characteristics.

22. What is edge computing? Explain with example.

23. Illustrate the hierarchy of edge, fog and cloud with a neat diagram.

INTERNET OF THINGS TECHNOLOGY 17CS81 Module-1 Notes

 INTERNET OF THINGS TECHNOLOGY 17CS81 Module-1 Notes

 

ALGORITHMS AND DATABASE MANAGEMENT SYSTEMS LABORATORY    

Course Code:  20SCSL16

 

1.       Program to implement Ford-Fulkerson method

2.       Program to implement Naive algorithm.

3.       Program to implement Rabin - Karp algorithm.

4.       Program to implement Boyer – Moore algorithm.

5.       Program to implement Monte Carlo algorithm.

 

1.       Develop a database application to demonstrate storing and retrieving of BLOB and CLOB objects.

a. Write a binary large object (BLOB) to a database as either binary or character (CLOB) data, depending on the type of the field in your data source. To write a BLOB value to the database, issue the appropriate INSERT or UPDATE statement and pass the BLOB value as an input parameter. If your BLOB is stored as text, such as a SQL Server text field, pass the BLOB as a string parameter. If the BLOB is stored in binary format, such as a SQL Server image field, pass an array of type byte as a binary parameter.

 b. Once storing of BLOB and CLOB objects is done, retrieve them and display the results accordingly

program uses MySQL PHP. (installed through XAMPP)

Start Apache and Mysql using XAMPP control

store PHP files in XAMPP/htdocs

Store image file in htdoc folder

db.php

insert.php

listimage.php

Open php file in local browser

database output_images(

imageId

tinyint(3)

imageType

varchar(25)

imageData

mediumblob

 )

 2.       Develop a database application to demonstrate the representation of multi valued attributes, and the use of nested tables to represent complex objects. Write suitable queries to demonstrate their use. Consider Purchase Order Example: This example is based on a typical business activity: managing customer orders. Need to demonstrate how the application might evolve from relational to object-relational, and how you could write it from scratch using a pure object-oriented approach.

 a. Show how to implement the schema -- Implementing the Application under the Relational Model -- using only Oracle's built-in data types. Build an object-oriented application on top of this relational schema using object views

Solution using Oracle 11g Express edition 

 

 

3.        Design and develop a suitable Student Database application by considering appropriate attributes. Couple of attributes to be maintained is the Attendance of a student in each subject for which he/she has enrolled and Internal Assessment Using TRIGGERS, write active rules to do the following:

a.         Whenever the attendance is updated, check if the attendance is less than 85%; if so, notify   

the Head of the Department concerned.

b.        Whenever, the marks in an Internal Assessment Test are entered, check if the marks are

less than 40%; if so, notify the Head of the Department concerned.

 

  

INTERNET OF THINGS TECHNOLOGY

[As per Choice Based Credit System (CBCS) scheme]

(Effective from the academic year 2017 - 2018)

SEMESTER – VIII                                        IA Marks 40                                        Exam Marks 60

Subject Code  17CS81                                                                       Number of Lecture Hours/Week 04

Total Number of Lecture Hours 50                 Exam Hours 03                       CREDITS – 04

Module – 1                                                                                                                              10 Hours

What is IoT, Genesis of IoT, IoT and Digitization, IoT Impact, Convergence of IT and IoT, IoT Challenges, IoT Network Architecture and Design, Drivers Behind New Network Architectures, Comparing IoT Architectures, A Simplified IoT Architecture, The Core IoT Functional Stack, IoT Data Management and Compute Stack.

 

Module – 2                                                                                                                              10 Hours

Smart Objects: The “Things” in IoT, Sensors, Actuators, and Smart Objects, Sensor  Networks, Connecting Smart Objects, Communications Criteria, IoT Access Technologies.

 

Module – 3                                                                                                                              10 Hours

IP as the IoT Network Layer, The Business Case for IP, The need for Optimization, Optimizing IP for IoT, Profiles and Compliances, Application Protocols for IoT, The Transport Layer, IoT Application Transport Methods.

 

Module – 4                                                                                                                              10 Hours

Data and Analytics for IoT, An Introduction to Data Analytics for IoT, Machine Learning, Big Data Analytics Tools and Technology, Edge Streaming Analytics, Network Analytics, Securing IoT, A Brief History of OT Security, Common Challenges in OT Security, How IT and OT Security Practices and Systems Vary, Formal Risk  Analysis Structures: OCTAVE and FAIR, The Phased Application of Security in an Operational Environment

 

Module – 5

IoT Physical Devices and Endpoints - Arduino UNO: Introduction to Arduino, Arduino UNO, Installing the Software, Fundamentals of Arduino Programming.  IoT Physical Devices and Endpoints - RaspberryPi: Introduction to RaspberryPi, About the RaspberryPi Board: Hardware Layout, Operating Systems on RaspberryPi, Configuring RaspberryPi, Programming RaspberryPi with Python, Wireless Temperature Monitoring System Using Pi, DS18B20 Temperature Sensor, Connecting Raspberry Pi via SSH, Accessing Temperature from DS18B20 sensors, Remote access to RaspberryPi, Smart and Connected Cities, An IoT Strategy for Smarter Cities, Smart City IoT Architecture, Smart City Security Architecture, Smart City Use-Case Examples

 

Course Outcomes: After studying this course, students will be able to:

·          Interpret the impact and challenges posed by IoT networks leading to new architectural

  models.

·          Compare and contrast the deployment of smart objects and the technologies to connect them

  to network.

·          Appraise the role of IoT protocols for efficient network communication.

·          Elaborate the need for Data Analytics and Security in IoT.

·          Illustrate different sensor technologies for sensing real world entities and identify the

      applications of IoT in Industry.

Text Books:

 

1. David Hanes, Gonzalo Salgueiro, Patrick Grossetete, Robert Barton, Jerome Henry,"IoT Fundamentals: Networking Technologies, Protocols, and Use Cases for the Internet of Things”, 1 st Edition, Pearson Education (Cisco Press Indian Reprint). (ISBN: 978-9386873743)

2. Srinivasa K G, “Internet of Things”,CENGAGE Leaning India, 2017

Reference Books:

 

1. Vijay Madisetti and ArshdeepBahga, “Internet of Things (A Hands-on-Approach)”,  1 st      

     Edition, VPT, 2014. (ISBN: 978-8173719547) (good illustration with examples for practical IoT approach) 

2. Raj Kamal, “Internet of Things: Architecture and Design Principles”, 1 st Edition,McGraw

    Hill Education, 2017. (ISBN: 978-9352605224)

 AIML module 3 Planning PPT 

 PPT AIML Module II PPT Module 2 AIML

 PPT for AI & ML first module

* source PPT by Saroj Kaushik and few slides added by faculty

 Final  marks after 3 test and assignment evaluated for 10 marks

If you find any discrepancy contact dhananjay.maktedar@gmail.com

mbno 9448754514

check your marks do not edit 

 Group formation for assignment 

Link for group details

 

Module – 5 Evaluating Hypothesis: Motivation, Estimating hypothesis accuracy, Basics of sampling theorem, General approach for deriving confidence intervals, Difference in error of two hypothesis, Comparing learning algorithms. Instance Based Learning: Introduction, k-nearest neighbor learning, locally weighted regression, radial basis function, cased-based reasoning, Reinforcement Learning: Introduction, Learning Task, Q Learning

Text book 1, Sections: 5.1-5.6, 8.1-8.5, 13.1-13.3

Module 5 PPT

Module 5 Notes

Module 5 QB

 Neural Network Matlab examples for solving assignment

download copy and use for reference to solve problem in ML lab

https://drive.google.com/file/d/1aPfX8fOFc2ukn2NA0aNF5X3pWBaJsRuH/view?usp=sharing

 

Module – 4 Bayesian Learning: Introduction, Bayes theorem, Bayes theorem and concept learning, ML and LS error hypothesis, ML for predicting probabilities, MDL principle, Naive Bayes classifier, Bayesian belief networks, EM algorithm

Text book 1, Sections: 6.1 – 6.6, 6.9, 6.11, 6.12

 

Module 4 Notes

Module 4 PPT

Module 4 QB

Module 3

Module – 3 Artificial Neural Networks: Introduction, Neural Network representation, Appropriate problems, Perceptrons, Backpropagation algorithm.

Text book 1, Sections: 4.1 – 4.6

 

Module 3 Notes

Module 3 PPT

Module 3 QB

Module 2 Machine Learning

Module – 2 Decision Tree Learning: Decision tree representation, Appropriate problems for decision tree learning, Basic decision tree learning algorithm, hypothesis space search in decision tree learning, Inductive bias in decision tree learning, Issues in decision tree learning.

Text Book1, Sections: 3.1-3.7 

Module 2 Notes

Module 2 PPT

Module 2 Question Bank 

PPT for Module 1

 

Module – 1 Introduction: Well posed learning problems, Designing a Learning system, Perspective and Issues in Machine Learning. Concept Learning: Concept learning task, Concept learning as search, Find-S algorithm, Version space, Candidate Elimination algorithm, Inductive Bias.

Text Book1, Sections: 1.1 – 1.3, 2.1-2.5, 2.7 10 Hours 

Module 1 Notes

Module 1 PPT

Module Question Bank 

MACHINE LEARNING

[As per Choice Based Credit System (CBCS) scheme]

(Effective from the academic year 2017 - 2018)

SEMESTER – VII Subject Code 17CS73 IA Marks 40 Number of Lecture Hours/Week 03 Exam Marks 60 Total Number of Lecture Hours 50 Exam Hours 03 CREDITS – 04

Module – 1 Introduction: Well posed learning problems, Designing a Learning system, Perspective and Issues in Machine Learning. Concept Learning: Concept learning task, Concept learning as search, Find-S algorithm, Version space, Candidate Elimination algorithm, Inductive Bias.

Text Book1, Sections: 1.1 – 1.3, 2.1-2.5, 2.7 10 Hours

Module – 2 Decision Tree Learning: Decision tree representation, Appropriate problems for decision tree learning, Basic decision tree learning algorithm, hypothesis space search in decision tree learning, Inductive bias in decision tree learning, Issues in decision tree learning.

Text Book1, Sections: 3.1-3.7 10 Hours

Module – 3 Artificial Neural Networks: Introduction, Neural Network representation, Appropriate problems, Perceptrons, Backpropagation algorithm.

Text book 1, Sections: 4.1 – 4.6 08 Hours

Module – 4 Bayesian Learning: Introduction, Bayes theorem, Bayes theorem and concept learning, ML and LS error hypothesis, ML for predicting probabilities, MDL principle, Naive Bayes classifier, Bayesian belief networks, EM algorithm

Text book 1, Sections: 6.1 – 6.6, 6.9, 6.11, 6.12 10 Hours

Module – 5 Evaluating Hypothesis: Motivation, Estimating hypothesis accuracy, Basics of sampling theorem, General approach for deriving confidence intervals, Difference in error of two hypothesis, Comparing learning algorithms. Instance Based Learning: Introduction, k-nearest neighbor learning, locally weighted regression, radial basis function, cased-based reasoning, Reinforcement Learning: Introduction, Learning Task, Q Learning

Text book 1, Sections: 5.1-5.6, 8.1-8.5, 13.1-13.3 12 Hours

Text Books: 1.

Tom M. Mitchell, Machine Learning, India Edition 2013, McGraw Hill Education.

Reference Books: 1. Trevor Hastie, Robert Tibshirani, Jerome Friedman, h The Elements of Statistical Learning, 2nd edition, springer series in statistics.

2. Ethem Alpaydın, Introduction to machine learning, second edition, MIT press.

Pre Requisite for Machine Learning

Coding Capabilities: 

The ease of converting logical statements into code can go a long way while becoming an ML practitioner.

Most of the open-source libraries are available in Python and R(especially data science libraries).

A good knowledge of Python can accelerate the learning curve.

Some of the important Python packages are

1. Tensorflow (Parallel and Distributed Computation for Machine Learning and Deep Learning)

2. numpy (Efficient Matrices Computations)

3. OpenCV (Python’s Image Processing Toolbox)

4. R Studio

5. Pycharm

6. iPython/Jupyter Notebook

7. Julia

8. Spyder

9. Anaconda

10. Rodeo

11. Google –Colab

Online environment to learn python

1. FOSS IIT Bombay

2. Spoken – tutorials IIT Bombay

3. Open course of MIT on python ( Offered by Coursera MIT OCW) free

4. Python Documentation

Online environment for deep learning

1. Google colabs

2. AWS

3. IBM blue mix

4. Microsoft Azure

5. ML flow

1. OpenCV (Python’s Image Processing Toolbox)

2. Python

3. R

4. Matlab

5. Octave

6. Julia

7. C++

8. C

Algorithms and Data Structures:

Data structures are efficient data models and are designed for efficiency in terms of memory and time consumed. Knowing how to handle data can fasten the processing. It also helps design better and faster algorithms, be it for pre-processing of data or designing the algorithm itself.

Calculus:

The heart of neural networks is the back propagation algorithm, based totally on differentiation. Hence we recommend a basic outline of calculus would help in the understanding of the training process.

Linear Algebra:

Gilbert Strang teaches the subject in the most fluidic manner and thus we strongly suggest his course on MIT OCW, available on YouTube. Linear Algebra is important because the data we deal with is multi-dimensional. For example, when we try to predict the price of a house, the various dimensions are location, area, facilities available, etc. Matrices are the most ideal way to deal with higher dimensions.

Statistics:

The basic understanding of mean, median, and mode of various probability distributions, especially the gaussian distribution is useful, as most of the data found in the real world can be modelled via these probability distributions and thus simplify the data to a fewer number of parameters.

Vector Algebra