KPR INSTITUTE OF ENGINEERING AND TECHNOLOGY M.E. COMPUTER …

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KPR INSTITUTE OF ENGINEERING AND TECHNOLOGY

(Autonomous Institution)

M.E. COMPUTER SCIENCE AND ENGINEERING

REGULATIONS – 2019

CHOICE BASED CREDIT SYSTEM

DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING

1. Vision and Mission of the Institution.

Vision of the Institution:

To become a premier institute of academic excellence by imparting technical,

intellectual, and professional skills to students for meeting the diverse needs of the

industry, society, the nation and the world at large.

Mission of the Institution:

1. Commitment to offer value based education and enhancement of practical skills.

2. Continuous assessment of teaching and learning process through scholarly activities.

3. Enriching research and innovative activities in collaboration with industry and institute

of repute.

4. Ensuring the academic process to uphold culture, ethics and social responsibility.

2. Vision and Mission of the Department.

Vision of the Department:

To foster the students by providing learner centric teaching environment, continuous

learning, research and development to become thriving professionals and entrepreneurs to

excel in the field of computer science and contribute to the society.

Mission of the Department:

1. Providing value based education and contented learning experience to the students.

2. Educating the students with the state of art technologies and cultivating their proficiency in

analytical and designing skills.

3. Enabling the students to achieve a successful career in Computer Science and Engineering

or related fields to meet the changing needs of various stakeholders.

4. Guiding the students in research by nurturing their interest in continuous learning towards

serving the society and the country.

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POs:

Engineering Graduates will be able to:

1. Engineering Knowledge: Apply the knowledge of mathematics, science, engineering

Fundamentals and an engineering specialization to the solution of complex engineering

problems.

2. Problem Analysis: Identify, formulate, review research literature, and analyze complex

engineering problems reaching substantiated conclusions using first principles of mathematics,

natural sciences, and engineering sciences.

3. Design/development of solutions: Design solutions for complex engineering problems and

design system components or processes that meet the specified needs with appropriate

consideration for the public health and safety, and the cultural, societal, and environmental

considerations.

4. Conduct investigations of complex problems: Use research-based knowledge and research

methods including design of experiments, analysis and interpretation of data, and synthesis of

the information to provide valid conclusions.

5. Modern tool usage: Create, select, and apply appropriate techniques, resources, and modern

engineering and IT tools including prediction and modeling to complex engineering activities

with an understanding of the limitations.

6. The engineer and society: Apply reasoning informed by the contextual knowledge to assess

societal, health, safety, legal and cultural issues and the consequent responsibilities relevant to

the professional engineering practice.

7. Environment and sustainability: Understand the impact of the professional engineering

solutions in societal and environmental contexts, and demonstrate the knowledge of, and need

for sustainable development.

8. Ethics: Apply ethical principles and commit to professional ethics and responsibilities and

norms of the engineering practice.

9. Individual and team work: Function effectively as an individual, and as a member or leader

in diverse teams, and in multidisciplinary settings.

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10. Communication: Communicate effectively on complex engineering activities with the

engineering community and with society at large, such as, being able to comprehend and write

effective reports and design documentation, make effective presentations, and give and receive

clear instructions.

11. Project management and finance: Demonstrate knowledge and understanding of the

engineering and management principles and apply these to one’s own work, as a member and

leader in a team, to manage projects and in multidisciplinary environments.

12. Life-long learning: Recognize the need for, and have the preparation and ability to engage

in independent and life-long learning in the broadest context of technological change.

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KPR INSTITUTE OF ENGINEERING AND TECHNOLOGY

(Autonomous Institution)

M.E. COMPUTER SCIENCE AND ENGINEERING

REGULATIONS – 2019

CHOICE BASED CREDIT SYSTEM

CURRICULA AND SYLLABI

COURSES OF STUDY

(For the candidates admitted from 2019-20 onwards)

SEMESTER I

S.NO COURSE

CODE COURSE TITLE CATEGORY L T P C

THEORY

1 P19MA102 Probability, Statistics and Graph

Theory FC 3 1 0 4

2 P19CS101 Modern Operating Systems PC 3 0 0 3

3 P19CS102 Agile Software Development and

Usability Engineering PC 3 0 0 3

4 P19CS103 Machine Learning PC 3 0 0 3

5 - Professional Elective I PE 3 0 0 3

6 P19CS104 Research Methodology FC 3 0 0 3

PRACTICAL(S)

7 P19CS105 Design and Analysis of Algorithms

Lab PC 0 0 2 2

TOTAL 18 1 2 21

SEMESTER II

S.NO COURSE


THEORY

1 P19CS201 Protocol Design and Verification PC 3 0 0 3

2 P19CS202 Internet of Things PC 3 0 0 3

3 P19CS203 Big Data Analytics PC 3 0 0 3

4 - Professional Elective II PE 3 0 0 3

5 - Professional Elective III PE 3 0 0 3

6 - Professional Elective IV PE 3 0 0 3

PRACTICAL(S)

7 P19CS204 Data Analytics Laboratory PC 0 0 2 2

8 P19CS205 Mini Project EEC 0 0 2 2

9 P19CS206 Internship EEC - - - 1

TOTAL 18 0 4 23

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SEMESTER III

S.NO COURSE


THEORY

1 - Professional Elective V PE 3 0 0 3

2 - Professional Elective VI PE 3 0 0 3

PRACTICAL(S)

3 P19CS301 Project Work Phase I EEC 0 0 12 6

TOTAL 6 0 12 12

SEMESTER IV

S.NO COURSE


PRACTICAL(S)

1 P19CS401 Project Work Phase II EEC 0 0 24 12

TOTAL 0 0 24 12

SUB. TOTAL CREDITS: 67

INTERNSHIP: 01

TOTAL NO. OF CREDITS: 68

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PROFESSIONAL ELECTIVES (PE)

S. NO. COURSE

CODE COURSE TITLE L T P C

1 P19CSP01 Cloud Computing Technologies 3 0 0 3

2 P19CSP02 Mobile and Pervasive Computing 3 0 0 3

3 P19CSP03 Information Retrieval Techniques 3 0 0 3

4 P19CSP04 Image Processing and Analysis 3 0 0 3

5 P19CSP05 Real Time Systems 3 0 0 3

6 P19CSP06 Performance Analysis of Computer Systems 3 0 0 3

7 P19CSP07 Identity and Access Management 3 0 0 3

8 P19CSP08 Web Engineering 3 0 0 3

9 P19CSP09 Formal Models of Software Systems 3 0 0 3

10 P19CSP10 Data Visualization 3 0 0 3

11 P19CSP11 Software Defined Networks 3 0 0 3

12 P19CSP12 Augmented Reality 3 0 0 3

13 P19CSP13 Parallel Programming Paradigms 3 0 0 3

14 P19CSP14 Fault Tolerant Computing Systems 3 0 0 3

15 P19CSP15 Natural Language Processing 3 0 0 3

16 P19CSP16 Enterprise Computing 3 0 0 3

17 P19CSP17 Reconfigurable Computing 3 0 0 3

18 P19CSP18 Software Quality Assurance and Testing 3 0 0 3

19 P19CSP19 Cyber Physical Systems 3 0 0 3

20 P19CSP20 Deep Learning 3 0 0 3

21 P19CSP21 Language Technologies 3 0 0 3

22 P19CSP22 Computer Vision 3 0 0 3

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23 P19CSP23 Speech Processing and Synthesis 3 0 0 3

24 P19CSP24 Embedded Software Development 3 0 0 3

25 P19CSP25 Information Storage Management 3 0 0 3

26 P19CSP26 Social Network Analysis 3 0 0 3

27 P19CSP27 Bio-inspired Computing 3 0 0 3

EMPLOYABILITY ENHANCEMENT COURSES (EEC) – PRACTICAL COURSES

AND PROJECT WORK

S. NO. COURSE


1 P19CS205 Mini Project EEC 0 0 2 2

2 P19CS301 Project Work Phase I EEC 0 0 12 6

3 P19CS401 Project Work Phase II EEC 0 0 24 12

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P19MA102

PROBABILITY, STATISTICS AND GRAPH THEORY

L T P C

3 1 0 4

PRE-REQUISITES: Category: FC

Probability and Queuing Theory/ Statistics

COURSE OBJECTIVES:

To understand the basics of probability, random variables, standard distributions, and

statistics

To be familiar the applications of graph theory for real world problems

To learn the fundamentals of machine learning

UNIT – I : PROBABILITY (10)

Discrete time Markov Chain - Computation of n-step Transition Probabilities - State Classification

and Limiting Probabilities - Distribution of Times between State Changes - Markov Modulated

Bernoulli Process - Irreducible Finite Chains with Aperiodic States.

UNIT – II : SAMPLING DISTRIBUTION (8)

Random samples, sampling distributions of estimators, Methods of Moments and Maximum

Likelihood.

UNIT – III : STATISTICS (9)

Statistical inference, Introduction to multivariate statistical models: regression and classification

problems, principal components analysis, the problem of over fitting model assessment.

UNIT – IV : GRAPH THEORY (9)

Isomorphism – Planar graphs- Graph colouring- Hamilton circuits and Euler cycles- Permutations

and Combinations with and without repetition.

UNIT – V : MACHINE LEARNING IN BIOINFORMATICS (9)

Support Vector Machine- Prediction of Protein Secondary Structure of DNA sequence - Genome

analysis with software tools-Case Study.

Total: 60

REFERENCES:

1. K. Trivedi, Probability and Statistics with Reliability, Queuing, and Computer Science

Applications, John Wiley & Sons 2016.

2. Alan Tucker, Applied Combinatorics, 6th Edition John Wiley & Sons, 2012.

3. PierriBaldi and Soren Brunak, Bioinformatics-Machine Learning Approach, 2nd Edition

(Ebook).

4. John Vince, Foundation Mathematics for Computer Science, Springer.

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5. Devore, J. L. Probability and Statistics for Engineering and the Sciences, 8th Edition,

Cengage Learning, 2014.

6. Gupta S.C. and Kapoor V.K., Fundamentals of Mathematical Statistics, Sultan and

Sons, New Delhi, 2001.

COURSE OUTCOMES:

Upon completion of the course, the student will be able to:

S. No. CO’s Outcomes Level

1 CO1 Compute transition probabilities and limiting probabilities of

various process Applying

2 CO2 Find the sampling distributions of estimators and to estimate

the moments Applying

3 CO3 Identify the methods of statistical inference, to apply principal

component analysis and to solve over fitting model Applying

4 CO4 Apply the knowledge of graph theory in to model a real time

problem Applying

5 CO5 Use machine learning techniques to analyze genome structure Applying

6 CO6 Make use of probability, statistics and graph theory in

computer science filed applications Applying

COURSE ARTICULATION MATRIX:

CO PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 P

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P

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CO1 3 2 1 1

CO2 3 1 1 2

CO3 3 2 1 1

CO4 3 2 1 2

CO5 3 2 1 2

CO6 3 2 1 2

Note: 1: Low, 2: Medium, 3: High

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P19CS101

MODERN OPERATING SYSTEMS

L T P C

3 0 0 3

PRE-REQUISITES: Category: PC

Operating System

COURSE OBJECTIVES:

To understand the concepts of distributed systems

To get an insight into the various issues and solutions in distributed operating systems

To learn about mobile and cloud operating systems

UNIT – I : DISTRIBUTED SYSTEMS (9)

Introduction of Distributed Computing System – Distributed Computing System Models –

Distributed Operating Systems – Issues In Distributed Operating Systems.

UNIT – II : SYNCHRONIZATION (9)

Clock Synchronization – Event Ordering – Mutual Exclusion – Deadlock Modelling – Deadlock

Prevention – Deadlock Avoidance – Deadlock Detection and Recovery - Election Algorithms.

UNIT – III : DISTRIBUTED SHARED MEMORY (9)

General Architecture – Structure of Shared Memory Space – Issues in design and implementation of

Distributed Shared Memory - Consistency Models – Replacement Strategy – Thrashing.

UNIT – IV : DISTRIBUTED FILE SYSTEMS (9)

Distributed File Systems – File Models – File Accessing Models – File Sharing Semantics – File

Caching Semantics – File Replication – Atomic Transactions – Case Studies.

UNIT – V : CLOUD AND MOBILE OS (9)

Cloud OS - Introduction to Cloud Computing, Features of Cloud OS, Case Studies - Mobile OS -

Introduction to Mobile Computing, Features of Mobile OS, Case Studies.

Total: 45

REFERENCES:

1. Pradeep K. Sinha, Distributed Operating Systems Concepts and Design, Prentice Hall

of India Private Limited, 2008.

2. M. Singhal, N. Shivaratri, Advanced Concepts in Operating Systems, Tata McGraw-

Hill, 2008.

3. Andrew S. Tanenbaum, Maarten Van Steen, Distributed Systems Principles and

Paradigms, Pearson Education, 2007.

4. Pattnaik, Prasant, Kumar, Mall, Rajib, Fundamentals of Mobile Computing, PHI, 2012.

5. Asoke K Talukder, Roopa Yavagal, Mobile Computing - Technology, Applications,

and Service Creation – 1st edition, McGraw-Hill, 2006.

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6. Thomas A. Limoncelli Strata R. Chalup, Christina J. Hogan, The Practice of Cloud

System Administration: Designing and Operating Large Distributed Systems, Addison-

Wesley Professional; 1st Edition, 2014.

7. Thomas Erl, Ricardo Puttini, ZaighamMahmood, Cloud Computing: Concepts,

Technology & Architecture, Prentice Hall; 1st Edition, 2013.

COURSE OUTCOMES:


S. No CO’s Outcomes Level

1 CO1

Apply the concepts of operating system to a distributed

environment and identify the features specific to distributed

systems

Applying

2 CO2 Apply the process synchronization concepts for the given

scenario in distributed environment Applying

3 CO3 Illustrate the different consistency model, replacement

strategy in distributed shared memory (DSM) Understanding

4 CO4 Apply the distributed file system concepts for a given scenario Applying

5 CO5 Identify the role of operating system in cloud and mobile

environment Applying

6 CO6 Identify the different features of mobile and real-time

operating systems Applying



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CO1 2

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P19CS102

AGILE SOFTWARE DEVELOPMENT AND

USABILITY ENGINEERING

L T P C

3 0 0 3


Software Engineering

COURSE OBJECTIVES:

To understand agile software development process, planning and management

To use advanced software testing techniques

To understand process of usability engineering

UNIT – I : AGILE SOFTWARE DEVELOPMENT (9)

Agile vs Traditional models, Agile manifesto, Agile methodologies- DSDM, FDD, Crystal, Scrum,

Agile Modeling, Extreme Programming, Lean Software Development, Unified Process (UP).

UNIT – II : MANAGING AND PLANNING AGILE PROJECTS (9)

Gathering software requirements -Eliciting requirements from users, Adopting Agile values, writing

user stories. Planning Agile Projects- Prioritizing and estimating work, organizing projects by

features, dividing features into tasks.

UNIT – III : REPORTING TEAM PROGRESS (9)

Documenting work completed with backlogs, tracking progress with burn down charts, Projecting

project costs and completion dates, Monitoring work in progress with task boards.

UNIT – IV : TEST-DRIVEN DEVELOPMENT AND USABILITY

ENGINEERING (9)

Unit, integration, system and Acceptance testing, exploratory testing, automated and manual testing,

exercising boundary conditions, driving development through constant testing. Usability

engineering: Usability engineering life cycle, Human-computer interaction and user interface

design, Importance of interface design in software design, benefits of good interface design.

UNIT – V : USABILITY ENGINEERING (9)

User interface development - Needs analysis, Systems analysis, User profiling, Preliminary design,

Rapid prototyping. GUI design- navigation and information hierarchy, user interaction diagrams,

GUI design heuristics, usability testing. Usability across interface types: Web, Desktop, Mobile,

touch and video games.

Total: 45

REFERENCES:

1. Mike Holcombe, Running an Agile Software Development Project, Wiley, 2008.

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2. Laura M. Leventhal, Julie A. Barnes, Usability Engineering: Process, Products, and

Examples, Pearson/Prentice Hall, 2008.

3. Orit Hazzan, Yael Dubinsky, Agile software engineering, Springer, 2014.

4. Jakob Nielsen, Usability Engineering, Academic Press, 1993.

COURSE OUTCOMES:




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1 CO1 Write user stories for given software specification. Understanding

2 CO2 Plan iterations based on relative effort and business value Applying

3 CO3 Create backlogs and burn-down charts to monitor progress of

a project Applying

4 CO4 Increase quality with test-driven development Applying

5 CO5 Design an interface by applying usability guidelines and

standards for given system development problems Applying

6 CO6 Design a usability test plan based on requirements

specification Applying

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P19CS103

MACHINE LEARNING

L T P C

3 0 0 3


Principles of data mining

COURSE OBJECTIVES:

To introduce students to the basic concepts and techniques of Machine Learning.

To have a thorough understanding of the Supervised and Unsupervised learning

techniques

To study the various probability based learning techniques and graphical models of

machine learning algorithms

UNIT – I : INTRODUCTION (9)

Introduction – Probability-Review, Designing a Learning system. Supervised learning– K-NN,

Decision trees and rule learning, Naïve Bayes algorithm, Linear regression, Logistic regression, The

Perceptron Algorithm, Neural Networks and Belief Networks, SVMs and Margin Classifiers, SVM:

duality and kernels.

UNIT – II : COMPUTATIONAL LEARNING THEORY (9)

Introduction, PAC learning, Mistake Bounds (Find-S, Halving Algorithm), Weighted Majority

Algorithm, Complexity for infinite hypotheses spaces: VC dimension for Neural Networks.

UNIT – III : UNSUPERVISED LEARNING (9)

Clustering- K-means – EM Algorithm- Mixtures of Gaussians. The Curse of Dimensionality –

Dimensionality Reduction – Factor analysis – Principal Component Analysis – Probabilistic PCA-

Independent components analysis.

UNIT – IV : REINFORCEMENT LEARNING (9)

Q-learning, Nondeterministic rewards and actions, Temporal difference learning, Single state case-

elements of reinforcement learning – Model-based learning – Generalization – Partially observable

states.

UNIT – V : SCALABLE LEARNING AND APPLICATIONS (9)

Practical aspects of implementing parallel Machine Learning methods, Biotechnology, NLP, Image

processing.

Total: 45

REFERENCES:

1. Christopher M.Bishop, Pattern recognition and machine learning, Springer, 2007.

2. Tom M. Mitchell, Machine learning, McGraw Hill, 1997.

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3. Kevin Murphy, Machine Learning - A Probabilistic Perspective, Adaptive Computation

and Machine Learning, MIT Press, 2012.

4. Ethem Alpaydin, Introduction to machine learning, The MIT Press, 2004.

5. Stephen Marsland, Machine learning: An algorithmic perspective, CRC, 2009.

COURSE OUTCOMES:




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CO2 3 3 3 2 1

CO3 3 3 3 2 1

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1 CO1 Illustrate the steps involved in designing a machine learning

algorithm Understanding

2 CO2

Construct training and prediction algorithms for classification

using decision trees, artificial neural networks and Support

Vector Machines

Applying

3 CO3 Construct learning algorithms using Bayesian probabilistic

models for complex applications. Applying

4 CO4 Illustrate the fundamentals of computational learning theory

with an understanding of the mistake bounds Understanding

5 CO5

Construct learning algorithms which involves linear

regression with a comprehension of regularization, bias-

variance and evidence approximation

Applying

6 CO6

Compare the available design options and apply supervised

and unsupervised learning algorithms to solve complex

problems with an understanding of the trade-offs involved

Applying

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P19CS104

RESEARCH METHODOLOGY L T P C

3 0 0 3

PRE-REQUISITES: Category: FC

None

COURSE OBJECTIVES:

To understand basic concepts of research and methodologies

To select and define appropriate research problem and parameters

To write a research report and thesis

UNIT – I : MEANING OF RESEARCH - FUNCTION OF RESEARCH (9)

Meaning of Research - Function of Research – Characteristics of Research – Steps involved in

Research – Research in Pure and Applied Sciences - Inter Disciplinary Research. Factors which

hinder Research – Significance of Research - Research and scientific methods – Research Process–

Criteria of good Research – Problems encountered by Researchers – Literature review.

UNIT – II : IDENTIFICATION OF RESEARCH PROBLEM (9)

Selecting the Research problem – Necessity of defining the problem – Goals and Criteria for

identifying problems for research. Perception of Research problem – Techniques involved in defining

the problem – Source of problems – Personal consideration.

UNIT – III : RESEARCH DESIGN (9)

Formulation of Research design – Need for Research design – Features of a good design – Important

concepts related to Research design. Different research designs – Basic principles of experimental

designs – Computer and internet in designs.

UNIT – IV : INTERPRETATION AND REPORT WRITING (9)

Meaning and Technique of interpretation – Precautions in interpretation – Significance of report

writing – Different steps in writing a report – Layout of a Research report. Types of report –

Mechanics of writing a research report – Precautions for writing a research report – Conclusion.

UNIT – V : STATISTICAL TECHNIQUES AND TOOLS (9)

Introduction of statistics – Functions – Limitations – Measures of central tendency - Arithmetic mean

– Median – Mode – Standard deviation – Co-efficient of variation (Discrete serious and continuous

serious) – Correlation - Regression – Multiple Regression. Sampling distribution – Standard error –

Concept of point and interval estimation – Level of significance – Degree of freedom – Analysis of

variance – One way and two way classified data – ‘F’-test.

Total: 45

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REFERENCES:

1. A Hand Book of Methodology of Research, Rajammall, P. Devadoss and K.

Kulandaivel, RMM Vidyalaya Press, 1976.

2. Research Methodology Methods & Techniques, C.R. Kothari – New Age international

Publishers, Reprint 2008.

3. Thesis and Assignment Writing, J. Anderson, Wiley Eastern Ltd., 1997.

4. Research Methodology, Mukul Gupta, Deepa Gupta – PHI Learning Private Ltd., New

Delhi, 2011.

5. Fundamentals of Mathematical statistics, S.C. Gupta and V.K. Kapoor, Sultan Chand

& Sons, New Delhi, 1999.

6. Statistical Methods, G.W. Snedecor and W.G. Cochrans, Lowa state University Press,

1967.

COURSE OUTCOMES:




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CO1 3 1 1 2

CO2 3 2 2 2

CO3 3 2 2 2

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1 CO1 Explain the functions of research and process involved in

literature review Understating

2 CO2 Formulate and discuss research problem Applying

3 CO3 Frame different research design methodologies Applying

4 CO4 Analyze research related information Analyzing

5 CO5 Write research reports by following research ethics Applying

6 CO6 Utilize sampling, statistical techniques and tools for effective

reporting Applying

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P19CS105

DESIGN AND ANALYSIS OF ALGORITHMS LAB L T P C

0 0 2 2


Nil

COURSE OBJECTIVES:

To implement basic algorithms of data structure

To analyze the performance of algorithms

List of Experiments:

Crossword puzzles as Constraint Satisfaction problems

Graph coloring problem by backtracking and constraint propagation (using heuristics)

Shortest path in multi-stage graph using dynamic programming

Ford–Fulkerson algorithm to compute the maximum flow in a graph

Maximum clique problem using branch and cut method

Boyer–Moore string search algorithm for substring search

Implement any two clock synchronization algorithms and compare their performances.

(Berkeley algorithm, Cristian's algorithm, Intersection algorithm, Marzullo's algorithm)

Lamport's distributed mutual exclusion algorithm

Banker's algorithm for deadlock avoidance

Approximation algorithm for the problems like Graph coloring, Vertex cover problem,

maximal flow, shortest path problems, maximum subsequence generation etc.

Total: 30

COURSE OUTCOMES:




S

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CO2 3 2 2 1


S. No CO’s Outcomes Levels

1 CO1 Develop algorithms on encryption, decryption and distributed

mutual exclusion and deadlock concepts Applying

2 CO2

Develop approximation, randomization, linear and non-linear

algorithms for various problems like scheduling, graph,

network, string and subsequence problems

Applying

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P19CS201

PROTOCOL DESIGN AND VERIFICATION L T P C

3 0 0 3


Nil

COURSE OBJECTIVES:

To learn about process synchronization

To understand the working of basic network protocols with protocol encoding

To study the evolution made in network security mechanisms

UNIT – I : INTRODUCTION (9)

CSP Descriptions and Proof Rules - Processes and Process Synchronisation - Channel History

Semantics - Failure Semantics - Protocols and Services - Providing a service-Service Features - OSI

and other layered architectures.

UNIT – II : BASIC PROTOCOL MECHANISMS (9)

Sequence Error and Flow control – change of service- Multiplexing – splitting –Segmenting –

Reassembly – Prioritization- Multipeer Consensus – Reliable broadcasts – Election – Commitment

– Byzantine Agreement – Clock Synchronization.

UNIT – III : SECURITY (9)

Crypto systems – Integrity – Digital Signature – Entity Authentication –Key Exchange - Naming

Addressing and Routing – General Principle – Addressing Structures – routing – Congestion.

UNIT – IV : PROTOCOL ENCODING (9)

Simple binary encoding – TLV –ASN.1 – ASCII Encoding - Protocols in the OSI Lower Layers –

Data Link Layer– Network layer –Transport Layer.

UNIT – V : APPLICATION SUPPORT PROTOCOLS (9)

Session Layer- Presentation Layer –Application Layer – Commitment -Concurrency and recovery -

Client Server Systems- Security Middle ware - Application Protocols – FTP – Distributed

Transaction Processing Notation – Data types Inference Rules.

Total: 45

REFERENCES:

1. Gerard J. Holzmann, Design and Validation of Computer Protocols, Prentice Hall; 1st

edition.

2. König, Hartmut, Protocol Engineering, ISBN 978-3-642-29145-6, Springer-Verlag

Berlin Heidelberg.

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COURSE OUTCOMES:



1 CO1 Apply CSP descriptions and rules and synchronize services Applying

2 CO2 Apply basic protocol Mechanisms for multiplexing and

segmenting Applying

3 CO3 Achieve integrity by adopting integrity and authentication Applying

4 CO4 Compare and contrast and select relevant encoding

mechanisms Understanding

5 CO5 Compare and contrast distributed transaction processing

protocols Understanding



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P19CS202

INTERNET OF THINGS L T P C

3 0 0 3


Nil

COURSE OBJECTIVES:

To understand the fundamentals of Internet of Things and its protocols

To build a small low cost embedded system using Raspberry Pi

To apply the concept of Internet of Things in the real world scenario

UNIT – I : INTRODUCTION TO IoT (9)

Internet of Things - Physical Design- Logical Design- IoT Enabling Technologies - IoT Levels

&Deployment Templates - Domain Specific IoTs - IoT and M2M - IoT System Management with

NETCONF-YANG- IoT Platforms Design Methodology.

UNIT – II : IoT ARCHITECTURE (9)

M2M high-level ETSI architecture - IETF architecture for IoT - OGC architecture - IoT reference

model - Domain model - information model - functional model - communication model – IoT

reference architecture.

UNIT – III : IoT PROTOCOLS (9)

Protocol Standardization for IoT - Efforts - M2M and WSN Protocols - SCADA and RFID Protocols

- Unified Data Standards - Protocols - IEEE 802.15.4 - BACNet Protocol Modbus -Zigbee

Architecture - Network layer - 6LowPAN - CoAP– Security.

UNIT – IV : BUILDING IoT WITH RASPBERRY PI & ARDUINO (9)

Building IOT with RASPERRY PI- IoT Systems - Logical Design using Python IoT Physical Devices

& Endpoints - IoT Device -Building blocks -Raspberry Pi -Board - Linux on Raspberry Pi -Raspberry

Pi Interfaces -Programming Raspberry Pi with Python - Other IoT Platforms - Arduino.

UNIT – V : CASE STUDIES AND REAL-WORLD APPLICATIONS (9)

Real world design constraints - Applications - Asset management, Industrial automation, smartgrid,

Commercial building automation, Smart cities - participatory sensing - Data Analytics for IoT

Software & Management Tools for IoT Cloud Storage Models & Communication APIs – Cloud for

IoT - Amazon Web Services for IoT.

Total: 45

REFERENCES:

1. Arshdeep Bahgya, Vijay Madisetti, “Internet of Things-A hands-on approach”,

Universities Press, 2015.

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2. Dieter Uckelmann, Mark Harrison, Michahelles, Floran (Eds), “Architecting the

Internet of Things”, Springer, 2011.

3. Honbo Zhou, “The Internet of Things in the Cloud: A Middleware Perspective”, CRS

Press, 2012.

4. Jan Ho¨ller, Vlasios Tsiatsis, Catherine Mulligan, Stamatis, Karnouskos, Stefan

Avesand. David Boyle, "From Machine-to-Machine to the Internet of Things

Introduction to a New Age of Intelligence", Elsevier, 2014.

5. Olivier Hersent, David Boswarthick, Omar Elloumi, “The Internet of Things-Key

applications and Protocols”, Wiley, 2012.

COURSE OUTCOMES:



1 CO1 Analyze various protocols for IoT Analyzing

2 CO2 Develop web services to access/control IoT devices. Applying

3 CO3 Design a portable IoT using Rasperry Pi Applying

4 CO4 Deploy an IoT application and connect to the cloud. Applying

5 CO5 Analyze applications of IoT in real time scenario Analyzing

6 CO6 Apply the concept of Internet of Things in the real-world

scenario Applying



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P19CS203

BIG DATA ANALYTICS L T P C

3 0 0 3


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COURSE OBJECTIVES:

To understand the competitive advantages of big data analytics

To understand the big data frameworks and data analysis methods

To gain knowledge on Hadoop related tools such as HBase, Cassandra, Pig, and Hive

for big data analytics

UNIT – I : INTRODUCTION TO BIG DATA (9)

Big Data Definition, Characteristic Features Big Data Applications - Big Data vs Traditional Data -

Risks of Big Data - Structure of Big Data - Challenges of Conventional Systems – Web Data

Evolution of Analytic Scalability - Evolution of Analytic Processes, Tools and methods - Analysis

vs Reporting - Modern Data Analytic Tools.

UNIT – II : HADOOP FRAMEWORK (9)

Distributed File Systems - Large-Scale File System Organization HDFS concepts – Map Reduce

Execution, Algorithms using Map Reduce, Matrix-Vector Multiplication Hadoop YARN.

UNIT – III : DATA ANALYSIS (9)

Statistical Methods: Regression modelling, Multivariate Analysis - Classification: SVM& Kernel

Methods - Rule Mining - Cluster Analysis, Types of Data in Cluster Analysis, Partitioning Methods,

Hierarchical Methods, Density Based Methods, Grid Based Methods, Model Based Clustering

Methods, Clustering High Dimensional Data - Predictive Analytics Data analysis using R.

UNIT – IV : MINING DATA STREAMS (9)

Streams: Concepts Stream Data Model and Architecture - Sampling data in a stream – Mining Data

Streams and Mining Time-series data - Real Time Analytics Platform (RTAP) Applications - Case

Studies - Real Time Sentiment Analysis, Stock Market Predictions.

UNIT – V : BIG DATA FRAMEWORKS (9)

Introduction to NoSQL Aggregate Data Models Hbase: Data Model and Implementations Hbase

Clients Examples. Cassandra: Data Model Examples Cassandra Clients Hadoop Integration. Pig

Grunt Pig Data Model Pig Latin developing and testing Pig Latin scripts. Hive Data Types and File

Formats HiveQL Data Definition HiveQL Data Manipulation HiveQL Queries.

Total: 45

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REFERENCES:

1. Bill Franks, “Taming the Big Data Tidal Wave: Finding Opportunities in Huge Data

Streams with Advanced Analytics”, Wiely and SAS Business Series, 2012.

2. David Loshin, "Big Data Analytics: From Strategic Planning to Enterprise Integration

with Tools, Techniques, NoSQL, and Graph", 2013.

3. Micheal Berthold, David J. Hand, “Intelligent Data Analysis”, Springer, Second

Edition, 2007.

4. Michael Minelli, Michelle Chambers, and Ambiga Dhiraj, "Big Data, Big Analytics:

Emerging Business Intelligence and Analytic Trends for Today's Businesses", Wiley,

2013.

5. P. J. Sadalage and M. Fowler, "NoSQL Distilled: A Brief Guide to the Emerging World

of Polyglot Persistence", Addison-Wesley Professional, 2012.

6. Richard Cotton, "Learning R A Step-by-step Function Guide to Data Analysis”,

O’Reilly Media, 2013.

COURSE OUTCOMES:



1 CO1 Explain how to leverage the insights from big data analytics Understanding

2 CO2 Analyze data by utilizing various statistical and data mining

approaches Analyzing

3 CO3 Perform analytics on real-time streaming data Analyzing

4 CO4 Discuss the various NoSql alternative database models Understanding

5 CO5 Illustrate steam computing and various data analysis methods Understanding

6 CO6 Apply Hadoop related tools such as HBase, Cassandra, Pig,

and Hive for big data analytics for suitable applications Applying



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P19CS204

DATA ANALYTICS LABORATORY L T P C

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COURSE OBJECTIVES:

To implement Map Reduce programs for processing big data

To realize storage of big data using Hbase, Mongo DB

To analyze big data using machine learning techniques such as SVM / Decision tree

classification and clustering

List of Experiments:

Hadoop

Install, configure and run Hadoop and HDFS

Implement word count / frequency programs using MapReduce

Implement an MR program that processes a weather dataset R

Implement Linear and logistic Regression

Implement SVM / Decision tree classification techniques

Implement clustering techniques

Visualize data using any plotting framework

Implement an application that stores big data in Hbase / MongoDB / Pig using Hadoop

/ R.

Total: 45

COURSE OUTCOMES:



1 CO1 Process big data using Hadoop framework Applying

2 CO2 Build and apply linear and logistic regression models Applying

3 CO3 Perform data analysis with machine learning methods Analyzing

4 CO4 Perform graphical data analysis Analyzing

5 CO5 Make use of SVM/ Decision tree algorithms for a given

scenario Applying

6 CO6 Implement Hbase, Mongo DB for big data storage Applying

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