IIoT PROGRAMS

DEPARTMENT	PROGRAM
IOT PLATFORM	IOT BASIC INTRODUCTION
GENERATOR MONITORING SYSTEM	COMMUNICATION & STANDARD INTERFACES SMART INDUSTRIAL CONNECTITY
SMART PUBLIC TRANSPORT	DATA SCIENCE AND ANALYTICS
SMART MANUFACTURING	MACHINE LEARNING
SMART CITY	ARTIFICIAL INTELLIGENCES
MAJOR PROJECT IN EACH PROGRAM	–

1. IoT BASIC INTRODUCTION

OBJECTIVES

To learn the concepts of IOT.
To identify the different technology.
To learn different applications in IOT.
To learn different protocols used in IOT.
To learn the concepts of smart city development in IOT.
To learn how to analysis the data in IOT

OUTCOMES

Apply the concepts of IOT.
Identify the different technology.
Apply IOT to different applications using Thingworx.
Analysis and evaluate protocols used in IOT.
Design and develop smart things in IOT using Thingworx.
Analysis and evaluate the data received through sensors in IOT.

SCOPE

IIOT Designer,
IIOT Developer,
IIOT Analyst,
IIOT Tester
Entrepreneurship

PROJECTS

Develop mini IoT Project
LDR based Darkness activation system
Home Security Alarm System Using Arduino
Arduino Alarm Clock
Portable Ultrasonic Range Meter

2. COMMUNICATION & STANDARD INTERFACES

OBJECTIVES

Address the real world problems and find the required solution.
Design the problem solution as per the requirement analysis done.
Study the basic concepts of programming/ hardware/ emulator for Raspberry pi/Arduino.
Fabricate and implement the mini project intended solution for project based learning.
Build and test the mini project successfully.
Improve the team building, communication and management skills of the students.

OUTCOMES

Identify the requirements for the real world problems.
Building Mashup and Widgets using Thingworx.
Study and enhance software/ hardware skills.
Demonstrate and build the project successfully by hardware requirements, coding, emulating and testing.
To report and present the findings of the study conducted in the preferred task.
Demonstrate an ability to work in teams and manage the conduct of the research study

SCOPE

IIOT Designer,
IIOT Developer,
IIOT Analyst,
IIOT Tester
Entrepreneurship

PROJECTS

Smart Dustbin based
Obstacle Avoiding Robot
Keypad Security for Home Automation

3. SMART INDUSTRIAL CONNECTIVITY

OBJECTIVES

To gain knowledge of installing Android Studio and Cross Platform Integrated Development Environment.
To learn designing of User Interface and Layouts for Android App.
To learn how to use intents to broadcast data within and between Applications.
To use Content providers and Handle Databases using SQLite.
To introduce Android APIs for Camera and Music system.
To discuss various security issues with Android Platform.

OUTCOMES

Experiment on Integrated Development Environment for Android Application Development.
Design and Implement User Interfaces and Layouts of Android App.
Use Intents for activity and broadcasting data in Android App.
Design and Implement Database Application and Content Providers.
Experiment with Camera and music systems.
Develop Android App with Security features.

SCOPE

Android Developer and Android Tester
Entrepreneurship

PROJECTS

Smart Genset
Smart Street Light

4. DATA SCIENCE AND ANALYTICS

OBJECTIVES

This course will serve as a comprehensive introduction to various topics in machine learning.
Conceptualization and summarization of big data and machine learning, trivial data versus big data, big data computing technologies, machine learning techniques, and scaling up machine learning approaches.
To provide Conceptualization and summarization: Representation of data. Modeling of machine learning techniques. Application of big data computing technologies.
To provide Trivial data versus Big data: Representation learning. Publicly available datasets. Scalability and Scaling up techniques. Report writing using Latex.

OUTCOMES

At the end of the course the students should be able to design and implement machine learning solutions to classification, regression, and clustering problems; and be able to evaluate and interpret the results of the algorithms.
Ability to identify the characteristics of datasets and compare the trivial data and big data for various applications.
Ability to select and implement machine learning tech
niques and computing environment that are suitable for the applications under consideration.
Ability to solve problems associated with batch learning and online learning, and the big data characteristics such as high dimensionality, dynamically growing data and in particular scalability issues.

SCOPE

Business Analyst,
Product Analyst,
Machine Learning Engineer,
Data Scientist

PROJECTS

Smart Transportation

5. MACHINE LEARNING

OBJECTIVES

To provide Big data computing environment: Modern data analytics technologies like Hadoop and MapReduce. Suitable programming languages like Python, Java and C. Big data friendly machine learning scikit-learn libraries. Software platforms like Matlab or R.
To provide Machine learning techniques: Three phases of machine learning. types of learning support vector machine. decision trees and random forests. deep learning.
To provide Scaling up machine learning: Dimensionality reduction techniques like principal component analysis and feature hashing. Online processing technique called stochastic gradient descent. Big data machine learning models.

OUTCOMES

Ability to understand and apply scaling up machine learning techniques and associated computing techniques and technologies.
Ability to recognize and implement various ways of selecting suitable model parameters for different machine learning techniques.
Ability to integrate machine learning libraries and mathematical and statistical tools with modern technologies like hadoop and mapreduce.

SCOPE

Business Analyst
Product Analyst
Machine Learning Engineer
Data Scientist

PROJECTS

Smart Manufacturing

6. ARTIFICIAL INTELLIGENCES

OBJECTIVES

To create appreciation and understanding of both the achievements of AI and the theory underlying those achievements.
To introduce the concepts of a Rational Intelligent Agent and the different types of Agents that can be designed to solve problems
To review the different stages of development of the AI field from human like behavior to Rational Agents.
To impart basic proficiency in representing difficult real life problems in a state space representation so as to solve them using AI techniques.
To create an understanding of the basic issues of knowledge representation Logic & blind & heuristic search, as well as an understanding of other topics such as minimal, resolution, etc. that play an important role in AI programs.
To introduce advanced topics of AI such as planning, Bayes networks, natural language processing and Cognitive Computing

OUTCOMES

Demonstrate knowledge of the building blocks of AI as presented in terms of intelligent agents.
Analyze and formalize the problem as a state space, graph, design heuristics and select amongst different search.
Develop intelligent algorithms for constraint satisfaction problems and also design intelligent systems for Game Playing
Attain the capability to represent various real life problem domains using logic based techniques and use this to perform inference or planning.
Formulate and solve problems with uncertain information using Bayesian approaches.
Apply concept Natural Language processing to problems leading to understanding of cognitive computing.

SCOPE

AL Developer and AI Analyst
Data Scientist

PROJECTS

Smart City Project

PROJECTS UNDER IIoT

Access control to restricted areas and detection of people in non-authorized areas.
Asset indoor location by using active (ZigBee) and passive tags (RFID/NFC).
Assistance for elderly or disabled people living independent.
Cold Chain Monitoring and Smart Tracking
Control micro-climate conditions to maximize the production of fruits and vegetables and its quality.
Control of CO2 emissions of factories, pollution emitted by cars and toxic gases generated in farms.
Control of conditions inside freezers storing vaccines, medicines and organic elements.
Control of rotation of products in shelves and warehouses to automate restocking processes.
Control of routes followed for delicate goods like medical drugs, jewels or dangerous merchandises.
Control remotely the swimming pool conditions.
Control the exact conditions of plants grown in water to get the highest efficiency crops.
Detect leakages and wastes of factories in rivers.
Detection of gas levels and leakages in industrial environments, surroundings of chemical factories and inside mines.
Detection of windows and doors openings and violations to prevent intruders.
E-Health Sensor Platform for Biometric and Medical applications.
E-Health: Low Cost Sensors for Early Detection of Childhood Disease
Energy and water supply consumption monitoring to obtain advice on how to save cost and resources.
Energy consumption monitoring and management.
Environmental monitoring by Public Transportation.
Information collection from Can Bus to send real time alarms to emergencies or provide advice to drivers.
Intelligent and weather adaptive lighting in street lights
Intelligent Highways with warning messages and diversions according to climate conditions and unexpected events like accidents or traffic jams.
Liquid detection in data centers, warehouses and sensitive building grounds to prevent break downs and corrosion.
Measurement of water pressure in water transportation systems.
Monitoring and optimization of performance in solar energy plants.
Monitoring greenhouse conditions to develop new products in the food industry.
Monitoring of combustion gases and premptive fire conditions to define alert zones.
Monitoring of conditions inside museums and art warehouses.

Key Indicator	Extended Profile
1.1	Number of students year wise during last five years
2.1	Number of full time teachers during last five years
3.1	Expenditures excluding salary component year wise during last five years

Key Indicator	Criterion7–Institutional Values and Best Practices	100
7.1	Institutional Values and Social Responsibilities	50
7.2	Best Practices	30
7.3	Institutional Distinctiveness	20

Key Indicator	Criterion 6- Governance, Leadership and Management	100
6.1	Institutional Vision and Leadership	10
6.2	Strategy Development and Deployment	10
6.3	Faculty Empowerment Strategies	35
6.4	Financial Management and Resource Mobilization	15
6.5	Internal Quality Assurance System	30

Key Indicator	Criterion 5- Student Support and Progression	140
5.1	Student Support	50
5.2	Student Progression	30
5.3	Student Participation and Activities	50
5.4	Alumni Engagement	10

Key Indicator	Criterion 4 – Infrastructure and Learning Resources	100
4.1	Physical Facilities	30
4.2	Library as a learning Resource	20
4.3	IT Infrastructure	30
4.4	Maintenance of Campus Infrastructure	20

Key Indicator	Criteria 3- Research, Innovations and Extension	110
3.1	Resource Mobilization for Research	10
3.2	Innovation Ecosystem	15
3.3	Research Publication and Awards	25
3.4	Extension Activities	40
3.5	Collaboration	20

Key Indicator	Criteria 2- Teaching- Learning and Evaluation	350
2.1	Student Enrolment and Profile	40
2.2	Student Teacher Ratio	40
2.3	Teaching- Learning Process	40
2.4	Teacher Profile and Quality	40
2.5	Evaluation Process and Reforms	40
2.6	Student Performance and Learning Outcome	90
2.7	Student Satisfaction Survey	60

Key Indicator	Criterion 1 – Curricular Aspects	100
1.1	Curricular Planning and Implementation	20
1.2	Academic Flexibility	30
1.3	Curriculum Enrichment	30
1.4	Feedback System	20