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B-SC in Computer Science Internet Of Things at SRM Institute of Science and Technology
Chengalpattu, Tamil Nadu
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About the Specialization
What is Computer Science (Internet of Things) at SRM Institute of Science and Technology Chengalpattu?
This B.Sc. Computer Science with a specialization in Internet of Things (IoT) program at SRM Institute of Science and Technology focuses on equipping students with expertise in connected devices, data sensing, network communication, and cloud integration. It addresses the rapidly growing demand in the Indian market for professionals skilled in designing, developing, and deploying IoT solutions across various sectors like smart cities, healthcare, and industrial automation. The program emphasizes both theoretical foundations and practical applications through hands-on labs and projects.
Who Should Apply?
This program is ideal for fresh graduates from science or computer backgrounds seeking entry into the dynamic IoT sector, particularly those interested in hardware-software integration and data analytics. It also suits working professionals looking to upskill in emerging technologies, or career changers aiming to transition into the fast-growing IoT industry in India. Candidates with a strong aptitude for logical reasoning, problem-solving, and an interest in futuristic technologies will find this program rewarding.
Why Choose This Course?
Graduates of this program can expect diverse India-specific career paths as IoT Developers, Embedded Systems Engineers, Data Analysts (IoT), IoT Solution Architects, and Network Administrators. Entry-level salaries typically range from INR 3-5 LPA, growing significantly with experience to INR 8-15+ LPA in leading Indian tech companies and startups. The curriculum aligns with industry demands, preparing students for roles in smart infrastructure, automotive, agriculture, and healthcare IoT, fostering growth trajectories in a connected world.
Student Success Practices
Foundation Stage
Master Programming and Logic Fundamentals- (Semester 1-2)
Dedicate significant time to thoroughly grasp fundamental programming concepts in Python and Java, alongside discrete mathematics and data structures. Practice daily coding challenges to build strong problem-solving skills and algorithmic thinking essential for computer science.
Tools & Resources
HackerRank, LeetCode, GeeksforGeeks, Python documentation, Java tutorials
Career Connection
A solid foundation in programming and data structures is crucial for cracking coding interviews and developing efficient IoT applications and backend systems, which are core to many tech roles.
Build a Strong Academic Network- (Semester 1-2)
Actively participate in class discussions, form study groups, and engage with professors. Seek guidance for challenging topics and explore research opportunities or mini-projects to deepen understanding and build collaborative skills within the academic community.
Tools & Resources
Campus academic clubs, peer study circles, faculty office hours
Career Connection
Networking builds peer support, opens doors to academic mentorship, and fosters teamwork skills essential for future group projects and professional roles in any industry.
Develop Basic Hardware Understanding- (Semester 1-2)
Alongside software, start exploring basic electronics and microcontrollers like Arduino and Raspberry Pi through personal projects or online tutorials. Understand digital logic concepts practically to bridge the software-hardware gap.
Tools & Resources
Arduino Starter Kit, Raspberry Pi kits, online courses (Coursera, Udemy), basic electronics tutorials
Career Connection
Early exposure to hardware empowers students to integrate software with physical devices, a core skill for any IoT engineer and a significant differentiator in campus placements.
Intermediate Stage
Undertake IoT Mini-Projects and Internships- (Semester 3-5)
Apply theoretical knowledge from core IoT subjects (Sensors, Protocols, Embedded Systems) by building small-scale IoT projects. Actively seek and complete internships during summer breaks in relevant companies to gain real-world experience.
Tools & Resources
ESP32/ESP8266 boards, IoT platforms (AWS IoT, Azure IoT), company career portals, college placement cell
Career Connection
Practical projects demonstrate skill application to potential employers, while internships provide invaluable industry exposure and a potential pre-placement offer, accelerating career entry.
Specialize through Electives and Certifications- (Semester 3-5)
Strategically choose electives that align with career interests, such as Cloud Computing, Machine Learning for IoT, or Mobile App Development. Pursue relevant industry certifications to validate specialized skills.
Tools & Resources
NPTEL courses, Coursera/edX for certifications, industry-specific training platforms (e.g., AWS Certified IoT Specialty)
Career Connection
Specialization enhances employability, distinguishes candidates in a competitive job market, and directly maps to advanced roles within specific IoT domains, leading to better opportunities.
Participate in Hackathons and Technical Competitions- (Semester 3-5)
Join university-level and external hackathons focused on IoT, embedded systems, or smart solutions. This fosters rapid prototyping skills, teamwork, and innovative problem-solving under real-world pressure.
Tools & Resources
Major hackathon platforms (Devfolio, Hackerearth), college technical clubs
Career Connection
Winning or even participating successfully in competitions enhances resumes, showcases practical abilities, and provides invaluable networking opportunities with industry professionals, fostering career growth.
Advanced Stage
Develop a Capstone Major Project- (Semester 6)
Work on a comprehensive major project that integrates various aspects of IoT including hardware, software, cloud integration, security, and analytics. Focus on solving a real-world problem or developing an innovative solution with clear application.
Tools & Resources
Advanced IoT kits, enterprise-grade cloud platforms, project management tools, faculty mentors
Career Connection
A strong, well-documented major project serves as a powerful portfolio centerpiece, showcasing advanced skills and readiness for industry roles, significantly attracting recruiters.
Refine Communication and Presentation Skills- (Semester 6)
Actively practice presenting project work, participating in technical debates, and honing overall communication skills. Attend workshops on resume building, interview techniques, and group discussions to become a confident communicator.
Tools & Resources
College career services, Toastmasters clubs (if available), mock interview sessions
Career Connection
Excellent communication and presentation skills are paramount for technical interviews, effective team collaboration, and clearly conveying complex technical ideas in professional settings, improving career progression.
Strategic Placement Preparation- (Semester 6)
Begin rigorous preparation for placements well in advance of the final semester. This includes extensive practice of Data Structures and Algorithms, reviewing core computer science concepts, and preparing for technical and HR interviews specific to IoT roles.
Tools & Resources
InterviewBit, LeetCode, GeeksforGeeks, company-specific interview guides, SRMIST Placement Cell resources
Career Connection
Targeted preparation ensures students are well-equipped to tackle the demanding placement process, maximizing their chances of securing desirable job offers in the competitive IoT industry.
Program Structure and Curriculum
Eligibility:
- Candidate should have passed 10+2 or equivalent examination with a minimum of 50% aggregate marks.
Duration: 6 semesters / 3 years
Credits: 109 (derived from detailed course structure; note: summary table on page 16 of the source document states 140 credits, which appears to be a discrepancy) Credits
Assessment: Internal: 50%, External: 50%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21LEH101T | Communicative English | Core | 3 | Language Skills Development, Grammar and Vocabulary Enhancement, Reading Comprehension Strategies, Public Speaking and Presentation Skills, Listening and Note-taking |
| 21LCS101J | Foundation of Computer Science | Core | 3 | Introduction to Computer Systems, Hardware and Software Components, Operating Systems Basics, Number Systems and Data Representation, Algorithmic Thinking and Problem Solving |
| 21LCS102J | Problem Solving using Python | Core | 3 | Python Fundamentals and Syntax, Data Types, Operators, and Expressions, Control Flow Statements (Conditional, Looping), Functions and Modules, Object-Oriented Programming Concepts, File Handling and Exception Handling |
| 21LCS103J | Digital Logic and Computer Organization | Core | 3 | Digital Systems and Binary Logic, Boolean Algebra and Logic Gates, Combinational Logic Circuits, Sequential Logic Circuits, Memory Organization and Addressing, Basic Computer Organization |
| 21LCS104J | Discrete Mathematics | Core | 3 | Mathematical Logic and Proof Techniques, Set Theory, Relations, and Functions, Graph Theory and Trees, Combinatorics and Counting Principles, Introduction to Probability |
| 21LBS101L | Soft Skills | Skill Enhancement | 1 | Self-Management and Self-Awareness, Effective Communication Skills, Presentation and Public Speaking, Teamwork and Collaboration, Time Management and Goal Setting |
| 21LCS105P | Digital Logic and Computer Organization Lab | Lab | 1 | Implementation of Basic Logic Gates, Boolean Function Realization, Design of Combinational Circuits (Adders, Decoders), Experiments with Sequential Circuits (Flip-Flops, Counters), Introduction to Computer Organization Simulation |
| 21LCS106P | Python Programming Lab | Lab | 1 | Developing Python Programs for Problem Solving, Implementing Basic Data Structures, Debugging and Testing Python Code, Working with File I/O Operations, Applying Object-Oriented Concepts in Python |
| 21LEH102L | General English | Skill Enhancement | 1 | Listening Comprehension Practice, Spoken English and Fluency Development, Group Discussion Techniques, Interview Skills Training, Vocabulary Building Exercises |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21LBS201T | Mathematics for Computer Science | Core | 3 | Matrices and Determinants, Differential and Integral Calculus, Ordinary Differential Equations, Vector Calculus, Probability Distributions and Statistics |
| 21LCS201J | Data Structures and Algorithms | Core | 3 | Arrays, Linked Lists, Stacks, and Queues, Trees and Binary Search Trees, Graphs and Graph Traversal Algorithms, Sorting and Searching Techniques, Algorithm Design Paradigms, Time and Space Complexity Analysis |
| 21LCS202J | Database Management Systems | Core | 3 | DBMS Concepts and Architecture, Entity-Relationship (ER) Model, Relational Model and Relational Algebra, Structured Query Language (SQL), Normalization and Dependency Theory, Transaction Management and Concurrency Control |
| 21LCS203J | Object-Oriented Programming using Java | Core | 3 | OOP Principles: Encapsulation, Inheritance, Polymorphism, Java Language Fundamentals (Classes, Objects), Abstract Classes and Interfaces, Exception Handling and Multithreading, I/O Streams and File Handling, Collection Framework |
| 21LCS204J | Computer Networks | Core | 3 | Network Models (OSI, TCP/IP), Physical Layer and Data Transmission, Data Link Layer and Error Detection/Correction, Network Layer (IP Addressing, Routing), Transport Layer (TCP, UDP, Congestion Control), Application Layer Protocols (HTTP, FTP, DNS) |
| 21LCS205P | Data Structures and Algorithms Lab | Lab | 1 | Implementation of Linear Data Structures (Stack, Queue, List), Implementation of Non-Linear Data Structures (Trees, Graphs), Practical Application of Sorting Algorithms, Practical Application of Searching Algorithms, Algorithm Efficiency Analysis |
| 21LCS206P | Database Management Systems Lab | Lab | 1 | Writing SQL Queries (DDL, DML, DCL), Database Design and Schema Implementation, Advanced SQL Features (Joins, Subqueries, Views), PL/SQL Programming Basics, Database Connectivity (e.g., JDBC) |
| 21LCS207P | Object-Oriented Programming using Java Lab | Lab | 1 | Developing Java Programs with OOP Concepts, GUI Applications with AWT/Swing, Handling Exceptions in Java Programs, Implementing Multithreading Applications, File Operations in Java |
| 21LEA201L | Environmental Science | Skill Enhancement | 1 | Ecosystems and Biodiversity, Environmental Pollution and Control, Natural Resources and Conservation, Climate Change and Global Warming, Environmental Ethics and Policies |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21LCS301J | Operating Systems | Core | 3 | Operating System Concepts and Structure, Process Management and CPU Scheduling, Memory Management Techniques, File Systems and I/O Management, Deadlocks and Concurrency Control |
| 21LIT301J | Internet of Things | Core | 3 | IoT Architecture and Paradigms, IoT Devices, Sensors, and Actuators, IoT Communication Technologies (Wireless, Wired), Data Acquisition and Management, IoT Cloud Platforms and Services, IoT Applications and Use Cases |
| 21LIT302J | Introduction to Web Technology | Core | 3 | HTML5 and CSS3 Essentials, JavaScript for Client-Side Scripting, Web Servers and Web Architecture, Introduction to Web Security, Responsive Web Design Principles, Basic Web Development Tools |
| 21LIT303J | Embedded Systems | Core | 3 | Microcontrollers and Microprocessors Architecture, Embedded System Design Principles, Interfacing with Peripherals (I/O, Memory), Embedded C Programming, Real-Time Operating Systems (RTOS) Concepts, Debugging and Testing Embedded Systems |
| 21LCS302P | Operating Systems Lab | Lab | 1 | Linux Commands and Shell Scripting, Process Management Experiments, CPU Scheduling Algorithm Implementation, Memory Management Techniques, File System Operations |
| 21LIT304P | Internet of Things Lab | Lab | 1 | IoT Device Programming (Arduino/ESP Platforms), Sensor and Actuator Interfacing, IoT Communication Protocols (MQTT, CoAP), Data Logging and Basic Visualization, Cloud Platform Integration (e.g., Node-RED) |
| 21LIT305P | Web Technology Lab | Lab | 1 | HTML/CSS Page Development and Styling, JavaScript for Dynamic Content, Forms and Input Validation, AJAX and JSON for Asynchronous Data, Introduction to Frontend Frameworks |
| 21LIT306J | Cloud Computing | Elective (Program Specific, chosen as representative) | 3 | Cloud Computing Paradigms and Models, Virtualization Technologies, Cloud Service Models (IaaS, PaaS, SaaS), Cloud Deployment Models (Public, Private, Hybrid), Cloud Security and Data Management, Big Data Analytics on Cloud |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21LIT401J | Sensors and Actuators for IoT | Core (Specialization) | 3 | Types of Sensors and Transducers, Actuator Mechanisms and Control Systems, Data Acquisition Systems, Signal Conditioning and Processing, Interfacing Techniques with Microcontrollers, Sensor Calibration and Measurement Accuracy |
| 21LIT402J | IoT Communication Protocols | Core (Specialization) | 3 | Wireless Communication Technologies (Wi-Fi, Bluetooth, Zigbee), LPWAN Technologies (LoRaWAN, NB-IoT), MQTT and CoAP Protocols for IoT Messaging, HTTP and REST APIs for IoT, Network Topologies and Architectures for IoT, Protocol Security for IoT |
| 21LCS401J | Cryptography and Network Security | Core | 3 | Security Concepts and Common Attacks, Symmetric Key Cryptography (DES, AES), Asymmetric Key Cryptography (RSA), Hash Functions and Digital Signatures, Network Security (Firewalls, VPN, IDS), Web Security Fundamentals |
| 21LIT403J | Data Analytics for IoT | Core (Specialization) | 3 | IoT Data Collection and Preprocessing, Statistical Analysis for IoT Data, Machine Learning Algorithms for IoT Applications, Time Series Analysis for Sensor Data, Data Visualization Techniques, Edge Analytics and Fog Computing Concepts |
| 21LIT404P | Sensors and Actuators for IoT Lab | Lab | 1 | Interfacing Various Sensors (Temperature, Humidity, Motion), Controlling Actuators (Motors, LEDs), Data Acquisition from Sensors, Analog to Digital Conversion, Embedded Programming for I/O Control |
| 21LIT405P | IoT Communication Protocols Lab | Lab | 1 | MQTT Broker and Client Implementation, CoAP Communication Setup, Wi-Fi and Bluetooth Module Interfacing, Data Transmission over LPWAN, Network Packet Analysis |
| 21LCS402P | Cryptography and Network Security Lab | Lab | 1 | Implementation of Encryption Algorithms, Digital Signature Generation, Network Vulnerability Scanning, Firewall Configuration, Intrusion Detection System Basics |
| 21LIT307J | Mobile Application Development (Android) | Elective (chosen as representative) | 3 | Android Architecture and Components, User Interface Design with XML, Activities, Intents, and Services, Data Storage (SQLite, Shared Preferences), Network Connectivity and REST APIs, Debugging and Publishing Apps |
| 21LVP401 | Value Added Course (VAC) I | Skill Enhancement | 1 |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21LIT501J | IoT Architecture and Design | Core (Specialization) | 3 | IoT Reference Architectures (e.g., IEEE P2413), IoT Design Methodologies and Principles, Edge, Fog, and Cloud Computing in IoT, Microservices Architecture for IoT, Scalability and Interoperability Considerations, IoT System Integration and Deployment |
| 21LIT502J | IoT Security and Privacy | Core (Specialization) | 3 | IoT Threat Landscape and Vulnerabilities, Device Security and Authentication, Network Security for IoT (TLS, DTLS), Data Privacy and GDPR Compliance, Blockchain for IoT Security, Secure Firmware Updates and Key Management |
| 21LIT503J | Wireless Sensor Networks | Core (Specialization) | 3 | WSN Architecture and Components, Routing Protocols for WSNs, Localization and Tracking Techniques, Time Synchronization in WSNs, Energy Efficiency and Power Management, WSN Applications and Challenges |
| 21LIT504P | IoT Design and Security Lab | Lab | 1 | Designing IoT Solutions with various components, Implementing Security Protocols for IoT Devices, Vulnerability Assessment of IoT Systems, Secure Communication Setup, Privacy-Enhancing Technologies in IoT |
| 21LIT505P | Minor Project | Project | 2 | Project Planning and Management, Requirement Analysis and Design, Implementation and Testing, Documentation and Reporting, Presentation and Demonstration |
| 21LES304J | Machine Learning | Elective (chosen as representative) | 3 | Introduction to Machine Learning Concepts, Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering, PCA), Neural Networks and Deep Learning Basics, Model Evaluation and Validation, Feature Engineering and Selection |
| 21LES318P | Machine Learning Lab | Elective Lab | 1 | Implementing ML Algorithms in Python, Data Preprocessing with Pandas, Model Training and Evaluation (Scikit-learn), Data Visualization for ML Results, Hyperparameter Tuning and Model Optimization |
| 21LVA501 | Value Added Course (VAC) II | Skill Enhancement | 1 |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| 21LCS601J | Professional Ethics and Human Values | Core | 3 | Ethical Theories and Dilemmas, Professionalism in the IT Industry, Cyber Ethics and Data Privacy, Intellectual Property Rights and Plagiarism, Human Values and Work Ethics, Corporate Social Responsibility |
| 21LIT601J | Industrial IoT | Core (Specialization) | 3 | Introduction to IIoT and Industry 4.0, IIoT Architectures and Platforms, Sensors and Actuators in Industrial Settings, SCADA and PLC Integration, Digital Twins and Predictive Maintenance, IIoT Security and Safety Considerations |
| 21LIT602P | Major Project | Project | 5 | Comprehensive Project Planning and Execution, Advanced System Design and Architecture, Implementation and System Integration, Rigorous Testing and Validation, Detailed Technical Report Writing, Final Presentation and Demonstration |
| 21LES301J | Artificial Intelligence | Elective (chosen as representative) | 3 | Introduction to AI and Intelligent Agents, Problem Solving by Search Algorithms, Knowledge Representation and Reasoning, Expert Systems and Logic Programming, Machine Learning Fundamentals, Game Playing and Adversarial Search |
| 21LES319P | Artificial Intelligence Lab | Elective Lab | 1 | Implementing Search Algorithms (BFS, DFS, A*), Prolog/Lisp Programming for AI, Knowledge-Based Systems Implementation, Mini-projects in AI, Working with AI Tools and Libraries |
| 21LIEXXX | Internship | Internship | 2 | Real-world Industry Exposure, Application of Academic Knowledge to Practical Problems, Problem Solving in a Professional Setting, Teamwork and Communication Skills Development, Internship Report Writing and Presentation |
