Internet of ThingsSemester 3-2 syllabus

The second semester of third year deepens the data and platform side of IoT with analytics, cloud computing, and machine learning as core subjects, then opens up two professional-elective slots plus an open elective. Two labs reinforce cloud and analytics skills, and the semester closes with soft-skills/IELTS training, a technical-writing and IPR audit course, and a mandatory 8-week industry internship over the following summer. Total load is 20-1-8 contact hours for 23 credits.

Subjects

IoT Data Analytics

• Total: 3-0-0, 3 credits (Professional Core)
• Unit 1: Big-data platforms for IoT and interoperability challenges across smart-city applications.
• Unit 2: RFID authentication issues and adaptive neural approaches to self-aware IoT systems.
• Unit 3: Fog computing as a distributed analytics platform and metadata management for smart grids.
• Unit 4: Web-enabled building automation, intelligent transportation, and smart health-tracking systems.
• Unit 5: Sustainability analytics in cloud-based M2M systems and social-network analysis for smart environments.

Cloud Computing

• Total: 3-0-0, 3 credits (Professional Core)
• Unit 1: Cloud fundamentals — service models (IaaS/PaaS/SaaS) and deployment models.
• Unit 2: Cloud-enabling technologies — parallel/distributed computing, SOA, and virtualization.
• Unit 3: Virtualization and containers, including Docker and orchestration with Kubernetes.
• Unit 4: Cloud challenges — interoperability, scalability, energy efficiency, and security architecture.
• Unit 5: Advanced topics — serverless computing, cloud-centric IoT, edge/fog computing, and DevOps.

Machine Learning

• Total: 3-0-0, 3 credits (Professional Core)
• Unit 1: Machine learning paradigms, learning stages, and data representation.
• Unit 2: Nearest-neighbor models, distance measures, and classifier/regression performance evaluation.
• Unit 3: Decision-tree models, random forests, and the Bayes classifier.
• Unit 4: Linear discriminants — perceptrons, SVMs, logistic regression, and multi-layer perceptrons.
• Unit 5: Clustering techniques — k-means, fuzzy c-means, and spectral clustering.

Professional Elective-II options:

Students choose one of the following four subjects (or an approved 12-week MOOC) as Professional Elective-II.

DevOps

• Unit 1: DevOps lifecycle, workflow principles, and CI/CD automation concepts.
• Unit 2: Source-code management with Git and code-quality analysis with tools like SonarQube.
• Unit 3: Continuous integration using Jenkins, including pipelines and master/agent architecture.
• Unit 4: Continuous delivery and containerization with Docker.
• Unit 5: Configuration management with Ansible and container orchestration with Kubernetes/OpenShift.

IoT Security

• Unit 1: Fundamentals of the IoT security ecosystem and cryptographic building blocks.
• Unit 2: Cloud computing service models relevant to IoT deployments.
• Unit 3: Benefits and challenges of cloud computing, including public-versus-private trade-offs.
• Unit 4: Core security concepts for IoT devices — confidentiality, integrity, and authentication.
• Unit 5: IoT security threats and countermeasures, including virtualization-specific attacks.

Multi Agent Systems

• Unit 1: Foundations of agent systems and their relationship to objects, expert systems, and distributed systems.
• Unit 2: Intelligent-agent architectures — reactive, reasoning, and hybrid agents.
• Unit 3: Multi-agent communication standards and cooperative distributed problem solving.
• Unit 4: Multi-agent decision-making — game-theoretic equilibria and computational social choice.
• Unit 5: Resource allocation, auction mechanisms, bargaining strategies, and logical foundations of multi-agent reasoning.

Automata Theory & Compiler Design

• Unit 1: Finite automata (DFA/NFA) and an introduction to compiler phases.
• Unit 2: Regular expressions/languages and the lexical-analysis phase of compilation.
• Unit 3: Context-free grammars, parse trees, and top-down parsing.
• Unit 4: Pushdown automata and bottom-up/LR parsing techniques.
• Unit 5: Turing machines, decidability, and later compiler phases such as code generation.

Professional Elective-III options:

Students choose one of the following four subjects (or an approved 12-week MOOC) as Professional Elective-III.

Blockchain Technologies

• Unit 1: Blockchain fundamentals, consensus mechanisms, and cryptocurrency basics.
• Unit 2: Public blockchain systems (Bitcoin, Ethereum) and smart contracts.
• Unit 3: Private and consortium blockchain systems, plus initial coin offerings.
• Unit 4: Blockchain security — privacy, scalability, and identity-management challenges.
• Unit 5: Industry case studies and hands-on blockchain development with Python and Hyperledger Fabric.

Natural Language Processing

• Unit 1: Language modeling, morphology, and text normalization/tokenization.
• Unit 2: N-gram models and part-of-speech tagging approaches.
• Unit 3: Syntactic analysis — context-free grammars, parsing, and probabilistic CFGs.
• Unit 4: Semantics and pragmatics, including word-sense disambiguation.
• Unit 5: Discourse analysis, coreference resolution, and standard lexical resources like WordNet.

Security Assessment and Risk Analysis

• Unit 1: Core computer-security principles and threat modeling.
• Unit 2: Secure software design representations and vulnerability-aware design review.
• Unit 3: Software assurance models and risk-based security testing.
• Unit 4: Enterprise security — cryptography, authentication schemes, and PKI.
• Unit 5: Security frameworks for internet-based e-commerce and e-service systems.

Android Application Development

• Unit 1: Mobile device security issues and secure development strategies.
• Unit 2: WAP/mobile HTML security and common web-style application attacks.
• Unit 3: Bluetooth technology architecture and its security vulnerabilities.
• Unit 4: SMS/MMS and WAP protocol-level attacks.
• Unit 5: Enterprise mobile security controls — encryption, sandboxing, and app signing/permissions.

Open Elective-III

• Total: 3-0-0, 3 credits
• Selected from the university-wide open elective pool available to IoT students that semester.

Cloud Computing Lab

• Total: 0-0-3, 1.5 credits (Professional Core)
• Web-services and IPC/messaging exercises.
• Virtual-machine setup with VirtualBox/VMware and cloud instance provisioning on AWS EC2/OpenStack.
• Google App Engine deployment and Docker container web-server setup.
• Hadoop single-node cluster setup, OpenFaaS serverless demos, and CloudSim scheduling simulations.

IoT Data Analytics Lab

• Total: 0-0-3, 1.5 credits (Professional Core)
• Core Java data-structure implementations (linked lists, stacks, queues, sets, maps).
• Hadoop installation across standalone, pseudo-distributed, and fully distributed modes.
• MapReduce programs — word count, weather-data mining, shortest path, and PageRank.
• Pig and Hive exercises for data sorting, grouping, and querying.

Soft Skills or IELTS

• Total: 0-1-2, 2 credits (Skill Enhancement Course)
• Unit 1: Communication skills — intrapersonal/interpersonal skills and verbal/non-verbal communication.
• Unit 2: Critical thinking — active listening, analytical reasoning, and case analysis.
• Unit 3: Problem solving and decision making, including conflict resolution.
• Unit 4: Emotional intelligence and stress management.
• Unit 5: Leadership skills — team building, public speaking, and time management.

Technical Paper Writing & IPR (Audit Course)

• Total: 2-0-0, 0 credits
• Unit 1: Technical report writing fundamentals and structuring conventions.
• Unit 2: Drafting, illustrations, and plain-English editing practices.
• Unit 3: Proofreading, summarizing, and presenting final reports.
• Unit 4: Word-processing tools for reports — tables of contents, tracked changes, and citations.
• Unit 5: Intellectual property fundamentals — patents, copyrights, and the patenting process.

Note: A mandatory industry internship of 8 weeks runs during the following summer vacation.