LSA Based Smart Assessment Methodology for SDN Infrastructure in IoT Environment

  • Farhan Ullah
  • Junfeng Wang
  • Muhammad Farhan
  • Sohail Jabbar
  • Muhammad Kashif Naseer
  • Muhammad Asif
Part of the following topical collections:
  1. Special Issue on Emerging Technology for Software Defined Network Enabled Internet of Things


The Software Defined Network (SDN) is merged in the Internet of Things (IoT) to interconnect large and complex networks. It is used in the education system to interconnect students and teacher by heterogenous IoT devices. In this paper, the SDN-based IoT model for students’ Interaction is proposed which interconnects students to a teacher in a smart city environment. The students and teachers are free to move to anywhere, anytime and with any hardware. An architecture model for students’ teacher’s interaction in IoT is proposed which shows the details procedure about the interaction of teacher with students for electronic assessment. The SDN solves the scalability and interoperability issues between their heterogenous IoT devices. A Methodology for Students’ Answer Assessment using Latent Semantic Analysis (LSA) is proposed which calculates the semantic similarity between teacher’s question and students’ answers. The LSA is used to calculate semantic similarity between text documents. It is used to mark the students’ answers automatically by semantics. The Students’ can see results through their IoT devices just after finishing the examination with more accurate marks We have collected fifty (50) undergraduate students’ data from Learning Management System (LMS) of Virtual University (VU) of Pakistan. The experiment is implemented on eighteen (18) students’ answers in R Studio with R version 3.4.2. Teachers are provided with four (4) bins of the mark while the proposed method assigns accurate marks. The experimental results show that the proposed methodology gave accurate results as compared to teacher’s marks.


Software define network Internet of Things Latent Semantic Analysis Machine learning Semantic similarity Technology enhanced assessment 



This work was supported by the National Key Research and Development Program (2016YFB0800605, 2016QY06X1205), and the Technology Research and Development Program of Sichuan, China (18DYF2039, 17ZDYF2583).


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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Computer ScienceSichuan UniversityChengduChina
  2. 2.School of Aeronautics and Astronautics and College of Computer ScienceSichuan UniversityChengduChina
  3. 3.COMSATS Institute of Information TechnologySahiwalPakistan
  4. 4.Department of Computer ScienceNational Textile UniversityFaisalabadPakistan
  5. 5.Department of Computer EngineeringBahria UniversityIslamabadPakistan

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