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An Optical‐Fog assisted EEG‐based virtual reality framework for enhancing E‐learning through educational games
Author(s) -
Sood Sandeep K.,
Singh Kiran D.
Publication year - 2018
Publication title -
computer applications in engineering education
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.478
H-Index - 29
eISSN - 1099-0542
pISSN - 1061-3773
DOI - 10.1002/cae.21965
Subject(s) - computer science , cloud computing , multimedia , energy consumption , software deployment , virtual reality , quality of experience , electroencephalography , software , human–computer interaction , simulation , artificial intelligence , computer network , quality of service , software engineering , engineering , operating system , psychology , psychiatry , electrical engineering
Abstract Virtual reality (VR) combined with cloud computing has become the pioneer to provide e‐learning for making education accessible across the globe. To enhance e‐learning experience, electroencephalography (EEG) based educational games are widely used to improve the cognitive and learning skills of students. This method records the electrical activities of the brain of participating students to enhance prevailed knowledge and experience by using a virtual environment. In the present cloud computing paradigm, VR faces many challenges to adapt EEG‐based educational games for e‐learning due to their intensive demands of low‐delay, optimum bandwidth, and minimum energy consumption. In this paper, an Optical‐Fog assisted EEG‐based virtual reality framework is proposed that uses the resources of the optical network to enhance the e‐learning experience. An EEG‐based VR framework is designed with six modules those are deployed at both Edge‐Fog layer and Optical‐Fog layer in the Optical‐Fog network. In addition, a Software Defined Network (SDN) is used to reduce the delay that improves the Quality of experience (QoE). For simulating the experiment, IFogSim is used to implement both Optical‐Fog‐based and cloud‐based deployment scenarios for playing games. Subsequently, a novel algorithm is proposed for implementing Optical‐Fog‐based module placement strategy. The performance of the framework is evaluated by computing average delay, network usage, and energy consumption. The results show the significant advantages of the optical network for playing EEG‐based games.