Human machine interaction-assisted smart educational system for rural children. (April 2022)

• Record Type: Journal Article
• Authors: WEI, Na; YANG, Feng; Muthu, BalaAnand; Shanthini, A.
• Is Part Of: Computers & electrical engineering. Volume 99(2022)
• Publication Date: 2022-04
• Subjects: Cyber-physical system -- Human-machine interaction -- Smart educational system -- Rural education; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.107812
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: School systems are primarily dependant on government and government-supported institutions for a country's rural education. It is not easy for rural schooling; rural children are faced with various obstacles before they are done. Either rural student access to specialized training resources such as multimedia schooling, non-school books, and digital education are inaccessible or lack access. Therefore, this study proposes a Human Machine Interaction-assisted Smart Educational System (HMI-aSES) for Rural Children. This paper includes a cyber-physical system (CPS) with optical fog computing to improvise intelligent education system monitoring. This technology uses fog devices with an optical fog layer for early decisions and activities without hesitating, minimum energy, and maximum bandwidth. The proposed architecture provides a flexible smart optical node for many applications powered by CPS through the Fog Manager in the middleware of Cloud and surveillance equipment and facilities. The Fog structure decision suggests an ideal positioning algorithm for putting CPS-based activities on a neighbouring optical fog machine. The latency in the range of 6.2–7.34 ms has been generated by the experimental analysis's proposed device. The energy usage of tracking cameras is dramatically decreased. The network utilization is even smaller than the traditional method by way of the proposed structure. Thus the experimental results shows HMI-aSES increases performance ratio (94.3%), network consumption (95.4%), smart education growth (97.7%), interaction ratio (98.6%) and energy consumption (95.4%).