Adaptation challenges of edge computing model in educational institute

Author:

Momena Alaa Fouad1,Gazi Kamal Hossain2,Mukherjee Asesh Kumar3,Salahshour Soheil45,Ghosh Arijit6,Mondal Sankar Prasad2

Affiliation:

1. Department of Industrial Engineering, College of Engineering, Prince Sattam Bin Abdulaziz University, Al Kharj, Saudi Arabia

2. Department of Applied Mathematics, Maulana Abul Kalam Azad University of Technology, West Bengal, Nadia, West Bengal, India

3. Department of Mathematics, Haringhata Mahavidyalaya, Subarnapur, Nadia, West Bengal, India

4. Faculty of Engineering and Natural Sciences, Bahcesehir University, Istanbul, Turkey

5. Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon

6. Department of Mathematics, St. Xavier’s College (Autonomous), Kolkata, India

Abstract

Use of the Internet of Everything (IoE), the number of smart gadgets increasing rapidly giving the side effect of huge data, which has led to issues with traditional cloud computing models like inadequate security, slow response times, poor privacy, and bandwidth overload. Conventionally, cloud computing is no longer adequate for supporting the diversified needs of the user and the extraordinary society of data processing, so edge computing technologies have been revealed. This study considers edge computing in an educational institute in a scientific way. Multi criteria decision making (MCDM) is one of the most suitable decision making processes that propose to choose optimal alternatives by considering multiple conflicting criteria. Entropy weighted method is considered to evaluate factor weight. Weighted Aggregated Sum Product Assessment (WASPAS) and Combined Compromise Solution (CoCoSo) based MCDM methodologies examine the ranking of alternatives for this study. Multiple decision makers (DMs) give opinions with Pentagonal Fuzzy Soft Set (PFSS) to express the uncertainty and fuzziness of the data set. The set operations and arithmetic operations of PFSS are discussed in detail. Also, a new de-fuzzification method of PFSS is proposed in this study. Calculated the criteria weight and prioritized the alternative based on source data. Lastly, sensitivity analysis and comparative analysis are conducted to check the stability of the result.

Publisher

IOS Press

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