Work place: Department of Computer Science, Kennesaw State University, Kennesaw, Georgia, USA
E-mail: jhe4@kennesaw.edu
Website:
Research Interests: Computational Mathematics, Computer systems and computational processes, Computational Learning Theory, Computer Networks
Biography
Dr. Jing (Selena) He is currently an Assistant Professor in the Department of Computer Science at Kennesaw State University. She received her PH.D from the Department of Computer Science at Georgia State University. Her dissertation title is "Connected Dominating Set Based Topology Control in Wireless Sensor Networks", and her academic advisors are Prof. Yi Pan and Prof. Ying shu Li. She is now an IEEE student member and an N^2 Women member.
Before joining GSU, she received a B.S. degree in Electronic Engineering from Wuhan Institute of Technology, Wuhan, Hubei China. She received M.S. degrees in computer science concentrated in Artificial Intelligence from Utah State University and emphasised in Wireless Networking from Georgia State University. Her research interests include computational intelligence, machine learning, wireless networks, wireless sensor networks, network optimization, and cognitive radio networks. She also had intern experiences in IT industry. She worked with Electronic Arts in the Salt Lake Branch as a software engineer.
By Akhilesh Vishwanath Ramya Peruri Jing Selena
DOI: https://doi.org/10.5815/ijitcs.2016.05.03, Pub. Date: 8 May 2016
Cloud computing is considered as one of the most exciting technology because of its flexibility and scalability. The main problem that occurs in cloud is security. To overcome the problems or issues of security, a new technique called fog-computing is evolved. As there are security issues in fog even after getting the encrypted data from cloud, we implemented the process of encryption using AES algorithm to check how it works for the fog. So far, to our analysis AES algorithm is the most secured process of encryption for security. Three datasets of different types are considered and applied the analysed encryption technique over those datasets. On validation, entire data over datasets is being accurately encrypted and decrypted back as well. We took android mobile as an edge device and deployed the encryption over datasets into it. Further, performance of encryption is evaluated over selected datasets for accuracy if the entire data is correctly encrypted and decrypted along with the time, User load, Response time, Memory Utilization over file size. Further best and worst cases among the datasets are analysed thereby evaluating the suitability of AES in fog.
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