Open AccessA LSTM Approach for Secure Energy Efficient Computational Offloading in Mobile Edge Computing
Author(s) -
S Anoop,
Dr.J. Amar Pratap Singh
Publication year - 2021
Publication title -
webology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.259
H-Index - 18
ISSN - 1735-188X
DOI - 10.14704/web/v18i2/web18359
Subject(s) - computer science , mobile edge computing , computation offloading , energy consumption , distributed computing , edge computing , cloud computing , mobile device , mobile cloud computing , efficient energy use , scheduling (production processes) , computational complexity theory , enhanced data rates for gsm evolution , reinforcement learning , mobile computing , server , computer network , artificial intelligence , algorithm , mathematical optimization , ecology , mathematics , electrical engineering , biology , engineering , operating system
Mobile technologies is evolving so rapidly in every aspect, utilizing every single resource in the form of applications which creates advancement in day to day life. This technological advancements overcomes the traditional computing methods which increases communication delay, energy consumption for mobile devices. In today’s world, Mobile Edge Computing is evolving as a scenario for improving in these limitations so as to provide better output to end users. This paper proposed a secure and energy-efficient computational offloading scheme using LSTM. The prediction of the computational tasks done using the LSTM algorithm. A strategy for computation offloading based on the prediction of tasks, and the migration of tasks for the scheme of edge cloud scheduling based on a reinforcement learning routing algorithm help to optimize the edge computing offloading model. Experimental results show that our proposed algorithm Intelligent Energy Efficient Offloading Algorithm (IEEOA), can efficiently decrease total task delay and energy consumption, and bring much security to the devices due to the firewall nature of LSTM.