scholarly journals Delay-aware and Energy-Efficient Computation Offloading in Mobile Edge Computing Using Deep Reinforcement Learning

2021 ◽  
pp. 1-1
Author(s):  
Laha Ale ◽  
Ning Zhang ◽  
Xiaojie Fang ◽  
Xianfu Chen ◽  
Shaohua Wu ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Energy Efficient ◽  
Edge Computing ◽  
Computation Offloading ◽  
Mobile Edge Computing ◽  
Efficient Computation

scholarly journals A LSTM Approach for Secure Energy Efficient Computational Offloading in Mobile Edge Computing

Webology ◽  
2021 ◽  
Vol 18 (2) ◽  
pp. 856-874
Author(s):  
S. Anoop ◽  
Dr.J. Amar Pratap Singh
Keyword(s):  
Reinforcement Learning ◽  
Energy Consumption ◽  
Energy Efficient ◽  
Routing Algorithm ◽  
Computing Methods ◽  
Edge Computing ◽  
Mobile Technologies ◽  
Computation Offloading ◽  
Mobile Edge Computing ◽  
Computational Offloading

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.

scholarly journals Energy Efficient Computation Offloading Mechanism in Multi-Server Mobile Edge Computing—An Integer Linear Optimization Approach

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 1010 ◽  
Author(s):  
Prince Waqas Khan ◽  
Khizar Abbas ◽  
Hadil Shaiba ◽  
Ammar Muthanna ◽  
Abdelrahman Abuarqoub ◽  
...  
Keyword(s):  
Energy Harvesting ◽  
Mobile Devices ◽  
Energy Efficient ◽  
Linear Optimization ◽  
Edge Computing ◽  
Computation Offloading ◽  
Optimization Approach ◽  
Mobile Edge Computing ◽  
Efficient Computation ◽  
Integer Linear Optimization

Conserving energy resources and enhancing computation capability have been the key design challenges in the era of the Internet of Things (IoT). The recent development of energy harvesting (EH) and Mobile Edge Computing (MEC) technologies have been recognized as promising techniques for tackling such challenges. Computation offloading enables executing the heavy computation workloads at the powerful MEC servers. Hence, the quality of computation experience, for example, the execution latency, could be significantly improved. In a situation where mobile devices can move arbitrarily and having multi servers for offloading, computation offloading strategies are facing new challenges. The competition of resource allocation and server selection becomes high in such environments. In this paper, an optimized computation offloading algorithm that is based on integer linear optimization is proposed. The algorithm allows choosing the execution mode among local execution, offloading execution, and task dropping for each mobile device. The proposed system is based on an improved computing strategy that is also energy efficient. Mobile devices, including energy harvesting (EH) devices, are considered for simulation purposes. Simulation results illustrate that the energy level starts from 0.979 % and gradually decreases to 0.87 % . Therefore, the proposed algorithm can trade-off the energy of computational offloading tasks efficiently.

scholarly journals Advanced Energy-Efficient Computation Offloading Using Deep Reinforcement Learning in MTC Edge Computing

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 82867-82875 ◽  
Author(s):  
Israr Khan ◽  
Xiaofeng Tao ◽  
G. M. Shafiqur Rahman ◽  
Waheed Ur Rehman ◽  
Tabinda Salam
Keyword(s):  
Reinforcement Learning ◽  
Energy Efficient ◽  
Edge Computing ◽  
Computation Offloading ◽  
Efficient Computation
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