scholarly journals Energy-Efficient Collaborative Task Computation Offloading in Cloud-Assisted Edge Computing for IoT Sensors

Sensors ◽  
2019 ◽  
Vol 19 (5) ◽  
pp. 1105 ◽  
Author(s):  
Fagui Liu ◽  
Zhenxi Huang ◽  
Liangming Wang
Keyword(s):  
Energy Consumption ◽  
Energy Efficient ◽  
Edge Computing ◽  
Computation Offloading ◽  
Mixed Integer ◽  
Integer Programming Problem ◽  
Efficient Computation ◽  
Collaborative Task ◽  
Computing Paradigm ◽  
Task Dependency

As an emerging and promising computing paradigm in the Internet of things (IoT),edge computing can significantly reduce energy consumption and enhance computation capabilityfor resource-constrained IoT devices. Computation offloading has recently received considerableattention in edge computing. Many existing studies have investigated the computation offloadingproblem with independent computing tasks. However, due to the inter-task dependency in variousdevices that commonly happens in IoT systems, achieving energy-efficient computation offloadingdecisions remains a challengeable problem. In this paper, a cloud-assisted edge computing frameworkwith a three-tier network in an IoT environment is introduced. In this framework, we first formulatedan energy consumption minimization problem as a mixed integer programming problem consideringtwo constraints, the task-dependency requirement and the completion time deadline of the IoT service.To address this problem, we then proposed an Energy-efficient Collaborative Task ComputationOffloading (ECTCO) algorithm based on a semidefinite relaxation and stochastic mapping approachto obtain strategies of tasks computation offloading for IoT sensors. Simulation results demonstratedthat the cloud-assisted edge computing framework was feasible and the proposed ECTCO algorithmcould effectively reduce the energy cost of IoT sensors.

scholarly journals Energy-Efficient Computation Offloading and Transmit Power Allocation Scheme for Mobile Edge Computing

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Xiaohui Gu ◽  
Li Jin ◽  
Nan Zhao ◽  
Guoan Zhang
Keyword(s):  
Energy Consumption ◽  
Power Allocation ◽  
Energy Efficient ◽  
Completion Time ◽  
Computation Offloading ◽  
Battery Life ◽  
Transmission Power ◽  
Mobile Edge Computing ◽  
Efficient Computation ◽  
Allocation Scheme

Mobile edge computing (MEC) is considered a promising technique that prolongs battery life and enhances the computation capacity of mobile devices (MDs) by offloading computation-intensive tasks to the resource-rich cloud located at the edges of mobile networks. In this study, the problem of energy-efficient computation offloading with guaranteed performance in multiuser MEC systems was investigated. Given that MDs typically seek lower energy consumption and improve the performance of computing tasks, we provide an energy-efficient computation offloading and transmit power allocation scheme that reduces energy consumption and completion time. We formulate the energy efficiency cost minimization problem, which satisfies the completion time deadline constraint of MDs in an MEC system. In addition, the corresponding Karush–Kuhn–Tucker conditions are applied to solve the optimization problem, and a new algorithm comprising the computation offloading policy and transmission power allocation is presented. Numerical results demonstrate that our proposed scheme, with the optimal computation offloading policy and adapted transmission power for MDs, outperforms local computing and full offloading methods in terms of energy consumption and completion delay. Consequently, our proposed system could help overcome the restrictions on computation resources and battery life of mobile devices to meet the requirements of new applications.

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.

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