Facial reconstruction

Search LJMU Research Online

Browse Repository | Browse E-Theses

Smart Application Division and Time Allocation Policy for Computational Offloading in Wireless Powered Mobile Edge Computing

Numani, A, Ali, Z, Abbas, ZH, Abbas, G, Baker, T and Al-Jumeily, D (2021) Smart Application Division and Time Allocation Policy for Computational Offloading in Wireless Powered Mobile Edge Computing. Mobile Information Systems, 2021. ISSN 1574-017X

[img]
Preview
Text
Smart Application Division and Time Allocation Policy for Computational Offloading in Wireless Powered Mobile Edge Computing.pdf - Published Version
Available under License Creative Commons Attribution.

Download (1MB) | Preview

Abstract

Limited battery life and poor computational resources of mobile terminals are challenging problems for the present and future computation-intensive mobile applications. Wireless powered mobile edge computing is one of the solutions, in which wireless energy transfer technology and cloud server’s capabilities are brought to the edge of cellular networks. In wireless powered mobile edge computing systems, the mobile terminals charge their batteries through radio frequency signals and offload their applications to the nearby hybrid access point in the same time slot to minimize their energy consumption and ensure uninterrupted connectivity with hybrid access point. However, the smart division of application into subtasks as well as intelligent partitioning of time slot for harvesting energy and offloading data is a complex problem. In this paper, we propose a novel deep-learning-based offloading and time allocation policy (DOTP) for training a deep neural network that divides the computation application into optimal number of subtasks, decides for the subtasks to be offloaded or executed locally (offloading policy), and divides the time slot for data offloading and energy harvesting (time allocation policy). DOTP takes into account the current battery level, energy consumption, and time delay of mobile terminal. A comprehensive cost function is formulated, which uses all the aforementioned metrics to calculate the cost for all number of subtasks. We propose an algorithm that selects the optimal number of subtasks, partial offloading policy, and time allocation policy to generate a huge dataset for training a deep neural network and hence avoid huge computational overhead in partial offloading. Simulation results are compared with the benchmark schemes of total offloading, local execution, and partial offloading. It is evident from the results that the proposed algorithm outperforms the other schemes in terms of battery life, time delay, and energy consumption, with 75% accuracy of the trained deep neural network. The achieved decrease in total energy consumption of mobile terminal through DOTP is 45.74%, 36.69%, and 30.59% as compared to total offloading, partial offloading, and local offloading schemes, respectively.

Item Type: Article
Uncontrolled Keywords: 0805 Distributed Computing, 0806 Information Systems
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Computer Science & Mathematics
Publisher: Hindawi Publishing Company
Date Deposited: 20 Jul 2021 09:30
Last Modified: 20 Jul 2021 09:30
DOI or Identification number: 10.1155/2021/9993946
URI: https://researchonline.ljmu.ac.uk/id/eprint/15306

Actions (login required)

View Item View Item