Wu, J., Ahmed Haider, Sami, Soni, M., Kalra, A. and Deb, N. ORCID: https://orcid.org/0000-0002-1860-5548 (2022) Blockchain based energy efficient multi-tasking optimistic scenario for mobile edge computing. PeerJ. Computer science, 8 (e1118). pp. 1-20. ISSN 2376-5992
Text (PubRouter Upload)
article.pdf - Published Version Restricted to Repository staff only Download (5MB) | Request a copy |
|
Preview |
Text
Binder1.pdf - Published Version Available under License Creative Commons Attribution. Download (5MB) | Preview |
Abstract
Mobile edge computational power faces the difficulty of balancing the energy consumption of many devices and workloads as science and technology advance. Most related research focuses on exploiting edge server computing performance to reduce mobile device energy consumption and task execution time during task processing. Existing research, however, shows that there is no adequate answer to the energy consumption balances between multi-device and multitasking. The present edge computing system model has been updated to address this energy consumption balance problem. We present a blockchain-based analytical method for the energy utilization balance optimization problem of multi-mobile devices and multitasking and an optimistic scenario on this foundation. An investigation of the corresponding approximation ratio is performed. Compared to the total energy demand optimization method and the random algorithm, many simulation studies have been carried out. Compared to the random process, the testing findings demonstrate that the suggested greedy algorithm can improve average performance by 66.59 percent in terms of energy balance. Furthermore, when the minimum transmission power of the mobile device is between five and six dBm, the greedy algorithm nearly achieves the best solution when compared to the brute force technique under the classical task topology.
Item Type: | Article |
---|---|
Uncontrolled Discrete Keywords: | Energy balance, Greedy Algorithm, Blockchain, Task Offloading, Mobile Edge Computing |
Divisions: | College of Business, Psychology and Sport > Worcester Business School |
Related URLs: | |
Copyright Info: | Copyright 2022 Wu et al. Distributed under Creative Commons CC-BY 4.0 OPEN ACCESS |
SWORD Depositor: | Prof. Pub Router |
Depositing User: | Katherine Small |
Date Deposited: | 10 May 2024 13:07 |
Last Modified: | 28 Nov 2024 19:15 |
URI: | https://eprints.worc.ac.uk/id/eprint/12680 |
Actions (login required)
View Item |