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DOI: 10.14569/IJACSA.2023.0140378
PDF

An Energy‐Aware Technique for Task Allocation in the Internet of Things using Krill Herd Algorithm

Author 1: Dejun Miao
Author 2: Rongyan Xu
Author 3: Jiusong Chen
Author 4: Yizong Dai

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 3, 2023.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: The Internet of Things (IoT) is an innovative technology that connects the digital and physical worlds as well as allows physical devices capable of different capacities to share resources to accomplish tasks. Most IoT objects have limited battery life and are heterogeneous. Assignment of these objects is, therefore, extremely challenging. Energy consumption and reliability are the primary objectives of task allocation algorithms. We present an optimization solution to the IoT task allocation problem based on the krill herd algorithm. The algorithm increases the energy efficiency and stability of the network while providing a reliable task allocation solution. An extensive test of the proposed algorithm has been conducted using the MATLAB simulator. Compared to the most relevant method in the literature, our algorithm provides a higher level of energy efficiency.

Keywords: Internet of things; resource allocation; task scheduling; energy efficiency

Dejun Miao, Rongyan Xu, Jiusong Chen and Yizong Dai, “An Energy‐Aware Technique for Task Allocation in the Internet of Things using Krill Herd Algorithm” International Journal of Advanced Computer Science and Applications(IJACSA), 14(3), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140378

@article{Miao2023,
title = {An Energy‐Aware Technique for Task Allocation in the Internet of Things using Krill Herd Algorithm},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140378},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140378},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {3},
author = {Dejun Miao and Rongyan Xu and Jiusong Chen and Yizong Dai}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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