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Research Article | Open Access |

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) · Vol. 14, No. 3 · Published 2023

DOI: https://doi.org/10.14569/IJACSA.2023.0140378

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.

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How to Cite this Article

Miao, D., Xu, R., Chen, J., & Dai, Y. (2023). An Energy‐Aware Technique for Task Allocation in the Internet of Things using Krill Herd Algorithm. International Journal of Advanced Computer Science and Applications, 14(3). https://doi.org/10.14569/IJACSA.2023.0140378

Miao, Dejun, et al.. "An Energy‐Aware Technique for Task Allocation in the Internet of Things using Krill Herd Algorithm." International Journal of Advanced Computer Science and Applications, vol. 14, no. 3, 2023, https://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},
  volume    = {14},
  number    = {3},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Dejun Miao and Rongyan Xu and Jiusong Chen and Yizong Dai},
  doi       = {10.14569/IJACSA.2023.0140378},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140378}
}

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