- Title
- Intelligent energy prediction techniques for fog computing networks
- Creator
- Farooq, Umar; Shabir, Muhammad; Javed, Muhammad; Imran, Muhammad
- Date
- 2021
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/179027
- Identifier
- vital:15502
- Identifier
-
https://doi.org/10.1016/j.asoc.2021.107682
- Identifier
- ISBN:1568-4946 (ISSN)
- Abstract
- Energy Efficiency is a key concern for future fog-enabled Internet of Things (IoT). Since Fog Nodes (FNs) are energy-constrained devices, task offloading techniques must consider the energy consumption of the FNs to maximize the performance of IoT applications. In this context, accurate energy prediction can enable the development of intelligent energy-aware task offloading techniques. In this paper, we present two energy prediction techniques, the first one is based on the Recursive Least Square (RLS) filter and the second one uses the Artificial Neural Network (ANN). Both techniques use inputs such as the number of tasks and size of the tasks to predict the energy consumption at different fog nodes. Simulation results show that both techniques have a root mean square error of less than 3%. However, the ANN-based technique shows up to 20% less root mean square error as compared to the RLS-based technique. © 2021 Elsevier B.V.
- Publisher
- Elsevier Ltd
- Relation
- Applied Soft Computing Vol. 111, no. (2021), p.
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright 2021 Elsevier B.V.
- Rights
- Open Access
- Subject
- 0102 Applied Mathematics; 0801 Artificial Intelligence and Image Processing; 0806 Information Systems; Artificial neural network; Energy prediction; Fog computing
- Full Text
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