Modeling of smartphones’ power using neural networks
| Autor: | Assim Sagahyroon, Sameer Alawnah |
|---|---|
| Rok vydání: | 2017 |
| Předmět: |
Battery (electricity)
Structure (mathematical logic) General Computer Science Artificial neural network business.industry Computer science Work (physics) 020206 networking & telecommunications 02 engineering and technology Machine learning computer.software_genre Power (physics) Identification (information) Control and Systems Engineering Power consumption 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer Energy (signal processing) Simulation Computer Science(all) |
| Zdroj: | EURASIP Journal on Embedded Systems. 2017 |
| ISSN: | 1687-3963 |
| DOI: | 10.1186/s13639-017-0070-1 |
| Popis: | In the work presented in this paper, we use data collected from mobile users over several weeks to develop a neural network-based prediction model for the power consumed by a smartphone. Battery life is critical to the designers of smartphones, and being able to assess scenarios of power consumption, and hence energy usage is of great value. The models developed attempt to correlate power consumption to users’ behavior by using power-related data collected from smartphones with the help of specially designed logging tool or application. Experiences gained while developing the model regarding the selection of input parameters to the model, the identification of the most suitable NN (neural network) structure, and the training methodology applied are all described in this paper. To the best of our knowledge, this is the first attempt where NN is used as a vehicle to model smartphones’ power, and the results obtained demonstrate that NNs models can provide reasonably accurate estimates, and therefore, further investigation of their use in this modeling problem is justified. |
| Databáze: | OpenAIRE |
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