A Deep Neural Network Approach for Customized Prediction of Mobile Devices Discharging Time

Gentil, Mattia; Galeazzi, Alessandro; Chiariotti, Federico; Polese, Michele; Zanella, Andréa; Zorzi, Michele

doi:10.1109/glocom.2017.8254532

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…The structure of artificial neural networks (ANN) consists of input, hidden, and output layers. If the structure of an ANN consists of more than one hidden layer, it is considered a deep neural network (DNN) [13]. The structure of the created DNN is presented in Fig.…”

Section: ) Deep Neural Network (Dnn)mentioning

confidence: 99%

Deep Belief Network Based Wireless Sensor Network Connectivity Analysis

AKBAŞ,

BUYRUKOĞLU

2023

Balkan Journal of Electrical and Computer Engineering

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Wireless sensor networks (WSNs) are widely used in various fields, and their deployment is critical to ensure area coverage and full network connectivity to achieve the maximum network lifetime. In this study, we present a mixed-integer programming (MIP) model that deeply investigates deployment parameters to optimize lifetime and analyze network connectivity. We further analyze the obtained results using Deep Belief Network (DBN) and Deep Neural Network (DNN) algorithms to achieve higher accuracy rates. Our evaluation shows that the DBN outperforms the DNN with an accuracy rate of 81.2%, precision of 81.2%, recall of 99.1%, and an F1-Score of 0.78. We also utilize two different datasets to justify the efficiency of the DBN in this research. The findings of this study emphasize the validity of our DBN algorithm and encourage further research into lifetime optimization and connectivity analysis in WSNs.

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Section: ) Deep Neural Network (Dnn)mentioning

confidence: 99%

Deep Belief Network Based Wireless Sensor Network Connectivity Analysis

AKBAŞ,

BUYRUKOĞLU

2023

Balkan Journal of Electrical and Computer Engineering

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“…In the research work [305], ML algorithms, especially DNN is used for the prediction of the battery depletion time by drawing a personalized battery-usage pattern from time and location information provided by the mobile device and also taking into account the individual users' discharging patterns and personal behaviour information.…”

Section: ) Prediction Through Dnnmentioning

confidence: 99%

Power Consumption Analysis, Measurement, Management, and Issues: A State-of-the-Art Review of Smartphone Battery and Energy Usage

et al. 2019

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The advancement and popularity of smartphones have made it an essential and all-purpose device. But lack of advancement in battery technology has held back its optimum potential. Therefore, considering its scarcity, optimal use and efficient management of energy are crucial in a smartphone. For that, a fair understanding of a smartphone's energy consumption factors is necessary for both users and device manufacturers, along with other stakeholders in the smartphone ecosystem. It is important to assess how much of the device's energy is consumed by which components and under what circumstances. This paper provides a generalized, but detailed analysis of the power consumption causes (internal and external) of a smartphone and also offers suggestive measures to minimize the consumption for each factor. The main contribution of this paper is four comprehensive literature reviews on: 1) smartphone's power consumption assessment and estimation (including power consumption analysis and modelling); 2) power consumption management for smartphones (including energy-saving methods and techniques); 3) state-of-the-art of the research and commercial developments of smartphone batteries (including alternative power sources); and 4) mitigating the hazardous issues of smartphones' batteries (with a details explanation of the issues). The research works are further subcategorized based on different research and solution approaches. A good number of recent empirical research works are considered for this comprehensive review, and each of them is succinctly analysed and discussed.

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Smartphone power management based on ConvLSTM model

Çiçek

Gören

2021

Neural Comput & Applic

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A Deep Neural Network Approach for Customized Prediction of Mobile Devices Discharging Time

Cited by 4 publications

References 18 publications

Deep Belief Network Based Wireless Sensor Network Connectivity Analysis

Deep Belief Network Based Wireless Sensor Network Connectivity Analysis

Power Consumption Analysis, Measurement, Management, and Issues: A State-of-the-Art Review of Smartphone Battery and Energy Usage

Smartphone power management based on ConvLSTM model

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