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Modeling battery behavior on sensory operations for context-aware smartphone sensing.

Yurur O, Liu CH, Moreno W - Sensors (Basel) (2015)

Bottom Line: Energy consumption is a major concern in context-aware smartphone sensing.Second, this paper models the energy consumption behavior of accelerometers analytically and then provides extensive simulation results and a smartphone application to examine the proposed sensor model.By doing this, this paper aims at achieving a fine efficiency in power consumption caused by sensory operations, while maintaining the accuracy of smartphone applications based on sensor usages.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical Engineering, University of South Florida, Tampa, FL 33620, USA. oyurur@mail.usf.edu.

ABSTRACT
Energy consumption is a major concern in context-aware smartphone sensing. This paper first studies mobile device-based battery modeling, which adopts the kinetic battery model (KiBaM), under the scope of battery non-linearities with respect to variant loads. Second, this paper models the energy consumption behavior of accelerometers analytically and then provides extensive simulation results and a smartphone application to examine the proposed sensor model. Third, a Markov reward process is integrated to create energy consumption profiles, linking with sensory operations and their effects on battery non-linearity. Energy consumption profiles consist of different pairs of duty cycles and sampling frequencies during sensory operations. Furthermore, the total energy cost by each profile is represented by an accumulated reward in this process. Finally, three different methods are proposed on the evolution of the reward process, to present the linkage between different usage patterns on the accelerometer sensor through a smartphone application and the battery behavior. By doing this, this paper aims at achieving a fine efficiency in power consumption caused by sensory operations, while maintaining the accuracy of smartphone applications based on sensor usages. More importantly, this study intends that modeling the battery non-linearities together with investigating the effects of different usage patterns in sensory operations in terms of the power consumption and the battery discharge may lead to discovering optimal energy reduction strategies to extend the battery lifetime and help a continual improvement in context-aware mobile services.

No MeSH data available.


Related in: MedlinePlus

Power consumption ratio analysis in comparison to the aggressive sampling (DC = 100%, fs = 100 Hz) (results are averaged with respect to the experiments).
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f5-sensors-15-12323: Power consumption ratio analysis in comparison to the aggressive sampling (DC = 100%, fs = 100 Hz) (results are averaged with respect to the experiments).

Mentions: Each method given by Equations (16)–(18) is applied differently to the semi-Markov reward process for the similar user activity profile within the same experimental setups in order to analyze the power efficiency or battery life extension achieved by the smartphone accelerometer, shown in Figure 5, while keeping overall 10% accuracy loss.


Modeling battery behavior on sensory operations for context-aware smartphone sensing.

Yurur O, Liu CH, Moreno W - Sensors (Basel) (2015)

Power consumption ratio analysis in comparison to the aggressive sampling (DC = 100%, fs = 100 Hz) (results are averaged with respect to the experiments).
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4507616&req=5

f5-sensors-15-12323: Power consumption ratio analysis in comparison to the aggressive sampling (DC = 100%, fs = 100 Hz) (results are averaged with respect to the experiments).
Mentions: Each method given by Equations (16)–(18) is applied differently to the semi-Markov reward process for the similar user activity profile within the same experimental setups in order to analyze the power efficiency or battery life extension achieved by the smartphone accelerometer, shown in Figure 5, while keeping overall 10% accuracy loss.

Bottom Line: Energy consumption is a major concern in context-aware smartphone sensing.Second, this paper models the energy consumption behavior of accelerometers analytically and then provides extensive simulation results and a smartphone application to examine the proposed sensor model.By doing this, this paper aims at achieving a fine efficiency in power consumption caused by sensory operations, while maintaining the accuracy of smartphone applications based on sensor usages.

View Article: PubMed Central - PubMed

Affiliation: Department of Electrical Engineering, University of South Florida, Tampa, FL 33620, USA. oyurur@mail.usf.edu.

ABSTRACT
Energy consumption is a major concern in context-aware smartphone sensing. This paper first studies mobile device-based battery modeling, which adopts the kinetic battery model (KiBaM), under the scope of battery non-linearities with respect to variant loads. Second, this paper models the energy consumption behavior of accelerometers analytically and then provides extensive simulation results and a smartphone application to examine the proposed sensor model. Third, a Markov reward process is integrated to create energy consumption profiles, linking with sensory operations and their effects on battery non-linearity. Energy consumption profiles consist of different pairs of duty cycles and sampling frequencies during sensory operations. Furthermore, the total energy cost by each profile is represented by an accumulated reward in this process. Finally, three different methods are proposed on the evolution of the reward process, to present the linkage between different usage patterns on the accelerometer sensor through a smartphone application and the battery behavior. By doing this, this paper aims at achieving a fine efficiency in power consumption caused by sensory operations, while maintaining the accuracy of smartphone applications based on sensor usages. More importantly, this study intends that modeling the battery non-linearities together with investigating the effects of different usage patterns in sensory operations in terms of the power consumption and the battery discharge may lead to discovering optimal energy reduction strategies to extend the battery lifetime and help a continual improvement in context-aware mobile services.

No MeSH data available.


Related in: MedlinePlus