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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.Furthermore, the total energy cost by each profile is represented by an accumulated reward in this process.

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

The KiBaM discharge model where C = 1400 mAh = 5040 As, c = 0.625, k = 4.5E−5/s, p = 0.1, λ = 2fs, fs = {50, 100} Hz, r = nΔt, n = {1/2, 3/4,1}.
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f2-sensors-15-12323: The KiBaM discharge model where C = 1400 mAh = 5040 As, c = 0.625, k = 4.5E−5/s, p = 0.1, λ = 2fs, fs = {50, 100} Hz, r = nΔt, n = {1/2, 3/4,1}.

Mentions: Figure 2 shows an example investigating KiBaM behavior under different load profiles and also at fixed system parameters. The load profiles are characterized by mixture pairs of sampling frequency, fs, and duty cycling on the load. Thereby, the load is defined by λ = 2fs where fs = {50, 100} Hz and r = nΔt where n = {1/2, 3/4, 1}. For instance, if n = 1, this means the load has a constant discharge profile, i.e., 100% duty cycling, whereas if n ∈ {1/2, 3/4}, this means duty cycling values of {50%, 75%} are applied on the load. In addition, the same power consumption rate per unit time is considered during discharge. On the other hand, the battery parameters for KiBaM are chosen as in C = 1400 mAh, c = 0.625, p = 0.1 and k = 4.5 E−5(1/s). These parameters can differ from one battery to another. However, with this example, it is intended to see how a battery discharges differently with respect to variant load profiles.


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

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

The KiBaM discharge model where C = 1400 mAh = 5040 As, c = 0.625, k = 4.5E−5/s, p = 0.1, λ = 2fs, fs = {50, 100} Hz, r = nΔt, n = {1/2, 3/4,1}.
© Copyright Policy
Related In: Results  -  Collection

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

f2-sensors-15-12323: The KiBaM discharge model where C = 1400 mAh = 5040 As, c = 0.625, k = 4.5E−5/s, p = 0.1, λ = 2fs, fs = {50, 100} Hz, r = nΔt, n = {1/2, 3/4,1}.
Mentions: Figure 2 shows an example investigating KiBaM behavior under different load profiles and also at fixed system parameters. The load profiles are characterized by mixture pairs of sampling frequency, fs, and duty cycling on the load. Thereby, the load is defined by λ = 2fs where fs = {50, 100} Hz and r = nΔt where n = {1/2, 3/4, 1}. For instance, if n = 1, this means the load has a constant discharge profile, i.e., 100% duty cycling, whereas if n ∈ {1/2, 3/4}, this means duty cycling values of {50%, 75%} are applied on the load. In addition, the same power consumption rate per unit time is considered during discharge. On the other hand, the battery parameters for KiBaM are chosen as in C = 1400 mAh, c = 0.625, p = 0.1 and k = 4.5 E−5(1/s). These parameters can differ from one battery to another. However, with this example, it is intended to see how a battery discharges differently with respect to variant load profiles.

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.Furthermore, the total energy cost by each profile is represented by an accumulated reward in this process.

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