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

Ozgur Yurur1, Chi Harold Liu2, Wilfrido Moreno3

  • 1Department of Electrical Engineering, University of South Florida, Tampa, FL 33620, USA. oyurur@mail.usf.edu.

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Area of Science:

  • Mobile Computing
  • Energy Efficiency
  • Sensor Networks

Background:

  • Smartphone sensing is crucial for context-aware applications but leads to significant energy consumption.
  • Battery non-linearities and varying sensor loads complicate accurate energy modeling.
  • Optimizing sensor usage is vital for extending battery life and ensuring service continuity.

Purpose of the Study:

  • To develop an analytical model for accelerometer energy consumption in smartphones.
  • To investigate the impact of sensor usage patterns on battery non-linearities and discharge.
  • To propose strategies for efficient power management in context-aware mobile services.

Main Methods:

  • Utilized the kinetic battery model (KiBaM) to study battery non-linearities under variant loads.
  • Analytically modeled accelerometer energy consumption and validated with simulations and a smartphone app.
  • Integrated a Markov reward process to create energy consumption profiles based on duty cycles and sampling frequencies.

Main Results:

  • Established a link between sensor operation patterns, battery non-linearity, and overall energy consumption.
  • Demonstrated that different usage patterns significantly affect battery discharge and power drain.
  • Quantified the energy cost of various sensor operation profiles through accumulated rewards.

Conclusions:

  • Modeling battery non-linearities alongside sensor usage patterns is key to discovering optimal energy reduction strategies.
  • The proposed methods can help achieve power efficiency in sensory operations without compromising application accuracy.
  • This research contributes to extending smartphone battery lifetime and improving context-aware mobile services.