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.
Highlights
New generation mobile devices with sensing capabilities, such as smartphones and tablets, will constitute a significant part of future mobile technologies
This paper investigates mobile device-based battery behavior with respect to variant sensory operations in a smartphone application
The paper models sensory operations by providing the smartphone accelerometer as an example to analyze the linkage between battery discharge and power consumption caused by the sensor
Summary
New generation mobile devices with sensing capabilities, such as smartphones and tablets, will constitute a significant part of future mobile technologies. A major challenge standing up to these sensor-rich devices is resource limitation in terms of power, memory and bandwidth as compared to the capabilities of PCs and servers In this sense, the design of mobile device-based context-aware middleware needs to create abstract models for the representation of the interested phenomena for the application services, and to exploit the heterogeneous and unobtrusive physical world, as well as providing energy-efficient optimal sensor sensing and actuating solutions. Continuously capturing user context through sensors in a context-aware application imposes heavy workloads on computations and hardware, e.g., the processor and relevant hardware peripherals, which, in return, makes the battery drain rapidly Thereby, topics, such as the extension of battery lifetime, estimation of energy delivery or battery discharge and optimal energy management, have drawn much research interest in mobile computing
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