BatterySense

The aim of this project is to investigate the performance of smartphone power management techniques. The power management consists of three components; charging controller, fuel gauge, and the battery pack. The performance of charging controller lies on how the devices charge their batteries and the consequence of such charging on battery life. I am actively working on this project. We combine smartphone battery analytics data from Carat data set with practical measurements. Thanks to the Carat team for sharing their data set. We are working on data-driven methods to estimate the battery capacity loss, charging performance of the device and user, performance of the fuel gauges in estimate SOC. The methods for are coming user applications. For mobile vendors we are working on APIs on top of Spark to understand the performance of the power management system of their mobile devices.

[arXiv] Mohammad A. Hoque, Matti Siekkinen, Jonghoe Koo, and Sasu Tarkoma. Accurate Online Full Charge Capacity Modeling of Smartphone Batteries. Jun 2016.
[ACM DL] [pdf] Mohammad A. Hoque and Sasu Tarkoma. Characterizing Smartphone Power Management in the Wild. To appear in the 7th International Workshop on Hot Topics in Planet-Scale Measurement, HotPlanet ’16.
[ACM DL] [pdf] Mohammad A. Hoque, Sasu Tarkoma Sudden drop in the battery level?: understanding smartphone state of charge anomaly. ACM SIGOPS Oper. Syst. Rev. 49, 2 (January 2016), 70-74.
[ACM DL] [pdf] Mohammad Ashraful Hoque, Matti Siekkinen, Kashif Nizam Khan, Yu Xiao, Sasu Tarkoma. Modeling, Profiling, and Debugging the Energy Consumption of Mobile Devices. ACM Comput. Surv. 48, 3, Article 39 (December 2015), 40 pages.

Reviewer: Xinfei Guo (ACM Computing Reviews)
"Because mobile devices, like smartphones, are usually powered with small-size batteries that have limited capacity and life, hardware engineers need to be smart at designing energy-efficient systems to run the applications with minimal energy. On the other hand, software engineers need to be aware of the energy consumption behaviors of the hardware components. Therefore, analyzing and estimating the energy consumption of the devices during runtime is crucial. There are usually two ways of doing this. The first strategy is hardware based, which is to measure the current and power consumption with instruments, like a source meter. The limitation of this solution is that it is not portable, and it requires opening the devices physically during the measurement. A better solution would be software based, which is called power/energy profiling. The idea is to characterize power/energy at the software level based on the power models that are trained using power measurements and system logs.

In this paper, Hoque and colleagues provide a comprehensive survey of the existing software-based energy profiling solutions for analyzing and eliminating smartphone energy consumption. The survey covers a broad range of solutions, from the basic ones that are just able to report total system power to the most advanced ones that are able to provide the energy consumption profile on the program code. It also covers several energy diagnosis engines, which can detect abnormal energy use by different applications and analyze the reasons for this energy use so that developers are aware of the behaviors and can make decisions on how to optimize the applications. The survey starts by discussing the necessary steps to construct an energy profiler and then presenting different methods for each step and substep. This makes the paper very clear and easy to follow.

This paper provides a very complete design space analysis by comparing each solution and analyzing the tradeoffs. It is a very helpful guide for researchers who have just started working with software-based energy profilers and want to learn the field. Also, it will help software developers choose the right energy profilers based on different applications or power/energy requirements."
This paper investiagtes the performance of smartphones in their SOC estiamtion as the full charge capacity (FCC) of their batteries decrease. The battery voltage behaves differently when the FCC decreases while charging. The SOC anomaly or SOC correction increases while dscharging as the full charge capacity decreases.

[ACM DL] [pdf] Mohammad A. Hoque, Sasu Tarkoma Sudden drop in the battery level?: understanding smartphone state of charge anomaly. In Proceedings of the Workshop on Power-Aware Computing and Systems - HotPower, September 2015. (One of the best papers to appear in ACM Oprt. Sys. Rev.)

Master Thesis

Javad Sadeqzadeh Boroujeni, Modeling Smartphone Battery Temperature