A Measurement Tool to Track Drones Battery Consumption During Flights

The autonomy of mobile systems depends greatly on the capability of the power source that supplies the necessary energy. Typically these sources are limited batteries that cannot keep up with the functionality and services that modern mobile equipment features. This situation motivates researchers and practitioners to develop strategies to promote efficient energy usage on mobile platforms. However, to reduce the energy consumption it is required to have reliable means to measure the devices behavior and its relationship to the battery discharge. This problem is relevant in platforms that depend strongly on batteries like cellphones, tablets, wearables, or drones. This paper focuses on drones, introducing a software system that acquires data during a drone mission, featuring an online battery discharge analyzer. The goal of this software is to provide a means to identify the operations that spend more energy and, as consequence, deliver the necessary information to avoid energy expensive movements and extend the battery lifetime for improved drone autonomy.

[1]  Lei Yang,et al.  Accurate online power estimation and automatic battery behavior based power model generation for smartphones , 2010, 2010 IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS).

[2]  Witold Pedrycz,et al.  Predicting Development Effort from User Stories , 2011, 2011 International Symposium on Empirical Software Engineering and Measurement.

[3]  Luis Corral,et al.  Energy-Aware Performance Evaluation of Android Custom Kernels , 2015, 2015 IEEE/ACM 4th International Workshop on Green and Sustainable Software.

[4]  Jason Flinn,et al.  Energy-aware adaptation for mobile applications , 1999, SOSP.

[5]  H. Thode.  Testing For Normality , 2002 .

[6]  Alberto Sillitti,et al.  Method reallocation to reduce energy consumption: an implementation in Android OS , 2014, SAC.

[7]  S. Shapiro,et al.  An Analysis of Variance Test for Normality (Complete Samples) , 1965 .

[8]  Alberto Sillitti,et al.  Failure Prediction based on Log Files Using the Cox Proportional Hazard Model , 2011, SEKE.

[9]  Roel Wieringa,et al.  The Green Lab: Experimentation in Software Energy Efficiency , 2015, 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering.

[10]  Grace A. Lewis,et al.  Architectural tactics for cyber-foraging: Results of a systematic literature review , 2015, J. Syst. Softw..

[11]  H. B. Mann,et al.  On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other , 1947 .

[12]  Ming Zhang,et al.  Where is the energy spent inside my app?: fine grained energy accounting on smartphones with Eprof , 2012, EuroSys '12.

[13]  Lijuan Cao,et al.  Support vector machines experts for time series forecasting , 2003, Neurocomputing.

[14]  Kathryn S. McKinley,et al.  The model is not enough: Understanding energy consumption in mobile devices , 2012, 2012 IEEE Hot Chips 24 Symposium (HCS).

[15]  Roger Clarke,et al.  Understanding the drone epidemic , 2014, Comput. Law Secur. Rev..

[16]  Mahadev Satyanarayanan,et al.  Tactics-based remote execution for mobile computing , 2003, MobiSys '03.

[17]  Hojung Cha,et al.  AppScope: Application Energy Metering Framework for Android Smartphone Using Kernel Activity Monitoring , 2012, USENIX Annual Technical Conference.

[18]  Narseo Vallina-Rodriguez,et al.  Energy Management Techniques in Modern Mobile Handsets , 2013, IEEE Communications Surveys & Tutorials.