Analysis of Energy Consumption on Android Devices for Developers: A Systematic Mapping Study




Android apps, Android smartphone, Battery Saving, Energy consumption, Energy Efficiency, Mobile Computing


Reducing energy consumption is a major challenge that mobile computing must deal with. Smartphones are constantly evolving to match traditional computers in many aspects, especially in processing and memory. However, user experience is severely impacted by the rapid discharge of batteries in smartphones. This article aims to identify which metrics and assessment techniques are applied to evaluate energy consumption on Android smartphones, by summarizing factors that cause high energy consumption through a Systematic Mapping Study (SMS). The methodology of this SMS consisted of performing searches on the digital libraries ACM, IEEE, and Scopus. Sixty articles were obtained, of which 17 were identified as relevant for this study. Among the main methods identified, energy consumption is collected at time intervals based on information on battery voltage/current or on information from features such as Wi-Fi, cellular networks, screen brightness, screen duration on, Bluetooth usage, and others. Regarding the tools and applications that collect such information, there are Android batterystats, applications developed specially for each research like BatteryHub. The data is mainly analyzed by techniques such as clustering (17.65%), covariance (17.65%), Bayesian classification, and decision trees (11.76%). From these techniques, it was identified that user profile is the main factor affecting battery performance, being present in 23.53% of the articles, followed by mobile networks and Wi-Fi (17.65%), in addition to applications and services in background present at 11.76%. Finally, two articles in this SMS provide recommendations to reduce consumption based on users’ usage profiles. In summary, the analysis has shown a significant correlation between user habits and energy consumption. As a result, it is recommended that developers prioritize the exploration of artificial intelligence techniques to automatically adjust smartphone usage settings based on usage context. This approach can lead to significant battery power savings.


Download data is not yet available.

Author Biographies

Edwin Monteiro, Federal University of Amazonas

Master and Bachelor in Computer Science from the Federal University of Amazonas in 2021 and 2018 respectively. He is currently a doctoral student at the Graduate Program in Informatics at the Federal University of Amazonas. He has experience in Computer Science, with an emphasis on methods for Automatic Assessment such as Item Response Theory, Formative Feedback, Educational Data Mining, Automatic Code Generation and Non-Relational Database. He also has experience in Formal Methods, working mainly on the following topics: Finite State Process (FSP), Modeling of Distributed Systems, Construction of Grammars for Formal Methods; and Mobile Development (Android). Areas of interest: Informatics in Education, Formal Methods, Embedded Systems, Mobile Computing, Machine Learning and Educational Data Mining.

Helena Cavalcante, Federal University of Amazonas

She is an undergraduate student in Electrical Engineering with an emphasis on Electronics. She participates in research projects in partnership with companies in the area of digital security, automated testing and embedded systems. She is interested in the areas of cyber-physical systems and artificial intelligence.

Raimundo Barreto, Federal University of Amazonas

He holds a Bachelor's degree in Data Processing from the Federal University of Amazonas (1991), a Master's in Computer Science from the Federal University of Minas Gerais (1997) and a PhD in Computer Science from the Federal University of Pernambuco (2005). He did a postdoctoral internship at the University of Southampton (UK) in 2011. He is currently an associate professor at the Federal University of Amazonas. He has experience in Basic Software, working mainly on the following topics: real-time systems, embedded systems, software synthesis and fault tolerance.

Rosiane de Freitas, Federal University of Amazonas

She holds a PhD in Systems and Computer Engineering, in the field of Algorithms and Combinatorics, from the Federal University of Rio de Janeiro. Master's in Computer Science from the State University of Campinas and a bachelor's degree in Computing from the Federal University of Amazonas. Currently it is linked to the Institute of Computing of the Federal University of Amazonas. She works in the area of ​​Computer Science, with emphasis on Algorithms and Combinatorial Optimization, working mainly on the following topics: theoretical scheduling, exact and approximate algorithms, computational complexity, graph theory, mathematical programming and network optimization. Leader of the CNPq research group on "Optimization, Algorithms and Computational Complexity". She coordinates and participates in research projects in partnership with researchers from important national and foreign universities, being a member of committees, scientific societies and reviewer of important national and international journals. It develops R&D projects involving important research institutes and laboratories. Currently, member of the Management Committee of the Special Committee on Algorithms, Combinatorics and Optimization of the Brazilian Computer Society (SBC), member of the Executive Committee of the Programming Marathon (ICPC/ACM Brasil) and vice-president of events of the Latin American Association of Operational Research (ALIO).


Almasri, A. and Sameh, A. (2019). Rating google-play apps' energy consumption on android smartphones. In 2019 2nd IEEE Middle East and North Africa COMMunications Conference (MENACOMM), pages 1-6. DOI: 10.1109/MENACOMM46666.2019.8988554.

Barreto Neto, A. C. S., Farias, F., Mialaret, M. A. T., Cartaxo, B., Lima, P. A., and Maciel, P. R. M. (2020). Building energy consumption models based on smartphone user's usage patterns. CoRR, abs/2012.10246. DOI: 10.1016/j.knosys.2020.106680.

Cañete, A., Horcas, J.-M., Ayala, I., and Fuentes, L. (2020). Energy efficient adaptation engines for android applications. Information and Software Technology, 118:106220. DOI: 10.1016/j.infsof.2019.106220.

Dai, Z., Wang, W., and Wu, Y. (2020). Static energy consumption analysis for android applications. IOP Conference Series: Earth and Environmental Science, 512:012011. DOI: 10.1088/1755-1315/512/1/012011.

Di Nucci, D., Palomba, F., Prota, A., Panichella, A., Zaidman, A., and Lucia, A. (2017). Petra: A software-based tool for estimating the energy profile of android applications. International Conference on Software Engineering. DOI: 10.1109/ICSE-C.2017.18.

Dick, Zhuoqing Morley Mao, L. Y. (2011). Power tutor description. Available online [link] Accessed: 2021-11-24.

Duan, L., Lawo, M., Rügge, I., and Yu, X. (2017). Power Management of Smartphones Based on Device Usage Patterns, pages 197-207. Springer. DOI: 10.1007/978-3-319-45117-6_18.

Elliot, J., Kor, a.-l., and Omotosho, O. (2017). Energy consumption in smartphones: An investigation of battery and energy consumption of media related applications on android smartphones. International SEEDS Conference 2017. Available online [link].

Google (2021). Battery stats description. Available online [link] Accessed: 2021-11-24.

Guo, Y., Wang, C., and Chen, X. (2017). Understanding application-battery interactions on smartphones: A large-scale empirical study. IEEE Access, 5:13387-13400. DOI: 10.1109/ACCESS.2017.2728620.

Harihar, V. K. and Sukumaran, S. (2018). Behaviour comprehension and prediction using time series analysis of data for code offloading in mobile cloud computing. In 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information Communication Technology (RTEICT), pages 367-375. DOI: 10.1109/RTEICT42901.2018.9012270.

Kitchenham, B. A. and Charters, S. (2007). Guidelines for performing systematic literature reviews in software engineering. Technical Report EBSE 2007-001, Keele University and Durham University Joint Report. Available online [link].

Linares-Vásquez, M., Bavota, G., Bernal-Cárdenas, C., Penta, M. D., Oliveto, R., and Poshyvanyk, D. (2018). Multi-objective optimization of energy consumption of guis in android apps. ACM Transactions on Software Engineering and Methodology (TOSEM), 27(3):1-47. DOI: 10.1145/3241742.

Lopes, C. V., Maj, P., Martins, P., Saini, V., Yang, D., Zitny, J., Sajnani, H., and Vitek, J. (2017). D'ej`avu: A map of code duplicates on github. Proc. ACM Program. Lang., 1(OOPSLA). DOI: 10.1145/3133908.

Malavolta, I., Grua, E. M., Lam, C.-Y., de Vries, R., Tan, F., Zielinski, E., Peters, M., and Kaandorp, L. (2020). A framework for the automatic execution of measurement-based experiments on android devices. 2020 35th IEEE/ACM International Conference on Automated Software Engineering Workshops (ASEW). DOI: 10.1145/3417113.3422184.

Mehrotra, D., Srivastava, R., Nagpal, R., and Nagpal, D. (2021). Multiclass classification of mobile applications as per energy consumption. Journal of King Saud University - Computer and Information Sciences, 33(6):719-727. DOI: 10.1016/j.jksuci.2018.05.007.

Oliner, A. J., Iyer, A. P., Stoica, I., Lagerspetz, E., and Tarkoma, S. (2013). Carat: Collaborative energy diagnosis for mobile devices. In Proceedings of the 11th ACM Conference on Embedded Networked Sensor Systems, SenSys '13, New York, NY, USA. Association for Computing Machinery. DOI: 10.1145/2517351.2517354.

Oliveira, W., Oliveira, R., and Castor, F. (2017). A study on the energy consumption of android app development approaches. In 2017 IEEE/ACM 14th International Conference on Mining Software Repositories (MSR), pages 42-52. DOI: 10.1109/MSR.2017.66.

Oliveira, W., Oliveira, R., Castor, F., Fernandes, B., and Pinto, G. (2019). Recommending energy-efficient java collections. In 2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR), pages 160-170. DOI: 10.1109/MSR.2019.00033.

Pang, C., Hindle, A., Adams, B., and Hassan, A. E. (2016). What do programmers know about software energy consumption? IEEE Software, 33(03):83-89. DOI: 10.1109/MS.2015.83.

Pereira, R., Matalonga, H., Couto, M., Castor, F., Cabral, B., Carvalho, P., Sousa, S., and Fernandes, J. (2021). Greenhub: a large-scale collaborative dataset to battery consumption analysis of android devices. Empirical Software Engineering, 26. DOI: 10.1007/s10664-020-09925-5.

Perez, J Diaz, B. T. (2020). Systematic literature reviews in software engineering—enhancement of the study selection process using cohen’s kappa statistic. Journal of Systems and Software, 168:110657. DOI: 10.1016/j.jss.2020.110657.

Qualcomm (2017). Trepn description. Available online [link] Accessed: 2021-11-24.

Rua, R., Couto, M., and Saraiva, J. (2019). Greensource: A large-scale collection of android code, tests and energy metrics. IEEE/ACM 16th International Conference on Mining Software Repositories (MSR), pages 176-180. DOI: 10.1109/MSR.2019.00035.

Statcounter (2021). Operating system market share worldwide. Available online [link] Acessed: 25/10/2021.

UFSCar (2021). Start-lapes-ufscar. Available online [link] Acessed: 25/10/2021.

Wang, C., Guo, Y., Shen, P., and Chen, X. (2017). E-spector: Online energy inspection for android applications. 2017 IEEE/ACM International Symposium on Low Power Electronics and Design (ISLPED), pages 1-6. DOI: 10.1109/ISLPED.2017.8009207.

Xu, F., Liu, Y., Li, Q., and Zhang, Y. (2013). V-edge: Fast self-constructive power modeling of smartphones based on battery voltage dynamics. In 10th USENIX Symposium on Networked Systems Design and Implementation (NSDI 13), pages 43-55, Lombard, IL. USENIX Association. Available online [link].

Zhang, X., Xiao, X., He, L., Ma, Y., Huang, Y., Liu, X., Xu, W., and Liu, C. (2019). Pifa: An intelligent phase identification and frequency adjustment framework for time-sensitive mobile computing. 2019 IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), pages 54-64. [link]. DOI: 10.1109/RTAS.2019.00013.




How to Cite

Monteiro, E., Cavalcante, H., Barreto, R., & de Freitas, R. (2023). Analysis of Energy Consumption on Android Devices for Developers: A Systematic Mapping Study. SBC Reviews on Computer Science, 3(1), 1–18.