{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:33:04Z","timestamp":1760239984291,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,2,26]],"date-time":"2019-02-26T00:00:00Z","timestamp":1551139200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>With self-provisioning of resources as premise, dew computing aims at providing computing services by minimizing the dependency over existing internetwork back-haul. Mobile devices have a huge potential to contribute to this emerging paradigm, not only due to their proximity to the end user, ever growing computing\/storage features and pervasiveness, but also due to their capability to render services for several hours, even days, without being plugged to the electricity grid. Nonetheless, misusing the energy of their batteries can discourage owners to offer devices as resource providers in dew computing environments. Arguably, having accurate estimations of remaining battery would help to take better advantage of a device\u2019s computing capabilities. In this paper, we propose a model to estimate mobile devices battery availability by inspecting traces of real mobile device owner\u2019s activity and relevant device state variables. The model includes a feature extraction approach to obtain representative features\/variables, and a prediction approach, based on regression models and machine learning classifiers. On average, the accuracy of our approach, measured with the mean squared error metric, overpasses the one obtained by a related work. Prediction experiments at five hours ahead are performed over activity logs of 23 mobile users across several months.<\/jats:p>","DOI":"10.3390\/info10030086","type":"journal-article","created":{"date-parts":[[2019,2,26]],"date-time":"2019-02-26T11:00:44Z","timestamp":1551178844000},"page":"86","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Towards Integrating Mobile Devices into Dew Computing: A Model for Hour-Wise Prediction of Energy Availability"],"prefix":"10.3390","volume":"10","author":[{"given":"Mathias","family":"Longo","sequence":"first","affiliation":[{"name":"Viterbi School of Engineering, University of Southern California, Los Angeles, CA 90007, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7021-3501","authenticated-orcid":false,"given":"Mat\u00edas","family":"Hirsch","sequence":"additional","affiliation":[{"name":"ISISTAN-UNICEN-CONICET, Tandil B7001BBO, Buenos Aires, Argentina"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5761-1898","authenticated-orcid":false,"given":"Cristian","family":"Mateos","sequence":"additional","affiliation":[{"name":"ISISTAN-UNICEN-CONICET, Tandil B7001BBO, Buenos Aires, Argentina"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9537-3541","authenticated-orcid":false,"given":"Alejandro","family":"Zunino","sequence":"additional","affiliation":[{"name":"ISISTAN-UNICEN-CONICET, Tandil B7001BBO, Buenos Aires, Argentina"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,26]]},"reference":[{"key":"ref_1","first-page":"16","article-title":"Scalable distributed computing hierarchy: Cloud, fog and dew computing","volume":"2","author":"Skala","year":"2015","journal-title":"Open J. 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Proceedings of the First Edition of the Workshop on Mobile Cloud Computing, Helsinki, Finland.","DOI":"10.1145\/2342509.2342513"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1109\/MCOM.2017.1700120","article-title":"Bringing computation closer toward the user network: Is edge computing the solution?","volume":"55","author":"Ahmed","year":"2017","journal-title":"IEEE Commun. Mag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"e3496","DOI":"10.1002\/ett.3496","article-title":"Minimizing dependency on internetwork: Is dew computing a solution?","volume":"30","author":"Ray","year":"2019","journal-title":"Trans. Emerg. Telecommun. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"723","DOI":"10.1109\/ACCESS.2017.2775042","article-title":"An Introduction to Dew Computing: Definition, Concept and Implications","volume":"6","author":"Ray","year":"2018","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1504\/IJCC.2015.071717","article-title":"Cloud-dew architecture","volume":"4","author":"Wang","year":"2015","journal-title":"Int. J. Cloud Comput."},{"key":"ref_9","first-page":"8","article-title":"Doing more with the dew: A new approach to cloud-dew architecture","volume":"3","author":"Fisher","year":"2016","journal-title":"Open J. Cloud Comput."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Gusev, M. (2017, January 22\u201326). A dew computing solution for IoT streaming devices. Proceedings of the 40th International Convention on Information and Communication Technology, Electronics and Microelectronics (MIPRO), Opatija, Croatia.","DOI":"10.23919\/MIPRO.2017.7973454"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Martins, V., Rufino, J., Silva, L., Almeida, J., Miguel Fernandes Silva, B., Ferreira, J., and Fonseca, J. (2019). Towards Personal Virtual Traffic Lights. Information, 10.","DOI":"10.3390\/info10010032"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1109\/MPRV.2018.011591063","article-title":"NoCloud: Exploring network disconnection through on-device data analysis","volume":"17","author":"Rawassizadeh","year":"2018","journal-title":"IEEE Pervasive Comput."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1515\/eng-2018-0012","article-title":"Parallel Processing of Images in Mobile Devices using BOINC","volume":"8","author":"Curiel","year":"2018","journal-title":"Open Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.advengsoft.2016.11.005","article-title":"Parallelisation of an interactive lattice-Boltzmann method on an Android-powered mobile device","volume":"104","author":"Harwood","year":"2017","journal-title":"Adv. Eng. Softw."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"644","DOI":"10.1016\/j.future.2018.06.005","article-title":"Augmenting computing capabilities at the edge by jointly exploiting mobile devices: A survey","volume":"88","author":"Hirsch","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Tapparello, C., Karaoglu, C.F.B., Ba, H., Hijazi, S., Shi, J., Aquino, A., and Heinzelman, W. (2015). Volunteer Computing on Mobile Devices: State of the Art and Future. Enabling Real-Time Mobile Cloud Computing through Emerging Technologies, IGI Global.","DOI":"10.4018\/978-1-4666-8751-6.ch095"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/s10723-016-9387-6","article-title":"A two-phase energy-aware scheduling approach for cpu-intensive jobs in mobile grids","volume":"15","author":"Hirsch","year":"2017","journal-title":"J. Grid Comput."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1145\/2627534.2627553","article-title":"Device Analyzer: Large-scale mobile data collection","volume":"41","author":"Wagner","year":"2014","journal-title":"ACM SIGMETRICS Perform. Eval. Rev."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"338","DOI":"10.5626\/JCSE.2011.5.4.338","article-title":"Personalized battery lifetime prediction for mobile devices based on usage patterns","volume":"5","author":"Kang","year":"2011","journal-title":"J. Comput. Sci. Eng."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Longo, M., Mateos, C., and Zunino, A. (2018). A Model for Hour-Wise Prediction of Mobile Device Energy Availability. Information Technology-New Generations, Springer.","DOI":"10.1007\/978-3-319-77028-4_47"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"e4103","DOI":"10.1002\/cpe.4103","article-title":"A quantum-inspired binary gravitational search algorithm\u2013based job-scheduling model for mobile computational grid","volume":"29","author":"Singh","year":"2017","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1226","DOI":"10.1002\/cpe.3297","article-title":"Energy efficient and robust allocation of interdependent tasks on mobile ad hoc computational grid","volume":"27","author":"Shah","year":"2015","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"847","DOI":"10.1007\/s11704-014-3223-6","article-title":"A scheduling algorithm with dynamic properties in mobile grid","volume":"8","author":"Lee","year":"2014","journal-title":"Front. Comput. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1109\/MPRV.2005.75","article-title":"Crawdad: A community resource for archiving wireless data at dartmouth","volume":"4","author":"Kotz","year":"2005","journal-title":"IEEE Pervasive Comput."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s00607-012-0245-5","article-title":"Energy-efficient job stealing for CPU-intensive processing in mobile devices","volume":"96","author":"Rodriguez","year":"2014","journal-title":"Computing"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.pmcj.2015.08.003","article-title":"Battery-aware centralized schedulers for CPU-bound jobs in mobile Grids","volume":"29","author":"Hirsch","year":"2016","journal-title":"Pervasive Mob. Comput."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1920","DOI":"10.1016\/j.comnet.2012.02.007","article-title":"Energy-aware resource sharing with mobile devices","volume":"56","author":"Waldhorst","year":"2012","journal-title":"Comput. Netw."},{"key":"ref_28","first-page":"1157","article-title":"An introduction to variable and feature selection","volume":"3","author":"Guyon","year":"2003","journal-title":"J. Mach. Learn. Res."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Guyon, I., and Elisseeff, A. (2006). An introduction to feature extraction. Feature Extraction, Springer.","DOI":"10.1007\/978-3-540-35488-8"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1080\/00401706.1970.10488634","article-title":"Ridge regression: Biased estimation for nonorthogonal problems","volume":"12","author":"Hoerl","year":"1970","journal-title":"Technometrics"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Sak, H., Senior, A., and Beaufays, F. (2014, January 14\u201318). Long short-term memory recurrent neural network architectures for large scale acoustic modeling. Proceedings of the Fifteenth Annual Conference of the International Speech Communication Association, Singapore.","DOI":"10.21437\/Interspeech.2014-80"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/10\/3\/86\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:34:49Z","timestamp":1760186089000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/10\/3\/86"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,2,26]]},"references-count":32,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2019,3]]}},"alternative-id":["info10030086"],"URL":"https:\/\/doi.org\/10.3390\/info10030086","relation":{},"ISSN":["2078-2489"],"issn-type":[{"type":"electronic","value":"2078-2489"}],"subject":[],"published":{"date-parts":[[2019,2,26]]}}}