dc.contributor.author | Trihinas, Demetris | en |
dc.contributor.author | Pallis, George C. | en |
dc.contributor.author | Dikaiakos, Marios D. | en |
dc.contributor.editor | Luo F. | en |
dc.contributor.editor | Ogan K. | en |
dc.contributor.editor | Zaki M.J. | en |
dc.contributor.editor | Haas L. | en |
dc.contributor.editor | Ooi B.C. | en |
dc.contributor.editor | Kumar V. | en |
dc.contributor.editor | Rachuri S. | en |
dc.contributor.editor | Pyne S. | en |
dc.contributor.editor | Ho H. | en |
dc.contributor.editor | Hu X. | en |
dc.contributor.editor | Yu S. | en |
dc.contributor.editor | Hsiao M.H.-I. | en |
dc.contributor.editor | Li J. | en |
dc.creator | Trihinas, Demetris | en |
dc.creator | Pallis, George C. | en |
dc.creator | Dikaiakos, Marios D. | en |
dc.date.accessioned | 2019-11-13T10:42:31Z | |
dc.date.available | 2019-11-13T10:42:31Z | |
dc.date.issued | 2015 | |
dc.identifier.isbn | 978-1-4799-9925-5 | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/55084 | |
dc.description.abstract | Real-time data processing while the velocity and volume of data generated keep increasing, as well as, energy-efficiency are great challenges of big data streaming which have transitioned to the Internet of Things (IoT) realm. In this paper, we introduce AdaM, a lightweight adaptive monitoring framework for smart battery-powered IoT devices with limited processing capabilities. AdaM, inexpensively and in place dynamically adapts the monitoring intensity and the amount of data disseminated through the network based on the current evolution and variability of the metric stream. Results on real-world testbeds, show that AdaM achieves a balance between efficiency and accuracy. Specifically, AdaM is capable of reducing data volume by 74%, energy consumption by at least 71%, while preserving a greater than 89% accuracy. © 2015 IEEE. | en |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en |
dc.source | Proceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015 | en |
dc.source | 3rd IEEE International Conference on Big Data, IEEE Big Data 2015 | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84963745231&doi=10.1109%2fBigData.2015.7363816&partnerID=40&md5=5748d795dcb32905f608737e25ddf843 | |
dc.subject | Internet | en |
dc.subject | Sampling | en |
dc.subject | Monitoring | en |
dc.subject | Filtering | en |
dc.subject | Data handling | en |
dc.subject | Filtration | en |
dc.subject | Battery powered | en |
dc.subject | Energy utilization | en |
dc.subject | Energy efficiency | en |
dc.subject | Big data | en |
dc.subject | Network-based | en |
dc.subject | Internet of thing (IOT) | en |
dc.subject | Processing capability | en |
dc.subject | Adaptive monitoring | en |
dc.subject | Battery management systems | en |
dc.subject | Data streaming | en |
dc.subject | Data volume | en |
dc.subject | Internet of Things | en |
dc.subject | Real-time data processing | en |
dc.title | AdaM: An adaptive monitoring framework for sampling and filtering on IoT devices | en |
dc.type | info:eu-repo/semantics/conferenceObject | |
dc.identifier.doi | 10.1109/BigData.2015.7363816 | |
dc.description.startingpage | 717 | |
dc.description.endingpage | 726 | |
dc.author.faculty | 002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences | |
dc.author.department | Τμήμα Πληροφορικής / Department of Computer Science | |
dc.type.uhtype | Conference Object | en |
dc.description.notes | <p>Sponsors: CCF | en |
dc.description.notes | et al. | en |
dc.description.notes | Huawi | en |
dc.description.notes | IEEE Computer Society | en |
dc.description.notes | National Science Foundation (NSF) | en |
dc.description.notes | Springer | en |
dc.description.notes | Conference code: 118870 | en |
dc.description.notes | Cited By :1</p> | en |
dc.contributor.orcid | Pallis, George C. [0000-0003-1815-5468] | |
dc.contributor.orcid | Dikaiakos, Marios D. [0000-0002-4350-6058] | |
dc.contributor.orcid | Trihinas, Demetris [0000-0002-9540-7342] | |
dc.gnosis.orcid | 0000-0003-1815-5468 | |
dc.gnosis.orcid | 0000-0002-4350-6058 | |
dc.gnosis.orcid | 0000-0002-9540-7342 | |