Improving Energy Saving Techniques by Ambient Intelligence Scheduling

Matteo Cristani, Erisa Karafili, Claudio Tomazzoli

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Energy saving is one of the most challenging aspects of modern ambient intelligence technologies, for both domestic and business usages. In this paper we show how to combine Ambient Intelligence and Artificial Intelligence techniques to solve the problem of scheduling a set of devices under a given set of constraints, like limits to the maximal energy usage (Energy Span) and maximal energy absorption (Energy Peak). We provide a method that can be used to schedule the usage of devices in a given environment in a way that respects the input constraints. We adapt an existent approach to scheduling for Ambient Intelligence to a specific framework and exhibit a sample usage for a real life system, Elettra, that is in use in an industrial context.
Original languageEnglish
Title of host publicationProceedings of the Proceedings IEEE 29th International Conference on Advanced Information Networking and Applications (AINA 2015)
EditorsLeonard Barolli, Makoto Takizawa, Fatos Xhafa, Tomoya Enokido, Jong Hyuk Park
PublisherIEEE
Publication date2015
Pages324-331
ISBN (Print)978-1-4799-7904-2
DOIs
Publication statusPublished - 2015
Event29th IEEE International Conference on Advanced Information Networking and Applications (AINA 2015) - Gwangju, Korea, Democratic People's Republic of
Duration: 25 Mar 201527 Mar 2015
Conference number: 29
http://voyager.ce.fit.ac.jp/conf/aina/2015/

Conference

Conference29th IEEE International Conference on Advanced Information Networking and Applications (AINA 2015)
Number29
CountryKorea, Democratic People's Republic of
CityGwangju
Period25/03/201527/03/2015
Internet address

Keywords

  • Communication, Networking and Broadcast Technologies
  • Computing and Processing
  • Absorption
  • Ambient intelligence
  • Energy consumption
  • Performance evaluation
  • Plugs
  • Servers
  • Time factors

Cite this

Cristani, M., Karafili, E., & Tomazzoli, C. (2015). Improving Energy Saving Techniques by Ambient Intelligence Scheduling. In L. Barolli, M. Takizawa, F. Xhafa, T. Enokido, & J. H. Park (Eds.), Proceedings of the Proceedings IEEE 29th International Conference on Advanced Information Networking and Applications (AINA 2015) (pp. 324-331). IEEE. https://doi.org/10.1109/AINA.2015.202
Cristani, Matteo ; Karafili, Erisa ; Tomazzoli, Claudio. / Improving Energy Saving Techniques by Ambient Intelligence Scheduling. Proceedings of the Proceedings IEEE 29th International Conference on Advanced Information Networking and Applications (AINA 2015). editor / Leonard Barolli ; Makoto Takizawa ; Fatos Xhafa ; Tomoya Enokido ; Jong Hyuk Park. IEEE, 2015. pp. 324-331
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title = "Improving Energy Saving Techniques by Ambient Intelligence Scheduling",
abstract = "Energy saving is one of the most challenging aspects of modern ambient intelligence technologies, for both domestic and business usages. In this paper we show how to combine Ambient Intelligence and Artificial Intelligence techniques to solve the problem of scheduling a set of devices under a given set of constraints, like limits to the maximal energy usage (Energy Span) and maximal energy absorption (Energy Peak). We provide a method that can be used to schedule the usage of devices in a given environment in a way that respects the input constraints. We adapt an existent approach to scheduling for Ambient Intelligence to a specific framework and exhibit a sample usage for a real life system, Elettra, that is in use in an industrial context.",
keywords = "Communication, Networking and Broadcast Technologies, Computing and Processing, Absorption, Ambient intelligence, Energy consumption, Performance evaluation, Plugs, Servers, Time factors",
author = "Matteo Cristani and Erisa Karafili and Claudio Tomazzoli",
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Cristani, M, Karafili, E & Tomazzoli, C 2015, Improving Energy Saving Techniques by Ambient Intelligence Scheduling. in L Barolli, M Takizawa, F Xhafa, T Enokido & JH Park (eds), Proceedings of the Proceedings IEEE 29th International Conference on Advanced Information Networking and Applications (AINA 2015). IEEE, pp. 324-331, 29th IEEE International Conference on Advanced Information Networking and Applications (AINA 2015), Gwangju, Korea, Democratic People's Republic of, 25/03/2015. https://doi.org/10.1109/AINA.2015.202

Improving Energy Saving Techniques by Ambient Intelligence Scheduling. / Cristani, Matteo; Karafili, Erisa; Tomazzoli, Claudio.

Proceedings of the Proceedings IEEE 29th International Conference on Advanced Information Networking and Applications (AINA 2015). ed. / Leonard Barolli; Makoto Takizawa; Fatos Xhafa; Tomoya Enokido; Jong Hyuk Park. IEEE, 2015. p. 324-331.

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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N2 - Energy saving is one of the most challenging aspects of modern ambient intelligence technologies, for both domestic and business usages. In this paper we show how to combine Ambient Intelligence and Artificial Intelligence techniques to solve the problem of scheduling a set of devices under a given set of constraints, like limits to the maximal energy usage (Energy Span) and maximal energy absorption (Energy Peak). We provide a method that can be used to schedule the usage of devices in a given environment in a way that respects the input constraints. We adapt an existent approach to scheduling for Ambient Intelligence to a specific framework and exhibit a sample usage for a real life system, Elettra, that is in use in an industrial context.

AB - Energy saving is one of the most challenging aspects of modern ambient intelligence technologies, for both domestic and business usages. In this paper we show how to combine Ambient Intelligence and Artificial Intelligence techniques to solve the problem of scheduling a set of devices under a given set of constraints, like limits to the maximal energy usage (Energy Span) and maximal energy absorption (Energy Peak). We provide a method that can be used to schedule the usage of devices in a given environment in a way that respects the input constraints. We adapt an existent approach to scheduling for Ambient Intelligence to a specific framework and exhibit a sample usage for a real life system, Elettra, that is in use in an industrial context.

KW - Communication, Networking and Broadcast Technologies

KW - Computing and Processing

KW - Absorption

KW - Ambient intelligence

KW - Energy consumption

KW - Performance evaluation

KW - Plugs

KW - Servers

KW - Time factors

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Cristani M, Karafili E, Tomazzoli C. Improving Energy Saving Techniques by Ambient Intelligence Scheduling. In Barolli L, Takizawa M, Xhafa F, Enokido T, Park JH, editors, Proceedings of the Proceedings IEEE 29th International Conference on Advanced Information Networking and Applications (AINA 2015). IEEE. 2015. p. 324-331 https://doi.org/10.1109/AINA.2015.202