skip to main content
10.1145/3167020.3167022acmotherconferencesArticle/Chapter ViewAbstractPublication PagesmedesConference Proceedingsconference-collections
research-article

A New Two-Layered Architecture for Efficient Situations Management in Smart Environments

Published: 07 November 2017 Publication History

Abstract

In the field of smart environments, many devices and multimedia oriented connected objects has gained a significant attention in the last years. In such a domain, there is a large number of heterogeneous specific profiles (user devices, connected objects, smart homes, health sensors...). These profiles are highly dynamic, according to different contexts (user profile, user environment, monitoring, social activities...). There is a critical need to offer to users a flexible and efficient services selection among a large set of candidates based on their surrounding environments, user's current needs and situations. In this paper, we propose a two layered architecture including a local server (Fog computing), a central Cloud providing an efficient situation management and good scalability. We particularly focus on context-aware e-health mobile applications for achieving the efficient and quality diagnosis of complex situations and for providing all distributed multimedia services that help users to access/broadcast multimedia documents.

References

[1]
Anagnostopoulos, C., Hadjiefthymiades, S.2008. Enhancing situation-aware systems through imprecise reasoning. IEEE Transactions on Mobile Computing 7, No. 10, 1153--1168.
[2]
Da K., Dalmau M., Roose P. 2014. Kalimucho: Middleware for Mobile Applications -- ACM SAC 2014 -- pp. 413--419.
[3]
Gherari, M., Amirat, A., & Oussalah, M. C. 2014. Towards smart cloud gate middleware: an approach based on profiling technique. In Conférence francophone sur les Architectures Logicielles (CAL 2014). Paris, France, 2014. 25--30.
[4]
Yus, R., Mena, E., Ilarri, S., Illarramendi A. 2014. SHERLOCK: Semantic management of Location-Based Services in wireless environments. Pervasive and Mobile Computing, (2014)15: 87--99.
[5]
Naqvi, N. Z., Preuveneers, D., Berbers, Y. 2014. A quality-aware federated framework for smart mobile applications in the cloud. Procedia Computer Science 32 (2014): 253--260.
[6]
Forkan, A., Khalil, I., & Tari, Z. 2014. CoCaMAAL: A cloud-oriented context-aware middleware in ambient assisted living. Future Generation Computer Systems 35: 114--127.
[7]
Aguilar, J., Jerez, M., Exposito, E., Thierry, V. 2015. CARMiCLOC: Context Awareness Middleware in Cloud Computing. CLEI. Latin American: IEEE, 2015. 1--10.
[8]
Buyya, R., Yeo, C., Venugopal, S., Broberg, J., Brandic, I. 2009. Cloud computing and emerging it platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Generation Computer Systems 25: 599--616.
[9]
Forough, S., Reza, J., 2012. A Comparative Study of Context Modeling Approaches and Applying in an Infrastructure, Canadian Journal on Data information and Knowledge Engineering, vol. 3, no. 1, 161--180.
[10]
Dromzee, C., Laborie S., Roose P.2013. A Semantic Generic Profile for Multimedia Documents Adaptation. Intelligent Multimedia Technologies for Networking Applications: Techniques and Tools. IGI Global, pp. 225--246, 2013.
[11]
Buchholz, S., Hamann, T., & Hubsch, G.2004. Comprehensive structured context profiles (cscp): Design and experiences. In Pervasive Computing and Communications Workshops. IEEE, pp. 43--47.
[12]
Gyrard, A., Bonnet, C., Boudaoud, K., & Serrano, M. 2016. LOV4IoT: A second life for ontology-based domain knowledge to build Semantic Web of Things applications. In 2016 IEEE 4th International Conference of Future Internet of Things and Cloud (FiCloud),. IEEE, 2016, 254--261.
[13]
George, P., Alexandra, P., et Peter, W., 2006. Event-condition-action rules on RDF metadata in P2P environments. Computer Networks, 1513--1532.
[14]
Alti, A., Lakehal, A., Laborie, S., Roose, P. 2016. Autonomic Semantic-Based Context-Aware Platform for Mobile Applications in Pervasive Environments. Future Internet 8(4), 2016: 48.
[15]
Bin, P., SONG, X., Enmin, X.A. 2013. CAMSPF: cloud-assisted mobile service provision framework supporting personalized user demands in pervasive computing environment. Wireless Communications and Mobile Computing Conference (IWCMC), 2013 9th International. IEEE, 2013. 649--654.
[16]
Rouvoy, R., Barone, P., Ding, Y. 2009. MUSIC: Middleware support for self-adaptation in ubiquitous and service-oriented environments. In: Software engineering for self-adaptive systems, Springer Berlin Heidelberg. 2009. 164--182.
[17]
Karchoud, R., Roose, P., Dalmau, M., de Courchelle, I., Dibon, P. Kalimucho for smart: One step towards eternal applications. In Industrial Technology (ICIT), 2015 IEEE International Conference. IEEE, 2015, March. 2426--2432.
[18]
Abdullah Alsaffar, A., Hung, P., Hong, C. Eui-Nam Huh, S., Aazam, M. 2016. An Architecture of IoT Service Delegation and Resource Allocation Based on Collaboration between Fog and Cloud Computing, pp. 1:15.
[19]
Alti, A., Achouri, M., Makhlouf D., Roose, P. 2014. Smart Semantic-based Approach for Mobile Applications in Pervasive Environments-- IEEE International Conference on Information Technology for Organizations Development (IT4OD-2016) -- 30/03-01/04 -- Fes -- Marroco -- IEEE Publisher --
[20]
Alti, A., Laborie, S., Roose, P. 2014. Dynamic semantic-based adaptation of multimedia documents -- Transaction on Emerging Telecommunication Technologies (2014) 25:239-- 258 --Wiley Online Library

Cited By

View all
  • (2019)Collection and analysis of physiological data in smart environments: a systematic mappingJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-019-01409-911:7(2883-2897)Online publication date: 26-Jul-2019

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences
MEDES '17: Proceedings of the 9th International Conference on Management of Digital EcoSystems
November 2017
299 pages
ISBN:9781450348959
DOI:10.1145/3167020
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

In-Cooperation

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 November 2017

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. E-Health
  2. Fog-based
  3. adaptation
  4. context
  5. emergency situation
  6. scalability
  7. similarity measure

Qualifiers

  • Research-article
  • Research
  • Refereed limited

Conference

MEDES '17

Acceptance Rates

MEDES '17 Paper Acceptance Rate 41 of 65 submissions, 63%;
Overall Acceptance Rate 267 of 682 submissions, 39%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)1
  • Downloads (Last 6 weeks)0
Reflects downloads up to 19 Feb 2025

Other Metrics

Citations

Cited By

View all
  • (2019)Collection and analysis of physiological data in smart environments: a systematic mappingJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-019-01409-911:7(2883-2897)Online publication date: 26-Jul-2019

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Figures

Tables

Media

Share

Share

Share this Publication link

Share on social media