Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.5555/2962664.2962670guideproceedingsArticle/Chapter ViewAbstractPublication PagesConference Proceedingsacm-pubtype
research-article
Free access

DevsServer: ambient intelligence and DEVS modeling based simulation server

Published: 03 April 2016 Publication History

Abstract

To improve disease surveillance systems (DSS) with faster and accurate outbreak detection and epidemics propagation capabilities, the availability of fine-tuned models is required along with the design of server based solution that simulate the effects of public health authorities' measures, and integrate Ambient Intelligence (AmI) capabilities to semantize epidemic models. Hosting Discrete Event System Specifications (DEVS) models, these AmI servers and their communication protocols are different, miscellaneous and require interoperability. The Triple Space Computing (TSC) paradigm addresses interoperability by sharing information represented in a semantic format through a common virtual space. In this this paper we present DevsServer, a fully distributed TSC simulation server solution (middleware) designed to meet the needs of parallel and distributed discrete event simulation. DevsServer defines an SOA (service oriented architecture) interface for the TSC operations. This interface convinces with DEVS formalism and focuses on simplicity, conviviality and modularity, so that a single or many simulations that support different models can still interact.

References

[1]
Al-zoubi K., Wainer G. (2013). RISE: A general simulation interoperability middleware container. Journal of Parallel and Distributed Computing, Elsevier. Vol. 73. Issue 5.
[2]
Amamra L., Mokaddem M., Atmani B., Mesure de la qualité de la vaccination guidée par les données. Sixième Atelier sur les Systèmes Décisionnels ASD'2012, 1-3 April 2012, Université Saad Dahlab, Blida, Algérie. ISBN 978-9947-0-3416-3.
[3]
Barabási A-L, Albert R. Emergence of scaling in random networks. Science 1999;286:509--12.
[4]
Barigou F., Mokaddem M., Atmani B., Beldjilali B. 'Towards an Automated System for Extracting Named Entity from Medical Reports' International Congress on Models Optimization and Security of Systems. Du 29-31 May 2010, Tiaret, Algeria.
[5]
Berners-Lee, T., Hendler, J., & Lassila, O. (2001). The semantic web. Scientific American, 284, 3443.
[6]
Brahami M., Atmani B., Mokaddem M., CARTOCEL: un outil de cartographie des connaissances guidée par la machine cellulaire CASI. EGC 2010: 625--626.
[7]
Fujimoto R. (2000). Parallel and Distribution Simulation Systems, John Wiley & Sons, New York.
[8]
Gelernter, D. (1985). Generative communication in Linda. ACM Transactions on Programming Languages and Systems (TOPLAS), 7, 80112.
[9]
Gómez-Goiri A., López-de-Ipiña D. (2012). Assessing data dissemination strategies within triple spaces on the web of things. In: Sixth International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS), (pp. 763--769).
[10]
Gómez Goiri A., Orduña P., Diego J., López-de-Ipiña D., (2014). Otsopack: Lightweight semantic framework for interoperable ambient intelligence applications" In Computers in Human Behavior. vol. 30. p. 460--467.
[11]
Guinard, D. (2011). A web of things application architecture integrating the real-world into the web. Ph.D. ETH Zurich.
[12]
Hethcote HW. The mathematics of infectious diseases. SIAM Rev 2000;42:599--653.
[13]
Honkola, J., Laine, H., Brown, R., & Tyrkko, O. Smart-M3 information sharing platform. In 2010 IEEE Symposium on Computers and Communications (ISCCs) (pp. 1041--1046). IEEE. 2010.
[14]
Jafer, S; Liu, Q; Wainer, G, (2012) Synchronization methods in parallel and distributed, DEVS Integrative M&S Symposium, SpringSim Multi-Conference.
[15]
Khushraj, D., Lassila, O., & Finin, T. (2004). sTuples: Semantic tuple spaces. In The first annual international conference on mobile and ubiquitous systems: Networking and services, 2004. MOBIQUITOUS 2004 (pp. 268--277).
[16]
Liu J, Zhang T. Epidemic spreading of an SEIRS model in scale-free networks. Commun Nonlinear Sci Numer Simul 2011;16:3375--84.
[17]
Liu Z, Hu B. Epidemic spreading in community networks. Europhys Lett 2005;72:315--21.
[18]
Mokaddem M., Bahnes A., Atmani B., 'création dynamique orientée services de contenu pédagogique en e-Learning', JDLIO'2011, Journées Doctorales du Laboratoire d'informatique d'Oran, 2011, Oran, Algérie
[19]
Mokeddem S., Atmani B., Mokaddem M. Supervised Feature Selection for Diagnosis of Coronary Artery Disease Based on Genetic Algorithm. First International Conference on Computational Science and Engineering (CSE 2013). Dubai, UAE. May 2013. pp 53--64. ISSN 2231-5403, ISBN 978-1-921987-23-6.
[20]
Mokeddem S., Atmani B., Mokaddem M. 'An Effective Feature Selection Approach Driven Genetic Algorithm Wrapped Bayes Naïve'.Int. Nat. Journal of Data Analysis Technics and Strategies, (2015).ISSN online:1755-8069 ISSN print: 1755-8050.
[21]
Moreno Y, Pastor-Satorras R, Vespignani A. Epidemic outbreaks in complex heterogeneous networks. Eur Phys J B 2002;26:521--9.
[22]
Nixon, L. J., Simperl, E., Krummenacher, R., & Martin-Recuerda, F. (2008). Tuple space-based computing for the semantic web: A survey of the state-ofthe-art. Knowledge Engineering Review, 23, 181212.
[23]
Olinky R, Stone L. Unexpected epidemic thresholds in heterogeneous networks: the role of disease transmission. Phys Rev E 2004;70:030902(R).
[24]
Pastor-Satorras R, Vespignani A. Epidemic dynamics in scale-free networks. Phys Rev Lett 2001;86:3200.
[25]
Pirkkalainen, H., & Pawlowski, J. M. (2012). The knowledge intervention integration process: A process-oriented view to enable global social knowledge management. International Journal of Knowledge Society Research (IJKSR), 3, 45--57.
[26]
S. Mittal, "Extending DoDAF to Allow DEVS-Based Modeling and Simulation", Special Issue on DoDAF, Journal of Defense Modeling and Simulation, Vol III, No. 2, 2006
[27]
Saurabh Mittal, José L. Risco-Martín and Bernard P. Zeigler (2009), "DEVS/SOA: A Cross-Platform Framework for Net-centric Modeling and Simulation in DEVS Unified Process", SIMULATION., July, 2009. Vol. 85(7), pp. 419--450.
[28]
Saurabh Mittal, José L. Risco-Martín and Bernard P. Zeigler (2007), "DEVSML: automating DEVS execution over SOA towards transparent simulators", In SpringSim '07: Proceedings of the 2007 spring simulation multiconference. San Diego, CA, USA, pp. 287--295. Society for Computer Simulation International.
[29]
Saurabh Mittal, José L. Risco-Martín and Bernard P. Zeigler (2007), "DEVS-based simulation web services for net-centric T&E", In SCSC: Proceedings of the 2007 summer computer simulation conference. San Diego, CA, USA, pp. 357--366. Society for Computer Simulation International.
[30]
Silva M. J., da Silva F. A. B., Lopes L. F., Couto F. M., (2010). Building a Digital Library for Epidemic Modelling, Proceedings of ICDL 2010 -- The International Conference on Digital Libraries New Delhi, India, February.
[31]
Tolk A., (2010). Interoperability and composability, in: C. Banks, J. Sokolowski (Eds.), Modeling and Simulation Fundamentals: Theoretical Underpinnings and Practical Domains, Wiley, New Jersey, pp. 373--402.
[32]
Varela-Candamio L., García-Álvarez M. T. (2012). Analysis of information and communication technologies in higher education: A case study of business degree. International Journal of Engineering Education, 28, 1301--1308.
[33]
Wainer G., (2009). Discrete-Event Modeling and Simulation: A Practitioner's Approach, CRC press, Taylor & Francis Group, Boca Raton, Florida.
[34]
Wainer G., Al-Zoubi K., Mittal S., Risco Martín J., Sarjoughian H., Zeigler B. (2010), in: G. Wainer, P. Mosterman (Eds.). Discrete-Event Modeling and Simulation: Theory and Applications, CRC Press. Taylor and Francis, pp. 389--494 (Chapters 15-18).
[35]
Wainer G., Madhoun R., Al-Zoubi K. (2008). Distributed simulation of DEVS and Cell-DEVS models in CD++ using Web services, Simulation Modelling Practice and Theory 16 (9) 1266--1292.
[36]
Xiao Y, Zhou Y, Tang S. Modelling disease spread in dispersal networks at two levels. Math Med Biol 2011;28:227--44.
[37]
Yang R, Wang B-H, Ren J, Bai W-J, Shi Z-W, Wang W-X, Zhou T. Epidemic spreading on heterogeneous networks with identical infectivity. Phys Lett A 2007;364:189--93
[38]
Zeigler B., Praehofer H., Kim T., (2000). Theory of Modeling and Simulation, Academic Press, San Diego, CA.
[39]
Zhang H, Fu X. Spreading of epidemics on scale-free networks with nonlinear infectivity. Nonlinear Anal Theory Methods Appl 2009;70:3273--8.
[40]
Zhang J-P, Jin Z. The analysis of an epidemic model on networks. Appl Math Comput 2011;217:7053--64.
[41]
Zhang J-P, Jin Z. Epidemic spreading on complex networks with community structure. Appl Math Comput 2012;219:2829--38.
[42]
ACIMS software site: http://acims.asu.edu/software

Index Terms

  1. DevsServer: ambient intelligence and DEVS modeling based simulation server

        Recommendations

        Comments

        Information & Contributors

        Information

        Published In

        cover image Guide Proceedings
        MSCIAAS '16: Proceedings of the Modeling and Simulation of Complexity in Intelligent, Adaptive and Autonomous Systems 2016 (MSCIAAS 2016) and Space Simulation for Planetary Space Exploration (SPACE 2016)
        April 2016
        104 pages

        Publisher

        Society for Computer Simulation International

        San Diego, CA, United States

        Publication History

        Published: 03 April 2016

        Author Tags

        1. DEVS
        2. ambient intelligence
        3. epidemic modeling
        4. service oriented simulation
        5. triple space-based computing

        Qualifiers

        • Research-article

        Contributors

        Other Metrics

        Bibliometrics & Citations

        Bibliometrics

        Article Metrics

        • 0
          Total Citations
        • 95
          Total Downloads
        • Downloads (Last 12 months)54
        • Downloads (Last 6 weeks)3
        Reflects downloads up to 13 Jan 2025

        Other Metrics

        Citations

        View Options

        View options

        PDF

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        Login options

        Media

        Figures

        Other

        Tables

        Share

        Share

        Share this Publication link

        Share on social media