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Data-Centered Scheduling for Addressing Performance Metrics on WSN

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Applications of Evolutionary Computation (EvoApplications 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6625))

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Abstract

In this paper, a novel combination of cross-layer strategies for addressing timeliness, handling latency times on a data-centred way, and improving energy management in a non-mobile WSN scenario, is proposed.In this way, a set of performance metrics (for timeliness, latencies and energy management) are introduced and used for evaluating Periodic Scheduling and Simplified Forwarding strategies. The WSN is modelled as an four states Asynchronous Cellular Automaton with irregular neighbourhoods. Therefore, only information from local neighbourhood is needed for communication between nodes. Our results show that the proposed strategies and performance metrics are useful for sensing data accurately, without excessive oversampling.

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Author information

Authors and Affiliations

  1. Department of Systems Engineering ALIFE Research Group, Universidad Nacional de Colombia, Bogota, Colombia

    Lia Susana d. C. Silva-Lopez & Jonatan Gomez

Authors
  1. Lia Susana d. C. Silva-Lopez
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  2. Jonatan Gomez
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Editor information

Editors and Affiliations

  1. Department of Mathematics and Statistics, University of Strathclyde, 16 Richmond Street, G1 1XQ, Glasgow, UK

    Cecilia Di Chio

  2. School of Business, University College Dublin, 4, Belfield, Dublin, UK

    Anthony Brabazon

  3. Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA), Galleria 2, 6928, Manno-Lugano, Switzerland

    Gianni A. Di Caro

  4. Institute of Computer Science, University of Bremen, 28359, Bremen, Germany

    Rolf Drechsler

  5. Next Generation Intelligent Networks Research Center (nexGIN RC), National University of Computer & Emerging Sciences (FAST-NU), Islamabad, Pakistan

    Muddassar Farooq

  6. Department of Information Systems, Johannes Gutenberg-Universität, 55099, Mainz, Germany

    Jörn Grahl

  7. Mathematics and Computer Science Depatment, University of Richmond, 23173, Richmond, VA, USA

    Gary Greenfield

  8. ICD - OSI, University of Technology of Troyes, 12, Rue Marie Curie, BP 2060, 10010, Troyes, France

    Christian Prins

  9. Facultad de Informatica, Universidade de A Coruña, 15071, Coruña, CP, Spain

    Juan Romero

  10. Dipartimento di Automatica e Informatica, Politecnico di Torino, Italy

    Giovanni Squillero

  11. Consiglio Nazionale delle Ricerche (CNR), Istituto di Calcolo e Reti ad Alte Prestazioni (ICAR), Via P. Castellino 111, 80131, Naples, Italy

    Ernesto Tarantino

  12. Dipartimento di Tecnologie dell’Informazione, Università degli Studi di Milano, Via Bramante 65, 26013, Crema (CR), Italy

    Andrea G. B. Tettamanzi

  13. School of Computing, Edinburgh Napier University, 10 Clinton Road, EH10 5DT, Edinburgh, UK

    Neil Urquhart

  14. Computer Engineering Department, Istanbul Technical University, Room Nr. 3302, Maslak, 34469, Istanbul, Turkey

    A. Şima Uyar

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© 2011 Springer-Verlag Berlin Heidelberg

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Silva-Lopez, L.S.d.C., Gomez, J. (2011). Data-Centered Scheduling for Addressing Performance Metrics on WSN. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2011. Lecture Notes in Computer Science, vol 6625. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20520-0_8

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  • DOI: https://doi.org/10.1007/978-3-642-20520-0_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-20519-4

  • Online ISBN: 978-3-642-20520-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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