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Analysis of the Battery Level in Complex Wireless Sensor Networks Using a Two Time Scales Second Order Fluid Model

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Practical Applications of Stochastic Modelling (PASM 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1786))

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Abstract

Battery operated Wireless Sensor Networks (WSNs) are currently one of the most important research areas related to applications: the possibility of running complex algorithms and off-load tasks using Fog and Edge computing techniques, as well as the ability of increasing the battery lifetime adopting energy harvesting, together with the communication capabilities offered by infrastructures such as 5G, are just some of the reasons for which this topic continues to be one of the most challenging and interesting. In this paper we focus on the problem of modeling the battery evolution of the devices, focusing on the issues created by the two time scales at which system evolves: tasks execution, sensor readings, and network operations occur at a small time scale, while energy harvesting and battery depletion runs on a much larger time frame. We propose a fluid model for the entire system, and we analyze it in two steps: we first focus on the small time scales to produce a simplified Second Order Fluid Model (SOFM), which later is used to reproduce the evolution at the large time frame. We analyze the considered models using discrete event simulation.

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Notes

  1. 1.

    This is a simplification of the actual solicitation model, suitable for the purposes of this paper: we considered more realistic scenarios in [2].

  2. 2.

    Those rates must be set accordingly with the actual seasonal local weather phenomena: this is a simple modelization, but a similar approach as the upper part may be used to represent a more complex variability pattern. The effect of a token in the \(H_{OFF}\) place can alternatively represent both a lower efficiency or a deactivation of the solar panel, as needed by the real conditions of the actual system.

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Acknowledgements

This work has been partially funded by the internal competitive funding program “VALERE: VAnviteLli pEr la RicErca” of Università degli Studi della Campania “Luigi Vanvitelli” and is part of the research activities developed within the project PON “Ricerca e Innovazione” 2014–2020, action IV.6 “Contratti di ricerca su tematiche Green”, issued by Italian Ministry of University and Research.

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Authors and Affiliations

  1. Politecnico di Milano, via Ponzio 4/5, 20133, Milano, Italy

    Marco Gribaudo

  2. Università degli Studi della Campania “L. Vanvitelli”, viale Lincoln 5, 81100, Caserta, Italy

    Mauro Iacono

  3. Università degli Studi di Torino, Corso Svizzera 185, 10149, Torino, Italy

    Daniele Manini

  4. Università degli Studi di Salerno, via Giovanni Paolo II 132, 84084, Fisciano, Italy

    Michele Mastroianni

Authors
  1. Marco Gribaudo
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  2. Mauro Iacono
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  3. Daniele Manini
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  4. Michele Mastroianni
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Corresponding author

Correspondence to Mauro Iacono .

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Editors and Affiliations

  1. Newcastle University, Newcastle upon Tyne, UK

    Matthew Forshaw

  2. Universidad Miguel Hernández de Elche, Elche, Spain

    Katja Gilly

  3. Imperial College London, London, UK

    William Knottenbelt

  4. Newcastle University, Newcastle upon Tyne, UK

    Nigel Thomas

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Gribaudo, M., Iacono, M., Manini, D., Mastroianni, M. (2023). Analysis of the Battery Level in Complex Wireless Sensor Networks Using a Two Time Scales Second Order Fluid Model. In: Forshaw, M., Gilly, K., Knottenbelt, W., Thomas, N. (eds) Practical Applications of Stochastic Modelling. PASM 2022. Communications in Computer and Information Science, vol 1786. Springer, Cham. https://doi.org/10.1007/978-3-031-44053-3_3

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  • DOI: https://doi.org/10.1007/978-3-031-44053-3_3

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