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Hybrid Simulation of Energy Management in IoT Edge Computing Surveillance Systems

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Performance Engineering and Stochastic Modeling (EPEW 2021, ASMTA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 13104))

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Abstract

Internet of Things (IoT) is a well established approach used for the implementation of surveillance systems that are suitable for monitoring large portions of territory. Current developments allow the design of battery powered IoT nodes that can communicate over the network with low energy requirements and locally perform some computing and coordination task, besides running sensing and related processing: it is thus possible to implement edge computing oriented solutions on IoT, if the design encompasses both hardware and software elements in terms of sensing, processing, computing, communications and routing energy costs as one of the quality indices of the system. In this paper we propose a modeling approach for edge computing IoT-based monitoring systems energy related characteristics, suitable for the analysis of energy levels of large battery powered monitoring systems with dynamic and reactive computing workloads.

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References

  1. Huang, D., Wang, P., Niyato, D.: A dynamic offloading algorithm for mobile computing. IEEE Trans. Wireless Commun. 11(6), 1991–1995 (2012)

    Article  Google Scholar 

  2. Zhang, G., Chen, Y., Shen, Z., Wang, L.: Distributed energy management for multiuser mobile-edge computing systems with energy harvesting devices and QoS constraints. IEEE Internet Things J. 6(3), 4035–4048 (2019)

    Article  Google Scholar 

  3. Chen, X., Jiao, L., Li, W., Fu, X.: Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE/ACM Trans. Networking 24(5), 2795–2808 (2016)

    Article  Google Scholar 

  4. Shah-Mansouri, H., Wong, V.W.S.: Hierarchical fog-cloud computing for IoT systems: a computation offloading game. IEEE Internet Things J. 5(4), 3246–3257 (2018)

    Article  Google Scholar 

  5. Liu, L., Chang, Z., Guo, X.: Socially aware dynamic computation offloading scheme for fog computing system with energy harvesting devices. IEEE Internet Things J. 5(3), 1869–1879 (2018)

    Article  Google Scholar 

  6. Trinh, H., et al.: Energy-aware mobile edge computing and routing for low-latency visual data processing. IEEE Trans. Multimedia 20(10), 2562–2577 (2018)

    Article  Google Scholar 

  7. Campanile, L., Gribaudo, M., Iacono, M., Marulli, F., Mastroianni, M.: Computer network simulation with ns-3: a systematic literature review. Electronics 9(2), 272 (2020). https://doi.org/10.3390/electronics9020272

  8. Jha, D.N., et al.: IoTSim-edge: a simulation framework for modeling the behavior of internet of things and edge computing environments. Softw.: Pract. Exp. 50(6), 844–867 (2020)

    Google Scholar 

  9. Zurell, D., et al.: The virtual ecologist approach: simulating data and observers. Oikos 119(4), 622–635 (2010)

    Article  Google Scholar 

  10. Rowcliffe, J., Carbone, C., Kays, R., Kranstauber, B., Jansen, P.: Bias in estimating animal travel distance: the effect of sampling frequency. Methods Ecol. Evol. 3(4), 653–662 (2012)

    Article  Google Scholar 

  11. Palmer, S., Coulon, A., Travis, J.: Introducing a ‘stochastic movement simulator’ for estimating habitat connectivity. Methods Ecol. Evol. 2(3), 258–268 (2011)

    Article  Google Scholar 

  12. Signer, J., Fieberg, J., Avgar, T.: Animal movement tools (amt): R package for managing tracking data and conducting habitat selection analyses. Ecol. Evol. 9(2), 880–890 (2019)

    Article  Google Scholar 

  13. Vuilleumier, S., Metzger, R.: Animal dispersal modelling: handling landscape features and related animal choices. Ecol. Model. 190(1–2), 159–170 (2006)

    Article  Google Scholar 

  14. Watkins, K., Rose, K.: Evaluating the performance of individual-based animal movement models in novel environments. Ecol. Model. 250, 214–234 (2013)

    Article  Google Scholar 

  15. Technitis, G., Othman, W., Safi, K., Weibel, R.: From A to B, randomly: a point-to-point random trajectory generator for animal movement. Int. J. Geogr. Inf. Sci. 29(6), 912–934 (2015)

    Article  Google Scholar 

  16. Campanile, L., Iacono, M., Marulli, F., Mastroianni, M., Mazzocca, N.: Toward a fuzzy-based approach for computational load offloading of IoT devices. J. Univ. Comput. Sci. 26(11), 1455–1474 (2020)

    Google Scholar 

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Acknowledgments

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”.

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Authors and Affiliations

  1. Dipartimento di Matematica e Fisica, Università degli Studi della Campania, “L. Vanvitelli”, viale Lincoln 5, 81100, Caserta, Caserta, Italy

    Lelio Campanile, Mauro Iacono & Michele Mastroianni

  2. Dipartimento di Elettronica, Informatica e Bioingegneria, Politecnico di Milano, via Ponzio 5, 20133, Milan, Italy

    Marco Gribaudo

Authors
  1. Lelio Campanile
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  2. Marco Gribaudo
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  3. Mauro Iacono
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  4. Michele Mastroianni
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Corresponding author

Correspondence to Michele Mastroianni .

Editor information

Editors and Affiliations

  1. University Paris-Saclay, Gif-sur-Yvette, France

    Paolo Ballarini

  2. Telecom Sub Paris, Evry, France

    Hind Castel

  3. University of Patras, Patras, Greece

    Ioannis Dimitriou

  4. Università degli studi della Campania Luigi Vanvitelli, Caserta, Italy

    Mauro Iacono

  5. Faculty of Engineering, Information and Systems, Division of Policy and Planning Sciences, University of Tsukuba, Tsukuba, Japan

    Tuan Phung-Duc

  6. Ghent University, Gent, Belgium

    Joris Walraevens

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Campanile, L., Gribaudo, M., Iacono, M., Mastroianni, M. (2021). Hybrid Simulation of Energy Management in IoT Edge Computing Surveillance Systems. In: Ballarini, P., Castel, H., Dimitriou, I., Iacono, M., Phung-Duc, T., Walraevens, J. (eds) Performance Engineering and Stochastic Modeling. EPEW ASMTA 2021 2021. Lecture Notes in Computer Science(), vol 13104. Springer, Cham. https://doi.org/10.1007/978-3-030-91825-5_21

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  • DOI: https://doi.org/10.1007/978-3-030-91825-5_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-91824-8

  • Online ISBN: 978-3-030-91825-5

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