Virtualizing Closed-loop Sensor Networks: A Case Study

Priyanka Dattatri Kedalagudde, Michael Zink

Abstract

Closed loop sensor networks are cyber-physical systems that establish a tightly coupled connection between computational elements and the control of physical elements. Existing closed-loop sensor networks are based on dedicated, ’stove-pipe’ architectures that prevent the sharing of these networks. This paper addresses the problem of sharing these networks through virtualization. We propose scheduling algorithms that manage requests from competing applications and evaluate their impact on system utilization as compared to a dedicated network. These algorithms are evaluated through trace-driven simulations. We aim to demonstrate that the proposed scheduling algorithms result in cost savings due to shared network infrastructure without unduly affecting application utility. In our evaluations, we observe only a 20% reduction in average utility via the DSES scheduling approach.

References

  1. Allen, G., Nabrzyski, J., Seidel, E., van Albada, G. D., Dongarra, J. J., and Sloot, P. M. A., editors (2009). Computational Science - ICCS 2009. Springer-Verlag.
  2. Barham, P., Dragovic, B., Fraser, K., Hand, S., Harris, T., Ho, A., Neugebaue, R., Pratt, I., and Warfield, A. (2003). Xen and the art of virtualization. In Proceedings of the 19th ACM Symposium on Operating Systems Principles, Bolton Landing, NY, USA.
  3. Baumgartner, T., Chatzigiannakis, I., Danckwardt, M., Koninis, C., Kröller, A., Mylonas, G., Pfisterer, D., and Porter, B. (2010). Virtualising testbeds to support large-scale reconfigurable experimental facilities. In EWSN, pages 210-223.
  4. Bose, R. and Helal, A. (2008). Distributed mechanisms for enabling virtual sensors in service oriented intelligent environments. In Intelligent Environments, 2008 IET 4th International Conference on, pages 1 -8.
  5. Bose, R., Helal, A., Sivakumar, V., and Lim, S. (2007). Virtual sensors for service oriented intelligent environments. In Proceedings of the third conference on IASTED International Conference: Advances in Computer Science and Technology, ACST'07, pages 165- 170, Anaheim, CA, USA. ACTA Press.
  6. Brotzge, J., Chandrasekar, V., Droegemeier, K., Kurose, J., McLaughlin, D., Philips, B., Preston, M., and Sekelsky, S. (2004). Distributed collaborative adaptive sensing for hazardous weather detection, tracking, and predicting. In Proceeding of Computational Science - ICCS 2004, Krakow, Poland.
  7. Brouwers, N., Langendoen, K., and Corke, P. (2009). Darjeeling, a feature-rich vm for the resource poor. In Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems, SenSys 7809, pages 169- 182, New York, NY, USA. ACM.
  8. Bugnion, E., Devine, S., and Rosenblum, M. (1997). Disco: Running commodity operating systems on scalable multiprocessors. ACM Transactions on Computer Systems, 15(4):143-156.
  9. Council, N. R. (2008). Evaluation of the multifunction phased array radar planning process. The National Academic Press.
  10. Drake, P., McLaughlin, D., and Nolan, M. (2010). Collaborative and adaptive sensing of the atmosphere (casa) and multi-function sensor services network (mssn). In Integrated Communications Navigation and Surveillance Conference (ICNS), pages 1-33.
  11. Evensen, P. and Meling, H. (2009). Sensor virtualization with self-configuration and flexible interactions. In Proceedings of the 3rd ACM International Workshop on Context-Awareness for Self-Managing Systems, Casemans 7809, pages 31-38, New York, NY, USA. ACM.
  12. Hong, K., Park, J., Kim, T., Kim, S., Kim, H., Ko, Y., Park, J., Burgstaller, B., and Scholz, B. (2009). Tinyvm, an efficient virtual machine infrastructure for sensor networks. In Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems, SenSys 7809, pages 399-400, New York, NY, USA. ACM.
  13. Jayasumana, A., Han, Q., and Illangasekare, T. (2007). Virtual sensor networks - a resource efficient approach for concurrent applications. In Proceedings of the 4th International Conference on Information Technology: New Generations (ITNG), Las Vegas, NV, USA.
  14. Krishnappa, D. K., Irwin, D., Lyons, E., and Zink, M. (2012a). Cloudcast: Cloud computing for short-term mobile weather forecasts. In IPCCC 2012.
  15. Krishnappa, D. K., Lyons, E., Irwin, D., and Zink, M. (2012b). Network capabilities of cloud services for a real time scientific application. In LCN 2012.
  16. Lim, H. B., Iqbal, M., and Ng, T. J. (2009). A virtualization framework for heterogeneous sensor network platforms. In Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems, SenSys 7809, pages 319-320, New York, NY, USA. ACM.
  17. Massaguer, D., Mehrotra, S., and Venkatasubramanian, N. (2009). A semantic approach for building pervasive spaces. In Proceedings of the 6th Middleware Doctoral Symposium, MDS 7809, pages 2:1-2:6, New York, NY, USA. ACM.
  18. McLaughlin, D., Pepyne, D., V.Chandrasekar, Philips, B., Kurose, J., and et al., M. Z. (2009). Short-Wavelenth Technology and the Potential for Distributed Networks of Small Radar Systems. Bulletin of the American Meteorological Society (BAMS), 90(12):1797- 1817.
  19. Pajic, M. and Mangharam, R. (2010). Embedded virtual machines for robust wireless control and actuation. In Real-Time and Embedded Technology and Applications Symposium (RTAS), 2010 16th IEEE, pages 79- 88.
  20. Pumpichet, S. and Pissinou, N. (2010). Virtual sensor for mobile sensor data cleaning. In GLOBECOM 2010, 2010 IEEE Global Telecommunications Conference, pages 1 -5.
  21. Seawright, L. and MacKinnon, R. (1979). Vm/370 - a study of multiplicity and usefulness. IBM Systems Journal, pages 4-17.
  22. Sztipanovits, J. and Rajkumar, R., editors (2010). International Conference on Cyber-Physical Systems. ACM Press.
  23. Zink, M., Lyons, E., Westbrook, D., Kurose, J., and Pepyne, D. (2010). Closed-loop architecture for distributed collaborative adaptive sensing: Meteorogolical command & control. International Journal for Sensor Networks (IJSNET), 7(1/2).
  24. Zink, M., Lyons, E., Westbrook, D., Pepyne, D., Pilips, B., Kurose, J., and Chandrasekar, V. (2009). Meteorogical Command and Control: Architecture and performance evaluation. In Geoscience and Remote Sensing Symposium, 2008. IGARSS 2008. IEEE International.
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Paper Citation


in Harvard Style

Dattatri Kedalagudde P. and Zink M. (2017). Virtualizing Closed-loop Sensor Networks: A Case Study . In Proceedings of the 6th International Conference on Sensor Networks - Volume 1: SENSORNETS, ISBN 978-989-758-211-0, pages 188-195. DOI: 10.5220/0006209901880195


in Bibtex Style

@conference{sensornets17,
author={Priyanka Dattatri Kedalagudde and Michael Zink},
title={Virtualizing Closed-loop Sensor Networks: A Case Study},
booktitle={Proceedings of the 6th International Conference on Sensor Networks - Volume 1: SENSORNETS,},
year={2017},
pages={188-195},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006209901880195},
isbn={978-989-758-211-0},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Sensor Networks - Volume 1: SENSORNETS,
TI - Virtualizing Closed-loop Sensor Networks: A Case Study
SN - 978-989-758-211-0
AU - Dattatri Kedalagudde P.
AU - Zink M.
PY - 2017
SP - 188
EP - 195
DO - 10.5220/0006209901880195