Multimedia Systems

, Volume 18, Issue 5, pp 425–444 | Cite as

MultiSense: proportional-share for mechanically steerable sensor networks

  • Navin Sharma
  • David Irwin
  • Michael Zink
  • Prashant Shenoy
Regular Paper

Abstract

Steerable sensors, such as pan-tilt-zoom cameras and weather radars, expose programmable actuators to applications, which steer them to dictate the type, quality, and quantity of data they collect. Applications with different goals steer these sensors in different directions. Although being expensive to deploy and maintain, existing steerable sensor networks allow only a single application to control them due to the slow speed of their mechanical actuators. To address the problem, we design MultiSense to enable fine-grained multiplexing by (1) exposing a virtual sensor to each application and (2) optimizing the time to context-switch between virtual sensors and satisfy requests. We implement MultiSense in Xen, a widely used virtualization platform, and explore how well proportional-share scheduling, along with extensions for state restoration, request batching and merging, and anticipatory scheduling, satisfies the unique requirements of steerable sensors. We present experiments for pan-tilt-zoom cameras and weather radars that show MultiSense efficiently isolates the performance of virtual sensors, allowing concurrent applications to satisfy conflicting goals. As one example, we enable a tracking application to photograph an object moving at nearly 3 mph every 23 ft along its trajectory at a distance of 300 ft, while supporting a security application that photographs a fixed point every 3 s.

Keywords

Virtualization Sensor Camera Radar 

References

  1. 1.
    Netflix Watch Instantly. http://www.netflix.com/ (2012). Accessed Jan 2012
  2. 2.
    Barham, P., Dragovic, B., Fraser, K., Hand, S., Harris, T.L., Ho, A., Neugebauer, R., Pratt, I., Warfield, A.: Xen and the art of virtualization. In: Proceedings of the 9th ACM symposium on operating systems principles, Bolton Landing, 19–22 Oct 2003Google Scholar
  3. 3.
    Bennett, J.C.R., Zhang, H.: Wf2q: worst-case weighted fair queuing. In: Proceedings of the IEEE international conference on computer communications, Shanghai, June 2002Google Scholar
  4. 4.
    Bi, S., Chong, D., Kamal, A.T., Farrell, J.A., Roy-chowdhury, A.K.: Distributed camera networks. IEEE Signal Process. Mag. 28(3), 20–31 (2011)CrossRefGoogle Scholar
  5. 5.
    Bimbo, A.D., Dini, F., Grifoni, A., Pernici, F.: Uncalibrated framework for on-line camera cooperation to acquire human head imagery in wide areas. In: Proceeding of the fifth IEEE international conference on advanced video and signal based surveillance, Santa Fe, Sep 2008Google Scholar
  6. 6.
    Binsted, K., Bradley, N., Buie, M., Ibara, S., Kadooka, M., Shirae, D.: The Lowell telescope scheduler: a system to provide non-professional access to large automatic telescopes. In: Proceedings of the Internet and multimedia systems and applications conference, Grindelwald, Aug 2005Google Scholar
  7. 7.
    Bruno, J., Brustoloni, J., Gabber, E., Ozden, B., Silberschatz, A.: Disk scheduling with quality of service guarantees. In: Proceedings of the international conference on multimedia computing and systems, vol. 2, p. 400, Florence, July 1999Google Scholar
  8. 8.
    Cao, Q., Abdelzaher, T., Stankovic, J., He, T.: The LiteOS operating system: towards unix-like abstractions for wireless sensor networks. In: Proceedings of the 7th international conference on information processing in sensor networks, pp. 233–244, St. Louis, Apr 2008Google Scholar
  9. 9.
    Cao, Q., Fesehaye, D., Pham, N., Sarwar, Y., Abdelzaher, T.: Virtual battery: an energy reserve abstraction for embedded sensor networks. In: Proceedings of the real-time systems symposium, San Diego, Nov 2008Google Scholar
  10. 10.
    Francoeur, A.: Border patrol goes high tech. http://www.photonics.com (2009). Accessed on 24 Aug 2009
  11. 11.
    Goyal, P., Vin, H., Cheng, H.: Start-time fair queueing: a scheduling algorithm for integrated services packet switching networks. In: Proceedings of ACM SIGCOMM conference, Stanford, Aug 1996Google Scholar
  12. 12.
    Iyer, S., Druschel, P.: Anticipatory scheduling: a disk scheduling framework to overcome deceptive idleness in synchronous I/O. In: Proceedings of the 18th ACM symposium on operating systems principles, Banff, Oct 2001Google Scholar
  13. 13.
    Jones, M., Rosu, D., Rosu, M.: CPU reservations and time constraints: efficient, predictable scheduling of independent activities. In: Proceedings of the symposium on operating systems principles, Saint-Malo, Oct 1997Google Scholar
  14. 14.
    Klues, K., Handziski, V., Lu, C., Wolisz, A., Culler, D., Gay, D. and Philip Levis.: Integrating concurrency control and energy management in device drivers. In: Proceedings of the symposium on operating systems principles, Stevenson, Oct 2007Google Scholar
  15. 15.
    Levis, P., Maté, D. Culler.: A tiny virtual machine for sensor networks. In: Proceedings of the international conference on architectural support for programming languages and operating systems, San Jose, Oct 2002Google Scholar
  16. 16.
    Li, M., Yan, T., Ganesan, D., Lyons, E., Shenoy, P., Venkataramani, A., Zink, M.: Multi-user data sharing In radar sensor networks. In: Proceedings of the ACM conference on embedded networked sensor systems, Sydney, Nov 2007Google Scholar
  17. 17.
    Lorincz, K., Chen, B., Waterman, J., Werner-Allen, G., Welsh, M.: Resource aware programming in the Pixie operating system. In: Proceedings of the ACM conference on embedded networked sensor systems, Raleigh, Nov 2008Google Scholar
  18. 18.
    Magnuson, S.: New northern border camera system to avoid past pitfalls. In: National defense magazine, Sep 2009Google Scholar
  19. 19.
    McLaughlin, D., Pepyne, D., Chandrasekar, V., Philips, B., Kurose, J., Zink, M.: Short-wavelength technology and the potential for distributed networks of small radar systems. Bull. Am. Meteorol. Soc. 90(12), 1797–1817 (2009)Google Scholar
  20. 20.
    Micheloni, C., Rinner, B., Foresti, G.L.: Video analysis in pan-tilt-zoom camera networks. IEEE Signal Process. Mag. 27(5), 78–90 (2010)CrossRefGoogle Scholar
  21. 21.
    Nelson, T.: The device driver as state machine. C. Users J. 10(3), 41–60 (1992)Google Scholar
  22. 22.
    Qureshi, F.Z., Terzopoulos, D.: Surveillance camera acheduling: a virtual vision approach. ACM Multimed. Syst. J. 12(3), 269–283 (2006)CrossRefGoogle Scholar
  23. 23.
    Qureshi, F.Z., Terzopoulos, D.: Planning ahead for PTZ camera assignment and control. In: Proceedings of third ACM/IEEE international conference on distributed smart cameras, Como, Aug 2009Google Scholar
  24. 24.
    Raj, H., Seshasayee, B., Schwan, K.: VMedia: enhanced multimedia services in virtualized systems. In: Proceedings of the multimedia computing and networks conference, San Jose, Jan 2008Google Scholar
  25. 25.
    Salazar, J.L., Knapp, E.J., McLaughlin, D.J.: Dual-polarization performance of the phase-tilt antenna array in a CASA dense network radar. In: Proceedings of the 2010 IEEE international geoscience and remote sensing symposium, Honolulu, July 2010Google Scholar
  26. 26.
    Sharma, N., Irwin, D., Shenoy, P., Zink, M.: MultiSense: fine-grained multiplexing for steerable camera sensor networks. In: Proceedings of the 2011 ACM multimedia systems, San Jose, Feb 2011Google Scholar
  27. 27.
    Shenoy, P., Vin, H.: Cello: a disk scheduling framework for next generation operating systems. In: Proceedings of the international conference on measurement and modeling of computer systems, Madison, June 1998Google Scholar
  28. 28.
    Starzyk, W., Qureshi, F.Z.: Learning proactive control strategies for PTZ cameras. In: Proceedings of the 2011 fifth ACM/IEEE international conference on distributed smart cameras, Ghent, Aug 2011Google Scholar
  29. 29.
    Swift, M.M., Annamalai, M., Bershad, B.N., Levy, H.M.: Recovering device drivers. In: Proceedings of the sixth symposium on operating system design and implementation, San Francisco, Dec 2004Google Scholar
  30. 30.
    Wang, Y., Chandrasekar, V., Dolan, B.: Development of scan strategy for dual doppler retrieval in a networked radar system. In: Proceeding of the IEEE international geoscience and remote sensing symposium, Boston, July 2008Google Scholar
  31. 31.
    Warfield, A., Hand, S., Fraser, K., Deegan, T.: Facilitating the development of soft devices. In: Proceedings of the USENIX annual technical conference, Anaheim, 10–15 Apr 2005Google Scholar
  32. 32.
    Xia, L., Lange, J.: Towards virtual passthrough I/O on commodity devices. In: Proceedings of the workshop on I/O virtualization, USENIX Association, San Diego, Dec 2008Google Scholar
  33. 33.
    Zink, M., Lyons, E., Westbrook, D., Kurose, J., Pepyne, D.: Closed-loop architecture for distributed collaborative adaptive sensing of the atmosphere: meteorological command and control. Int. J. Sens. Netw. 7(1/2), 4–18 (2010)Google Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Navin Sharma
    • 1
  • David Irwin
    • 1
  • Michael Zink
    • 1
  • Prashant Shenoy
    • 1
  1. 1.University of MassachusettsAmherstUSA

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