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Engineering Energy Efficient Visual Sensor Network Applications Using Skeletons

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Abstract

Visual sensor networks (VSNs) perform complex scene analysis algorithms that require significant computations and communications. Under this respect, the use of skeletons contributes to reduce the complexity of VSN programming and may ensure an easier and better optimization of the code. In this context, we propose INS, a stencil based skeleton targeted for wireless/visual sensor networks (W/VSNs) and give a preliminary analysis of its benefits using tracking as a case study. INS abstracts a distributed approximation schema in which the estimation of a given metric is organized in a sequence of steps. Each step includes collecting estimates from some neighbor nodes and local computation of a new approximation. In particular, INS takes inspiration from some stencil based skeletons proposed for parallel computation and merges it with the classical event driven model typical of sensor programming. As a result, the execution of each step is triggered by the detection of a relevant event in the environment. Tracking consists in periodically predicting position and velocity of one or more mobile targets. We discuss how INS can be instantiated to a distributed version of Kalman filtering. As energy efficiency is central in W/VSNs, we derive analytic models for energy dissipation of the INS skeleton depending on different concepts of neighborhood for the data exchanged at each step. Then, these models are used to guide the deployment of our tracking application on a real scenario.

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References

  1. Aldinucci, M., Danelutto, M., Kilpatrick, P.: Autonomic management of multiple non-functional concerns in behavioural skeletons. In: Grids, P2P and Services Computing, pp. 89–103. Springer, US (2010)

  2. Baronti, P., Pillai, P., Chook, V., et al.: Wireless sensor networks: a survey on the state of the art and the 802.15.4 and zigbee standards. Comput. Commun. 30(7), 1655–1695 (2007)

    Article  Google Scholar 

  3. Bettstetter, C., Resta, G., Santi, P.: The node distribution of the random waypoint mobility model for wireless ad hoc networks. IEEE Trans. Mobile Comput. 2(3), 257–269 (2003). doi:10.1109/TMC.2003.1233531

    Article  Google Scholar 

  4. Bolla, R., Davoli, F., Bruschi, R., et al.: The potential impact of green technologies in next-generation wireline networks: is there room for energy saving optimization? IEEE Commun. Mag. 49(8), 80–86 (2011)

    Article  Google Scholar 

  5. Buettner, M., Yee, G.V., Anderson, E., Han, R.: X-mac: a short preamble mac protocol for duty- cycled wireless sensor networks. In: Proceedings of the 4th International Conference on Embedded Networked Sensor Systems, SenSys ’06, pp. 307–320. ACM, New York, NY, USA (2006). doi:10.1145/1182807.1182838

  6. Castalia: A Simulator for WSN (2011). http://castalia.npc.nicta.com.au/

  7. Chiaravalloti, S., Idzikowski, F., Budzisz, L.: Power consumption of WLAN network elements. Technival Report TKN-11-002, Technische Universitat, Berlin (2011)

  8. Cole, M.: Algorithmic Skeletons: Structured Management of Parallel Computation. MIT Press, Cambridge, MA, USA (1991)

    Google Scholar 

  9. Data sheet., P.: Cisco Aironet 802.11a/b/g Wireless PCI Adapter (2010). http://www.cisco.com/en/US/prod/collateral/wireless/ps6442/ps4555/ps5819/product_data_sheet09186a00801ebc33.pdf

  10. Dean, J., Ghemawat, S.: Mapreduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)

    Article  Google Scholar 

  11. Enmyren, J., Kessler, C.W.: Skepu: a multi-backend skeleton programming library for multi-gpu systems. In: Proceedings of the Fourth International Workshop on High-Level Parallel Programming and Applications, HLPP ’10, pp. 5–14. ACM, New York, NY, USA (2010)

  12. Escolar, S., Chessa, S., Carretero, J.: Optimization of quality of service in wireless sensor networks powered by solar cells. In: Proceedings of the 2012 IEEE 10th International Symposium on Parallel and Distributed Processing with Applications, ISPA ’12, pp. 269–276. IEEE Computer Society, Washington, DC, USA (2012)

  13. Fox, G.C.: Concurrent processing for scientific calculations. In: COMPCON’84, pp. 70–73 (1984)

  14. Gay, D., Levis, P., von Behren, R., et al.: The nesC language: a holistic approach to networked embedded systems. SIGPLAN Notices 38(5), 1–11 (2003)

    Article  Google Scholar 

  15. Gorlatch, S., Pelagatti, S.: A transformational framework for skeletal programs: overview and case study. In: Parallel and Distributed Processing. IPPS/SPDP’99 Workshops Proceedings. Lecture Notes in Computer Science 1586, pp. 123–137. Springer, Berlin (1999)

  16. Gupta, V., Tovar, E., Pinho, L., et al.: sMapReduce: a programming pattern for wireless sensor networks. In: Proceedings of the 2nd Workshop on Software Engineering for Sensor Network Applications, pp. 37–42. ACM (2011)

  17. Hnat, T., Sookoor, T., Hooimeijer, P., et al.: Macrolab: a vector-based macroprogramming framework for cyber-physical systems. In: Proceedings of the 6th ACM Conference on Embedded Network Sensor Systems, pp. 225–238. ACM (2008)

  18. Kamal, A.T., Ding, C., Song, B., Farrell, J.A., Roy-Chowdhury, A.K.: A generalized Kalman consensus filter for wide-area video networks. In: Decision and Control and European Control Conference (CDC-ECC), 2011 50th IEEE Conference on, pp. 7863–7869 (2011)

  19. Levis, P., Madden, S., Polastre, J., et al.: TinyOS: an operating system for sensor networks. In: In Ambient Intelligence. Springer (2004)

  20. Loke, S., Nadarajah, S.: MBMF: a framework for macroprogramming data-centric sensor network applications using the bird-meertens formalism. In: 2009 Joint Conferences on Pervasive Computing JCPC, pp. 359–364 (2009)

  21. Lutz, T., Fensch, C., Cole, M.: Partans: an autotuning framework for stencil computation on multi-gpu systems. ACM Trans. Archit. Code Optim. 9(4), 59:1–59:24 (2013)

    Google Scholar 

  22. Madden, S., Franklin, M., Hellerstein, J., et al.: Tag: a tiny aggregation service for ad-hoc sensor networks. SIGOPS Oper. Syst. Rev. 36(SI), 131–146 (2002)

    Article  Google Scholar 

  23. Madden, S., Franklin, M., Hellerstein, J., et al.: TinyDB: an acquisitional query processing system for sensor networks. ACM Trans. Database Syst. 30(1), 122–173 (2005)

    Article  Google Scholar 

  24. Mottola, L., Picco, G.: Logical neighborhoods: a programming abstraction for wireless sensor networks. In: Distributed Computing in Sensor Systems, LNCS, vol. 4026, pp. 150–168. Springer, Heidelberg (2006)

  25. Mottola, L., Picco, G.: Programming wireless sensor networks with logical neighborhoods. In: Proceedings of the 1st Internation Conference on Integrated Internet Ad hoc and Sensor Networks (InterSense) (2006)

  26. Murugesan, S.: Harnessing green it: principles and practices. IT Prof. 10(1), 24–33 (2008)

    Article  Google Scholar 

  27. Newton, R., Morrisett, G., Welsh, M.: The regiment macroprogramming system. In: Proceedings of the 6th International Conference on Information Processing in Sensor Networks, pp. 489–498. ACM (2007)

  28. Olfati-Saber, R.: Distributed kalman filtering for sensor networks. In: 46th IEEE Conference on Decision and Control, 2007, pp. 5492–5498 (2007)

  29. Pelagatti, S.: Structured Development of Parallel Programs. Taylor & Francis, London (1997)

    Google Scholar 

  30. Reed, D., Adams, L., Patrick, M.: Stencils and problem partitionings: their influence on the performance of multiple processor systems. IEEE Trans. Comput. C-36(7), 845–858 (1987)

    Google Scholar 

  31. Song, B., Kamal, A., Soto, C., Ding, C., Farrell, J., Roy-Chowdhury, A.: Tracking and activity recognition through consensus in distributed camera networks. IEEE Trans. Image Process. 19(10), 2564–2579 (2010)

    Article  MathSciNet  Google Scholar 

  32. Soro, S., Heinzelman, W.: A survey of visual sensor networks. In: Advances in Multimedia, 2009 (2009)

  33. Taj, M., Cavallaro, A.: Distributed and decentralized multicamera tracking. IEEE Signal Process. Mag. 28(3), 46–58 (2011). doi:10.1109/MSP.2011.940281

    Article  Google Scholar 

  34. Tavli, B., Bicakci, K., Zilan, R., et al.: A survey of visual sensor network platforms. Multimedia Tools Appl. 60(3), 689–726 (2012)

    Article  Google Scholar 

  35. Villalobos, J., Wilkinson, B.: Skeleton/pattern programming with an adder operator for grid and cloud platforms. In: International Conference on Grid Computing and Applications, pp. 122–128 (2010)

  36. Vishnu, A., Song, S., Marquez, A., et al.: Designing energy efficient communication runtime systems for data centric programming models. In: Proceedings of the 2010 IEEE/ACM Int’l Conference on GREENCOM-CPSCOM ’10, pp. 229–236. IEEE Computer Society, Washington, DC, USA (2010)

  37. Welch, G., Bishop, G.: An introduction to the Kalman filter. Department of Technical Report TR95041. Computer Science, University of North Carolina, Chapel Hill (2000)

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Authors and Affiliations

  1. Dipartimento di Informatica, Università di Pisa, Largo B. Pontecorvo, 3, Pisa, Italy

    Stefano Chessa, Susanna Pelagatti & Nicoletta Triolo

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  1. Stefano Chessa
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  2. Susanna Pelagatti
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  3. Nicoletta Triolo
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Correspondence to Nicoletta Triolo.

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Chessa, S., Pelagatti, S. & Triolo, N. Engineering Energy Efficient Visual Sensor Network Applications Using Skeletons. Int J Parallel Prog 42, 663–680 (2014). https://doi.org/10.1007/s10766-013-0260-y

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  • DOI: https://doi.org/10.1007/s10766-013-0260-y

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