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PCTMC Models of Wireless Sensor Network Protocols

  • Conference paper
Book cover Computer Performance Engineering (EPEW 2012, UKPEW 2012)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 7587))

Abstract

Wireless Sensor Networks (WSNs) consist of a large number of spatially distributed embedded devices (nodes), which communicate with one another via radio. Over the last decade improvements in hardware and a steady decrease in cost have encouraged the application of WSNs in areas such as industrial control, security and environmental monitoring. However, despite increasing popularity, the design of end-to-end software for WSNs is still an expert task since the choice of middleware protocols heavily influences the performance of resource-constrained WSNs. As a consequence, WSN designers resort to discrete event simulation prior to deploying networks. While such simulations are reasonably accurate, they tend to be computationally expensive to run, especially for large networks. This particularly limits the number of distinct protocol configurations that engineers can test in advance of construction and hence their final setup may be suboptimal. To mitigate this effect we discuss how highly efficient mean-field techniques can be brought to bear on models of wireless sensor networks. In particular, we consider the practical modelling issues involved in constructing appropriately realistic Population CTMC (PCTMC) models of WSN protocols.

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References

  1. Viani, F., Oliveri, G., Donelli, M., Lizzi, L., Rocca, P., Massa, A.: WSN-based Solutions for Security and Surveillance. Computer, 1762–1765 (September 2010)

    Google Scholar 

  2. Al-Fares, M.S., Sun, Z.: Self-Organizing Routing Protocol to achieve QoS in Wireless Sensor Network for Forest Fire Monitoring. Systems Research, 211–216 (2009)

    Google Scholar 

  3. Xu, N., Rangwala, S., Chintalapudi, K.K., Ganesan, D., Broad, A., Govindan, R., Estrin, D.: A wireless sensor network For structural monitoring. In: Proceedings of the 2nd International Conference on Embedded Networked Sensor Systems SenSys 2004, vol. 20(7), pp. 13–24 (2004)

    Google Scholar 

  4. Akhondi, M.R., Talevski, A., Carlsen, S., Petersen, S.: Applications of Wireless Sensor Networks in the Oil, Gas and Resources Industries. In: 2010 24th IEEE International Conference on Advanced Information Networking and Applications, pp. 941–948 (2010)

    Google Scholar 

  5. Bagree, R., Jain, V.R., Kumar, A., Ranjan, P.: TigerCENSE: Wireless Image Sensor Network to Monitor Tiger Movement. In: Marron, P.J., Voigt, T., Corke, P., Mottola, L. (eds.) REALWSN 2010. LNCS, vol. 6511, pp. 13–24. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  6. Micallef, J., Grech, I., Brincat, A., Traver, V., Monto, E.: Body area network for wireless patient monitoring. IET Communications 2(2), 215–222 (2008)

    Article  Google Scholar 

  7. Boulis, A.: Castalia: revealing pitfalls in designing distributed algorithms in WSN. In: Jha, S. (ed.) Proceedings of the 5th International Conference on Embedded Networked Sensor Systems, pp. 407–408. ACM (2007)

    Google Scholar 

  8. Ns, The Network Simulator - ns-2 (2002)

    Google Scholar 

  9. Levis, P., Lee, N.: TOSSIM: A Simulator for TinyOS Networks, UC Berkeley, pp. 1–17 (September 2003)

    Google Scholar 

  10. Bergamini, L., Crociani, C., Vitaletti, A., Nati, M.: Validation of WSN simulators through a comparison with a real testbed. In: Proceedings of the 7th ACM Workshop on Performance Evaluation of Wireless Ad Hoc Sensor and Ubiquitous Networks, pp. 103–104. ACM (2010)

    Google Scholar 

  11. Egea-Lopez, E., Vales-Alonso, J., Martinez-Sala, A., Pavon-Mario, P., Garcia-Haro, J.: Simulation Scalability Issues in Wireless Sensor Networks. IEEE Communications Magazine 44(7), 64–73 (2006)

    Article  Google Scholar 

  12. Opper, M., Saad, D.: Advanced Mean Field Methods: Theory and Practice. MIT Press (2001)

    Google Scholar 

  13. Van Kampen, N.G.: Stochastic Processes in Physics and Chemistry. North-Holland personal library, vol. 11. North-Holland (1992)

    Google Scholar 

  14. Ciocchetta, F., Hillston, J.: Bio-PEPA: A framework for the modelling and analysis of biological systems. Theoretical Computer Science 410(33-34), 3065–3084 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  15. Hillston, J.: Fluid flow approximation of PEPA models. In: Second International Conference on the Quantitative Evaluation of Systems, QEST 2005, pp. 33–42 (2005)

    Google Scholar 

  16. Hayden, R.A.: Mean-field approximations for performance models with generally-timed transitions. Accepted for publication in ACM SIGMETRICS Performance Evaluation Review (2011)

    Google Scholar 

  17. Gribaudo, M., Cerotti, D., Bobbio, A.: Analysis of On-off policies in Sensor Networks Using Interacting Markovian Agents. In: 6th IEEE International Conference on Pervasive Computing and Communications, PerCom 2008, pp. 300–305 (2008)

    Google Scholar 

  18. Bruneo, D., Scarpa, M., Bobbio, A., Cerotti, D., Gribaudo, M.: Markovian agent modeling swarm intelligence algorithms in wireless sensor networks. Performance Evaluation 69(3-4), 135–149 (2012)

    Article  Google Scholar 

  19. Förster, A., Murphy, A.L.: A Critical Survey and Guide to Evaluating WSN Routing Protocols. In: The First International Workshop on Networks of Cooperating Objects (CONET), Stockholm (2010)

    Google Scholar 

  20. Anastasi, G., Conti, M., Francesco, M.D.: A Comprehensive Analysis of the MAC Unreliability Problem in IEEE 802. 15. 4 Wireless Sensor Networks. IEEE Transactions on Industrial Informatics 7(1), 52–65 (2011)

    Article  Google Scholar 

  21. Zimmerling, M., Ferrari, F., Mottola, L., Voigt, T., Thiele, L.: pTunes: Runtime Parameter Adaptation for Low-power MAC Protocols. In: Proceedings of the 11th International Conference on Information Processing in Sensor Networks - IPSN 2012, p. 173. ACM Press, New York (2012)

    Chapter  Google Scholar 

  22. Crossbow: Crossbow datasheet on MicaZ (2006)

    Google Scholar 

  23. Sohrabi, K., Manriquez, B., Pottie, G.J.: Near ground wideband channel measurement in 800-1000 MHz. In: 1999 IEEE 49th Vehicular Technology Conference Cat No99CH36363, vol. 1(3), pp. 571–574 (1999)

    Google Scholar 

  24. Van Dam, T., Langendoen, K.: An adaptive energy-efficient MAC protocol for wireless sensor networks. In: Akyildiz, I.F., Estrin, D., Culler, D.E., Srivastava, M.B. (eds.) Proceedings of the First International Conference on Embedded Networked Sensor Systems, SenSys 2003, vol. 03, p. 171. ACM Press (2003)

    Google Scholar 

  25. Yick, J., Mukherjee, B., Ghosal, D.: Wireless sensor network survey. Computer Networks 52(12), 2292–2330 (2008)

    Article  Google Scholar 

  26. Yadav, P.: Cross-Layer Protocols to Support Periodic Data Collection and Event Driven Wireless Sensor Network Applications. Phd thesis, Imperial College (2011)

    Google Scholar 

  27. Paone, M., Paladina, L., Bruneo, D., Puliafito, A.: A Swarm-based Routing Protocol for Wireless Sensor Networks. In: Sixth IEEE International Symposium on Network Computing and Applications, NCA 2007, vol. 3(Nca), pp. 265–268 (2007)

    Google Scholar 

  28. Bachir, A., Dohler, M., Watteyne, T., Leung, K.K.: MAC Essentials for Wireless Sensor Networks. IEEE Communications Surveys Tutorials 12(2), 222–248 (2010)

    Article  Google Scholar 

  29. Römer, K., Kasten, O., Mattern, F.: Middleware challenges for wireless sensor networks. ACM SIGMOBILE Mobile Computing and Communications Review 6(4), 59–61 (2002)

    Article  Google Scholar 

  30. Chatzigiannakis, I., Mylonas, G., Nikoletseas, S.: 50 ways to build your application: A survey of middleware and systems for Wireless Sensor Networks. In: 2007 IEEE Conference on Emerging Technologies Factory Automation, EFTA 2007, pp. 466–473 (2007)

    Google Scholar 

  31. Tong, S.: An Evaluation Framework for middleware approaches on Wireless Sensor Networks. Tech. Rep., Helsinki University of Technology, Helsinki (2007)

    Google Scholar 

  32. Wang, M.-M., Cao, J.-N., Li, J., Dasi, S.K.: Middleware for Wireless Sensor Networks: A Survey. Journal of Computer Science and Technology 23(3), 305–326 (2008)

    Article  Google Scholar 

  33. Liu, T., Martonosi, M.: Impala: a middleware system for managing autonomic, parallel sensor systems. In: System, PPoPP 2003, vol. 38, pp. 107–118. ACM (2003)

    Google Scholar 

  34. Buckl, C., Sommer, S., Scholz, A., Knoll, A., Kemper, A.: Generating a Tailored Middleware for Wireless Sensor Network Applications. In: IEEE International Conference on Sensor Networks Ubiquitous and Trustworthy Computing, SUTC 2008, pp. 162–169 (2008)

    Google Scholar 

  35. Gillespie, D.T.: Exact stochastic simulation of coupled chemical reactions. Journal of Physical Chemistry 81(25), 2340–2361 (1977)

    Article  Google Scholar 

  36. Wangersky, P.J.: Lotka-Volterra population models. Annual Review of Ecology and Systematics 9(1), 189–218 (1978)

    Article  Google Scholar 

  37. Stefanek, A., Hayden, R.A., Bradley, J.T.: Fluid computation of the performance-energy trade-off in large scale Markov models. Accepted for Publication in ACM SIGMETRICS Performance Evaluation Review (2011)

    Google Scholar 

  38. Benaim, M., Le Boudec, J.: A class of mean field interaction models for computer and communication systems. Performance Evaluation 65(11-12), 823–838 (2008)

    Article  Google Scholar 

  39. Stefanek, A., Guenther, M.C., Bradley, J.T.: Normal and inhomogeneous moment closures for stochastic process algebras. In: 10th Workshop on Process Algebra and Stochastically Timed Activities (PASTA 2011), Ragusa (2011)

    Google Scholar 

  40. Galpin, V.: Towards a spatial stochastic process algebra. In: Proceedings of the 7th Workshop on Process Algebra and Stochastically Timed Activities, PASTA, Edinburgh (2008)

    Google Scholar 

  41. Liu, A.-F., Ma, M., Chen, Z.-G., Gui, W.-H.: Energy-Hole Avoidance Routing Algorithm for WSN. In: Fourth International Conference on Natural Computation, ICNC 2008, vol. 1, pp. 76–80 (2008)

    Google Scholar 

  42. Halkes, G.P., Langendoen, K.G.: Experimental Evaluation of Simulation Abstractions for Wireless Sensor Network MAC Protocols. EURASIP Journal on Wireless Communications and Networking 2010, 1–10 (2010)

    Article  Google Scholar 

  43. Perla, E., Catháin, A.O., Carbajo, R.S., Huggard, M., Mc Goldrick, C.: PowerTOSSIM z: Realistic Energy Modelling for Wireless Sensor Network Environments. In: Proceedings of the 3rd ACM Workshop on Performance Monitoring and Measurement of Heterogeneous Wireless and Wired Networks, pp. 35–42. ACM (2008)

    Google Scholar 

  44. Jongerden, M.R., Haverkort, B.R.: Which battery model to use? IET Software 3(6), 445 (2009)

    Article  Google Scholar 

  45. Chaintreau, A., Le Boudec, J.Y., Ristanovic, N.: The age of gossip: spatial mean field regime. Evolution, 109–120 (2009)

    Google Scholar 

  46. Caravagna, G., Hillston, J.: Modeling biological systems with delays in Bio-PEPA. In: Electronic Proceedings in Theoretical Computer Science, MeCBIC, vol. 40, pp. 85–101 (2010)

    Google Scholar 

  47. Guenther, M.C., Bradley, J.T.: Mean-field analysis of data flows in Wireless Sensor Networks. Submitted to VALUETOOLS (2012), http://www.doc.ic.ac.uk/~mcg05/wsnrouting

  48. Galpin, V., Bortolussi, L., Hillston, J.: HYPE: A Process Algebra for Compositional Flows and Emergent Behaviour. In: Bravetti, M., Zavattaro, G. (eds.) CONCUR 2009. LNCS, vol. 5710, pp. 305–320. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

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Authors and Affiliations

  1. Imperial College London, 180 Queen’s Gate, London, SW7 2AZ, United Kingdom

    Marcel C. Guenther & Jeremy T. Bradley

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  1. Marcel C. Guenther
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  2. Jeremy T. Bradley
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Editors and Affiliations

  1. Institut für Informatik, Ludwig-Maximilians-Universität, München, Germany

    Mirco Tribastone

  2. School of Informatics, Informatics Forum, The University of Edinburgh, 10 Crichton Street, EH8 9AB, Edinburgh, UK

    Stephen Gilmore

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Guenther, M.C., Bradley, J.T. (2013). PCTMC Models of Wireless Sensor Network Protocols. In: Tribastone, M., Gilmore, S. (eds) Computer Performance Engineering. EPEW UKPEW 2012 2012. Lecture Notes in Computer Science, vol 7587. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36781-6_12

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  • DOI: https://doi.org/10.1007/978-3-642-36781-6_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-36780-9

  • Online ISBN: 978-3-642-36781-6

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