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2014 | OriginalPaper | Buchkapitel

2. Stochastic Modeling of Delay, Energy Consumption, and Lifetime

verfasst von : Yunbo Wang, Mehmet C. Vuran, Steve Goddard

Erschienen in: The Art of Wireless Sensor Networks

Verlag: Springer Berlin Heidelberg

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Abstract

Emerging applications of wireless sensor networks (WSNs) require real-time quality of service (QoS) guarantees to be provided by the network. Due to the non-deterministic impacts of the wireless channel and queuing state, probabilistic analysis of QoS is essential. For most WSNs applications, the end-to-end delay for packet delivery and the energy consumption are the most important QoS metrics. In this chapter, a comprehensive cross-layer probabilistic analysis framework is presented to investigate the probabilistic evaluation of QoS performance provided by WSNs. In particular, the QoS performance is evaluated in two levels. In the node level, using a Discrete-Time Markov queueing model, the distribution of single-hop delay and single-node energy consumption and lifetime are analyzed. In the network level, based on the node level analysis, the distributions of end-to-end delay, the network lifetime, and the event detection delay are then analyzed. Fluid models are utilized in the network level analysis. The framework also considers a realistic channel environments. Compared to the first-order QoS statistics, such as the mean and the variance, the distribution of QoS metrics reveals the relationship between the performance and reliability with QoS-based operations in WSNs. Using the framework, effective network development can be performed.

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Vorheriges Kapitel Introduction

Nächstes Kapitel Barrier Coverage: Foundations and Design

In the following, we drop the indices \(m\) and \(b\), where appropriate, to simplify the notation.

With a slight abuse of denotation, this infinitesimal area is henceforth denoted by \(\mathrm{d}\varvec{x}\).

Although a discrete time Markov process is used for the model, the energy consumption is continuous. Thus the pdf, as opposed to the pmf, is used to characterize the distribution.

T. Abdelzaher, S. Prabh, R. Kiran, On real-time capacity limits of multihop wireless sensor networks, in Proceedings of IEEE RTSS 2004, pp. 359–370 (2004). doi:10.1109/REAL.2004.37

K. Akkaya, M. Younis, A survey on routing protocols for wireless sensor networks. Ad Hoc Netw. 3(3), 325–349 (2005)CrossRef

I.F. Akyildiz, T. Melodia, K.R. Chowdhury, A survey on wireless multimedia sensor networks. Comput. Netw. 51(4), 921–960 (2007). doi:http://dx.doi.org/10.1016/j.comnet.2006.10.002

I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, Wireless sensor networks: a survey. Comput. Netw. J. (Elsevier) 38(4), 393–422 (2002). doi:http://dx.doi.org/10.1016/S1389-1286(01),00302-4

G. Armitage, Quality of Service in IP Networks: Foundations for a Multi-Service Internet (Macmillan Publishing Co., Inc. Indianapolis, 2000)

G. Bianchi, Performance analysis of the IEEE 802.11 distributed coordination function. IEEE J. Sel. Areas Commun. 18(3), 535–547 (2000). doi:10.1109/49.840210

N. Bisnik, A. Abouzeid, Queuing network models for delay analysis of multihop wireless ad hoc networks, in IWCMC 2006: Proceedings of the 2006 International Conference on Wireless Communications and Mobile Computing, pp. 773–778. Vancouver, British Columbia, Canada (2006). doi:10.1145/1143549.1143704

M. Buettner, G.V. Yee, E. Anderson, R. Han, X-MAC: a short preamble MAC protocol for duty-cycled wireless sensor networks, in Proceedings of ACM SenSys 2006. Boulder, CO (2006)

A. Burchard, J. Liebeherr, S. Patek, A min-plus calculus for end-to-end statistical service guarantees. IEEE Trans. Inf. Theory 52(9), 4105–4114 (2006). doi: 10.1109/TIT.2006.880019

10.

Q. Cao, T. Yan, J. Stankovic, T. Abdelzaher, Analysis of target detection performance for wireless sensor networks, in Proceedins of DCOSS 2005, pp. 276–292. Marina del Rey, CA (2005)

11.

S. Chakrabarti, A. Mishra, QoS issues in ad hoc wireless networks. IEEE Commun. Mag. 39(2), 142–148 (2002)CrossRef

12.

D. Chen, P. Varshney, QoS support in wireless sensor networks: a survey, in International Conference on Wireless Networks, pp. 227–233. Citeseer (2004)

13.

C. Chiasserini, R. Gaeta, M. Garetto, M. Gribaudo, D. Manini, M. Sereno, Fluid models for large-scale wireless sensor networks. Perform. Eval. 64(7–8), 715–736 (2007)CrossRef

14.

E. Crawley, R. Nair, B. Rajagopalan, H. Sandick, A Framework for QoS-based Routing in the Internet. RFC2386, August (1998)

15.

R. Cruz, A calculus for network delay. I. Network elements in isolation. IEEE Trans. Inf. Theory 37(1), 114–131 (1991). doi: 10.1109/18.61109

16.

W. Dargie, X. Chao, M. Denko, Modelling the energy cost of a fully operational wireless sensor network. Telecommun. Syst. 44(1–2), 3–15 (2010)CrossRef

17.

O. Dousse, C. Tavoularis, P. Thiran, Delay of intrusion detection in wireless sensor networks, in Proceedings of ACM MobiHoc 2006, p. 165. Florence, Italy (2006)

18.

E. Duarte-Melo, M. Liu, Analysis of energy consumption and lifetime of heterogeneous wireless sensor networks, in Proceedings of IEEE GLOBECOM 2002. Taipei, Taiwan (2002)

19.

E. Duarte-Melo, M. Liu, A. Misra, A modeling framework for computing lifetime and information capacity in wireless sensor networks, in WiOpt 2004. Cambridge, UK (2004)

20.

E.J. Duarte-melo, M. Liu, Data-gathering wireless sensor networks: Organization and capacity. Comput. Netw. 43, 519–537 (2003)CrossRefMATH

21.

E. Felemban, C.G. Lee, E. Ekici, R. Boder, S. Vural, Probabilistic QoS guarantee in reliability and timeliness domains in wireless sensor networks, in Proceedings of IEEE INFOCOM 2005, vol. 4, pp. 2646–2657. Miami, FL (2005). doi:10.1109/INFCOM.2005.1498548

22.

M. Fidler, An end-to-end probabilistic network calculus with moment generating functions, in Proceedings of IEEE IWQoS, pp. 261–270. New Haven, CT (2006). doi: 10.1109/IWQOS.2006.250477

23.

M. Franceschetti, O. Dousse, D. Tse, P. Thiran, Closing the gap in the capacity of wireless networks via percolation theory. IEEE Trans. Inf. Theory 53(3), 1009–1018 (2007)CrossRefMathSciNet

24.

R.G. Gallager, Discrete Stochastic Processes (Kluwer Academic Publishers, New York, 1995)

25.

L. Galluccio, S. Palazzo, End-to-End delay and network lifetime analysis in a wireless sensor network performing data aggregation, in Proceedings of IEEE GLOBECOM 2009, pp. 146–151. Honolulu, HI (2009)

26.

K. Gopalan, T.C. Chiueh, Y.J. Lin, Probabilistic delay guarantees using delay distribution measurement, in Proceedings of ACM MULTIMEDIA 2004, pp. 900–907. New York, NY (2004). doi: http://doi.acm.org/10.1145/1027527.1027734

27.

M. Gribaudo, D. Manini, A. Nordio, C. Nordio, Analysis of IEEE 802.15. 4 sensor networks for event detection, in Proceedings of IEEE Globecom 2009, pp. 152–157. Honolulu, Hawaii (2009)

28.

G.R. Gupta, N.B. Shroff, Delay analysis for multi-hop wireless networks, in Proceedings of IEEE INFOCOM 2009, pp. 412–421. Rio de Janeiro, Brazil (2009)

29.

P. Gupta, P.R. Kumar, The capacity of wireless networks. IEEE Trans. Inf. Theory IT-46(2), 388–404 (2000)

30.

J. Haapola, Z. Shelby, C. Pomalaza-Raez, P. Mahonen, Cross-layer energy analysis of multihop wireless sensor networks, in Proceedings of IEEE EWSN 2005 (2005)

31.

T. Issariyakul, E. Hossain, Analysis of end-to-end performance in a multi-hop wireless network for different hop-level ARQ policies, in Proceedings of IEEE GLOBECOM 2004, vol. 5, pp. 3022–3026. Dallas, TX (2004). doi:10.1109/GLOCOM.2004.1378907

32.

D. Jung, T. Teixeira, A. Savvides, Sensor node lifetime analysis: models and tools. ACM Trans. Sens. Netw. 5(1), 1–33 (2009). doi: 10.1145/1464420.1464423

33.

J. Kim, X. Lin, N. Shroff, Optimal anycast technique for delay-sensitive energy-constrained asynchronous wireless sensor networks, in Proceedings of IEEE INFOCOM 2009, pp. 412–421. Rio de Janeiro, Brazil (2009)

34.

A. Koubaa, M. Alves, E. Tovar, Modeling and worst-case dimensioning of cluster-tree wireless sensor networks, in Proceedings of IEEE RTSS 2006, pp. 412–421. Rio de Janeiro, Brazil (2006). doi: 10.1109/RTSS.2006.29

35.

S. Kulkarni, A. Iyer, C. Rosenberg, An address-light, integrated mac and routing protocol for wireless sensor networks. IEEE/ACM Trans. Netw 14(4), 793–806 (2006). doi: 10.1109/TNET.2006.880163

36.

V.G. Kulkarni, Fluid models for single buffer systems. Front. Queue. Models Appl. Sci. Eng., pp. 321–338 (1997)

37.

S. Kumar, A. Arora, T. Lai, On the lifetime analysis of always-on wireless sensor network applications, in Proceedings of IEEE MASS 2005. Washington, DC (2005)

38.

J. Lehoczky, Real-time queueing network theory, in Proceedings of IEEE RTSS 1997, pp. 58–67. San Francisco, CA (1997). doi: 10.1109/REAL.1997.641269

39.

P. Levis, N. Lee, M. Welsh, D. Culler, TOSSIM: accurate and scalable simulation of entire tinyos applications, in Proceedings of ACM SenSys 2003. Los Angeles, CA (2003)

40.

H. Li, P. Shenoy, K. Ramamritham, Scheduling messages with deadlines in multi-hop real-time sensor networks, in Proceedings of IEEE RTAS 2005, pp. 415–425. San Francisco, CA (2005). doi: 10.1109/RTAS.2005.48

41.

S. Liu, K.W. Fan, P. Sinha, CMAC: An energy efficient mac layer protocol using convergent packet forwarding for wireless sensor networks, in Proceedings of SECON 2007, pp. 11–20. San Diego, CA (2007). doi: 10.1109/SAHCN.2007.4292813

42.

Y. Liu, F. Lo Presti, V. Misra, D. Towsley, Y. Gum Fluid models and solutions for large-scale IP networks, in Proceedings of ACM SIGMETRICS 2003, pp. 91–101. San Diego, CA (2003)

43.

D. Marco, E.J. Duarte-Melo, M. Liu, D.L. Neuhoff, On the many-to-one transport capacity of a dense wireless sensor network and the compressibility of its data, in Proceedings of IPSN’03. Palo Alto, CA (2003)

44.

W. Marcus, An architecture for QoS analysis and experimentation. IEEE/ACM Trans. Netw. (TON) 4(4), 603 (1996)

45.

D. McDysan, QoS and Traffic Management in IP and ATM Networks (McGraw-Hill, New York, 1999)

46.

T. Melodia, M.C. Vuran, D. Pompili, The state of the art in cross-layer design for wireless sensor networks, in Wireless Systems and Network Architectures in Next Generation Internet, pp. 78–92 (2006)

47.

P. Mohapatra, J. Li, C. Gui, QoS in mobile ad hoc networks. IEEE Wireless Commun. 10(3), 44–53 (2003)CrossRef

48.

A. Muhammad Mahtab, B. Olivier, M. Daniel, A. Thomas, S. Olivier, A hybrid model for accurate energy analysis of WSN nodes. EURASIP J. Embed. Syst. 2011 (2011)

49.

Y. Nazarathy, G. Weiss, The asymptotic variance rate of the output process of finite capacity birth-death queues. Queue. Syst. 59(2), 135–156 (2008). doi: 10.1007/s11134-008-9079-4

50.

R. Nelson, Probability, Stochastic Processes, and Queueing Theory: The Mathematics of Computer Performance Modeling (Springer, New York, 1995)CrossRefMATH

51.

M. Neuts, J. Guo, M. Zukerman, H.L. Vu, The waiting time distribution for a TDMA model with a finite buffer and state-dependent service. IEEE Trans. Commun. 53(9), 1522–1533 (2005). doi: 10.1109/TCOMM.2005.855014

52.

M.F. Neuts, Matrix-Geometric Solutions in Stochastic Models: an Algorithmic Approach (Dover, New York, 1981)

53.

M. Noori, M. Ardakani, Lifetime analysis of random event-driven clustered wireless sensor networks. IEEE Trans. Mob. Comput. 10(10), 1448–1458 (2010)CrossRef

54.

R. Oliver, G. Fohler, Probabilistic estimation of end-to-end path latency in wireless sensor networks, in Proceedings of IEEE MASS 2009, pp. 423–431. Macau, China (2009). doi: 10.1109/MOBHOC.2009.5336970

55.

W. Pak, J.G. Choi, S. Bahk, Tier based anycast to achieve maximum lifetime by duty cycle control in wireless sensor networks, in Wireless Communications and Mobile Computing Conference, 2008. IWCMC 2008. International, pp. 123–128 (2008). doi:10.1109/IWCMC.2008.22

56.

P. Park, P. Di Marco, P. Soldati, C. Fischione, K. Johansson, A generalized markov chain model for effective analysis of slotted IEEE 802.15.4, in Proceedings of IEEE MASS 2009, pp. 130–139. Macau, China (2009). doi: 10.1109/MOBHOC.2009.5337007

57.

J. Polastre, J. Hill, D. Culler, Versatile low power media access for wireless sensor networks, in Proceedings of ACM SenSys 2004. Baltimore, MA (2004). doi: http://doi.acm.org/10.1145/1031495.1031508

58.

S. Pollin, M. Ergen, S. Ergen, B. Bougard, F. Catthoor, A. Bahai, P. Varaiya, Performance analysis of slotted carrier sense ieee 802.15.4 acknowledged uplink transmissions, in Proceedings of IEEE WCNC 2008, pp. 1559–1564. Las Vegas, NV (2008). doi: 10.1109/WCNC.2008.279

59.

T. Sakurai, H. Vu, MAC access delay of IEEE 802.11 DCF. IEEE Trans. Wireless Commun. 6(5), 1702–1710 (2007). doi: 10.1109/TWC.2007.360372

60.

J. Schmitt, F. Zdarsky, L. Thiele, A comprehensive worst-case calculus for wireless sensor networks with in-network processing, in RTSS 2007, pp. 193–202 (2007). doi: 10.1109/RTSS.2007.17

61.

O. Tickoo, B. Sikdar, Modeling queueing and channel access delay in unsaturated IEEE 802.11 random access MAC based wireless networks. IEEE/ACM Trans. Netw. 16(4), 878–891 (2008). doi:http://dx.doi.org/10.1109/TNET.2007.904010

62.

MICA2 sensor node. http://www.xbow.com

63.

TelosB sensor node. http://www.xbow.com

64.

S. Toumpis, L. Tassiulas, Packetostatics: deployment of massively dense sensor networks as an electrostatics problem, in Proceedings of IEEE INFOCOM 2005. Miami, FL (2005)

65.

L. Van Hoesel, T. Nieberg, J. Wu, P. Havinga, Prolonging the lifetime of wireless sensor networks by cross-layer interaction, in IEEE Wireless Communications, p. 79 (2004)

66.

M.C. Vuran, I.F. Akyildiz, XLP: a cross layer protocol for efficient communication in wireless sensor networks. IEEE Trans. Mob. Comput. 9(11), 1578–1591 (2010)CrossRef

67.

Q. Wang, M. Hempstead, W. Yang, A realistic power consumption model for wireless sensor network devices, in Proceedings of IEEE SECON 2006, pp. 286–295. Reston, VA (2006)

68.

Y. Wang, M.C. Vuran, S. Goddard, Cross-layer analysis of the end-to-end delay distribution in wireless sensor networks, in Proceedings of IEEE RTSS 2009. Washington, DC (2009)

69.

Y. Wang, M.C. Vuran, S. Goddard, Stochastic analysis of energy consumption in wireless sensor networks, in Proceedings of IEEE SECON 2010. Boston, MA (2010)

70.

Y. Wang, M.C. Vuran, S. Goddard: Analysis of event detection delay in wireless sensor networks, in Proceedings of IEEE INFOCOM 2011. Shanghai, China (2011)

71.

Y. Wang, M.C. Vuran, S. Goddard, Cross-layer analysis of the end-to-end delay distribution in wireless sensor networks. IEEE Trans. Netw. 20(1), 305–318 (2012)CrossRef

72.

M. Xie, M. Haenggi, Towards an end-to-end delay analysis of wireless multihop networks. Ad Hoc Netw. 7(5), 849–861 (2009). doi: 10.1016/j.adhoc.2008.04.010

73.

Y. Xue, M.C. Vuran, B. Ramamurthy, Cost-efficiency of anycast-based forwarding in duty-cycled wsns with lossy channel, in Proceedings of IEEE SECON 2010. Boston, MA (2010)

74.

W. Ye, J. Heidemann, D. Estrin, Medium access control with coordinated adaptive sleeping for wireless sensor networks. IEEE/ACM Trans. Netw. 12(3), 493–506 (2004). doi: http://dx.doi.org/10.1109/TNET.2004.828953

75.

S.N. Yeung, J. Lehoczky, End-to-end delay analysis for real-time networks, in Proceedings of IEEE RTSS 2001, pp. 299–309. London, UK (2001)

76.

M. Zúniga, B. Krishnamachari, An analysis of unreliability and asymmetry in low-power wireless links. ACM Trans. Sens. Netw. 3(2) (2007)

Titel: Stochastic Modeling of Delay, Energy Consumption, and Lifetime
verfasst von: Yunbo Wang
Mehmet C. Vuran
Steve Goddard
Verlag: Springer Berlin Heidelberg
Buch: The Art of Wireless Sensor Networks
Print ISBN: 978-3-642-40065-0

Electronic ISBN: 978-3-642-40066-7

Copyright-Jahr: 2014
DOI: https://doi.org/10.1007/978-3-642-40066-7_2

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