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Delay-Constrained Optimal Data Aggregation in Hierarchical Wireless Sensor Networks

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Abstract

A lot of realistic applications with wireless sensor networks adopt hierarchical architecture in which sensor nodes are grouped into clusters, with each cluster relying on a gateway node for local data aggregation and long-distance radio transmission. Compared to normal sensor nodes, the gateway nodes, also called application nodes (ANs), are equipped with relatively powerful transceivers and have more energy. Nevertheless, since an AN is the main gateway for sensor nodes within its clusters, its energy may be depleted more quickly than normal sensor nodes. As such, it is important to find methods to save energy for ANs. This paper presents a Delay-Constrained Optimal Data Aggregation (DeCODA) framework that considers the unique feature of traffic patterns and information processing at ANs for energy saving. Mathematical models and analytical results are provided, and simulation studies are performed to verify the effectiveness of the DeCODA framework.

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References

  1. Bateman A (1989) A general analysis of bit error probability for reference based BPSK mobile data transmission. IEEE Trans Commun 37:398–402

    Article  Google Scholar 

  2. Berry R, Gallager R (2002) Communication over fading channels with delay constraints. IEEE Trans Inf Theory 48(5):1135–1149, May

    Article  MathSciNet  Google Scholar 

  3. Cheung S-Y, Coleri S, Dundar B, Ganesh S, Tan C-W, Varaiya P (2004) A sensor network for traffic monitoring. In: Plenary talk at IPSN 04, Berkeley, April 2004

  4. Coleri S, Cheung SY, Varaiya P (2004) Sensor networks for monitoring traffic. In: Forty-second annual allerton conference on commuinication, control, and computing, University of Illinois, Urbana, September 2004

  5. De Couto SJ (2004) High-throughput routing for multi-hop wireless networks. Ph.D. Thesis, MIT, June

  6. Emekci F, Tuna SE, Agrawal D, Abbadi AE (2004) BINOCULAR: a system monitoring framework. In: Proceedings of first workshop on data management for sensor networks (DMSN 2004), Toronto, August 2004

  7. Fan KW, Liu S, Sinha P (2007) Structure-free data aggregation in sensor networks. IEEE Trans Mob Comput 6(8):929–942

    Article  Google Scholar 

  8. Fu A, Modiano E, Tsitsiklis J (2003) Optimal energy allocation for delay-constrained data transmission over a time-varying channel. In: Proceedings of IEEE Infocom 2003, San Francisco, April 2003

  9. El Gamal A, Uysal E, Prabhakar B (2001) Energy-efficient transmission over a wireless link via lazy packet scheduling. In: Proceedings of IEEE Infocom 2001, Anchorage, April 2001, pp 386–394

  10. Heinzelman WR, Chandrakasan A, Balakrishnan H (2000) Energy-efficient communication protocol for wireless micro sensor networks. In: IEEE proceedings of the Hawaii international conference on system sciences, Maui, January 2000

  11. Kahn J, Katz R, Pister K (1999) Next century challenges: mobile networking for smart dust. In: Proceedings of MobiCom 99, Seattle, August 1999, pp 263–270

  12. Karenos K, Kalogeraki V, Krishnamurthy SV (2008) Cluster-based congestion control for sensor networks. ACM Trans Sens Netw (TOSN) 4(1)

  13. Kleinrock L (1967) Queueing theory, vol 1. Wiley, New York

    Google Scholar 

  14. Kompella RR, Snoeren AC (2003) Practical lazy scheduling in sensor networks. In: Proceedings of ACM SenSys 03, Los Angeles, November 2003

  15. Krishnamachari B, Estrin D, Wicker SB (2002) The impact of data aggregation in wireless sensor networks. In: ICDCS workshop on distributed event-based systems (DEBS), Vienna, July 2002

  16. Liu C, Wu K, Tsao M (2005) Energy efficient information collection with the ARIMA model in wireless sensor networks. In: IEEE GlobeCom, St. Louis, November 2005

  17. Liu C, Wu K, Pei J (2005) A dynamic clustering and scheduling approach to energy saving in data collection from wireless sensor networks. In: Proceedings of second annual IEEE communications society conference on sensor and ad hoc communications and networks, Santa Clara, September 2005

  18. Mainwaring A, Polastre J, Szewczyk R, Culler D, Anderson J (2002) Wireless sensor networks for habitat monitoring. In: Proceedings of first international workshop on wireless sensor networks and applications (WSNA’02), Atlanta, September 2002, pp 88–97

  19. Pan J, Hou YT, Cai L, Shi Y, Shen S.X (2003) Topology control for wireless sensor networks. In: Proceedings of MobiCom 03, San Diego, September 2003, pp 286–299

  20. Pattem S, Krishnamachari B, Govindan R (2004) The impact of spatial correlation on routing with compression in wireless sensor networks. In: Proceedings of third international symposium on information processing in sensor networks, Berkeley, April 2004, pp 28–35

  21. Ragoler I, Matias Y, Aviram N (2004) Adaptive probing and communication in sensor networks. In: Proceedings of third international conference on ad-hoc networks and wireless, Vancouver, July 2004, pp 280–293

  22. Sayood K (2000) Introduction to data compression, 2nd edn. Morgan Kaufmann, San Mateo

    Google Scholar 

  23. Tian D, Georganas ND (2002) A coverage-preserved node scheduling scheme for large wireless sensor networks. In: Proceedings of first international workshop on wireless sensor networks and applications (WSNA’02), Atlanta, September 2002, pp 32–41

  24. Wang HS, Moayeri N (1995) Finite-state Markov channel—a useful model for radio communication channels. IEEE Trans Veh Technol 44(1):163–171

    Article  Google Scholar 

  25. Mark J, Zhuang W (2003) Wireless communications and networking. Pearson Education, Harlow

    Google Scholar 

  26. Warneke B, Last M, Leibowitz B, Pister KSJ (2001) Smart dust: communicating with a cubic-millimeter computer. IEEE Comput Mag 34(1):44–51

    Google Scholar 

  27. engim.com (2004) Wideband multi-channel wireless LAN chipsets for next generation infrastructure solutions. http://www.engim.com

  28. Wu K, Liu C, Xiao Y, Liu JC (2007) Coarse-grained scheduling for gateway nodes in wireless sensor networks. In: Proceedings of IEEE wireless communications and networking conference (WCNC 07), Hong Kong, March 2007

  29. Ye F, Zhong G, Lu S, Zhang L (2003) PEAS: a robust energy conserving protocol for long-lived sensor networks. In: Proceedings of 23rd international conference on distributed computing systems (ICDCS ’03), Providence, May 2003

  30. Ye F, Luo H, Cheng J, Lu S, Zhang L (2001) A two-tier data dissemination model for large scale wireless sensor networks. In: Proceedings of MobiCom 01, Rome, July 2001, pp 148–159

  31. Yu Y, Krishnamachari B, Prasanna VK (2004) Energy-latency tradeoffs for data gathering in wireless sensor networks. In: Proceedings of Infocom 04, Hongkong, March 2004

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Correspondence to Yang Xiao.

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This is a much extended version of our conference paper in IEEE WCNC 07 [28]. This research was partially supported by Natural Sciences and Engineering Research Council of Canada, Canada Foundation for Innovation, and the British Columbia Knowledge Development Fund, as well as US National Science Foundation (NSF) under the grants CCF-0829827 and CNS-0716211.

Appendix

Appendix

In this appendix, we use the same method as in [14] to derive the energy function in this paper. A similar function was provided in [9] without proof.

It is commonly known that for an AWGN channel with average signal power constraint S and noise power constraint N, the maximum capacity that an optimal coding scheme can achieve is given by

$$C = \frac{1}{2} \log(1+S/N) \text{ bits/transmission}.$$
(24)

If we assume a sub-optimal code with rate Rate = ϕC where ϕ ≈ 1, from Eq. 24, it is easy to get

$$S =N(2^{2\phi Rate} -1).$$
(25)

Consider a channel with the maximum transmission speed of 1 Mbps and with Rate = 6 [9]. Assume that the transmission duration of a 128 bits packet is τ since the actual transmission speed may be smaller than the full speed to save energy [9]. The relationship of actual \(\hat{Rate}\) and τ can be expressed as follows,

$$\hat{Rate} = \frac{0.000128*6}{\tau}.$$
(26)

Assuming a noise level of 1, the energy per bit is \(w = S/\hat{Rate}\), and the energy per packet is given by

$$w(\tau) = 128* \frac{\tau}{0.000768}\big(2^\frac{0.001536}{\tau} -1\big),$$
(27)

where 128 is the packet size in bits.

Using the same method, if we assume the packet size is 10 Kb and the maximum transmission speed is 1 Mbps as in [9], we can obtain an energy function that is the same as Equation (15) in [9]. Note that the power level is a relative value normalized by the noise level, which is 1 in this case. Assuming a different noise level may change the required power level and thus the energy consumption to achieve the same bit error rate, but all conclusions in this paper still hold since the shape of the energy function remains the same.

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Wu, K., Liu, C., Xiao, Y. et al. Delay-Constrained Optimal Data Aggregation in Hierarchical Wireless Sensor Networks. Mobile Netw Appl 14, 571–589 (2009). https://doi.org/10.1007/s11036-008-0119-4

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