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Cross-Layer Duty Cycle Scheduling with Data Aggregation Routing in Wireless Sensor Networks

  • Yean-Fu Wen
  • Frank Yeong-Sung Lin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4096)

Abstract

Well-scheduled communications, in conjunction with the aggregation of data reduce the energy waste on idle listening and redundant transmissions. In addition, the adjustable radii and the number of retransmissions are considered to reduce the energy consumption. Thus, to see that the total energy consumption is minimized, we propose a mathematical model that constructs a data aggregation tree and schedules the activities of all sensors under adjustable radii and collision avoidance conditions. As the data aggregation tree has been proven to be a NP-complete problem, we adopt a LR method to determine a near-optimal solution and furthermore verify whether the proposed LR-based algorithm, LRA, achieves energy efficiency and ensures the latency within a reasonable range. The experiments show the proposed algorithm outperforms other general routing algorithms, such as SPT, CNS, and GIT algorithms. It improves energy conservation, which it does up to 9.1% over GIT. More specifically, it also improves energy conservation up to 65% over scheduling algorithms, such as S-MAC and T-MAC.

Keywords

Sensor Node Wireless Sensor Network Medium Access Control Total Energy Consumption Data Aggregation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Ahuja, R.K., Magnanti, T.L., Orlin, J.B.: Network Flows: Theory, Algorithms, and Applications, ch. 16. Prentice-Hall, Englewood Cliffs (1993)Google Scholar
  2. 2.
    Akyildiz, I.F., Su, W., Sankarasubramaniam, Y., Cayirci, E.: A Survey on Sensor Networks. IEEE Communications Magazine 40(8), 102–114 (2002)CrossRefGoogle Scholar
  3. 3.
    ANSI/IEEE: 802.11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications. IEEE Standard 802.11 (2000)Google Scholar
  4. 4.
    Bhardwaj, M., Chandrakasan, A.P.: Bounding the Lifetime of Sensor Networks via Optimal Role Assignments. In: Proc. IEEE INFOCOM, New York, pp. 1587–1596 (2002)Google Scholar
  5. 5.
    Bianchi, G.: Performance Analysis of the IEEE 802.11 Distributed Coordination Function. IEEE Journal on Selected Areas in Communications 18(2), 535–547 (2000)CrossRefGoogle Scholar
  6. 6.
    Carle, J., Simplot, D.: Energy Efficient Area Monitoring by Sensor Networks. IEEE Computer 37(2), 40–46 (2004)Google Scholar
  7. 7.
    Chang, J.H., Tassiulas, L.: Maximum Lifetime Routing in Wireless Sensor Networks. IEEE/ACM Transactions on Networking (TON) 12(4), 609–619 (2004)CrossRefGoogle Scholar
  8. 8.
    Dam, T.V., Langendoen, K.: An Adaptive Energy-Efficient MAC Protocol for Wireless Sensor Networks. In: Proc. ACM SenSys Los Angeles, pp. 171–180 (2003)Google Scholar
  9. 9.
    Fisher, M.L.: The Lagrangian Relaxation Method for Solving Integer Programming Problems. Management Science 27(1), 1–18 (1981)MATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-completeness. Freeman, San Francisco (1979)MATHGoogle Scholar
  11. 11.
    Kalpakis, K., Dasgupta, K., Namjoshi, P.: Efficient Algorithms for Maximum Lifetime Data Gathering and Aggregation in Wireless Sensor Networks. Computer Networks Journal 42(6), 697–716 (2003)MATHCrossRefGoogle Scholar
  12. 12.
    Krishnamachari, B., Estrin, D., Wicker, S.: Modeling Data-Centric Routing in Wireless Sensor Networks. USC Computer Engineering Technical Report CENG (2002)Google Scholar
  13. 13.
    Lin, F.Y.S., Yen, H.H., Lin, S.P.: MAC Aware Energy-Efficient Data-Centric Routing in Wireless Sensor Networks. In: Proc. IEEE ICC, Istanbul, Turkey (2006)Google Scholar
  14. 14.
    Liu, H., Wan, P.J., Yi, C.W., Jia, X., Makki, S., Pissinou, N.: Maximal Lifetime Scheduling in Sensor Surveillance Networks. In: Proc. INFOCOM, Miami, pp. 2482–2491 (2005)Google Scholar
  15. 15.
    Lu, G., Krishnamachari, B., Raghavendra, C.: An Adaptive Energy-Efficient and Low-Latency MAC for Data Gathering in Sensor Networks. In: Proc. WMAN (2004)Google Scholar
  16. 16.
    Lu, G., Sadagopan, N., Krishnamachari, B., Goel, A.: Delay Efficient Sleep Scheduling In Wireless Sensor Network. In: Proc. INFOCOM, Miami, Florida, USA, pp. 2470–2481 (2005)Google Scholar
  17. 17.
    Stemm, M., Katz, R.H.: Measuring and Reducing Energy Consumption of Network Interfaces in Hand-held Devices. IEICE Transactions on Communications E80-B(8), 1125–1131 (1997)Google Scholar
  18. 18.
    Sheu, S.T., Tsai, T.H., Chen, J.H.: MR2RP: The Multi-Rate and Multi-Range Routing Protocol for IEEE 802.11 Wireless Ad Hoc Networks. ACM/Kluwer Wireless Networks 9(8), 165–177 (2003)MATHCrossRefGoogle Scholar
  19. 19.
    Shiou, C.W., Lin, F.Y.S., Cheng, H.C., Wen, Y.F.: Optimal Energy-Efficient Routing for Wireless Sensor Networks. In: Proc. IEEE AINA, Taipei, Taiwan, pp. 325–330 (2005)Google Scholar
  20. 20.
    Wieselthier, J.E., Nguyen, G.D., Ephremides, A.: Energy-Efficient Broadcast and Multicast Trees in Wireless Networks. Mobile Networks and Applications (MONET) 7(2), 481–492 (2002)CrossRefGoogle Scholar
  21. 21.
    Ye, W., Heidemann, J., Estrin, D.: An Energy-efficient MAC Protocol for Wireless Sensor Networks. In: Proc. IEEE INFOCOM, New York, pp. 1567–1576 (2002)Google Scholar
  22. 22.
    Yen, H.H., Lin, F.Y.S., Lin, S.P.: Energy-Efficient Data-Centric Routing in Wireless Sensor Networks. IEICE Trans. on Communications E88-B(16), 4470–4480 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yean-Fu Wen
    • 1
    • 2
  • Frank Yeong-Sung Lin
    • 1
  1. 1.National Taiwan UniversityTaiwan (R.O.C.)
  2. 2.China University of TechnologyTaiwan (R.O.C.)

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