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Path management method using partially connected neural network in large-scale heterogeneous sensor network

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Abstract

In this paper, we develop the cost function of the path management method for data delivery in large-scale heterogeneous sensor network. Usually, the most conventional methods determine the optimal coefficients in the cost function, without considering the node surrounding environments, such as the wireless propagation environment or the topological environment. Due to this reason, there is the limitation to improve the performance of path management, such as data delivery ratio and delay of data delivery. To solve this problem, we derive a new cost function using the concept of partially connected neural network (PCNN) that is modeled according to the input types whether inputs are correlated or uncorrelated. In our application, we assume that all inputs of the cost function are uncorrelated. Thus, we connect all inputs to the hidden layer of the PCNN in an uncoupled way. We also propose the training technique for finding the optimal weights in the PCNN. Our PCNN is trained to maximize the packet transmission success ratio. In the experimental section, we show that our PCNN method outperforms other conventional methods in terms of the quality of data delivery, such as data delivery ratio and delay of data delivery.

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References

  1. Kulkarni RV, Förster A, Venayagamoorthy GK (2011) Computational intelligence in wireless sensor networks: a survey. IEEE Commun Surv Tutor 13(1):68–96

    Article  Google Scholar 

  2. Culler D, Estrin D, Srivastava M (2004) Overview of sensor networks. IEEE Comput 37(8):41–49

    Article  Google Scholar 

  3. Romer K, Mattern F (2004) The design space of wireless sensor networks. IEEE Wirel Commun 11(6):54–61

    Article  Google Scholar 

  4. Al-Karaki JN, Kamal AE (2004) Routing techniques in wireless sensor networks: a survey. IEEE Wirel Commun 11(6):6–28

    Article  Google Scholar 

  5. Niculescu D (2005) Communication paradigms for sensor net-works. IEEE Commun Mag 43(3):116–122

    Article  MathSciNet  Google Scholar 

  6. Song WZ, Huang R, Xu M, Shirazi BA, Lahusen R (2010) Design and deployment of sensor network for real-time high-fidelity volcano monitoring. IEEE Trans Parallel Distrib Syst 21(11):1674–1685

    Article  Google Scholar 

  7. Sankarasubramaniam Y, Akan OB, Akyildiz IF (2003) ESRT: Event-to-sink reliable transport in wireless sensor networks. In: Proceedings of ACM annual international conference on mobile computing and networking, MOBICOM2003, Annapolis, pp 177–188

  8. Chen H, Tse CK, Feng J (2009) Minimizing effective energy consumption in multi-cluster sensor networks for source extraction. IEEE Trans Wirel Commun 8(3):1480–1489

    Article  Google Scholar 

  9. Kuhn F, Wattenhofer R, Zollinger A (2008) An algorithmic approach to geographic routing in ad hoc and sensor networks. IEEE Trans Netw 16(1):51–62

    Article  Google Scholar 

  10. Haykin S (1998) Neural networks: a comprehensive foundation. Prentice-Hall, Upper Saddle River

    Google Scholar 

  11. Heinzelman WR, Chandrakasan A, Balakrishnan H (2000) Energy-efficient communication protocol for wireless microsensor networks. In: Proceedings of the Hawaii international conference on system science, pp 1–10

  12. Lindsey S, Raghavendra CS (2002) Pegasis: power-efficient gathering in sensor information systems. In: Proceedings of IEEE aerospace conference

  13. De Couto D, Aguayo D, Bicket J, Morris R (2005) High-throughput path metric for multi-hop wireless routing. ACM Wirel Netw 11(4):419–434

    Article  Google Scholar 

  14. Draves R, Padhye J, Zill B (2004) Routing in multi-radio, multi-hop wireless mesh networks. In: Proceedings of ACM annual international conference on mobile computing and networking (MOBICOM), Philadelphia, pp 114–128

  15. Lindsey S, Raghavendra C (2002) PEGASIS: power-efficient gathering in sensor information systems. In: Proceedings of IEEE aerospace conference, pp 1125–1130

  16. Rodoplu V, Meng TH (1999) Minimum energy mobile wireless networks. IEEE J Sel Areas Commun 17(8):1333–1344

    Article  Google Scholar 

  17. Shah R, Rabaey J (2002) Energy aware routing for low energy ad hoc sensor networks. In: Proceedings of IEEE wireless communications & networking conference (WCNC), Orlando, pp 350–355

  18. Younis M, Youssef M, Arisha K (2002) Energy-aware routing in cluster-based sensor networks. In: Proceedings of IEEE/ACM international symposium on modeling, analysis and simulation of computer and telecommunication systems (MASCOTS), pp 129–136

  19. Youssef M, Younis M, Arisha K (2002) A constrained shortest-path energy-aware routing algorithm for wireless sensor networks. In: Proceedings of IEEE wireless communications & networking conference (WCNC), Orlando, pp 749–799

  20. Szymanski BK, Chen BG (2008) Computing with time: from neural networks to sensor networks. Comput J Br Comput Soc 51(4):511–522

    Google Scholar 

  21. Zhao W, Liu D, Jiang Y (2009) Distributed neural network routing algorithm based on global information of wireless sensor network. In: Proceedings of IEEE international conference on communications and mobile computing (CMC), pp 552–555

  22. Toumpis S, Goldsmith AJ (2003) Capacity regions for wireless ad hoc networks. IEEE Trans Wirel Commun 2(4):736–748

    Article  Google Scholar 

  23. Rayanchu S, Mishra A, Agrawal D, Saha S, Banerjee S (2008) Diagnosing wireless packet losses in 802.11: separating collision from weak signal. In: Proceedings of IEEE conference on computer communications (INFOCOM), pp 735–743

  24. Kang S, Isik C (2005) Partially connected feedforward neural networks structured by input types. IEEE Trans Neural Netw 16(1):175–184

    Article  Google Scholar 

  25. The network simulator, ns-2. Available on-line at http://www.isi.edu/nsnam/ns

  26. Rappaport TS (1996) Wireless communications, principles and practice. Prentice Hall, Upper Saddle River

    Google Scholar 

  27. IEEE Std. 802.15.4 (2003) Wireless medium access control (MAC) and physical layer (PHY) specifications for lower-rate wireless personal area networks (LR-WPANs)

Download references

Acknowledgment

This work was supported by an Inha University Research Grant.

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Authors and Affiliations

  1. Department of Information Media, University of Suwon, San 2-2, Wau-ri, Bongdam-eup, Hwaseong-si, Gyeonggi-do, 445-743, Korea

    Yujin Lim

  2. Department of Computer Science and Computer Engineering, Inha University, 253 Younghyun-dong, Man-gu, Incheon, 420-751, Korea

    Sanggil Kang

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  1. Yujin Lim
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  2. Sanggil Kang
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Correspondence to Sanggil Kang.

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Lim, Y., Kang, S. Path management method using partially connected neural network in large-scale heterogeneous sensor network. Neural Comput & Applic 21, 1931–1936 (2012). https://doi.org/10.1007/s00521-011-0664-9

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  • DOI: https://doi.org/10.1007/s00521-011-0664-9

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