Small World-Based Wireless Sensor Network Power Control Algorithm for Airborne PHM

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 295)

Abstract

Aircraft Prognostic and Health Management (PHM) is a system which could diagnose device faults and assess its health status. Due to the complexity of the airborne environment, the aircraft PHM uses Wireless Sensor Network (WSN) technology to collect and transmit data. In this paper, a Power Control Algorithm based on Small World (PCS) is proposed to reduce the network delay. The PCS algorithm adds several shortcuts into the network based on the small world theory and uses genetic algorithm to optimize the shortcuts. Simulation results demonstrate that this method can effectively shorten the average path length and reduce the network delay.

Keywords

Aircraft PHM Wireless sensor network Small world Genetic algorithm 

Notes

Acknowledgments

This work is supported by the National Natural Science Foundation of China under grant NO.61262020; the Aeronautical Science Foundation of China NO.2012ZD56; the Natural Science Foundation of Education Bureau of Jiangxi Province NO.GJJ12460; the Nanchang Hang Kong University doctoral Sustentation Fund NO.EA201120180.

References

  1. 1.
    ZENG S-K, Michael G, Pecht, WU Ji (2005) Status and perspeetives of prognostics and health management technologies. ACTA AERONUTICA ET AST RONAUTICA SINICA 5:25626–25632Google Scholar
  2. 2.
    Zhou Yang, Bo Jing (2011) Application of wireless sensor network on airborne of prognostics and health mangement. J Comput Res Dev S2:338–342Google Scholar
  3. 3.
    Andy H (2001) The joint strike fighter(JSF) prognostics and health management. In: NDIA 4th annual systems engineering conference, pp 2799–2813Google Scholar
  4. 4.
    Harri H, Andre S, Pekka O (2010) Distributed algorithms for lifetime maximization in sensor networks viaMinCMax spanning subgraphs. Wireless Netw 16:875C887Google Scholar
  5. 5.
    Demo J, Steiner A, Friedersdorf F (2010) Development of a wireless miniaturized smart sensor network for aircraft corrosion monitoring. In: Proceedings of IEEE aerospace conference, pp 1–9Google Scholar
  6. 6.
    Cheng SF, Azarian MH, Pecht MG (2010) Sensor system for prognostics and health management. Sensors 10(6):5774–5797Google Scholar
  7. 7.
    Gkoutioudi K, Karatza HD (2012) A simulation study of multi-criteria scheduling in grid based on genetic algorithms. In: Proceedings of IEEE 10th international symposium, IEEE Press, Leganes, pp 317–324Google Scholar
  8. 8.
    Aziz, AA, Sekercioglu YA, Fitzpatrick P (2013) A survey on distributed topology control techniques for extending the lifetime of battery powered wireless sensor networks. IEEE Commun Surv Tutorials 1(15):121–144Google Scholar
  9. 9.
    Kubisch M, Karl H, Wolisz A (2003) Distributed algorithm for transmission power control in wireless sensor networks. In: Proceedings of IEEE WCNC 2003, IEEE Press, New OrleansGoogle Scholar
  10. 10.
    Wattenhofer R, Zollinger A (2004) XTC: a practical topology control algorithm for ad-hoc networks. In: Proceedings of the international parallel and distributed processing symposium, IEEE Press, New Mexico, pp 216–223Google Scholar
  11. 11.
    Kawadia V, Kumar PR (2003) Power control and clustering in ad-hoc networks. In: Proceedings of the IEEE Conference on Computer Communications, pp 459–469Google Scholar
  12. 12.
    HUANG H-J, HU G-M, YU F-C (2011) Cross-layer power controlled routing in wireless ad hoc networks. Appl Res Comput 28(5):1793–1798Google Scholar
  13. 13.
    Utku GA (2010) Weak state routing for large-scale dynamic networks. IEEE/ACM Trans Networking 54(4):573–588Google Scholar
  14. 14.
    Sheng M, Li J, Li H, Shi Y (2011, June) Small world based cooperative routing protocol for large scale wireless ad hoc networks. In Communications (ICC), 2011 IEEE International Conference on, IEEE, Chicago, pp 1–5Google Scholar
  15. 15.
    Helmy A (2003) Small worlds in wireless networks. IEEE Commun Lett 7(10):490–492CrossRefGoogle Scholar
  16. 16.
    Helmy A (2002) Small large-scale wireless networks: mobility-assisted resource discovery. arXiv preprint cs/0207069
  17. 17.
    Cavalcanti D (2004) Exploiting the small-world effect to increase connectivity in wireless ad hoc networks. In: International conference on telecommunications, Fortaleza, p 388–393Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Internet of Things Technology InstituteNanchang Hang Kong UniversityNanchangPeople Republic of China

Personalised recommendations