Skip to main content

Optimal Data Collection of MP-MR-MC Wireless Sensors Network for Structural Monitoring

  • Conference paper
  • First Online:
Multi-disciplinary Trends in Artificial Intelligence (MIWAI 2018)

Abstract

Structural health monitoring (SHM) is a kind of data-intensive applications for wireless sensors networks, which usually requires high network bandwidth. However, the bandwidth of traditional single-radio single-channel (SR-SC) WSN is quite limited. In order to meet the requirement of structural monitoring, multi-radio multi-channel (MR-MC) WSN is emerging. In this paper, we address the optimal data collection problem in MR-MC WSN by modelling it as an integer linear programming problem. Combining the advantages of the particle swarm optimization (PSO) algorithm and flower pollination optimization (FPA) algorithm, we propose a new hybrid algorithm BFPA-PSO to solve the optimization problem under the constraint of time slot and multi-power multi-radio multi-channel (MP-MR-MC). Theoretical analysis and simulation experiments are carried out and the results show that the proposed method has good performance in improving network capacity as well as reducing energy consumption.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Risodkar, Y.R., Pawar, A.S.: A survey: structural health monitoring of bridge using WSN. In: International Conference on Global Trends in Signal Processing. Information Computing and Communication, Jalgaon, pp. 615–618. IEEE (2017)

    Google Scholar 

  2. Chen, Z., Casciati, F.: A low-noise, real-time, wireless data acquisition system for structural monitoring applications. Struct. Control. Health Monit. 21(7), 1118–1136 (2014)

    Article  Google Scholar 

  3. Chen, Z.C., Casciati, S., Faravelli, L.: In-situ validation of a wireless data acquisition system by monitoring a pedestrian bridge. Adv. Struct. Eng. 18(1), 97–106 (2014)

    Article  Google Scholar 

  4. Kim, S., Pakzad, S., Culler, D.: Health monitoring of civil infrastructures using wireless sensor networks. In: International Symposium on Information Processing in Sensor Networks, Cambridge, pp. 254–263. IEEE (2007)

    Google Scholar 

  5. Mehrabi, A., Kim, K.: General framework for network throughput maximization in sink-based energy harvesting wireless sensor networks. IEEE Trans. Mob. Comput. 16(7), 1881–1896 (2017)

    Article  Google Scholar 

  6. Lian, J., Naik, K., Agnew, G.B.: Data capacity improvement of wireless sensor networks using non-uniform sensor distribution. Int. J. Distrib. Sens. Netw. 2(2), 121–145 (2006)

    Article  Google Scholar 

  7. Ji, S., Cai, Z., Li, Y., Jia, X.: Continuous data collection capacity of dual-radio multi-channel wireless sensor networks. IEEE Trans. Parallel Distrib. Syst. 23(10), 1844–1855 (2012)

    Article  Google Scholar 

  8. Kodialam, M., Nandagopal, T.: Characterizing the capacity region in multi-radio multi-channel wireless mesh networks. In: International Conference on Mobile Computing and Networking, Cologne, pp. 73–87. DBLP (2005)

    Google Scholar 

  9. Fan, B., Li, J., Guo, L., Liu, X.: A multi-power multi-channel data collection scheduling algorithm in dual-radio sensor networks. Telecommun. Sci. 28(2), 36–45 (2012)

    Google Scholar 

  10. Babatunde, O., Armstrong, L., Leng, J., Diepeveen, D.: A genetic algorithm-based feature selection. Br. J. Math. Comput. Sci. 5(4), 889–905 (2014)

    Google Scholar 

  11. Mirjalili, S., Mirjalili, S.M., Yang, X.S.: Binary bat algorithm. Neural Comput. Appl. 25(3–4), 663–681 (2014)

    Article  Google Scholar 

  12. Rodrigues, D., Yang, X.-S., de Souza, A.N., Papa, J.P.: Binary flower pollination algorithm and its application to feature selection. In: Yang, X.-S. (ed.) Recent Advances in Swarm Intelligence and Evolutionary Computation. SCI, vol. 585, pp. 85–100. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-13826-8_5

    Chapter  Google Scholar 

  13. Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S.: BGSA: binary gravitational search algorithm. Natural Comput. 9(3), 727–745 (2010)

    Article  MathSciNet  Google Scholar 

  14. Kennedy, J., Eberhart, R.C.: A discrete binary version of the particle swarm algorithm. In: IEEE International Conference on Systems. Man, and Cybernetics, Orlando, pp. 4104–4108. IEEE (1997)

    Google Scholar 

  15. Chen, Z.C.: Energy efficiency strategy for a general real-time wireless sensor platform. Smart Struct. Syst. 14(4), 617–641 (2014)

    Article  Google Scholar 

  16. Li, J., Guo, X., Guo, L.: Joint routing, scheduling and channel assignment in multi-power multi-radio wireless sensor networks. In: IEEE International Performance Computing and Communications Conference, Orlando, pp. 1–8. IEEE (2011)

    Google Scholar 

  17. Gupta, P., Kumar, P.R.: The capacity of wireless networks. IEEE Trans. Inf. Theory 46(2), 388–404 (2002)

    Article  MathSciNet  Google Scholar 

  18. Yang, X.-S.: Flower pollination algorithm for global optimization. In: Durand-Lose, J., Jonoska, N. (eds.) UCNC 2012. LNCS, vol. 7445, pp. 240–249. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32894-7_27

    Chapter  Google Scholar 

  19. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Sammut, C., Webb, G.I. (eds.) Encyclopedia of Machine Learning, vol. 4, pp. 1942–1948. Springer, Boston (2011). https://doi.org/10.1007/978-1-4419-1153-7_200581

    Chapter  Google Scholar 

  20. Vijayalakshmi, K., Anandan, P.: A multi objective Tabu particle swarm optimization for effective cluster head selection in WSN. Cluster Comput. 6, 1–8 (2018)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Zhicong Chen .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Li, Q., Chen, Z., Wu, L., Cheng, S., Lin, P. (2018). Optimal Data Collection of MP-MR-MC Wireless Sensors Network for Structural Monitoring. In: Kaenampornpan, M., Malaka, R., Nguyen, D., Schwind, N. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2018. Lecture Notes in Computer Science(), vol 11248. Springer, Cham. https://doi.org/10.1007/978-3-030-03014-8_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-03014-8_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03013-1

  • Online ISBN: 978-3-030-03014-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics