Skip to main content
SpringerLink
Log in

Location Planning of UAVs for WSNs Data Collection Based on Adaptive Search Algorithm

  • Conference paper
  • First Online:
Machine Learning for Cyber Security (ML4CS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 12487))

Included in the following conference series:

Abstract

Unmanned Aerial Vehicles (UAVs) have been widely used in data collection, tracking and monitoring in wireless sensor networks (WSNs). By considering the three factors of sensor coverage, energy consumption and Quality of Service (QoS), the WSNs data collection problem is transformed into a location planning model for optimizing K-location of UAVs. Besides, an adaptive search algorithm contains two crucial methods are proposed to address this issue, form which one is the optimal matching method between sensors and UAVs, and the other is automatic location generation strategy of UAVs. Finally, analytical and simulation-based results show that the proposed algorithm has obvious advantages over the KMeans algorithm in location planning option of UAVs.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 99.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 129.99
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. Tao, M., Ota, K., Dong, M.: Locating compromised data sources in IoT-enabled smart cities: a great-alternative-region-based approach. IEEE Trans. Ind. Inform. 14(6), 2579–2587 (2018)

    Article  Google Scholar 

  2. Tao, M., Li, X., Yuan, H., Wei, W.: UAV-aided trustworthy data collection in federated-WSN-enabled IoT applications. Inf. Sci. 532, 155–169 (2020)

    Article  Google Scholar 

  3. Bera, S., Misra, S., Roy, S.K., et al.: Soft-WSN: software-defined WSN management system for IoT applications. IEEE Syst. J. 12(3), 2074–2081 (2018)

    Article  Google Scholar 

  4. Vijay, G., Bdira, E.B.A., Ibnkahla, M.: Cognition in wireless sensor networks: a perspective. IEEE Sens. J. 11(3), 582–592 (2011)

    Article  Google Scholar 

  5. Zhao, M., Yang, Y., Wang, C.: Mobile data gathering with load balanced clustering and dual data uploading in wireless sensor networks. IEEE Trans. Mob. Comput. 14(4), 770–785 (2018)

    Article  Google Scholar 

  6. Xie, K., Ning, X., Wang, X.: An efficient privacy-preserving compressive data gathering scheme in WSNs. Inf. Sci. 390, 82–94 (2017)

    Article  Google Scholar 

  7. Rani, S., Ahmed, S.H., Talwar, R., et al.: Can sensors collect big data? An energy-efficient big data gathering algorithm for a WSN. IEEE Trans. Ind. Inform. 13(4), 1961–1968 (2017)

    Article  Google Scholar 

  8. Farzana, A.H.F., Neduncheliyan, S.: Ant-based routing and QoS-effective data collection for mobile wireless sensor network. Wirel. Netw. 23(6), 1697–1707 (2016). https://doi.org/10.1007/s11276-016-1239-6

    Article  Google Scholar 

  9. Joshi, Y.K., Younis, M.: Restoring connectivity in a resource constrained WSN. J. Netw. Comput. Appl. 66, 151–165 (2016)

    Article  Google Scholar 

  10. Wu, Q., Liu, L., Zhang, R.: Fundamental tradeoffs in communication and trajectory design for UAV enabled wireless network. IEEE Wirel. Commun. 26(1), 36–44 (2019)

    Article  Google Scholar 

  11. Miao, Y., Sun, Z., Wang, N., et al.: Time efficient data collection with mobile sink and vMIMO technique in wireless sensor networks. IEEE Syst. J. 12(1), 639–647 (2018)

    Article  Google Scholar 

  12. Zhou, Z., Du, C., Shu, L.: An energy-balanced heuristic for mobile sink scheduling in hybrid WSNs. IEEE Trans. Ind. Inform. 12(1), 28–40 (2016)

    Article  Google Scholar 

  13. Chang, J.Y., Shen, T.H.: An efficient tree-based power saving scheme for wireless sensor networks with mobile sink. IEEE Sens. J. 16(20), 7545–7557 (2016)

    Article  Google Scholar 

  14. Ekanayake, J., Pallickara, S.: Map Reduce for data intensive scientific analysis. In: IEEE eScience, Piscataway, pp. 277–284 (2008)

    Google Scholar 

Download references

Acknowledgments

This work is supported by the PhD Start-Up Fund of Dongguan University of Technology (GC300502-3),the Higher Education Innovation Strong School Project of Guangdong Province of China (2017KQNCX190), the Natural Science Foundation of Guangdong Province (Grant No. 2018A030313014), the research team project of Dongguan University of Technology (Grant No. TDY-B2019009), and the Guangdong University Key Project (2019KZDXM012).

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, Dongguan University of Technology, Dongguan, 523808, People’s Republic of China

    Xueqiang Li & Ming Tao

Authors
  1. Xueqiang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ming Tao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xueqiang Li .

Editor information

Editors and Affiliations

  1. Xidian University, Xi'an, China

    Xiaofeng Chen

  2. Guangzhou University, Guangzhou, China

    Hongyang Yan

  3. Michigan State University, East Lansing, MI, USA

    Qiben Yan

  4. Division of Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science, Thuwal, Saudi Arabia

    Xiangliang Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Li, X., Tao, M. (2020). Location Planning of UAVs for WSNs Data Collection Based on Adaptive Search Algorithm. In: Chen, X., Yan, H., Yan, Q., Zhang, X. (eds) Machine Learning for Cyber Security. ML4CS 2020. Lecture Notes in Computer Science(), vol 12487. Springer, Cham. https://doi.org/10.1007/978-3-030-62460-6_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-62460-6_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-62459-0

  • Online ISBN: 978-3-030-62460-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation