Skip to main content
Log in

Experience data excavating based distributed occasional communication establishing for swarm in remote region

  • Published:
Cluster Computing Aims and scope Submit manuscript

Abstract

This paper focuses on the issue of distributed occasional communication establishing in a fickle complex remote region with declining communication frequency points, where the communication frequency point condition is depicted by both communication frequency point quality and the coverage of jammers. A frequency point condition behavior regulation is established to depict the declining communication frequency point. By exploring communication frequency point quality and exploring the jammers activity jointly, an experience excavating behavior-based algorithm is put forwarded for occasional communication establishing. To reduce complexity and achieve relatively improved traffic complete capability, a voracious method is put forwarded, where user selects a communication frequency point with the best expected traffic complete rate to optimize the immediate benefit in the current period. By observing the simulation outputs, the put forwarded method achieves improved traffic complete capacity than the current works under changing conditions.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

References

  1. Purohit, A., Sun, Z., Zhang, P.: Sugarmap: location-less coverage for micro-aerial sensing swarms. In: Proceedings of ACM International Conference on Information Processing in Sensor Networks, pp. 253–264 (2013)

  2. Zhan, P., Yu, K., Swindlehurst, A.: Wireless relay communications with unmanned aerial vehicles: performance and optimization. IEEE Trans. Aerosp. Electron. Syst. 47(3), 2068–2085 (2011)

    Article  Google Scholar 

  3. Ortiz, G.G., et al.: Design and development of a robust ATP subsystem for the Altair UAV-to-Ground Lasercomm 2.5-Gbps demonstration. Proc. SPIE 4975, 103–114 (2003)

    Article  Google Scholar 

  4. Saleem, Y., Rehmani, M.H., Zeadally, S.: Integration of cognitive radio technology with unmanned aerial vehicles: issues, opportunities, and future research challenges. J. Netw. Comput. Appl. 50, 15–31 (2015)

    Article  Google Scholar 

  5. Wang, H., Huo, D., Alidaee, B.: Position unmanned aerial vehicles in the mobile ad hoc network. J. Intell. Robot. Syst. 74(1/2), 455–464 (2014)

    Article  Google Scholar 

  6. Burdakov, O., Doherty, P., Holmberg, K., Olsson, P.-M.: Optimal placement of UV-based communications relay nodes. J. Glob. Optim. 48(4), 511–531 (2010)

    Article  MathSciNet  Google Scholar 

  7. Bekmezci, I., Sahingoz, O.K., Temel, Ş.: Flying ad-hoc networks (FANETs): a survey. Ad Hoc Netw. 11(3), 1254–1270 (2013)

    Article  Google Scholar 

  8. Ono, F., Ochiai, H., Miura, R.: A wireless relay network based on unmanned aircraft system with rate optimization. IEEE Trans. Wirel. Commun. 15(11), 7699–7708 (2016)

    Article  Google Scholar 

  9. Hyung, C.D., Kim, S.H., Sung, D.K.: Energy-efficient maneuvering and communication of a single UAV-based relay. IEEE Trans. Aerosp. Electron. Syst. 50(3), 2320–2327 (2014)

    Article  Google Scholar 

  10. Zhu, Y., et al.: Design and evaluation of airborne communication networks. In: 7th International Conference on Ubiquitous and Future Networks, July 2015, pp. 277–282 (2015)

  11. Puri, A.: A survey of unmanned aerial vehicles (UAV) for traffic surveillance. PhD Dissertation, Department of Computer Science and Engineering, University of South Florida (2005)

  12. Ni, W., Collings, I.B., Liu, R.P.: Decentralized user-centric scheduling with low rate feedback for mobile small cells. IEEE Trans. Wirel. Commun. 12(12), 6106–6120 (2013)

    Article  Google Scholar 

  13. Peng, L., Lipinski, D., Mohseni, K.: Dynamic data driven application system for plume estimation using UAVs. J. Intell. Robot. Syst. 74(1/2), 421–436 (2014)

    Article  Google Scholar 

  14. Feng, L., et al.: A distributed gateway selection algorithm for UAV networks. IEEE Trans. Emerg. Top. Comput. 3(1), 22–33 (2015)

    Article  Google Scholar 

  15. Yin, C., et al.: Enhanced routing protocol for fast flying UAV network. In: IEEE International Conference on Communication Systems, December 2016, pp. 1–6 (2016)

  16. Qing, Z., Lang, T., Swami, A.: “Decentralized cognitive MAC for opportunistic spectrum access in ad hoc networks: a POMDP framework. IEEE Sel. Areas Commun. 25(3), 589–600 (2007)

    Article  Google Scholar 

  17. Chen, Y., Qing, Z., Swami, A.: Bursty traffic in energy-constrained opportunistic spectrum access. In: IEEE Global Telecommunications Conference, vol. 1–11, pp. 4641–4646 (2007)

  18. Chen, Y., Qing, Z., Swami, A.: Joint design and separation principle for opportunistic spectrum access in the presence of sensing errors. IEEE Trans. Inf. Theory 54, 2053–2071 (2008)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Lei Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yao, C., Wang, L. & Yu, X. Experience data excavating based distributed occasional communication establishing for swarm in remote region. Cluster Comput 22 (Suppl 6), 15409–15416 (2019). https://doi.org/10.1007/s10586-018-2610-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10586-018-2610-4

Keywords

Navigation