Skip to main content

Multi-threshold Hysteresis-Based Congestion Control for UAV-Based Detection Sensor Network

  • Conference paper
  • First Online:
Internet of Things, Smart Spaces, and Next Generation Networks and Systems (NEW2AN 2022)

Abstract

Nowadays, unmanned aerial vehicles (UAV) are considered for a variety of different applications. One of the important ones is automatic collection of sensory data. In this work, we study the use of UAV-based for secure remote monitoring network enabled via LoRaWAN lower-power wide area (LPWA) network. In the considered use-case the UAVs carry sensors collecting data and LoRa modules to transmit collected data to the stationary gateway. The work focuses on congestion control on the gateway. Specifically, we propose a multi-threshold hysteresis-based control mechanism to alleviate system overload by dropping some of the data send by the sensors. The considered system is modeled using the queuing system with multiple thresholds. The work studies time-dependent characteristics of the system: average time the system spends in overload and reduced load states. Our results demonstrate that using hysteresis control allows for the system to accept priority data traffic even under relatively high system load.

The research was supported by RSF (project No. 21-79-00157).

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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. Aliyu, B., Machnev, E.A., Mokrov, E.V.: Hysteretic congestion control in wireless cloud sensor networks. Inform. Appl. 16(3), 83–89 (2022). https://doi.org/10.14357/19922264220311

    Article  Google Scholar 

  2. Bagaa, M., Challal, Y., Ksentini, A., Derhab, A., Badache, N.: Data aggregation scheduling algorithms in wireless sensor networks: solutions and challenges. Commun. Surv. Tutor. 16, 1339–1368 (2014). https://doi.org/10.1109/SURV.2014.031914.00029

    Article  Google Scholar 

  3. Gapeyenko, M., Petrov, V., Moltchanov, D., Andreev, S., Himayat, N., Koucheryavy, Y.: Flexible and reliable UAV-assisted backhaul operation in 5G mmWave cellular networks. IEEE J. Sel. Areas Commun. 36(11), 2486–2496 (2018)

    Article  Google Scholar 

  4. Ghazali, M.H.M., Teoh, K., Rahiman, W.: A systematic review of real-time deployments of UAV-based LoRa communication network. IEEE Access 9, 124817–124830 (2021). https://doi.org/10.1109/ACCESS.2021.3110872

    Article  Google Scholar 

  5. Ghorbel, M.B., Rodríguez-Duarte, D., Ghazzai, H., Hossain, M.J., Menouar, H.: Joint position and travel path optimization for energy efficient wireless data gathering using unmanned aerial vehicles. IEEE Trans. Veh. Technol. 68(3), 2165–2175 (2019). https://doi.org/10.1109/TVT.2019.2893374

    Article  Google Scholar 

  6. Kavuri, S., Moltchanov, D., Ometov, A., Andreev, S., Koucheryavy, Y.: Performance analysis of onshore NB-IoT for container tracking during near-the-shore vessel navigation. IEEE Internet Things J. 7(4), 2928–2943 (2020)

    Article  Google Scholar 

  7. Komarov, M., Moltchanov, D.: System design and analysis of UAV-assisted BLE wireless sensor systems. In: Mamatas, L., Matta, I., Papadimitriou, P., Koucheryavy, Y. (eds.) WWIC 2016. LNCS, vol. 9674, pp. 284–296. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-33936-8_22

    Chapter  Google Scholar 

  8. Petrov, V., Gapeyenko, M., Moltchanov, D., Andreev, S., Heath, R.W.: Hover or perch: comparing capacity of airborne and landed millimeter-wave UAV cells. IEEE Wireless Commun. Lett. 9(12), 2059–2063 (2020)

    Article  Google Scholar 

  9. Polonelli, T., Qin, Y., Yeatman, E.M., Benini, L., Boyle, D.: A flexible, low-power platform for UAV-based data collection from remote sensors. IEEE Access 8, 164775–164785 (2020). https://doi.org/10.1109/ACCESS.2020.3021370

    Article  Google Scholar 

  10. Stusek, M., et al.: Optimizing NB-IoT communication patterns for permanently connected mMTC devices. In: 2022 IEEE Wireless Communications and Networking Conference (WCNC), pp. 1413–1418. IEEE (2022)

    Google Scholar 

  11. Tafintsev, N., et al.: Handling spontaneous traffic variations in 5G+ via offloading onto mmWave-capable UAV “bridges’’. IEEE Trans. Veh. Technol. 69(9), 10070–10084 (2020)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Bashir Aliyu , Evgeny Mokrov or Konstantin Samouylov .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Aliyu, B., Mokrov, E., Samouylov, K. (2023). Multi-threshold Hysteresis-Based Congestion Control for UAV-Based Detection Sensor Network. In: Koucheryavy, Y., Aziz, A. (eds) Internet of Things, Smart Spaces, and Next Generation Networks and Systems. NEW2AN 2022. Lecture Notes in Computer Science, vol 13772. Springer, Cham. https://doi.org/10.1007/978-3-031-30258-9_52

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-30258-9_52

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-30257-2

  • Online ISBN: 978-3-031-30258-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics