Skip to main content
SpringerLink
Log in
Book cover

International Conference on Software Engineering Research, Management and Applications

SERA 2019: Software Engineering Research, Management and Applications pp 1–22Cite as

A QoS Aware Uplink Scheduler for IoT in Emergency Over LTE/LTE-A Networks

  • Chapter
  • First Online:

Part of the book series: Studies in Computational Intelligence ((SCI,volume 845))

Abstract

A massive number of Internet-of-Things (IoT) devices are deployed to monitor and control a variety of physical objects as well as support a body of smart-world applications. How to efficiently allocate network resources becomes a challenging issue with the rapidly growing connected IoT devices. Depending on applications, the burst of IoT traffic could lead to the bandwidth deficiency within a short period of time and further deteriorates network performance. To tackle this issue, in this paper we first propose a Quality of Service (QoS) aware Normal Round Robin Uplink Scheduler (QNRR-US) over Long-Term Evolution (LTE)/LTE-Advance (LTE-A) networks. QNRR-US assigns a higher priority to IoT data that requires urgent treatment over normal IoT data, and then builds IoT devices’ scheduling queues based on priorities of data traffic. Thus, QNRR-US guarantees high priority data transmission. To provide fairness to IoT data, QNRR-US reserves some bandwidth for low priority data traffic. Based on QNRR-US, we then propose the QoS aware Bound Round Robin Uplink Scheduler (QBRR-US), which separates enormous IoT devices with burst data traffics and pushes them into service and waiting queue. The IoT devices in service queue take part in round robin resource allocation until the transmission of urgent data from the IoT device is complete and the new IoT device enters service queue from waiting queue for the next turn of resource allocation. Through simulations in NS-3, our experimental results show that QBRR-US outperforms the traditional proportional fair (PF) scheduler and QNRR-US with respect to throughput, packet loss ratio, and packet delay.

This is a preview of subscription content, 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   139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   179.99
Price excludes VAT (USA)
  • Durable hardcover 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

Learn about institutional subscriptions

References

  1. Lin, J., Yu, W., Zhang, N., Yang, X., Zhang, H., & Zhao, W. (2017, October). A survey on Internet of Things: architecture, enabling technologies, security and privacy, and applications. IEEE Internet of Things Journal, 4(5), 1125–1142.

    Google Scholar 

  2. Gao, W., Nguyen, J. H., Yu, W., Lu, C., Ku, D. T., & Hatcher, W. G. (2017, October). Toward emulation-based performance assessment of constrained application protocol in dynamic networks. IEEE Internet of Things Journal, 4(5), 1597–1610.

    Google Scholar 

  3. Yu, W., Liang, F., He, X., Hatcher, W. G., Lu, C., Lin, J., et al. (2018). A survey on the edge computing for the Internet of Things. IEEE Access, 6, 6900–6919.

    Article  Google Scholar 

  4. Xu, H., Yu, W., Griffith, D., & Golmie, N. (2018). A survey on industrial Internet of Things: A cyber-physical systems perspective. IEEE Access, 6, 78238–78259.

    Article  Google Scholar 

  5. Mallapuram, S., Ngwum, N., Yuan, F., Lu, C., & Yu, W. (2017, May). Smart city: The state of the art, datasets, and evaluation platforms. In 2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS) (pp. 447–452).

    Google Scholar 

  6. Lin, J., Yu, W., Yang, X., Yang, Q., Fu, X., & Zhao, W. (2015, June). A novel dynamic en-route decision real-time route guidance scheme in intelligent transportation systems. In 2015 IEEE 35th International Conference on Distributed Computing Systems (pp. 61–72).

    Google Scholar 

  7. Yang, Q., An, D., Min, R., Yu, W., Yang, X., & Zhao, W. (2017, July). On optimal pmu placement-based defense against data integrity attacks in smart grid. IEEE Transactions on Information Forensics and Security, 12(7), 1735–1750.

    Google Scholar 

  8. Wang, W., Wang, Q., & Sohraby, K. (2017). Multimedia sensing as a service (MSaaS): Exploring resource saving potentials of at cloud-edge IoT and fogs. IEEE Internet of Things Journal, 4(2), 487–495.

    Google Scholar 

  9. Xu, J., Guo, H., & Wu, S. (2018, October). Indoor multi-sensory self-supervised autonomous mobile robotic navigation. In 2018 IEEE International Conference on Industrial Internet (ICII) (pp. 119–128).

    Google Scholar 

  10. Wu, S., Rendall, J. B., Smith, M. J., Zhu, S., Xu, J., Wang, H., et al. (2017, June). Survey on prediction algorithms in smart homes. IEEE Internet of Things Journal, 4(3), 636–644.

    Article  Google Scholar 

  11. Yao, R., Wang, W., Farrokh-Baroughi, M., Wang, H., & Qian, Y. (2013). Quality-driven energy-neutralized power and relay selection for smart grid wireless multimedia sensor based IoTs. IEEE Sensors Journal, 13(10), 3637–3644.

    Article  Google Scholar 

  12. Xu, G., Yu, W., Griffith, D., Golmie, N., & Moulema, P. (2017, February). Toward integrating distributed energy resources and storage devices in smart grid. IEEE Internet of Things Journal, 4(1), 192–204.

    Google Scholar 

  13. Yang, Q., Yang, J., Yu, W., An, D., Zhang, N., & Zhao, W. (2014, March). On false data-injection attacks against power system state estimation: Modeling and countermeasures. IEEE Transactions on Parallel and Distributed Systems, 25(3), 717–729.

    Google Scholar 

  14. Lin, J., Yu, W., Yang, X., Xu, G., & Zhao, W. (2012, April). On false data injection attacks against distributed energy routing in smart grid. In 2012 IEEE/ACM Third International Conference on Cyber-Physical Systems (pp. 183–192).

    Google Scholar 

  15. Lin, J., Yu, W., & Yang, X. (2016, January). Towards multistep electricity prices in smart grid electricity markets. IEEE Transactions on Parallel and Distributed Systems, 27(1), 286–302.

    Google Scholar 

  16. Akpakwu, G. A., Silva, B. J., Hancke, G. P., & Abu-Mahfouz, A. M. (2018). A survey on 5G networks for the Internet of Things: Communication technologies and challenges. IEEE Access, 6, 3619–3647.

    Article  Google Scholar 

  17. Yu, W., Xu, H., Zhang, H., Griffith, D., & Golmie, N. (2016, August). Ultra-dense networks: Survey of state of the art and future directions. In 2016 25th International Conference on Computer Communication and Networks (ICCCN) (pp. 1–10).

    Google Scholar 

  18. Global internet of things market size 2009–2019. https://www.statista.com/statistics/485136/global-internet-of-things-market-size/ (Online). Accessed May 2, 2018.

  19. Al-Zihad, M., Akash, S. A., Adhikary, T., & Razzaque, M. A. (2017, December). Bandwidth allocation and computation offloading for service specific IoT edge devices. In 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC) (pp. 516–519).

    Google Scholar 

  20. Wang, L., Zhang, X., Wang, S., & Yang, J. (2017). An online strategy of adaptive traffic offloading and bandwidth allocation for green M2M communications. IEEE Access, 5, 6444–6453.

    Article  Google Scholar 

  21. Wu, Y., Yu, W., Griffith, D.W., & Golmie, N. (2018). Modeling and performance assessment of dynamic rate adaptation for M2M communications. IEEE Transactions on Network Science and Engineering, 1.

    Google Scholar 

  22. Yu, W., Xu, H., Nguyen, J., Blasch, E., Hematian, A., & Gao, W. (2018). Survey of public safety communications: User-side and network-side solutions and future directions. IEEE Access, 6, 70397–70425.

    Article  Google Scholar 

  23. nsnam. ns-3 network simulator. https://www.nsnam.org/. Accessed November 4, 2018.

  24. Sesia, S., Baker, M., & Toufik, I. (2011). LTE-the UMTS long term evolution: From theory to practice. Chichester: Wiley.

    Book  Google Scholar 

  25. Abu-Ali, N., Taha, A. M., Salah, M., & Hassanein, H. (2014). Uplink scheduling in LTE and LTE-advanced: Tutorial, survey and evaluation framework. IEEE Communications Surveys & Tutorials, 16(3), 1239–1265.

    Article  Google Scholar 

  26. Ghavimi, F., Lu, Y., & Chen, H. (2017, July). Uplink scheduling and power allocation for M2M communications in sc-fdma-based LTE-A networks with QoS guarantees. IEEE Transactions on Vehicular Technology, 66(7), 6160–6170.

    Google Scholar 

  27. Ragaleux, A., Baey, S., & Karaca, M. (2017, August). Standard-compliant LTE—A uplink scheduling scheme with quality of service. IEEE Transactions on Vehicular Technology, 66(8), 7207–7222.

    Google Scholar 

  28. Yu, C., Yu, L., Wu, Y., He, Y., & Lu, Q. (2017). Uplink scheduling and link adaptation for narrowband Internet of Things systems. IEEE Access, 5, 1724–1734.

    Article  Google Scholar 

  29. Liu, Q., Zoppi, S., Tan, G., Kellerer, W., & Steinbach, E. (2017, October). Quality-of-control-driven uplink scheduling for networked control systems running over 5G communication networks. In 2017 IEEE International Symposium on Haptic, Audio and Visual Environments and Games (HAVE) (pp. 1–6).

    Google Scholar 

  30. Elhamy, A., & Gadallah, Y. (2015). BAT: A balanced alternating technique for M2M uplink scheduling over LTE. In 2015 IEEE 81st Vehicular Technology Conference (VTC Spring) (pp. 1–6). IEEE.

    Google Scholar 

  31. Carlesso, M., Antonopoulos, A., Granelli, F., & Verikoukis, C. (2015). Uplink scheduling for smart metering and real-time traffic coexistence in LTE networks. In 2015 IEEE International Conference on Communications (ICC) (pp. 820–825). IEEE.

    Google Scholar 

  32. Wang, C., Kuo, J., Yang, D., & Chen, W. (2018, December). Surveillance-aware uplink scheduling for cellular networks. IEEE Transactions on Mobile Computing, 17(12), 2939–2952.

    Google Scholar 

  33. He, Y., Li, N., Xie, W., & Wang, C. (2017, October). Uplink scheduling and power allocation with M2M/H2H co-existence in LTE—A cellular networks. In 2017 IEEE 17th International Conference on Communication Technology (ICCT) (pp. 528–533).

    Google Scholar 

  34. Chuang, T., Tsai, M., & Chuang, C. (2015, March). Group-based uplink scheduling for machine-type communications in LTE-advanced networks. In 2015 IEEE 29th International Conference on Advanced Information Networking and Applications Workshops (pp. 652–657).

    Google Scholar 

  35. Amarasekara, B., Ranaweera, C., Evans, R., & Nirmalathas, A. (2017). Dynamic scheduling algorithm for lte uplink with smart-metering traffic. Transactions on Emerging Telecommunications Technologies, 28(10), e3163.

    Article  Google Scholar 

  36. Wu, Y., Yu, W., Zhang, J., Griffith, D., Golmie, N., & Lu, C. (2018). A 3D topology optimization scheme for M2M communications. In 2018 19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) (pp. 15–20). IEEE.

    Google Scholar 

  37. Wu, Y., Cui, Y., Yu, W., Lu, C., & Zhao, W. (2019). Modeling and forecasting of timescale network traffic dynamics in m2m communications. In Proceedings of 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS). IEEE.

    Google Scholar 

  38. Zhao, X., Laverty, D. M., McKernan, A., Morrow, D. J., McLaughlin, K., & Sezer, S. (2017, September). GPS-disciplined analog-to-digital converter for phasor measurement applications. IEEE Transactions on Instrumentation and Measurement, 66(9), 2349–2357.

    Google Scholar 

  39. Policy and charging control architecture. 3GPP Standard, (TS 23.203 release 15), 2017.

    Google Scholar 

  40. Ieee standard for synchrophasors for power systems c37.118. (IEEE C37.118.2), 2011.

    Google Scholar 

  41. Evolved universal terrestrial radio access (e-utra); base station (bs) radio transmission and reception. 3GPP Standard, (TS36.104 release 12), 2015.

    Google Scholar 

Download references

Acknowledgements

The work was supported in part by the US National Science Foundation (NSF) under grants: CNS 1350145. Any opinions, findings and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding agency.

Author information

Authors and Affiliations

  1. Department of Computer and Information Systems, Towson University, Towson, MD, 21252, USA

    Jin Zhang, Yalong Wu, Wei Yu & Chao Lu

Authors
  1. Jin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yalong Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wei Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chao Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wei Yu .

Editor information

Editors and Affiliations

  1. Software Engineering and Information Technology Institute, Central Michigan University, Mount Pleasant, MI, USA

    Roger Lee

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Zhang, J., Wu, Y., Yu, W., Lu, C. (2020). A QoS Aware Uplink Scheduler for IoT in Emergency Over LTE/LTE-A Networks. In: Lee, R. (eds) Software Engineering Research, Management and Applications. SERA 2019. Studies in Computational Intelligence, vol 845. Springer, Cham. https://doi.org/10.1007/978-3-030-24344-9_1

Download citation

Publish with us

Policies and ethics

Search

Navigation