Skip to main content
Log in

Assessment of LTE Wireless Accessing for Managing Traffic Flow of IoT Services

  • Published:
Mobile Networks and Applications Aims and scope Submit manuscript

Abstract

There is a tremendous growth in the traffic of the Internet of Things (IoT) devices. Some of the IoT application scenarios may prefer the wireless access supported by a Long Term Evolution (LTE) network. Due to the limited available spectrum, it is very important for the LTE network to efficiently control traffic flow and allocate uplink radio resources. To perform the above tasks, an Exponentially Weighted Moving Average (EWMA) rate was defined to measure the data rate of an LTE bearer. Based on the EWMA rate, an Allocate as Granted (AAG) resource allocation scheme was also proposed. With the clear definition of EWMA rate, the AAG scheme has outstanding performance in terms of User Equipment (UE) satisfaction, packet delay, and system throughput. This paper investigates the characteristics of the EWMA rate and explains why it is suitable to compromise the expectation of both the UE and the Evolved Node B (eNB). Based on the EWMA rate, this paper also describes how traffic parameters are related to the performance of the traffic and the eNB, as well as the rate of charging. This issue is also discussed by taking examples of Facebook webcast traffic patterns, which could be similar to that of monitoring devices of IoT.

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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Husain S, Kunz APA, Papageorgiou A, Song J (2014) Recent trends in IoT/M2M related standards. Presented at the 21st annual conference of IEEE Industrial Electronics society

  2. Ratasuk R, Prasad A, Li Z, Ghosh A, Uusitalo MA (2015) Recent advancements in M2M communications in 4G networks and evolution towards 5G. In: 18th International Conference on Intelligence in Next Generation Networks (ICIN). IEEE

  3. Lien S-Y, Deng D-J, Tsai H-L, Lin Y-P, Chen K-C (2017) Vehicular radio access to unlicensed spectrum. IEEE Wirel Commun 24(6):46–54

    Article  Google Scholar 

  4. Mehmood Y, Görg C, Muehleisen M, Timm-Giel A (2015) Mobile M2M communication architectures, upcoming challenges, applications, and future directions. EURASIP J Wirel Commun Netw 2015(1):250

    Article  Google Scholar 

  5. Ruiz de Temino L, Berardinelli G, Frattasi S, Mogensen P (2008) Channel-aware scheduling algorithms for SC-FDMA in LTE uplink. In: IEEE 19th International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC

  6. Liu F, She X, Chen L, Otsuka H (2010) Improved recursive maximum expansion scheduling algorithms for uplink single carrier FDMA system, In: IEEE 71st Vehicular Technology Conference (VTC 2010-Spring), pp 1–5

  7. Iosif O, Banica I (2011) LTE uplink analysis using two packet scheduling models. In: 19th Telecommunications Forum (TELFOR), pp 394–397

  8. Lee S-B, Pefkianakis I, Meyerson A, Xu S, Lu S (2009) Proportional fair frequency-domain packet scheduling for 3GPP LTE uplink. In: IEEE INFOCOM, pp 2611–2615

  9. Calabrese FD et al (2008) Search-tree based uplink channel aware packet scheduling for UTRAN LTE. In: IEEE Vehicular Technology Conference, VTC Spring, pp 1949–1953

  10. 3GPP (2015) TS 36.300 v. 12.4.0 evolved universal terrestrial radio access (E-UTRA) and evolved universal terrestrial radio access network (E-UTRAN); overall description; stage 2

  11. Hatoum R, Hatoum A, Ghaith A, Pujolle G (2014) Qos-based joint resource allocation with link adaptation for SC-FDMA uplink in heterogeneous networks. Presented at the 12th ACM international symposium on Mobility management and wireless access

  12. Safa H, El-Hajj W, Tohme K (2013) A QoS-aware uplink scheduling paradigm for LTE networks. In: IEEE 27th International Conference on Advanced Information Networking and Applications, AINA, pp 1097–1104

  13. Marwat SNK, Zaki Y, Goerg C, Weerawardane T, Timm-Giel A (2012) Design and performance analysis of bandwidth and QoS aware LTE uplink scheduler in heterogeneous traffic environment. In: 8th International Wireless Communications and Mobile Computing Conference (IWCMC), pp 499–504

  14. 3GPP (2015) TS 23.203 v. 14.4.0 policy and charging control architecture

  15. 3GPP (2015) TS 23.401 v. 13.2.0 general packet radio service (GPRS) enhancements for evolved universal terrestrial radio access network (E-UTRAN) access

  16. Exponentially weighted moving average (EWMA). Available: en.wikipedia.org/wiki/Moving_average

  17. Kuo F-C, Wang H-C, Ting K-C, Tseng C-C, Liu P-E (2012) Robust LTE uplink scheduling based on call admission control. In: 2012 International Conference on ICT Convergence (ICTC), pp 548–552

  18. Kuo F-C, Ting K-C, Wang H-C, Tseng C-C, Chen M-W (2016) Differentiating and scheduling LTE uplink traffic based on exponentially weighted moving average of data rate. Mobile Networks and Applications:1–12. https://doi.org/10.1007/s11036-016-0693-9, First Online: 15 February 2016

  19. Kuo F-C, Ting K-C, Wang H-C, Tseng C-C (2017) On demand resource allocation for LTE uplink transmission based on logical channel groups. Mobile Networks and Applications 22:868–879. Online

    Article  Google Scholar 

  20. 3GPP (2015) TS 36.321 v. 11.6.0 evolved universal terrestrial radio access (E-UTRA); Medium access control (MAC) protocol specification

  21. 3GPP (2016) TS 36.331, evolved universal terrestrial radio access (E-UTRA);. Radio resource control (RRC);. Protocol specification

  22. Chandrasekaran B Survey of network traffic models. Available: www.cs.wustl.edu/~jain/cse567-06/ftp/traffic_models3/

  23. Doulamis AD, Doulamis ND, Kollias SD (2003) An adaptable neural-network model for recursive nonlinear traffic prediction and modeling of MPEG video sources. IEEE Trans Neural Netw 14(1):150–166

    Article  Google Scholar 

  24. Ying L, Feng-ju K, Lian-jiong Z, Xiang-yang L (2011) Impromvent method of MAC protocol based on adhoc network traffic characteristic. In: 2011 6th IEEE Joint International Information Technology and Artificial Intelligence Conference, vol 1, pp 464–467

  25. Xie Y, Shensheng T, Xiangnong H (2011) A new model for generating burst traffic based on hierarchical HMM. In: 2011 Eighth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), vol 4, pp 2212–2216

  26. 3GPP (2016) TS 36.213, v. 14.3.0 evolved universal terrestrial radio access (E-UTRA); physical layer procedures, release 13

  27. Chang C-Y, Yen H-C, Lin C-C, Deng D-J (2015) QoS/QoE support for H. 264/AVC video stream in IEEE 802.11 ac WLANs. IEEE Systems Journal

Download references

Acknowledgements

The author would like to thank Jia-Hao Xu and Zhen-Hao Huang for helping the simulation work.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Fang-Chang Kuo.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kuo, FC. Assessment of LTE Wireless Accessing for Managing Traffic Flow of IoT Services. Mobile Netw Appl 24, 853–863 (2019). https://doi.org/10.1007/s11036-018-1092-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11036-018-1092-1

Keywords

Navigation