ANN-based optimization framework for performance enhancement of Restricted Access Window mechanism in dense IoT networks

Abstract

IEEE 802.11ah, marketed as Wi-Fi HaLow, operates at Sub 1 GHz spectrum to provide broad coverage, high throughput, energy efficiency and scalability. This makes IEEE 802.11ah a promising candidate for the Internet of Things (IoT). One of the major enhancements in the MAC layer is the Restricted Access Window (RAW) mechanism, which focuses on mitigating the channel contention in dense networks. The RAW mechanism reduces the channel contention among the group of devices by restricting their channel access to the allocated RAW slots. Since the standard does not specify the optimal RAW configuration parameters, choosing the number of RAW slots has significant impact on the performance of the RAW mechanism. In this paper, we develop an optimization framework by exploiting the Multilayer Perceptron Artificial Neural Network (MLP-ANN) to find the optimal number of RAW slots that can maximize the performance of the RAW mechanism in terms of throughput, delay and energy consumption. We train the ANN using the network size, Modulation and Coding Schemes, duration of the RAW period and the optimal number of RAW slots found using the analytical model presented in this paper. Further, we evaluate the performance of the RAW mechanism by choosing the optimal number of RAW slots provided by the ANN-based optimization framework. Results show that the proposed scheme significantly enhances the performance of the RAW mechanism. Finally, the analytical results are corroborated using extensive simulations done in ns-3.

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Notes

  1. 1.

    In uniform groping scheme the number of RAW slots is equal to the number of groups.

  2. 2.

    We consider \(m=5\), \(R=7\), \(CW_{min}=32\) and \(CW_{max}=1024\) according to [3].

  3. 3.

    Although multiple packets can be sent in a TXOP, we assume only one packet for simplicity. \(\Delta _{\textit{DATA}}\) and \(\Delta _{\textit{ACK}}\) represent the time taken to transmit the data packet and acknowledgment corresponding to the MCS used.

Abbreviations

N :

Network size

n :

Index of a device in the network

G :

Number of groups

g :

Index of a device in a group

\(\Delta _{\textit{RAW}}\) :

Duration of RAW period

\(\Delta _{\textit{slot}}\) :

Duration of RAW slot

\(\xi \) :

Transmission Opportunity (TXOP)

\(\xi '\) :

TXOP+DIFS

\(\Delta _{\textit{DATA}}\) :

Time taken to transmit a data packet

\(\Delta _{\textit{ACK}}\) :

Time take to transmit acknowledgment

\(\delta _{s}\) :

SIFS duration

\(\delta _{d}\) :

DIFS duration

\(\rho \) :

Duration of mini-slot

m :

Number of back-off stages

\(m'\) :

Maximum re-transmission limit

s(t):

Stochastic process representing the back-off stage

b(t):

Stochastic process representing the back-off counter

l :

Number of transactions

\(\Delta _{h}\) :

Duration of holding time

\(\Delta _{g}\) :

Guard interval

\(B_l\) :

Number of back-off slots before \(l{th}\) transaction

\(\Delta _{l}\) :

Duration of l transactions

\(\psi \) :

Number of mini-slot in a RAW slot

\({\mathcal {T}}_u\) :

Maximum number of transaction in a RAW slot

References

  1. 1

    Palattella M R, Accettura N, Vilajosana X, Watteyne T, Grieco L A,  Boggia G and Dohler M 2013 Standardized protocol stack for the Internet of (important) things. IEEE Commun. Surv. Tut. 15(3): 1389–1406

    Article  Google Scholar 

  2. 2

    Kocan E, Domazetovic B and Pejanovic-Djurisic M 2017 Range extension in IEEE 802.11ah systems through relaying. Wireless Pers. Commun. 97(2): 1889–1910

    Article  Google Scholar 

  3. 3

    Man L A N, Standards Committee and IEEE Computer 2013 IEEE Standard 802.11ac-2013: Part 11: Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) Specifications, Amendment 4 Enhancements for Very High Throughput for Operation in Bands below 6 GHz.

  4. 4

    Ahmed N, Rahman H and Hussain M I 2018 An IEEE 802.11ah-based scalable network architecture for Internet of Things. Ann. Telecommun. 73(7): 499–509

    Article  Google Scholar 

  5. 5

    Wang C and Lin T 2008 Application of neural networks for achieving 802.11 QoS in heterogeneous channels. Comput. Netw. 52(3): 581–592

    Article  Google Scholar 

  6. 6

    Singh J P, Dutta P and Pal A 2012 Delay prediction in mobile ad hoc network using artificial neural network. In: Procedia Technol. 4: 201–206

    Article  Google Scholar 

  7. 7

    Wang C 2013 Dynamic ARF for throughput improvement in 802.11 WLAN via a machine-learning approach. J. Netw. Comput. Appl. 36(2): 667–676

    Article  Google Scholar 

  8. 8

    Lin P and Lin T 2009 Machine-learning-based adaptive approach for frame-size optimization in wireless LAN environments. IEEE Trans. Veh. Technol. 58(9): 5060–5073

    Article  Google Scholar 

  9. 9

    Afsharizadeh M and Mohammadi M 2016 Prediction-based reversible image watermarking using artificial neural networks. Turk. J. Electr. Eng. Comput. Sci. 24: 896–910

    Article  Google Scholar 

  10. 10

    Hornik K, Stinchcombe M and White H 1989 Multilayer feedforward networks are universal approximators. Neural Netw. 2(5): 359–366

    Article  Google Scholar 

  11. 11

    Hassoun M H 1995 Fundamentals of artificial neural networks, 1st ed. Cambridge, MA, USA: MIT Press

    MATH  Google Scholar 

  12. 12

    Park C W, Hwang D and Lee T J 2014 Enhancement of IEEE 802.11ah MAC for M2M communications. IEEE Commun. Lett. 18(7): 1151–1154

    Article  Google Scholar 

  13. 13

    Khorov E, Lyakhov A, Krotov A and Guschin A 2015 A survey on IEEE 802.11ah: an enabling networking technology for smart cities. Comput. Commun. 58: 53–69

    Article  Google Scholar 

  14. 14

    Sun W, Choi M and Choi S 2013 IEEE 802.11ah: a long range 802.11 WLAN at sub 1 GHz. J. ICT Standardiz. 1: 1–26

    Google Scholar 

  15. 15

    Adame T, Bel A, Bellalta B, Barcelo J and Oliver M 2014 IEEE 802.11ah: the WiFi approach for M2M communications. IEEE Wireless Commun. 21(6): 144–152

    Article  Google Scholar 

  16. 16

    Hazmi A, Rinne J and Valkama M 2012 Feasibility study of IEEE 802.11ah radio technology for IoT and M2M use cases. In: Proceedings of the IEEE GLOBECOM Workshops, pp. 1687–1692

  17. 17

    Aust S and Ito T 2012 Sub 1 GHz wireless LAN propagation path loss models for urban smart grid applications. In: Proceedings of the International Conference on Computing, Networking and Communications (ICNC), pp. 116–120

  18. 18

    Casas R A, Papaparaskeva V, Mao X, Kumar R, Kaul P and Hijazi S 2015 An IEEE 802.11ah programmable modem. In: Proceedings of the 2015 16th IEEE International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM), pp. 1–6

  19. 19

    Yoon S G, Seo J O and Bahk S 2016 Regrouping algorithm to alleviate the hidden node problem in 802.11ah networks. Comput. Netw. 105: 22–32

    Article  Google Scholar 

  20. 20

    Damayanti W, Kim S and Yun J H 2016 Collision chain mitigation and hidden device-aware grouping in large-scale IEEE 802.11ah networks. Comput. Netw. 108: 296–306

    Article  Google Scholar 

  21. 21

    Dong M, Wu Z, Gao X and Zhao H 2016 An efficient spatial group restricted access window scheme for IEEE 802.11ah networks. In: Proceedings of the 2016 Sixth International Conference on Information Science and Technology (ICIST), pp. 168–173

  22. 22

    Chang T C, Lin C H, Lin K C J and Chen W T 2015 Load-balanced sensor grouping for IEEE 802.11ah networks. In: Proceedings of the 2015 IEEE Global Communications Conference (GLOBECOM), pp. 1–6

  23. 23

    Wang Y, Li Y, Chai K K, Chen Y and Schormans J 2015 Energy-aware adaptive restricted access window for IEEE 802.11ah based smart grid networks. In: Proceedings of the 2015 IEEE International Conference on Smart Grid Communications (SmartGridComm), pp. 581–586

  24. 24

    Wang Y, Chai K K, Chen Y, Schormans J and Loo J 2017 Energy-aware Restricted Access Window control with retransmission scheme for IEEE 802.11ah (Wi-Fi HaLow) based networks. In: Proceedings of the 2017 13th Annual Conference on Wireless On-demand Network Systems and Services (WONS), pp. 69–76

  25. 25

    Nawaz N, Hafeez M, Zaidi S A R, McLernon D C and Ghogho M 2017 Throughput enhancement of restricted access window for uniform grouping scheme in IEEE 802.11ah. In: Proceedings of the 2017 IEEE International Conference on Communications (ICC), pp. 1–7

  26. 26

    Pandya B and Chiueh T D 2018 Interference aware coordinated multiuser access in multi-band WLAN for next generation low power applications. Wireless Netw. 25(4): 1965–1981

    Article  Google Scholar 

  27. 27

    Lei X and Rhee S H 2017 Performance improvement of Sub 1 GHz WLANs for future IoT environments. Wireless Pers. Commun. 93(4): 933–947

    Article  Google Scholar 

  28. 28

    Tian L, Famaey J and Latre S 2016 Evaluation of the IEEE 802.11ah Restricted Access Window mechanism for dense IoT networks. In: Proceedings of the 2016 17th IEEE International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM), pp. 1–9

  29. 29

    Wireless LAN Medium Access Control (MAC) and Physical Layer (PHY) specifications—amendment 4: enhancements for very high throughput for operation in bands below 6 GHz. IEEE Std 802.11ac(TM)-2013

  30. 30

    Adame T, Bel A, Bellalta B, Barcelo J, Gonzalez J and Oliver M 2013 Capacity analysis of IEEE 802.11Ah WLANs for M2M communications. In: Proceedings of the 6th International Workshop on Multiple Access Communcations, pp. 139–155

  31. 31

    Hsu C C and Liang S T 2017 An efficient retransmission mechanism for the RAW-based IEEE 802.11ah networks. In: Proceedings of the 2017 International Conference on E-Society, E-Education and E-Technology, pp. 8–11

  32. 32

    Chatzimisios P, Boucouvalas A C and Vitsas V 2003 IEEE 802.11 packet delay—a finite retry limit analysis. In: Proceedings of the Global Telecommunications Conference, GLOBECOM ’03, IEEE, vol. 2, pp. 950–954

  33. 33

    Fausett L (Ed.) 1994 Fundamentals of neural networks: architectures, algorithms, and applications. Upper Saddle River, NJ, USA: Prentice-Hall, Inc.

  34. 34

    Demuth H and Beale M 1993 Neural network toolbox for use with MATLAB, http://www.mathworks.com/

  35. 35

    Lera G and Pinzolas M 2002 Neighborhood based Levenberg–Marquardt algorithm for neural network training. IEEE Trans. Neural Netw. 13(5): 1200–1203

    Article  Google Scholar 

  36. 36

    Zhai X, Ali A A S, Amira A and Bensaali F 2016 MLP neural network based gas classification system on Zynq SoC. IEEE Access 4: 8138–8146

    Article  Google Scholar 

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Correspondence to Miriyala Mahesh.

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Mahesh, M., Harigovindan, V.P. ANN-based optimization framework for performance enhancement of Restricted Access Window mechanism in dense IoT networks. Sādhanā 45, 52 (2020). https://doi.org/10.1007/s12046-020-1287-6

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Keywords

  • Internet of Things
  • IEEE 802.11ah
  • Restricted Access Window
  • Artificial Neural Network