Random Access in IoT Using Naïve Bayes Classification

Authors: Alhusein Almahjoub, Dongyu Qiu

Abstract:

This paper deals with the random access procedure in next-generation networks and presents the solution to reduce total service time (TST) which is one of the most important performance metrics in current and future internet of things (IoT) based networks. The proposed solution focuses on the calculation of optimal transmission probability which maximizes the success probability and reduces TST. It uses the information of several idle preambles in every time slot, and based on it, it estimates the number of backlogged IoT devices using Naïve Bayes estimation which is a type of supervised learning in the machine learning domain. The estimation of backlogged devices is necessary since optimal transmission probability depends on it and the eNodeB does not have information about it. The simulations are carried out in MATLAB which verify that the proposed solution gives excellent performance.

Keywords: Random access, LTE/LTE-A, 5G, machine learning, Naïve Bayes estimation.

References:

[1] Saadi, M., Noor, M.T., Imran, A., Toor, W.T., Mumtaz, S. and Wuttisittikulkij, L., 2020. IoT enabled quality of experience measurement for next generation networks in smart cities. Sustainable Cities and Society, 60, p.102266.
[2] A. A. Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari and M. Ayyash, "Internet of Things: A survey on enabling technologies protocols and applications", IEEE Commun. Surveys Tuts., vol. 17, no. 4, pp. 2347-2376, 4th Quart., 2015.
[3] J. Granjal, E. Monteiro and J. Sá Silva, "Security for the Internet of Things: A survey of existing protocols and open research issues", IEEE Commun. Surveys Tuts., vol. 17, no. 3, pp. 1294-1312, 3rd Quart. 2015.
[4] J. Lin, W. Yu, N. Zhang, X. Yang, H. Zhang and W. Zhao, "A survey on Internet of Things: Architecture enabling technologies security and privacy and applications", IEEE Internet Things J., vol. 4, no. 5, pp. 1125-1142, Oct. 2017.
[5] K. A. da Costa, J. P. Papa, C. O. Lisboa, R. Munoz and V. H. C. de Albuquerque, "Internet of Things: A survey on machine learning-based intrusion detection approaches", Comput. Netw., vol. 151, pp. 147-157, Mar. 2019.
[6] M. S. Mahdavinejad et al., "Machine learning for Internet of Things data analysis: A survey", Digit. Commun. Netw., vol. 4, no. 3, pp. 161-175, Aug. 2018.
[7] S. T. Vieira, R. L. Rosa, D. R. Zegarra, A. Ramírez, M. Saadi, L. Wuttisittikulkij. Q-Meter: Quality Monitoring System for Telecommunication Services Based on Sentiment Analysis Using Deep Learning. Sensors, 21(5), pp.1880, 2021
[8] H. Jin, W. T. Toor, B. C. Jung and J. B. Seo, "Recursive Pseudo-Bayesian Access Class Barring for M2M Communications in LTE Systems", IEEE Transactions on Vehicular Technology, vol. 66, no. 9, pp. 8595-8599, Sept 2017.
[9] C. Tsai, C. Lai, M. Chiang and L. T. Yang, "Data mining for Internet of Things: A survey", IEEE Commun. Surveys Tuts., vol. 16, no. 1, pp. 77-97, 1st Quart. 2014.
[10] https://www.mathworks.com/, accessed on January 2, 2021
[11] Alvi, M., Abualnaja, K.M., Toor, W.T. and Saadi, M., 2021. Performance analysis of access class barring for next generation IoT devices. Alexandria Engineering Journal, 60(1), pp.615-627.
[12] M. Tavana,V. Shah-Mansouri, andV.W. S.Wong, “Congestion control for bursty M2M traffic in LTE networks,” in Proc. IEEE Int. Conf. Commun., Jun. 2015, pp. 5815–5820.
[13] W. T. Toor and H. Jin, "Comparative study of access class barring and extended access barring for machine type communications", Proc. ICTC, pp. 604-609, Oct. 2017.