Model Formulation and Adaptive Neuro Fuzzy Inference System (ANFIS) to Monitor the Network Performance of Call Quality

Kadiri Kamoru Oluwatoyin*, Enem Theophilus Aniemeka **
* Department of Electrical and Electronic Engineering, Federal Polytechnic, Offa, Kwara, Nigeria.
** Department of Computer Science, Air Force Institute of Technology, Nigerian Air Force Base, Kaduna, Nigeria.
Periodicity:August - October'2019
DOI : https://doi.org/10.26634/jcs.8.4.17321

Abstract

Poor voice quality in Voice over Internet Protocol (VoIP) models during communication is a common occurrence that VoIP users experience, and can be frustrating when users cannot communicate efficiently. Most people find it difficult when they make calls and there is an echo. In addition time, effort and energy are wasted without any compensation. Users are also frustrated by not receiving, not transmitting or misunderstanding voice messages correctly. This study aims to reduce the threat of bad calls and improve the quality of voice calls. The work is designed to solve the need of the community of users by helping them to reduce communication costs and at the same time enabling them to communicate with greater clarity of signal using a unified communication system such as phones, computers, and tablets with one another. However, when the service requirement is greater than or equal to the threshold, it is segmented into less than or equal to the threshold value in smaller packets with a maximum service requirement. Besides this, if this node is vacant or has a single packet in the service unit, a packet is not saved at an intermediate node. These findings show the validity of hybrid switching that includes three fundamental computer network switching methods: packet, signal, and circuit switching. The results show that the maximum value of the limit is always available, which minimizes the network delay for nearly all traffic concentrations.

Keywords

Adaptive, Call Quality, Fuzzy, Inference System, Neuro-Fuzzy.

How to Cite this Article?

Oluwatoyin, K. K. and Aniemeka, E. T. (2019). Model Formulation and Adaptive Neuro Fuzzy Inference System (ANFIS) to Monitor the Network Performance of Call Quality. i-manager's Journal on Communication Engineering and Systems, 8(4), 9-14. https://doi.org/10.26634/jcs.8.4.17321

References

[1]. Abichandani, J., Baenke, J., Irizarry, M. S., Saxena, N., Vyas, P., Prasad, S., MAda, S., & Tafesse, Y. Z. (2017). A comparative study of voice quality and coverage for voice over long term evolution calls using different codec mode-sets. IEEE Access, 5, 10315-10322. https://doi.org/ 10.1109/ACCESS.2017.2707080
[2]. Bora, G., Bora, S., Singh, S., & Arsalan, S. M. (2014). OSI reference model: An overview. International Journal of Computer Trends and Technology (IJCTT), 7(4), 214- 218. https://doi.org/10.14445/22312803%2FIJCTTV7P151
[3]. Dahivadkar, S. K., & Limkar, M. (2017). Echo cancelation system in VOIP using Matlab. International Journal of Scientific Research Engineering & Technology (IJSRET), 6(1), 42-44.
[4]. Hidayat, A., & Saputra, I. P. (2019). Implementation voice over internet protocol (VOIP) as a communication media between unit at University Muhammadiyah Metro. International Journal of Information System and Computer Science (IJISCS), 2(2), 59-66.
[5]. Jalendry, S., & Verma, S. (2015). A detail review on voice over internet protocol (VoIP). International Journal of Engineering Trends and Technology (IJETT), 23(4), 161- 166. https://doi.org/10.14445/22315381%2Fijettv23p232
[6]. Jocić, J., & Veličković, Z. (2015). Measurement QoS parameters of VoIP codecs as a function of the network traffic level. In 2015 5th International Conference on Information Society and Technology (pp. 422-426).
[7]. Okonji, E. (2016). VoLTE Service Will Enhance Voice Calls in Nigeria. This Day. Retrieved from https://www. this daylive.com/index.php/2016/04/14/volte-service-willenhance- voice-calls-in-nigeria/
[8]. Rattal, S., Badri, A., & Moughit, M. (2014). A new wireless VoIP signaling device supporting SIP and H. 323 protocols. Journal of Computer Networks and Communications. 1-8. https://doi.org/10.1155/2014/ 605274
[9]. Singh, T. R., Singh, I. T & Sinam, T. (2016). Analysis of Skype and its detection. International Journal of Recent Technology and Engineering (IJRET), 5(4), 1-6.
[10]. Slavata, O., & Holub, J. (2015). Impact of the codec and various QoS methods on the final quality of the transferred voice in an IP network. In Journal of Physics: Conference Series (Vol. 588, No. 1, pp. 1-7. IOP Publishing. https://doi.org/10.1088/1742-6596/588/ 1/012011
[11]. Tauna, A. (2018). 140 Million Nigerians use Mobile Phones – NCC. The daily post. Retrieved from http:// dailypost.ng/2018/03/02/140-million-nigerians-usemobile- phones-ncc/
[12]. Yu, J., Lee, D., & Han, W. (2016). Preferred listening levels of mobile phone programs when considering subway interior noise. Noise & Health, 18(80), 36-41. https://doi.org/10.4103%2F1463-1741.174383
[13]. Waburi, N. (2009). The Contribution of Mobile Phones to the Kenyan Economy. Market & Social Research, Nairobi, Kenya.
If you have access to this article please login to view the article or kindly login to purchase the article

Purchase Instant Access

Single Article

North Americas,UK,
Middle East,Europe
India Rest of world
USD EUR INR USD-ROW
Online 15 15

Options for accessing this content:
  • If you would like institutional access to this content, please recommend the title to your librarian.
    Library Recommendation Form
  • If you already have i-manager's user account: Login above and proceed to purchase the article.
  • New Users: Please register, then proceed to purchase the article.