CFP last date
20 January 2025
Reseach Article

A Multidimensional Fuzzy Knowledge-based System for Optimizing Wireless Local Area Networks Performance

by Imeh J. Umoren, Samuel B. Okon
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 183 - Number 1
Year of Publication: 2021
Authors: Imeh J. Umoren, Samuel B. Okon
10.5120/ijca2021921238

Imeh J. Umoren, Samuel B. Okon . A Multidimensional Fuzzy Knowledge-based System for Optimizing Wireless Local Area Networks Performance. International Journal of Computer Applications. 183, 1 ( May 2021), 8-19. DOI=10.5120/ijca2021921238

@article{ 10.5120/ijca2021921238,
author = { Imeh J. Umoren, Samuel B. Okon },
title = { A Multidimensional Fuzzy Knowledge-based System for Optimizing Wireless Local Area Networks Performance },
journal = { International Journal of Computer Applications },
issue_date = { May 2021 },
volume = { 183 },
number = { 1 },
month = { May },
year = { 2021 },
issn = { 0975-8887 },
pages = { 8-19 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume183/number1/31890-2021921238/ },
doi = { 10.5120/ijca2021921238 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:15:32.770003+05:30
%A Imeh J. Umoren
%A Samuel B. Okon
%T A Multidimensional Fuzzy Knowledge-based System for Optimizing Wireless Local Area Networks Performance
%J International Journal of Computer Applications
%@ 0975-8887
%V 183
%N 1
%P 8-19
%D 2021
%I Foundation of Computer Science (FCS), NY, USA
Abstract

With the dawn of Wireless Local Area Networks (WLAN), network operators of third generation (3G) and fourth generation (4G) networks can properly address traffic requirements through subscribers and hotspot locations. Primarily, a significant aspect to consider is the issue of performance leading to Quality of Service (QoS) of mobile data networks allow subscribers to experience seamless and ubiquitous services as well as very high data rates. In this paper, we study the existing problem of network degradation which impact the provision of such seamless connectivity. In network performance, most indicators for performance optimization, includes Packet loss, Packet delay and Jitter (PLPDJ. As wireless networks evolve, demand for information services with high reliability, quick response times (QRT) and ubiquitous connectivity continues to upsurge rapidly. These issues are commonly affected by wireless networks inherent variances from wireline networks. Hence, network traffic metrics; latency, packet Loss and packet delay in certain wireless environments experienced some challenges in networks performance. To overcome these challenges, we consider network performance optimization techniques and proposed a framework using Type 1 Fuzzy knowledge-based approach for efficient WLAN performance. First, a performance measures on a typical wireless local area network with IP address 102.89.2.166 was carried out for a period of twenty-one (21) days based on specified performance metrics. Results shows that the average latency on a given WLAN was 11399ms (0.19m) as compared to jitter which was 1076ms (0.017m). Again, the download speed was established at 191.46Mbs compared to Upload speed which was at 35.7Mbps. Secondly, we obtain statistical operational field data and carried out simulation with the proposed model. Results indicates a minimization on congestions on the representative network environment which shows efficient network performance. Consequently, the evaluation carried out with Triangular Membership Functions (TMF) demonstrates an optimized WLAN Performance with QOS provisioning.

References
  1. Akram M. and Hatem M. (2017). Computer Network Performance management using a Simple Network Management Protocol, International Journal of Computing Academic Research (IJCAR) ISSN 2305-9184, Volume 6, Number 2, pp.50-58, Egypt.
  2. Akhilesh K. and Ompal S, (2012). 5G Technology – Redefining wireless Communication in upcoming years, International Journal of Computer Science and Management Research Vol 1 Issue 1.
  3. Alikira R (2012). Evaluation of WLAN Security and Performance, Munich, GRIN Verlag, https://www.grin.com/document/205389.
  4. Carlos A., Octavio J., Miguel J., (2017). Performance Optimization Model n802.11n Networks Using Multi-Objective Programming. Applied Mathematical Sciences, Vol. 11, 2017, no. 59, 2907 - 2918 HIKARI.
  5. Kuteyi A (2015). Network Performance Analysis Within A Local Area network. https://www.academia.edu/14397915/
  6. Kaidioglu, R (2010). Performance benchmarking of cellular network operators in Turkey. Graduate School of Natural and Applied Sciences of ATILIM University Turkey, Electrical and Electronic Engineering.
  7. Lopa, J. (2015). Evolution of Mobile Generation Technology: 1G to 5G and Review of Upcoming Wireless Technology 5G. International Journal of Modern Trends in Engineering and Research (IJMTER) Volume 02, Issue 10, ISSN (Online):2349–9745; ISSN (Print):2393-8161.
  8. Mendel, J. M., Hongwei,W. (2007). New results about 6 the centroid of an interval type-2 fuzzy set, including the centroid of a fuzzy granule. Information Sciences, 177(2):360-377.
  9. Munam, Ghazanfar, Carsten, Khurram, (2011). Network Performance Optimization: A Case Study of Enterprise Network Simulated in OPNET.
  10. Nandhini, Jagadhesan, Anusuja (2016). An Optimization of Wireless Network Security System, International Journal of Computer Science and Information Technology Research ISSN 2348-120X (online) Vol. 4, Issue 3, pp: (296-301).
  11. Nosiri C, Onyenwe M, Ekwueme U, (2019). Electrical and Electronic Engineering Department, Federal University of Technology, Fuzzy Logic Implementation for Enhanced WCDMA Network Using Selected KPIs Owerri, Nigeria
  12. Richa R, Shrivastava S, and Sarita S, (2016). Performance Analysis of Fuzzy based RED for Congestion Control in MANET International Journal of Smart Home Vol. 10, No. 5, pp. 231-240
  13. Umoh U, Nwachukwu E, and Okure O., (2010). Fuzzy rule-based framework for effective control of profitability in a paper recycling plant, Global J. Computer. Sci. Technol. pp 56–67.
  14. Imeh J. Umoren, Prince Asagba and Olumide Owolabi (2014). Handover Manageability and Performance Modeling in CDMA Mobile Communication Networks, International Institute for Science, Technology and Education (IISTEE) and Computing Information Systems Development Informatics and Allied research -Journal (CISDA). Vol 5 No. 1, ISSN: 2167 -1710, page 27-42.
  15. Imeh J. Umoren, Daniel E. Asuquo, Onukwugha Gilean, Mfon Esang (2019. Performability of Retransmission of Loss Packets in Wireless Sensor Networks. Comput. Inf. Sci. 12(2): 71-86, Canada.
  16. Imeh J. Umoren and Saviour J. Inyang (2021). Methodical Performance Modelling of Mobile Broadband Networks with Soft Computing Model. International Journal of Computer Applications 174(25):7-21, NY, USA.
  17. Ye, Quanmin (2014). Cross-layer schemes for performance optimization in wireless networks (Doctoral Dissertations. 2328.
  18. Yogesh Misra (2012). A review on application of fuzzy logic in increasing the Efficiency of Industrial process” (IJLTET) Vol. 1 ,11 ISSN: 2278- 621X.
Index Terms

Computer Science
Information Sciences

Keywords

Quick Response Time (QRT) Quality of Service (QoS) Packet loss Packet delay and Jitter