Anaysis of Controller Based IEEE 802.11 System with Similarity Measure Clustering Evaluation of Channel Allocation Efficiency by Change Detection

被引:0
作者
Gal, Zoltan [1 ]
Terdik, Gyorgy [1 ]
机构
[1] Univ Debrecen, Fac Informat, Debrecen, Hungary
来源
2017 5TH INTERNATIONAL SYMPOSIUM ON DIGITAL FORENSIC AND SECURITY (ISDFS) | 2017年
关键词
Clustering; Complex Event Processing; Davies-Bouldin index; Hurst exponent; Internet of Things; Self-Similarity; Sensor Network; Special Event Detection; WiFi LAN;
D O I
暂无
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
The efficiency of a WiFi system with dozens of base stations in relatively small physical area is determined by the optimal allocation of the radio channels to the mobile devices. Based on the increased penetration rate of the high traffic capable smartphones and accentuated usage of these devices in densely populated buildings intelligent hardware tools are needed to offer QoS level to the users. The Radio Resource Management (RRM) of IEEE 802.11 network is provided by a wireless LAN controller. This node maintains an allocated control connection to each of the base stations providing enhanced quality level of the WiFi hot zone services. Modern base stations have the capability to listen to the radio channels periodically to detect the received signal intensity. The controller samples periodically each of the radio channels without affecting the own radio frame processing and collects in this way radio resource usage. Based on specific criteria like the number of active nodes, traffic intensity, interference, noise intensity the periodically executed RRM management algorithm modifies the distribution of active channels on the supervised hot zone level. The radio signal intensity scanning task is considered to be performed by the base stations as a sampling process of individual sensors distributed in physical space. Eighteen WiFi access points with thirteen channel sensors in 2.4 GHz range and sixteen channel sensors in 5 GHz range were used to capture radio signal intensity in a densely populated building. Holding special criteria in the scanned signal intensity values is considered as a complex event. Clustering method based on similarity measure was used to analyse sensor grouping behavior of the wireless controller RRM algorithm. Our work is focused on the usability of different statistical metrics (i.e. Hurst, Davies-Bouldin, etc.) to characterize intrusion detection efficiency of the wireless LAN controller.
引用
收藏
页数:6
相关论文
共 10 条
  • [1] Using self-similarity to cluster large data sets
    Barbará, D
    Chen, P
    [J]. DATA MINING AND KNOWLEDGE DISCOVERY, 2003, 7 (02) : 123 - 152
  • [2] A Novel Distance for Clustering to Support Mixed Data Attributes and Promote Data Reliability and Network Lifetime in Large Scale Wireless Sensor Networks
    Devi, N. Chitra
    Palanisamy, V.
    Baskaran, K.
    Prabeela, S.
    [J]. INTERNATIONAL CONFERENCE ON COMMUNICATION TECHNOLOGY AND SYSTEM DESIGN 2011, 2012, 30 : 669 - 677
  • [3] Outliers detection and classification in wireless sensor networks
    Fawzy, Asmaa
    Mokhtar, Hoda M. O.
    Hegazy, Osman
    [J]. EGYPTIAN INFORMATICS JOURNAL, 2013, 14 (02) : 157 - 164
  • [4] Fernandes R., 2016, P 8 INT C UB FUT NET, P1
  • [5] Gal Z., 2013, 2013 IEEE 11th International Symposium on Intelligent Systems and Informatics (SISY), P215, DOI 10.1109/SISY.2013.6662573
  • [6] Multifractal study of Internet traffic
    Gál, Z
    Terdik, G
    Iglói, E
    [J]. 2001 IEEE WORKSHOP ON HIGH PERFORMANCE SWITCHING AND ROUTING, 2001, : 197 - 201
  • [7] Clustering of Complex Data-sets using Fractal Similarity Measures and Uncertainties
    Hoecker, Maximilian
    Polsterer, Kai Lars
    Kuegler, Sven Dennis
    Heuveline, Vincent
    [J]. 2015 IEEE 18TH INTERNATIONAL CONFERENCE ON COMPUTATIONAL SCIENCE AND ENGINEERING (CSE), 2015, : 82 - 91
  • [8] Indu Y., 2016, INT J ENG TECHNOLOGY, V8, P1095
  • [9] Saitta S, 2007, LECT NOTES ARTIF INT, V4571, P174
  • [10] Levy Flights and Fractal Modeling of Internet Traffic
    Terdik, Gyoergy
    Gyires, Tibor
    [J]. IEEE-ACM TRANSACTIONS ON NETWORKING, 2009, 17 (01) : 120 - 129