Keywords and Phrases
Agglomerative Clustering; Received signal strength indication; Link Quality Indication
"In today's information world, different wireless sensor network topologies are permitted to exist in the same geographical region. For example, a city contains thousands of enterprise networks; a school might include different wireless network topologies used by students and faculties respectively. Even within a building, there might be multiple companies that use their own wireless networks. These networks might cause interference to other networks. How to detect, determine and locate the unknown wireless topologies in the same geographical area becomes quite significant in the wireless industry, especially in military use, such as spy-nodes detection and communication orientation systems. In this thesis, two easily collectable and available parameters in wireless networking, RSSI and LQI, are used as the test data. These two parameters are recorded from an unknown wireless topology. Then, a shape driven clustering technique - GSD (Granulometric Size Distribution), a statistical distance clustering approach - Agglomerative Clustering technique and a traditional clustering method - PAM Clustering technique are separately explored to classify these RSSI & LQI data into certain number of groups in order to determine the number of active sensor nodes in the unknown wireless topology. The corresponding methodology and theoretical results for each clustering method are presented in this thesis. Matlab, SAS and Mathematica were used to classify the input signal data to prove that RSSI & LQI data are capable to determine the number of active communication nodes in wireless topologies"--Abstract, page iii.
Zawodniok, Maciej Jan, 1975-
Electrical and Computer Engineering
M.S. in Electrical Engineering
Missouri University of Science and Technology
ix, 49 pages
© 2013 Yanwen Wang, All rights reserved.
Thesis - Restricted Access
Wireless communication systems -- Data processing
Telecommunication -- Data processing
Telecommunication -- Traffic
Print OCLC #
Electronic OCLC #
Link to Catalog Record
Electronic access to the full-text of this document is restricted to Missouri S&T users. Otherwise, request this publication directly from Missouri S&T Library or contact your local library.http://merlin.lib.umsystem.edu:80/record=b10115802~S5
Wang, Yanwen, "A study of clustering RSSI and LQI from wireless network communications" (2013). Masters Theses. 4461.
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