Masters Theses
Keywords and Phrases
Agglomerative Clustering; Received signal strength indication; Link Quality Indication
Abstract
"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.
Advisor(s)
Guardiola, Ivan
Committee Member(s)
Shi, Yiyu
Zawodniok, Maciej Jan, 1975-
Department(s)
Electrical and Computer Engineering
Degree Name
M.S. in Electrical Engineering
Publisher
Missouri University of Science and Technology
Publication Date
Spring 2013
Pagination
ix, 49 pages
Note about bibliography
Includes bibliographical references (pages 47-48).
Rights
© 2013 Yanwen Wang, All rights reserved.
Document Type
Thesis - Open Access
File Type
text
Language
English
Subject Headings
Wireless communication systems -- Data processingTelecommunication -- Data processingTelecommunication -- TrafficTopologyData mining
Thesis Number
T 10335
Print OCLC #
860988731
Electronic OCLC #
909400896
Recommended Citation
Wang, Yanwen, "A study of clustering RSSI and LQI from wireless network communications" (2013). Masters Theses. 4461.
https://scholarsmine.mst.edu/masters_theses/4461