Methodologies for measuring IEEE 802.11 networks with application

Pao Yue-kong Library Electronic Theses Database

Methodologies for measuring IEEE 802.11 networks with application


Author: Ng, Yat-fai
Title: Methodologies for measuring IEEE 802.11 networks with application
Degree: M.Sc.
Year: 2008
Subject: Hong Kong Polytechnic University -- Dissertations.
Sensor networks.
IEEE 802.11 (Standard)
Network performance (Telecommunication)
Department: Dept. of Computing
Pages: xvi, 115 leaves : ill. (chiefly col.) ; 30 cm.
Language: English
InnoPac Record:
Abstract: This study is devoted on the effectiveness of various methodologies for measuring IEEE 802.11 network. Besides of the methodology explanation, I have developed a set of new tools for measurement control and data presentation. Also, the new "Auto-Selecting the Rate Adaptation Algorithm" (ASRAA) is designed for automatic / intentional Rate Control Algorithms (RCA) selection in wireless network driver. And, a RCA competition is accomplished for network performance analysis in heavy traffic environment. Most likely, successful network measurement is relied on the effective data capturing method. These captured information are the original source for any decided analysis. Currently, there are two types of capturing arrangement. One is active capturing system which is running in the member node in the measured network. Thus, the capturing overhead might affect precise measurement in that node. The most disadvantage is that it is appliable for domestic network only. The other is passive sniffing system which is running in the additional invisible node for the target network, i.e. no link state is registered in the network. There is no overhead to be added to any former nodes in the original network. It can be applied on any touchable or untouchable network. Because of this, the passive sniffing is focused in this study. The Multiband Atheros Driver for WiFi (Mad-WiFi) [4] for Linux and the AirPcap [1] for Windows are common passive sniffer. The comparison shows that the MadWiFi is capable to capture more data in a congested network. But, the AirPcap is very good for exploring strange network. The "RemoteCall" and "RemoteExecute" are new tools to control large testbed, i.e. network with many nodes. They are worked as client / server structure. Control signal are transmitted via wired IP network that is separated from the measured wireless network. No overhead is added to influence the target network. They are small JAVA program to be run on all common platform. The "RemoteCall" is the centralized controller for test procedure setup and execution trigger. The "RemoteExecute" is actual command setup and execution master on every nodes in the network. Meanwhile, the "XGraph" is a Java based IEEE 802.11 network traffic analyzer, it reads in libpcap formatted output file and generates time-aligned graphs for traffic analysis. Multiple nodes are supported. The graph includes rich information, such as data / ACK packets count, transmission rate, packet loss detect, signal strength and etc. Also, the "Auto-Selecting the Rate Adaptation Algorithm" (ASRAA) will be introduced as a new concept of rate control. Usually, there is a single RCA to be implemented for each network driver. The ASRAA is a combination of multiple RCA logic and provides a policy based selection mechanism to choose a RCA for each transmission. The Auto-RCA-Select engine is counting on statistical status and current application behavior, such as size of data block and TCP / UDP port for application classification. The on-line packet analysis is deployed to predict the outgoing packet pattern which is useful for an appropriate RCA selection for effective transmission. Finally, I use all of above to measure the performance of various Rate Control Algorithms (RCA) in a busy network. It is different from usual performance analysis. They check the RCA against distant or signal strength for peer-to-peer network. I will check for channel competition on multiple nodes network. It is because the single client network is not a practical operational environment. In my study, there are four RCAs to be selected for comparison. They are ARF [7], AMRR [8], ONOE [3], and Sample [6]. ARF is the most referential RCA and is implemented in my testbed. AMRR, ONOE and Sample are built-in RCA for MadWiFi driver. The objective of the measurement is to illustrate their behavior in a congested network under different scenarios, such as all nodes using the same RCA or different RCA for each node. The result shows that the network performance is seriously degraded when four nodes make heavy transmission to wireless access point. It is far from usual expectation for busy wireless network.

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