Identifying Wi-Fi Interference by End-Users Richard Meng, Xiang
Identifying Wi-Fi Interference by End-Users
Richard Meng, Xiang Ying Qian, Kyung-Hwa Kim, Henning Schulzrinne
Dept. of CS and EE, Columbia University in the City of New York
Motivation & Background
Identifying causes of WLAN performance degradation is
Most access points (IEEE 802.11b / IEEE 802.11g) are
deployed in the 2.4GHz wireless band, which causes
Most significant interference sources:
Difficult for end-users to identify the devices that cause
Additional hardware is required (e.g., Wi-Spy)
Monitors RF activity within a given frequency range
(Wi-Spy by Metageek, $84, metageek.net)
Our goal is to identify the source of Wi-Fi interference
without any hardware support.
Monitor and analyze the patterns of various
parameters related to Wi-Fi performance.
Obtain data from other collaborative nodes to see
whether others also observe the same interference
Train the pattern analyzer with the dataset obtained
from different environments and nodes
Provide users the best matched devices that are
supposed to cause the interference
Is there a microwave
oven nearby your
Is there a Bluetooth
device nearby your
In our experiment, Bluetooth devices and microwave
ovens showed different patterns (e.g., the magnitude of
standard deviation of retry count, percentage of ACK
failures, i.e. microwave oven tends to cause more
unstable network condition)
This result enables to identify some interference
However, the patterns are difficult to be resolved by
A Machine learning method is needed to achieve more
accurate identification of interference sources
count per frame
Existing waves makes measuring interference difficult
We measured network throughput, SNR, 802.11 retry
counter, and other variables to infer a characteristic of
On Linux: Analyze the information of Radiotap 802.11
header in the data link frames captured by integrating
Wireshark, Jpcap, and Alpacka library.
On Windows: Analyze built-in parameters in network
systems collected by Windows Native Wi-Fi API.
802.11g Cisco Linksys AP (Channel 1,6,7)
Measurement on laptops
Experiment with and without microwave ovens /
Sharing information using DYSWIS framework
Scale factor of small to large =23 =46 Scale factor of large to small =64 = 32 Core Lesson. The Scale Factor is the ratio of the lengths of corresponding sides in similar figures. Consider these two similar parallelograms. Since...
An additional son of Chief, Valiant, was not heterozygous for the putative mutation, but was included in the study as a control. The genomes of Chief, Ivanhoe Chief and Valiant were sequenced using sequencing by synthesis chemistry on an Illumina...
Planck's Hypothesis. In 1900 Max Planck proposed a formula for the intensity curve which did fit the experimental data quite well. He then set out to find a set of assumptions -- a model -- that would produce his formula.
The "Total Score" column shows numerical values (0-10) that help describe the magnitude of an overall threat rank at a finer level than is given by the Very High, High, Medium, and Low ranks. These scores are used for creating...
Natural Language Semantics for Musicologists. Winfried Lechner. National and Kapodistrian University of Athens. Lecture. series . Language & Music. National and Kapodistrian University of Athens June 12, 2019
12th Grade English Literature Vocab. Week 2 buffoon The girl was embarrassed by her father who was clowning around and acting as a buffoon. buffoon a person who amuses others by tricks, jokes, odd gestures and postures, etc. Synonym clown,...
Beneficial when feeding Beneficial when mating Fishes - Biology Migration Generally related to feeding and/or reproduction Diel Horizontal Ex: Grunts (day on reef, night feeding in seagrass beds) Vertical Ex: Mesopelagic fishes Large Scale Ex: Skipjack tuna feed in Eastern...
Ready to download the document? Go ahead and hit continue!