Resilient Average and Distortion Detection in Sensor Networks

In this paper a resilient sensor network is built in order to lessen the effects of a small portion of corrupted sensors when an aggregated result such as the average needs to be obtained. By examining the variance in sensor readings, a change in the pattern can be spotted and minimized in order to maintain a stable aggregated reading. Offset in sensors readings are also analyzed and compensated to help reduce a bias change in average. These two analytical techniques are later combined in Kalman filter to produce a smooth and resilient average given by the readings of individual sensors. In addition, principal components analysis is used to detect variations in the sensor network. Experiments are held using real sensors called MICAz, which are use to gather light measurements in a small area and display the light average generated in that area.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Computer Science

Year

2009

Author dc:creator

• Aguirre Jurado, Ricardo

Contributors dc:contributor

• Richard III, Golden G.
• Nino, Jaime
• Abdelguerfi, Mahdi

• Sensors
• Correlation
• Kalman Filter
• Principal Component Analysis
• PCA

Repository record dc:identifier

https://scholarworks.uno.edu/td/962

OAI identifier oai:identifier

oai:scholarworks.uno.edu:td-1943