Title

Outliers in Wireless Sensor Networks

Presenter Information

Ryan Birmingham

Department

Computer Science

Major

Computer Science and Computer Engineering

Research Advisor

Madria, Sanjay Kumar
McDonald, Dylan

Advisor's Department

Computer Science

Abstract

Detecting outliers has been a well studied problem in many applicable fields of research. Yet, wireless sensor networks present challenges because of their limited resources and unique need for real-time detection. Wireless sensor networks are deployed for a variety of reasons, and whether they are being used by the military, as weather sensors, or for another use, accuracy is always a necessity. With our research we examine the existing methods for outlier detection in wireless networks and propose new directions for future research. We not only will present a method to more confidently predict what data is an outlier, but to also find this information with as little delay as possible.

Biography

Ryan Birmingham is a dual major in Computer Science and Computer Engineering at the Missouri University of Science and Technology. He is participating in undergraduate research with Dr. Sanjay Madria and Dylan McDonald. This summer he will be participating in a NSF REU research program.

Research Category

Sciences

Presentation Type

Poster Presentation

Document Type

Poster

Location

Upper Atrium/Hallway

Presentation Date

07 Apr 2010, 9:00 am - 11:45 am

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Apr 7th, 9:00 AM Apr 7th, 11:45 AM

Outliers in Wireless Sensor Networks

Upper Atrium/Hallway

Detecting outliers has been a well studied problem in many applicable fields of research. Yet, wireless sensor networks present challenges because of their limited resources and unique need for real-time detection. Wireless sensor networks are deployed for a variety of reasons, and whether they are being used by the military, as weather sensors, or for another use, accuracy is always a necessity. With our research we examine the existing methods for outlier detection in wireless networks and propose new directions for future research. We not only will present a method to more confidently predict what data is an outlier, but to also find this information with as little delay as possible.