Graduation Year

2006

Document Type

Thesis

Degree

M.S.Cp.E.

Degree Granting Department

Computer Science and Engineering

Major Professor

Robin Murphy, Ph.D.

Co-Major Professor

Miguel Labrador, Ph.D.

Committee Member

Phillip DuMas

Keywords

teleoperation, frame rate, bandwidth regulation, human factors, user contention

Abstract

Robot and sensor networks are needed for safety, security, and rescue applicationssuch as port security and reconnaissance during a disaster. These applications rely on realtimetransmission of images, which generally saturate the available wireless networkinfrastructure. Knowledge-based Compression is a strategy for reducing the video frametransmission rate between robots or sensors and remote operators. Because images mayneed to be archived as evidence and/or distributed to multiple applications with differentpost processing needs, lossy compression schemes, such as MPEG, H.26x, etc., are notacceptable. This work proposes a lossless video server system consisting of three classesof filters (redundancy, task, and priority) which use different levels of knowledge (localsensed environment, human factors associated with a local task, and relative globalpriority of a task) at the application layer of the network. It demonstrates the redundancyand task filters for realistic robot search scenarios. The redundancy filter is shown toreduce the overall transmission bandwidth by 24.07% to 33.42%, and when combinedwith the task filter, reduces overall transmission bandwidth by 59.08% to 67.83%. Byitself, the task filter has the capability to reduce transmission bandwidth by 32.95% to33.78%. While Knowledge-based Compression generally does not reach the same levels ofreduction as MPEG, there are instances where the system outperforms MPEG encoding.

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