Accession Number : ADA310932
Title : Robust Transfiguring Network Protocols.
Descriptive Note : Final technical rept. Mar 92-Mar 95,
Corporate Author : SRI INTERNATIONAL MENLO PARK CA
Personal Author(s) : Ogler, Richard G. ; Wong, Julie S. ; Khan, Irfan H.
PDF Url : ADA310932
Report Date : APR 1996
Pagination or Media Count : 35
Abstract : In RTNP, we have developed a protocol that uses two artificial intelligence methods, neural networks and evidential reasoning, to recognize and predict adverse network conditions, and that uses fuzzy logic to dynamically control the parameters of a tunable routing protocol in response to the perceived environment. Examples of the tunable protocol parameters are: (1) a parameter that controls the degree to which traffic is spread over multiple paths; (2) a link bias parameter that, when large, increases stability by forcing traffic over minimum-hop paths; and (3) a parameter that determines how often routing updates are sent. Examples of measurements used to recognize adverse conditions are: (1) congestion; (2) probability of a successful transmission on a link; (3) jamming characteristics; and (4) degree of routing oscillations. Neural network methods were developed for predicting link-states and congestion, based on network measurements and estimates. These methods were shown in simulations to predict link states and queuing delay much more accurately than other methods.
Descriptors : *NEURAL NETS, *DATA TRANSMISSION SYSTEMS, *ARTIFICIAL INTELLIGENCE, COMPUTER PROGRAMS, ALGORITHMS, COMPUTERIZED SIMULATION, MEASUREMENT, QUEUEING THEORY, REASONING, MATHEMATICAL LOGIC, PATHS, ADVERSE CONDITIONS, DATA ACQUISITION, DELAY, TUNING, JAMMING, FUZZY SETS, ROUTING, OSCILLATION.
Subject Categories : Computer Systems
Distribution Statement : APPROVED FOR PUBLIC RELEASE