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Network coding via evolutionary algorithms
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Karunarathne, Lalith (2012) Network coding via evolutionary algorithms. PhD thesis, University of Warwick.
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Official URL: http://webcat.warwick.ac.uk/record=b2684519~S1
Abstract
Network coding (NC) is a relatively recent novel technique that generalises
network operation beyond traditional store-and-forward routing, allowing
intermediate nodes to combine independent data streams linearly. The rapid
integration of bandwidth-hungry applications such as video conferencing and HDTV
means that NC is a decisive future network technology.
NC is gaining popularity since it offers significant benefits, such as throughput
gain, robustness, adaptability and resilience. However, it does this at a potential
complexity cost in terms of both operational complexity and set-up complexity. This
is particularly true of network code construction.
Most NC problems related to these complexities are classified as non
deterministic polynomial hard (NP-hard) and an evolutionary approach is essential to
solve them in polynomial time. This research concentrates on the multicast scenario,
particularly: (a) network code construction with optimum network and coding
resources; (b) optimising network coding resources; (c) optimising network security
with a cost criterion (to combat the unintentionally introduced Byzantine
modification security issue).
The proposed solution identifies minimal configurations for the source to deliver
its multicast traffic whilst allowing intermediate nodes only to perform forwarding
and coding. In the method, a preliminary process first provides unevaluated
individuals to a search space that it creates using two generic algorithms (augmenting
path and linear disjoint path. An initial population is then formed by randomly
picking individuals in the search space. Finally, the Multi-objective Genetic
algorithm (MOGA) and Vector evaluated Genetic algorithm (VEGA) approaches
search the population to identify minimal configurations. Genetic operators
(crossover, mutation) contribute to include optimum features (e.g. lower cost, lower
coding resources) into feasible minimal configurations. A fitness assignment and
individual evaluation process is performed to identify the feasible minimal
configurations. Simulations performed on randomly generated acyclic networks are used to
quantify the performance of MOGA and VEGA.
Item Type: | Thesis (PhD) | ||||
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Subjects: | Q Science > QA Mathematics > QA76 Electronic computers. Computer science. Computer software T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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Library of Congress Subject Headings (LCSH): | Coding theory, Computer networks -- Mathematical models, Data transmission systems, Evolutionary computation | ||||
Official Date: | November 2012 | ||||
Dates: |
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Institution: | University of Warwick | ||||
Theses Department: | School of Engineering | ||||
Thesis Type: | PhD | ||||
Publication Status: | Unpublished | ||||
Supervisor(s)/Advisor: | Leeson, Mark S., 1963- | ||||
Sponsors: | Engineering and Physical Sciences Research Council (EPSRC) | ||||
Extent: | xvi, 218 leaves : illustrations, charts. | ||||
Language: | eng |
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