Network coding has demonstrated to be very effective in increasing wireless network capacity and reliability. However, the benefits of network coding are only applicable in networks consisting of reliable and trustworthy nodes. Non-negligible network coding (NC) noise and network pollution can cause substantial performance decrease. There is a cogent need for adaptive network coding schemes that can optimize network level throughput by exploiting NC noise and NC pollution. This research develops efficient adaptive model that can provide optimal network level throughput by designing of adaptive network coding schemes, pollution detection model, and joint treatment of coding and dynamic routing. The overall objective is threefold. First, it develops adaptive characteristic model and efficient estimation on the NC noise and NC pollution. Based on this, novel on-the-fly adaptive network coding schemes are developed. Second, this research develops theoretical characterization and effective pollution detection and immunization schemes in relay networks through source privacy protection. Third, this research establishes the mathematical structure for the wireless relay node, and designs cost function for optimal throughput computation. Based on this, provably optimal algorithms with polynomial time complexity are developed under the given pollution constraint and security requirement. In addition, this project also includes a significant education component aimed at integrating frontier research with undergraduate and graduate curricula.

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Michigan State University
East Lansing
United States
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