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Mr. Anas Abu Dagga, Full-Time COE PhD Student, will defend his PhD Dissertation (ONLINE) on Wednesday, December 09, 2020 at 08:00 a.m. His PhD dissertation title is " CODING-BASED PEER-TO-PEER CONTENT DISTRIBUTION NETWORK." His dissertation advisor is "Dr. Ashraf Mahmoud, Associate Professor, COE Department." You are cordially invited to attend by using the following link ( https://bit.ly/36nlvEf )
Abstract:                                                                                                                                                                                                                                              Network coding (NC) is known as a helpful method for increasing the content availability, accelerating the download process, and robustness against churn in P2P content distribution networks. In the first part of this work, an improved applicable network coding scheme referred to as Super Generation Network Coding (SGNC) is proposed. SGNC maximizes the generation size so that it is as close as possible to the optimal size without adding computational overhead. Theoretical analysis and experimental work show that SGNC outperforms classical and all previous coding based schemes for P2P content distribution systems in terms of content availability, download time, overhead, and decodability for all piece scheduling policies. In the second part, the problem of NC coefficients overhead is revisited. A novel approach based on modular arithmetic and prime numbers called One Item Network Coding Vector (OINCV) is proposed to reduce the coefficients overhead by augmenting only one item coefficient of size two or maximum four bytes within the payload packet. OINCV successfully addresses all limitations of the previous methods including the limitations on the generation size and density of the packets within the generation, recoding at the intermediate nodes, and creating innovative coding vectors. Probabilistic theoretical analysis proves OINCV viability and reliability. Experimentally, the results show that OINCV far outperforms all the previous methods in terms of coding coefficients overhead, download time, throughput, number of dropped packets at the receiver side.    

Expiry: 10 Jan 2021