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Shojafar, M., Abawajy, J.H., Delkhah, Z., Ahmadi, A., Pooranian, Z. and Abraham, A. (2013) An Efficient and Distributed File Search in Unstructured Peer-to-Peer Networks. Peer to Peer Network Application, 8, 120-136.
http://dx.doi.org/10.1007/s12083-013-0236-0

has been cited by the following article:

  • TITLE: Highly Available Hypercube Tokenized Sequential Matrix Partitioned Data Sharing in Large P2P Networks

    AUTHORS: C. G. Ravichandran, J. Lourdu Xavier

    KEYWORDS: Peer-to-Peer (P2P), Video-on-Demand, Hypercube, Sequential Matrix Partition, Data Mapping, Data Availability

    JOURNAL NAME: Circuits and Systems, Vol.7 No.9, July 8, 2016

    ABSTRACT: Peer-to-peer (P2P) networking is a distributed architecture that partitions tasks or data between peer nodes. In this paper, an efficient Hypercube Sequential Matrix Partition (HS-MP) for efficient data sharing in P2P Networks using tokenizer method is proposed to resolve the problems of the larger P2P networks. The availability of data is first measured by the tokenizer using Dynamic Hypercube Organization. By applying Dynamic Hypercube Organization, that efficiently coordinates and assists the peers in P2P network ensuring data availability at many locations. Each data in peer is then assigned with valid ID by the tokenizer using Sequential Self-Organizing (SSO) ID generation model. This ensures data sharing with other nodes in large P2P network at minimum time interval which is obtained through proximity of data availability. To validate the framework HS-MP, the performance is evaluated using traffic traces collected from data sharing applications. Simulations conducting using Network simulator-2 show that the proposed framework outperforms the conventional streaming models. The performance of the proposed system is analyzed using energy consumption, average latency and average data availability rate with respect to the number of peer nodes, data size, amount of data shared and execution time. The proposed method reduces the energy consumption 43.35% to transpose traffic, 35.29% to bitrev traffic and 25% to bitcomp traffic patterns.