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An Effective and Secure Data Sharing in P2P Network Using Biased Contribution Index Based Rumour Riding Protocol (BCIRR)
Optical Memory and Neural Networks ( IF 1.0 ) Pub Date : 2020-12-23 , DOI: 10.3103/s1060992x20040104
Dharmendra Kumar , Mayank Pandey

Abstract

Data sharing in the Peer to Peer (P2P) networks became an important function in the trustworthy computing. Secure and load balancing control in file sharing is vital to enhance the overall performance of P2P file sharing system. In literature many methods of load balancing control and security control have been used but it is not able to attain the best results in P2P networks. Hence, in this paper, Biased Contribution Index Based Rumour Riding Protocol (BCIRR) is developed to attain the load balancing control and security enhancement of P2P networks. The proposed method is concentrated to achieve two main objective function such as load balancing control and security enhancement. The proposed protocol is a combination of Biased Contribution Index (BCI) and Enhanced Rumour Riding protocol (ERR). Here, load balancing control of the P2P network is attained with the utilization of the BCI and security enhancement of the P2P network is attained with the utilization of the ERR. The proposed protocol will be implemented in the Matlab platform and the performance of the proposed protocol is analysed with different performance metrics such as Packet loss, Delivery ratio, Average end to end delay and Throughput. To analysis the effectiveness of proposed method, it will be compared with the existing method of Catching Algorithms (CA).



中文翻译:

使用基于偏贡献指数的谣言骑马协议(BCIRR)在P2P网络中进行有效且安全的数据共享

摘要

对等(P2P)网络中的数据共享成为可信赖计算中的重要功能。文件共享中的安全和负载平衡控制对于增强P2P文件共享系统的整体性能至关重要。在文献中,已经使用了许多负载平衡控制和安全控制方法,但是在P2P网络中无法获得最佳效果。因此,在本文中,开发了基于偏见贡献指数的谣言骑马协议(BCIRR)以实现P2P网络的负载平衡控制和安全性增强。所提出的方法集中于实现两个主要目标功能,例如负载平衡控制和安全性增强。拟议的协议是偏见贡献指数(BCI)和增强的谣言骑马协议(ERR)的组合。这里,通过使用BCI可以实现P2P网络的负载平衡控制,并且可以通过使用ERR来实现P2P网络的安全性增强。拟议的协议将在Matlab平台中实施,并使用不同的性能指标(例如数据包丢失,传输率,平均端到端延迟和吞吐量)来分析拟议协议的性能。为了分析所提出方法的有效性,将其与现有的“捕获算法”(CA)方法进行比较。平均端到端延迟和吞吐量。为了分析所提出方法的有效性,将其与现有的“捕获算法”(CA)方法进行比较。平均端到端延迟和吞吐量。为了分析所提出方法的有效性,将其与现有的“捕获算法”(CA)方法进行比较。

更新日期:2020-12-23
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