Open AccessBiased contribution index: a new faster convergent index to maintain the fairness in peer‐to‐peer networks
Author(s) -
Awasthi S.K.,
Singh Y.N.
Publication year - 2018
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
ISSN - 1350-911X
DOI - 10.1049/el.2018.5649
Subject(s) - upload , computer science , convergence (economics) , download , index (typography) , mechanism (biology) , peer to peer , incentive , distributed computing , value (mathematics) , mathematical optimization , computer network , mathematics , machine learning , microeconomics , world wide web , economics , philosophy , epistemology , economic growth
The free‐riding and large difference between upload and download amount of resources is a fundamental problem in a peer‐to‐peer network. An incentive mechanism, which can be implemented in a distributed fashion, can solve this problem. Global contribution (GC) approach is one such mechanism, but its speed of convergence is slow. This letter proposes a new index named biased contribution index (BCI). It is proved that BCI always converges at a certain value. Simulation results show that it converges faster than the GC.