A swarm intelligence learning model of adaptive incentive protocols for P2P networks
by Zheng Wang
International Journal of Communication Networks and Distributed Systems (IJCNDS), Vol. 20, No. 2, 2018

Abstract: Incentive protocols are critical for promoting contribution and cooperation among peers in P2P networks. The behaviour of peers has a significant impact on the effects of incentive protocols. Inspired by the biological systems, a swarm intelligence learning model of adaptive incentive protocols is proposed for P2P networks. The learning model is designed by having peers as particles in the moving swarm. The learning and adaption of peers are guided by the current best strategy as well as the best strategy in history. Simulation results demonstrate that the proposed learning model has a faster convergence rate towards at least the quasi-optimum than the two existing learning models.

Online publication date: Fri, 09-Feb-2018

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Communication Networks and Distributed Systems (IJCNDS):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com