On-demand multipath protocol with adaptive routing method by back propagation neural networks
by Fang Jing, R.S. Bhuvaneswaran, Yoshiaki Katayama, Naohisa Takahashi
International Journal of Communication Networks and Distributed Systems (IJCNDS), Vol. 3, No. 1, 2009

Abstract: Two representative multipath routing protocol strategies have been utilised currently, one is described as primary routing protocol and the other is load-balancing protocol. In the former routing protocol, the source selects the pre-computed shortest or fastest route as the primary one for the following data transmission. On the other hand, the latter routing protocol utilises multiple routes in rotation to equalise transmission load in the network. However, these solutions suffer during high mobility since they are lacking in global perspective for the following data transmission, resulting in low packet delivery ratio and prolonged delay time. Hence, to find the optimum routing protocol strategy, we present an adaptive multipath routing protocol by means of applying golden section search and back propagation neural networks. We evaluated our protocol using Omnet simulator. Simulation results show that the proposed solution has a good real-time performance than those of other protocols.

Online publication date: Sun, 21-Jun-2009

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