A fault tolerance based route optimisation and data aggregation using artificial intelligence to enhance performance in wireless sensor networks
by Vinod Kumar Menaria; S.C. Jain; A. Nagaraju
International Journal of Wireless and Mobile Computing (IJWMC), Vol. 14, No. 2, 2018

Abstract: In the on-demand usage of wireless sensor networks over the internet, fault tolerance is an exigent task to improve the overall performance of service computing. In the proposed research work, an attempt has been made to make use of an artificial bee colony approach to find data aggregation for providing fault tolerance in wireless sensor networks (WSNs) and to make effective utilisation of the existing resources over the internet. In this paper, it is tried to apply quadratic minimum spanning tree (Q-MST) which is an artificial intelligence technique to provide fault tolerance along with data aggregation in WSN. Q-MST is used to improve the fault tolerance in WSN to transmit data packets from the source node to sink node. Ant colony, PRIMS and Particle Swarm Optimisation (PSO) algorithms are used for generating minimum spanning tree (MST) which can be used for data aggregation. The Q-MST is an improved version of minimum spanning tree where an ordered pair of distinct edges would be considered for implementing an alternative edge for the existing edge failure in MST.

Online publication date: Wed, 11-Apr-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 Wireless and Mobile Computing (IJWMC):
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