A fault tolerance based route optimisation and data aggregation using artificial intelligence to enhance performance in wireless sensor networks

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.