An adaptive cellular automata scheme for diagnosis of fault tolerance and connectivity preserving in wireless sensor networks

Abstract

Wireless sensor networks (WSN) construct the infrastructure for a wide collection of applications concerning security, military services, and monitoring. Fault control and tolerance in WSN is a complicated issue since nodes in WSNs are susceptible to come to be a failure due to energy deterioration, hardware malfunction, communication link mistakes, and harmful attacks. As a result, fault tolerance can be one of the vital aspects in WSNs. One of the successful strategies to regulate fault tolerate is with the aid of clustering. In this paper, we propose a novel idea of an Irregular Cellular Automata (ICA) model in WSN to identify the faulty nodes based on clustering and cluster-head selection without consuming massive computational overhead and bandwidth. The suggested system is based on a node’s data structure that contains many parameters such as node energy level, a number of neighbors, coverage and connectivity variables. This data structure contributes to the selection of cluster head based on a set of rules inside ICA so that when the cluster head breaks down, it is going to be replaced by one more supplementary cluster head with the most affordable cost, and the route will come to be re-organized. Compared with existing approaches in which the re-clustering step can be commonly worked regularly in the full network, re-clustering step in the recommended approach can be ran locally anytime it is actually projected. Simulation outcomes illustrate that this kind of schemes possesses a substantial energy preservation, a low end-to-end packet transport latency, and a confident network lifetime ratio.