Abstract
A wireless sensor network (WSN) is a collection of various tiny devices known as sensor nodes, which are also called motes. Due to high-energy consumption, the possibility of hardware, link or node failure, and some malicious attacks, sensor networks are considered error-prone networks. Hence, fault tolerance (FT) in WSN is one of the prominent issues. This article presents a novel FT approach named node-link failure fault tolerance model (NLFFT Model) in WSN, to handle the faults that occur either by link or node failure during data transmission from the sensor to the sink or base station. The NLFFT model consists of an improved quadratic minimum spanning tree (Imp-QMST) approach. This approach helps in finding the alternate link whenever it fails due to various situations and also an improved-handoff (Imp-Handoff) algorithm to support the node failure to the fault tolerance. Improved QMST presents a novel mechanism to find an alternate edge in place of the broken or failed edge in the spanning tree, to improve the fault tolerance in WSN. Imp-Handoff suggests a novel way to find the faulty node owing to less battery power and replaces a defective node by an appropriate neighbor to shift the tasks performed by a faulty node in WSN. Simulation results clearly state that as compared to the basic techniques i.e. Q-MST and Handoff algorithm, the proposed NLFFT model improvises the performance of WSN around by 7%. The results prove that the Imp-QMST gives about 6% improved throughput, 5% less end-to-end delay, and 6% less power consumption than the QMST algorithm. Similarly, Imp-Handoff improves about 4% throughput, 6% less end-to-end delay, and utilizes 7% less power consumption.
Highlights
A wireless sensor network (WSN) comprises of many tiny devices known as sensor nodes and base stations
The sensor nodes in the cluster are connected by hop-to-hop with each other based on the coverage capacity among the existing sensors nodes and each cluster has a cluster header to communicate with other clusters
The clusters are interconnected through its cluster headers and aggregated data from all the clusters is transferred to the base station (BS), to process the information further or transfer to another heterogeneous network
Summary
A WSN comprises of many tiny devices known as sensor nodes and base stations. Such networks are useful for monitoring and passing environmental and physical constraints viz. Four swarm intelligence-based algorithms have been used to generate spanning trees in WSN and the performance for different characteristics, i.e., throughput, end-to-end delay, and power dissipation of proposed methods to existing methods Q-MST [3,4,5] and Handoff [10] has been evaluated.
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