NLFFT: A Novel Fault Tolerance Model Using Artificial Intelligence to Improve Performance in Wireless Sensor Networks

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.

Keywords

• Fault tolerance
• handoff mechanism
• MST
• Q-MST
• swarm intelligence
• WSN