Fault Tolerance Mechanism Combining Static Backup and Dynamic Timing Monitoring for Cluster Heads

Abstract

Internet of things (IoT) monitoring systems have been extensively applied in smart homes, underwater monitoring, volcano monitoring, and health monitoring. In IoT applications, a wireless sensor network (WSN) is deployed to collect data. Hierarchical routing protocols that effectively maintain the energy consumed by sensor nodes (SNs) are usually employed in WSNs. Cluster heads (CHs) are important in this type of protocol. An effective fault tolerance mechanism for CHs in this system can guarantee reliable data acquisition. In this paper, a fault tolerance mechanism that combines CH static backup and dynamic timing monitoring (SBDTM) is proposed, a CH reliability model based on the Markov model is developed, and the minimum number of CHs necessary to satisfy the given reliability requirement is obtained. The data structures and fault-tolerant operations are described, and the energy consumption and the latency of the recovery of the SBDTM mechanism are quantitatively analysed. Simulations were carried out to compare the total network energy consumption, number of dead nodes, throughput, and packet loss rate of the proposed model with those of other methods presented in the literature. The simulation results show that the proposed SBDTM fault tolerance mechanism is superior to current models. This study presents important theoretical and application-based knowledge that can guarantee reliable data acquisition for IoT-based monitoring systems.