A Robust Distributed Clustered Fault-Tolerant Scheduling for Wireless Sensor Networks (RDCFT)

Numerous researchers have suggested numerous solutions based on routing and energy to overcome the prevalence of WSN faults in the current literature. Clustering is the most commonly used energy-efficient and fault-tolerant management technique in WSNs, grouping sensors to manage them and perform distributed activities such as resource management. Although clustering methods are most commonly used to reduce energy consumption, they can also achieve various quality-driven goals, such as network fault tolerance capability. As a result of WSN's unpredictable nature, developing fault tolerance mechanisms is essential. Redundancy and diversity methods are commonly used in networks to increase fault tolerance. As a network management challenge, clustering methods should tolerate malfunctioning sensors while maintaining the network's coverage and stability. Sensors are prone to failure due to their operation in hostile environments with limited battery power. The state of the deployed sensor or cluster head (CH) can be active, redundant, or dead/faulty for the same target region of interest (R). The network's overall lifetime and fault tolerance capability can also be extended by using these backup sensors. By putting redundant sensors into sleep mode, can extend the network lifetime without compromising the quality of service. Sensor networks are generally designed to endure far longer than individual sensors. It is commonly performed by putting sensors to sleep. We propose a robust distributed clustered fault tolerance scheduling (RDCFT) approach that efficiently checks the status of the deployed sensor periodically (based on a sweep-line redundancy check algorithm) in WSNs. Simulation results are based on analytical and experimental studies to validate the effectiveness and usefulness of the proposed work.