## 1 Introduction  
Wireless sensor networks (WSNs) have a large number of sensory devices that are deployed in an area and integrated to work wirelessly together. As a result, implementing a sensor node is a key difficulty in WSNs. This has a significant impact on efficiency and costs. So, when we deploy a sensor node, we must consider factors such as the deployment's goal, as well as the topography and coverage capability of the sensor node. A WSN's primary goal is to provide broad coverage with as few sensor nodes as possible. Wireless communication has been around for a long time, dating back to the first infrared link. People have been attempting to develop wireless connections between electronic items and computers for a long time. Due to the rapid development of compact, dense radio-frequency integrated circuits (RFICs) and filters during the last five to ten years. In addition to scientific advances in spread-spectrum technologies, many wireless technology standards have been developed, allowing many manufacturers and implementers to achieve interoperability between many devices [1]. In the subject of comprehensive monitoring, habitat monitoring often entails continuous operation for many months, whereas structural monitoring (such as bridge monitoring) may take several years. A recent study has demonstrated that these wireless networks can cause numerous system problems in dispersed networks, known as Byzantine flows. A system issue can be identified by the fact that some network nodes fail to react or respond with inaccurate information. This can be accomplished by dynamically adjusting node duty cycles in dense sensor networks. As a result, many academics have specialized in putting specific nodes into hibernation (or failure-mode coverage) while the remaining active nodes continue to provide uninterrupted service. A fundamental issue is minimizing the number of active nodes while maintaining an adequate level of service for applications. Maintaining enough sensing coverage and network connectivity with active nodes, in particular, are critical criteria in sensor networks [2]. A system error is a circumstance that infects an operating system, particularly a distributed computer system, in which a component may fail and information regarding whether or not a component has failed is insufficient. The word is derived from the story \"The Problem of the Byzantine Generals\" [3], which was designed to illustrate a circumstance in which the hardware components of the system have failed. A chief node can exist in both failed and working failure-detection systems, displaying various symptoms to different observers. It is difficult for the other components to announce it failed and disconnect it from the network since they must first agree on which component failed. As a result, system fault tolerance (SFT) refers to a fault-tolerant network system's resistance to such scenarios [4]. As a result, typical WSN research has focused on network configuration, network operations, and performance evaluation of WSNs that execute the primary function. The Internet of Things (IoT) is becoming a reality as sensor technology advances, allowing the installation of inexpensive, compact, and computationally efficient sensor devices. Although collecting and monitoring sensor data does not necessitate considerable local data processing for decision-making at sensor nodes, some new WSN applications necessitate fast and dependable device decision-making. Because of the large number of IoT devices that require high bandwidth and cause network connectivity challenges, on-device self-configuration eventually leads to increased computing capabilities of sensor nodes to supplement local data monitoring. Efforts are being made in the proposed self-configuration management (SCM) to use fault tolerance mechanisms to restore the network to its original state. This study will address the programmatic platform for WSNs as follows: (1) Self-detection for infected device nodes in each cluster.