A wireless sensor network is a network formed through self-organization by a large number of low-cost sensor nodes capable of sensing, data processing, and wireless communication [1]. Operating independently of infrastructure such as base stations or mobile routers, these nodes self-organize into a network using specific distributed protocols. The network collaboratively monitors, senses, and collects information regarding the environment or specific targets within its coverage area and processes this data, enabling users to access extensive, detailed, and reliable information anytime, anywhere, and under various conditions—particularly in scenarios where only wireless communication is feasible. Consequently, such network systems find wide application in fields including national defense and security, environmental monitoring, traffic management, healthcare, manufacturing, counter-terrorism, and disaster relief.

  As the scope of wireless sensor network applications continues to expand, these networks are frequently deployed in extreme environments to collect data. Due to the limited power, storage, and computational capabilities of sensor nodes—combined with harsh operating conditions—these nodes are more prone to failure than those in traditional networks. Maintaining high-quality service while minimizing energy consumption under such circumstances presents a significant challenge, and effective fault management plays a crucial role in achieving these objectives. Consequently, fault management in wireless sensor networks is of great importance.

  When a network or system failure occurs, network fault management becomes the primary tool for administrators; consequently, fault management is arguably the most critical aspect of overall network management.
  However, the situation is complicated by the fact that network faults involve equipment from various vendors and of different types, complex network topologies, and differing criteria used by various organizations to classify fault types.

 From the user's perspective, the goal is for network operations to run smoothly in daily life and work, ensuring that information transmission remains uninterrupted by network failures. Conversely, from the perspective of network operators and administrators, the priority is to rapidly identify the root cause of any failure that occurs during network operation. These various factors have contributed to the relatively slow pace of research into fault management for wireless sensor networks in recent years. Drawing on fault management practices in traditional networks, the following section outlines fault management in wireless sensor networks by categorizing it into three distinct stages: fault detection, fault diagnosis, and fault recovery .

  To identify the existence of a fault, it is necessary to collect data regarding the network's status. Generally, when a network fault occurs, network devices enter an abnormal state; by obtaining device status information, faults can be detected promptly. There are two methods for collecting network status information: devices reporting critical network events to the management system, and the network management system periodically querying the status of network devices—a process known as active polling.
  Typically, network management systems employ a combination of these two methods. When the status of network components is monitored, minor or simple faults are usually recorded in error logs without requiring special handling, whereas more serious faults must be reported via the network manager—a process referred to as "alarming."

  Based on the location of the entity performing the fault detection, fault detection methods for wireless sensor networks can be classified into centralized and distributed approaches.

  Wireless sensor networks (WSNs) are widely applied, and since they are generally considered to constitute the foundational layer of the Internet of Things (IoT), research into WSNs offers valuable guidance for practical applications. This paper classifies, describes, and analyzes fault detection methods for wireless sensor networks, providing significant insight for future research in this field.