## 1 Introduction  
Recently, the increased performance of radio and embedded systems and their decreased price have led to the widespread use of Wireless Sensor Networks (WSNs) [1, 2, 3]. WSNs commonly consist of numerous sensors in the surveillance area, and the sensing unit of a sensor can sense environmental information (e.g., wind speed, temperature, humidity, solar radiation, etc.) [4, 5]. The sensing data are transferred to the sink through a wireless communication unit in the sensor. Users can access these sensing data through wireless communication devices, such as terminal hosts, personal computers, smart devices, and laptops. As one of the critical technologies of WSNs, target tracking is the basis for applications such as military surveillance, wildlife observation, biomedical health care, and vehicle tracking [6, 7]. Generally, the main aim of target tracking in WSNs is to detect the existence of the target and to estimate its future position based on historical data of the target movement. In the tracking process, most of the sensors spend a significant amount of time in a state in which they do not participate actively in communications [8]. In this case, we can turn off the radio module of these sensors to save energy. Therefore, how to schedule the state of the sensors in the network to achieve efficient data transmission is a crucial issue for target tracking applications. To deal with this problem, clustering methods are widely used as a means to facilitate cooperative data processing and manage the resources of sensor networks [9, 10, 11]. As shown in Figure 1, in clustering methods, sensors are organized into clusters, and each cluster has a cluster head (CH) and multiple cluster members (CMs). CMs send sensing data to CH, and CH is responsible for collecting data, performing data fusion, and sending a report toward the sink. In this way, not only can the energy consumption in the network be reduced to achieve the purpose of prolonging the network lifetime, but also the scalability is robust, and the network delay can be reduced. Existing studies have designed different clustering methods for target-tracking-based applications, among which the most representative one is forming an on-demand cluster between static clusters [12]. As shown in Figure 2, when the target is located between static clusters, only one on-demand cluster is activated for work, and the data can be transmitted to one CH. The activated cluster is adaptively formed according to the position of the moving target, and the prediction mechanism is used to calculate the target trajectory. One of the CH will fuse the data from CMs in the on-demand cluster to provide the system with a more accurate target position estimate. Although this structure has better energy efficiency than traditional clustering methods, it cannot avoid periodic cluster reconstruction caused by the reselection of CHs. The reason is that the CH not only has to give CMs activation instructions according to the target position, but also undertakes the task of sending, receiving, and fusing data. The death of CH means that the network needs to update the role of the CH in time, and this short-cycle CH rotation will cause additional communication overhead for the system. To solve the above drawback of existing methods, we propose a novel clustering method for target tracking, in which we abandon the static clustering structure and decentralize the tasks undertaken by CHs. The main objective is to avoid the communication overhead caused by the periodic reconstruction of the cluster structure and to prolong the network lifetime. The major contribution of this paper is summarized as follows:  
* Tracking anchors are introduced to indicate sensor activation based on the target position. Since there is no task of sending, receiving, and fusing data, the tracking anchor consumes little energy and therefore does not need to be reselected periodically.