## 2 Related Work  
With the rapid development of WSNs, the way of the network organization has attracted the attention of many scholars. However, compared with other typical network structures, the clustering method is a more effective method to reduce the energy consumption of sensor nodes and prolong the network lifetime. In this section, we provide a description of previous works related to clustering methods for target tracking in WSNs. [PERSON] et al. proposed a hybrid cluster-based target tracking (HCTT) distributed mobility management protocol to solve the boundary problem [12]. By integrating an on-demand cluster into the static cluster structure, HCTT enabled nodes belonging to different clusters share information, thereby ensuring smoothly target tracking and achieving a tradeoff between energy consumption and local sensor collaboration. In [13], [PERSON] et al. proposed a dynamic clustering method called EEAOC, in which the CH re-adjustment and cluster migration technique were used to activate appropriate  
Figure 2: On-demand cluster between static cluster. clusters for continuous event monitoring via a 2-logical-overlapping clustering scheme. In EEAOC, the cluster topology can be switched according to the target movement with low communication overhead. The authors in [14] proposed a dynamic clustering protocol (EEDC) for target tracking and continuous event monitoring. In EEDC, the overlapping cluster structure was dynamically reconstructed to adapt to the changing location of targets, and candidate cluster heads were selected according to rough fuzzy C-Means (RFCM) and genetic algorithm (GA). EEDC can significantly reduce system energy consumption as compared to other clustering protocols. To provide a good tradeoff between energy efficiency and tracking accuracy, a convolution-based method was presented to quantify the relationship between the cluster parameters and the energy-quality metrics of the tracking system in [15]. The proposed method can provide Pareto-optimal parameters to obtain the optimal energy efficiency of a cluster-based WSN tracking system. In [16], [PERSON] et al. proposed a dynamic chain-based sensor collaboration (DCBC) method for target tracking, which has a good reference value for clustering methods. Compared to clustering, chain structures involve a large number of unnecessary nodes and unacceptable transmission delays. Therefore, in DCBC, the authors dynamically adjusted the network topology by pruning and adding nodes instead of re-establishing the chain structure. The experimental results showed that DCBC can reduce and balance the average energy consumption of the network. [PERSON] et al. proposed an energy-efficient adaptive clustering scheme (EEAC) for target tracking with adaptive cluster size to keep minimal energy consumption while obtaining high tracking accuracy [17]. In EEAC, the authors presented a model for the prediction of target location and described how adaptive clusters were formed and how CHs were selected. It was observed that EEAC required less energy consumption as compared to other approaches. In [18], aiming to solve the problem of collaborative tracking, [PERSON] et al. laid out a cluster-based architecture to address the limitations in computational power, battery capacity, and communication capacities of sensors. Target tracking was formulated as a cross-layer optimization problem for the purpose of maximizing the utility function in the cluster. Experiments indicated that the proposed architecture can reduce the energy consumption caused by node selection in a dynamic scenario. [PERSON] et al. proposed a hierarchical clustering node collaborative scheduling (HCNCS) protocol in [11]. In HCNCS, nodes were classified into sensing disks for sensing and transmitting data. Node rotation was used to balance energy consumption, and cooperative sensing can improve the coverage area of sensing disks. In this way, the coverage performance of the network can be improved, and energy consumption can be balanced.