## 2 Related Work
Using state prediction technology and the Particle Swarm Optimizer (PSO), [PERSON] designed a tracking mechanism to achieve coordination between nodes and continuous target tracking in [19]. The author used state prediction technology to calculate the target trajectories and used PSO to optimize the network topology during tracking to save overall overhead. The experimental results indicated that the proposed method has good performance in energy management and can effectively prolong the network lifetime. In [20], a fault-tolerant sensor scheduling (FTSS) approach was proposed to reduce the target loss probability for target tracking in WSNs. In FTSS, the fault-tolerant domain was introduced, and the binary Gray Wolf Optimizer (bGWO) was used to activate the optimal tracking node set. Experiments indicated that reducing the target loss probability by FTSS can not only improve the tracking accuracy but also save the extra energy consumption brought by the recovery mechanism. A dynamic cluster task allocation scheme is proposed in [21] to perform collaborative task allocation for target tracking in WSNs. By considering energy consumption and residual energy balance, an energy efficient model was proposed. Moreover, a tracking accuracy model based on the area-sum principle was presented using triangulation localization. The two models were combined to establish a CM selection method based on the genetic algorithm (GA) for multi-target tracking. Simulation results indicated the high energy efficiency of the model and the validity of GA in CM selection. In [22], [PERSON] et al. proposed an improved algorithm, LEACH-MTC, which was based on the LEACH protocol. First, using the extended Kalman filter (EKF), the future location of the moving target can be obtained. After that, combining the state prediction of the target and the performance of collaborative monitoring, an elliptical monitoring area was constructed to align with the moving direction of the target. It was shown that LEACH-MTC could increase the survival number of working nodes and reduce network energy consumption. [PERSON] et al. proposed a rapid and efficient clustering method in [23]. The main aim is to achieve optimal sensor allocation for localization uncertainty reduction in target tracking. By maximizing the expected posterior of the moving target, the derived cluster was used as the search space for optimal sensor allocation. The proposed method was indicated to be superior in terms of achieving greater uncertainty reduction. Considering failures happening in target tracking applications, a novel fault-tolerant target tracking (FTTT) protocol based on clustering was proposed in [9]. In FTTT, a static clustering method was presented, which can facilitate the CH recovery procedure. Moreover, redundant nodes were scheduled into a sleep state to reduce the cost of fault tolerance. FTTT has been proven to effectively recover faulty nodes and reduce the total energy consumption in the network. In [24], [PERSON] et al. proposed a clustering method for target tracking with distributed information compression called CTCI, in which a novel cooperative positioning approach was developed to keep the information of measurement uncertainties. Moreover, CTCI allowed the active nodes to use information compression for energy conservation. The Bayesian framework was utilized to provide a complete distribution of target position estimation. Simulation results indicated that CTCI achieved a balanced performance in terms of tracking accuracy, transmission data size, and energy consumption. In general, almost all the clustering methods for target tracking focus on solving the energy consumption issue. Currently, making dynamic adjustments to the classical static cluster structure is the most effective way to track moving objects. CHs need to schedule their CMs to perform operations such as cluster migration and on-demand cluster formation according to the target position.