## 4 The Proposed Tracking Anchor Based Clustering Method
### Tracking Anchor Determination
5: For each solution
6: If Equation (25) is satisfied then
7: Calculate the objective value by Equation (23) to be \(\mathcal{U}\)
8: If \(\mathcal{U}<Q\)then
9: Set \(Q=\mathcal{U}\) in the third constraint of Equation (25)
10: Else
11: The solution is eliminated. 12: End for
13: The value of \(Q\) cannot be smaller, and the optimal solution can be obtained, which denotes the optimal status of CMs. ```

**Algorithm 2** State Scheduling for CMs

Overall, the proposed algorithm works as follows: First, using RFCM, the position of the tracking anchors and the memberships of nodes are determined. Instead of taking on the task of sending, receiving, and fusing data, the tracking anchors are only responsible for broadcasting activation messages to nodes in their lower approximation and boundary regions when a target is predicted to move nearby. Then, we will maintain a membership table, in which we record each node's membership to the anchors. According to the information provided by the membership table, a node will only be activated when it has received activation messages from all anchors to which it belongs. After that, the nodes activated by the tracking anchors will form a dynamic cluster. The CH is selected using a delayed broadcast related to distance and energy. To reduce redundant data transmission, we design a sensing state and sensing-transmitting state for CMs and use linear 0-1 programming to select the appropriate working states for CMs. Finally, the CMs in the sensing-transmitting state will send data to the CH, and the CH will fuse the data and forward them to the sink. The workflow of the proposed algorithm is shown in Figure 8.

In the proposed algorithm, tracking anchors play a crucial role in indicating the activation of tracking nodes, which can be used as a substitute for traditional static CHs. Since there is no heavy transmission task like the static CHs, the rotation period of the tracking anchors can be longer. Thus, the communication overhead caused by cluster structure reconstruction can be saved. Moreover, after the dynamic cluster is formed, we set two working states for the CMs and do not require all CMs to transmit their sensing data. In the scheduling process, we try to avoid redundant transmission within the cluster while meeting the tracking quality requirements. As a result, the proposed algorithm can further save energy and extend network lifetime. ## 5 Performance Evaluation

In this section, we evaluate the performance of the proposed algorithm in the WSN, and the existing HCTT [12], EEAOC [13], and EEDC [14] algorithms are used in comparison in the simulation experiments. In HCTT, static clusters and on-demand dynamic clusters alternately perform the tracking tasks. By scheduling boundary nodes to form an on-demand cluster, HCTT can solve the problem of limited sensor information sharing between static clusters. EEAOC is a clustering algorithm with a 2-logical cluster structure. The affiliation between nodes and different CHs will change according to the movement of the target. In EEDC, a dynamic cluster capable of omnidirectional tracking is constructed to cope with the random movement of the target. RFCM and GA algorithms are used to select on-demand candidate CHs. In general, the above three comparison algorithms are all energy efficient clustering protocols proposed for target tracking applications in WSNs. Figure 8: Workflow of the proposed algorithm. ### Simulation Setup

Simulation experiments were carried out using MATLAB 2016a (MathWorks, Natick, MA, USA). For the convenience of comparison, we refer to [12; 13; 14] to make a comprehensive experiment parameter setting. The parameters that appeared in simulations are listed in Table 1. A wireless sensor network consisting of 200-500 homogeneous sensor nodes randomly distributed in 500 m \(\times\) 500 m square area with identical initial energies is considered. The sink is placed at the coordinate (250, 250).