## 4 The Proposed Tracking Anchor Based Clustering Method
### Tracking Anchor Determination
It can be seen that the clustering methods that do not count on static cluster structure, such as EEDC and the proposed TACM, need to activate fewer sensors than HCTT and EEAOC in the process of target tracking. The reason is that, in HCTT, all the nodes in the nearby static cluster need to remain active to establish an on-demand cluster as CHs instructed while the target moves

Figure 10: Experimental results for total energy consumption in the network. Figure 9: Experimental results for average residual energy of sensor nodes. across clusters. In EEAOC, the logical overlapping cluster needs to be switched when the target crosses clusters, so the node is activated to interact. Moreover, compared with TACM, more nodes are activated in EEDC because its introduced candidate CHs need to be activated on standby for tracking tasks. We calculate the energy mean square deviation of the living nodes to measure the energy distribution in the network, and the results are shown in Figure 12. The energy mean square deviation is \(2.4483\times 10^{-4}\) for TACM, \(2.085\times 10^{-4}\) for EEAOC, \(1.7181\times 10^{-4}\) for EEDC, and \(1.2395\times 10^{-4}\) for HCTT. It can be seen that the slope of TACM is smaller than that of the other three methods, indicating that the load balancing performance of TACM is the best. The reason is that the tracking anchors in TACM are only responsible for node activation, not the task of sending and receiving data, so they have a light workload. Moreover, CH is temporarily selected by volunteering according to the position of the target and the residual energy of the node so that the node serving as CH will not consume too much energy. In comparison, the CHs in EEAOC and HCTT cannot be changed in time like TACM, resulting in significant differences in energy consumption between nodes. Although the CH in EEDC is also temporarily selected, in the process of performing the candidate CHs selection process, the original CH undertakes additional information collection and calculation tasks, so the node serving as the CH will have a heavier burden than TACM. At last, we use the network lifetime to make an overall measure of the energy efficiency of the four clustering methods. Figure 13 shows the network lifetime with different numbers of nodes, and (a), (b), and (c) are the simulation results measured based on metrics called First Node Dies (FND), Half of the Nodes Die (HND), and Last Node Dies (LND). It can be seen that the proposed TACM method shows the best performance in all three metrics. In addition to avoiding frequent CH rotation and balancing node workload, as mentioned earlier, TACM also schedules the transmission status of CMs in the cluster. Specifically, only a part of the nodes that meet the tracking requirements with a miniature sensing overlap \begin{table}
\begin{tabular}{c c} \hline \hline
**Clustering Methods** & **Average Number of Activated Nodes** \\ \hline HCTT & 21.3 \\ EEAOC & 15.6 \\ EEDC & 12.3 \\ TACM & 10.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average number of activated nodes in 1000 time steps. Figure 11: Experimental result for number of activated nodes in the network. area between each other can transmit data. In this way, the transmission of redundant data can be reduced, thereby further saving energy and prolonging the network lifetime. ### Threats to Validity

In this subsection, we discuss some threats to the validity of our conclusions. In the surveillance area of the network, we use the RFCM algorithm to determine the position of tracking anchors and the membership of nodes. Based on an ideal assumption, the anchors can be placed directly at the calculated position using transport, which is environmentally demanding and requires additional resources. Therefore, in the experiments, we designate the sensor node closest to the calculated position to work as the tracking anchor.