## 4 The Proposed Tracking Anchor Based Clustering Method
### Tracking Anchor Determination
However, due to slight changes in anchor positions, some nodes will be classified in the wrong positions, which leads to the risk of unreasonable clustering or even isolated nodes in the network. In future work, we will continue to improve this work to compensate for the performance loss due to anchor position bias. ## 6 Conclusions

Aimed at the tracking requirements for the moving target in WSNs, we propose a novel clustering method named TACM in this paper. Abandoning the static clusters formed by selecting CHs in the surveillance area, we introduce tracking anchors as indicators of sensor activation. Tracking anchors do not undertake the work of data transceiver and

Figure 12: Experimental result for number of energy mean square deviation of nodes. Figure 13: Experimental result for network lifetime (**a**) First node dead; (**b**) Half node dead; (**c**) Last node dead. fusion so that they can avoid the overhead caused by frequent rotation. We utilize the RFCM algorithm to determine the location of the anchors and then build a membership table based on whether the sensor is located in the lower approximation region or boundary region. After that, sensors are activated to form a cluster according to activation rules and the membership table. The activated sensors will voluntarily become the CH using a distance and energy related delay broadcast. Since the CH in TACM is determined temporarily, there is no overload problem that exists with static CHs, and the load balance can be guaranteed. Furthermore, we set sensing and sensing-transmitting states for CMs in the cluster. Using linear 0-1 programming, we schedule the optimal work states for CMs. On the premise of meeting the tracking requirement, the CMs in the sensing state do not need to send data to the CH. In this way, the energy waste caused by redundant transmission can be reduced. The proposed method is compared with some fundamental and state-of-the-art existing clustering methods, and the simulation results indicate that TACM can achieve a more efficient data transmission process and can significantly reduce the overall energy consumption and prolong the network lifetime. [PERSON] developed the basic idea, devised the main methodology, worked on simulations, and wrote this paper. [PERSON] is an expert in the application research of wireless sensor networks. He supervised and reviewed this work with extreme care. All authors have read and agreed to the published version of the manuscript. This work is supported by the National Key R&D Program of China under Grant #2021 YFE0111900. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Conflicts of Interest:** The authors declare no conflict of interest. ## References

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