## 3 The Proposed Sensor Deployment Algorithms
### The Particle Swarm/Genetic Hybrid Algorithm
The simulation results show that the model proposed in this paper can better adapt to specific business requirements and improve processing efficiency. Compared to other algorithms, this hybrid approach demonstrates superior performance in reducing deployment costs while maintaining a lower time complexity. Also, there are some limitations in the paper; this study primarily relies on simulation experiments and lacks testing in real-world applications. Future research could further expand the conclusion to highlight potential applications of the proposed method in real-world industrial scenarios, such as production monitoring, predictive maintenance, and energy efficiency optimization. These examples are based on industry-relevant cases and demonstrate the practical value of our method in various environments. Conceptualization, FX. ; Investigation, FX. ; Methodology, X.L. and L.N. ; Software, Y.L. ; Supervision, C.Z. ; Visualization, Y.L. ; Writing--original draft, X.L. and L.N. All authors have read and agreed to the published version of the manuscript. This research was funded by the Beijing Information Science and Technology University Research Fund (Grant Number:2023 XJ002). The data presented in this study are available on request from the corresponding author ([EMAIL_ADDRESS]). The data are not publicly available due to in-depth research based on these data. The authors declare that they have no conflicts of interest related to this work. The decision to submit this report for publication was made solely by the authors, and the sponsors had no involvement in this decision-making process. ## References

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