Hsiao C.-HYEONG-SUNG LINYang H.-JHuang YChen Y.-FTu C.-WZhang S.-Y.2022-11-182022-11-18202114248220https://www.scopus.com/inward/record.uri?eid=2-s2.0-85117957547&doi=10.3390%2fs21217121&partnerID=40&md5=7f730068ae5705fe6ba6acd2b07cc4b6https://scholars.lib.ntu.edu.tw/handle/123456789/625768As wireless sensor networks have become more prevalent, data from sensors in daily life are constantly being recorded. Due to cost or energy consumption considerations, optimization-based approaches are proposed to reduce deployed sensors and yield results within the error tolerance. The correlation-aware method is also designed in a mathematical model that combines theoretical and practical perspectives. The sensor deployment strategies, including XGBoost, Pearson correlation, and Lagrangian Relaxation (LR), are determined to minimize deployment costs while maintaining estimation errors below a given threshold. Moreover, the results significantly ensure the accuracy of the gathered information while minimizing the cost of deployment and maximizing the lifetime of the WSN. Furthermore, the proposed solution can be readily applied to sensor distribution problems in various fields. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.Lagrangian Relaxation; Network deployment; Pearson correlation; Wireless sensor networks (WSNs); XGBoostCorrelation methods; Cost estimating; Energy utilization; Errors; Estimation; Lagrange multipliers; Bounded estimation error; Daily lives; Deployment costs; Energy-consumption; Lagrangian relaxations; Network deployment; Optimisations; Pearson correlation; Wireless sensor network; Xgboost; Wireless sensor networks; computer network; information processing; theoretical model; wireless communication; Computer Communication Networks; Models, Theoretical; Records; Wireless Technologyjournal article10.3390/s212171212-s2.0-85117957547