It is more practical and efficient to deploy sensors in critical areas rather than common areas to ensure indoor positioning accuracy and reduce deployment cost. This study focused on the sensor placement optimization for critical-grid coverage problem with two objectives: accuracy and cost. After reviewing some related works, this article proposed a multi-objective optimization model for critical-grid coverage problem of indoor positioning considering k-coverage problem as well as the topological rationality of sensor distribution. Then, NSGA-II algorithm was used to solve the optimizing model of sensor placement. At last, the simulation experiment and real environment validation were conducted for proposed method. The results showed that the optimized schemes obtain a lower error (1.13, 1.21 m) and a higher reduction of sensor deployment cost than the uniform deployment scheme (1.44 m). As a conclusion, the proposed method could reduce the cost of sensor deployment while ensuring the accuracy of indoor positioning for critical areas. It also provides a new direction for improving the accuracy of indoor positioning.

中文翻译：

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室内定位临界网格覆盖问题的传感器放置优化

在关键区域部署传感器比在公共区域部署传感器更加实用和高效，以确保室内定位精度并降低部署成本。本研究重点关注关键网格覆盖问题的传感器放置优化，其目标有两个：准确性和成本。在回顾了相关工作后，本文提出了一种考虑k-coverage问题以及传感器分布拓扑合理性的室内定位临界网格覆盖问题的多目标优化模型。然后采用NSGA-II算法求解传感器放置的优化模型。最后，对所提出的方法进行了仿真实验和真实环境验证。结果表明，优化方案获得了较低的误差（1.13, 1. 21 m) 和比统一部署方案 (1.44 m) 更高的传感器部署成本降低。总之，所提出的方法可以降低传感器部署成本，同时保证关键区域室内定位的准确性。也为提高室内定位精度提供了新的方向。