A Novel Deployment Strategy Based on Improved Gradient‐Based Optimizer for <scp>BLE</scp> Anchor Nodes

ABSTRACT Bluetooth low energy (BLE) technology has gained attraction for indoor localization in recent years due to its low power consumption and cost‐effectiveness. However, deploying BLE anchor nodes in indoor environments, particularly in areas with obstacles that can interfere with signal strength, remains a major challenge. This paper proposes a novel deployment strategy that utilizes an improved gradient‐based optimizer (GBO) algorithm to address these challenges. The primary objective is to improve both the coverage of BLE anchor nodes and the accuracy of localization. To assess the effectiveness of the deployment schemes generated by the algorithm, we introduce a comprehensive performance evaluation metric. This metric integrates multiple indicators, including capability rate, geometric accuracy factor, penalty term, sensor network topology, and network uniformity. An obstacle perception model is also introduced to mitigate the impact of deviations between measured values from the RSSI ranging model in obstacle‐rich environments and actual ground truth values. This model adjusts the measurements to enhance the reliability of the localization process. The presented algorithm is tested in an indoor space with obstacles. Experimental results demonstrate that the novel algorithm significantly optimizes anchor node layouts, enhancing coverage, and localization accuracy. Compared with the existing algorithms, the proposed method shows better performances and highlights its potential for more accurate and efficient BLE indoor localization. In particular, our research achieved an 86% probability of positioning errors within 3 m, surpassing the best results of other deployment schemes by approximately 11%. This highlights the potential of our approach for more accurate and efficient BLE‐based indoor localization.