Several Wireless Fidelity (WiFi) fingerprint datasets based on Received Signal Strength (RSS) have been shared for indoor localization. However, they can’t meet all the demands of WiFi RSS-based localization. A supplementary open dataset for WiFi indoor localization based on RSS, called as SODIndoorLoc, covering three buildings with multiple floors, is presented in this work. The dataset includes dense and uniformly distributed Reference Points (RPs) with the average distance between two adjacent RPs smaller than 1.2 m. Besides, the locations and channel information of pre-installed Access Points (APs) are summarized in the SODIndoorLoc. In addition, computer-aided design drawings of each floor are provided. The SODIndoorLoc supplies nine training and five testing sheets. Four standard machine learning algorithms and their variants (eight in total) are explored to evaluate positioning accuracy, and the best average positioning accuracy is about 2.3 m. Therefore, the SODIndoorLoc can be treated as a supplement to UJIIndoorLoc with a consistent format. The dataset can be used for clustering, classification, and regression to compare the performance of different indoor positioning applications based on WiFi RSS values, e.g., high-precision positioning, building, floor recognition, fine-grained scene identification, range model simulation, and rapid dataset construction.

中文翻译：

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基于接收信号强度的 WiFi 室内定位补充开放数据集

几个基于接收信号强度 (RSS) 的无线保真 (WiFi) 指纹数据集已被共享用于室内定位。但是，它们并不能满足基于 WiFi RSS 的本地化的所有需求。在这项工作中，提出了一个基于 RSS 的 WiFi 室内定位补充开放数据集，称为 SODIndoorLoc，覆盖了三个具有多个楼层的建筑物。该数据集包括密集且均匀分布的参考点 (RP)，两个相邻 RP 之间的平均距离小于 1.2 m。此外，预装接入点 (AP) 的位置和信道信息汇总在 SODIndoorLoc 中。此外，还提供各楼层的计算机辅助设计图。SODIndoorLoc 提供九份培训和五份测试表。探索了四种标准机器学习算法及其变体（共八种）来评估定位精度，最佳平均定位精度约为2.3 m。因此，SODIndoorLoc 可以作为 UJIIndoorLoc 的补充，格式一致。该数据集可用于聚类、分类和回归，以比较基于 WiFi RSS 值的不同室内定位应用的性能，例如高精度定位、建筑物、楼层识别、细粒度场景识别、距离模型模拟和快速数据集构建。