The rise of the Internet of Things (IoT) and mobile internet applications has spurred interest in location-based services (LBS) for commercial, military, and social applications. While the global positioning system (GPS) dominates outdoor localization, its efficacy wanes indoors due to signal challenges. Indoor localization systems leverage wireless technologies like Wi-Fi, ZigBee, Bluetooth, UWB, selecting based on context. Received signal strength indicator (RSSI) technology, known for its accuracy and simplicity, is widely adopted. This study employs machine learning algorithms in three phases: supervised regressors, supervised classifiers, and ensemble methods for RSSI-based indoor localization. Additionally, it introduces a weighted least squares technique and pseudo-linear solution approach to address non-linear RSSI measurement equations by approximating them with linear equations. An experimental testbed, utilizing diverse wireless technologies and anchor nodes, is designed for data collection, employing IoT cloud architectures. Pre-processing involves investigating filters for data refinement before algorithm training. The study employs machine learning models like linear regression, polynomial regression, support vector regression, random forest regression, and decision tree regressor across various wireless technologies. These models estimate the geographical coordinates of a moving target node, and their performance is evaluated using metrics such as accuracy, root mean square errors, precision, recall, sensitivity, coefficient of determinant, and the f1-score. The experiment's outcomes provide insights into the effectiveness of different supervised machine learning techniques in terms of localization accuracy and robustness in indoor environments.

翻译：物联网（IoT）与移动互联网应用的兴起推动了基于位置的服务（LBS）在商业、军事及社会领域的需求增长。尽管全球定位系统（GPS）在室外定位中占据主导地位，但其在室内环境中因信号挑战导致效能下降。室内定位系统利用Wi-Fi、ZigBee、蓝牙、超宽带（UWB）等无线技术，并根据具体场景进行选择。其中，基于接收信号强度指示（RSSI）的技术因其精度高、实现简便而被广泛采用。本研究分三个阶段运用机器学习算法：监督回归、监督分类及集成方法，实现基于RSSI的室内定位。同时，引入加权最小二乘技术及伪线性求解方法，通过线性近似逼近非线性RSSI测量方程。实验平台采用多类无线技术及锚节点进行数据采集，并基于物联网云架构设计。数据预处理阶段，在算法训练前对滤波器进行优化探究。本研究应用线性回归、多项式回归、支持向量回归、随机森林回归及决策树回归等机器学习模型，跨多种无线技术场景进行实验。这些模型用于估算移动目标节点的地理坐标，并通过准确率、均方根误差、精确率、召回率、灵敏度、决定系数及F1分数等指标评估其性能。实验结果揭示了不同监督机器学习技术在室内定位精度与鲁棒性方面的有效性。