Philippopoulos Panos I, Koutrakis Kostas N, Tsafaras Efstathios D, Papadopoulou Evangelia G, Sigalas Dimitrios, Tselikas Nikolaos D, Ougiaroglou Stefanos, Vassilakis Costas
Digital Systems Department, University of the Peloponnese, GR-23100 Sparta, Greece.
Informatics and Telecommunications Department, University of the Peloponnese, GR-22100 Tripoli, Greece.
Sensors (Basel). 2025 Apr 25;25(9):2713. doi: 10.3390/s25092713.
RSSI-based proximity positioning is a well-established technique for indoor localization, featuring simplicity and cost-effectiveness, requiring low-price and off-the-shelf hardware. However, it suffers from low accuracy (in NLOS traffic), noise, and multipath fading issues. In large complex spaces, such as museums, where heavy visitor traffic is expected to seriously impact the ability to maintain LOS, RSSI coupled with Bluetooth Low Energy (BLE) seems ideal in terms of market availability, cost-/energy-efficiency and scalability that affect competing technologies, provided it achieves adequate accuracy. Our work reports and discusses findings of a BLE/RSSI-based pilot, implemented at the Museum of Modern Greek Culture in Athens, involving eight buildings with 47 halls with diverse areas, shapes, and showcase layouts. Wearable visitor BLE beacons provided cell-level location determined by a prototype tool (VTT), integrating in its architecture different functionalities: raw RSSI data smoothing with Kalman filters, hybrid positioning provision, temporal methods for visitor cell prediction, spatial filtering, and prediction based on popular machine learning classifiers. Visitor movement modeling, based on critical parameters influencing signal measurements, provided scenarios mapped to popular behavioral models. One such model, "ant", corresponding to relatively slow nomadic cell roaming, was selected for basic experimentation. Pilot implementation decisions and methods adopted at all layers of the VTT architecture followed the overall concept of simplicity, availability, and cost-efficiency, providing a maximum infrastructure cost of 8 Euro per m covered. A total 15 methods/algorithms were evaluated against prediction accuracy across 20 RSSI datasets, incorporating diverse hall cell allocations and visitor movement patterns. RSSI data, temporal and spatial management with simple low-processing methods adopted, achieved a maximum prediction accuracy average of 81.53% across all datasets, while ML algorithms (Random Forest) achieved a maximum prediction accuracy average of 87.24%.
基于接收信号强度指示（RSSI）的近场定位是一种成熟的室内定位技术，具有简单性和成本效益，只需使用低价的现成硬件。然而，它存在精度低（在非视距通信中）、噪声和多径衰落等问题。在大型复杂空间，如预计有大量游客流量会严重影响保持视距能力的博物馆中，考虑到影响竞争技术的市场可用性、成本/能源效率和可扩展性，若能达到足够的精度，将RSSI与低功耗蓝牙（BLE）相结合似乎是理想的选择。我们的工作报告并讨论了在雅典现代希腊文化博物馆实施的基于BLE/RSSI的试点项目的结果，该项目涉及八栋建筑，有47个面积、形状和展示布局各异的展厅。可穿戴式游客BLE信标提供由原型工具（VTT）确定的单元级位置，该工具在其架构中集成了不同功能：使用卡尔曼滤波器对原始RSSI数据进行平滑处理、提供混合定位、用于游客单元预测的时间方法、空间滤波以及基于流行机器学习分类器的预测。基于影响信号测量的关键参数进行的游客移动建模提供了映射到流行行为模型的场景。选择了一种这样的模型，即对应相对缓慢的游牧式单元漫游的“蚂蚁”模型进行基础实验。VTT架构各层采用的试点实施决策和方法遵循简单性、可用性和成本效益的总体理念，每平方米覆盖面积的基础设施成本最高为8欧元。针对20个RSSI数据集的预测精度评估了总共15种方法/算法，这些数据集包含不同的展厅单元分配和游客移动模式。采用简单的低处理方法进行RSSI数据的时间和空间管理，在所有数据集中实现的最大预测精度平均为81.53%，而机器学习算法（随机森林）实现的最大预测精度平均为87.24%。
