论文标题
使用物联网的无内存技术和无线技术用于室内本地化
Memoryless Techniques and Wireless Technologies for Indoor Localization with the Internet of Things
论文作者
论文摘要
近年来，物联网（IoT）已成长为通过使用室内定位系统（IPS）和基于位置的服务（LBS）来跟踪设备的跟踪。设计IP时，一种流行的方法涉及使用无线网络从具有预定位置的设备中计算目标的近似位置。在许多智能建筑应用中，LB对于开发有效的工作区是必要的。在本文中，我们检查了两种无记忆的定位技术，K-Nearest邻居（KNN）和幼稚的贝叶斯，并将它们与简单的三材进行比较，以准确性，精度和复杂性进行比较。我们通过使用三种流行的物联网无线技术（Zigbee，bluetooth Low Energy（BLE）和WiFi（2.4 GHz频段））以及三个实验场景来验证多个环境的结果，通过使用三种流行的物联网无线技术（Zigbee，蓝牙低能（BLE））进行了全面分析。根据实验结果，KNN是最准确的定位技术，也是最精确的。所有实验的RSSI数据集可在线获得。
In recent years, the Internet of Things (IoT) has grown to include the tracking of devices through the use of Indoor Positioning Systems (IPS) and Location Based Services (LBS). When designing an IPS, a popular approach involves using wireless networks to calculate the approximate location of the target from devices with predetermined positions. In many smart building applications, LBS are necessary for efficient workspaces to be developed. In this paper, we examine two memoryless positioning techniques, K-Nearest Neighbor (KNN), and Naive Bayes, and compare them with simple trilateration, in terms of accuracy, precision, and complexity. We present a comprehensive analysis between the techniques through the use of three popular IoT wireless technologies: Zigbee, Bluetooth Low Energy (BLE), and WiFi (2.4 GHz band), along with three experimental scenarios to verify results across multiple environments. According to experimental results, KNN is the most accurate localization technique as well as the most precise. The RSSI dataset of all the experiments is available online.
