基于蓝牙信标的自学习神经网络室内定位

Kisu Ok, Dongwoo Kwon, Youngmin Ji
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引用次数: 3

摘要

随着信息通信技术的发展,利用物联网(IoT)的服务已经在各个领域实现。其中,使用信标的位置服务的优点是可以半永久地使用蓝牙低功耗(BLE)。在本文中,我们利用这些优势来推断信标的室内定位。在建筑物的一层安装多个信标收发器,并使用神经网络学习来学习信标发送器的位置。结果表明,神经网络学习具有较高的室内定位精度。
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Bluetooth Beacon-Based Indoor Localization Using Self-Learning Neural Network
With the development of ICT technology, services using the Internet of Things (IoT) have been implemented in various fields. Among them, location-based services using beacons have the advantage that they can be used semi-permanently using Bluetooth Low Energy (BLE). In this paper, we utilize these advantages to infer indoor localization of beacon. Install multiple beacon transceivers on one floor of the building and learn the location of the beacon transmitter using neural network learning. As a result, neural network learning showed high indoor localization accuracy.
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A Case for Two-stage Inference with Knowledge Caching Bluetooth Beacon-Based Indoor Localization Using Self-Learning Neural Network Enhanced Partitioning of DNN Layers for Uploading from Mobile Devices to Edge Servers Exploring Image Reconstruction Attack in Deep Learning Computation Offloading
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