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基于蓝牙5.0新特性的低功耗蓝牙发现过程的提议与评估

Proposal and Evaluation of BLE Discovery Process Based on New Features of Bluetooth 5.0.

作者信息

Hernández-Solana Ángela, Perez-Diaz-de-Cerio David, Valdovinos Antonio, Valenzuela Jose Luis

机构信息

Aragon Institute for Engineering Research (I3A), University of Zaragoza, 50018 Zaragoza, Spain.

Signal Theory and Communications Department, Universitat Politècnica de Catalunya, Esteve Terrades 7, 08860 Castelldefels, Spain.

出版信息

Sensors (Basel). 2017 Aug 30;17(9):1988. doi: 10.3390/s17091988.

Abstract

The device discovery process is one of the most crucial aspects in real deployments of sensor networks. Recently, several works have analyzed the topic of Bluetooth Low Energy (BLE) device discovery through analytical or simulation models limited to version 4.x. Non-connectable and non-scannable undirected advertising has been shown to be a reliable alternative for discovering a high number of devices in a relatively short time period. However, new features of Bluetooth 5.0 allow us to define a variant on the device discovery process, based on BLE scannable undirected advertising events, which results in higher discovering capacities and also lower power consumption. In order to characterize this new device discovery process, we experimentally model the real device behavior of BLE scannable undirected advertising events. Non-detection packet probability, discovery probability, and discovery latency for a varying number of devices and parameters are compared by simulations and experimental measurements. We demonstrate that our proposal outperforms previous works, diminishing the discovery time and increasing the potential user device density. A mathematical model is also developed in order to easily obtain a measure of the potential capacity in high density scenarios.

摘要

设备发现过程是传感器网络实际部署中最关键的方面之一。最近,一些研究通过仅限于4.x版本的分析或仿真模型,对低功耗蓝牙(BLE)设备发现这一主题进行了分析。不可连接且不可扫描的无向广告已被证明是在相对较短的时间段内发现大量设备的可靠替代方法。然而,蓝牙5.0的新特性使我们能够基于BLE可扫描无向广告事件,在设备发现过程中定义一种变体,这会带来更高的发现能力以及更低的功耗。为了描述这种新的设备发现过程,我们通过实验对BLE可扫描无向广告事件的实际设备行为进行建模。通过仿真和实验测量,比较了不同数量的设备和参数下的未检测数据包概率、发现概率和发现延迟。我们证明,我们的方案优于先前的研究成果,减少了发现时间并增加了潜在用户设备密度。还开发了一个数学模型,以便在高密度场景中轻松获得潜在容量的度量。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/269a/5621146/fb688585ebb2/sensors-17-01988-g001.jpg

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