Accurate Analysis on Bluetooth Low Energy Neighbor Discovery

The basic concept of Bluetooth Low Energy (BLE) is short packet transmission and transient connection. It can quickly establish a connection, send data, and quickly disconnect, so that neighbor discovery is frequent and becomes an important issue. In the neighbor discovery which includes advertising and scanning, the BLE specification defines several important parameters. The parameters on the advertiser side include advertising interval, advertising duration, etc. On the scanner side, there are scan interval, scan window, etc. How to configure these parameters for quick neighbor discovery has been troublesome for BLE implementers. Prior analyses on BLE discovery process also showed some disagreements or made some incorrect assumptions. In this paper, we use rigorous probability-theory based derivations to obtain different kinds of successful discovery probabilities. We clarify disagreements in prior works and also provide insights on how to configure parameters for maximizing discovery probability. In particular, we prove that the discovery probabilities on each of the three channels are correlated. We also find that, when the advertising duration is set close to some multiples of the scan interval, an ill-fated synchronization problem will occur. To have a high discovery probability, both scan window and scan interval should be set at a large value, though it might not be good for energy saving.

Developing a New Security Framework for Bluetooth Low Energy Devices

Wearable devices are becoming more popular in our daily life.They are usually used to monitor health status,track fitness data,or even do medical tests,etc.Since the wearable devices can obtain a lot of personal data,their security issues are very important.Motivated by the consideration that the current pairing mechanisms of Bluetooth Low Energy(BLE)are commonly impractical or insecure for many BLE based wearable devices nowadays,we design and implement a security framework in order to protect the communication between these devices.The security framework is a supplement to the Bluetooth pairing mechanisms and is compatible with all BLE based wearable devices.The framework is a module between the application layer and the GATT(Generic Attribute Profile)layer in the BLE architecture stack.When the framework starts,a client and a server can automatically and securely establish shared fresh keys following a designed protocol;the services of encrypting and decrypting messages are provided to the applications conveniently by two functions;application data are securely transmitted following another protocol using the generated keys.Prudential principles are followed by the design of the framework for security purposes.It can protect BLE based wearable devices from replay attacks,Man-in-The-Middle attacks,data tampering,and passive eavesdropping.We conduct experiments to show that the framework can be conveniently deployed with practical operational cost of power consumption.The protocols in this framework have been formally verified that the designed security goals are satisfied.

基于核密度估计模型优化手术调度的设计与应用

目的针对现有手术患者转运平台转运流程中,手术状态节点变动存在人工操作依赖性和滞后性、手术衔接不高效的问题,改进手术患者转运平台的手术流转流程。方法结合集成蓝牙低能耗技术的智能手环、人员定位系统,运用核密度估计模型估算手术平均时长,从而改进手术患者转运平台的手术流转流程,并对模型的有效性进行验证。结果应用改进的手术转运平台后,首台延迟率、手术衔接时间较改进前下降6.2%、8.0 min,日均麻醉利用率和日间手术利用率较应用前提高了6.9%、6.4%,且差异均有统计学意义(P<0.01)。结论通过应用改进后的手术患者转运平台,手术室手术衔接效率更高,手术调度更加智能化,可有效提高手术室效率。

多特征融合的半监督流形约束定位方法

针对无线保真(WiFi)和低功耗蓝牙(BLE)指纹定位方法需要大量标记训练样本以及单模定位精度和稳定性难以满足大规模定位场景需求的问题,提出一种融合WiFi和BLE信号的半监督流形约束定位方法.实验结果表明:该方法与单一特征相比,每一维度归一化方差稳定在0.08以下,定位精度约提高25个百分点;使用分别构建流形约束的半监督学习方法时,能使定位过程中所需标记样本数量减少约90%.因此,该方法能极大减少需标记的样本数量,并有效提高定位的稳定性和精度.

基于ECA-CNN的混合指纹室内定位方法

在指纹法的定位中,指纹特征和定位模型是影响定位精度的2个关键因素.在指纹特征的选取方面,可见光强度稳定性较高,但位置特征区分度低;无线信号强度区分度较高,但波动性较强.在定位模型的构建方面,基于卷积神经网络(convolutional neural network, CNN)的定位模型在特征提取时不能有效地突出重要特征.针对上述问题,提出一种基于ECA-CNN的混合指纹室内定位方法(hybrid fingerprint indoor localization method based on ECA-CNN, ECACon-HF).首先,利用可见光强度和低功耗蓝牙(Bluetooth low energy, BLE)接收信号强度指示(received signal strength indication, RSSI)来构建混合指纹,降低BLE指纹不稳定的影响,并增强不同位置之间的区分度.同时,基于高效通道注意力(efficient channel attention, ECA)模块改进CNN定位模型,ECA能够通过跨通道交互策略自适应地提取指纹中的重要信息,抑制指纹中的环境干扰,增强混合指纹特征表达能力,更有效地利用混合指纹优势.实验结果显示,ECAConHF在构建的混合指纹库上可以达到0.316 m的定位精度,高于在单一指纹上的精度;并且基于同一指纹库,ECACon-HF相比其他室内定位方法,定位精度具有明显优势.

BLE测距精度影响因素评估分析

针对低功耗蓝牙(BLE)测距精度不稳定且误差较大的问题,提出一种BLE测距精度影响因素评估分析方法:选取BLE信距转换模型、信号传输角度、BLE协议版本和遮蔽环境等4种影响因素进行实测实验,分析全域函数模型、分段函数模型、终端和BLE信标相对角度大小、4.2和5.0不同协议版本、人体/易拉宝/纸板/穿孔铁板遮蔽环境等与BLE测距精度的关系。结果表明,基于分段拟合函数的BLE信距转换模型能小幅度提高拟合精度;信号传输角度越大等价距离圆上信号接收强度(RSS)值越小;同距离下不同BLE协议版本的RSS不相等;人体遮挡等遮蔽环境破坏BLE信号衰减规律而导致RSS降低。

基于Q-Learning的动态BLE Mesh网络高能效路由算法

针对动态低功耗蓝牙(Bluetooth Low Energy, BLE)Mesh网络规范采用的管理式泛洪路由机制所导致的数据包冗余和高能耗的问题,提出了动态BLE Mesh网络高能效路由算法。通过建立基于Q-Learning的BLE Mesh网络路由模型,将BLE Mesh网络中节点的剩余能量、转发成本、移动因子以及接收信号强度指示(Received Signal Strength Indicator, RSSI)纳入到奖励函数的设计中。同时对BLE Mesh网络中的心跳机制进行改进,使网络中的节点更加高效地获取环境信息。采用基于探索的路由发现机制来更新网络中节点的Q值,使其能更加准确反映节点移动所导致的网络拓扑变化。仿真结果表明,该算法在节点能耗和网络开销上均优于传统的管理式泛洪路由机制。

一种BLE蓝牙技术在智能家电中的应用设计

BLE蓝牙技术由于具有低功耗、毫秒级连接延时、简单的配网交互等优点,在智能家电领域应用的越来越广泛。智能家电领域正经历由传统的Wi-Fi模块向Wi-Fi+Ble模块切换的阶段。本文提出了一种使用移动设备添加BLE家电的安全流程和方法设计,提高了智能家电产品在使用中的便捷与安全性。

Real Time Vehicle Status Monitoring under Moving Conditions Using a Low Power IoT System

In the era of the Internet of Things(IoT),the ever-increasing number of devices connected to the IoT networks also increases the energy consumption on the edge.This is prohibitive since the devices living on the edge are generally resource constrained devices in terms of energy consumption and computational power.Thus,trying to tackle this issue,in this paper,a fully automated end-to-end IoT system for real time monitoring of the status of a moving vehicle is proposed.The IoT system consists mainly of three components:(1)the ultra-lowpower consumptionWireless SensorNode(WSN),(2)the IoT gateway and(3)the IoT platform.In this scope,a selfpoweredWSN having ultra-low energy consumption(less than 10 mJ),which can be produced by environmental harvesting systems,is developed.WSN is used for collecting sensors’measurements from the vehicle and transmitting them to the IoT gateway,by exploiting a low energy communication protocol(i.e.,BLE).A powerful IoT gateway gathers the sensors’measurements,harmonizes,stores temporary and transmits them wirelessly,to a backend server(i.e.,LTE).And finally,the IoT platform,which in essence is a web application user interface(UI),used mainly for almost real time visualization of sensors’measurements,but also for sending alerts and control signals to enable actuators,installed in the vehicle near to the sensors field.The proposed system is scalable and it can be adopted for monitoring a large number of vehicles,thus providing a fully automatic IoT solution for vehicle fleet management.Moreover,it can be extended for simultaneous monitoring of additional parameters,supporting other low energy communication protocols and producing various kinds of alerts and control signals.

基于BLE Mesh的资产定位系统设计

针对当前资产定位系统定位精度、建设成本和部署灵活性难以有效平衡的问题,基于BLE Mesh采用多维标度分析(MultiDimensional Scaling-Map,MDS-MAP)定位算法设计了一种资产定位系统。系统首先对原始接收信号强度(Received Signal Strength Indicator,RSSI)进行高斯-卡尔曼融合滤波,提高了RSSI值的准确性;然后利用生存时间(Time To Live,TTL)对中继节点进行约束,提高了数据传输的有效性;最后利用半径弥补法与Bellman-Ford融合迭代方案对生成的距离矩阵进行修正,减小了测距误差。实验结果表明,所设计的系统可有效完成蓝牙标签信息更新以及位置展示,平均定位精度达到了0.94 m。本系统具有成本低、工程实施方便的优点,有一定的应用价值和发展前景。

基于蓝牙和LoRa的广区域无线电目标监测系统

随着万物互联时代的到来,物联网技术得到了普遍的推广和应用。蓝牙是一种优秀的低功耗技术,LoRa是一种性能优异的远距离通信技术,二者在其各自的领域都得到了广泛的研究和应用,然而却鲜有兼顾二者特点的结合研究。针对野外高原鼠兔监测这一广区域目标监测场景,本文基于蓝牙和LoRa提出了一种广区域无线电目标监测系统,突破了续航和通信距离方面的瓶颈,具有低成本、低功耗等特点。

可穿戴式头部摆动姿态检测系统

为解决传统人机交互方式对于部分重度残疾人士或在某些特定场合不适用的问题,设计了一种可穿戴式头部摆动姿态检测系统.首先,由头部摆动姿态检测模块采集头部的原始姿态数据;其次,在时域上分析三轴加速度数据,并利用四元数姿态解算三轴角速度数据;然后,结合三轴加速度和角速度数据确定5种头部摆动姿态相对应的阈值范围,通过滑动窗口法区分指令动作和无意识动作,完成头部摆动姿态检测;最后,采用蓝牙无线通信方式将指令传输至蓝牙子系统模块,并通过串口在上位机显示结果.试验结果表明,该系统可以准确检测头部摆动姿态、区分无意识头部动作,头部摆动姿态检测平均正确率达96%.该头部摆动姿态检测系统具有结构简单、准确率高、成本低、阈值可调等优点.

一种应用于低功耗蓝牙的解帧电路

为了满足低功耗蓝牙在传输过程中的实时性、准确性和低功耗要求,提出一种应用于低功耗蓝牙链路层的解帧电路。电路基于低功耗蓝牙数据帧的结构和数据处理过程特点,通过状态机控制解帧过程中各个电路模块的工作状态,实现低功耗蓝牙接收设备在数据传输过程中的解帧处理。仿真结果表明,所提电路能够有效地实现对接收的数据帧起止时刻的定位并解析出协议数据单元的具体数据,同时满足低功耗的要求。

Bluetooth Low Energy Device Identification Based on Link Layer Broadcast Packet Fingerprinting

With the rapid development of the Internet of Things(IoT),wireless technology has become an indispensable part of modern computing platforms and embedded systems.Wireless device fingerprint identification is deemed as a promising solution towards enhancing the security of device access authentication and communication process in the IoT scenario.However,the extraction of features from the network layer and its upper layers often confront restrictions from specific devices:the association with a certain wireless network and the access to the plaintext of the payload.Meanwhile,Bluetooth Low Energy(BLE)packets have been encrypted above the link layer,which makes those features difficult to extract.To tackle these problems,we introduce a novel method to identify BLE devices based on the fingerprint features in the data link layer.Initially,the BLE packets are collected through a receiver based on software-defined radio technology.Then,fields that reflect device differences in BLE broadcast packets are extracted through traffic analysis.Finally,a MultiLayer Perceptron(MLP)model is employed to recognize the category of BLE devices.An experimental result on a dataset with 15 types of BLE devices shows that the identification accuracy of the proposed method can reach 99.8%,which accomplishes better performance over previous work.

Side-Channel Analysis for the Re-Keying Protocol of Bluetooth Low Energy

In the era of the Internet of Things,Bluetooth low energy(BLE/BTLE)plays an important role as a wellknown wireless communication technology.While the security and privacy of BLE have been analyzed and fixed several times,the threat of side-channel attacks to BLE devices is still not well understood.In this work,we highlight a side-channel threat to the re-keying protocol of BLE.This protocol uses a fixed long term key for generating session keys,and the leakage of the long term key could render the encryption of all the following(and previous)connections useless.Our attack exploits the side-channel leakage of the re-keying protocol when it is implemented on embedded devices.In particular,we present successful correlation electromagnetic analysis and deep learning based profiled analysis that recover long term keys of BLE devices.We evaluate our attack on an ARM Cortex-M4 processor(Nordic Semiconductor nRF52840)running Nimble,a popular open-source BLE stack.Our results demonstrate that the long term key can be recovered within only a small amount of electromagnetic traces.Further,we summarize the features and limitations of our attack,and suggest a range of countermeasures to prevent it.

基于手机蓝牙的低功耗防丢器

该设计采用新蓝牙技术4.0(BLE),该技术是三个技术的集合,分别是传统蓝牙技术、低功耗蓝牙技术、高速蓝牙技术,综合应用了蓝牙模块HC-08、蜂鸣器、YX-117AM调速芯片和0820扁平振动马达组成的振动模块、液晶显示屏LCD12864、ISD1820语音播报模块,以及锂电池自动充电。就低功耗蓝牙防丢器而言,它不仅要具有报警功能,更要具有便于携带和低功耗等特点。文章综述了低功耗蓝牙防丢器的设计过程,并且基于Android进行手机APP开发。

基于低功耗蓝牙的军用物资运输环境实时监控系统研究

针对军用物资公路运输保障过程中存在的风险问题,提出了一种物资运输环境实时监控系统.本文提出的实时监控系统基于低功耗蓝牙BLE技术,选取NB-IoT窄带物联网作为无线通信模块,在运输过程中对物资的多项状态参数进行实时数据采集、跟踪与监控,并将采集到的数据实时上传到云平台,能及时发现安全隐患并及时上报解决,实现危险品在运输过程中状态可视化、易掌控,做到及时发现和消除隐患,保证物资运输过程安全、高效.

BLE Audio技术及其在电视互动应用中的实时音频通信

随着智能电视的普及和电视观看体验的不断提升,用户对于更方便、更自由的音频传输方式的需求日益增加。蓝牙低功耗音频(Bluetooth Low Energy Audio,BLE Audio)技术是一种基于蓝牙低功耗(Bluetooth Low Energy,BLE)标准的无线音频传输技术,由BLE技术升级而来,为音频设备提供高质量的无线音频传输,同时具有低功耗、低延迟和简单的设备连接特性。BLE Audio技术适用于各种应用场景,包括耳机、音箱、智能家居系统以及可穿戴设备等。基于此,详细分析BLE、BLE Audio技术以及BLE Audio技术在电视互动场景中的应用。

基于短距无线通信的物联网智能锁安全设计

为解决短距无线通信技术在物联网智能锁中存在的安全隐患问题,文章提出物联网智能锁安全设计方案。首先,分析了短距无线通信技术运用;其次,toys安全模型解析法研究了智能锁目前存在的安全隐患问题;最后,结合物联网智能锁远程攻击方法,提出相关安全改进方案,并仿真分析了安全设计后的通信模型,发现该通信模型可以有效地抵御和避免非法重放攻击现象,将窃听攻击出现概率降到最低。

适应矿井线形空间的BLE多连接网络移动节点漫游通信方法

低功耗蓝牙(BLE,Bluetooth low energy)是一种智能手机中标配的无线通信技术。在矿井巷道线形受限场景中,空间作业人员、行走作业的机械设备等移动节点需要接入无线通信网络,并保持与网络的连接以及感知数据的交互,为此提出了一种移动节点在线形BLE多连接网络中的漫游通信方法,通过BLE移动终端持续监测和评估BLE基站的RSSI(Received signal strength index),采用去极值平均滤波算法,对RSSI值进行滤波,择优建立用于传输数据的主要通信链路和保持连接的次要通信链路,移动过程中通过主次通信链路切换以及重建次要通信链路以保证数据传输通道的稳定性,通过BLE基站持续广播更新数据传输路径以保证移动通信路径的准确性。试验结果表明,该方法可实现移动终端自主择优切换与基站的连接,保持传输链路稳定,具有良好的鲁棒性。