Improving location awareness in indoor spaces using RFID technology

Location awareness is a key issue to improve the development of autonomous entities that are embedded into ubiquitous computing environments. GPS seems to be the best solution to develop outdoor location systems, but the performance of these systems is not good enough to locate objects or humans within indoor environments, mainly if accuracy and precision are required. In this article we propose the use of a cheap and reliable technology as RFID to develop a passive RFID-based indoor location system that is able to accurately locate autonomous entities, such as robots and people, within a defined surface. This system is applied to solve the robot tracking problem. We include the evaluation of the proposal by comparing our system technology performance with other alternatives built on different technologies (Wi-Fi, Bluetooth, IrDA, ultrasound, etc.). We have also performed a location awareness proof concept test to analyze the viability of the approach.     

Context-aware,self-scaling Fuzzy ArtMap for received signal strength based location systems

Location awareness is the key capability of mobile computing applications. Despite high demand, indoor location technologies have not become truly ubiquitous mainly due to their requirements of costly infrastructure and dedicated hardware components. Received signal strength (RSS) based location systems are poised to realize economical ubiquity as well as sufficient accuracy for variety of applications. Nevertheless high resolution RSS based location awareness requires tedious sensor data collection and training of classifier which lengthens location system development life cycle. We present a rapid development approach based on online and incremental learning method which significantly reduces development time while providing competitive accuracy in comparison with other methods. ConSelFAM (Context-aware, Self-scaling Fuzzy ArtMap) extends the Fuzzy ArtMap neural network system. It enables on the fly expansion and reconstruction of location systems which is not possible in previous systems.     

基于深度学习的无线电作弊信号发现与定位系统

随着无线电技术的飞速发展和无线电作弊手段的日新月异,考试中利用无线电设备进行作弊的情况也逐年增加。为保证考试的公平性,如何有效地发现和定位无线电作弊信号、建设智慧考场已成当前迫切需要解决的新课题。鉴于此,在室内定位和频谱监测技术的基础上,本文设计了基于深度学习的无线电作弊信号发现与定位系统,系统实现了无线电作弊设备判决、定位、告警、以及移动终端实时显示等功能,为监考人员提供了直观、远程和实时的考场环境的安全情况,为广大考生创造了一个公平的竞争环境。     

Scalable and consistent radio map management using participatory sensing

Although Wi-Fi fingerprinting is a promising solution for indoor localization, its widespread use is limited due to the necessity of time-consuming site surveys. Recently, active research has been conducted to reduce site-survey costs with participatory sensing. While previous work focused on the expansion of radio map coverage, in this paper, we deal with the issues on the scalability and consistency of radio map. In participatory sensing, radio map construction should be able to handle massive data collected from many people over a long period with limited storage capacity. The radio map should also guarantee consistency, which means consistent accuracy regardless of the RSS variances caused by environmental dynamics. This paper proposes a scalable and consistent radio map management scheme. Using multiple fingerprints per location, we minimize accuracy degradation caused by the RSS variance problem. To overcome the scalability issue, we control the number of fingerprints by a two-phase fingerprints selection algorithm. For each location, the proposed scheme first clusters the collected fingerprints and removes all fingerprints except for the centroids. Then, an optimal set of fingerprints is found by comparing the fingerprints in neighboring locations. We validate the efficiency of the proposed scheme with real experiments in various environments.     

SRS: Spatial-tagged radio-mapping system combining LiDAR and mobile-phone data for indoor location-based services

One of the most challenging issues in radio received signal strength (RSS)-based localization systems is the generation and distribution of a radio map with a coordinate system linked with spatial information in a large indoor space. This study proposes a novel spatial-tagged radio-mapping system (SRS) that effectively combines the heterogeneous properties of LiDAR and mobile phones to simultaneously perform both spatial and radio mappings. The SRS consists of synchronization, localization, and map building processes, and enables real-time spatial and radio mapping. In the synchronization process, the distance range, motion data, and radio signals obtained through the LiDAR and mobile phone are collected in nodal units according to the sensing time. In the localization process, a feature variance filter is used to control the number of features generated from LiDAR and estimate the positions at which the nodes are generated in real time according to the motion data and radio signals. In map building, the estimated positions of the nodes are used to extract spatial and radio maps by using a unified location coordinate system. To ensure mobility, the SRS is manufactured in the form of a backpack supporting LiDAR and a mobile phone; the usefulness of the system is experimentally verified. The experiments are performed in a large indoor shopping mall with a complex structure. The experimental results demonstrated that a common coordinate system could be used to build spatial and radio maps with high accuracy and efficiency in real time. In addition, the field applicability of the SRS to location-based services is experimentally verified by applying the constructed radio map to well-known fingerprinting algorithms using the heterogeneous mobile phones.     

Smartphone-based Wi-Fi tracking system exploiting the RSS peak to overcome the RSS variance problem

The Wi-Fi fingerprinting (WF) technique normally suffers from the Received Signal Strength (RSS) variance problem caused by environmental changes that are inherent in both the training and localization phases. Several calibration algorithms have been proposed but they only focus on the hardware variance problem. Moreover, smartphones were not evaluated and these are now widely used in WF systems. In this paper, we analyzed various aspects of the RSS variance problem when using smartphones for WF: device type, device placement, user direction, and environmental changes over time. To overcome the RSS variance problem, we also propose a smartphone-based, indoor pedestrian-tracking system. The scheme uses the location where the maximum RSS is observed, which is preserved even though RSS varies significantly. We experimentally validate that the proposed system is tolerant to the RSS variance problem.     

Automated construction of Wi-Fi-based indoor logical location predictor using crowd-sourced photos with Wi-Fi signals

Owing to the recent proliferation of smartphones and the SNS, a large number of images taken by smartphones at various places have been uploaded to SNSs. In addition, smartphones are equipped with various sensors such as Wi-Fi modules that enable us to generate an image associated with the sensory information that represents the context in which the image was captured. This study demonstrates the benefits of images associated with Wi-Fi signals in the automated construction of a Wi-Fi-based indoor logical location classifier that predicts a semantic location label of a user’s position for shopping complexes. In this study, a logical location class refers to the store class label in a shopping complex, such as Starbucks and H&M. Given a collection of images associated with Wi-Fi signals taken at a shopping complex and the complex’s floor plan, the proposed method first estimates the store label at which an image was taken by analyzing the image and crawled online images of branch stores. Then, the 2D coordinates of the images taken at branch stores on the floor coordinate system can be estimated using the floor plan. Subsequently, by using the Wi-Fi signals of the branch store images and their estimated 2D coordinates, we construct a transformation function that maps Wi-Fi signals onto the 2D coordinates, and we adopt this function to predict an indoor location class of an observed Wi-Fi scan from a smartphone possessed by an end user. The proposed transformation function comprises an ensemble of sub-functions designed based on CVAEs. Finally, we demonstrate the effectiveness of the proposed method for three actual shopping complexes.     

基于无线定位的个性化导览关键技术及其在博物馆中的应用

本文提出一种基于Wi-Fi无线定位网络能够满足相关应用精度需求的室内导览方法,该方法使用智能手机自身处理能力实时进行信号强度概率分布以及位置指纹匹配计算,使用基于动态权值的方法来对室内环境进行建模,引入加权线性公式组合推荐算法实现基于优化A星算法的路线规划。本文同时给出了该方法应用于构建博物馆个性化导览系统的应用示例,实验结果表明该方法具有较高的定位精度和推荐准确率。本文所提室内导览方法具有通用性好和组网成本低的特点,能够较好满足博物馆等室内导览系统应用需求,具备进一步进行商业化应用的潜力。     

Fuzzy online location management in mobile computing environments

This paper presents a new approach, namely Intelligent Fuzzy Online Location Management Strategy (IFOLMS), based on Fuzzy clustering techniques to solve the mobile location management problem. Using a Fuzzy location estimator in this technique, mobile users’ past movements are used in making future paging decisions by the network. IFOLMS has the potential to lead to massive savings in the number of network signal transactions that must be made to locate users. Performance of the proposed approach has been measured by using several test networks; it shows promising results — around 50% reduction in network cost — when compared to many of the existing location management techniques (including GSM). Results also provide new insights into the mobility management problem and its associated performance issues.     

Adaptive radio maps for pattern-matching localization via inter-beacon co-calibration

A growing number of location-based applications are based on indoor positioning, and much of the research effort in this field has focused on the pattern-matching approach. This approach relies on comparing a pre-trained database (or radio map) with the received signal strength (RSS) of a mobile device. However, such methods are highly sensitive to environmental dynamics. A number of solutions based on added anchor points have been proposed to overcome this problem. This paper proposes an approach using existing beacons to measure the RSS from other beacons as a reference, which we call inter-beacon measurement, for the calibration of radio maps on the fly. This approach is feasible because most current beacons (such as Wi-Fi and ZigBee stations) have both transmitting and receiving capabilities. This approach would relieve the need for additional anchor points that deal with environmental dynamics. Simulation and experimental results are presented to verify our claims.     

Wi-Fi multi-floor indoor positioning considering architectural aspects and controlled computational complexity

This paper focuses on the positioning of Wi-Fi nodes in multi-floor indoor environments. A target radio-frequency (RF) fingerprint – measured by the MS to be localized – is compared with georeferenced RF fingerprints, previously stored in a correlation database (CDB). Therefore, this strategy lies within the so-called Database Correlation Methods (DCM) used to locate mobile stations (MS) in wireless networks. To obtain best matches in terms of architectural structures such as floors, doors, aisles, among others, the authors apply two combined techniques that improve localization accuracy: unsupervised clustering (K-medians and Kohonen layer) and majority voting committees of backpropagation artificial neural networks (ANNs). The unsupervised clustering is employed to allow collected data (the fingerprints) to group freely in their natural space, without precluding – through the imposition of architectural constraints – any natural arrangement of the collected fingerprints. The proposed combined strategy improves floor identification accuracy, which in indoor multi-floor positioning must be high. The effects of the proposed solution on the DCM positioning accuracy are experimentally evaluated using actual measured data. In the trial the floor identification accuracy ranged from 91% to 97%, and the average 2D positioning error ranged from 4.5 to 1.7 m, depending on the size of the measurement window (from 1 to 25 samples).     

A smartphone-based multimodal indoor tracking system

The great popularity of smartphones, together with the increasingly important aim of providing context-aware services, has spurred interest in developing indoor tracking systems. Accurate tracking and localization systems are seen as key services for most context-aware applications. Research projects making use of radio signals detected by radio interfaces and the data captured by sensors commonly integrated in most smartphones have already shown promising and better results than location solutions based on a single data source. In this paper, we present a multi-sensor tracking system built by incrementally integrating state-of-the-art models of the Wi-Fi interface and the accelerometer, gyroscope and magnetometer sensors of a smartphone. Our proposal consists of a simple calibration phase of the tracking system, which involves enabling simultaneous data gathering from all three sensors and the Wi-Fi interface. Taking the Wi-Fi signal model as baseline, four different configurations are evaluated by incrementally adding and integrating the models of the other three sensors. The experimental results reveal a mean error accuracy of 60 cm in the case when the tracking system makes use of all four data sources. Our results also include a spatial characterization of the accuracy and processing power requirements of the proposed solution. Our main findings demonstrate the feasibility of developing accurate localization indoor tracking systems using current smartphones without the need for additional hardware.     

一种无源被动室内区域定位方法的研究

室内区域定位在医疗养老、智慧大楼等领域有着广泛的应用.室内区域定位中最突出的问题是无线电信道效应的动态和不可预测性(如多径传播、信道衰落等)对接收信号强度(received signal strength, RSS)的干扰影响.为了降低无线电的干扰,提出了一种新的基于注意力机制的CNN-BiLSTM的室内区域定位模型,该模型通过捕获粗细粒度特征与定位区域的对应关系来减弱RSS序列对信道变化的依赖.首先,利用卷积神经网络(convolutional neural network, CNN)学习捕捉RSS序列的特征来抽取区域中心点的细粒度特征.然后,利用双向长短时记忆(bidirectional long short-term memory, BiLSTM)网络的存储记忆特性,学习当前与过去RSS序列中隐含区域范围的粗粒度特征.最后,利用注意力机制,通过融合粗细粒度特征,建立RSS序列特征与区域位置的映射关系,获取区域位置信息.真实室内环境下区域定位的实验结果表明,与目前定位效果最好的网格区域综合概率定位模型相比,提出的方法在降低计算复杂度的同时提高了区域定位的准确度和对环境的适应能力.     

基于时间反转的二阶段Wi-Fi室内定位方法

受Wi-Fi系统有限物理带宽限制,时间反转定位算法的定位精度难以得到提升。当定位范围较大时,在线定位阶段所需的匹配运算量更大,导致定位时间增长。针对上述问题,本文提出了一种基于时间反转的二阶段Wi-Fi室内定位方法。首先对接收信号强度和信道频率响应进行离线采集,利用接收信号强度和k近邻匹配算法进行位置粗估计,大致确定待测点所在范围。随后根据粗估计结果筛选原始指纹库,构建指纹库子集。在位置精估计阶段,计算待测点信道频率响应与指纹库子集中各参考点处信道频率响应的信号组合共振能量,通过最大值搜索寻找组合共振能量最大的参考点,将其坐标值作为位置估计结果。实验结果表明,所提算法相比于传统定位算法在精度和运行速度上有明显提升,在非直射环境下仍能保证较高的定位精度。     

Spoofing attacks and countermeasures in fm indoor localization system

FM-based indoor localization identifies the location of an user by looking at the received signal strength (RSS) at the user’s location. In this paper, we discover a spoofing attack which is able to cause the FM-based indoor localization system to malfunction. The newly discovered, easy launched spoofing attack which enables the adversary to deceive a victim user to obtain a fake indoor location through remotely manipulating the RSS at the user’s location simultaneously. By analyzing the features of the received and FM signals in the frequency domain, we propose a defense method to deal with this attack. The proposed method contains two levels of detections. Specifically, the first level of detection distinguishes between the normal signal and the attack/noise signal, and the second level of detection finally detects the existence of the attack signal. We perform real-world experiments on Universal Software Radio Peripherals (USRPs) to spoof a target location to four different locations 6 - 32 meters away from the target location. The experiment results show a promising performance of the proposed defense method with a false negative rate and a false alarm rate of 3.9% and 6.4%, respectively.     

面向复杂机房环境的室内定位技术应用研究#br#

考虑到机房数据中心运维智能化管理迫切需要精准的室内定位服务,针对机房金属遮挡、电磁辐射和其他机房设备干扰严重的复杂环境以及非视距传播误差显著降低传统室内定位精度问题,提出一种运用最优化理论原理和K最邻近算法的抗非视距传播误差的室内三维三边定位算法,在此基础上设计和开发基于超宽带技术的机房室内定位系统,用于复杂机房环境的室内定位导航。实验证明,该定位系统比Wi-Fi、蓝牙指纹定位具有更高的定位精度,在复杂的机房环境下基于非线性最小二乘法进行目标函数求解的最优化方法与KNN算法相结合的室内定位方案具备较好的定位性能,为机房数据中心运维智能化的位置定位导航服务提供了有效的方法和手段。     

基于Wi-Fi和自适应蒙特卡洛的移动机器人定位方法的研究

提出一种基于Wi-Fi和自适应蒙特卡洛的移动机器人定位方法。通过对实验环境中Wi-Fi信号的分布进行测试和分析,利用Wi-Fi信号强度的三角定位法,在ROS平台上实现Wi-Fi-AMCL室内初始化定位系统。对该方法和传统定位方法设计实验进行比较,结果表明前者不仅可以有效加快粒子的收敛速度、缩短机器人的定位时间,而且在一定限度上提高了机器人的初始化定位精度,改善了定位效果。     

From one to crowd: a survey on crowdsourcing-based wireless indoor localization

Wireless indoor localization has attracted growing research interest in the mobile computing community for the last decade. Various available indoor signals, including radio frequency, ambient, visual, and motion signals, are extensively exploited for location estimation in indoor environments. The physical measurements of these signals, however, are still limited by both the resolution of devices and the spatial-temporal variability of the signals. One type of noisy signal complemented by another type of signal can benefit the wireless indoor localization in many ways, since these signals are related in their physics and independent in noise. In this article, we survey the new trend of integrating multiple chaotic signals to facilitate the creation of a crowd-sourced localization system. Specifically, we first present a three-layer framework for crowdsourcing-based indoor localization by integrating-multiple signals, and illustrate the basic methodology for making use of the available signals. Next, we study the mainstream signals involved in indoor localization approaches in terms of their characteristics and typical usages. Furthermore, considering multiple different outputs from different signals, we present significant insights to integrate them together, to achieve localizability in different scenarios.     

基于多边限定的室内定位方法设计与系统实现

随着基于位置服务（Location Based Services，LBS）的发展与智能移动设备的普及，室内定位算法与系统受到了广泛研究与关注。为提高室内定位精度、增强系统鲁棒性，提出了基于多边限定的fingerprint定位方法。基于Wi-Fi RSSI（Received Signal Strength Indication）信号处理建立离线fingerprint数据库；通过对拟合距离-RSSI函数分析，提出了多边限定的方法确定一个最佳参考点（Reference Point，RP）集合，缩小在线定位阶段的搜索范围。在此基础上，再利用fingerprint定位方法进行定位。此外，实现了基于提出方法的室内定位系统原型用于算法性能评估。通过大量真实场景实验分析、验证了相较于传统fingerprint方法，基于多边限定的fingerprint定位方法能有效提高室内定位精度，增强系统鲁棒性。     

SCaNME: Location tracking system in large-scale campus Wi-Fi environment using unlabeled mobility map

In this paper, we propose a novel location tracking system called SCaNME (Shotgun Clustering-aided Navigation in Mobile Environment) which iteratively sequences the clusters of sporadically recorded received signal strength (RSS) measurements and adaptively construct a mobility map of the environment for location tracking. In the SCaNME system, the location tracking problem is solved by first matching the people’s locations to the location points (LPs) with small Kullback–Leibler (KL) divergence. Then, Allen’s logics are applied to reveal the person’s activities, assist the on-line location tracking and finally obtain a refined path estimate. The experimental results conducted on the large-scale HKUST campus demonstrate that the SCaNME tracking system provides better precision and reliability than the conventional location tracking systems. Furthermore, the experiments of SCaNME tracking system show its capability of providing people’s real-time locations without fingerprint calibration in large-scale Wi-Fi environment.