A Polygonal Method for Ranging-Based Localization in an Indoor Wireless Sensor Network

In this paper, we propose an indoor localization method in a wireless sensor network based on IEEE 802.15.4 specification. The proposed method follows a ranging-based approach using not only the measurements of received signal strength (RSS) but also the coordinates of the anchor points (APs). The localization accuracy depends on the errors in the distance estimation with the RSS measurements and the size of the polygon composed of the APs used for the lateration. Since errors are inevitably involved in the RSS measurement, we focus on reducing the size of the polygon to increase the localization accuracy. We use the centroid of the polygon as a reference point to estimate the relative location of a target in the polygon composed of the APs hearing the target. Once the relative position is estimated, only the APs covering the area are used for localization. We implement the localization method and evaluate the accuracy of the proposed method in various radio propagation environments. The experimental results show that the proposed method improves the localization accuracy and is robust against the dynamically changing radio propagation environments over time.     

Landmark‐based device calibration and region‐based modeling for RSS‐based localization

During the past decades, many fingerprint‐based indoor positioning systems have been proposed and have achieved great success. However, uncontrolled effects of device diversity, signal noise, and dynamic obstacles could recognizably degrade the performance of modern fingerprint‐based indoor localization systems. In this paper, to amend the variations in radio signal strengths (RSSs) caused by device diversity, we proposed an automatic device calibration process. Because of device diversity, the sensed RSS would deviate from the trained radio map and thus leads to poor positioning. An RSS transform function could be adopted to calibrate the RSS variation between different devices and overcome the device diversity problem. However, to train the transform function, a data collection process is required. Unlike conventional calibration methods requiring manual data collection, we proposed a landmark‐based automatic collection process. Based on the detection of Wi‐Fi landmarks, our system could automatically collect pair‐wise RSS samples between devices and train the RSS transform function without extra human power. In addition, to well represent the effects of signal noise and dynamic obstacles, a region‐based RSS modeling method was also proposed. The proposed modeling method allows our system to perform region‐based target localization and utilize more robust region information for localization. Experiments in various environments demonstrate that our system could give a better positioning performance by properly handling the RSS variation caused by signal noise, dynamic environment, and device diversity. Copyright © 2015 John Wiley & Sons, Ltd.     

Green wireless local area network received signal strength dimensionality reduction and indoor localization based on fingerprint algorithm

Green wireless local area network (WLAN) is an emerging technology to achieve both the purposes of power conservation and high‐speed accessing to the Internet because of the working on‐demand strategy adoption and high density access points (APs) deployment. Although it is good news to data traffic service, Green WLAN brings severe challenges to the indoor localization service based on fingerprint algorithm. Redundant APs will greatly enlarge the radio map and introduce a much heavier computation burden to the terminal for localization in the online phase. In addition, APs in Green WLAN are powered on and off to make balances between data traffic service demand and energy saving goals so that the received signal strength (RSS) sampled online and recorded in the radio map offline are rarely matched in the same detected AP number, which leads to asymmetric matching problem occurring in the fingerprint algorithm. In this paper, we propose to make a nonlinear dimensionality reduction on the RSS by local discriminant embedding algorithm to realize both the computation burden decreasing and asymmetric matching problem resolving for the fingerprint algorithm in Green WLAN. The simulation results show that our proposed methods could effectively reduce the computation burden in the online phase and make the fingerprint algorithm operate more robustly when the RSS is reduced to the intrinsic dimensionality in Green WLAN. Copyright © 2013 John Wiley & Sons, Ltd.     

Robust Indoor Positioning Provided by Real-Time RSSI Values in Unmodified WLAN Networks

The positioning methods based on received signal strength (RSS) measurements, link the RSS values to the position of the mobile station(MS) to be located. Their accuracy depends on the suitability of the propagation models used for the actual propagation conditions. In indoor wireless networks, these propagation conditions are very difficult to predict due to the unwieldy and dynamic nature of the RSS. In this paper, we present a novel method which dynamically estimates the propagation models that best fit the propagation environments, by using only RSS measurements obtained in real time. This method is based on maximizing compatibility of the MS to access points (AP) distance estimates. Once the propagation models are estimated in real time, it is possible to accurately determine the distance between the MS and each AP. By means of these distance estimates, the location of the MS can be obtained by trilateration. The method proposed coupled with simulations and measurements in a real indoor environment, demonstrates its feasibility and suitability, since it outperforms conventional RSS-based indoor location methods without using any radio map information nor a calibration stage.     

An Adaptive AP Selection Scheme Based on RSS for Enhancing Positioning Accuracy

In recent years, the indoor positioning technologies have been recognized as core technologies for realizing smart space, a ubiquitous society, context awareness, and various location-based services. There are several approaches for positioning with radio signals, but the received signal strength (RSS)-based technology is considered a promising scheme because of its simplicity and practicality in implementation. In this paper, the positioning performance of the RSS value-based scheme is analyzed with respect to the location of access points (APs) and the number of APs in an indoor environment. An adaptive AP selection scheme and a base AP changing scheme are then proposed to enhance the positioning accuracy. In order to estimate the RSS characteristics, RSS values are measured as the distance between the AP and the receiver increases. The positioning performance is evaluated with differing AP numbers, which form a triangle or a quadrilateral. The performance of the proposed schemes is evaluated via experiments using wireless local area network APs. Results show that the performance of proposed schemes is enhanced compared to that of conventional scheme.     

An indoor radio propagation model considering angles for WLAN infrastructures

Wireless local area network fingerprint‐based indoor location system is a hot topic these years because it needs no extra hardware and is very easy to deploy. However, it demands a database containing the distribution of received signal strength (RSS) of the area of interest,called radio map. Conventionally, we need to grid the area densely and manually measure RSS values on intersections, which will consume a lot of time and human resources. What is worse, change of the environment may render this database totally useless. Our consideration is to measure RSS on a small amount of these intersections and use them to build a radio propagation model. Then, this model can be deployed to predict RSS values of other intersections and reconstruct the radio map. In other words, we only need to collect a very small part the radio map and utilize the radio propagation model to recover the whole one. So far, many models have been proposed, among which the one suggested by Seidel, named floor attenuation factor propagation model, achieves great balance between computational request and accuracy. But it is not compatible with environments in some scenarios. So as to compensate for this deficiency, we take into account the angles formed by signal and surfaces of obstacles, and the results show better compatibility. The proposed model has four parameters that are related to the environments, and our second contribution in this paper is to propose a method to determine them. In fact, after collecting a small part of the radio map, we can estimate these parameters with least square method. Then, these parameters can be used to predict the signal strength at any other points in the same environment, and the whole radio map is rebuilt. According to practical experiments, performance of the radio map built by the proposed model is not as good as the manually collected one, but 80% of collecting labor is saved. Copyright © 2015 John Wiley & Sons, Ltd.     

Zero-configuration indoor localization over IEEE 802.11 wireless infrastructure

With the technical advances in ubiquitous computing and wireless networking, there has been an increasing need to capture the context information (such as the location) and to figure it into applications. In this paper, we establish the theoretical base and develop a localization algorithm for building a zero-configuration and robust indoor localization and tracking system to support location-based network services and management. The localization algorithm takes as input the on-line measurements of received signal strengths (RSSs) between 802.11 APs and between a client and its neighboring APs, and estimates the location of the client. The on-line RSS measurements among 802.11 APs are used to capture (in real-time) the effects of RF multi-path fading, temperature and humidity variations, opening and closing of doors, furniture relocation, and human mobility on the RSS measurements, and to create, based on the truncated singular value decomposition (SVD) technique, a mapping between the RSS measure and the actual geographical distance. The proposed system requires zero-configuration because the on-line calibration of the effect of wireless physical characteristics on RSS measurement is automated and no on-site survey or initial training is required to bootstrap the system. It is also quite responsive to environmental dynamics, as the impacts of physical characteristics changes have been explicitly figured in the mapping between the RSS measures and the actual geographical distances. We have implemented the proposed system with inexpensive off-the-shelf Wi-Fi hardware and sensory functions of IEEE 802.11, and carried out a detailed empirical study in our departmental building, Siebel Center for Computer Science. The empirical results show the proposed system is quite robust and gives accurate localization results.     

Location Estimation Based on Semi-Supervised Locally Linear Embedding (SSLLE) Approach for Indoor Wireless Networks

Location aware computing is popularized and location information use has important due to huge application of mobile computing devices and local area wireless networks. In this paper, we have proposed a method based on Semi-supervised Locally Linear Embedding for indoor wireless networks. Previous methods for location estimation in indoor wireless networks require a large amount of labeled data for learning the radio map. However, labeled instances are often difficult, expensive, or time consuming to obtain, as they require great efforts, meanwhile unlabeled data may be relatively easy to collect. So, the use of semi-supervised learning is more feasible. In the experiment 101 access points (APs) have been deployed so, the RSS vector received by the mobile station has large dimensions (i.e. 101). At first, we use Locally Linear Embedding to reduce the dimensions of data, and then we use semi-supervised learning algorithm to learn the radio map. The algorithm performs nonlinear mapping between the received signal strengths from nearby access points and the user??s location. It is shown that the proposed scheme has the advantage of robustness and scalability, and is easy in training and implementation. In addition, the scheme exhibits superior performance in the nonline-of-sight (NLOS) situation. Experimental results are presented to demonstrate the feasibility of the proposed SSLLE algorithm.     

Deployment of RSS-Based Indoor Positioning Systems

Location estimation based on Received Signal Strength (RSS) is the prevalent method in indoor positioning. For such positioning systems, a massive collection of training samples is needed for their calibration. The accuracy of these methods is directly related to the placement of the reference points and the radio map used to compute the device location. Traditionally, deploying the reference points and building the radio map require human intervention and are extremely time-consuming. In this paper we present an approach to reduce the manual calibration efforts needed to deploy an RSS-based localization system, both when using only one RF technology or when using a combination of RF technologies. It is an automatic approach both to build a radio map in a given workspace by means of a signal propagation model, and to assess the system calibration that best fits the required accuracy by using a multi-objective genetic algorithm.     

Investigation of radio channel uncertainty in distance estimation in wireless sensor networks

The distance estimation between nodes is a crucial requirement for localization and object tracking. Received signal strength (RSS) measurement is one of the used methods for the distance estimation in wireless networks. Its main advantage is that there are no additional hardware requirements. This paper describes a lateration approach for localization and distance estimation using RSS. For the purpose of investigation of RSS uncertainty, several scenarios were designed for both indoor and outdoor measurements. The first set of RSS measurement scenarios was proposed with the intention of hardware independent investigation of radio channel. For the second set of measurements, we employed IRIS sensor nodes to evaluate the distance estimation with certain devices. The experiments considered also obstacles in the radio channel. The results obtained in the proposed scenarios present usability of the method under different conditions. There is also a signal propagation model constructed from measured data at a node, which subsequently serves for distance determination.     

Reduced-complexity fingerprinting in WLAN-based indoor positioning

In this paper, a two-stage complexity reduction technique is implemented for a fingerprinting-based indoor positioning system. Several computation reduction techniques are applied during the offline and online phases of fingerprinting. Specifically, dimensionality reduction algorithms, clustering techniques and fast search strategies are integrated to achieve an ultimate reduction in the computational requirements of fingerprinting. The computational cost of fingerprinting is first reduced by restricting the location fingerprints to signal strength values received from informative access points (APs). Afterwards, clustering techniques are employed to speed up the online search for the target best match. Finally, selective matching between the target RSS and the pre-stored fingerprints is proposed to reduce the computational cost even further. In particular, this paper studies different dimensionality reduction methods and chooses the method that minimizes the positioning error. Moreover, a hybrid search solution of clustering and fast search strategies is proposed to minimize the search operations to find a user position in the radio map.     

Scene analysis indoor positioning enhancements

The emergence of innovative location-oriented services and the great advances in mobile computing and wireless networking motivated the development of positioning systems in indoor environments. However, despite the benefits from location awareness within a building, the implicating indoor characteristics and increased user mobility impeded the implementation of accurate and time-efficient indoor localizers. In this paper, we consider the case of indoor positioning based on the correlation between location and signal intensity of the received Wi-Fi signals. This is due to the wide availability of WLAN infrastructure and the ease of obtaining such signal strength (SS) measurements by standard 802.11 cards. With our focus on the radio scene analysis (or fingerprinting) positioning method, we study both deterministic and probabilistic schemes. We then describe techniques to improve their accuracy without increasing considerably the processing time and hardware requirements of the system. More precisely, we first propose considering orientation information and simple SS sample processing during the training of the system or the entire localization process. For dealing with the expanded search space after adding orientation-sensitive information, we suggest a hierarchical pattern matching method during the real-time localization phase. Numerical results based on real experimental measurements demonstrated a noticeable performance enhancement, especially for the deterministic case which has additionally the advantage of being less complex compared to the probabilistic one.     

基于接收信号强度非齐性分布特征的半监督学习室内定位指纹库构建

室内定位中半监督学习的指纹库构建方法能够降低人力开销,但忽略了高维接收信号强度(RSS)数据不均匀的非齐分布特点,影响定位精度,针对此问题该文提出一种基于RSS非齐性分布特征的半监督流形对齐指纹库构建方法。该算法运用局部RSS尺度参数以及共享近邻相似性构造权重矩阵,得到精确反映RSS数据流形结构的权重图,利用该权重图通过求解流形对齐的目标函数最优解,实现运用少量标记数据对大量未标记数据的位置标定。实验结果表明,该算法可以显著降低离线阶段数据采集的工作量,同时可以取得较高的定位精度。     

基于RSS相关性的位置指纹室内定位方法

解决设备差异性造成的Wi-Fi信号强度不确定问题是位置指纹室内定位应用与推广的关键.一种基于设备间接收信号强度(Received Signal Strength,RSS)相关性的位置指纹室内定位方法被提出.以智能手机为用户终端,离线阶段,通过智能手机扫描的Wi-Fi信号强度信息,经过数据处理,筛选稳定的接入点(Access Point,AP),构建离线指纹数据库;在线定位阶段,对于实时获取的Wi-Fi信号强度信息,进行筛选处理后,挑选与离线指纹共同拥有的AP,并根据该AP集合,形成新的离线指纹和在线指纹.对离线指纹按RSS的大小降序排序;在线指纹,则以同一次序对RSS排序,然后利用皮尔逊相关系数和杰卡德相似系数,计算指纹相似度并排序,通过K最近邻(K-Nearest Neighbor,KNN)算法实现用户定位.实验表明该方法可有效解决设备差异性问题,并实现精确定位,平均定位误差达到1.7 m.     

Histogram Based Particle Filtering with Online Adaptation for Indoor Tracking in WLANs

Indoor localization using signal strength in Wireless Local Area Networks is becoming increasingly prevalent in today??s pervasive computing applications. In this paper, we propose an indoor tracking algorithm under the Bayesian filtering and machine learning framework. The main idea is to apply a graph-based particle filter to track a person??s location on an indoor floor map, and to utilize the machine learning method to approximate the likelihood of an observation at various locations based on the calibration data. Histograms are used to approximate the RSS distributions at the survey points, and Nadaraya?CWatson kernel regression is adopted to recover the distributions at non-survey points only from the nearby locations. In addition, we also propose a simple algorithm to continuously update the radio map with the online measurements. A series of experiments are carried out in an office environment. Results show that the proposed Histogram Based Particle Filtering (HBPF)/HBPF with Online Adaptation achieves superior performance than other existing algorithms while retaining low computational complexity.     

基于距离估计和NNSS的室内定位算法

基于室内RSS,在IEEE802.11信道模型基础上,文中提出了一种综合考虑数据库特性和信号传输信道特性的WLAN定位算法,通过仿真试验证明,该算法能够达到较高的定位精度,对参与定位的AP数量的灵敏度较低,从而具有很好的定位精度稳定性.     

面向WLAN室内定位的T检验样本容量优化方法

WLAN（Wireless Local Area Networks）室内定位已受到人们广泛的关注,而在离线指纹采集阶段常常容易造成位置指纹RSS数据采集的盲目性和不可靠性,并忽略所需采集RSS（Received Signal Strength）样本容量与定位性能的关系.为了解决这一问题,本文提出一种面向WLAN室内定位的T检验样本容量优化方法.该方法在离线阶段利用OC（Operating Characteristics）函数优化指纹数据库允许的最小RSS样本容量,而在在线阶段利用T检验方法对目标终端进行粗定位,并进而提出基于T检验的KNN（K-Nearest Neighbour）算法以完成对目标终端的精定位.此方法用有限的样本容量获得较稳定的定位性能分析结果,显著地减少了大量的人力和时间开销.     

Multi‐system‐multi‐operator localization in PLMN using neural networks

Providing the localization algorithm for context‐aware services is the focus of many studies. This paper explores the properties of positioning models based on received signal strength (RSS) in PLMN (Public Land Mobile Network) networks. The effects of using the RSS at a mobile terminal from various systems, such as GSM and UMTS, as well as from multiple operators, have been investigated and discussed. Twenty‐two models, based on artificial neural networks, have been developed and verified using the data from an immense measurement campaign. The obtained results show that augmenting the model with additional RSS data, even from systems with poor radio‐visibility, may improve the positioning accuracy to as much as a 35thinspacem median distance error in a light urban environment. The degradation of accuracy in indoor environments and the complexity and latency of the models were also scrutinized. Copyright © 2011 John Wiley & Sons, Ltd.     

TOA/RSS混合信息室内可见光定位方法

为提高室内定位精度,提出一种基于混合到达时间/接收信号强度(TOA/RSS)信息的定位方法。针对室内可见光定位中存在的多径效应造成的定位非线性误差,引入前置无迹卡尔曼滤波的粒子滤波算法,将TOA信息与RSS信息相融合,达到修正非线性误差的目的。然后综合考虑接收端惯性传感参数,对接收端进行运动分析,提升估算坐标的精度。在长宽均为5 m、高度为3 m的室内进行定位仿真,在12 W发光二极管(LED)发射功率下,所提方法获得了平均定位误差为2.02 cm的定位精度。仿真结果证明,所提定位方法的定位性能总体优于指纹定位方法和三边定位的RSS定位方法,具有较强的鲁棒性和较低的定位延迟。     

Finding Near-Optimal Regularization Parameter for Indoor Device-free Localization

Device-free Localization (DfL) systems offer real-time indoor localization of people without any electronic devices attached on their bodies. The human body influences the radio wave propagation between wireless links and changes the Received Signal Strength (RSS). Wireless Sensor Networks (WSNs) nodes easily measure these RSS changes and appropriate Radio Tomographic Imaging (RTI) algorithms can then process the RSS data and allow human localization. This paper investigates how to choose near-optimal regularization parameter during the regularization process for indoor DfL and describes an experimental indoor DfL setup realized with a Sun SPOT based WSN. The work elaborates on the numerical calculation of the near-optimal regularization parameter by usage of the trade-off curve criterion. The calculated parameter enables conclusive RTI image with sufficient localization precision for eHealth or other ambient-assisted-living applications where the error tolerance is at a scale of several tens of centimeters. The value for the regularization parameter matches the empirical derived value obtained in the authors’previous work.