Online Received Signal Strength Research Articles

A real-time fingerprint-based indoor positioning using deep learning and preceding states

In fingerprint-based positioning methods, the received signal strength (RSS) vectors from access points are measured at reference points and saved in a database. Then, this dataset is used for the training phase of a pattern recognition algorithm. Several noise types impact the signals in radio channels, and RSS values are corrupted correspondingly. These noises can be mitigated by averaging the RSS samples. In real-time applications, the users cannot wait to collect uncorrelated RSS samples to calculate their average in the online phase of the positioning process. In this paper, we propose a solution for this problem by leveraging the distribution of RSS samples in the offline phase and the preceding state of the user in the online phase.In the first step, we propose a fast and accurate positioning algorithm using a deep neural network (DNN) to learn the distribution of available RSS samples instead of averaging them at the offline phase. Then, the similarity of an online RSS sample to the RPs’ fingerprints is obtained to estimate the user’s location. Next, the proposed DNN model is combined with a novel state-based positioning method to more accurately estimate the user’s location. Extensive experiments on both benchmark and our collected datasets in two different scenarios (single RSS sample and many RSS samples for each user in the online phase) verify the superiority of the proposed algorithm compared with traditional regression algorithms such as deep neural network regression, Gaussian process regression, random forest, and weighted KNN.

Indoor WLAN Personnel Intrusion Detection Using Transfer Learning-Aided Generative Adversarial Network with Light-Loaded Database

The Internet of Everything (IoE) provides a platform that allows devices to be remotely connected, sensed, and controlled across the network infrastructure. The smart home in the era of the IoE is born on the basis of the high integration of emerging communication technologies such as big data, sensors, and machine learning. In this paper, we focus on wireless detection technologies using smartphones and computers in smart homes. Among them, the indoor Wireless Local Area Network (WLAN) personnel intrusion detection technology based on the database construction has become one of the comprehensive detection technologies by advantages of the convenient accessibility of the WLAN signal and minimal hardware requirement. However, the considerable labor and time cost involved in the database construction affects the popularity and application of database-based intrusion detection systems. To cope with this problem, we propose a new indoor WLAN personnel intrusion detection approach with the reduced overhead of the database construction. Specifically, first of all, the offline database is extended by fake Received Signal Strength (RSS) data, which are generated by the Generative Adversarial Network (GAN) based supervised learning from actual labeled RSS data. Second, the difference between the extended database and online RSS data caused by the time-variant environment noise is reduced by minimizing the Maximum Mean Discrepancy (MMD) between marginal distributions of RSS data through the transfer learning. Finally, the intrusion detection is achieved by classifying online RSS data with classifiers trained from the extended database. Furthermore, experimental results show that the proposed approach can not only perform well in reducing the database overhead and the difference of data in source and target domains, which are corresponding to the same environment state but also detect environment states with satisfactory accuracy.

Indoor intrusion detection based on deep signal feature fusion and minimized-MKMMD transfer learning

Indoor intrusion detection based on Wireless Local Area Network (WLAN) has been widely used for security monitoring, smart homes, entertainment games, and many other fields in the Beyond 5G (B5G) wireless communication network environment. Most existing indoor intrusion detection methods directly exploit the Received Signal Strength (RSS) data collected by Monitor Points (MPs) and pay little attention to the instability of WLAN signals in complex indoor environments. In response to this problem, this paper proposes a novel WLAN indoor intrusion detection method based on deep signal feature fusion and Minimized Multiple Kernel Maximum Mean Discrepancy (Minimized-MKMMD). Firstly, the multi-branch deep convolutional neural network is used to conduct the dimensionality reduction and feature fusion of the RSS data, and the tags are obtained according to the features of the offline and online RSS fusion features that are corresponding to the silence and intrusion states, and then based on this, the source domain and target domain are constructed respectively. Secondly, the optimal transfer matrix is constructed by minimizing MKMMD. Thirdly, the transferred RSS data in the source domain is utilized for training the classifiers that are applying in getting the classification of the RSS fusion features in the target domain in the same shared subspace. Finally, the intrusion detection of the target environment is realized by iteratively updating the process above until the algorithm converges. The experimental results show that the proposed method can effectively improve the accuracy and robustness of the intrusion detection system.

Wi-Fi based indoor localization using trilateration and fingerprinting methods

Nowadays, mobile devices such as personal computers and smartphones are emerging as a major key in today’s computing platforms for indoor object localization systems due to the object localization in indoor areas strongly suffers from limitation of using GNSS (Global navigation satellite system) systems due to low satellite availability and high signal attenuation. During the last decade, many researchers have developed indoor localization systems which are the process of obtaining user or device location through mobile devices using Wireless Fidelity (Wi-Fi) network signals with promising results and acceptable performance. In these Wi-Fi based localization systems, indoor positioning relies on different types of measurements including Time-Of-Arrival (TOA), Time-Difference-Of-Arrival (TDOA), Angle-Of-Arrival (AOA), and Received Signal Strength (RSS) of Wi-Fi signal.In this paper, the techniques and algorithms that used for the RSS-based localization such as Trilateration and Fingerprinting which depend on the RSS from the access point (WI-FI). Using Received Signal Strength Ranging approach in the Trilateration method which depends on database that contains path-loss-exponent and shadowing parameter that differ according to the environment, solving the equation using different Access Points (APs) at least 3 APs, the no of the APs and there locations were varied to get the best accuracy which depends on the horizontal dilution of position (HDOP). At the Fingerprinting method depends on matching the recorded offline RSS from nearby access points (AP) to the online RSS received by the user on the move is reviewed. A comparison of location fingerprinting methods involving deterministic method (k-nearest neighbor method and weighted k-nearest neighbor method), probabilistic methods by estimation of likelihood functions with several approaches (non-parametric and parametric)are also explained. The performance parameters of this study include the two-dimensional root mean square error (2D-rms) which measures the localization accuracy. Moreover, the effect of increasing/decreasing the number of APs on the system accuracy is also discussed. The aim of this paper is to announce which method can provide better performance than the others and under what conditions.

Expectation Maximization Indoor Localization Utilizing Supporting Set for Internet of Things

With the growth of WLAN infrastructure, received signal strength (RSS) fingerprint-based WiFi indoor positioning systems have received considerable attention recently. Some existing RSS fingerprint-based localization methods estimate locations by directly matching an online testing sample with an offline database, and thus show low accuracy because RSS is known to be vulnerable to variations caused by changing environment and heterogeneous hardware. To overcome the above drawbacks, we propose an expectation maximization indoor localization approach by leveraging supporting set (EMSS). In the offline phase, we first divide a positioning area into ${G}$ grid points and index each grid by a label. All the indices of grid points form a label set $ {\Psi =\{1,2,\ldots, G\}}$ . Then, we collect the RSS fingerprints to construct an offline database for all labeled grid points. In the online phase, given an online RSS testing sample, we first construct a supporting set (SS), which is a subset of $ {\Psi }$ , selected by the similarity between the online RSS sample and offline database. So, SS is a latent space that likely includes the true label (location) of the user. Based on the SS, we then derive an expectation maximization (EM) algorithm by incorporating the fingerprint quality into the estimation of the true label. EM can intelligently estimate the location of the user by evaluating the fingerprint quality of SS. Furthermore, we propose an optimal size selection algorithm based on Bayesian information criterion to adaptively determine the size of SS. Our method can effectively mitigate the impacts of changing environment and heterogeneous hardware without fingerprint and hardware calibrations, and can thus be practically applied. Experimental results verify that EMSS performs significantly better than some existing fingerprint-based methods.

Improving Wi-Fi Fingerprint Positioning with a Pose Recognition-Assisted SVM Algorithm

The fingerprint method has been widely adopted for Wi-Fi indoor positioning. In the fingerprint matching process, user poses and user body shadowing have serious impact on the received signal strength (RSS) data and degrade matching accuracy; however, this impact has not attracted large attention. In this study, we systematically investigate the impact of user poses and user body shadowing on the collected RSS data and propose a new method called the pose recognition-assisted support vector machine algorithm (PRASVM). It fully exploits the characteristics of different user poses and improves the support vector machine (SVM) positioning performance by introducing a pose recognition procedure. This proposed method firstly establishes a fingerprint database with RSS and sensor data corresponding to different poses in the offline phase, and fingerprints of different poses in the database are extracted to train reference point (RP) classifiers of different poses and a pose classifier using an SVM algorithm. Secondly, in the online phase, the poses of RSS data measured online are recognised by a pose classifier, and RSS data measured online are grouped with different poses. Then online RSS data from each group at an unknown user location are reclassified as corresponding RPs by the RP classifiers of the corresponding poses. Finally, user location is determined by grouped RSS data corresponding to coordinates of the RPs. By considering the user pose and user body shadowing, the observed RSS data matches the fingerprint database better, and the classification accuracy of grouped online RSS data is remarkably improved. To verify performances of the proposed method, experiments are carried out: one in an office setting, and the other in a lecture hall. The experimental results show that the positioning accuracies of the proposed PRASVM algorithm outperform the conventional weighted k-nearest neighbour (WKNN) algorithm by 52.29% and 40.89%, outperform the SVM algorithm by 73.74% and 60.45%, and outperform the pose recognition-assisted WKNN algorithm by 34.76% and 21.86%, respectively. As a result, the PRASVM algorithm noticeably improves positioning accuracy.

Transferred Knowledge Aided Positioning via Global and Local Structural Consistency Constraints

The fluctuation of received signal strength (RSS) induced by changing environment is the main hindrance from practical applications of the fingerprint-based indoor positioning methods. Transfer learning can mitigate the fluctuation of RSS by transferring knowledge from a source domain (off-line RSS data) to a target domain (online RSS data). However, the existing transfer learning approaches do not fully take into account the full constraints in Global and LOcal Structural conSistency (GLOSS), thus resulting in insufficient knowledge transfer. To overcome the above drawback, we propose a Transferred knowlEdge-Aided POsiTioning (TEAPOT) approach via GLOSS constraints in this paper. TEAPOT imposes the global structural consistency by minimizing the differences between the marginal and conditional distributions of the source and target domains and maximizing the samples variance in a latent subspace. Simultaneously, it also imposes the local structural consistency by minimizing within class variance and maximizing between class variance to retain the source discriminative information and preserving the local neighborhood relationship by using manifold regularization. Furthermore, a nonlinear TEAPOT is derived to improve the ability of TEAPOT to alleviate the limitation of linear projection. Compared with the existing methods, two of our proposed TEAPOT approaches via GLOSS constraints show higher accuracy and better ability in handling out-of-sample generalization. The experimental results verify that the proposed method significantly outperforms the existing methods.

An Adaptive Weighted KNN Positioning Method Based on Omnidirectional Fingerprint Database and Twice Affinity Propagation Clustering.

The human body has a great influence on Wi-Fi signal power. A fixed K value leads to localization errors for the K-nearest neighbor (KNN) algorithm. To address these problems, we present an adaptive weighted KNN positioning method based on an omnidirectional fingerprint database (ODFD) and twice affinity propagation clustering. Firstly, an OFPD is proposed to alleviate body’s sheltering impact on signal, which includes position, orientation and the sequence of mean received signal strength (RSS) at each reference point (RP). Secondly, affinity propagation clustering (APC) algorithm is introduced on the offline stage based on the fusion of signal-domain distance and position-domain distance. Finally, adaptive weighted KNN algorithm based on APC is proposed for estimating user’s position during online stage. K initial RPs can be obtained by KNN, then they are clustered by APC algorithm based on their position-domain distances. The most probable sub-cluster is reserved by the comparison of RPs’ number and signal-domain distance between sub-cluster center and the online RSS readings. The weighted average coordinates in the remaining sub-cluster can be estimated. We have implemented the proposed method with the mean error of 2.2 m, the root mean square error of 1.5 m. Experimental results show that our proposed method outperforms traditional fingerprinting methods.

A Comparison Study of Indoor Localization Methods Using Available WI-FI Signals

Object localization in indoor areas strongly suffers from limitation of using GNSS (Global navigation satellite system ) systems due to low satellite availability and high signal attenuation. Nowadays, mobile devices such as personal computers and smart phones are emerging as a major key in today’s computing platforms for indoor object localization systems due to the rapid developments in wireless communications and mobile computing. During last decade, many researchers have developed indoor localization systems through mobile devices using Wireless Fidelity (Wi-Fi) network signals with promising results and acceptable performance. In these Wi-Fi based localization systems, indoor positioning relies on different types of measurements including Time-Of-Arrival (TOA), Time-Difference-Of-Arrival (TDOA), Angle-Of-Arrival (AOA), and Received Signal Strength (RSS) of Wi-Fi signal. In this paper, the algorithms and techniques used for the RSS-based localization systems using fingerprinting method which depends on matching the recorded offline RSS from nearby access points (AP) to the online RSS received by the user on the move is reviewed. A comparison of location fingerprinting methods involving deterministic method (k-nearest neighbor method and weighted k-nearest neighbor method), probabilistic methods by estimation of likelihood functions with several approaches (non-parametric and parametric)are also explained. The performance parameters of this comparative study include the two-dimensional root mean square error (2D-rms) which measures the localization accuracy. Moreover, the effect of increasing/decreasing the number of APs on the system accuracy is also discussed. The aim of this comparison is to announce which method can provide better performance than the others and under what conditions.

Structured Group Sparsity: A Novel Indoor WLAN Localization, Outlier Detection, and Radio Map Interpolation Scheme

This paper introduces novel schemes for indoor localization, outlier detection, and radio map interpolation using wireless local area networks. The localization method consists of a novel multicomponent optimization technique that minimizes the squared $\ell _{2}$ -norm of the residuals between the radio map and the online received signal strength (RSS) measurements, the $\ell _{1}$ -norm of the user's location vector, and weighted $\ell _{2}$ -norms of layered groups of reference points (RPs). RPs are grouped using a new criterion based on the similarity between the so-called access point (AP) coverage vectors. In addition, since AP readings are prone to containing inordinate readings, called outliers, an augmented optimization problem is proposed to detect the outliers and localize the user with cleaned online measurements. Moreover, a novel scheme to record fingerprints from a smaller number of RPs and estimate the radio map at RPs without recorded fingerprints is developed using sparse recovery techniques. All localization schemes are tested on RSS fingerprints collected from a real environment. The overall scheme has comparable complexity with competing approaches, while it performs with high accuracy under a small number of APs and finer granularity of RPs.

Robust, cost-effective and scalable localization in large indoor areas

Indoor location information plays a fundamental role in supporting various interesting location-aware indoor applications. Widely deployed WiFi networks make it feasible to perform indoor localization by first establishing a received signal strength (RSS) map covering the whole area based on a signal propagation model, then determining a location from an online RSS measurement given the RSS map. However, challenges remain in practical deployments, due to inaccurately estimated RSS values in the RSS map and an insufficient number of access points (APs) in large indoor areas. To address these challenges, we develop a robust, cost-effective and scalable localization system (REAL). Our approach adaptively searches for the best model parameters with limited training resources. In addition, REAL utilizes a probabilistic approach for location searching by considering errors from the signal propagation model. It also exploits information regarding unobserved APs at a given location and an optimal clustering method. We systematically evaluate the accuracy of the propagation model with different configurations. Our intensive real-world experimental results demonstrate that REAL achieves considerable localization accuracy at a very low training cost. In addition, the comparisons over two large indoor environments show that REAL consistently outperforms other state-of-the-art systems and can be effectively applied to various real-world scenarios.

Kalman Filter-Based Indoor Position Tracking with Self-Calibration for RSS Variation Mitigation

This paper investigates the indoor position tracking problem under the variation of received signal strength (RSS) characteristic from the changes of device statuses and environmental factors. A novel indoor position tracking algorithm is introduced to provide reliable position estimates by integrating motion sensor-based positioning (i.e., dead-reckoning) and RSS-based fingerprinting positioning with Kalman filter. In the presence of the RSS variation, RSS-based fingerprinting positioning provides unreliable results due to different characteristics of RSS measurements in the offline and online phases, and the tracking performance is degraded. To mitigate the effect of the RSS variation, a recursive least square estimation-based self-calibration algorithm is proposed that estimates the RSS variation parameters and provides the mapping between the offline and online RSS measurements. By combining the Kalman filter-based tracking algorithm with the self-calibration, the proposed algorithm can achieve higher tracking accuracy even in severe RSS variation conditions. Through extensive computer simulations, we have shown that the proposed algorithm outperforms other position tracking algorithms without self-calibration.

Received signal strength recovery in green WLAN indoor positioning system using singular value thresholding.

Green WLAN is a promising technique for accessing future indoor Internet services. It is designed not only for high-speed data communication purposes but also for energy efficiency. The basic strategy of green WLAN is that all the access points are not always powered on, but rather work on-demand. Though powering off idle access points does not affect data communication, a serious asymmetric matching problem will arise in a WLAN indoor positioning system due to the fact the received signal strength (RSS) readings from the available access points are different in their offline and online phases. This asymmetry problem will no doubt invalidate the fingerprint algorithm used to estimate the mobile device location. Therefore, in this paper we propose a green WLAN indoor positioning system, which can recover RSS readings and achieve good localization performance based on singular value thresholding (SVT) theory. By solving the nuclear norm minimization problem, SVT recovers not only the radio map, but also online RSS readings from a sparse matrix by sensing only a fraction of the RSS readings. We have implemented the method in our lab and evaluated its performances. The experimental results indicate the proposed system could recover the RSS readings and achieve good localization performance.

Error Estimation Method for Matrix Correlation-Based Wi-Fi Indoor Localization

A novel neighbor selection-based fingerprinting algorithm using matrix correlation (MC) for Wi-Fi localization is presented in this paper. Compared with classic fingerprinting algorithms that usually employ a single received signal strength (RSS) sample, the presented algorithm uses multiple on-line RSS samples in the form of a matrix and measures correlations between the on-line RSS matrix and RSS matrices in the radio-map. The algorithm makes efficient use of on-line RSS information and considers RSS variations of reference points (RPs) for localization, so it offers more accurate localization results than classic neighbor selection-based algorithms. Based on the MC algorithm, an error estimation method using artificial neural network is also presented to fuse available information that includes RSS samples and localization results computed by the MC algorithm and model the nonlinear relationship between the available information and localization errors. In the on-line phase, localization errors are estimated and then used to correct the localization results to reduce negative influences caused by a static radio-map and RP distribution. Experimental results demonstrate that the MC algorithm outperforms the other neighbor selection-based algorithms and the error estimation method can reduce the mean of localization errors by nearly half.

Kalman/Map Filtering-Aided Fast Normalized Cross Correlation-Based Wi-Fi Fingerprinting Location Sensing

A Kalman/map filtering (KMF)-aided fast normalized cross correlation (FNCC)-based Wi-Fi fingerprinting location sensing system is proposed in this paper. Compared with conventional neighbor selection algorithms that calculate localization results with received signal strength (RSS) mean samples, the proposed FNCC algorithm makes use of all the on-line RSS samples and reference point RSS variations to achieve higher fingerprinting accuracy. The FNCC computes efficiently while maintaining the same accuracy as the basic normalized cross correlation. Additionally, a KMF is also proposed to process fingerprinting localization results. It employs a new map matching algorithm to nonlinearize the linear location prediction process of Kalman filtering (KF) that takes advantage of spatial proximities of consecutive localization results. With a calibration model integrated into an indoor map, the map matching algorithm corrects unreasonable prediction locations of the KF according to the building interior structure. Thus, more accurate prediction locations are obtained. Using these locations, the KMF considerably improves fingerprinting algorithm performance. Experimental results demonstrate that the FNCC algorithm with reduced computational complexity outperforms other neighbor selection algorithms and the KMF effectively improves location sensing accuracy by using indoor map information and spatial proximities of consecutive localization results.

Modeling, Real-Time Estimation, and Identification of UWB Indoor Wireless Channels

Stochastic differential equations (SDEs) are used to model ultrawideband (UWB) indoor wireless channels. We show that the impulse responses for time-varying indoor wireless channels can be approximated in a mean-square sense as close as desired by impulse responses that can be realized by SDEs. The state variables represent the inphase and quadrature components of the UWB channel. The expected maximization and extended Kalman filter are employed to recursively identify and estimate the channel parameters and states, respectively, from online received signal strength measured data. Both resolvable and nonresolvable multipath received signals are considered and represented as small-scaled Nakagami fading. The proposed models together with the estimation algorithm are tested using UWB indoor measurement data demonstrating the method’s viability and the results are presented.

Dynamic Propagation Modeling for Mobile Users' Position and Heading Estimation in Wireless Local Area Networks

A system for mobile users' position and heading estimation in IEEE 802.11 WLAN (WiFi) using received signal strength (RSS) approach is introduced. The basic contribution the system introduces is that it doesn't need offline training or extra special hardware. It makes use of the fact that only few online RSS measurements from visible access points (AP) around the user is needed to build local propagation model at run-time. Gaussian Process Regression (GPR) is used as a non-parametric modeling that handles non-equally spaced sparse data. Due to the few learning data points, Gaussian kernels calibration and prediction happen in a single step. This enables the system to autonomously adapt to environment changes. The estimated ranges from multiple access points (AP) are used to determine position using weighted least squares. Then, the rate of change of signal strength from multiple APs is used by a novel algorithm to estimate heading. Experiments show reliable meter-level positioning accuracy and heading estimation accuracy of 16.5 degrees.