Received Signal Strength Data Research Articles

Maximum Mean Discrepancy Minimization Based Transfer Learning for Indoor WLAN Personnel Intrusion Detection

Indoor personnel intrusion detection has been recognized as an active research topic over the last decade due to the remarkably growing demand for indoor security management, elderly monitoring, and smart home. In this circumstance, indoor wireless local area network (WLAN) personnel intrusion detection is one of the most promising approaches by considering its advantages of the handy accessibility of WLAN signal and convenient use of WLAN devices. Many existing studies rely on only the offline WLAN received signal strength (RSS) data to train a heuristic model, which is, then, used for online intrusion detection without considering the time-variant RSS property in the actual indoor environment. To address this problem, we propose a new maximum mean discrepancy (MMD) minimization based transfer learning approach for indoor WLAN personnel intrusion detection. Specifically, first of all, the source and target domains are constructed from offline labeled and online unlabeled RSS data, respectively. Second, the MMD of marginal distributions of the RSS data in source and target domains are calculated as the difference of these two domains. Third, the optimal transfer matrix corresponding to the minimum difference of source and target domains (or called minimum MMD) is constructed to transfer the RSS data in these two domains into the data in a same subspace. Finally, the classifiers used for intrusion detection are trained from these data with the purpose of enhancing the robustness of the proposed approach.

A hidden semi-Markov model for indoor radio source localization using received signal strength

Multipath propagation makes the use of received signal strength (RSS) unreliable as a signal propagation model for localization of a radio source based on RSS data. An approach to mitigation of this problem is the use of a Hidden Markov model (HMM) to represent the relationship between RSS and the radio source location by incorporating an environment prior and RSS source dynamics. The HMM structure forces a geometric form for the distribution for the sojourn time. This, combined with missing data problems, reduces confidence in location estimation. It is found, in this paper, that Hidden semi-Markov Models (HsMMs), with a more flexible sojourn time distribution are more able to represent source dynamics while retaining the advantages of HMMs for environmental constraints and improving resilience to missing measurements. The performance of the proposed HsMM algorithm is compared with a standard HMM via experiments and simulations involving indoor radio source localization using RSS data. Simulations use a ray tracing software-based simulator and the experiments for transmitter localization with RSS data are collected by software defined radios.

An Efficient RSS Localization for Underwater Wireless Sensor Networks.

Localization is a key-enabling technology for many applications in underwater wireless sensor networks. Traditional approaches for received signal strength (RSS)-based localization often require uniform distribution for anchor nodes and suffer from poor estimates according to unpredictable and uncontrollable noise conditions. In this paper, we establish an RSS-based localization scheme to determine the location of an unknown normal sensor from a certain measurement set of potential anchor nodes. First, we present a practical path loss model for wireless communication in underwater acoustic environments, where anchor nodes are deployed in a random circumstance. For a given area of interest, the RSS data collection is performed dynamically, where the measurement noises and the correlation among them are taken into account. For a pair of transmitter and receiver, we approximate the geometry distance between them according to a linear regression model. Thus, we can obtain a quick access for the range information, while keeping the error, the communication head and the response time low. We also present a method to correct noises in the distance estimate. Simulation results demonstrate that our localization scheme achieves a better performance for certain scenario settings. The successful localization probability can be up to 90%, where the anchor rate is fixed at 10%.

RSE-Assisted Lane-Level Positioning Method for a Connected Vehicle Environment

In this paper, a roadside equipment (RSE)–assisted positioning method, i.e., global positioning system (GPS)–received signal strength (RSS) hybrid, is developed for lane-level positioning, which is fundamental to many applications in intelligent transportation systems. By exploiting the potential of RSE in existing pilots all over the world, this key component in connected vehicle networks can be used to achieve greater positioning accuracy than GPS positioning. The proposed method utilizes RSS data, which is commonly available in all connected vehicle networks, to update the GPS position and improve its accuracy based on a Bayesian approach. A method for lane positioning at a specific point is presented first, and then an extension to enable real-time lane positioning is proposed. Two typical types of RSE deployments for different traffic flow demands are considered, and the performance of the proposed method in each deployment is assessed. The proposed method features higher accuracy than existing GPS positioning methods and low complexity. To evaluate the proposed method, simulations are conducted, and the results demonstrate high accuracy and robustness. Moreover, field tests are also conducted, and the outcomes show that the proposed method can recognize the lane in which the target vehicle is traveling.

Fingerprint Database Reconstruction Based on Robust PCA for Indoor Localization.

The indoor localization method based on the Received Signal Strength (RSS) fingerprint is widely used for its high positioning accuracy and low cost. However, the propagation behavior of radio signals in an indoor environment is complicated and always leads to the existence of outliers and noises that deviate from a normal RSS value in the database. The fingerprint database containing outliers and noises will severely degrade the performance of an indoor localization system. In this paper, an approach to reconstruct the fingerprint database is proposed with the purpose of mitigating the influences of outliers. More specifically, by exploiting the spatial and temporal correlations of RSS data, the database can be transformed into a low-rank matrix. Therefore, the RPCA (Robust Principle Component Analysis) technique can be applied to recover the low-rank matrix from a noisy matrix. In addition, we propose an improved RPCA model which takes advantage of the prior knowledge of a singular value and could remove outliers and structured noise simultaneously. The experimental results show that the proposed method can eliminate outliers and structured noise efficiently.

WLAN indoor target intrusion detection algorithm based on adaptive-depth ray tree

With the wide deployment of wireless local area network (WLAN) and general support of WLAN protocol by various intelligent terminals, the intrusion detection with respect to the indoor target can be realized by using the existing WLAN infrastructure. To achieve this goal, the adaptive-depth ray tree based quasi 3D ray-tracing model is constructed to model the received signal strength (RSS) propagation property under the indoor silence and intrusion scenarios. Then, the RSS mean, variance, maximum, minimum, range, and median are allied to construct the training database for the probabilistic neural network (PNN). Finally, after the training, the PNN is utilized to perform the multi-classification decision with respect to the newly-collected RSS data, and consequently achieve the indoor target intrusion detection and area localization. The experimental results indicate that the proposed algorithm is featured with high intrusion detection rate as well as low database construction cost.

Fast fingerprints construction via GPR of high spatial-temporal resolution with sparse RSS sampling in indoor localization

Effective indoor localization largely relies on the fingerprint database (model) of Received Signal Strength (RSS) in connection with Radio Frequency sources, such as the most widely used Bluetooth Low Energy (BLE) iBeacons. RSSs exhibit significant random variations in both the spatial and temporal domains. It is a notoriously onerous and challenging task to construct the fingerprint database for accurate localization, as the BLE RSSs must be captured via a full space scan from one point to another every few meters in a certain period of time. In order to tackle this problem, this study proposes an approach to fast fingerprints construction that only requires a sparse sampling of RSS of the space. First, a smartphone records the time series of RSS over a designated path, and a radio map for the path is then generated by a spatio-temporal mapping method using the Pedestrian Dead Reckoning algorithm. Second, the radio map of the entire space can be obtained via Gauss Process Regression (GPR), with outliers reduced to improve the reliability of the fingerprint database. Experiments have been performed in an underground carpark (38 m $$\times $$ 14 m), and the experimental results indicate that the proposed approach can construct the fingerprint database 300% faster than the conventional approach does. The localization accuracy of both approaches is quite similar (80% error in 2.8 m). The proposed approach offers potential for the construction of a large-scale fingerprint database for a wide-area Location Based Service (LBS) of Smart City indoor and outdoor integration, where big RSS data processing is a must.

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.

The Indoor Positioning System Using Fingerprint Method Based Deep Neural Network

Highly dynamic indoor environments being one of the challenge in the Indoor Positioning System (IPS). Collecting the Received Signal Strength (RSS) value from every WiFi access point known fingerprint method is presented by previous researchers. They proposed with different techniques in fingerprint methods to compete similar existing technology such as GPS in term of accuracy. The drawback using fingerprint is the IPS cannot maintain the high performance constantly. In this research, we propose the Deep Neural Network (DNN) algorithm for improving the fingerprint method in the IPS. Basically, the fingerprint method consists of two phases, Online and Offline phases. In the off-line, RSS values will be collected from several coordinates as known reference points and stored in the database. The online phase has different step which the current position will be compared to RSS values stored in the database. The DNN method was used to calculate the closest position estimation probability. The IPS using DNN was successfully applied using 5 layers consisting of a 1 input layer, 3 hidden layers and 1 output layer. The input and hidden layer have 28 nodes for each layers and output layer has 2 nodes. The simulation results from RSS data set has achieved 2 meters accuracy. It concluded that DNN performance depends on the number of hidden layers and the number of nodes in each hidden layer.

Dynamic Wireless Indoor Localization Incorporating With an Autonomous Mobile Robot Based on an Adaptive Signal Model Fingerprinting Approach

Indoor localization based on received signal strength (RSS) will result in a decreased precision after the environment changes. In this paper, we develop an adaptive wireless indoor localization system (ILS) for dynamic environments. The system consists of the following two components: an automated database updating process and a new fingerprinting algorithm called adaptive signal model fingerprinting (ASMF). In the ILS, a self-locating mobile robot is set up to continuously collect RSS measurement data within the localization space for autonomously updating the fingerprint database. ASMF is designed to reduce the time consumption and the amount of RSS data needed for updating the database. The fingerprint of the signal in ASMF is constructed by the position of the beacons and three signal models, which can be duly corrected based on the regression and optimization algorithm. Finally, we propose experiments for positioning targets in the static and dynamic environments and compare the results of the ASMF algorithm with traditional trilateration and k-nearest-neighbor fingerprinting algorithms. The experimental results demonstrate that the ASMF-based ILS provides much better performance in both static and dynamic environments; furthermore, the positioning accuracy can be actually maintained by the autonomous updated ASMF database.

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.

Online Learning-Based WIFI Radio Map Updating Considering High-Dynamic Environmental Factors

Indoor localization has been recognized as a promising research around the world, and fingerprint-based localization method which leverages WIFI Received Signal Strength (RSS) has been extensively studied since widespread deployment of Access Points (APs) makes WIFI signals omnipresent and easily be obtained. A primary weakness of WIFI-based fingerprinting localization approach lies in its vulnerability under environmental changes and alteration of AP deployment. Despite some studies focus on dealing with effects of AP alterations and low-dynamic environmental factors, such as humidity, temperature, etc., influences of high-dynamic factors, such as changes of crowds' density and position, on WIFI radio map have not been sufficiently studied. In this work, we propose OWUH, an Online Learning-based WIFI Radio Map Updating service considering influences of high-dynamic factors. OWUH utilizes sensors in smart phones as the source of RSS datasets, and it combines historical and newly collected RSS data and purposeful probe data as dataset to incrementally update radio map, which means, compared with traditional methods, the OWUH approach requires a smaller number of RSS data for frequent updating of radio map. Moreover, in order to further enlarge our dataset, we take static data and low-dynamic data into account. An improved online learning method is proposed to recognize periodic pattern and update current radio map. Extensive experiments with 15 volunteers across 10 days indicate that OWUH effectively accommodates RSS variations over time and derives accurate prediction of fresh radio map with mean errors of less than 5dB, outperforming existing approaches.

A novel radio map construction method with reduced human efforts for Wi-Fi localisation system

Fingerprint based Wi-Fi localisation system often takes a lot of human efforts to measure the received signal strength (RSS) of dense grid in an indoor environment. In this paper, we propose a novel fingerprint database construction method with reduced human effort to obtain optimal length of RSS time series and grid division. We verify the chaotic characteristics of RSS time series, and use phase space reconstruction algorithm to calculate the optimal length of RSS data to be collected at each reference point. Then Gaussian process regression (GPR) for fingerprinting based indoor localisation is used to construct the database with information of limited reference points. The hyper-parameters of GPR is calculated by conjugate gradient descent algorithm. The performance of the proposed radio map construction framework is validated in real indoor environment, and with using Bayesian positioning method, the localisation error mean can be 1.5 m while the construction time of radio map is greatly reduced with ensuring accuracy.

Local Average Signal Strength Estimation for Indoor Multipath Propagation

A comprehensive study of approaches for the estimation of the local average of the received signal strength (RSS) for indoor multipath propagation is presented. The effects of the required number of the RSS data samples and the Euclidean distances between the neighboring samples are investigated over 1D, 2D, and 3D configurations. It was found that the effect of fast fading was reduced to an acceptable level using a 2D horizontal arrangement with a relatively large spacing configuration. Furthermore, averaging was enhanced with a larger spacing compared to smaller spacing when considering the same number of samples.

Effect of electromagnetic induction spectroscopy of screw crack implant on electromagnetic signal strength

Abstract Screws in bone implant are widely used in medical technology which focus on stabilizing and supporting the broken bone in means of healing. Improvement in bone implant are required in order to ensure better healing process because some failures may happen due to the crack of the screw implant. Previous studies showed there are lack of investigation on screw crack detection in bone screw implant. Early detection of screw implant loosening is crucial for faster repairing and a healthy ready-to-operate. The purpose of this study is to investigate the received signal strength of electromagnetic field induction on implant screw by employing a single channel magnetic induction spectroscopy using flat spiral coil sensor. This study also to evaluate the screw implant under the action of electromagnetic field induction. The electromagnetic method is choose to be implement using the COMSOL Multiphysics’s simulation software. The magnetic induction spectroscopy technique was used in this study to evaluate the received signal strength data for the different screw crack and non-crack. In this study, a high-cycle fatigue test using the flat coil sensor was performed in order to monitor cracks in Titanium Ti-6Al-4V screw.. The results suggested the feasibility of the proposed electromagnetic induction spectroscopy.

INDOOR POSITIONING SYSTEM BERDASARKAN FINGERPRINTING RECEIVED SIGNAL STRENGTH (RSS) WIFI DENGAN ALGORITMA K-NEAREST NEIGHBOR (K-NN)

Wireless networks other than communication media can be used to find out the existence of an object. Positioning technology that is commonly used is Global Positioning System (GPS). GPS can receive location information accurately in outdoor, this situation is contradictory in indoor environment, GPS signal is interrupted by signal attenuation caused by building materials and types of physical barriers. This study aims as an alternative solution for indoor positioning using RSS (Received Signal Strength) WiFi. Fingerprinting technique is used to collect RSS data on 5 access points in 3 test locations, RSS data collected is 243 data. This study uses Euclidean Distance and K-Nearest Neighbor (K-NN) method. The accuracy of the system is tested using the 10-Fold Cross Validation method based on the results of the test shows that the system is able to determine the location with an accuracy rate of 96.71%.

Research on RSS Data Optimization and DFL Localization for Non-Empty Environments.

Device-free localization (DFL) is a new technique which can estimate the target location through analyzing the shadowing effect on surrounding radio frequency (RF) links. In a relatively complex environment, the influences of random disturbance and the multipath effect are more serious. There are kinds of noises and disturbances in the received signal strength (RSS) data of RF links and the data itself can even be distorted, which will seriously affect the DFL accuracy. Most of the common filtering methods adopted in DFL field are not targeted and the filtering effects are unstable. This paper researches the characteristics of RSS data with random disturbances and proposes two-dimensional double correlation (TDDC) distributed wavelet filtering. It can filter out the random disturbances and noise while preserving the RSS fluctuations which are helpful for the DFL, thus improving the quality of RSS data and localization accuracy. Furthermore, RSS variation rules for the links are different in complex environments and hence, it is difficult for the collected training samples to cover all possible patterns. Therefore, a single machine learning model with poor generalization ability finds it difficult to achieve ideal localization results. In this paper, the Adaboost.M2 ensemble learning model based on the Gini decision tree (GDTE) is proposed to improve the generalization ability for unknown patterns. Extensive experiments performed in two different drawing rooms demonstrate that the TDDC distributed wavelet filtering and the GDTE localization model have obvious advantages compared with other methods. The localization accuracy rates of 87% and 95% can be achieved respectively in the two environments.

Robust Neighborhood Graphing for Semi-Supervised Indoor Localization With Light-Loaded Location Fingerprinting

The indoor localization systems based on wireless local area network received signal strength (RSS) have been widely applied due to the simplicity of system deployment as well as easy implementation on various mobile devices like the smartphones. However, they are often suffered by the major drawback of the extensive effort for location fingerprinting which is significantly labor-intensive and time-consuming. In response to this compelling problem, we design an improved manifold alignment approach to construct a cost-efficient radio map which consists of the sparsely collected location fingerprints and crowdsourcing RSS data with the purpose of reducing the overall fingerprints calibration effort. A new graph construction scheme which is proved to be the optimal choice to model the smoothness assumption in semi-supervised learning is proposed to explore the informativeness conveyed by location fingerprints during the process of radio map construction. In addition, the concept of execution characteristic function is considered to minimize the RSS sample capacity at each reference point to reduce fingerprints calibration effort further. Finally, the extensive experimental results demonstrate the performance improvement by the proposed system with the probability of localization errors within 3 m, 79.60%, which is at most 26.30 percentages higher than the one by the existing systems using location fingerprints solely.

Low-Dimensionality of Noise-Free RSS and Its Application in Distributed Massive MIMO

We examine the dimensionality of noise-free uplink received signal strength (RSS) in a distributed massive multiple-input multiple-output system. Upon applying principal component analysis to the noise-free uplink RSS data, we observe that it spans over a low-dimensional principal subspace. We make use of this unique property to propose a reconstruction-based Gaussian process regression (RecGP) method which predicts user locations from their uplink RSS. Considering noise-free RSS for training and noisy RSS for testing purposes, RecGP reconstructs the noisy test RSS from a low-dimensional principal subspace of the noise-free training RSS. This reconstruction allows RecGP to achieve lower prediction error than the standard Gaussian process regression method which directly uses the original test RSS for location prediction.

Indoor Pedestrian Motion Detection Via Spatial Clustering and Mapping

The proliferation of location-based services has driven the rapid development of indoor localization technology. The conventional location fingerprinting based localization approach suffers from the time-consuming and labor-intensive process of fingerprint database construction, and meanwhile, the widely known simultaneous localization and mapping seriously depends on various motion sensors. In response to these compelling problems, we propose a new indoor pedestrian motion detection approach without the assistance of location fingerprinting or motion sensing. Specifically, first of all, the motion paths are constructed by the motion model of the pedestrian in the target environment. Second, after collecting the wireless local area network (WLAN) received signal strength (RSS) sequence on each motion path, the concept of density-based spatial clustering is adopted to generate the RSS clusters, and in each, the RSS data are with the similar motion patterns. Third, based on the hotspot mapping relations between the physical subareas and RSS clusters, the target is located into the physical subarea mapped by the RSS cluster, which is most similar to the newly collected RSS data. Finally, the experimental results demonstrate that the proposed approach is capable of performing target localization as well as exploring the motion behavior of the pedestrian in an indoor environment.