Received Signal Strength Data Research Articles

Selection of Signal Sources Influence at Indoor Positioning System

The indoor positioning task is an exciting problem because of the practical applications and the scientific challenges. This work emphasises the impact of signal source selection on the effectiveness of positioning models relying on Wi-Fi received signal strength data by eliminating unstable signal sources such as mobile access points. Two localisation algorithms, based on k-Nearest Neighbour and Random Forest, were applied to data from three academic buildings to test the effectiveness of various access points (APs) selection filters. Additionally, new hierarchical selection models were proposed. The methods divide building into zones to improve the characteristics of the positioning error by a local selection of APs. Hierarchical methods reduced the median number of AP to 67 in comparison to over 100 selected by existing methods, obtaining a statistically similar median error of 6.01 m.

BLE Beacons for Sample Position Estimation in A Life Science Automation Laboratory

Abstract Estimation of the sample position is essential for working process monitoring and management in the life science automation laboratory. Bluetooth low-energy (BLE) beacons have the advantage of low price, small size, and low-energy consumption, which make them a promising solution for sample position estimation in the automated laboratory. Several fingerprinting models have been proposed to achieve indoor localization with the received signal strength (RSS) data. However, most of the research depends on intensive beacon installation. Proximity estimation, which depends entirely on one beacon, is more suitable for sample position estimation in large automated laboratories. The complexity of the life science automation laboratory environment brings challenges to the traditional path loss model (PLM), which is a widely used radio wave propagation model-based proximity estimation method. In this paper, BLE sensing devices for sample position estimation are proposed. The BLE beacon-based proximity estimation is discussed in the framework of machine learning, in which the support vector regression (SVR) is utilized to model the nonlinear relationship between the RSS data and distance, and the Kalman filter is utilized to decrease the RSS data deviation. The experimental results over different environments indicate that the SVR outperforms the PLM significantly, and provides 1 m absolute errors for more than 95% of the testing samples. The Kalman filter brings benefits to stable distance predictions. Apart from proximity-based sample position estimation, the proposed framework turned out to be effective in position estimation between parallel workbenches and position estimation on an automated workstation.

A new device-free localization method for RSS data with considering correlations

Device-free localization (DFL) is an emerging intelligent sensing technique with challenging issues due to the highly correlated data. Localization performance has been demonstrated from algorithmic as well as modelling perspectives, for example, by including various sparsity constraints. The general sparse regularization method simultaneously estimates parameters and selects features, but its performance relies heavily on the correlations among features, which leads to generalization errors. Our goal in this study is to formulate a DFL method based on independently interpretable sparse coding to improve the accuracy and efficiency of localization. The scheme uses a novel correlation constraint of sensing matrix columns to handle the high correlation of the received signal strength (RSS) data, overcoming not only the fluctuation of RSS due to environmental changes but also avoiding overfitting through identification of informative variables that provide interpretable results. Meanwhile, the minimax concave penalty (MCP) is proposed as a sparse regularizer to obtain strong sparsity and an exact estimate. We propose using the difference of convex functions algorithm (DCA) and non-convex proximal splitting to address the non-convexity problem. Extensive experiments were performed in a cluttered indoor laboratory and open outdoor environment to verify the efficacy of the proposed method.

An Indoor Fingerprint Positioning Algorithm Based on WKNN and Improved XGBoost.

Considering the low indoor positioning accuracy and poor positioning stability of traditional machine-learning algorithms, an indoor-fingerprint-positioning algorithm based on weighted k-nearest neighbors (WKNN) and extreme gradient boosting (XGBoost) was proposed in this study. Firstly, the outliers in the dataset of established fingerprints were removed by Gaussian filtering to enhance the data reliability. Secondly, the sample set was divided into a training set and a test set, followed by modeling using the XGBoost algorithm with the received signal strength data at each access point (AP) in the training set as the feature, and the coordinates as the label. Meanwhile, such parameters as the learning rate in the XGBoost algorithm were dynamically adjusted via the genetic algorithm (GA), and the optimal value was searched based on a fitness function. Then, the nearest neighbor set searched by the WKNN algorithm was introduced into the XGBoost model, and the final predicted coordinates were acquired after weighted fusion. As indicated in the experimental results, the average positioning error of the proposed algorithm is 1.22 m, which is 20.26-45.58% lower than that of traditional indoor positioning algorithms. In addition, the cumulative distribution function (CDF) curve can converge faster, reflecting better positioning performance.

Exploiting Environmental Information Using HsMMs for Smartphone User Tracking

The extensive deployment of wireless infrastructure provides alternative low-cost methods for location awareness of mobile phone users (MPUs) in indoor environments by processing the received signal strength (RSS) of the mobile phone. In such a signal-processing framework, hidden Markov models (HMMs) are often used to model the uncertainties of RSS data and incorporate environmental information into localization. Since hidden semi-Markov models (HsMMs) outperform HMMs in their ability to model state duration more flexibly, employing HsMMs for indoor user positioning is a promising research direction. In this aspect, a user’s personal preference for staying in a particular area, and the functionality of certain areas, such as a dining room, as well as navigation landmarks, can be utilized in the HsMM to assist localization. This article proposes an online HsMM forward recursion (HsMM-FR) algorithm to incorporate this information for real-time smartphone user tracking. We apply the proposed HsMM-FR algorithm to simulated, synthesized, and real RSS datasets in typical indoor environments for validation.

Enhanced Radio Map Interpolation Methods Based on Dimensionality Reduction and Clustering

The received signal strength (RSS) based Wi-Fi fingerprinting method is one of the most potential and easily deployed approaches for a reliable indoor positioning system. However, due to the labor intensive and time-consuming radio map construction process, interpolation is often incorporated. To ensure the interpolated radio map is robust against environmental noise and RSS fluctuations, we propose two novel interpolation methods, termed as DimRed and DimRedClust, for an improved radio map construction. The former performs dimensionality reduction prior to the interpolation while the latter employs both the dimensionality reduction and clustering before interpolating the radio map. For dimensionality reduction, principal component analysis (PCA) or truncated singular value decomposition (TSVD) is adopted to profoundly extract essential features from the RSS data while the K-means algorithm is used to partition the reference points (RPs) into several clusters. Subsequently, the RSS for all virtual points are interpolated via inverse distance weighting (IDW). Numerical results based on the real-world multi-floor multi-building dataset confirm the supremacy of the proposed schemes over the baseline IDW interpolation. Compared to the baseline IDW, the proposed PCA-K-means-IDW, TSVD-K-means-IDW, PCA-IDW, and TSVD-IDW could attain a performance gain in terms of average positioning error of up to 30.17%, 30.93%, 19.33%, and 21.61%, respectively.

Smartphone User Tracking by Incorporating User Orientation Using a Double-Layer HMM

We consider the problem of localizing a smartphone user using received signal strength (RSS) measured by a set of known network nodes in a harsh indoor environment. While the RSS of a wireless signal can be conveniently accessed, using it to estimate location is non-trivial in the presence of multipath propagation, shadowing and radio interference. Auxiliary information, such as the indoor building map and user's orientation information, potentially can help to improve localization performance. As the indoor layout is usually known as a priori, a user's moving direction or orientation in a given indoor map may contain valuable information to assist for reducing location ambiguities at estimation, typically when the radio signal channel is corrupted with noise. In this paper, we propose a double-layer hidden Markov model (DHMM) within a Bayesian learning framework for combining user orientation information and processing RSS data in the localization process to deal with RSS fluctuations induced by human body shadowing and multipath interference. Simulation and experimental results show that incorporating user orientation can potentially provide promising indoor positioning results.

Improving Weighted Multiple Linear Regression Algorithm for Radiolocation Estimation in LoRaWAN

In location based services, Weighted Multiple Linear Regression (WMLR) algorithm is used for radio device position estimation. Nevertheless, WMLR provides coarse location estimate, because weights apportioned to the received signal strength (RSS) for each hearable base station during matrix weight formation are not properly distributed. In an attempt to address the problem articulated above, an improved WMLR that enhanced the accuracy of radio device position estimate is proposed in this work. Min-Max scaling was used to determine the weight for each RSS values logged at different BS, as such forming a refined matrix weight. Public on-site outdoor Long Range Wide Area Network (LoRaWAN) RSS data set was used to assess the improved WMLR estimation algorithm on the basis of accuracy. The location accuracy of the proposed method is validated with the existing WMLR algorithm and Federal Communication Commission (FCC) maximum location error benchmark. Results show that the location accuracy of the improved approach outperformed that of the existing WMLR localization method.

Device Free Localization Using Prior Correction-Based Logistic Regression

In this letter, we focus on a device free localization (DFL) system which is based on logistic regression model using received signal strength (RSS) measurements in each link as training and testing data. To reduce the computational complexity of the logistic regression model prediction and improve the localization accuracy by reduction of the redundant training features, we obtain principal component using the measured RSS data through principal component analysis (PCA). Moreover, to overcome the imbalanced data problem, the number of training data belonging to major class is decreased through the sampling technique based on the distance between the reference points. The modified logistic regression model is derived from Bayesian formula based prior correction method due to the change in the ratio of the major and the minor classes in sampled training data. By substituting the transformed principal components of testing dataset to the probability model with the optimal regression coefficients, the localization probabilities of a target are computed at every reference point.

3.5 GHz Outdoor Radio Signal Strength Prediction With Machine Learning Based on Low-Cost Geographic Features

In the fifth-generation (5G) and beyond 5G (B5G) networks, received signal strength (RSS) prediction has always been a fundamental task in network planning and optimization phases. The existing machine learning (ML)-based RSS prediction models seldom testify to the model validity with reliable drive test data in 5G networks and tend to neglect the low-cost extraction in feature contriving. This article presents an ML scheme based on the geographic feature to predict RSS. We elaborately select four features closely related to RSS from the easily acquired geographic dataset and design low-cost methods for computing them. Experiments are executed in the large-scale outdoor scenario at 3.5 GHz for a 5G network where the real RSS data are collected by the field measurement in the urban city. We adopt four state-of-the-art ML algorithms for the proposed scheme and compare the algorithm accuracy with a Stanford University interim (SUI) model, an ECC-33 model, and a ray tracing method. Experiment results show the validity of the extracted features. Besides, the proposed scheme performs well in its model accuracy and computational efficiency compared with the existing methods. Furthermore, it is promising to build a universal, accurate, and efficient ML-based RSS prediction scheme based on massive field data in 5G or B5G networks.

Sparsity-enabled radio tomographic imaging using quantized received signal strength observations

Detection and tracking of objects in wireless sensor networks (WSN) is one of the active research areas in the signal processing community, where device-free localization (DFL) techniques are the potential contributors. Radio tomographic imaging (RTI) is one such DFL technique where the loss field surrounded by any obstacle can be visualized with the help of the received signal strength (RSS) of various transceiver nodes. Due to strict communication constraints and the necessity of low-complexity sensors, we are motivated towards quantized RSS (q-RSS) observations in the RTI system. The RSS data is digitized before sending it to the fusion center (FC). Therefore, uncertainty due to RSS quantization error leads to momentous performance degradation of the RTI system. The proposed support vector regression (SVR) technique reduces this uncertainty. The ϵ-SVR and ν-SVR techniques are affected by noise levels, and their performance is enhanced by the use of fused l2-SVR (F-l2-SVR). However, these estimators have higher computational complexity due to the large number of support vectors. Hence, a robust sparsity-based estimator using the linear programming SVR (LP-SVR) is proposed to reduce the computational complexity. Further, a novel fused lasso-based l1-SVR (FL-l1-SVR) estimator is proposed that provides estimated spatial loss field (SLF) as close as to the true SLF. Finally, the performance metrics and the reduced number of support vectors lead to the efficacy of the proposed FL-l1-SVR estimator over other estimators under q-RSS observations in the RTI system.

Robust UWB Indoor Localization for NLOS Scenes via Learning Spatial-Temporal Features

Ultra-wide band (UWB) localization system suffers from deteriorating performance in complex scenes, especially in non-line-of-sight (NLOS) conditions. In order to improve the accuracy and robustness of the localization system in NLOS environments, we propose an end-to-end deep neural network with both distance and received signal strength (RSS) measurements. On one hand, high-level spatial-temporal features can be learned through the proposed network from both RSS and distance data, which benefits the localization performance. On the other hand, the proposed network is robust to the variance in the number of available anchors, leading to high adaptability to different scenes. Specifically, three modules are designed in the deep network: 1) a module based on convolutional neural network (CNN) is presented to extract the local spatial features from the input data, and the structure of this module lends itself to varying-dimension input. 2) To manage the correlations between consecutive frames, we develop a deep long short-term memory (LSTM) model to extract temporal features and provide a high-level representation for a series of input data. 3) Finally, the fully-connected layers are utilized to estimate the 3D positions of the UWB tag. We conduct extensive experiments in three real-world scenarios to evaluate the proposed deep network. The experimental results indicate that the proposed network can significantly improve the accuracy and robustness of the UWB localization results, especially in NLOS situations.

Exploiting temporal dependency of RSS data with deep learning for IoT‐oriented wireless indoor localization

With the ubiquitous demand for indoor location‐based services and the pervasive deployment of Wi‐Fi hotspots, wireless indoor localization has been widely studied by utilizing various Wi‐Fi signal measurements. Most existing schemes leverage the Received Signal Strength (RSS) of Wi‐Fi to conduct cost‐efficient indoor localization. However, the RSS data are not only prone to multi‐path effects, but also sensitive to time‐varying environmental dynamics, making it quite daunting to achieve robust indoor localization. In contrast to existing solutions that focus on spatial features of RSS, in this article, we exploit the temporal dependency of RSS time‐series data by integrating the Kalman filter with deep neural networks. In particular, to tame time‐varying noises and preserve valuable temporal features in RSS measurements, we propose a time‐varying RSS filtering algorithm based on the Kalman filter and a refined post‐processing module. Moreover, a deep learning model based on deep neural network (DNN) is further utilized for effective feature extraction on one‐dimension RSS fingerprints. The experiment results show that the proposed Kalman‐DNN model improves at least 25% localization accuracy in comparison with conventional DNN model. Furthermore, with the localization time as 0.02 ms, the Kalman‐DNN model outperforms the Kalman‐CNN model in localization accuracy by at least 10%.

Passive Fingerprinting Reinforced by Active Radiomap for WLAN Indoor Positioning System

Passive fingerprinting collects the received signal strength (RSS) of wireless local area network (WLAN) signals emitted from a smartphone to create fingerprints and then to construct a radiomap for an indoor positioning system (IPS). However, human-impassable paths and irregular signal collection periods result in missing values in fingerprints. Moreover, missing fingerprints in shaded areas result in the degradation of the positioning accuracy. In this paper, we propose a novel passive fingerprinting method, introducing the concept of a WiFi monitor adjacency matrix and a method of transforming legacy active fingerprints to passive fingerprints by a linear regression model. Our system estimates walking paths with RSS data, removing outliers of human-impassable paths. Moreover, we can construct a high-performance radiomap by supplementing the missing signals as well as missing fingerprints in shaded areas. In an experiment conducted in a complex building environment, the proposed method achieved a positioning accuracy of 2.18 m, which is 32% better than that of a state-of-the-art system. The radiomap construction time was also greatly reduced.

On the High Fluctuations of Received Signal Strength Measurements With BLE Signals for Contact Tracing and Proximity Detection

This paper presents a measurement-based analysis of the Received Signal Strength (RSS) of Bluetooth Low Energy (BLE) signals, under Line-of-Sight (LOS) and Non-Line-of-Sight (NLOS) scenarios, performed in tandem at two universities in Tampere, Finland, and Bucharest, Romania. We adopted the same hardware and methodology for measurements in both places, and paid particular attention to the impact of RSS on various environmental factors, such as LOS and NLOS scenarios and interference in 2.4 GHz band. In addition, we considered the receiver orientation and the different frequencies of BLE advertising channels. We show that snapshot RSS measurements typically have high variability, not easily explainable by classical path-loss models. A snapshot recording is defined here as one continuous recording at fixed device locations in a static setup. Our observations also show that aggregated RSS data (i.e., considering several snapshot measurements together) is more informative from a statistical point of view and more in agreement with current theoretical path-loss models than snapshot measurements. However, in BLE applications such as contact tracing and proximity detection, the receivers typically have access only to snapshot measurements (e.g., taken over a short duration of 10–20 minutes or less), so the accuracy of contact-tracing and proximity detection can be highly affected by RSS instabilities. In addition to presenting the measurement-based BLE RSS analysis in a comprehensive and well-documented format, our paper also emphasizes open challenges when BLE RSS is used for contact tracing, ranging, and positioning applications.

Characterization of rain fade dynamics for Ku band satellite communication systems in a tropical location

Rain fade has always been regarded as the main obstacle to the terrestrial and satellite communication links operating at higher frequencies, particularly in the tropics. The dynamic features of rain fade such as duration and slope of fade are important parameters for system engineers to design and plan attenuation techniques for high reliability Ku-band services and other higher frequency bands. Twenty - four (24) consecutive months (January 2017 – December 2018) received signal strength data at 12.245 GHz (Ku-band) from EUTELSAT-W4 (geostationary at 36°E) is collected concurrently with rain intensity data at The Federal University of Technology, Akure (7.17°N, 5.18°E) Nigeria. Measured fade duration is compared with ITU-R P. 1623 model and the Cheffena-Amaya prediction model. The results show that the ITU-R model is close to the data measured, while the Cheffena-Amaya model overestimates the measurement for all timeframes taken into account. A modification of the ITU-R fade duration model is suggested according to the data measured. The proposed modified ITU-R prediction model is a recommended model to be adopted for Nigeria climate. These results can be used in the planning and design of fade mitigation techniques to overcome the effects of intense rain on terrestrial-satellite microwave links operating in the Ku-band in tropical regions.

Satellite-Signal Dependence on Rain and Atmospheric Temperature

In this paper, we present the Ku band signal strength relationship with rain rate and temperature. The correlation analysis of rain intensity with signal data indicates that rain rate negatively impacts the Ku band link while the atmospheric temperature has a strong positive correlation with the received signal strength. The statistical analysis showed that both temperature and rain intensity significantly influenced the received-signal strength data. This study is a preliminary analysis and aims to draw the association between a Ku band signal with rain intensity and temperature.

Autonomous Fingerprinting and Large Experimental Data Set for Visible Light Positioning.

This paper presents an autonomous method of collecting data for Visible Light Positioning (VLP) and a comprehensive investigation of VLP using a large set of experimental data. Received Signal Strength (RSS) data are efficiently collected using a novel method that utilizes consumer grade Virtual Reality (VR) tracking for accurate ground truth recording. An investigation into the accuracy of the ground truth system showed median and 90th percentile errors of 4.24 and 7.35 mm, respectively. Co-locating a VR tracker with a photodiode-equipped VLP receiver on a mobile robotic platform allows fingerprinting on a scale and accuracy that has not been possible with traditional manual collection methods. RSS data at 7344 locations within a 6.3 × 6.9 m test space fitted with 11 VLP luminaires is collected and has been made available for researchers. The quality and the volume of the data allow for a robust study of Machine Learning (ML)- and channel model-based positioning utilizing visible light. Among the ML-based techniques, ridge regression is found to be the most accurate, outperforming Weighted k Nearest Neighbor, Multilayer Perceptron, and random forest, among others. Model-based positioning is more accurate than ML techniques when a small data set is available for calibration and training. However, if a large data set is available for training, ML-based positioning outperforms its model-based counterparts in terms of localization accuracy.

Integrated Statistical Test of Signal Distributions and Access Point Contributions for Wi-Fi Indoor Localization

With the broad deployment of Wi-Fi networks, the Received Signal Strength (RSS) based Wi-Fi indoor localization has attained much interest of both academia and industry. At present, most of the currently available Wi-Fi indoor localization techniques focus on increasing the localization accuracy. However, few of them take into account the diversity of Wi-Fi signal distributions and the measurement error associated with RSS values owing to the complicated indoor environment, which consequently results in the low robustness of indoor localization systems. Thus, with the motivation to tackle this gripping problem, we design a new hybrid hypothesis test based on the idea of Asymptotic Relative Efficiency (ARE), which exploits signal distributions by considering different Access Point (AP) contributions to the Wi-Fi indoor localization accuracy. In concrete terms, first of all, the Jarque-Bera (JB) test is used to perform the normality test on the Wi-Fi signal distribution at each Reference Point (RP), and then the Chi-squared Automatic Interaction Detection (CHAID) approach is applied to obtain each AP contribution degree. Secondly, based on the evaluation of the JB test on the Wi-Fi signal distribution, the hybrid Mann-Whitney U and T test is applied to find the set of matching RPs corresponding to each newly-collected RSS data. Finally, the target location estimate is acquired by using the K-Nearest Neighbor (KNN), where the contribution degree of each AP is assigned as the weight during the calculation to find matching RPs. From the extensive experimental results, it is evident that the proposed approach can successfully improve the system performance by achieving a higher localization accuracy and enhanced robustness when compared with the state-of-the-art Wi-Fi indoor localization techniques.

Statistical tuning of cost 231 Hata model in deployed 1800mhz GSM networks for a rural environment

Radio propagation planning requires the use of propagation models in planning cell size as well as frequency assignment. This paper presents a comparative study of path loss predicted using COST 231 Hata model and ECC-33 model on received signal strength data collected from three deployed GSM networks at 1800MHz in Nigerian Institute for Oil Palm Research environment (NIFOR), Edo State, Nigeria. Based on the Mean Prediction Error (MPE) and Root Mean Square Error (RMSE) values obtained from the comparison, the COST 231 Hata model was tuned using the least square approach. The result obtained after tuning shows that for Network A; MPE andRMSE values reduces to 1.17 dB and 5.5dB. For Network B, MPE and RMSE values reduces to 2.26 dB and 7.16dB. While, for Network C; MPE and RMSE values reduces to 6.21 dB and 10.78dB. The results obtained show that the tuned COST 231 Hata model can be used for radio planning in the study environment as well as other environment with similar terrain profile.
 Keywords: Propagation model, Path loss, COST 231 Hata model, ECC-33 model, Least square tuning approach, MPE and RMSE