Received Signal Strength Values Research Articles

IBeacon indoor localization using trusted-ranges model

In this article, we propose an efficient approach to address mobile indoor localization using received signal strength from iBeacon combined with trusted-ranges model. In order to overcome the inconsistency of radio signal propagation, the trusted-ranges model supplies reliable ranges of received signal strength values from a certain number of nearest neighbor iBeacon nodes by classifying received signal strength values into various levels of range. By observing the signal propagation, the trusted-ranges model is built to provide important information for the training phase. Based on this, a partition scheme is applied to effectively determine the position of mobile devices. The experimental results show fast, robust, and accurate localization performance in the proposed method.

A novel approach to improve handover performance

Nowadays, mobile communications have heterogeneous wireless networks offering variable coverage and quality of service (QoS). The availability of alternatives generates a problem of occurrence of unnecessary handoff that results in wastage of network resources. In order to maintain seamless mobility, service continuity and optimise usage of network resources, it is desirable to minimise handover failures and unnecessary handovers. In this paper, a handover necessity estimation algorithm is introduced. This algorithm is made up of two modules. The first module estimates handoff necessity. It involves received signal strength (RSS) prediction, travelling distance estimation and velocity estimation. In a second module, candidate networks are discovered from the available networks. Based upon the predicted future values of RSS and travelling distance inside the wireless local area network (WLAN), the proposed algorithm improves the probability of unnecessary handoff and handover failures.

A Sparse Manifold Learning Approach to Robust Indoor Positioning Based on Wi-Fi RSS Fingerprinting

The emerging location-based applications depend on the fast and accurate positioning of mobile targets. Wi-Fi received signal strength (RSS) fingerprinting provides a promising solution to localize an object in indoor environments. Among the factors challenging the RSS fingerprinting based algorithms are the site survey cost and the time-varying environment, given the unreliable signal qualities. Here we present a novel approach to indoor object positioning using the manifold assumption on the radio map in RSS-location space. Thinking the measured RSS from one access point (AP) in different locations are randomly drawn from a nonlinear manifold (ground truth), we propose an expectation-maximization (EM) style algorithm to reconstruct the sparse representation of this manifold from the noisy RSS observations. Motivated by the observation that the radio map has a strong local correlation in the RSS-location space, we introduce a multi-scale constrained quadratic programming to approximate the manifold. Within limited iterations, we can estimate the ground truth RSS values and parameters simultaneously. As a result, the learned manifold is exploited to predict the object's position: we develop a positioning algorithm by minimizing the manifold distortion effort which integrates both measurement error and manifold shape preservation. We conducted extensive simulations and experiments in different settings, testing the datasets collected in a building in the last 8 months. The results showed that the proposed approach was adaptive to the varying environmental noise levels, presenting robust positioning performance.

Indoor Positioning System using Regression-based Fingerprint Method

Indoor Positioning System has opportunity to be used in different business platform. Based on past research, optimized localization method for Bluetooth Low Energy (BLE) to predict position of person or object with high accuracy has not been found yet. Most recent research that have solve Received Signal Strength (RSS) inconsistent value is using fingerprint method. This paper proposed a deep regression machine learning using convolutional neural network (CNN) with regression-based fingerprint model to estimate real position. The model used 5 nearest fingerprints as reference RSS values with their location (x or y) label as inputs to produce output of single value position (x or y), then repeat the process to produce second value of position to create complete coordinate of estimated position. To evaluate the proposed model, a comparison between training data with validation data using Root Mean Squared Error (RMSE) is used. The comparisons are with Multilayer Perceptron model and with the weighted sum method as benchmark. The experiment Gave results of mean distance and 90th percentile distance between proposed model with the benchmark. CNN model achieved accuracies of lower than 330cm at 90th percentile with mean distance lower than 185cm. Weighted sum model achieved accuracies lower than 360cm at 90th percentile with mean distance higher than 185cm, and MLP is in between them. The result demonstrates that the proposed method outperformed the benchmark methods.

Low-Profile ESPAR Antenna for RSS-Based DoA Estimation in IoT Applications

In this paper, we have introduced a low-profile electronically steerable parasitic array radiator (ESPAR) antenna that can successfully be used to estimate the direction-of-arrival (DoA) of incoming signals in wireless sensor network (WSN) applications, in which the height of the complete antenna has to be low. The proposed antenna is over three times lower than high-profile ESPAR antenna designs currently available in the literature for the DoA estimation; it can provide eight unique main beam directions and relies on simplified beam steering, which makes it applicable to simple and inexpensive WSN nodes. Measurements using our fabricated ESPAR antenna prototype indicate that relying solely on the received signal strength values recorded at the antenna output port, it is possible to achieve accurate DoA estimation results with error levels similar to those available for high-profile ESPAR antennas relying on the similar energy-efficient simplified beam steering concept and having 12 unique main beam directions. As a consequence, the overall time required for the DoA estimation using the proposed antenna can be reduced by 33%.

City Radio Map Construction for Wi-Fi-Based Citywide Indoor Positioning

A Wi-Fi fingerprint consists of received signal strength values at a particular location along with the location information. A Wi-Fi radio map is constructed by maintaining a database of Wi-Fi fingerprints at various points in a building. A city radio map is a collection of radio maps for most buildings in a city. A highly precise citywide indoor positioning service is possible, if an accurate city radio map is available. However, tremendous time and effort are required to construct a city radio map through manual calibration. This paper proposes a method for constructing a city radio map through the crowdsourcing of Wi-Fi fingerprints from numerous smartphones. The proposed method classifies the buildings in a city into three categories: buildings in residential areas, commercial areas, and public areas. Then, it develops location-labeling techniques appropriate for the collected fingerprints from buildings in each category. Experiments conducted in the cities of Daejeon and Seoul revealed that the proposed method can construct a precise city radio map with minimal cost in a short period of time. Once city radio maps are constructed for most cities around the world, the global indoor positioning system will be completed.

Crowdsourcing Indoor Positioning by Light-Weight Automatic Fingerprint Updating via Ensemble Learning

In recent years, Wi-Fi-based indoor positioning has attracted increasing research attention due to its ubiquitous deployment. Although extensive research has been conducted on Wi-Fi fingerprint-based positioning, especially, in complex environments and long-term deployments, the automatic adaptation of radio map has not been fully studied and the problems remain open. When the positions of some Access Points (APs) change, the traditional approach regularly conducts site surveying which is time-consuming and labor-intensive. In this paper, we propose a crowdsourcing indoor positioning approach based on ensemble learning for automatic Altered APs Identification and Fingerprints Updating, namely AAIFU. We propose an algorithm to detect and identify the altered APs in crowdsourcing data. After getting the altered APs, we use the relationship between the received signal strength values of the altered APs and the unaltered APs in the crowdsourcing data to train a prediction model used to update the radio map. We also handle the device diversity on all the processes of AAIFU. Our proposed solution is light-weight which does not rely on additional infrastructure and inertial sensors with high power consumption. The comprehensive experiments have been carried out in our teaching building to evaluate the effectiveness of AAIFU. The results show that our proposed AAIFU can effectively adapt the radio map to the movement of APs and improve positioning accuracy. Correspondingly, we achieve an average positioning accuracy of 2.6m which outperforms the fingerprinting approach with the original radio map by 63.9%.

A Fusion Algorithm of Indoor Positioning Based on PDR and RSS Fingerprint

The pedestrian dead reckoning (PDR) positioning has small noise and high stability in the short term, but it cannot use for long term because of its cumulative error. The received signal strength (RSS) fingerprint positioning has wide coverage and no accumulative error, but the amount of data leads to the long positioning time and discontinuous trajectory in the fingerprint database, where the stability of wireless signal is poor to result in a positioning error. Based on above method’s issues, this paper proposes a fusion indoor positioning algorithm based on PDR and RSS fingerprint. This algorithm presents a new filtering method based on the comparison of two adjacent RSS values, which can be used to deal with the PDR positioning method to solve the noise point caused by the shaking because the tester handheld terminal is not stable enough in the test and be used for processing of RSS fingerprint positioning method to inhibit the effect of wireless signal instability in a certain extent, such as the jumping point noise. Moreover, a fusion algorithm based on adaptive parameters is proposed in this paper with differences from the fusion method proposed in the reference literature, which is adaptive parameter with different weights according to the accuracy of the two positioning methods in the testing. Xiaomi Mi Pad of the Android system verifies the proposed method, and experimental results show that the proposed fusion positioning algorithm can effectively improve the positioning accuracy.

Localization Reliability Improvement Using Deep Gaussian Process Regression Model.

With the widespread use of the Global Positioning System, indoor positioning technology has attracted increasing attention. Many systems with distinct deployment costs and positioning accuracies have been developed over the past decade for indoor positioning. The method that is based on received signal strength (RSS) is the most widely used. However, manually measuring RSS signal values to build a fingerprint database is costly and time-consuming, and it is impractical in a dynamic environment with a large positioning area. In this study, we propose an indoor positioning system that is based on the deep Gaussian process regression (DGPR) model. This model is a nonparametric model and it only needs to measure part of the reference points, thus reducing the time and cost required for data collection. The model converts the RSS values into four types of characterizing values as input data and then predicts the position coordinates using DGPR. Finally, after reinforcement learning, the position coordinates are optimized. The authors conducted several experiments on a simulated environment by MATLAB and physical environments at Tianjin University. The experiments examined different environments, different kernels, and positioning accuracy. The results showed that the proposed method could not only retain the positioning accuracy, but also save the computation time that is required for location estimation.

A RSS-based localization method using HMM-based error correction

ABSTRACTIndoor localization and tracking systems are harvesting more attention from the researchers. Recently, several approaches for localization systems have been proposed that use the sensors which are available on smartphones. In this paper, a new filtering approach based on Hidden Markov Model (HMM) techniques to enhance the accuracy of the localization system is presented. The proposed approach filtered out the undesirable Received Signal Strength (RSS) values which affect the accuracy of the system using the hidden states. The proposed approach helps in the direction and the distance estimation. The results of the proposed approach show a significant improvement in terms of distance estimation and the filtering of the RSS values.

Optimization of 5G Virtual Cell Based Coordinated Multipoint Networks Using Deep Machine Learning

Providing seamless mobility and a uniform user experience, independent of location, is an important challenge for 5G wireless networks. The combination of Coordinated Multipoint (CoMP) networks and Virtual- Cells (VCs) are expected to play an important role in achieving high throughput independent of the mobile’s location by mitigating inter-cell interference and enhancing the cell-edge user throughput. Userspecific VCs will distinguish the physical cell from a broader area where the user can roam without the need for handoff, and may communicate with any Base Station (BS) in the VC area. However, this requires rapid decision making for the formation of VCs. In this paper, a novel algorithm based on a form of Recurrent Neural Networks (RNNs) called Gated Recurrent Units (GRUs) is used for predicting the triggering condition for forming VCs via enabling Coordinated Multipoint (CoMP) transmission. Simulation results, show that based on the sequences of Received Signal Strength (RSS) values of different mobile nodes used for training the RNN, the future RSS values from the closest three BSs can be accurately predicted using GRU, which is then used for making proactive decisions on enabling CoMP transmission and forming VCs.

PRAPD: A novel received signal strength–based approach for practical rogue access point detection

Rogue access point attack is one of the most important security threats for wireless local networks and has attracted great attention from both academia and industry. Utilizing received signal strength information is an effective solution to detect rogue access points. However, the received signal strength information is formed by multi-dimensional received signal strength vectors that are collected by multiple sniffers, and these received signal strength vectors are inevitably lacking in some dimensions due to the limited wireless transmission range and link instability. This will result in high false alarm rate for rogue access point detection. To solve this issue, we propose a received signal strength–based practical rogue access point detection approach, considering missing received signal strength values in received signal strength vectors collected in practical environment. First, we present a preprocessing scheme for received signal strength vectors, eliminating missing values by means of data filling, filtering, and averaging. Then, we perform clustering analysis on the received signal strength vectors, where we design a distance measurement method that dynamically uses partial components in received signal strength vectors to minimize the distance deviation due to missing values. Finally, we conduct the experiments to evaluate the performance of the practical rogue access point detection. The results demonstrate that the practical rogue access point detection can significantly reduce the false alarm rate while ensuring a high detection rate.

Visible light signal strength optimization using genetic algorithm in non-line-of-sight optical wireless communication

Visible light communication (VLC) using the light emitting diode (LED) attracts attention from researchers as a future low power optical wireless communication. VLC generally considers a line-of-sight (LOS) link, although the LOS link can be affected by the obstacles between LED transmitter and photodetector (PD) receiver in a practical scenario. In the case of non-line-of-sight (NLOS) link, it is important to find a link with maximum possible received signal strength (RSS) to ensure optimum communication quality in VLC. In this paper, we propose a novel scheme for optimizing the RSS using a meta-heuristic searching algorithm known as genetic algorithm (GA). The proposed scheme is simulated using two white light emitting diodes (LEDs) as the transmitters and a PD as the receiver. The genetic algorithm (GA) is used to calculate the maximum RSS on the NLOS link within an indoor environment. GA determines an orientation of the maximum RSS at the receiver instead of measuring the RSS for every angle. Simulation results reveal that the proposed scheme accurately estimates the orientation that produces an optimum RSS value at the receiver.

Optimization of Handover Parameters for LTE/LTE-A in-Building Systems

The optimization of handover (HO) parameters for in-building systems is investigated in this paper. We proposed a novel methodology that provides in-building base stations with the flexibility to customize HO parameters to specific radio frequency conditions at the cell-edge for different loading scenarios. We propose the use of machine learning and data mining techniques to allow the base stations to autonomously learn and identify characteristic patterns in the received signal strength values (reported by users during the HO process), and apply optimal HO parameters for each case. Our optimization strategy jointly considers the radio frequency conditions at the cell-edge and the load levels of the base stations, to determine optimal HO parameters that maximize the quality of service and guarantee the continuity of service at the cell-edge. We evaluated our methodology with experimental data collected from two fully operational LTE in-building systems deployed in a university campus. Our results show that with our methodology the spectral efficiency at the cell-edge can be greatly improved. Downlink data rate gains at the cell-edge reached a value close to 150% for a certain loading scenario compared to the traditional approach of selecting a unique set of HO parameters for the entire in-building system.

Robust System Design Using BILP for Wireless Indoor Positioning Systems

In wireless indoor positioning system designs, reference node (RN) failures during the online phase cause received signal strength values to be unavailable. This leads to accuracy performance degradation and a lack of system reliability in smart office systems. Moreover, the major design concern in the reliability of indoor positioning systems under the faulty RNs during the online phase has not been yet investigated in previous works. To address these gaps, we propose a novel mathematical formulation using a Binary Integer Linear Programming (BILP) approach that employs the Simulated Annealing (SA) solution technique. The proposed robust system design aims to put in place a suitable number of RNs and to determine their optimum locations, which may be located on a single floor or on multiple floors. In particular, the proposed system design provisions to support robust operation both during a normal situation and when there are some RN failures. Experimental results and comparative performance evaluation revealed that the proposed robust system design outperformed other system designs and was able to achieve the highest location accuracy performance in both fault-free and RN-failure scenarios. Specifically, when nine of the RNs in a three-story building failed, the proposed system design achieved 84.6%, 54.7%, and 32.9% more accurate performance than the Uniform, the MSMR, and the PhI-Uni, respectively.

A 3-D Positioning Algorithm for AOA-Based VLP With an Aperture-Based Receiver

We consider a visible light positioning system using modulated LEDs at the transmitter and photodiodes (PDs) combined with apertures at the receiver. The layout of the aperture-based receiver is designed in order to have angular diversity, implying it can detect the direction from which light is coming, by simply comparing the relative differences in received signal strength values in the different PDs. Hence, with this receiver, it is possible to extract the angle-of-arrival (AOA) of the light without needing the knowledge of the transmitted optical power. In this paper, we consider an algorithm, based on the maximum likelihood (ML) principle, to estimate the AOA, and obtain the position of the receiver in 3-D through triangulation. The ML algorithm, of which the practical implementation searches for the optimal value of the AOA starting from an initial estimate, suffers from convergence problems if the initial estimate is too far from the true AOA. Hence, we propose an initial low-complexity coarse estimation algorithm for the AOA, and make the algorithm iterative, where in each iteration, the initial estimate for the AOA is updated based on the previous position estimate. We show that the algorithm yields centimeter performance, i.e., an accuracy of 10 cm or better, using a limited number of LEDs, e.g., four LEDs for a $5\,\,\text {m} \times 5$ m area.

A Grid-Based Localization Algorithm for Wireless Sensor Networks Using Connectivity and RSS Rank

To improve localization accuracy and reduce system costs, a new range-free localization algorithm for wireless sensor networks using connectivity and received signal strength (RSS) rank is proposed. An unknown node is first localized in an initial residence area according to the connectivity constraints. Then, the residence area is refined using the RSS rank vector. The RSS rank vector of an unknown node is formed from the ranks of the RSS values obtained from neighboring anchors. The estimated location of the unknown node is the centroid of the refined residence area. The proposed localization algorithm uses a grid scan approach to avoid the complex geometric computations. This algorithm is improved by using an adaptive strategy to determine the grid size. The performance of these methods is examined via analysis and simulation. The results obtained verify that the proposed approaches provide better localization accuracy than competing algorithms in the literature.

A novel indoor localization scheme based on refined fingerprint-based autoencoder network

Indoor positioning systems have attracted increasing interests for the emergency of location based service in indoor environments. Wi-Fi fingerprint-based localization scheme has become a promising indoor localization technique due to the availability of access point (AP) and its low cost. However, the received signal strength (RSS) values are easily fluctuated by the interference of multi-path effects, which introduce propagation errors into localization results. In order to address the issue, a fingerprint-based autoencoder network scheme is proposed to learn the essential features from the measured coarse RSS values and extract the trained weight parameters of autoencoder network as refined fingerprints. The extracted fingerprints are able to represent the environmental properties and display strong robustness with fluctuated signals. The proposed scheme is further implemented in complex indoor scenes, which substantiate the effectiveness and accuracy improvement compared with other RSS-based schemes.

RSS-Based DoA Estimation Using ESPAR Antennas and Interpolated Radiation Patterns

In this letter, it is shown how an algorithm, which employs received signal strength values in order to estimate direction of arrival (DoA) of impinging signals in wireless sensor network (WSN) nodes equipped with electronically steerable parasitic array radiator (ESPAR) antennas, can easily be improved by applying an interpolation algorithm to radiation patterns recorded in the calibration phase of the DoA estimation process. The proposed method allows one to measure ESPAR antenna's radiation patterns during the initial calibration phase with much coarser than 1° angular resolution, while the overall DoA estimation accuracy can be kept at the similar level. Measurements using a fabricated ESPAR antenna have indicated that anechoic chamber calibration procedure of WSN nodes equipped with such antennas can be done almost 50% faster, which in consequence will reduce system deployment costs in practical WSN applications where a number of WSN nodes can easily reach hundreds.

Indoor Localization using Voronoi Tessellation

Recently the use of received signal strength values from a wireless local area network has received significant research interest for indoor localization. This work investigates a Vo ...