Indoor localization improvement via adaptive RSS fingerprinting database

With growing technology, Location Based Services (LBSs) are becoming a vital part of life. The rapid development of mobile internet has offered an opportunity for Wi-Fi indoor positioning to come under the spotlight due to its low cost. Here, indoor positioning is performed using Wi-Fi. The earlier solutions like GPS does not provide efficient positioning in indoor environment due to weak signal reception which makes Wi-Fi more reliable. However, nowadays the accuracy of the traditional indoor positioning algorithms like trilateration cannot meet the practical requirement. This paper proposes the performance analysis of indoor positioning algorithms using Wi-Fi technology based on Received Signal Strength (RSS) from the access points in the network. The positioning algorithms are based on location fingerprinting consisting of two stages: The offline database creation and the online positioning. The RSS values obtained by the mobile device at reference points are sent to the server where all the location fingerprints of all reference points are stored in a database. Coordinates of user position are calculated using positioning algorithms and the position is displayed in terms of 2Dcoordinates of the room and indoor map. Basic Fingerprint algorithm, RSS Fingerprint with minimum Euclidean Distance algorithm and RSS Fingerprint with Euclidean Distance and k-nearest neighbors' algorithm were implemented. Moreover comparison of their performance was carried out in terms of accuracy of position. From the result, it is found that an average accuracy of 0.4962 meters was achieved by RSS Fingerprint with minimum Euclidean distance algorithm.

Received signal strength (RSS) based location fingerprinting is a powerful wireless positioning technique. It estimates the target location by consulting a preliminary database and searching for the best matched RSS fingerprints. The construction and maintenance of a sufficient fingerprint database could be laborious and problematic. This paper proposes a new approach that utilizes the Kriging spatial interpolation algorithm to build complete fingerprint databases from sparsely collected measurements. The interpolation performance is analyzed over various extents of sparsity and number of measurements. The constructed fingerprint databases are utilized to locate a static target and the localization performances are analyzed. It is shown that the Kriging algorithm can be used to build RSS fingerprint databases of good accuracy based on sparsely collected measurements.

Received signal strength (RSS) fingerprint-based indoor localization has received increasing popularity over the past decades. However, it suffers from the high calibration effort for fingerprint collection. In this paper, a Centralized indooR localizatioN method using Pseudo-label (CRNP) is proposed, which employs a small set of labeled data (RSS fingerprint) along with large volumes of unlabeled data (RSS values without coordinates) to reduce the workload of labeled data collection and improve the indoor localization performance. However, the rich location data is large in quantity and privacy sensitive, which may lead to high network cost (i.e., data transmission cost, data storage cost) and potential privacy leakage for data transmission to the central server. Therefore, a decentralized indoor localization method incorporating CRNP and federated learning is devised, which keeps the location data on local users' devices and improves the shared CRNP model by aggregating users' updates of the model. The experiment results demonstrate that (i) the proposed CRNP enables to improve the indoor localization accuracy by using unlabeled crowdsourced data; (ii) the designed decentralized scheme is robust to different data distribution and is capable to reduce the network cost and prevent users' privacy leakage.

Abstract. Indoor localization has attracted the attention of researchers for wide applications in areas like construction, facility management, industries, logistics, and health. The Received Signal Strength (RSS) based fingerprinting method is widely adopted because it has a lower cost over other methods. RSS is a measurement of the power present in the received radio signal. While this fingerprinting method is very popular, there is a significant amount of effort required for collecting fingerprints for indoor space. In this paper, we propose an RSS fingerprinting method using Augmented Reality (AR) that does not rely on an external sensor resulting in ease of use and maintenance. This method uses spatial mapping techniques to help align the floor plan of existing buildings; then, after the alignment, we map local device coordinates to global coordinates. After this process, we partition the space in equally distanced reference points for RSS fingerprint collection. We developed an application for Microsoft HoloLens to align the floor plan and collect fingerprints on reference points. Then we tested collected fingerprints with existing RSS based indoor localization methods for its accuracy and performance.

The indoor pedestrian positioning methods are affected by substantial bias and errors because of the use of cheap microelectromechanical systems (MEMS) devices (e.g., gyroscope and accelerometer) and the users’ movements. Moreover, because radio-frequency (RF) signal values are changed drastically due to multipath fading and obstruction, the performance of RF-based localization systems may deteriorate in practice. To deal with this problem, various indoor localization methods that integrate the positional information gained from received signal strength (RSS) fingerprinting scheme and the motion of the user inferred by dead reckoning (DR) approach via Bayes filters have been suggested to accomplish more accurate localization results indoors. Among the Bayes filters, while the particle filter (PF) can offer the most accurate positioning performance, it may require substantial computation time due to use of many samples (particles) for high positioning accuracy. This paper introduces a pedestrian localization scheme performed on a mobile phone that leverages the RSS fingerprint-based method, dead reckoning (DR), and improved PF called a double-stacked particle filter (DSPF) in indoor environments. As a key element of our system, the DSPF algorithm is employed to correct the position of the user by fusing noisy location data gained by the RSS fingerprinting and DR schemes. By estimating the position of the user through the proposal distribution and target distribution obtained from multiple measurements, the DSPF method can offer better localization results compared to the Kalman filtering-based methods, and it can achieve competitive localization accuracy compared with PF while offering higher computational efficiency than PF. Experimental results demonstrate that the DSPF algorithm can achieve accurate and reliable localization with higher efficiency in computational cost compared with PF in indoor environments.

The Internet of Things (IoT) is an emerging paradigm to integrate the physical world into cyber systems by connecting various devices, which sense and control the surrounding objects. For the upcoming IoT era, narrow-band signals will play an important role in the exchange of sensor data and control signals in IoT networks. Indoor positioning is critical for many IoT applications, such as asset tracking, smart buildings, smart e-health, and smart cities, but it is very challenging to achieve accurate indoor positioning with narrowband signals because of multipath propagation. In this paper, we design a positioning system using narrowband signals, particularly ZigBee signals, based on an enhanced fingerprinting algorithm by fusing received signal strength (RSS) and time information. We first investigate the feasibility of time-based fingerprinting by handling the challenges as synchronization compensation among anchor nodes and design of sub-sample timestamps with a resolution of nanoseconds. Second, we design a feature-based fusion approach to fuse and standardize time and RSS fingerprints. Third, we adopt a random forest regression model to design an enhanced pattern matching algorithm for fingerprinting. We implement the proposed algorithms with software-defined radio techniques. In the given experiment results, we find that the positioning system achieves a mean positioning accuracy of 1.61 m, which represents a 36.1% improvement over traditional RSS-based fingerprinting.

In this paper, we propose a surface correlation based fingerprinting method in sparse Wi-Fi environment. The proposed method can estimate location even with very few Wi-Fi APs by using accumulated spatial RSS pattern. We verify performance of the proposed method through several tests in a building with complex paths. Many studies have developed localization technologies that use measurements such as RSS (Received Signal Strength) or RTT (Round Trip Time) for Wi-Fi signal. All RF have signal noise and distortion problems as well as multipath problem in indoor environments. As the distance from the RF signal source increases, the discrimination of the RSS decreases due to RF signal noise and distortion. Therefore, ambiguity of RSS increases as the distance increases, the performance deteriorates. The conventional RF based localization method have tried to maintain dense RF environment to guarantee stable and accurate performance. However, in order to maintain the dense RF environment, it is becoming a barrier to commercialization of localization technology due to high cost of constructing RF infrastructure and maintenance difficulty. In this paper, we propose a Surface Correlation (SC) based fingerprinting technique to maintain high positioning performance even with few Wi-Fi APs. The surface is the accumulated RSS pattern data during the movement of a pedestrian, which means the spatial RSS pattern. Conventional fingerprinting methods using kNN (k-Nearest Neighbors) and WkNN (Weighted kNN) are very vulnerable to signal noise and distortion because they utilize instantaneous RSS data. The performance of Particle Filter (PF) based localization technology is also degraded in areas where Wi-Fi AP is sparsely distributed. However, the proposed method is very robust against signal noise and distortion because it compares the spatial RSS pattern with the reference RSS pattern on the fingerprinting database. This increases localization accuracy and availability for Wi-Fi coverage. For localization, the proposed method conduct correlation process for the spatial RSS pattern of a pedestrian with reference RSS pattern of database. So, the reference RSS pattern should be extracted from a reference trajectory that matches a pedestrian`s walking path. The proposed method utilize a database of ‘link-node’ structure to dynamically generate a reference trajectory in indoor environment with complicated path. It generate candidate reference trajectories using only MAC address and turn event of Pedestrian Dead-Reckoning (PDR). The proposed method fix a reference trajectory between these candidate trajectories through correlation process of measured RSS pattern with each reference RSS pattern. A reference point on the fixed reference having most similar RSS pattern to a pedestrian`s is considered as a current location. In order to improve performance at a fork where RSS ambiguity increases, the proposed method estimates preferentially for a reference trajectory matching with PDR turn event. The turn event can be wrong due to various motion of a pedestrian. The proposed method selects a correct reference trajectory by continuously performing correlation process on candidate reference trajectories. In order to verify the performance of the proposed surface correlation based fingerprinting method, we conducted several tests in a three-story building. Before the tests, we conducted a survey of Wi-Fi environment to construct fingerprinting database in the building. As a result of the survey, 170 APs are distributed in the building. We reduced the number of APs by 20% from 170 to 17, and analyzed performance of the proposed and conventional localization methods. The test results show that the performance of the conventional localization methods degrade as the number of applied APs decreases. The proposed fingerprinting method shows a 3 meter localization error even with very few Wi-Fi APs, and unlike the conventional fingerprinting method, the localization error is bounded to be within 5m due to the use of spatial RSS pattern. In this paper, we presented the fingerprinting method using the surface correlation in sparse Wi-Fi environment. The proposed fingerprinting method accumulates Wi-Fi RSS during movement of a pedestrian. The proposed method compares the spatial RSS pattern, which is accumulated RSS data, with reference RSS patterns of fingerprinting database. By comparing the RSS patterns, the proposed method greatly improves accuracy as well as stability. In addition, it can be effectively applied in a sparse Wi-Fi environment by improving the availability of ambiguous areas of Wi-Fi coverage. Through these results, we expect to be able to provide accurate and reliable location information even in indoor weak RF environment.

In this paper, a real WiFi fingerprint based indoor localization system is considered for experiments, including three primary components: the APP in the smart phone, the server system and the embedded localization algorithm. As we all know, one of the main drawbacks in fingerprint based localization is the labor intensity and time consumption of data collection. This paper proposes an improved graph-based semi-supervised learning (I-GSSL) to better overcome this problem. Apart from taking advantage of the indoor propagation model, the I-GSSL algorithm is proposed to handle the existing out-of-sample problem where an elastic regularization is considered as an extra constraint. Meanwhile, due to unequal amount of location information in the received signal strength (RSS) from different access points (APs) and the redundancy of RSS at APs, a double weighted K nearest neighbor (DWKNN) algorithm is proposed for localization. Experimental results show the proposed scheme achieves a better label propagation and localization accuracy.

An RSS transform–based weighted k-nearest neighbor (WKNN) indoor positioning algorithm, Q-WKNN, is proposed to improve the positioning accuracy and real-time performance of Wi-Fi fingerprint–based indoor positioning. To smooth the RSS fluctuation difference caused by acquisition equipment, time, and environment changes, base Q is introduced in Q-WKNN to transform RSS to Q-based RSS, based on the relationship between the received signal strength (RSS) and physical distance. Analysis of the effective range of base Q indicates that Q-WKNN is more suitable for regions with noticeable environmental changes and fixed access points (APs). To reduce the positioning time, APs are selected to form a Q-WKNN similarity matrix. Adaptive K is applied to estimate the test point (TP) position. Commonly used indoor positioning algorithms are compared to Q-WKNN on Zenodo and underground parking databases. Results show that Q-WKNN has better positioning accuracy and real-time performance than WKNN, modified-WKNN (M-WKNN), Gaussian kernel (GK), and least squares-support vector machine (LS-SVM) algorithms.

Crowdsensing is more and more used nowadays for indoor localization based on Received Signal Strength (RSS) fingerprinting. It is a fast and efficient solution to maintain fingerprinting databases and to keep them up-to-date. There are however several challenges involved in crowdsensing RSS fingerprinting data, and these have been little investigated so far in the current literature. Our goal is to analyse the impact of various error sources in the crowdsensing process for the purpose of indoor localization. We rely our findings on a heavy measurement campaign involving 21 measurement devices and more than 6800 fingerprints. We show that crowdsensed databases are more robust to erroneous RSS reports than to malicious fingerprint position reports. We also evaluate the positioning accuracy achievable with crowdsensed databases in the absence of any available calibration.

Fingerprinting is one of the most reliable technique for indoor positioning. The concept of the fingerprinting is to compare the offline received signal strength (RSS) database and online RSS measurement to find the most similar reference point to represent the user position. However, fingerprinting is sensitive to the environmental changes, where the offline database is required to be updated. In this paper, we discuss the offline and online measurement mismatch when the transmission power is altered by the Wi-Fi access point. We propose dynamic adjustment (DA) algorithm to reduce the influence caused by the power variation. Experimental results validate that the proposed DA algorithm maintains the positioning accuracy under the power adjustment.

Indoor localization of mobile nodes is emerging as a promising field in the past decades due to the advances in mobile devices and the increasing number of wireless services and applications. Wi-Fi fingerprints which is a vector of Received Signal Strength (RSS) values in a specific location are considered a good feature for indoor localization due to their simplicity. However, the existing methods such as decision tree (DT) and Naïve Bayes (NB) techniques have some drawbacks. DT has low efficiency because it uses the Boolean function, and NB provides a low accuracy of prediction for the sparse dataset. In this research, fingerprint referencing with two important methods, Support Vector Machine (SVM) and Long-Short Term Memory (LSTM) machine learning algorithms, are used to enable accurate indoor localization. Furthermore, data normalization is implemented to reduce positioning error by increasing the inconsistency of the RSS values. Further, several experiments have been conducted by using three mobile devices in the area of 280 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> to create our dataset by taking 480 RSS records for both training and testing phases. The results show that the accuracy LSTM algorithm is 0.9 m, whereas the SVM algorithm accuracy is 1.1 m. Both experimental results show better accuracy for indoor environments can be achieved by the proposed approach than results recorded by several prior studies.

Smartphone-based Wi-Fi tracking system exploiting the RSS peak to overcome the RSS variance problem

RSS Based Energy Efficient Scheme for the Reduction of Overhearing and Rebroadcast for MANET

Device-free localization (DFL) methods use measured changes in the received signal strength (RSS) between many pairs of RF nodes to provide location estimates of a person inside the wireless network. Fundamental challenges for RSS DFL methods include having a model of RSS measurements as a function of a person's location, and maintaining an accurate model as the environment changes over time. Current methods rely on either labeled empty-area calibration or labeled fingerprints with a person at each location. Both need to be frequently recalibrated or retrained to stay current with changing environments. Other DFL methods only localize people in motion. In this paper, we address these challenges by, first, introducing a new mixture model for link RSS as a function of a person's location, and second, providing the framework to update model parameters without ever being provided labeled data from either empty-area or known-location classes. We develop two new Bayesian localization methods based on our mixture model and experimentally validate our system at three test sites with seven days of measurements. We demonstrate that our methods localize a person with non-degrading performance in changing environments, and, in addition, reduce localization error by $\mathbf {11-51}$ 11 - 51 percent compared to other DFL methods.