Path-Loss-Based Fingerprint Localization Approach for Location-Based Services in Indoor Environments

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.

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

Location-Based Services (LBS) are technologies that utilize user location data to provide relevant location-based information. This study aims to examine various LBS applications in smart systems through a review of previous literature. LBS are applied in both outdoor and indoor environments. Outdoor LBS typically use Global Positioning System (GPS) technology for applications such as attendance systems, local business searches, transportation, and healthcare services. Meanwhile, indoor LBS use technologies like Pedestrian Dead Reckoning (PDR) and Simultaneous Localization and Mapping (SLAM) to navigate environments where GPS signals are obstructed. This study also identifies the use of hybrid systems that integrate both indoor and outdoor LBS to ensure consistent service. In addition, challenges related to accuracy, privacy, and energy efficiency in LBS applications across various sectors are discussed. The findings of this research highlight the significant potential of LBS in supporting smart systems while emphasizing the need for further research to address existing challenges, particularly in technology integration and privacy improvements. The implementation of hybrid systems combining indoor and outdoor technologies is expected to provide a more comprehensive solution for future development.

Fingerprint localization (FL) is an effective localization method in rich scattering environments. However, conventional FL solutions are based on received signal strength (RSS) information, and they usually suffer from considerable performance degradation due to complex environmental dynamics. Therefore, hybrid FL methods were recently proposed to improve the accuracy performance by fully exploiting the features of different fingerprints. At the same time, since reference points (RPs) in most FL methods are distributed with lattice pattern, the establishment of fingerprint database requires huge amount of offline training, and thus different techniques were used to reduce the offline training cost for FL methods. Inspired by the above work, this paper presents an improved hybrid FL method, in which the RSS and direction of arrival (DOA) are collected as fingerprints. Moreover, instead of the lattice RPs layout (LRL) used in most existing hybrid FL methods, a circular RPs layout (CRL) is introduced into the proposed hybrid FL scheme to reduce the overhead of offline training. Simulation results show that when the signal-to-noise ratio of the system is 15 dB, the averaged localization error of the proposed RSS-DOA-CRL algorithm is reduced by 80.0% compared to the RSS-based FL, and 33.5% compared to the hybrid RSS-DOA with LRL respectively.

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.

The fingerprint-based localization technique has become a focus in indoor localization since it has the merits of strong anti-interference ability, good network stability, high quality data transmission and wide applicability. For fingerprint-based localization, however, offline site survey is usually time-consuming and labor-intensive. Therefore, offline site survey reduction becomes an important issue for fingerprint-based indoor localization. In this paper, we first model the fingerprints of all reference points (RPs) as a low-tubal-rank tensor and then propose a two-pass adaptive sampling scheme where under a given budget of samples, a fixed proportion of samples are firstly allocated uniformly at random, and then the remaining samples are assigned according to the leverage scores of the underlying tensor. Afterward, we apply a tensor completion algorithm such as ADMM, t-TNN and TenALS to solve the weighted nuclear norm minimization problem as above. The advantage is that the scheme has ability to identify the highly informative RPs that are conductive to fingerprint map reconstruction to mitigate the sample complexity for offline site survey. Simulation results show that the proposed adaptive sampling scheme can significantly improve the performance in terms of both reconstruction error and localization accuracy compared with the random uniform sampling method.

Due to the wide deployment of wireless local area networks (WLANs) and the easy acquirement of received signal strength (RSS), indoor localization based on RSS has attracted considerable attention in both academia and industry. In this paper, we extract the scatter factor from RSS and then propose a novel indoor localization scheme based on location fingerprinting and curve fitting techniques. The scheme is set up in two phases. In the offline phase, we create a fingerprint and apply the curve fitting technique to construct fitted RSS-distance functions in LOS (Line Of Sight) and NLOS (None Line Of Sight) condition, respectively. In online positioning phase, the proposed optimized fingerprinting-based localization algorithm (OFPL) uses trilateration technique to assist the fingerprinting localization in choosing the proper location of the receiver. Through conducting field and extensive experiments, we can draw the conclusion that our proposed algorithm can obtain some improvement in localization accuracy compared with the classical fingerprinting-based localization.

The development of the Internet of Things has accelerated research in the indoor location fingerprinting technique, which provides value-added localization services for existing WLAN infrastructures without the need for any specialized hardware. The deployment of a fingerprinting based localization system requires an extremely large amount of measurements on received signal strength information to generate a location fingerprint database. Nonetheless, this requirement can rarely be satisfied in most indoor environments. In this paper, we target one but common situation when the collected measurements on received signal strength information are insufficient, and show limitations of existing location fingerprinting methods in dealing with inadequate location fingerprints. We also introduce a novel method to reduce noise in measuring the received signal strength based on the maximum likelihood estimation, and compute locations from inadequate location fingerprints by using the stochastic gradient descent algorithm. Our experiment results show that our proposed method can achieve better localization performance even when only a small quantity of RSS measurements is available. Especially when the number of observations at each location is small, our proposed method has evident superiority in localization accuracy.

Two obstacles lie in the traditional Signal Strength Fingerprint Positioning method. Initially, the algorithm cannot converge quickly and accurately due to massive data generated by large indoor environment. Secondly, it is difficult to determine a specific floor in a building using the received Signal Strength(RSS). This article proposes a method, which uses convolutional neural network (CNN) to classify the floor and location of Bluetooth RSS as well as magnetic field data to calculate the final coordinates, could apply Fingerprint Positioning into indoor environment with large areas and multiply floors. The method involves converting the collected Bluetooth RSS into the “fingerprint image” required for calculation and establishing the CNN for classification training. Subsequently, the real-time Bluetooth RSS are imported into the CNN to classify the floor and determine the transmitters' location. Additionally, the observer's coordinates are matched using the magnetic field data. Our experiments suggested that the proposed method can classify floors and transmitters' locations with predictable bunds of 0.9667 and 0.9333, respectively. At the same time, the average positioning error is less than 1.2 m, which is 43.32% and 44.67% higher than the traditional Bluetooth and magnetic field fingerprint positioning. The accuracy of dynamic positioning is also within 1.55 meters.

Indoor localization schemes have significant potential for use in location-based services in areas such as smart factories, mixed reality, and indoor navigation. In particular, received signal strength (RSS)-based fingerprinting is used widely, given its simplicity and low hardware requirements. However, most studies tend to focus on estimating the 2D position of the target. Moreover, it is known that the fingerprinting scheme is computationally costly, and its positioning accuracy is readily affected by random fluctuations in the RSS values caused by fading and the multipath effect. We propose an indoor 3D localization scheme based on both fingerprinting and a 1D convolutional neural network (CNN). Instead of using the conventional fingerprint matching method, we transform the 3D positioning problem into a classification problem and use the 1D CNN model with the RSS time-series data from Bluetooth low-energy beacons for classification. By using the 1D CNN with the time-series data from multiple beacons, the inherent drawback of RSS-based fingerprinting, namely, its susceptibility to noise and randomness, is overcome, resulting in enhanced positioning accuracy. To evaluate the proposed scheme, we developed a 3D positioning system and performed comprehensive tests, whose results confirmed that the scheme significantly outperforms the conventional common spatial pattern classification algorithm.

In recent times, social and commercial interests in location-based services (LBS) are significantly increasing due to the rise in smart devices and technologies. The global navigation satellite systems (GNSS) have long been employed for LBS to navigate and determine accurate and reliable location information in outdoor environments. However, the GNSS signals are too weak to penetrate buildings and unable to provide reliable indoor LBS. Hence, GNSS’s incompetence in the indoor environment invites extensive research and development of an indoor positioning system (IPS). Various technologies and techniques have been studied for IPS development. This paper provides an overview of the available smartphone-based indoor localization solutions that rely on radio frequency technologies. As fingerprinting localization is mostly accepted for IPS development owing to its good localization accuracy, we discuss fingerprinting localization in detail. In particular, our analysis is more focused on practical IPS that are realized using a smartphone and Wi-Fi/Bluetooth Low Energy (BLE) as a signal source. Furthermore, we elaborate on the challenges of practical IPS, the available solutions and comprehensive performance comparison, and present some future trends in IPS development.

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.

With the rapid growth of the demand for indoor location-based services (LBS), Wi-Fi received signal strength (RSS) fingerprints database has attracted significant attention because it is easy to obtain. The fingerprints algorithm based on convolution neural network (CNN) is often used to improve indoor localization accuracy. However, the number of reference points used for position estimation has significant effects on the positioning accuracy. Meanwhile, it is always selected arbitraily without any guiding standards. As a result, a novel location estimation method based on Jenks natural breaks algorithm (JNBA), which can adaptively choose more reasonable reference points, is proposed in this paper. The output of CNN is processed by JNBA, which can select the number of reference points according to different environments. Then, the location is estimated by weighted K-nearest neighbors (WKNN). Experimental results show that the proposed method has higher positioning accuracy without sacrificing more time cost than the existing indoor localization methods based on CNN.

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.

The contribution of this paper is twofold: (1) to formulate a fusion problem encountered in the design of a multi-modal identity verification system as a particular classification problem, (2) to propose a simple classifier to solve this problem. The multi-modal identity verification system under consideration is built of d modalities in parallel, each one delivering as output a scalar number, called the score, stating how well the claimed identity is verified. A fusion module receiving as input the d scores has to take a binary decision: accept or reject identity. We have solved this fusion problem using a classic k-nearest-neighbor (k-NN) classifier. The most important problem encountered with this simple classifier is the unbalance between the number of reference points in either class. Adapting the classic k-NN classifier using distance weighting and vector quantization principles enables to reduce the influence and the number of impostor reference points respectively. This constitutes the originality of this paper. The performances of these different fusion modules have been evaluated on a multi-modal database, containing both vocal and visual modalities.