Fingerprinting-based Indoor Positioning System Research Articles

LSS-RM: Using Multi-Mounted Devices to Construct a Lightweight Site-Survey Radio Map for WiFi Positioning.

A WiFi-received signal strength index (RSSI) fingerprinting-based indoor positioning system (WiFi-RSSI IPS) is widely studied due to advantages of low cost and high accuracy, especially in a complex indoor environment where performance of the ranging method is limited. The key drawback that limits the large-scale deployment of WiFi-RSSI IPS is time-consuming offline site surveys. To solve this problem, we developed a method using multi-mounted devices to construct a lightweight site-survey radio map (LSS-RM) for WiFi positioning. A smartphone was mounted on the foot (Phone-F) and another on the waist (Phone-W) to scan WiFi-RSSI and simultaneously sample microelectromechanical system inertial measurement-unit (MEMS-IMU) readings, including triaxial accelerometer, gyroscope, and magnetometer measurements. The offline site-survey phase in LSS-RM is a client–server model of a data collection and preprocessing process, and a post calibration process. Reference-point (RP) coordinates were estimated using the pedestrian dead-reckoning algorithm. The heading was calculated with a corner detected by Phone-W and the preassigned site-survey trajectory. Step number and stride length were estimated using Phone-F based on the stance-phase detection algorithm. Finally, the WiFi-RSSI radio map was constructed with the RP coordinates and timestamps of each stance phase. Experimental results show that our LSS-RM method can reduce the time consumption of constructing a WiFi-RSSI radio map from 54 min to 7.6 min compared with the manual site-survey method. The average positioning error was below 2.5 m with three rounds along the preassigned site-survey trajectory. LSS-RM aims to reduce offline site-survey time consumption, which would cut down on manpower. It can be used in the large-scale implementation of WiFi-RSSI IPS, such as shopping malls, hospitals, and parking lots.

Received Signal Strength Based Indoor Positioning Using a Random Vector Functional Link Network

Fingerprinting based indoor positioning system is gaining more research interest under the umbrella of location-based services. However, existing works have certain limitations in addressing issues such as noisy measurements, high computational complexity, and poor generalization ability. In this work, a random vector functional link network based approach is introduced to address these issues. In the proposed system, a subset of informative features from many randomized noisy features is selected to both reduce the computational complexity and boost the generalization ability. Moreover, the feature selector and predictor are jointly learned iteratively in a single framework based on an augmented Lagrangian method. The proposed system is appealing as it can be naturally fit into parallel or distributed computing environment. Extensive real-world indoor localization experiments are conducted on users with smartphone devices and results demonstrate the superiority of the proposed method over the existing approaches.

Toward Low-Overhead Fingerprint-Based Indoor Localization via Transfer Learning: Design, Implementation, and Evaluation

This work aims at proposing a transfer learning (TL)-based framework to enhance system scalability of fingerprint-based indoor localization by reducing offline training overhead without jeopardizing the localization accuracy. The basic principle is to reshape data distributions in the target domain based on the transferred knowledge from the source domains, so that those data belonging to the same cluster will be logically closer to each other, whereas others will be further apart from each other. Specifically, the TL-based framework consists of two parts, metric learning and metric transfer, which are used to learn the distance metrics from source domains and identify the most suitable metric for the target domain, respectively. Furthermore, this work implements a prototype of the fingerprint-based indoor localization system with the proposed TL-based framework embedded. Finally, extensive real-world experiments are conducted to demonstrate the effectiveness and the generality of the TL-based framework.

RMapCS: Radio Map Construction From Crowdsourced Samples for Indoor Localization

Fingerprint crowdsourcing has recently been promoted as a promising solution for fingerprinting-based indoor localization systems to relieve the burden of site survey. When constructing an indoor localization map from crowd-sourced samples, the following challenges should be addressed: inaccurate sample annotation, unequal sample dimensionality, measurement device diversity, and nonuniform spatial distribution. In this paper, we propose the radio map construction from crowd-sourced sample (RMapCS) scheme to handle these challenges. The RMapCS consists of four main modules: outlier detection, source selection, fingerprint interpolation, and device calibration. For each device in each grid, we first propose an improved clustering algorithm to remove outliers and use a threshold-based approach to select only those important signal sources. For a grid without enough samples, we propose a fingerprint interpolation algorithm to construct its device-specific fingerprints. Then, we propose a device calibration algorithm to fuse samples from different devices to obtain grid fingerprints. We also propose a two-step online positioning algorithm consisting of both set comparison and similarity computation. We conduct field measurements and experiments to examine the localization performance. Results show that the proposed RMapCS can achieve significant improvements over the peer schemes and the average localization error can achieve around 1.5 m by using only the received signal strength-based fingerprints.

WinIPS: WiFi-Based Non-Intrusive Indoor Positioning System With Online Radio Map Construction and Adaptation

WiFi fingerprinting-based indoor positioning system (IPS) has become the most promising solution for indoor localization. However, there are two major drawbacks that hamper its large-scale implementation. First, an offline site survey process is required which is extremely time-consuming and labor-intensive. Second, the RSS fingerprint database built offline is vulnerable to environmental dynamics. To address these issues comprehensively, in this paper, we propose WinIPS, a WiFi-based non-intrusive IPS that enables automatic online radio map construction and adaptation, aiming for calibration-free indoor localization. WinIPS can capture data packets transmitted in existing WiFi traffic and extract the RSS and MAC addresses of both WiFi access points (APs) and mobile devices in a non-intrusive manner. APs can be used as online reference points for radio map construction. A novel Gaussian process regression model is proposed to approximate the non-uniform RSS distribution of an indoor environment. Extensive experiments were conducted, which demonstrated that WinIPS outperforms existing solutions in terms of both RSS estimation accuracy and localization accuracy.

Reduced-complexity fingerprinting in WLAN-based indoor positioning

In this paper, a two-stage complexity reduction technique is implemented for a fingerprinting-based indoor positioning system. Several computation reduction techniques are applied during the offline and online phases of fingerprinting. Specifically, dimensionality reduction algorithms, clustering techniques and fast search strategies are integrated to achieve an ultimate reduction in the computational requirements of fingerprinting. The computational cost of fingerprinting is first reduced by restricting the location fingerprints to signal strength values received from informative access points (APs). Afterwards, clustering techniques are employed to speed up the online search for the target best match. Finally, selective matching between the target RSS and the pre-stored fingerprints is proposed to reduce the computational cost even further. In particular, this paper studies different dimensionality reduction methods and chooses the method that minimizes the positioning error. Moreover, a hybrid search solution of clustering and fast search strategies is proposed to minimize the search operations to find a user position in the radio map.

Sample Size Determination Algorithm for fingerprint-based indoor localization systems

Provision of accurate location information is an important task in the Internet of Things (IoT) applications and scenarios. This need has boosted the research and development of fingerprint based, indoor localization systems, since GPS information is not available in indoor environments. Performance evaluation of such systems and their related localization algorithms, is usually based on sampling collection in predetermined test environments. The sample size determination and sampling methodology can significantly affect the reliability of the outcome. This work proposes an algorithm that calculates the minimum sample size of positioning data required for objective performance evaluation of fingerprint based localization systems. The use of a correct, independent, unbiased and representative sample size can speed up the training, evaluation and calibration procedures of a fingerprint based localization system, while ensuring that the system’s true accuracy is achieved. The proposed Sample Size Determination Algorithm (SSDA) takes into consideration the desired confidence level, the resulting standard deviation of a small size preliminary sample as well as the error approximation with respect to the actual error of the system and proposes the final sample size for the evaluation and/or calibration and/or training of the utilized radio-maps. Additionally, the SSDA, assumes random sample allocation in the area of interest in order to avoid biased results. Risks arising from the selection of a sample of convenience are also investigated. Finally, the performance of the proposed algorithm is tested in both measured and simulated radio-maps.

Accuracy-aware wireless indoor localization: Feasibility and applications

Fingerprint-based indoor localization has attracted extensive research efforts due to its potential for deployment without extensive infrastructure support. However, the accuracies of these different systems vary and it is difficult to compare and evaluate these systems systematically. In this work, we propose a Gaussian process based approach that takes the radio map and the localization algorithm as an input, and outputs the expected accuracy of the localization system. With an efficient error estimation algorithm, many applications such as landmark detection, localization algorithm selection and access point subset selection can be performed. Our evaluations show that our approach provides sufficient accuracy and can serve as a useful tool for system evaluation and performance tuning when developing fingerprint-based indoor localization systems.

Efficient Multi-Channel Signal Strength Based Localization via Matrix Completion and Bayesian Sparse Learning

Fingerprint-based location sensing technologies play an increasingly important role in pervasive computing applications due to their accuracy and minimal hardware requirements. However, typical fingerprint-based schemes implicitly assume that communication occurs over the same channel (frequency) during the training and the runtime phases. When this assumption is violated, the mismatches between training and runtime fingerprints can significantly deteriorate the localization performance. Additionally, the exhaustive calibration procedure required during training limits the scalability of this class of methods. In this work, we propose a novel, scalable, multi-channel fingerprint-based indoor localization system that employs modern mathematical concepts based on the Sparse Representations and Matrix Completion theories. The contribution of our work is threefold. First, we investigate the impact of channel changes on the fingerprint characteristics and the effects of channel mismatch on state-of-the-art localization schemes. Second, we propose a novel fingerprint collection technique that significantly reduces the calibration time, by formulating the map construction as an instance of the Matrix Completion problem. Third, we propose the use of sparse Bayesian learning to achieve accurate location estimation. Experimental evaluation on real data highlights the superior performance of the proposed framework in terms of reconstruction error and localization accuracy.

Quality-Driven Auction-Based Incentive Mechanism for Mobile Crowd Sensing

The recent paradigm of mobile crowd sensing (MCS) enables a broad range of mobile applications. A critical challenge for the paradigm is to incentivize phone users to be workers providing sensing services. While some theoretical incentive mechanisms for general-purpose crowdsourcing have been proposed, it is still an open issue as to how to incorporate the theoretical framework into the practical MCS system. In this paper, we propose an incentive mechanism based on a quality-driven auction (QDA). The mechanism is specifically for the MCS system, where the worker is paid off based on the quality of sensed data instead of working time, as adopted in the literature. We theoretically prove that the mechanism is truthful, individual rational, platform profitable, and social-welfare optimal. Moreover, we incorporate our incentive mechanism into a Wi-Fi fingerprint-based indoor localization system to incentivize the MCS-based fingerprint collection. We present a probabilistic model to evaluate the reliability of the submitted data, which resolves the issue that the ground truth for the data reliability is unavailable. We realize and deploy an indoor localization system to evaluate our proposed incentive mechanism and present extensive experimental results.

A Novel Technique for Human Traffic based Radio Map Updating in Wi-Fi Indoor Positioning Systems

With the fast-developing of mobile terminals, positioning techniques based on fingerprinting method draws attention from many researchers even world famous companies. To conquer some shortcomings of the existing fingerprinting systems and further improve its performance, we propose a radio map building and updating technique, which is able to customize the spatial and temporal dependency of radio maps. The method includes indoor propagation and penetration modeling and the analysis of human traffic. Based on the combination of Ray-Tracing Algorithm, Finite-Different Time-Domain and Rough Set Theory, the approach of indoor propagation modeling accurately represents the spatial dependency of the radio map. In terms of temporal dependency, we specifically study the factor of moving people in the interest area. With measurement and statistics, the factor of human traffic is introduced as the temporal updating component. We improve our existing indoor positioning system with the proposed building and updating method, and compare the localization accuracy. The results show that the enhanced system can conquer the influence caused by moving people, and maintain the confidence probability stable during week, which enhance the actual availability and robustness of fingerprinting-based indoor positioning system.

GROPING: Geomagnetism and cROwdsensing Powered Indoor NaviGation

Although a large number of WiFi fingerprinting based indoor localization systems have been proposed, our field experience with Google Maps Indoor (GMI), the only system available for public testing, shows that it is far from mature for indoor navigation. In this paper, we first report our field studies with GMI, as well as experiment results aiming to explain our unsatisfactory GMI experience. Then motivated by the obtained insights, we propose GROPING as a self-contained indoor navigation system independent of any infrastructural support. GROPING relies on geomagnetic fingerprints that are far more stable than WiFi fingerprints, and it exploits crowdsensing to construct floor maps rather than expecting individual venues to supply digitized maps. Based on our experiments with 20 participants in various floors of a big shopping mall, GROPING is able to deliver a sufficient accuracy for localization and thus provides smooth navigation experience.

Enhancing Wi-Fi fingerprinting for indoor positioning using human-centric collaborative feedback

Abstract Position information is an important aspect of a mobile device’s context. While GPS is widely used to provide location information, it does not work well indoors. Wi-Fi network infrastructure is found in many public facilities and can be used for indoor positioning. In addition, the ubiquity of Wi-Fi-capable devices makes this approach especially cost-effective. In recent years, “folksonomy”-like systems such as Wikipedia or Delicious Social Bookmarking have achieved huge successes. User collaboration is the defining characteristic of such systems. For indoor positioning mechanisms, it is also possible to incorporate collaboration in order to improve system performance, especially for fingerprinting-based approaches. In this article, a robust and efficient model is devised for integrating human-centric collaborative feedback within a baseline Wi-Fi fingerprinting-based indoor positioning system. Experiments show that the baseline system performance (i.e., positioning error and precision) is improved by collecting both positive and negative feedback from users. Moreover, the feedback model is robust with respect to malicious feedback, quickly self-correcting based on subsequent helpful feedback from users.