Received Signal Strength Fingerprints Research Articles

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.

Toward a Quality-Aware Online Pricing Mechanism for Crowdsensed Wireless Fingerprints

Fingerprinting localization systems are outstanding for its convenient deployment, where a major challenge is the high cost for collecting a huge number of received signal strength fingerprints. Mobile crowdsensing (MCS) paradigm is cost-effective for large-scale data collection; however, a quality-aware data pricing mechanism dedicated to MCSed fingerprints accommodating practical application situations including budget constraints and online data submission is still unavailable. In this paper, we present a data pricing scheme dedicated to MCSed fingerprints by enhancing the online learning technique. We first reveal the principle of fingerprints quality assessment for accurate localization. Based on the principle, we design corresponding loss and regret function, which is able to reflect values of the fingerprints with respect to localization accuracy. We then present an online pricing scheme for MCSed data, which results in that the worker's payoff is a random variable following an optimal probability density function leading to the minimum expected regret. Furthermore, we extend our scheme to application scenarios with different budget settings, where the pricing strategies for the scenarios of regret minimization with fixed budget and budget minimization for certain fingerprints quality level are investigated. Experimental results are presented to verify our theoretical analysis.

A Narrow-Band Indoor Positioning System by Fusing Time and Received Signal Strength via Ensemble Learning

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 narrow-band signals because of multipath propagation. In this paper, we design a positioning system using narrow-band 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.

R&P: An Low-Cost Device-Free Activity Recognition for E-Health

Activity recognition is important for taking care of patients and old men especially in e-Health. The activity recognition system without carrying any wearable devices is widely used in our daily life. Current methods employing uneconomical equipment or even dedicated devices lead to cost-inefficiency for large-scale deployments. This paper introduces R&P, a device-free activity recognition system only using cheap radio frequency identification devices (RFID) tags. Based on the analysis of RFID signals, we extract received signal strength fingerprints and phase fingerprints for each activity and synthesize these two kinds of fingerprints to accurately recognize activities. Moreover, we also modify the dynamic time warping (DTW) algorithm and propose T-DTW method to improve the recognition efficiency. We use commercial passive RFID hardware and verify R&Pin three different environments with different targets and six activities. The results demonstrate that our solution can recognize activities with an average accuracy of 87.9%.

Performance Analysis of RSS Fingerprinting Based Indoor Localization

Indoor localization has been an active research field for decades, where received signal strength (RSS) fingerprinting based methodology is widely adopted and induces many important localization techniques, such as the recently proposed one building fingerprints database with crowdsourcing. While efforts have been dedicated to improve accuracy and efficiency of localization, performance of the RSS fingerprinting based methodology itself is still unknown in a theoretical perspective. In this paper, we present a general probabilistic model to shed light on a fundamental issue: how good the RSS fingerprinting based indoor localization can achieve? Concretely, we present the probability that a user can be localized in a region with certain size. We reveal the interaction among accuracy, reliability, and the number of measurements in the localization process. Moreover, we present the optimal fingerprints reporting strategy that can achieve the best localization accuracy with given reliability and the number of measurements, which provides a design guideline for the RSS fingerprinting based indoor localization system. Further, we analyze the influence of imperfect database information on the reliability of localization, and find that the impact of imperfect information is still under control with reasonable number of samplings when building the database.

FUSION OF LOCATION FINGERPRINTING AND TRILATERATION BASED ON THE EXAMPLE OF DIFFERENTIAL WI-FI POSITIONING

Abstract. Positioning of mobile users in indoor environments with Wireless Fidelity (Wi-Fi) has become very popular whereby location fingerprinting and trilateration are the most commonly employed methods. In both the received signal strength (RSS) of the surrounding access points (APs) are scanned and used to estimate the user’s position. Within the scope of this study the advantageous qualities of both methods are identified and selected to benefit their combination. By a fusion of these technologies a higher performance for Wi-Fi positioning is achievable. For that purpose, a novel approach based on the well-known Differential GPS (DGPS) principle of operation is developed and applied. This approach for user localization and tracking is termed Differential Wi-Fi (DWi-Fi) by analogy with DGPS. From reference stations deployed in the area of interest differential measurement corrections are derived and applied at the mobile user side. Hence, range or coordinate corrections can be estimated from a network of reference station observations as it is done in common CORS GNSS networks. A low-cost realization with Raspberry Pi units is employed for these reference stations. These units serve at the same time as APs broadcasting Wi-Fi signals as well as reference stations scanning the receivable Wi-Fi signals of the surrounding APs. As the RSS measurements are carried out continuously at the reference stations dynamically changing maps of RSS distributions, so-called radio maps, are derived. Similar as in location fingerprinting this radio maps represent the RSS fingerprints at certain locations. From the areal modelling of the correction parameters in combination with the dynamically updated radio maps the location of the user can be estimated in real-time. The novel approach is presented and its performance demonstrated in this paper.

ScOFi: Schematic assisted optimum fingerprinting for Wi-Fi indoor localization using peer hand-shake

Received signal strength fingerprints based on Wi-Fi spectrum have been widely adopted in the recent years for indoor localization purposes due to cost-effectiveness and availability. However, until the peer hand-shake (PHS), existing work had not constrained the schematic dimension of the target area, which could dramatically reduce the localization error. As the demand for sensors everywhere schemes for 5G networks keeps on booming, effective signal propagation characterization with in indoors is very essential for Internet of things (IoT) indoor localization and navigation applications. We review, extend the validation of the PHS technique that leverages the schematic dimensions of the target area within the total indoor environment to construct, auto-dynamically transform and update fingerprint in complex indoor environments. Extensive experimental validation has been carried out in two scenarios; Scenario 1 categorizes lobby area while Scenario 2 categorizes corridor areas. We analyze the accuracy performance using Nearest Neighbor (NN) and the KNN algorithms. Experimental results show robustness of the PHS, achieving lower average localization error in diverse indoor dimensionalities than comparisons.

Structured Group Sparsity: A Novel Indoor WLAN Localization, Outlier Detection, and Radio Map Interpolation Scheme

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.

Fingerprints in the Air: Unique Identification of Wireless Devices Using RF RSS Fingerprints

Recent years have witnessed the proliferation of indoor localization, device identification, and wireless attendance security systems. These solutions typically leverage RF received signal strength fingerprints to locate persons. However, they entail an important albeit commonly ignored issue, i.e., detecting whether an individual is carrying more than one wireless device. In other words, additional devices should be excluded from the analysis. To detect the unique identification problem, bio-assisted methods, such as fingerprint, face, and gait recognition, are deployed near entrances. However, these methods are not only difficult to implement but also entail additional costs. This paper studies the unique identification problem using RF received signal strength fingerprints, which are collected and modeled as time series. The similarity of the time series is calculated to achieve unique identification. Specifically, a naive algorithm based on dynamic time warping is proposed to compute the similarity in the asynchronous time series. Then, an improved two-step algorithm based on feature extraction and spectral clustering is proposed to reduce the computational complexity of the similarity check. In addition, an effectiveness index is proposed to obtain the optimal number of clusters. The results of simulations and experiments show that our algorithms can detect the unique identification problem with moderate computational complexity in typical scenarios.

Fusing Bluetooth Beacon Data with Wi-Fi Radiomaps for Improved Indoor Localization.

Indoor user localization and tracking are instrumental to a broad range of services and applications in the Internet of Things (IoT) and particularly in Body Sensor Networks (BSN) and Ambient Assisted Living (AAL) scenarios. Due to the widespread availability of IEEE 802.11, many localization platforms have been proposed, based on the Wi-Fi Received Signal Strength (RSS) indicator, using algorithms such as K-Nearest Neighbour (KNN), Maximum A Posteriori (MAP) and Minimum Mean Square Error (MMSE). In this paper, we introduce a hybrid method that combines the simplicity (and low cost) of Bluetooth Low Energy (BLE) and the popular 802.11 infrastructure, to improve the accuracy of indoor localization platforms. Building on KNN, we propose a new positioning algorithm (dubbed i-KNN) which is able to filter the initial fingerprint dataset (i.e., the radiomap), after considering the proximity of RSS fingerprints with respect to the BLE devices. In this way, i-KNN provides an optimised small subset of possible user locations, based on which it finally estimates the user position. The proposed methodology achieves fast positioning estimation due to the utilization of a fragment of the initial fingerprint dataset, while at the same time improves positioning accuracy by minimizing any calculation errors.

An experimental study of indoor RSS-based RF fingerprinting localization using GSM and Wi-Fi signals

Localization of mobile users in indoor environments has many practical applications in daily life. In this paper, we study the performance of the received signal strength (RSS)-based radio frequency (RF) fingerprinting localization method in a shopping mall environment considering both calibration and practical measurement cases. In the calibration case, the test data for the RSS fingerprinting database are built offline by receiving signals from Global System for Mobile Communications (GSM) base stations, which are collected by a dedicated measurement tool, i.e. the Test Mobile System. In order to see the localization performance, the k-nearest neighbors (K-NN) and random decision forest (RDF) algorithms are implemented. The RDF algorithm provides a better localization performance than K-NN in this case. For the practical implementations, the RSS values of both GSM and Wi-Fi signals are collected by ordinary smartphones. Localization is performed using different classification algorithms, i.e. BayesNet, support vector machines, K-NN, RDF, and J48. Moreover, the effects of the received signal type, phone type, and number of reference points on localization performance are investigated.

Indoor Location Sensing with Invariant Wi-Fi Received Signal Strength Fingerprinting.

A method of location fingerprinting based on the Wi-Fi received signal strength (RSS) in an indoor environment is presented. The method aims to overcome the RSS instability due to varying channel disturbances in time by introducing the concept of invariant RSS statistics. The invariant RSS statistics represent here the RSS distributions collected at individual calibration locations under minimal random spatiotemporal disturbances in time. The invariant RSS statistics thus collected serve as the reference pattern classes for fingerprinting. Fingerprinting is carried out at an unknown location by identifying the reference pattern class that maximally supports the spontaneous RSS sensed from individual Wi-Fi sources. A design guideline is also presented as a rule of thumb for estimating the number of Wi-Fi signal sources required to be available for any given number of calibration locations under a certain level of random spatiotemporal disturbances. Experimental results show that the proposed method not only provides 17% higher success rate than conventional ones but also removes the need for recalibration. Furthermore, the resolution is shown finer by 40% with the execution time more than an order of magnitude faster than the conventional methods. These results are also backed up by theoretical analysis.

ACMI: FM-Based Indoor Localization via Autonomous Fingerprinting

We present ACMI, an FM-based indoor localization system that does not require proactive site profiling. ACMI constructs the fingerprint database based on pure estimation of indoor received signal strength (RSS) distribution, where only the signals transmitted from commercial FM radio stations are used. Based on extensive field measurement study, we established our own signal propagation model that harnesses FM radio characteristics and open information of FM transmission towers in combination with the floor-plan of a building. Output of the model is an RSS fingerprint database. Using the fingerprint database as a knowledge base, ACMI refines a positioning result via the two-step process; parameter calibration and path matching, during its runtime. Without site profiling, our evaluation indicates that ACMI in seven campus locations and three downtown buildings using eight distinguished FM stations finds positions with only about 6 and 10 meters of errors on average, respectively.

Improving indoor positioning precision by using received signal strength fingerprint and footprint based on weighted ambient Wi-Fi signals

Positioning is the foremost process in the location based service (LBS). With the GPS signal strength obstructed by the wall, indoor users cannot obtain their positions with the assistance from the global positioning satellites. Most of the indoor positioning systems have relied on received signal strengths (RSSs) from indoor wireless emitting devices, such as Wi-Fi access points (APs). Integrating indoor position information into the application on the modern handheld devices can increase the application diversity and quality in an indoor environment. In this paper, we propose a novel indoor positioning scheme assisted by the RSS fingerprint and footprint. Smartphone users can get their indoor position based on RSSs from the surrounding Wi-Fi APs. With the assistance of collecting ambient Wi-Fi RSSs from not only the intrinsic APs but also the extrinsic APs, filtering RSSs by directions/orientations, and mitigating signal fluctuation, our proposed scheme can overcome the severe signal instability problem in the indoor environment and raise the positioning accuracy. In order to reduce the time complexity of the indoor positioning procedure, we design a close designated location set (CDLS) algorithm that only uses the designated locations with the similar footprints of current user's position to determine the user's location. The proposed RSS fingerprint and footprint matching mechanism can speed up the positioning process. Meanwhile, to lessen the possible negative effect of extrinsic APs, the weighted voting positioning (WVP) algorithm would assign higher reference weights to the signals from the intrinsic APs, and adjust the weights to the signals from the extrinsic APs by their failure probability. The evaluation results show that our proposed scheme can achieve a certain level of accuracy in the indoor environments and outperform other solutions.

Distributed multi‐object localisation by consensus on compressive sampling received signal strength fingerprints

Recent growing interest for location-based services has created a demand on object localisation approaches with low cost and high accuracy. In this study, the problem of distributed multi-object localisation using fingerprints of received signal strength (RSS) is addressed by combining average consensus and compressed sensing. First, Bayesian compressed sensing is employed at each agent to recover the sparse index vector from RSS measurements, which are corrupted by noises. It relaxes the requirement on accurate prior position knowledge of beacon nodes and is applicable in non-line-of-sight conditions. Then, average consensus is adopted to compel all agents to reach an agreement on the index vector, and in turn, on the location of objects. By using only one-hop neighbours’ information, the proposed distributed localisation method is applicable to large-scale networks. Moreover, the final location of each object is obtainable from each individual agent, which makes the proposed method flexible to the network administration. Experimental results are included to demonstrate the effectiveness of the proposed method.

Received Signal Strength Database Interpolation by Kriging for a Wi-Fi Indoor Positioning System.

The main approach for a Wi-Fi indoor positioning system is based on the received signal strength (RSS) measurements, and the fingerprinting method is utilized to determine the user position by matching the RSS values with the pre-surveyed RSS database. To build a RSS fingerprint database is essential for an RSS based indoor positioning system, and building such a RSS fingerprint database requires lots of time and effort. As the range of the indoor environment becomes larger, labor is increased. To provide better indoor positioning services and to reduce the labor required for the establishment of the positioning system at the same time, an indoor positioning system with an appropriate spatial interpolation method is needed. In addition, the advantage of the RSS approach is that the signal strength decays as the transmission distance increases, and this signal propagation characteristic is applied to an interpolated database with the Kriging algorithm in this paper. Using the distribution of reference points (RPs) at measured points, the signal propagation model of the Wi-Fi access point (AP) in the building can be built and expressed as a function. The function, as the spatial structure of the environment, can create the RSS database quickly in different indoor environments. Thus, in this paper, a Wi-Fi indoor positioning system based on the Kriging fingerprinting method is developed. As shown in the experiment results, with a 72.2% probability, the error of the extended RSS database with Kriging is less than 3 dBm compared to the surveyed RSS database. Importantly, the positioning error of the developed Wi-Fi indoor positioning system with Kriging is reduced by 17.9% in average than that without Kriging.

Robust indoor localization and tracking using GSM fingerprints

The article presents an easy to implement approach for indoor localization and navigation that combines Bayesian filtering with support vector machine classifiers to associate high-dimensionality cellular telephone network received signal strength fingerprints to distinct spatial regions. The technique employs a “space sampling” and a “time sampling” scheme in the training procedure, and the Bayesian filter allows introducing a priori information on room layout and target trajectories, resulting in robust room-level indoor localization.

Cell Identification based on Received Signal Strength Fingerprints: Concept and Application towards Energy Saving in Cellular Networks

Cell Identification based on Received Signal Strength Fingerprints: Concept and Application towards Energy Saving in Cellular Networks

An Empirical Evaluation of a Probabilistic RF Signature for WLAN Location Fingerprinting

Localization for indoor environments has gained considerable attention over the last decade. The most popular technique is based on location fingerprinting using received signal strength (RSS) mainly due to the fact that it exploits the available wireless infrastructure and that RSS fingerprints are readily available using different wireless standards (IEEE 802.11, etc.). This simplicity however incurs a cost in accuracy and researchers focus on improving the performance from a pattern recognition perspective. Recently improvement in performance has been demonstrated using physical layer channel-based fingerprints such as the Channel Transfer Function (CTF) and Channel Impulse Response (CIR) at a cost of increased storage and computation requirements. In this paper we experimentally evaluate the performance of a probabilistic physical layer fingerprint that is based on entropy of the magnitude and phase of the CTF. We will show through extensive frequency domain channel measurements in an indoor office environment that entropy can be a practical alternative to RSS fingerprinting; where it shares the latter's simplicity of structure (scalar) but outperforms RSS and complex CIR fingerprints. We further investigate the impact of realistic channel and system impairments such as small-scale fading (Doppler), Signal-to-noise ratio (SNR) and interference on the performance of the proposed fingerprint signature.

Fingerprinting based localization in heterogeneous wireless networks

Due to advances in mobile technology, context-aware applications are continuously growing in importance; therefore, the ability of developing accurate and reliable localization system has become a necessity. Since methods based on received signal strength (RSS) fingerprints are today widely adopted and most of mobile devices comprise different wireless access technologies, it is feasible to use fingerprints from heterogeneous wireless networks (HWN) for localization purposes. In this paper we propose a novel approach for localization based on searching the area which best matches the test RSS fingerprint. We evaluated the proposed method in realistic environment in WLAN, GSM and UMTS networks, and compared it with other commonly used approaches. The results showed that our method compares favorably to others, and practically always achieves the lowest localization error. We also extended the proposed system using a model of cooperative positioning by combining the estimates obtained in the heterogeneous wireless network. The obtained results showed that with combined location estimate, significant improvement over any single system was achieved.