Location Fingerprint Database Research Articles

Next-Generation IoT: Harnessing AI for Enhanced Localization and Energy Harvesting in Backscatter Communications

Ongoing backscatter communications and localisation research have been able to obtain incredibly accurate results in controlled environments. The main issue with these systems is faced in complex RF environments. This paper investigates concurrent localization and ambient radio frequency (RF) energy harvesting using backscatter communication systems for Internet of Things networks. Dynamic real-world environments introduce complexity from multipath reflection and shadowing, as well as interference from movements. A machine learning framework leveraging K-Nearest Neighbors and Random Forest classifiers creates robustness against such variability. Historically, received signal measurements construct a location fingerprint database resilient to perturbations. The Random Forest model demonstrates precise localization across customized benches with programmable shuffling of chairs outfitted with RF identification tags. Average precision accuracy exceeds 99% despite deliberate placement modifications, inducing signal fluctuations emulating mobility and clutter. Significantly, directional antennas can harvest over −3 dBm, while even omnidirectional antennas provide −10 dBm—both suitable for perpetually replenishing low-energy electronics. Consequently, the intelligent backscatter platform localizes unmodified objects to customizable precision while promoting self-sustainability.

Indoor Location Fingerprinting Algorithm Based on Path Loss Parameter Estimation and Bayesian Inference

For indoor positioning methods based on Wi-Fi fingerprint, a high-density location fingerprint database usually needs to be constructed to ensure high-precision positioning requirements. At the same time, when the indoor area is huge or the number of reference points (RPs) is large and densely distributed, the computational burden of the online phase will be increased. To solve the above two problems, we propose an indoor location fingerprinting algorithm based on path loss parameter estimation and Bayesian inference (FA-PEBI). In the off-line phase, first, we collect location fingerprints from a few RPs in different rooms; second, the location fingerprints collected are filtered; third, the path loss parameters (PLPs) in different rooms are trained by using a logarithmic distance path loss (LDPL) model; and finally, predict the fingerprint information of the other locations in that room based on the trained parameters. In the online phase, the real-time fingerprint information of the user is first filtered, and then, the room where the user is located is predetermined by using the Bayesian inference method. Subsequently, the corresponding fingerprint database is selected to match based on the regional adaptive selection (RAS) algorithm. Eventually, the ultimate location of the user is obtained based on the Manhattan distance. The experimental results show that the proposed algorithm improves the localization accuracy by at least 16% compared with other existing methods, both in the Wireless Insite simulation data and MAN dataset, and still outperforms the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -nearest neighbor (KNN) algorithm when the fingerprint collection effort in the off-line phase is reduced by half.

Connectivity-Based Localization Scheme for Social Internet of Things

Different from the social network which only focuses on the interaction between people, the integration of social networks and the internet of things (IoT) leads to multi-directional interactions of human to human, human to thing, and thing to thing. The social IoT is composed of a large number of heterogeneous devices, which can improve the scalability of resource and service. Meanwhile, the heterogeneous devices have uneven computing power and different location information measurement types (e.g., the distance, angle, and hop count). Therefore, a localization approach with easy-to-obtain measurement data and low requirements on the computing power is needed. In this article, we propose a localization approach for the social IoT by combining the fuzzy rough set theory and the ridge regression extreme learning machine (RRELM). First of all, a location fingerprint database is constructed. Different from the traditional location fingerprint database, the location fingerprint database here stores the minimum hop counts between the reference node (RN) and the anchor node (AN) instead of the received signal strength (RSS). Second, the fuzzy rough set theory is used to compute the significant degree of each AN, and the ANs that contribute little to the positioning result are removed. This approach not only relieves the storage pressure of the location fingerprint database but also reduces the computational complexity of user position estimation. Third, the RRELM is trained by using the samples in the location fingerprint database. Finally, by inputting the newly collected minimum hop counts from the user to each AN into the trained RRELM, the user’s position is estimated. From the extensive experimental results, the proposed approach has high positioning accuracy and low computational complexity, which is suitable for the social IoT.

Indoor Localization Based on Optimized KNN

In recent years, with the continuous development of the economic situation, the price of low-end smart phones continues to reduce, the coverage of wireless local area network (WLAN) continues to improve, and individual users pay more and more attention to the real-time information around them, so indoor positioning technology has become a research hotspot. Among them, the indoor positioning based on the location fingerprint method quickly becomes the &amp;ldquo;Navigator&amp;rdquo; of indoor positioning direction by virtue of the simplicity of layout, the cost reduction of hardware facilities and the accuracy of positioning effect. However, the traditional indoor positioning methods usually rely on WiFi signal and KNN algorithm. When the KNN algorithm is implemented, there will be a lot of calculation and heavy workload to establish the location fingerprint database offline, and the efficiency and accuracy of online matching positioning points are low. This paper proposes an OKNN algorithm based on the improved KNN algorithm. By improving the efficiency of matching algorithm, the algorithm indirectly improves the positioning accuracy and optimizes the indoor positioning effect.

An Indoor Positioning Algorithm for Wearable Device Using Deep Learning Regression Prediction Model in IoT Applications

To solve the problem of low positioning accuracy and ease environmental impact of wearable devices in the Internet of things, a wearable device indoor positioning algorithm based on deep learning was proposed. Firstly, a basic model of deep learning composed of an input layer, hidden layer, and output layer is proposed to realize the continuous prediction and positioning with higher accuracy. Secondly, the automatic stacking encoder is trained with signal strength data, and features are extracted from a large number of signal strength samples with noise to build the location fingerprint database. Finally, the stacking automatic coding machine is used to obtain the signal strength characteristics of the points to be measured, which are matched with the signal strength characteristics in the fingerprint database, and the location of the points to be measured is estimated by the nearest neighbor algorithm. The experimental results show that the indoor positioning algorithm based on the stacking automatic coding machine has higher positioning accuracy, and the average error of points on the complete path can reach within 3 m in 93% cases.

Research on Intelligent Indoor Location Method Based on WI-FI Fingerprint

In order to improve indoor location performance based on Wi-Fi fingerprint, the novel algorithm is put forward based on K-mean algorithm and fuzzy support vector machine. Firstly, the basic theories of Wi-Fi location technology, indoor location principle based on location fingerprint database are studied. Secondly, the basic theories of K-means algorithm, fuzzy support vector machine and nearest incremental algorithm are discussed, and the corresponding mathematical model is deduced. Third, the Wi-Fi fingerprint location algorithm procedure is designed. Finally, the simulation analysis is carried out, and results show that the proposed method has higher location performance.

Self-Adaptive Dynamic Ranging Model-Based Real-Time Hybrid Algorithm for Accurate Indoor Localization

The reliability of location information still maintains a crucial impact on restricting the development of location based services in indoor environment. However, in wireless local area network, received signal strength indicator (RSSI) is prone to be interfered by indoor complex environment, resulting in low accuracy and instability of real-time positioning. Here, the new self-adaptive dynamic ranging model-based real-time hybrid algorithm was proposed to realize accurate and undisturbed localization in indoor scenes. A self-adaptive dynamic ranging model was initially constructed to update the environmental parameters and correct the ranging values of mobile terminals in real time. Based on this model, a hybrid KNN algorithm and a hybrid Bayesian algorithm were severally presented. Location fingerprint database and real-time RSSI data of test points were then obtained through data acquisition. Finally, the acquired data was further used to verify the two hybrid algorithms proposed, and compared with the results of several conventional algorithms. As a result, the stability and accuracy of dual hybrid algorithms were better than those of the traditional ones. The range of average location error of both hybrid algorithms maintained 1.26-1.38 m, which was significantly lower than the error level of 2-5 m under the current WLAN environment. This newly proposed hybrid algorithm could effectively improve the stability and accuracy of indoor localization with real-time positioning algorithm, providing a promising solution for RSSI-based indoor positioning system.

Indoor Fingerprint Localization Algorithm Based on Variable Weight Using Channel State Information

In order to solve the problems of indoor fingerprint localization algorithm based on static weight, such as low positioning accuracy and poor environmental adaptability, a variable weight indoor based on channel state information with Euclidean distance as weight reference is proposed. In the preprocessing stage, the collected CSI amplitude values are first subjected to Butterworth low-pass filtering denoising processing, and then the values of each sampling point are averaged, and the reference point location fingerprint database is established by combining the known coordinates. The weight index α is introduced in the online positioning stage, and the nearest neighbor is found by the KNN algorithm with the CSI eigenvalue reference. Then the weight index β is introduced, and the Euclidean distance is used as the weight reference, and the nearest neighbor is weighted. Get the coordinates of the target position. The experimental results show that the indoor fingerprint localization algorithm proposed by this scheme has higher positioning accuracy and less fluctuation of positioning error than the traditional KNN-based indoor fingerprint localization algorithm.

Calibrated Data Simplification for Energy-Efficient Location Sensing in Internet of Things

The Internet of Things (IoT) has gradually changed the way of people’s lives due to its ability of connecting everything together, and meanwhile the accurate location sensing plays a crucial role in achieving this goal. Up to now, as one of the most representative outdoor localization systems, the global positioning system has been widely used, but its performance may be dramatically declined in indoor environment due to the serious multipath effect and signal attenuation caused by the complicated indoor structure. At the same time, the location fingerprint-based localization approach has become a popular one in indoor environment, and meanwhile the corresponding calibrated signal simplification in location database construction has been primarily considered due to its significant practical meaning in avoiding the blind signal sampling. In this paper, we propose to use an information-theoretic lens to construct the energy-efficient location fingerprint database for the localization in IoT. Interestingly, by analyzing the information loss in signal sampling, we analogize the database construction process into the information propagation process in a lossy channel, and then formulate the relations of sample capacity and localization error from an information-theoretic view. After that, by selecting an appropriate time interval to sample the independent and nonredundant signal, the minimum number of sampled signal under the given expected localization accuracy is determined. Finally, the extensive experimental results show that compared with the state-of-the-art approaches, the proposed one can effectively simplify the calibrated data for the energy-efficient location database construction in different wireless localization networks.

Cost-efficient BLE fingerprint database construction approach via multi-quadric RBF interpolation

The demand for indoor localization is becoming urgent, but the traditional location fingerprint approach takes a lot of manpower and time to construct a fine-grained location fingerprint database. To address this problem, we propose to use the approach of combining dynamic collection of fingerprint samples with Radial Basis Function (RBF) interpolation. Specifically, the raw sparse fingerprint database is constructed from a small number of fingerprints collected on a few paths, in which the pedestrian track correction algorithm improves the validity and accuracy of the sparse fingerprint database. Then, the RBF interpolation approach is applied to enrich the sparse fingerprint database, in which the Genetic Algorithm (GA) is used to optimize the free shape parameter and the cut-off radius is determined according to the experimental results. Extensive experiments show that the proposed approach guarantees high interpolation and localization accuracy and also significantly reduces the effort of manual collection of fingerprint samples.

WLAN indoor positioning method based on gradient boosting and particle filtering

Indoor positioning technology has shown its great application prospects in smart cities. The main purpose of this paper is to study a low-cost, low-error indoor positioning method that can get a accurate indoor position when communicating with wireless local area networks (WLAN). The paper optimises the traditional WLAN indoor positioning method based on location fingerprint database, and algorithms about in indoor signal simulation, similarity matching of vector and continuously-positioning are tested in work of this paper, and a WLAN indoor positioning method based on gradient boosting and particle filtering is proposed. The paper finally shows the indoor positioning result with an average error of 1.7 metres. These research results verify the feasibility of WLAN indoor positioning and show that the positioning accuracy will be improved with the further optimisation of the positioning method. The potential application values of WLAN technology make it more convenient for internet of things (IoT) technology.

WLAN indoor positioning method based on gradient boosting and particle filtering

Indoor positioning technology has shown its great application prospects in smart cities. The main purpose of this paper is to study a low-cost, low-error indoor positioning method that can get a accurate indoor position when communicating with wireless local area networks (WLAN). The paper optimises the traditional WLAN indoor positioning method based on location fingerprint database, and algorithms about in indoor signal simulation, similarity matching of vector and continuously-positioning are tested in work of this paper, and a WLAN indoor positioning method based on gradient boosting and particle filtering is proposed. The paper finally shows the indoor positioning result with an average error of 1.7 metres. These research results verify the feasibility of WLAN indoor positioning and show that the positioning accuracy will be improved with the further optimisation of the positioning method. The potential application values of WLAN technology make it more convenient for internet of things (IoT) technology.

Indoor Positioning of RBF Neural Network Based on Improved Fast Clustering Algorithm Combined With LM Algorithm

In the indoor environment, due to weak receiver signals, environmental noise, multipath interference, and non-line-of-sight propagation, the traditional positioning algorithms based on received signal strength indication (RSSI) have many problems, such as inaccurate positioning results, great dependence on the signal propagation path loss model, and high time and labor costs. This paper studied the wireless indoor positioning algorithm based on neural network. A weighted median-Gaussian filtering method is proposed to preprocess RSSI and establish a location fingerprint database. An indoor positioning algorithm based on an improved fast clustering algorithm combined with a Levenberg–Marquardt (LM) algorithm is proposed. The improved clustering algorithm is used to design the network structure, initialize the number of radial basis function (RBF) neurons, find the local density peak as the cluster center to achieve rapid clustering of samples, and adjust the parameters of the kernel function of the hidden layer neurons. And the LM algorithm is used for numerical optimization. In order to verify the performance of the algorithm, positioning experiments are performed in the library. The error rate was reduced by 26.2% compared with the RBF network. The positioning results data confirm the effectiveness and applicability of the proposed algorithm.

Fast Signals of Opportunity Fingerprint Database Maintenance with Autonomous Unmanned Ground Vehicle for Indoor Positioning.

Indoor positioning technology based on Received Signal Strength Indicator (RSSI) fingerprints is a potential navigation solution, which has the advantages of simple implementation, low cost and high precision. However, as the radio frequency signals can be easily affected by the environmental change during its transmission, it is quite necessary to build location fingerprint database in advance and update it frequently, thereby guaranteeing the positioning accuracy. At present, the fingerprint database building methods mainly include point collection and line acquisition, both of which are usually labor-intensive and time consuming, especially in a large map area. This paper proposes a fast and efficient location fingerprint database construction and updating method based on a self-developed Unmanned Ground Vehicle (UGV) platform NAVIS, called Automatic Robot Line Collection. A smartphone was installed on NAVIS for collecting indoor Received Signal Strength Indicator (RSSI) fingerprints of Signals of Opportunity (SOP), such as Bluetooth and Wi-Fi. Meanwhile, indoor map was created by 2D LiDAR-based Simultaneous Localization and Mapping (SLAM) technology. The UGV automatically traverse the unknown indoor environment due to a pre-designed full-coverage path planning algorithm. Then, SOP sensors collect location fingerprints and generates grid map during the process of environment-traversing. Finally, location fingerprint database is built or updated by Kriging interpolation. Field tests were carried out to verify the effectiveness and efficiency of our proposed method. The results showed that, compared with the traditional point collection and line collection schemes, the root mean square error of the fingerprinting-based positioning results were reduced by 35.9% and 25.0% in static tests and 30.0% and 21.3% respectively in dynamic tests. Moreover, our UGV can traverse the indoor environment autonomously without human-labor on data acquisition, the efficiency of the automatic robot line collection scheme is 2.65 times and 1.72 times that of the traditional point collection and the traditional line acquisition, respectively.

An Indoor Pedestrian Localization Algorithm Based on Multi-Sensor Information Fusion

For existing indoor localization algorithm has low accuracy, high cost in deployment and maintenance, lack of robustness, and low sensor utilization, this paper proposes a particle filter algorithm based on multi-sensor fusion. The pedestrian’s localization in indoor environment is described as dynamic system state estimation problem. The algorithm combines the smart mobile terminal with indoor localization, and filters the result of localization with the particle filter. In this paper, a dynamic interval particle filter algorithm based on pedestrian dead reckoning (PDR) information and RSSI localization information have been used to improve the filtering precision and the stability. Moreover, the localization results will be uploaded to the server in time, and the location fingerprint database will be built incrementally, which can adapt the dynamic changes of the indoor environment. Experimental results show that the algorithm based on multi-sensor improves the localization accuracy and robustness compared with the location algorithm based on Wi-Fi.

Dealing with Insufficient Location Fingerprints in Wi-Fi Based Indoor Location Fingerprinting

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.

Construction Method of Location Fingerprint Database Based on Gaussian Process Regression Modeling

Construction Method of Location Fingerprint Database Based on Gaussian Process Regression Modeling

A Radio-Map Automatic Construction Algorithm Based on Crowdsourcing.

Traditional radio-map-based localization methods need to sample a large number of location fingerprints offline, which requires huge amount of human and material resources. To solve the high sampling cost problem, an automatic radio-map construction algorithm based on crowdsourcing is proposed. The algorithm employs the crowd-sourced information provided by a large number of users when they are walking in the buildings as the source of location fingerprint data. Through the variation characteristics of users’ smartphone sensors, the indoor anchors (doors) are identified and their locations are regarded as reference positions of the whole radio-map. The AP-Cluster method is used to cluster the crowdsourced fingerprints to acquire the representative fingerprints. According to the reference positions and the similarity between fingerprints, the representative fingerprints are linked to their corresponding physical locations and the radio-map is generated. Experimental results demonstrate that the proposed algorithm reduces the cost of fingerprint sampling and radio-map construction and guarantees the localization accuracy. The proposed method does not require users’ explicit participation, which effectively solves the resource-consumption problem when a location fingerprint database is established.

MRI: Model-Based Radio Interpolation for Indoor War-Walking

Location estimation methods using radio fingerprint have been studied extensively. The approach constructs a database that associates ambient radio signals with physical locations in training phase, and then estimates the location by finding the most similar signal pattern within the database. To achieve robust and accurate location estimation, the training phase should be conducted across the entire target space. In practice, however, a user may only access limited or authorized places in a building, that causes degradation in accuracy. In this paper, we present a smartphone-based autonomous indoor war-walking scheme, which automatically constructs the location fingerprint database, even covering unvisited locations. While a smartphone user explores the target area, the proposed system tracks the user's trajectory and simultaneously trains the location fingerprint database. Furthermore, our scheme interpolates radio signals in the database with an appropriate radio propagation model, and supplements fingerprints for unvisited places. As a result, although a user may sparsely explore the target site, the scheme returns the complete database. We implemented our solution and demonstrated the feasibility of the solution.

An effective location fingerprint model for wireless indoor localization

A model for predicting precision and accuracy performance of indoor fingerprint based positioning systems is very desirable for system designers as it helps estimate the probability of location selection before actual deployment. Such information can be used to tune the fingerprint database or improve the offline fingerprint collection phase. This paper presents a new analytical model that applies proximity graphs for approximating the probability distribution of error distance given a location fingerprint database using WLANs received signals, and its associated statistics. Simulations are used to validate the analytical model, which is found to produce results close to that from simulations. The model permits an analysis of the internal structure of location fingerprints. We employ the analysis of the fingerprint structure to identify and eliminate inefficient location fingerprints stored in the fingerprint database. Knowledge of where the inefficient fingerprints are can potentially be employed in a better location fingerprint collecting scheme from a grid system in the offline phase.