scholarly journals Indoor Floor Localization Based on Multi-Intelligent Sensors

2020 ◽  
Vol 10 (1) ◽  
pp. 6
Author(s):  
Min Zhao ◽  
Danyang Qin ◽  
Ruolin Guo ◽  
Xinxin Wang
Keyword(s):  
Machine Learning ◽  
Indoor Localization ◽  
Collaborative Work ◽  
Magnetic Data ◽  
Data Mapping ◽  
Localization Accuracy ◽  
Database Construction ◽  
Intelligent Sensors ◽  
Majority Principle ◽  
Fingerprint Database

With the continuous expansion of the market of indoor localization, the requirements of indoor localization technology are becoming higher and higher. Existing indoor floor localization (IFL) systems based on Wi-Fi signal and barometer data are susceptible to external environment changes, resulting in large errors. A method for indoor floor localization using multiple intelligent sensors (MIS-IFL) is proposed to decrease the localization errors, which consists of a fingerprint database construction phase and a floor localization phase. In the fingerprint database construction phase, data acquisition is performed using magnetometer sensor, accelerator sensor and gyro sensor in the smartphone. In the floor localization phase, an active pattern recognition is performed through the collaborative work of multiple intelligent sensors and machine learning classifiers. Then floor localization is performed using magnetic data mapping, Euclidean closest approximation and majority principle. Finally, the inter-floor detection link based on machine learning is added to improve the overall localization accuracy of MIS-IFL. The experimental results show that the performance of the proposed method is superior to the existing IFL.

scholarly journals Indoor Localization Based on Weighted Surfacing from Crowdsourced Samples

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2990 ◽  
Author(s):  
Junhong Lin ◽  
Bang Wang ◽  
Guang Yang ◽  
Mu Zhou
Keyword(s):  
Indoor Localization ◽  
Field Measurements ◽  
Localization Accuracy ◽  
Database Construction ◽  
Cross Domain ◽  
Site Survey ◽  
Intersection Algorithm ◽  
Promising Solution ◽  
Fingerprint Database ◽  
Sample Annotation

Fingerprinting-based indoor localization suffers from its time-consuming and labor-intensive site survey. As a promising solution, sample crowdsourcing has been recently promoted to exploit casually collected samples for building offline fingerprint database. However, crowdsourced samples may be annotated with erroneous locations, which raises a serious question about whether they are reliable for database construction. In this paper, we propose a cross-domain cluster intersection algorithm to weight each sample reliability. We then select those samples with higher weight to construct radio propagation surfaces by fitting polynomial functions. Furthermore, we employ an entropy-like measure to weight constructed surfaces for quantifying their different subarea consistencies and location discriminations in online positioning. Field measurements and experiments show that the proposed scheme can achieve high localization accuracy by well dealing with the sample annotation error and nonuniform density challenges.

scholarly journals DeepLocate: Smartphone Based Indoor Localization with a Deep Neural Network Ensemble Classifier

Sensors ◽  
2019 ◽  
Vol 20 (1) ◽  
pp. 133 ◽  
Author(s):  
Imran Ashraf ◽  
Soojung Hur ◽  
Sangjoon Park ◽  
Yongwan Park
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Indoor Localization ◽  
Ensemble Classifier ◽  
Location Based Services ◽  
Magnetic Data ◽  
Neural Network Ensemble ◽  
Localization Accuracy ◽  
Mean Error ◽  
The Impact

A quickly growing location-based services area has led to increased demand for indoor positioning and localization. Undoubtedly, Wi-Fi fingerprint-based localization is one of the promising indoor localization techniques, yet the variation of received signal strength is a major problem for accurate localization. Magnetic field-based localization has emerged as a new player and proved a potential indoor localization technology. However, one of its major limitations is degradation in localization accuracy when various smartphones are used. The localization performance is different from various smartphones even with the same localization technique. This research leverages the use of a deep neural network-based ensemble classifier to perform indoor localization with heterogeneous devices. The chief aim is to devise an approach that can achieve a similar localization accuracy using various smartphones. Features extracted from magnetic data of Galaxy S8 are fed into neural networks (NNs) for training. The experiments are performed with Galaxy S8, LG G6, LG G7, and Galaxy A8 smartphones to investigate the impact of device dependence on localization accuracy. Results demonstrate that NNs can play a significant role in mitigating the impact of device heterogeneity and increasing indoor localization accuracy. The proposed approach is able to achieve a localization accuracy of 2.64 m at 50% on four different devices. The mean error is 2.23 m, 2.52 m, 2.59 m, and 2.78 m for Galaxy S8, LG G6, LG G7, and Galaxy A8, respectively. Experiments on a publicly available magnetic dataset of Sony Xperia M2 using the proposed approach show a mean error of 2.84 m with a standard deviation of 2.24 m, while the error at 50% is 2.33 m. Furthermore, the impact of devices on various attitudes on the localization accuracy is investigated.

scholarly journals A Two-Level WiFi Fingerprint-Based Indoor Localization Method for Dangerous Area Monitoring

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4243 ◽  
Author(s):  
Fei Li ◽  
Min Liu ◽  
Yue Zhang ◽  
Weiming Shen
Keyword(s):  
Indoor Localization ◽  
Search Space ◽  
High Accuracy ◽  
Cluster Center ◽  
Support Vector ◽  
Localization Accuracy ◽  
Localization Method ◽  
Fingerprint Database ◽  
Positioning Algorithm ◽  
Area Monitoring

Localization technologies play an important role in disaster management and emergence response. In areas where the environment does not change much after an accident or in the case of dangerous areas monitoring, indoor fingerprint-based localization can be used. In such scenarios, a positioning system needs to have both a high accuracy and a rapid response. However, these two requirements are usually conflicting since a fingerprint-based indoor localization system with high accuracy usually has complex algorithms and needs to process a large amount of data, and therefore has a slow response. This problem becomes even worse when both the size of monitoring area and the number of reference nodes increase. To address this challenging problem, this paper proposes a two-level positioning algorithm in order to improve both the accuracy and the response time. In the off-line stage, a fingerprint database is divided into several sub databases by using an affinity propagation clustering (APC) algorithm based on Shepard similarity. The online stage has two steps: (1) a coarse positioning algorithm is adopted to find the most similar sub database by matching the cluster center with the fingerprint of the node tested, which will narrow the search space and consequently save time; (2) in the sub database area, a support vector regression (SVR) algorithm with its parameters being optimized by particle swarm optimization (PSO) is used for fine positioning, thus improving the online positioning accuracy. Both experiment results and actual implementations proved that the proposed two-level localization method is more suitable than other methods in term of algorithm complexity, storage requirements and localization accuracy in dangerous area monitoring.

scholarly journals BLocate: A Building Identification Scheme in GPS Denied Environments Using Smartphone Sensors

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3862 ◽  
Author(s):  
Imran Ashraf ◽  
Soojung Hur ◽  
Yongwan Park
Keyword(s):  
Magnetic Field ◽  
Indoor Localization ◽  
Magnetic Data ◽  
Accelerometer Data ◽  
Identification Method ◽  
Isolated Points ◽  
A Minor ◽  
Fingerprint Database ◽  
The Impact ◽  
Building Identification

Indoor localization systems assume that the user’s current building is known by the GPS (Global Positioning System). However, such assumptions do not hold true in GPS denied environments or where the GPS cannot determine the user’s definite location. We present a novel solution to identify the building where the user is present now. The proposed building identification method works on the pervasive magnetic field using a smartphone. The accelerometer data determines the user’s activity of being stationary or walking. An Artificial Neural Network is used to identify the user’s activities and it shows good results. The magnetometer data is used to identify the user’s current building using the fingerprinting approach. Contrary to a traditional fingerprinting approach which stores intensity values, we utilize the patterns formed by the magnetic field strength in the form of a Binary Grid (BG). The BG approach overcomes the limitation of Dynamic Time Warping (DTW) whose performance is degraded when the magnitude of the magnetic data is changed. The experiments are performed with Samsung Galaxy S8 for eight various buildings with different altitudes and number of floors in Yeungnam University, Korea. The results demonstrate that the proposed building identification method can potentially be deployed for building identification. The precision, UAR (Unweighted Average Recall), F score, and Cohen’s Kappa values are used to determine the performance of the proposed system. The proposed systems shows very promising results. The system operates without any aid from any infrastructure dependent technologies like GPS or WiFi. Furthermore, we performed many experiments to investigate the impact of isolated points data to build fingerprint database on system’s accuracy with 1 m and 2 m distance. Results illustrate that by trading off a minor accuracy, survey labor can be reduced by 50 percent.

scholarly journals Fingerprint Database Enhancement by Applying Interpolation and Regression Techniques for IoT-based Indoor Localization

2022 ◽  
Vol 4 ◽  
pp. 167-189
Author(s):  
Dwi Joko Suroso ◽  
Farid Yuli Martin Adiyatma ◽  
Panarat Cherntanomwong ◽  
Pitikhate Sooraksa
Keyword(s):  
Indoor Localization ◽  
Polynomial Regression ◽  
Polynomial Interpolation ◽  
Received Signal Strength Indicator ◽  
Database Construction ◽  
3D Environments ◽  
Regression Techniques ◽  
Fingerprint Database ◽  
2D And 3D ◽  
Classical Interpolation

Most applied indoor localization is based on distance and fingerprint techniques. The distance-based technique converts specific parameters to a distance, while the fingerprint technique stores parameters as the fingerprint database. The widely used Internet of Things (IoT) technologies, e.g., Wi-Fi and ZigBee, provide the localization parameters, i.e., received signal strength indicator (RSSI). The fingerprint technique advantages over the distance-based method as it straightforwardly uses the parameter and has better accuracy. However, the burden in database reconstruction in terms of complexity and cost is the disadvantage of this technique. Some solutions, i.e., interpolation, image-based method, machine learning (ML)-based, have been proposed to enhance the fingerprint methods. The limitations are complex and evaluated only in a single environment or simulation. This paper proposes applying classical interpolation and regression to create the synthetic fingerprint database using only a relatively sparse RSSI dataset. We use bilinear and polynomial interpolation and polynomial regression techniques to create the synthetic database and apply our methods to the 2D and 3D environments. We obtain an accuracy improvement of 0.2m for 2D and 0.13m for 3D by applying the synthetic database. Adding the synthetic database can tackle the sparsity issues, and the offline fingerprint database construction will be less burden. Doi: 10.28991/esj-2021-SP1-012 Full Text: PDF

Predicted Number of Pregnant Women in Aichi Prefecture, Japan: Estimation by Machine Learning Database Construction for Disaster Preparation

2021 ◽  
pp. 1-9
Author(s):  
Kanetoshi Hattori ◽  
Ritsuko Hattori
Keyword(s):  
Machine Learning ◽  
Pregnant Women ◽  
Disaster Preparedness ◽  
Correlation Coefficients ◽  
Database Construction ◽  
Aichi Prefecture ◽  
Cascade Correlation ◽  
Correlation Learning ◽  
Disaster Preparation ◽  
Area Data

Abstract Aichi prefecture, Japan is predicted to be hit by Mega-earthquake. Aichi Prefectural Association of Midwives has been making efforts to improve disaster preparedness for pregnant women. This project aims to acquire area data of pregnant women for simulated studies of rescue activities. Number of women in census survey areas in Nagoya City was acquired from nationwide data of pregnant women by machine learning (Cascade-Correlation Learning Architecture). Quite high correlation coefficients between actual data and estimation data were observed. Rescue simulations have been carried out based on the data acquired by this study.

scholarly journals An Indoor Localization System Using Residual Learning with Channel State Information

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 574
Author(s):  
Chendong Xu ◽  
Weigang Wang ◽  
Yunwei Zhang ◽  
Jie Qin ◽  
Shujuan Yu ◽  
...  
Keyword(s):  
Channel State Information ◽  
Indoor Environment ◽  
Indoor Localization ◽  
Location Based Services ◽  
Channel State ◽  
State Information ◽  
Localization Accuracy ◽  
Localization System ◽  
Increasing Demand ◽  
Degradation Problem

With the increasing demand of location-based services, neural network (NN)-based intelligent indoor localization has attracted great interest due to its high localization accuracy. However, deep NNs are usually affected by degradation and gradient vanishing. To fill this gap, we propose a novel indoor localization system, including denoising NN and residual network (ResNet), to predict the location of moving object by the channel state information (CSI). In the ResNet, to prevent overfitting, we replace all the residual blocks by the stochastic residual blocks. Specially, we explore the long-range stochastic shortcut connection (LRSSC) to solve the degradation problem and gradient vanishing. To obtain a large receptive field without losing information, we leverage the dilated convolution at the rear of the ResNet. Experimental results are presented to confirm that our system outperforms state-of-the-art methods in a representative indoor environment.

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