scholarly journals A Survey of Recent Indoor Localization Scenarios and Methodologies

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 8086
Author(s):  
Tian Yang ◽  
Adnane Cabani ◽  
Houcine Chafouk
Keyword(s):  
Indoor Localization ◽  
Measurement Data ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Bayesian Filtering ◽  
K Nearest Neighbors ◽  
Localization Accuracy ◽  
Trip Time ◽  
Network Methods ◽  
Received Signal Strength Indication

Recently, various novel scenarios have been studied for indoor localization. The trilateration is known as a classic theoretical model of geometric-based indoor localization, with uniform RSSI data that can be transferred directly into distance ranges. Then, a trilateration solution can be algebraically acquired from theses ranges, in order to fix user’s actual location. However, the collected RSSI or other measurement data should be further processed and classified to lower the localization error rate, instead of using the raw data influenced by multi-path effect, multiple nonlinear interference and noises. In this survey, a large number of existing techniques are presented for different indoor network structures and channel conditions, divided as LOS (light-of-sight) and NLOS (non light-of-sight). Besides, the input measurement data such as RSSI (received signal strength indication), TDOA (time difference of arrival), DOA (distance of arrival), and RTT (round trip time) are studied towards different application scenarios. The key localization techniques like RSSI-based fingerprinting technique are presented using supervised machine learning methods, namely SVM (support vector machine), KNN (K nearest neighbors) and NN (neural network) methods, especially in an offline training phase. Other unsupervised methods as isolation forest, k-means, and expectation maximization methods are utilized to further improve the localization accuracy in online testing phase. For Bayesian filtering methods, apart from the basic linear Kalman filter (LKF) methods, nonlinear stochastic filters such as extended KF, cubature KF, unscented KF and particle filters are introduced. These nonlinear methods are more suitable for dynamic localization models. In addition to the localization accuracy, the other important performance features and evaluation aspects are presented in our paper: scalability, stability, reliability, and the complexity of proposed algorithms is compared in this survey. Our paper provides a comprehensive perspective to compare the existing techniques and related practical localization models, with the aim of improving localization accuracy and reducing the complexity of the system.

scholarly journals A Comparison Analysis of BLE-Based Algorithms for Localization in Industrial Environments

Electronics ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 44 ◽  
Author(s):  
Davide Cannizzaro ◽  
Marina Zafiri ◽  
Daniele Jahier Pagliari ◽  
Edoardo Patti ◽  
Enrico Macii ◽  
...  
Keyword(s):  
Indoor Localization ◽  
Support Vector ◽  
Power Devices ◽  
K Nearest Neighbors ◽  
Distance Information ◽  
Localization Algorithms ◽  
Vector Machines ◽  
Environmental Variations ◽  
Industrial Environments ◽  
Received Signal Strength Indication

Proximity beacons are small, low-power devices capable of transmitting information at a limited distance via Bluetooth low energy protocol. These beacons are typically used to broadcast small amounts of location-dependent data (e.g., advertisements) or to detect nearby objects. However, researchers have shown that beacons can also be used for indoor localization converting the received signal strength indication (RSSI) to distance information. In this work, we study the effectiveness of proximity beacons for accurately locating objects within a manufacturing plant by performing extensive experiments in a real industrial environment. To this purpose, we compare localization algorithms based either on trilateration or environment fingerprinting combined with a machine-learning based regressor (k-nearest neighbors, support-vector machines, or multi-layer perceptron). Each algorithm is analyzed in two different types of industrial environments. For each environment, various configurations are explored, where a configuration is characterized by the number of beacons per square meter and the density of fingerprint points. In addition, the fingerprinting approach is based on a preliminary site characterization; it may lead to location errors in the presence of environment variations (e.g., movements of large objects). For this reason, the robustness of fingerprinting algorithms against such variations is also assessed. Our results show that fingerprint solutions outperform trilateration, showing also a good resilience to environmental variations. Given the similar error obtained by all three fingerprint approaches, we conclude that k-NN is the preferable algorithm due to its simple deployment and low number of hyper-parameters.

Comparing LR, GP, BPN, RBF and SVR for Self-Learning Pattern Matching in WSN Indoor Localization

2012 ◽  
Vol 3 (3) ◽  
pp. 49-62
Author(s):  
Ray-I Chang ◽  
Chi-Cheng Chuang
Keyword(s):  
Pattern Matching ◽  
Indoor Localization ◽  
Noise Signal ◽  
Divide And Conquer ◽  
Support Vector ◽  
Bayesian Filtering ◽  
Training Methods ◽  
Localization Accuracy ◽  
Backpropagation Network ◽  
Self Learning

It is a challenging issue to apply WSN (Wireless Sensor Network) to achieve accurate location information. PM (Pattern Matching), known as one of the most famous localization methods, has the drawback of requiring high initialization effort to predict/train MF (Matching Function). In this paper, the authors propose SPM (Self-learning PM) to improve not only the localization accuracy but also the initialization effort of PM. SPM applies a divide-and-conquer self-learning scheme to reduce the number of training patterns in training. Additionally, it introduces a Bayesian filtering scheme to remove the noise signal caused by multipath effects so as to enhance localization accuracy accordingly. This paper applies different training methods (linear regression, Gaussian process, backpropagation network, radial basis function, and support vector regression) to evaluate the performances of SPM and PM in a complicated indoor environment. Experiments show that SPM is better than PM for all training methods applied. SPM can use up to 72% fewer training patterns than PM to achieve the same localization accuracy. If the same number of training patterns is utilized, SPM can achieve up to 58% higher localization accuracy than PM.

scholarly journals Identification of NLOS and Multi-Path Conditions in UWB Localization Using Machine Learning Methods

2020 ◽  
Vol 10 (11) ◽  
pp. 3980 ◽  
Author(s):  
Cung Lian Sang ◽  
Bastian Steinhagen ◽  
Jonas Dominik Homburg ◽  
Michael Adams ◽  
Marc Hesse ◽  
...  
Keyword(s):  
Machine Learning ◽  
Indoor Localization ◽  
Measurement Data ◽  
Ultra Wideband ◽  
Machine Learning Techniques ◽  
Line Of Sight ◽  
Support Vector ◽  
Case Scenario ◽  
Worst Case ◽  
Worst Case Scenario

In ultra-wideband (UWB)-based wireless ranging or distance measurement, differentiation between line-of-sight (LOS), non-line-of-sight (NLOS), and multi-path (MP) conditions is important for precise indoor localization. This is because the accuracy of the reported measured distance in UWB ranging systems is directly affected by the measurement conditions (LOS, NLOS, or MP). However, the major contributions in the literature only address the binary classification between LOS and NLOS in UWB ranging systems. The MP condition is usually ignored. In fact, the MP condition also has a significant impact on the ranging errors of the UWB compared to the direct LOS measurement results. However, the magnitudes of the error contained in MP conditions are generally lower than completely blocked NLOS scenarios. This paper addresses machine learning techniques for identification of the three mentioned classes (LOS, NLOS, and MP) in the UWB indoor localization system using an experimental dataset. The dataset was collected in different conditions in different scenarios in indoor environments. Using the collected real measurement data, we compared three machine learning (ML) classifiers, i.e., support vector machine (SVM), random forest (RF) based on an ensemble learning method, and multilayer perceptron (MLP) based on a deep artificial neural network, in terms of their performance. The results showed that applying ML methods in UWB ranging systems was effective in the identification of the above-three mentioned classes. Specifically, the overall accuracy reached up to 91.9% in the best-case scenario and 72.9% in the worst-case scenario. Regarding the F1-score, it was 0.92 in the best-case and 0.69 in the worst-case scenario. For reproducible results and further exploration, we provide the publicly accessible experimental research data discussed in this paper at PUB (Publications at Bielefeld University). The evaluations of the three classifiers are conducted using the open-source Python machine learning library scikit-learn.

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 THE USE OF MACHINE LEARNING METHODS FOR BINARY CLASSIFICATION OF THE WORKING CONDITION OF BEARINGS USING THE SIGNALS OF VIBRATION ACCELERATION

2021 ◽  
pp. 15-22
Author(s):  
Ruslan Babudzhan ◽  
Konstantyn Isaienkov ◽  
Danilo Krasiy ◽  
Oleksii Vodka ◽  
Ivan Zadorozhny ◽  
...  
Keyword(s):  
Machine Learning ◽  
Binary Classification ◽  
Fractal Dimensions ◽  
Feature Space ◽  
Training Data ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Data Sets ◽  
Vibration Acceleration ◽  
K Nearest Neighbors

The paper investigates the relationship between vibration acceleration of bearings with their operational state. To determine these dependencies, a testbench was built and 112 experiments were carried out with different bearings: 100 bearings that developed an internal defect during operation and 12bearings without a defect. From the obtained records, a dataset was formed, which was used to build classifiers. Dataset is freely available. A methodfor classifying new and used bearings was proposed, which consists in searching for dependencies and regularities of the signal using descriptive functions: statistical, entropy, fractal dimensions and others. In addition to processing the signal itself, the frequency domain of the bearing operationsignal was also used to complement the feature space. The paper considered the possibility of generalizing the classification for its application on thosesignals that were not obtained in the course of laboratory experiments. An extraneous dataset was found in the public domain. This dataset was used todetermine how accurate a classifier was when it was trained and tested on significantly different signals. Training and validation were carried out usingthe bootstrapping method to eradicate the effect of randomness, given the small amount of training data available. To estimate the quality of theclassifiers, the F1-measure was used as the main metric due to the imbalance of the data sets. The following supervised machine learning methodswere chosen as classifier models: logistic regression, support vector machine, random forest, and K nearest neighbors. The results are presented in theform of plots of density distribution and diagrams.

scholarly journals Supervised machine learning methods in psychology: A practical introduction with annotated R code

2019 ◽  
Author(s):  
Hannes Rosenbusch ◽  
Felix Soldner ◽  
Anthony M Evans ◽  
Marcel Zeelenberg
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Psychological Research ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Learning Methods ◽  
Comprehensive Overview ◽  
K Nearest Neighbors ◽  
Machine Learning Methods ◽  
Out Of Sample

Machine learning methods for pattern detection and prediction are increasingly prevalent in psychological research. We provide a comprehensive overview of machine learning, its applications, and how to implement models for research. We review fundamental concepts of machine learning, such as prediction accuracy and out-of-sample evaluation, and summarize four standard prediction algorithms: linear regressions, ridge regressions, decision trees, and random forests (plus k-nearest neighbors, Naïve Bayes classifiers, and support vector machines in the supplementary material). This selection provides a set of powerful models that are implemented regularly in machine learning projects. We demonstrate each method with examples and annotated R code, and discuss best practices for determining sample sizes; comparing model performances; tuning prediction models; preregistering prediction models; and reporting results. Finally, we discuss the value of machine learning methods in maintaining psychology’s status as a predictive science.

scholarly journals Supervised binary classification methods for strawberry ripeness discrimination from bioimpedance data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Pietro Ibba ◽  
Christian Tronstad ◽  
Roberto Moscetti ◽  
Tanja Mimmo ◽  
Giuseppe Cantarella ◽  
...  
Keyword(s):  
Time Management ◽  
Binary Classification ◽  
Time Estimation ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Strawberry Fruit ◽  
K Nearest Neighbors ◽  
Promising Alternative ◽  
Machine Learning Classification ◽  
Unseen Data

AbstractStrawberry is one of the most popular fruits in the market. To meet the demanding consumer and market quality standards, there is a strong need for an on-site, accurate and reliable grading system during the whole harvesting process. In this work, a total of 923 strawberry fruit were measured directly on-plant at different ripening stages by means of bioimpedance data, collected at frequencies between 20 Hz and 300 kHz. The fruit batch was then splitted in 2 classes (i.e. ripe and unripe) based on surface color data. Starting from these data, six of the most commonly used supervised machine learning classification techniques, i.e. Logistic Regression (LR), Binary Decision Trees (DT), Naive Bayes Classifiers (NBC), K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Multi-Layer Perceptron Networks (MLP), were employed, optimized, tested and compared in view of their performance in predicting the strawberry fruit ripening stage. Such models were trained to develop a complete feature selection and optimization pipeline, not yet available for bioimpedance data analysis of fruit. The classification results highlighted that, among all the tested methods, MLP networks had the best performances on the test set, with 0.72, 0.82 and 0.73 for the F$$_1$$ 1 , F$$_{0.5}$$ 0.5 and F$$_2$$ 2 -score, respectively, and improved the training results, showing good generalization capability, adapting well to new, previously unseen data. Consequently, the MLP models, trained with bioimpedance data, are a promising alternative for real-time estimation of strawberry ripeness directly on-field, which could be a potential application technique for evaluating the harvesting time management for farmers and producers.

scholarly journals COVID-19 Prediction Applying Supervised Machine Learning Algorithms with Comparative Analysis Using WEKA

Algorithms ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 201
Author(s):  
Charlyn Nayve Villavicencio ◽  
Julio Jerison Escudero Macrohon ◽  
Xavier Alphonse Inbaraj ◽  
Jyh-Horng Jeng ◽  
Jer-Guang Hsieh
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Mean Absolute Error ◽  
Learning Algorithms ◽  
Absolute Error ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Use Of Technology

Early diagnosis is crucial to prevent the development of a disease that may cause danger to human lives. COVID-19, which is a contagious disease that has mutated into several variants, has become a global pandemic that demands to be diagnosed as soon as possible. With the use of technology, available information concerning COVID-19 increases each day, and extracting useful information from massive data can be done through data mining. In this study, authors utilized several supervised machine learning algorithms in building a model to analyze and predict the presence of COVID-19 using the COVID-19 Symptoms and Presence dataset from Kaggle. J48 Decision Tree, Random Forest, Support Vector Machine, K-Nearest Neighbors and Naïve Bayes algorithms were applied through WEKA machine learning software. Each model’s performance was evaluated using 10-fold cross validation and compared according to major accuracy measures, correctly or incorrectly classified instances, kappa, mean absolute error, and time taken to build the model. The results show that Support Vector Machine using Pearson VII universal kernel outweighs other algorithms by attaining 98.81% accuracy and a mean absolute error of 0.012.

scholarly journals Machine Learning Models for COVID-19 Detection in Brazil Based on Symptoms (Preprint)

2021 ◽  
Author(s):  
Íris Viana dos Santos Santana ◽  
Andressa C. M. da Silveira ◽  
Álvaro Sobrinho ◽  
Lenardo Chaves e Silva ◽  
Leandro Dias da Silva ◽  
...  
Keyword(s):  
Machine Learning ◽  
Early Stage ◽  
Area Under The Curve ◽  
Supervised Machine Learning ◽  
Gradient Boosting ◽  
Support Vector ◽  
Accuracy Score ◽  
K Nearest Neighbors ◽  
Runny Nose ◽  
Extreme Gradient Boosting

BACKGROUND controlling the COVID-19 outbreak in Brazil is considered a challenge of continental proportions due to the high population and urban density, weak implementation and maintenance of social distancing strategies, and limited testing capabilities. OBJECTIVE to contribute to addressing such a challenge, we present the implementation and evaluation of supervised Machine Learning (ML) models to assist the COVID-19 detection in Brazil based on early-stage symptoms. METHODS firstly, we conducted data preprocessing and applied the Chi-squared test in a Brazilian dataset, mainly composed of early-stage symptoms, to perform statistical analyses. Afterward, we implemented ML models using the Random Forest (RF), Support Vector Machine (SVM), Multilayer Perceptron (MLP), K-Nearest Neighbors (KNN), Decision Tree (DT), Gradient Boosting Machine (GBM), and Extreme Gradient Boosting (XGBoost) algorithms. We evaluated the ML models using precision, accuracy score, recall, the area under the curve, and the Friedman and Nemenyi tests. Based on the comparison, we grouped the top five ML models and measured feature importance. RESULTS the MLP model presented the highest mean accuracy score, with more than 97.85%, when compared to GBM (> 97.39%), RF (> 97.36%), DT (> 97.07%), XGBoost (> 97.06%), KNN (> 95.14%), and SVM (> 94.27%). Based on the statistical comparison, we grouped MLP, GBM, DT, RF, and XGBoost, as the top five ML models, because the evaluation results are statistically indistinguishable. The ML models` importance of features used during predictions varies from gender, profession, fever, sore throat, dyspnea, olfactory disorder, cough, runny nose, taste disorder, and headache. CONCLUSIONS supervised ML models effectively assist the decision making in medical diagnosis and public administration (e.g., testing strategies), based on early-stage symptoms that do not require advanced and expensive exams.

scholarly journals Opinion Mining using Machine Learning Techniques

2019 ◽  
Vol 9 (2) ◽  
pp. 4287-4292
Keyword(s):  
Machine Learning ◽  
Opinion Mining ◽  
Predictive Ability ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Significant Information ◽  
Learning Techniques

Sentiment analysis or opinion mining has gained much attention in recent years.With the constantly evolving social networks and internet marketing sites, reviews and blogs have been obtained among them, they act as an significant source for future analysis and better decision making. These reviews are naturally unstructured and thus require pre processing and further classification to gain the significant information for future use. These reviews and blogs can be of different types such as positive, negative and neutral . Supervised machine learning techniquess help to classify these reviews. In this paper five machine learning algorithms (K-Nearest Neighbors (KNN), Decision Tree, Artificial neural networks (ANNs), Naïve bayes and Support Vector Machine (SVM))are used for classification of sentiments. These algorithms are analyzed usingTwitter dataset. Performance analysis of these algorithms are done by using various performance measures such as Accuracy, precision, recall and F-measure. The evaluation of these techniques on Twitter datasetshowed predictive ability of Machine Learning in opinion mining

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