Machine learning based signal strength and uncertainty prediction for MEC mobility management

2021 ◽  
Author(s):  
Shangbin Wu ◽  
Junwei Ren ◽  
Tiezhu Zhao ◽  
Yue Wang
Keyword(s):  
Machine Learning ◽  
Mobility Management ◽  
Signal Strength ◽  
Uncertainty Prediction

Enhance Wi-Fi Fingerprinting Indoor-Positioning by Error Flag Framework

2014 ◽  
Vol 931-932 ◽  
pp. 942-946
Author(s):  
Shutchon Premchaisawatt ◽  
Nararat Ruangchaijatupon
Keyword(s):  
Machine Learning ◽  
Signal Strength ◽  
Indoor Positioning ◽  
Performance Comparison ◽  
Machine Learning Algorithms ◽  
Limited Information ◽  
Machine Learning Classifiers ◽  
Wireless Device ◽  
Learning Data

This research aims to purpose the new method, which is called Error Flag Framework (EFF) to enhance accuracy fingerprinting indoor positioning of wireless device by using machine learning algorithms. EFF is compared with well-known machine learning classifiers; i.e. Decision Tree, Naive Bayes, and Artificial Neural Networks, by exploiting the signal strength from limited information. The performance comparison is done in terms of accuracy of classification of positions, precision of distance classified, and effects of classification of positions on results from quantity of learning data. The result of this study can suggest that EFF can increase performance for indoor positioning of every well-known classifier, especially when the quantity of learning data is large enough. Hence, EFF is the alternate way for implementing in positioning software by using the fingerprinting method.

Hybrid Kernel Based Machine Learning Using Received Signal Strength Measurements for Indoor Localization

2018 ◽  
Vol 67 (3) ◽  
pp. 2824-2829 ◽  
Author(s):  
Jun Yan ◽  
Lin Zhao ◽  
Jian Tang ◽  
Yuwei Chen ◽  
Ruizhi Chen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Indoor Localization ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Hybrid Kernel

scholarly journals 3D Point Cloud-Based Tree Canopy Visualization for a Smart Deployment of Mobile Communication Systems

2021 ◽  
Author(s):  
Yunus Egi ◽  
Engin Eyceyurt
Keyword(s):  
Machine Learning ◽  
Mobile Communication ◽  
Communication Systems ◽  
Point Cloud ◽  
Smart Cities ◽  
Signal Strength ◽  
Propagation Model ◽  
3D Point Cloud ◽  
Mobile Communication Systems ◽  
Signal Fading

Mobile communication is one of the most important parameters of smart cities in terms of maintaining connectivity and interaction between humans and smart systems. However, In the deployment process of Mobile Communication Systems (MCS), Radio Frequency (RF) engineers use location depended empirical Signal Strength Path Loss (SSPL) models ending up with poor signal strength and slow data connection. This is due to the fact that empirical propagation models usually are restrained by the environment and do not implement state of the art technologies, including Unmanned Aerial Vehicles (UAV), Light Detection and Ranging (LiDAR), Image Processing, and Machine Learning to increase efficiency. Terrains involving buildings, hills, trees, mountains, and human-made structures are considered irregular terrains by telecommunication engineers. Irregular terrains, specifically trees, significantly affect MCS’s efficiency because of their complex pattern resulting in erroneous signal fading via multi-path reflection and absorption. Therefore, a virtual 3D environment is required to extract the required 3D terrain pattern and elevation data from the environment. Once this data is processed in the machine learning algorithm, an adaptive propagation model can be formed and can significantly improve SSPL prediction accuracy for MCS. This chapter presents 3D point cloud visualization via sensor fusion and 2D image color classification techniques, which lead to a novel propagation model for the smart deployment of MCS. The proposed system’s main contribution is to develop an intelligent environment that eliminates limitations and minimizes related signal fading prediction errors. In addition, having better connectivity and efficiency will resolve the communication problem of smart cities. The chapter also provides a case study that significantly outperforms other empirical models with an accuracy of 95.4%.

scholarly journals Hybrid Machine Learning Classifiers for Indoor User Localization Problem

2021 ◽  
Vol 10 (3) ◽  
pp. 49-53
Author(s):  
Hamza Turabieh ◽  
Ahmad S. Alghamdi
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Indoor Localization ◽  
Signal Strength ◽  
Access Point ◽  
Support Vector ◽  
Linear Discriminant ◽  
Machine Learning Classifiers ◽  
Learning Classifiers ◽  
User Location

Wi-Fi technology is now everywhere either inside or outside buildings. Using Wi-fi technology introduces an indoor localization service(s) (ILS). Determining indoor user location is a hard and complex problem. Several applications highlight the importance of indoor user localization such as disaster management, health care zones, Internet of Things applications (IoT), and public settlement planning. The measurements of Wi-Fi signal strength (i.e., Received Signal Strength Indicator (RSSI)) can be used to determine indoor user location. In this paper, we proposed a hybrid model between a wrapper feature selection algorithm and machine learning classifiers to determine indoor user location. We employed the Minimum Redundancy Maximum Relevance (mRMR) algorithm as a feature selection to select the most active access point (AP) based on RSSI values. Six different machine learning classifiers were used in this work (i.e., Decision Tree (DT), Support Vector Machine (SVM), k-nearest neighbors (kNN), Linear Discriminant Analysis (LDA), Ensemble-Bagged Tree (EBaT), and Ensemble Boosted Tree (EBoT)). We examined all classifiers on a public dataset obtained from UCI repository. The obtained results show that EBoT outperforms all other classifiers based on accuracy value/

scholarly journals Experimental Evaluation of Machine Learning Methods for Robust Received Signal Strength-Based Visible Light Positioning

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6109 ◽  
Author(s):  
Willem Raes ◽  
Nicolas Knudde ◽  
Jorik De Bruycker ◽  
Tom Dhaene ◽  
Nobby Stevens
Keyword(s):  
Machine Learning ◽  
Visible Light ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Superior Performance ◽  
Dust Particles ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Set Up ◽  
Gp Model

In this work, the use of Machine Learning methods for robust Received Signal Strength (RSS)-based Visible Light Positioning (VLP) is experimentally evaluated. The performance of Multilayer Perceptron (MLP) models and Gaussian processes (GP) is investigated when using relative RSS input features. The experimental set-up for the RSS-based VLP technology uses light-emitting diodes (LEDs) transmitting intensity modulated light and a single photodiode (PD) as a receiver. The experiments focus on achieving robustness to cope with unknown received signal strength modifications over time. Therefore, several datasets were collected, where per dataset either the LEDs transmitting power is modified or the PD aperture is partly obfuscated by dust particles. Two relative RSS schemes are investigated. The first scheme uses the maximum received light intensity to normalize the received RSS vector, while the second approach obtains RSS ratios by combining all possible unique pairs of received intensities. The Machine Learning (ML) methods are compared to a relative multilateration implementation. It is demonstrated that the adopted MLP and GP models exhibit superior performance and higher robustness when compared to the multilateration strategies. Furthermore, when comparing the investigated ML models, the GP model is proven to be more robust than the MLP for the considered scenarios.

scholarly journals Wireless Fingerprinting Uncertainty Prediction Based on Machine Learning

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 324 ◽  
Author(s):  
You Li ◽  
Zhouzheng Gao ◽  
Zhe He ◽  
Yuan Zhuang ◽  
Ahmed Radi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Indoor Localization ◽  
Dead Reckoning ◽  
Sensor Integration ◽  
Test Results ◽  
Walking Test ◽  
Uncertainty Prediction ◽  
Location Uncertainty ◽  
Measurement Noises ◽  
Artificial Neural Network Ann

Although wireless fingerprinting has been well researched and widely used for indoor localization, its performance is difficult to quantify. Therefore, when wireless fingerprinting solutions are used as location updates in multi-sensor integration, it is challenging to set their weight accurately. To alleviate this issue, this paper focuses on predicting wireless fingerprinting location uncertainty by given received signal strength (RSS) measurements through the use of machine learning (ML). Two ML methods are used, including an artificial neural network (ANN)-based approach and a Gaussian distribution (GD)-based method. The predicted location uncertainty is evaluated and further used to set the measurement noises in the dead-reckoning/wireless fingerprinting integrated localization extended Kalman filter (EKF). Indoor walking test results indicated the possibility of predicting the wireless fingerprinting uncertainty through ANN the effectiveness of setting measurement noises adaptively in the integrated localization EKF.

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