Let’s Forget About Exact Signal Strength: Indoor Positioning based on Access Point Ranking and Recurrent Neural Networks

2020 ◽  
Author(s):  
Nicola Saccomanno ◽  
Andrea Brunello ◽  
Angelo Montanari
Keyword(s):  
Neural Networks ◽  
Recurrent Neural Networks ◽  
Signal Strength ◽  
Access Point ◽  
Indoor Positioning

scholarly journals Side-Information-Aided Preprocessing Scheme for Deep-Learning Classifier in Fingerprint-Based Indoor Positioning

Electronics ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 982 ◽  
Author(s):  
Yue Liu ◽  
Rashmi Sharan Sinha ◽  
Shu-Zhi Liu ◽  
Seung-Hoon Hwang
Keyword(s):  
Deep Learning ◽  
Side Information ◽  
Success Probability ◽  
Dynamic Environment ◽  
Signal Strength ◽  
Access Point ◽  
Indoor Positioning ◽  
Received Signal Strength ◽  
Learning Classifier ◽  
Learning Classifiers

Deep-learning classifiers can effectively improve the accuracy of fingerprint-based indoor positioning. During fingerprint database construction, all received signal strength indicators from each access point are combined without any distinction. Therefore, the database is created and utilised for deep-learning models. Meanwhile, side information regarding specific conditions may help characterise the data features for the deep-learning classifier and improve the accuracy of indoor positioning. Herein, a side-information-aided preprocessing scheme for deep-learning classifiers is proposed in a dynamic environment, where several groups of different databases are constructed for training multiple classifiers. Therefore, appropriate databases can be employed to effectively improve positioning accuracies. Specifically, two kinds of side information, namely time (morning/afternoon) and direction (forward/backward), are considered when collecting the received signal strength indicator. Simulations and experiments are performed with the deep-learning classifier trained on four different databases. Moreover, these are compared with conventional results from the combined database. The results show that the side-information-aided preprocessing scheme allows better success probability than the conventional method. With two margins, the proposed scheme has 6.55% and 5.8% improved performances for simulations and experiments compared to the conventional scheme. Additionally, the proposed scheme, with time as the side information, obtains a higher success probability when the positioning accuracy requirement is loose with larger margin. With direction as the side information, the proposed scheme shows better performance for high positioning precision requirements. Thus, side information such as time or direction is advantageous for preprocessing data in deep-learning classifiers for fingerprint-based indoor positioning.

scholarly journals An effective algorithm to overcome the practical hindrance for Wi-Fi based indoor positioning system

2020 ◽  
Vol 10 (1) ◽  
pp. 117-123
Author(s):  
Bhulakshmi Bonthu ◽  
M Subaji
Keyword(s):  
Signal Strength ◽  
Access Point ◽  
Indoor Positioning ◽  
Positioning System ◽  
Radio Waves ◽  
Direct Path ◽  
Received Signal Strength Indicator ◽  
Access Points ◽  
Measuring Techniques ◽  
Indoor Positioning System

AbstractIndoor tracking has evolved with various methods. The most popular method is using signal strength measuring techniques like triangulation, trilateration and fingerprinting, etc. Generally, these methods use the internal sensors of the smartphone. All these techniques require an adequate number of access point signals. The estimated positioning accuracy depends on the number of signals received at any point and precision of its signal (Wi-Fi radio waves) strength. In a practical environment, the received signal strength indicator (RSSI) of the access point is hindered by obstacles or blocks in the direct path or Line of sight. Such access points become an anomaly in the calculation of position. By detecting the anomaly access points and neglecting it during the computation of an indoor position will improve the accuracy of the positioning system. The proposed method, Practical Hindrance Avoidance in an Indoor Positioning System (PHA-IPS), eliminate the anomaly nodes while estimating the position, so then enhances the accuracy.

scholarly journals A Mobile Positioning Method Based on Deep Learning Techniques

2018 ◽  
Author(s):  
Ling Wu ◽  
Chi-Hua Chen ◽  
Qishan Zhang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Cellular Networks ◽  
Recurrent Neural Network ◽  
Recurrent Neural Networks ◽  
Signal Strength ◽  
Location Estimation ◽  
Network Algorithms ◽  
Mobile Positioning ◽  
Positioning Method

This study proposes a mobile positioning method which adopts recurrent neural network algorithms to analyze the received signal strength indications from heterogeneous networks (e.g., cellular networks and Wi-Fi networks) for estimating the locations of mobile statioThis study proposes a mobile positioning method which adopts recurrent neural network algorithms to analyze the received signal strength indications from heterogeneous networks (e.g., cellular networks and Wi-Fi networks) for estimating the locations of mobile stations. The recurrent neural networks with multiple consecutive timestamps can be applied to extract the features of time series data for the improvement of location estimation. In practical experimental environments, there are 4,525 records, 59 different base stations, and 582 different Wi-Fi access points detected in Fuzhou University in China. The lower location errors can be obtained by the recurrent neural networks with multiple consecutive timestamps (e.g., 2 timestamps and 3 timestamps); the experimental results can be observed that the average error of location estimation was 9.19 meters by the proposed mobile positioning method with 2 timestamps.

Levenshtein Augmentation Improves Performance of SMILES Based Deep-Learning Synthesis Prediction

2020 ◽  
Author(s):  
Dean Sumner ◽  
Jiazhen He ◽  
Amol Thakkar ◽  
Ola Engkvist ◽  
Esben Jannik Bjerrum
Keyword(s):  
Neural Networks ◽  
Pattern Recognition ◽  
Deep Learning ◽  
Recurrent Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Sequence Similarity ◽  
Learning Models ◽  
Underlying Network

<p>SMILES randomization, a form of data augmentation, has previously been shown to increase the performance of deep learning models compared to non-augmented baselines. Here, we propose a novel data augmentation method we call “Levenshtein augmentation” which considers local SMILES sub-sequence similarity between reactants and their respective products when creating training pairs. The performance of Levenshtein augmentation was tested using two state of the art models - transformer and sequence-to-sequence based recurrent neural networks with attention. Levenshtein augmentation demonstrated an increase performance over non-augmented, and conventionally SMILES randomization augmented data when used for training of baseline models. Furthermore, Levenshtein augmentation seemingly results in what we define as <i>attentional gain </i>– an enhancement in the pattern recognition capabilities of the underlying network to molecular motifs.</p>

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