CCpos: WiFi Fingerprint Indoor Positioning System Based on CDAE-CNN

Abstract

WiFi is widely used for indoor positioning because of its advantages such as long transmission distance and ease of use indoors. To improve the accuracy and robustness of indoor WiFi fingerprint localization technology, this paper proposes a positioning system CCPos (CADE-CNN Positioning), which is based on a convolutional denoising autoencoder (CDAE) and a convolutional neural network (CNN). In the offline stage, this system applies the K-means algorithm to extract the validation set from the all-training set. In the online stage, the RSSI is first denoised and key features are extracted by the CDAE. Then the location estimation is output by the CNN. In this paper, the Alcala Tutorial 2017 dataset and UJIIndoorLoc are adopted to verify the performance of the CCpos system. The experimental results show that our system has excellent noise immunity and generalization performance. The mean positioning errors on the Alcala Tutorial 2017 dataset and the UJIIndoorLoc are 1.05 m and 12.4 m, respectively.

Multimodal Approaches for Indoor Localization for Ambient Assisted Living in Smart Homes

“…As outlined in Section 2, one the research challenges in this field of Indoor Localization is the need to develop an optimal machine learning model for Indoor Localization systems, Indoor Positioning Systems, and Location-Based Services. In [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25], researchers have used multiple machine learning approaches-Random Forest, Artificial Neural Network, Decision Tree, Support Vector Machine, k-NN, Gradient Boosted Trees, Deep Learning, and Linear Regression. However, none of these works implemented multiple machine learning models to evaluate and compare the associated performance characteristics to deduce the optimal machine learning approach.…”

“…However, none of these works implemented multiple machine learning models to evaluate and compare the associated performance characteristics to deduce the optimal machine learning approach. Due to the differences in the datasets used or the real-time data that was collected, the associated data preprocessing steps that were different, variations in train and test ratio of the data, and several other dissimilar steps that were associated with the developments of each of these machine learning models as presented in [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25], their final performance accuracies cannot be directly compared to deduce the best approach. Thus, analyzing the performance characteristics of multiple machine learning models, developed, implemented, and tested as per the same methodology, to deduce the optimal approach for development of such Indoor Localization systems serves as the main motivation for the work presented in the section.…”

“…These results are shown in Table 7. In this section, we followed the same steps as outlined in Section 4.3 and as per the flowchart shown in Figure 11, to develop, implement, and test this methodology by using all the machine learning methods that have been used by researchers [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] in this field. These machine learning methods included-Random Forest, Artificial Neural Network, Decision Tree, Support Vector Machine, k-NN, Gradient Boosted Trees, Deep Learning, and Linear Regression.…”

“…Need for an optimal machine learning-based approach for Indoor Localization: A range of machine learning approaches-Random Forest, Artificial Neural Network, Decision Tree, Support Vector Machine, k-NN, Gradient Boosted Trees, Deep Learning, and Linear Regression, have been used by several researchers [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] for development of various types of Indoor Localization systems for IoT-based environments. While each of these systems seem to perform reasonably well but none of these works attempted to develop an optimal machine learning model for Indoor Localization systems.…”

Using Smart Virtual-Sensor Nodes to Improve the Robustness of Indoor Localization Systems

“…In [ 26 ], the proposed CCPos positioning system (CADE-CNN positioning) used a convolutional noise-elimination autoencoder (CDAE) and convolutional neural network (CNN). The authors explained that in the offline stage, the system applied the K-means algorithm to extract the validation set from the complete and online training set; the RSSI was first demineralized, and the CDAE extracted key resources, so the location estimate was issued by the CNN.…”