Experimental Analysis on Weight &lt;inline-formula&gt; 
                     &lt;tex-math notation="LaTeX"&gt;${K}$ &lt;/tex-math&gt;
                  &lt;/inline-formula&gt;-Nearest Neighbor Indoor Fingerprint Positioning

Hu, Jiusong; Liu, Dawei; Yan, Zhi; Liu, Hongli

doi:10.1109/jiot.2018.2864607

Cited by 100 publications

(44 citation statements)

References 23 publications

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“…The algorithm used signal processing principles to detect and analyze the data coming from access points in the user's location to train the k-NN classifier. In [15], Hu et al proposed another k-NN based learning approach that detected the location of the user based on the nearest access points of the user. One of the key findings of the work was that the condition of k = 1 led to the best positioning performance accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

“…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.…”

Section: Deducing the Optimal Machine Learning Model For Indoor Localizationmentioning

confidence: 99%

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

Thakur

Han

2021

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This work makes multiple scientific contributions to the field of Indoor Localization for Ambient Assisted Living in Smart Homes. First, it presents a Big-Data driven methodology that studies the multimodal components of user interactions and analyzes the data from Bluetooth Low Energy (BLE) beacons and BLE scanners to detect a user’s indoor location in a specific ‘activity-based zone’ during Activities of Daily Living. Second, it introduces a context independent approach that can interpret the accelerometer and gyroscope data from diverse behavioral patterns to detect the ‘zone-based’ indoor location of a user in any Internet of Things (IoT)-based environment. These two approaches achieved performance accuracies of 81.36% and 81.13%, respectively, when tested on a dataset. Third, it presents a methodology to detect the spatial coordinates of a user’s indoor position that outperforms all similar works in this field, as per the associated root mean squared error—one of the performance evaluation metrics in ISO/IEC18305:2016—an international standard for testing Localization and Tracking Systems. Finally, it presents a comprehensive comparative study that includes Random Forest, Artificial Neural Network, Decision Tree, Support Vector Machine, k-NN, Gradient Boosted Trees, Deep Learning, and Linear Regression, to address the challenge of identifying the optimal machine learning approach for Indoor Localization.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Deducing the Optimal Machine Learning Model For Indoor Localizationmentioning

confidence: 99%

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

Thakur

Han

2021

Information

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“…The main reason lies in the presence of obstacles in the indoor environment, changes in natural conditions, and movement of people. The existing techniques, such as, K-nearest neighbor (KNN) [16], WKNN [17] and support vector machine (SVM) [18], are complicated and easily interfered, so the positioning performance cannot meet the practical requirements. With the successful application of deep learning in images, recurrent neural networks (RNN) [19] and deep neural network (DNN) [20] have been applied in the field of localization.…”

Section: Introductionmentioning

confidence: 99%

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

Feng

Zuo

Wang

2021

Sensors

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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.

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“…In the online phase, the mobile user (MU) measures the RSS at the positioning point. Then the measurements are compared with the data in the database using an appropriate matching algorithm and the position estimation is obtained [20]. Compared with trilateration, fingerprinting gives better results and avoids complex modeling of signal propagation [8].…”

Section: Introductionmentioning

confidence: 99%

Novel WiFi/MEMS Integrated Indoor Navigation System Based on Two-Stage EKF

et al. 2019

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Indoor navigation has been developing rapidly over the last few years. However, it still faces a number of challenges and practical issues. This paper proposes a novel WiFi/MEMS integration structure for indoor navigation. The two-stage structure uses the extended Kalman filter (EKF) to fuse the information from WiFi/MEMS sensors and contains attitude-determination EKF and position-tracking EKF. In the WiFi part, a partition solution called “moving partition” is originally proposed in this paper. This solution significantly reduces the computation time and enhances the performance of the traditional Weighted K-Nearest Neighbors (WKNN) method. Furthermore, the direction measurement is generated utilizing WiFi positioning results, and a “turn detection” is implemented to guarantee the effectiveness. The navigation performance of the presented integration structure has been verified through indoor experiments. The test results indicate that the proposed WiFi/MEMS solution works well. The root mean square (RMS) position error of WiFi/MEMS is 0.7926 m, which is an improvement of 20.59% and 36.60% when compared to MEMS and WiFi alone. Besides, the proposed algorithm still performs well with very few access points (AP) available and its stability has been proven.

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Experimental Analysis on Weight <inline-formula> <tex-math notation="LaTeX">${K}$ </tex-math> </inline-formula>-Nearest Neighbor Indoor Fingerprint Positioning

Cited by 100 publications

References 23 publications

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

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

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

Novel WiFi/MEMS Integrated Indoor Navigation System Based on Two-Stage EKF

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