Comparison of similarity approaches for indoor localization

Njima, Wafa; Ahriz, Iness; Zayani, Rafik; Terré, Michel; Bouallègue, Ridha

doi:10.1109/wimob.2017.8115821

Cited by 5 publications

(3 citation statements)

References 20 publications

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“…This is due to improved localization and reduced computational complexity, as concluded by Amr et al [ 19 ]. A detailed comparison of technologies and algorithms implementing the fingerprint technique for IoT indoor positioning has been carried out by [ 15 , 21 , 22 , 23 ].…”

Section: Related Workmentioning

confidence: 99%

Implementing Deep Learning Techniques in 5G IoT Networks for 3D Indoor Positioning: DELTA (DeEp Learning-Based Co-operaTive Architecture)

Boudani

Kanaris

Kokkinis

et al. 2020

Sensors

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In the near future, the fifth-generation wireless technology is expected to be rolled out, offering low latency, high bandwidth and multiple antennas deployed in a single access point. This ecosystem will help further enhance various location-based scenarios such as assets tracking in smart factories, precise smart management of hydroponic indoor vertical farms and indoor way-finding in smart hospitals. Such a system will also integrate existing technologies like the Internet of Things (IoT), WiFi and other network infrastructures. In this respect, 5G precise indoor localization using heterogeneous IoT technologies (Zigbee, Raspberry Pi, Arduino, BLE, etc.) is a challenging research area. In this work, an experimental 5G testbed has been designed integrating C-RAN and IoT networks. This testbed is used to improve both vertical and horizontal localization (3D Localization) in a 5G IoT environment. To achieve this, we propose the DEep Learning-based co-operaTive Architecture (DELTA) machine learning model implemented on a 3D multi-layered fingerprint radiomap. The DELTA begins by estimating the 2D location. Then, the output is recursively used to predict the 3D location of a mobile station. This approach is going to benefit use cases such as 3D indoor navigation in multi-floor smart factories or in large complex buildings. Finally, we have observed that the proposed model has outperformed traditional algorithms such as Support Vector Machine (SVM) and K-Nearest Neighbor (KNN).

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Section: Related Workmentioning

confidence: 99%

Implementing Deep Learning Techniques in 5G IoT Networks for 3D Indoor Positioning: DELTA (DeEp Learning-Based Co-operaTive Architecture)

Boudani

Kanaris

Kokkinis

et al. 2020

Sensors

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“…Indoor positioning systems (IPSs) have received significant attention in both academia and industry due to the growth of wireless communication infrastructure and the ever-increasing demand for location-based services (LBSs) [ 1 , 2 , 3 , 4 ]. Currently, more than 80% of the world’s population owns a smartphone [ 5 ], it is estimated that most people spend around 80% of their daily lives indoors [ 6 ], and 74% of smart device owners are active users of smartphone location-based applications [ 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

Heterogeneous Transfer Learning for Wi-Fi Indoor Positioning Based Hybrid Feature Selection

Gidey

Guo

et al. 2022

Sensors

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This paper presents the application of heterogeneous transfer learning (HetTL) methods which consider hybrid feature selection to reduce the training calibration effort and the noise generated by fingerprint duplicates obtained from multiple Wi-Fi access points. The Cramer–Rao Lower Bound analysis (CRLB) was also applied to evaluate and estimate a lower limit for the variance of a parameter estimator used to analyze positioning performance. We developed two novel algorithms for feature selection in fingerprint-based indoor positioning problems (IPP) to enhance positioning performance in the target domain with the HetTL. The algorithms comprised two scenarios: (i) a principal component analysis-based approach (PCA-based) and (ii) a hybrid approach that takes both PCA and correlation effect analysis into account (hybrid scenario). Accordingly, a new feature vector was constructed by retaining only the most significant predictors, and the most efficient feature dimensions were also determined by using a hybrid-based approach. Experimental results showed that the hybrid-based proposed algorithm has the minimum mean absolute error. The CRLB analysis also showed that the number of Wi-Fi access points could affect the lower bound location estimation error; however, identifying the most significant predictors is an effective approach to improve positioning performance.

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“…Due to its simplicity, RSSI is the most commonly used parameter in indoor localization, as it does not require additional hardware for time or phase synchronization and can be easily acquired. The existing methods based on RSSI can be classified into two categories; solutions based on fingerprinting [7], [8] and others based on trilateration [9].…”

Section: Introductionmentioning

confidence: 99%

Convolutional Neural Networks based Denoising for Indoor Localization

Njima

Chafii

Nimr

et al. 2021

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring)

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Indoor localization can be based on a matrix of pairwise distances between nodes to localize and reference nodes. This matrix is usually not complete, and its completion is subject to distance estimation errors as well as to the noise resulting from received signal strength indicator measurements. In this paper, we propose to use convolutional neural networks in order to denoise the completed matrix. A trilateration process is then applied on the recovered euclidean distance matrix (EDM) to locate an unknown node. This proposed approach is tested on a simulated environment, using a real propagation model based on measurements, and compared with the classical matrix completion approach, based on the adaptive moment estimation method, combined with trilateration. The simulation results show that our system outperforms the classical schemes in terms of EDM recovery and localization accuracy.

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Comparison of similarity approaches for indoor localization

Cited by 5 publications

References 20 publications

Implementing Deep Learning Techniques in 5G IoT Networks for 3D Indoor Positioning: DELTA (DeEp Learning-Based Co-operaTive Architecture)

Implementing Deep Learning Techniques in 5G IoT Networks for 3D Indoor Positioning: DELTA (DeEp Learning-Based Co-operaTive Architecture)

Heterogeneous Transfer Learning for Wi-Fi Indoor Positioning Based Hybrid Feature Selection

Convolutional Neural Networks based Denoising for Indoor Localization

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