Localization for Intelligent Systems Using Unsupervised Learning and Prediction Approaches

Mirdita, Paul; Khaliq, Zain; Refaey, Ahmed; Wang, Xianbin

doi:10.1109/icjece.2021.3062971

Cited by 5 publications

(3 citation statements)

References 36 publications

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“…Unsupervised learning [ 46 ] is another common learning strategy used to reduce the dimensionality of input data by learning the compact and intrinsic features from unlabeled data. Some recent works [ 47 , 48 , 49 ] use the learning strategy to reduce the complexity and storage cost of fingerprint data.…”

Section: Related Workmentioning

confidence: 99%

Wi-Fi-Based Indoor Localization and Navigation: A Robot-Aided Hybrid Deep Learning Approach

Lin

Gan

Jiang

et al. 2023

Sensors

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Indoor localization and navigation have become an increasingly important problem in both industry and academia with the widespread use of mobile smart devices and the development of network techniques. The Wi-Fi-based technology shows great potential for applications due to the ubiquitous Wi-Fi infrastructure in public indoor environments. Most existing approaches use trilateration or machine learning methods to predict locations from a set of annotated Wi-Fi observations. However, annotated data are not always readily available. In this paper, we propose a robot-aided data collection strategy to obtain the limited but high-quality labeled data and a large amount of unlabeled data. Furthermore, we design two deep learning models based on a variational autoencoder for the localization and navigation tasks, respectively. To make full use of the collected data, a hybrid learning approach is developed to train the models by combining supervised, unsupervised and semi-supervised learning strategies. Extensive experiments suggest that our approach enables the models to learn effective knowledge from unlabeled data with incremental improvements, and it can achieve promising localization and navigation performance in a complex indoor environment with obstacles.

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Section: Related Workmentioning

confidence: 99%

Wi-Fi-Based Indoor Localization and Navigation: A Robot-Aided Hybrid Deep Learning Approach

Lin

Gan

Jiang

et al. 2023

Sensors

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“…Regarding the online phase, the systems are trained via machine learning (ML) matching algorithms to identify the incoming online record of RSSI features from WAPs. The most modern and valuable ML techniques for positioning are weighted k-nearest neighbors ( Chen, 2021 ), support vector machine (SVM), random forest (RF), artificial neural network (ANN) ( Polak et al, 2021 ), multi perceptron (MLP), and LSTM as the well-known technique for time series information including Wi-Fi RSSI ( Mirdita et al, 2021 ) and deep neural network (DNN) ( Li, Lei & Zhang, 2020 ). The deep learning (DL) algorithms are more accurate than the rest of the methods.…”

Section: Introductionmentioning

confidence: 99%

Novel integrated matching algorithm using a deep learning algorithm for Wi-Fi fingerprint-positioning technique in the indoors-IoT era

Asaad

Maghdid

2023

PeerJ Computer Science

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The Internet-of-Things (IoT) has been used with greater frequency to track peoples’ daily activities, particularly those conducted indoors. Wi-Fi technology has been also been used as an alternative to global navigation satellite system (GNSS) technologies to track indoor activities. The received signal strength indicator (RSSI) is widely used to assist in the positioning of Wi-Fi signals. However, the RSSI-based technique suffers from multipath, non-line-of-sight (NLOS) problems and the fluctuation of RSSI measurements via Wi-Fi chipsets. One of the most well-known RSSI-based approaches is to apply the fingerprinting method to do the positioning. However, the fingerprinting-based form has an additional problem due to the lack of RSSI data samples, specifically in harsh area with a huge number of classes or reference points (RPs) and an unstable matching process algorithm. To mitigate the problems of the RSSI-based fingerprinting approach, this research proposes a novel matching process algorithm called Norm_MSATE_LSTM. We first performed the augmentation process to increase the RSSI data records via the Mean Stander deviation Augmentation TEchnique (MSATE). The RSSI records were normalized (norm), and the long short-term memory (LSTM) technique was applied to estimate the correct positions. Finally, the proposed matching algorithm was compared with the stand-alone matching algorithms, including the weighted k-nearest neighbors (WkNN) and LSTM. The results obtained from the experiments and the simulated experiments using OMNeT++ show that the proposed matching algorithm may improve positioning accuracy by 33.1% and 57.5% when only augmentation and augmentation with normalization are applied, respectively.

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“…Indoor localization is another heavily researched topic given that it can be used as an alternative to Global Positioning System (GPS) in areas where GPS signals are not available [6], and applications that require user's location such as museums tours, the guiding system for the visually impaired, etc. Whilst this area of study is beneficial for several applications, it may pose numerous risks in user privacy if not properly managed.…”

mentioning

confidence: 99%

6G-Enabled Mobile Access Point Placement via Dynamic Federated Learning Strategies

Mirdita

Bello

Refaey

et al. 2023

IEEE Open J. Commun. Soc.

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Advanced Indoor Positioning Systems (IPS) based on Received Signal Strength (RSS) fingerprints have been paramount in 6G network research and commercial exploitation due to their costeffectiveness and simplicity. Despite their popularity, the advent of 6G has prompted a shift towards exploring Deep Learning algorithms to further enhance their performance and precision. Deep Learning research typically demands large datasets, leading to reliance on data augmentation and crowdsourcing techniques for data collection. However, the traditional centralization of data in crowdsourcing poses privacy risks, and here is where Federated Learning (FL) comes into play. In light of this, our study introduces FL to bridge this divide in a decentralized way, eliminating the need for servers to acquire labeled data directly from users. This approach aims to minimize localization error in RSS fingerprints, preserve user privacy, and reduce system latency, all key goals for 6G networks. Moreover, we explore the use of power transmission techniques to further decrease the latency in the FL system. Our simulation outcomes confirm the superiority of FL over traditional Stochastic Gradient Descent (SGD) methods considering critical evaluation metrics like localization error and global loss, paving the way for efficient 6G implementation.

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Localization for Intelligent Systems Using Unsupervised Learning and Prediction Approaches

Cited by 5 publications

References 36 publications

Wi-Fi-Based Indoor Localization and Navigation: A Robot-Aided Hybrid Deep Learning Approach

Wi-Fi-Based Indoor Localization and Navigation: A Robot-Aided Hybrid Deep Learning Approach

Novel integrated matching algorithm using a deep learning algorithm for Wi-Fi fingerprint-positioning technique in the indoors-IoT era

6G-Enabled Mobile Access Point Placement via Dynamic Federated Learning Strategies

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