Visible light‐based indoor localization using <i>k‐means</i> clustering and linear regression

Saadi, Muhammad; Saeed, Zeeshan; Ahmad, Touqeer; Saleem, Maria; Wuttisittikulkij, Lunchakorn

doi:10.1002/ett.3480

Cited by 17 publications

(8 citation statements)

References 30 publications

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“…Similar to visible light positioning [18,90], a recent work [21] also divided RSSI identification and classification process into offline and online stages for a device localization problem in wireless sensor networks. Since the moving object (e.g., a walking person) continuously shadows the wireless links built by several pairs of fixed WiLoc sticks and therefore affects the RSSIs, the two stages system allows to build classification model based on RSSI data, and dynamically predict the location based on interference detection influenced by his moving trajectory.…”

Section: Machine Learning (Ml)-based Methodsmentioning

confidence: 99%

A Survey of Recent Indoor Localization Scenarios and Methodologies

Tian

Cabani

Chafouk

2021

Sensors

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Recently, various novel scenarios have been studied for indoor localization. The trilateration is known as a classic theoretical model of geometric-based indoor localization, with uniform RSSI data that can be transferred directly into distance ranges. Then, a trilateration solution can be algebraically acquired from theses ranges, in order to fix user’s actual location. However, the collected RSSI or other measurement data should be further processed and classified to lower the localization error rate, instead of using the raw data influenced by multi-path effect, multiple nonlinear interference and noises. In this survey, a large number of existing techniques are presented for different indoor network structures and channel conditions, divided as LOS (light-of-sight) and NLOS (non light-of-sight). Besides, the input measurement data such as RSSI (received signal strength indication), TDOA (time difference of arrival), DOA (distance of arrival), and RTT (round trip time) are studied towards different application scenarios. The key localization techniques like RSSI-based fingerprinting technique are presented using supervised machine learning methods, namely SVM (support vector machine), KNN (K nearest neighbors) and NN (neural network) methods, especially in an offline training phase. Other unsupervised methods as isolation forest, k-means, and expectation maximization methods are utilized to further improve the localization accuracy in online testing phase. For Bayesian filtering methods, apart from the basic linear Kalman filter (LKF) methods, nonlinear stochastic filters such as extended KF, cubature KF, unscented KF and particle filters are introduced. These nonlinear methods are more suitable for dynamic localization models. In addition to the localization accuracy, the other important performance features and evaluation aspects are presented in our paper: scalability, stability, reliability, and the complexity of proposed algorithms is compared in this survey. Our paper provides a comprehensive perspective to compare the existing techniques and related practical localization models, with the aim of improving localization accuracy and reducing the complexity of the system.

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Section: Machine Learning (Ml)-based Methodsmentioning

confidence: 99%

A Survey of Recent Indoor Localization Scenarios and Methodologies

Tian

Cabani

Chafouk

2021

Sensors

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“…In these studies the concept of smart manufacturing is approached that is related to the generation of the industrial revolution, which increases the use of sensors and actuators in the industrial sector (Silva et al, 2021). According to the recent market estimation & predictions, IoT devices are expected to arouse to 75.4 billion US dollars in 2025 (Saadi et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

A lightweight intelligent intrusion detection system for industrial internet of things using deep learning algorithms

et al. 2021

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With the substantial industrial growth, the industrial internet of things (IIoT) and many IoT avenues have emerged. However, the existing industrial architectures are still inefficient to deal with advanced security issues due to the distributed and distensible nature of the network IIoT communication networks. Therefore, solutions for improving intelligent decision-making actions to the IIoT are sorely necessary. Thus, in this paper, the main cybersecurity attacks are predicted by applying a deep learning model. The various security and integrity features such as the DoS, malevolent operation, data type probing, spying, scanning, intrusion detection, brute force, web attacks, and wrong setup is analysed and detected by a novel sparse evolutionary training (SET) based prediction model. To scrutinize the conduct of the proposed SET-based prediction model, evaluation parameters, such as, precision, accuracy, recall, and F1 score are measured and compared to other state-of-the-art algorithms, in which the proposed SET-based model achieved an average accuracy of 0.99% for an average testing time of 2.29 ms. Results reveal that the proposed model improved the attack detection accuracy by an average of 6.25% when compared with the other state-of-the-art machine learning models in a real scenario of IoT security in Industry 4.0.

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“…However, during an outbreak of a disease that causes breathing problems, radiologists may be overwhelmed with medical image analysis [2,8,13]. In this context, applying different machine learning models [14][15][16][17] during a pandemic is very useful, as it performs the automatic analysis of medical images. Deep learning models are the state of the art in identifying COVID-19 and other lung diseases by imaging [2,11,[18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%