A Review on Indoor Localization With Internet of Things

Chaimaa, Basri

doi:10.5194/isprs-archives-xliv-4-w3-2020-121-2020

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…T HE indoor localization system plays an essential role in the Internet of Things (IoT) applications, such as automatic vehicles, healthcare devices, logistics, industrial production, and wireless sensor networks, where accurate real-time position measurement is needed [1] - [5]. For example, smart unmanned robots will play an important role in future hospitals [6].…”

Section: Introductionmentioning

confidence: 99%

“…Due to the attenuation of obstacles such as concrete walls and windows, lots of indoor localization methods have been explored rather than those for outdoor environments (e.g., Global positioning system (GPS) and global navigation satellite system (GNSS)). These indoor techniques cover Wi-Fi, Bluetooth, Infrared, and ultra-wideband (UWB) [1]. The localization system using UWB measures time-of-flight (TOF) to estimate the distance to the target.…”

Section: Introductionmentioning

confidence: 99%

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Lab to Multiscene Generalization for Non-Line-of-Sight Identification With Small-Scale Datasets

Hua

Nielsen

et al. 2024

IEEE Trans. Artif. Intell.

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Ultra-wideband (UWB) wireless indoor positioning systems rely on time of flight (TOF) to estimate distances but can be biased and miscalculated due to non-line-of-sight (NLOS) transmission channels in complex environments. Therefore, to remove errors, several machine learning techniques have been proposed for identifying NLOS signals from Channel Impulse Responses (CIRs). However, as CIR signals could be heavily influenced by various environments, current NLOS classifiers are not universal to provide satisfactory accuracy for new scenarios and require detailed measurements on a large number of CIRs for training. Hence, we propose a generalization method based on data augmentation via noise injection and transfer learning to allow the deep neural network (DNN) trained under a lab condition to be applied to various and even harsh practical scenarios with the need to measure massive training data minimized. This paper presents the first demonstration that it is effective to utilize a lab-based pre-trained DNN for real-world transfer and white Gaussian noise data augmentation for ML-based NLOS identification on UWB CIRs to address the problem when it is not feasible to measure sufficient training data. Our testing results show that in two scenarios, corridor and parking lot, with only 50 CIR signals as the training set, the accuracy of the NLOS identification model after applying the proposed method is increased from 84.4% to 98.8% and from 81.1% to 97.1%, respectively.Impact Statement-In this paper, we propose a robust and data-efficient DNN-based method for identifying non-line-of-sight (NLOS) signals within ultra-wideband (UWB) indoor positioning signals to overcome distance estimation errors. For applications in a new environment or generalization across multiple environments, the need for sufficient data to train the DNN model can be largely lowered and higher accuracy can be offered. Furthermore, with our approach, the realization of accurate NLOS identification becomes possible in some harsh scenarios where collecting a large amount of data is costly, timeconsuming, or even impossible. In addition, we have investigated the possibility of applying noise injection to augment channel impulse response signals (CIRs) and to deal with environmental This work has been partly sponsored by the IoT Superproject, a strategic initiative of the

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Lab to Multiscene Generalization for Non-Line-of-Sight Identification With Small-Scale Datasets

Hua

Nielsen

et al. 2024

IEEE Trans. Artif. Intell.

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“…S INCE late 2019 COVID-19 has spread widely, disrupting people's lives, both socially and economically. The extensive spread is due, in part, to people's convergence [1]. Social distancing can reduce the risk of transmission of the COVID-19 disease [2].…”

Section: Introductionmentioning

confidence: 99%

Improved Control System Based on PSO and ANN for Social Distancing for Patients With COVID-19

et al. 2022

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The World Health Organization has declared the COVID-19 pandemic, with most countries being affected by this virus both socially and economically. It thus became necessary to develop solutions to help monitor and control disease spread by controlling medical workers' movements and warning them against approaching infected individuals in isolation rooms. This paper introduces a control system that uses improved particle swarm optimization (PSO), and artificial neural network (ANN) approaches to achieve social distancing. The distance between medical workers carrying mobile nodes and the beacon node (isolation room) was determined using the ZigBee wireless protocol's received signal strength indicator (RSSI). Two path loss models were developed to determine the distance from patients with COVID-19: the first is a log-normal shading model (LNSM), and the second is a polynomial function (POL). The coefficient values of the POL model were controlled based on PSO to improve model performance. A random-nonlinear time variation controller-PSO (RNT-PSO) approach was developed to avoid the local minima of the conventional PSO. As a result, social distancing for COVID-19 can be accurately determined. The measured RSSI and the distance were used as ANN inputs, while three control signals (alarming, warning, and closing) were used as ANN outputs. The results revealed that the hybrid model between POL and RNT-PSO, called RNT-PSO-POL, improved the system's performance by reducing the mean absolute error of distance to 1.433 m, compared to 1.777 m for the LNSM. The results show that the ANN achieves robust performance in terms of mean squared error.INDEX TERMS ANN, COVID-19, control system, distance estimation, improved, PSO, RSSI Thus, distancing between people has become crucial for

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Social Distancing elaboration for indoor environment using machine learning techniques

Brahmi

Boujnah

Abdennour

et al. 2022

2022 International Wireless Communications and Mobile Computing (IWCMC)

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A Review on Indoor Localization With Internet of Things

Cited by 4 publications

References 18 publications

Lab to Multiscene Generalization for Non-Line-of-Sight Identification With Small-Scale Datasets

Lab to Multiscene Generalization for Non-Line-of-Sight Identification With Small-Scale Datasets

Improved Control System Based on PSO and ANN for Social Distancing for Patients With COVID-19

Social Distancing elaboration for indoor environment using machine learning techniques

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