An Overview of Indoor Localization Technologies: Toward IoT Navigation Services

Billa, Arlind; Shayea, Ibraheem; Alhammadi, Abdulraqeb; Abdullah, Qazwan; Roslee, Mardeni

doi:10.1109/istt50966.2020.9279369

Cited by 31 publications

(16 citation statements)

References 33 publications

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“…In addition, to enhance the accuracy and robustness of Range-free localization, some modern research have proposed various improvements, such as incorporating localization algorithms with mobility prediction [64], deploying mobile anchor nodes [65] using directional antennas [66], and integrating additional contextual information, such as RSSIs and AOA measurements [67].…”

Section: ) Range-free Strategymentioning

confidence: 99%

Optimization Algorithms for Wireless Sensor Networks Node Localization: An Overview

Ahmad,

Alhasan,

Wazirali

et al. 2024

IEEE Access

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Wireless Sensor Networks (WSNs) play a critical role in numerous applications, and the accurate localization of sensor nodes is vital for their effective operation. In recent years, optimization algorithms have garnered significant attention as a means to enhance WSN node localization. This paper presents an in-depth exploration of the necessity of localization in WSN nodes and offers a comprehensive review of optimization algorithms used for this purpose. The review encompasses a diverse range of optimization techniques, including evolutionary algorithms, swarm intelligence, and metaheuristic approaches. Key factors, such as localization accuracy, scalability, computational complexity, and robustness, are systematically evaluated and compared across various optimization algorithms. Additionally, the paper sheds light on the strengths and limitations of each optimization approach and discusses their applicability in different WSN deployment scenarios. The insights provided in this review serve as a valuable resource for researchers and practitioners seeking to optimize WSN node localization, thus promoting the efficient and reliable operation of WSNs in diverse real-world applications.

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Section: ) Range-free Strategymentioning

confidence: 99%

Optimization Algorithms for Wireless Sensor Networks Node Localization: An Overview

Ahmad,

Alhasan,

Wazirali

et al. 2024

IEEE Access

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“…The process of monitoring an indoor place using data obtained from various sources such as wired or wireless networks is known as a indoor localization system [7]. The majority of indoor localization systems have been suggested using Zigbee, Bluetooth, Wi-Fi, and cellular, with various degrees of implementation complexity and accuracy.…”

Section: A An Overview On Indoor Localizationmentioning

confidence: 99%

Indoor Positioning via Gradient Boosting Enhanced with Feature Augmentation using Deep Learning

Goharfar

Babaki

Rasti

et al. 2022

2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring)

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With the emerge of the Internet of Things (IoT), localization within indoor environments has become inevitable and has attracted a great deal of attention in recent years. Several efforts have been made to cope with the challenges of accurate positioning systems in the presence of signal interference. In this paper, we propose a novel deep learning approach through Gradient Boosting Enhanced with Step-Wise Feature Augmentation using Artificial Neural Network (AugBoost-ANN) for indoor localization applications as it trains over labeled data. For this purpose, we propose an IoT architecture using a star network topology to collect the Received Signal Strength Indicator (RSSI) of Bluetooth Low Energy (BLE) modules by means of a Raspberry Pi as an Access Point (AP) in an indoor environment. The dataset for the experiments is gathered in the real world in different periods to match the real environments. Next, we address the challenges of the AugBoost-ANN training which augments features in each iteration of making a decision tree using a deep neural network and the transfer learning technique. Experimental results show more than 8% improvement in terms of accuracy in comparison with the existing gradient boosting and deep learning methods recently proposed in the literature, and our proposed model acquires a mean location accuracy of 0.77 m.

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“…However, the cost to deploy base stations is comparatively high. Recently, with the development of Internet-of-Things technologies, some researchers devote themselves to indoor location using wireless local networks, which have already been deployed in many indoor environments [ 7 , 8 ]. Alhammadi et al developed a three-dimensional indoor location system based on the Bayesian graphical model [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Indoor Location Technology with High Accuracy Using Simple Visual Tags

Gao

2023

Sensors

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To achieve low-cost and robustness, an indoor location system using simple visual tags is designed by comprehensively considering accuracy and computation complexity. Only the color and shape features are used for tag detection, by which both algorithm complexity and data storage requirement are reduced. To manage the nonunique problem caused by the simple tag features, a fast query and matching method is further presented by using the view field of the camera and the tag azimuth. Then, based on the relationship analysis between the spatial distribution of tags and location error, a pose and position estimation method using the weighted least square algorithm is designed and works together with the interactive algorithm by the designed switching strategy. By using the techniques presented, a favorable balance is achieved between the algorithm complexity and the location accuracy. The simulation and experiment results show that the proposed method can manage the singular problem of the overdetermined equations effectively and attenuate the negative effect of unfavorable label groups. Compared with the ultrawide band technology, the location error is reduced by more than 62%.

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An Overview of Indoor Localization Technologies: Toward IoT Navigation Services

Cited by 31 publications

References 33 publications

Optimization Algorithms for Wireless Sensor Networks Node Localization: An Overview

Optimization Algorithms for Wireless Sensor Networks Node Localization: An Overview

Indoor Positioning via Gradient Boosting Enhanced with Feature Augmentation using Deep Learning

Indoor Location Technology with High Accuracy Using Simple Visual Tags

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