Performance Evaluation of Wi-Fi Bluetooth Low Energy &amp; Li-Fi Technology in Indoor Positioning

Afzal, Muhammad Abrar; He, Di; Zhu, Ziyu; Yang, Yueming

doi:10.1109/icdsp.2018.8631602

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…Fingerprint-Based Positioning energy efficiency [3]. Thus, these new technologies (e.g., Ultrawideband, ZigBee, Li-Fi, and Augmented Reality) have been used for positioning in universities, shopping malls, airports [4], [5], [6], [7], [8], [9], and embedded in Internet of Things (IoT) [10], [11], [12], [13] and wearable devices [14]. However, fingerprinting is a widely used technique despite its limitations in accuracy and scalability [15], [16].…”

Section: Scalable and Efficient Clustering Formentioning

confidence: 99%

Scalable and Efficient Clustering for Fingerprint-Based Positioning

Torres-Sospedra

Quezada-Gaibor

Nurmi

et al. 2023

IEEE Internet Things J.

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Indoor positioning based on IEEE 802.11 wireless LAN (Wi-Fi) fingerprinting needs a reference data set, also known as a radio map, in order to match the incoming fingerprint in the operational phase with the most similar fingerprint in the data set and then estimate the device position indoors. Scalability problems may arise when the radio map is large, e.g., providing positioning in large geographical areas or involving crowdsourced data collection. Some researchers divide the radio map into smaller independent clusters, such that the search area is reduced to less dense groups than the initial database with similar features. Thus, the computational load in the operational stage is reduced both at the user devices and on servers. Nevertheless, the clustering models are machine-learning algorithms without specific domain knowledge on indoor positioning or signal propagation. This work proposes several clustering variants to optimize the coarse and fine-grained search and evaluates them over different clustering models and data sets. Moreover, we provide guidelines to obtain efficient and accurate positioning depending on the data set features. Finally, we show that the proposed new clustering variants reduce the execution time by half and the positioning error by ≈ 7% with respect to fingerprinting with the traditional clustering models.

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Section: Scalable and Efficient Clustering Formentioning

confidence: 99%

Scalable and Efficient Clustering for Fingerprint-Based Positioning

Torres-Sospedra

Quezada-Gaibor

Nurmi

et al. 2023

IEEE Internet Things J.

View full text Add to dashboard Cite

show abstract

“…Therefore, Li-Fi is faster than other wireless technologies like Wi-Fi, Bluetooth, etc. [28]. For example, using the 2.4 GHz frequency band and radio wave data transmission method, Wi-Fi transmits data at speeds of 150-600 Mbps.…”

Section: Li-fi Assessment In Test Environment Networkmentioning

confidence: 99%

A New Approach in Cyber Security of Industrial Control Systems: Li-Fi

et al. 2022

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Industrial Control Systems used in the control of critical infrastructures, especially in smart grids, have made a rapid transition from an isolated network architecture to wired and wireless external networks due to the factors that facilitate human life such as productivity, economy and speed. With this transition, industrial control systems specific protocols have become insufficient and hybrid protocols have started to be used. As a result, the vulnerabilities specific to the internet and new protocols besides the vulnerabilities specific to industrial control systems have started to threaten control systems. In this study, the usability of the Light Fidelity (Li-Fi) transmission infrastructure in data transmission was investigated as a solution proposal for the smart city / network infrastructure where attack of False Data Injection carried out. This study will make important contributions to the studies carried out for industrial control systems security.

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Virtual Training and Ergonomics Evaluation System for Industrial Production Safety Based on Visible Light Communication

Sun

et al. 2019

2019 IEEE 19th International Conference on Communication Technology (ICCT)

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Performance Evaluation of Wi-Fi Bluetooth Low Energy & Li-Fi Technology in Indoor Positioning

Cited by 5 publications

References 12 publications

Scalable and Efficient Clustering for Fingerprint-Based Positioning

Scalable and Efficient Clustering for Fingerprint-Based Positioning

A New Approach in Cyber Security of Industrial Control Systems: Li-Fi

Virtual Training and Ergonomics Evaluation System for Industrial Production Safety Based on Visible Light Communication

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