Bluetooth Low Energy Based Indoor Positioning on iOS Platform

Duong, Son Ngoc; Trinh, Anh Vu-Tuan; Dinh, Thai-Mai Thi

doi:10.1109/mcsoc2018.2018.00021

Cited by 10 publications

(7 citation statements)

References 13 publications

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“…In [ 32 ], the authors propose a system based on iBeacon RSS fingerprints. To improve the stability of RSS measurements, they employ Kalman filtering.…”

Section: Related Workmentioning

confidence: 99%

Received Signal Strength Fingerprinting-Based Indoor Location Estimation Employing Machine Learning

Polák

Rozum

Slanina

et al. 2021

Sensors

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The fingerprinting technique is a popular approach to reveal location of persons, instruments or devices in an indoor environment. Typically based on signal strength measurement, a power level map is created first in the learning phase to align with measured values in the inference. Second, the location is determined by taking the point for which the recorded received power level is closest to the power level actually measured. The biggest limit of this technique is the reliability of power measurements, which may lack accuracy in many wireless systems. To this end, this work extends the power level measurement by using multiple anchors and multiple radio channels and, consequently, considers different approaches to aligning the actual measurements with the recorded values. The dataset is available online. This article focuses on the very popular radio technology Bluetooth Low Energy to explore the possible improvement of the system accuracy through different machine learning approaches. It shows how the accuracy–complexity trade-off influences the possible candidate algorithms on an example of three-channel Bluetooth received signal strength based fingerprinting in a one dimensional environment with four static anchors and in a two dimensional environment with the same set of anchors. We provide a literature survey to identify the machine learning algorithms applied in the literature to show that the studies available can not be compared directly. Then, we implement and analyze the performance of four most popular supervised learning techniques, namely k Nearest Neighbors, Support Vector Machines, Random Forest, and Artificial Neural Network. In our scenario, the most promising machine learning technique being the Random Forest with classification accuracy over 99%.

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“…In [ 32 ], the authors propose a system based on iBeacon RSS fingerprints. To improve the stability of RSS measurements, they employ Kalman filtering.…”

Section: Related Workmentioning

confidence: 99%

Received Signal Strength Fingerprinting-Based Indoor Location Estimation Employing Machine Learning

Polák

Rozum

Slanina

et al. 2021

Sensors

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“…Many algorithms can be used for matching offline measurements with online measurements. The popular algorithms are the clustering-based algorithm [192] [210] [206], Bayesian inference based algorithms [209] [192], the traditional support vector machine learning algorithms [192] [208], the artificial neural network-based algorithms [192] [205]. Those algorithms are typical machine learning algorithms.…”

Section: Conventional Anchor Based Localization Systemmentioning

confidence: 99%

Survey on Localization Systems and Algorithms for Unmanned Systems

2021

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Intelligent unmanned systems have important applications, such as pesticide-spraying in agriculture, robot-based warehouse management systems, and missile-firing drones. The underlying assumption behind all autonomy is that the agent knows its relative position or egomotion with respect to some reference or scene. There exist thousands of localization systems in the literature. These localization systems use various combinations of sensors and algorithms, such as visual/visual-inertial SLAM, to achieve robust localization. The majority of the methods use one or more sensors from LIDAR, camera, IMU, UWB, GPS, compass, tracking system, etc. This survey presents a systematic review and analysis of published algorithms and techniques chronologically, and we introduce various highly impactful works. We provide insightful investigation and taxonomy on sensory data forming principle, feature association principle, egomotion estimation formation, and fusion model for each type of system. At last, some open problems and directions for future research are also included. We aim to survey the literature comprehensively to provide a complete understanding of localization methodologies, performance, advantages and limitations, and evaluations of various methods, shedding some light for future research.

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“…This is because changes in the ship environment are difficult to predict. For example, unpredictable dynamic factors can cause changes in the fingerprint map [18]. Such unforeseen emotional factors such as sailing speed, acceleration motion, turning motion, and weather conditions, it is inefficient and impractical to analyze and model the effects of each element.…”

Section: Previous Workmentioning

confidence: 99%

Sustainable Ship Management Post COVID-19 with In-Ship Positioning Services

Lin

Son

2021

Sustainability

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COVID-19 is spreading out in the world now. Passenger ships such as cruise ships are very critical in this situation. Boats’ hazardous areas need to be identified in advance and managed carefully to prevent the virus. Therefore, this paper proposes for the first time that three technologies are required to support the sustainable management of ships in the post-COVID-19 era. They are ship indoor positioning, close contact identification, and risk area calculation. Ship environment-aware indoor positioning algorithms are proposed for the first time for the moving ship environment, followed by a clustering algorithm for close contact identification. Then, the risk area is calculated using the convex hull algorithm. Finally, a sustainable management approach for ships post COVID-19 is proposed.

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Bluetooth Low Energy Based Indoor Positioning on iOS Platform

Cited by 10 publications

References 13 publications

Received Signal Strength Fingerprinting-Based Indoor Location Estimation Employing Machine Learning

Received Signal Strength Fingerprinting-Based Indoor Location Estimation Employing Machine Learning

Survey on Localization Systems and Algorithms for Unmanned Systems

Sustainable Ship Management Post COVID-19 with In-Ship Positioning Services

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