Beacon Placement Algorithm for Hybrid Indoor Positioning with Wi-Fi and Bluetooth Low Energy

Nuradha, Theshani; Gnanarathne, Induni; Perera, Louise; Denipitiyage, Dishanika; Dias, Dileeka

doi:10.1109/mercon.2019.8818845

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…There are also some hybrid solutions that combine distance-based localization techniques with the fingerprinting techniques to improve the position estimation accuracy [6,7]. These methods may improve localization accuracy at one location, but due to fluctuations in transmission power, especially in the case of Bluetooth, accurate position estimation is still a challenging task [8].…”

Section: Introductionmentioning

confidence: 99%

Linear Discriminant Analysis-Based Dynamic Indoor Localization Using Bluetooth Low Energy (BLE)

et al. 2020

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Due to recent advances in wireless gadgets and mobile computing, the location-based services have attracted the attention of computing and telecommunication industries to launch location-based fast and accurate localization systems for tracking, monitoring and navigation. Traditional lateration-based techniques have limitations, such as localization error, and modeling of distance estimates from received signals. Fingerprinting based tracking solutions are also environment dependent. On the other side, machine learning-based techniques are currently attracting industries for developing tracking applications. In this paper we have modeled a machine learning method known as Linear Discriminant Analysis (LDA) for real time dynamic object localization. The experimental results are based on real time trajectories, which validated the effectiveness of our proposed system in terms of accuracy compared to naive Bayes, k-nearest neighbors, a support vector machine and a decision tree.

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Section: Introductionmentioning

confidence: 99%

Linear Discriminant Analysis-Based Dynamic Indoor Localization Using Bluetooth Low Energy (BLE)

et al. 2020

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“…The Bluetooth Cell, beacons, [6][7][8] based approach of determining the proximity range for the location was adopted. One of the simplest ways to achieve meter-level accuracy in specific zones is by a proximity measurement.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Context-Aware Design Using Context State Information for the Internet of Things Paradigm

Ntalasha

Wang

2020

JMM

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In the Internet of Things (IoT) paradigm, context state information plays a critical role in advancing the development of adaptive pervasive applications. Pervasive services and context-aware computing are emerging as the next computing paradigms in which infrastructure and services are seamlessly available anywhere, anytime, and in any format. The IoT paradigm raises new opportunities and demands on the underlying systems, in particular, the need to have systems that are adaptive and context-aware using context state information. In this paper, we introduce a new adaptive context state design technique to model context-aware applications that are sensitive to context state information changes. Each context change event is captured, interpreted and reacted to so that applications and users use only the functionality and adaptability needs that are solutions to their needs. The solution is modeled using Finite State Machine (FSM) and semantic localization so that context state information within the IoT paradigm is aligned to events. The semantic localization process precisely estimates the proximity location of the user along with the quality of context (QoC) attributes using the Bluetooth cell-based approach. This semantic information is useful in determining and inferring the user activities in a location. The QoC attributes are used to determine the confidence of the user location and range of the Bluetooth beacons within the IoT domain. This will, in turn, be used to determine whether the user is in the location or not. The alignment technique in our model represents the proper and new solution concerning functionality and adaptability needs expressed by other user applications in the IoT environment. The experimental scenario results indicate that a user can continue to enjoy their daily activities while the IoT application adapts continuously to their changing needs and notifying service providers of the changes according to the events of the user.

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Design and Implementation of Indoor Positioning System using Bluetooth Low Energy

Chuang

et al. 2020

IOP Conf. Ser.: Mater. Sci. Eng.

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Most of the positioning systems nowadays require GPS. In indoor environments, however, GPS signals cannot be received properly due to the obstructions of the walls in the building. Therefore, other techniques such as RFID, Zigbee, WIFI or Bluetooth, are used based on the power loss, cost, or transmission range considerations. Our study, conducted at the AI+ Experience Center of Chung Hua University, adopts Bluetooth 5.0-based equipment and uses the received signal strength indication (RSSI) and triangulation method to perform indoor positioning. Our results show that Bluetooth positioning is more accurate in the sense that when the distance is within 1∼3 meters, the error with the actual position is about 1 meter, which is more accurate than the RFID or WIFI positioning with the error of 2∼3 meters.

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Beacon Placement Algorithm for Hybrid Indoor Positioning with Wi-Fi and Bluetooth Low Energy

Cited by 4 publications

References 12 publications

Linear Discriminant Analysis-Based Dynamic Indoor Localization Using Bluetooth Low Energy (BLE)

Linear Discriminant Analysis-Based Dynamic Indoor Localization Using Bluetooth Low Energy (BLE)

Adaptive Context-Aware Design Using Context State Information for the Internet of Things Paradigm

Design and Implementation of Indoor Positioning System using Bluetooth Low Energy

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