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Proceedings ArticleDOI

Indoor positioning of mobile devices with agile iBeacon deployment

TLDR
iBeacon is a new technology which provides a higher level of location awareness in indoor positioning of mobile devices using iBeacon, a built-in, cross-platform technology for Android and iOS devices, which utilizes Bluetooth Low Energy for long-last services.
Abstract
Position of mobile devices and their users provides a great amount of added value and opportunities. The penetration of tracking devices with sensory such as GPS devices, accelerators and specifically smart phones has impacted human lives extensively. Nowadays, many applications on smart phones and mobile devices exploit different techniques and inputs for positioning. Wireless positioning is generally divided into two categories: outdoor positioning and indoor positioning, depending on not only where they are used but also how they work. Powerful as it is, indoor positing is still a challenging problem because satellite-based approaches do not work properly inside buildings. Therefore, for indoor positioning, we need to use other technologies creatively. iBeacon, the focus of this paper, is a new technology which provides a higher level of location awareness in indoor positioning. iBeacon is a built-in, cross-platform technology for Android and iOS devices, which utilizes Bluetooth Low Energy (BLE) for long-last services. This technology has significant advantages compared to other types of indoor positioning technologies, such as less expensive hardware, less energy consumption, needless to internet connection, and being capable of receiving notifications in background. This technology will provide huge benefits for future location awareness applications. It will change the way retailers, event organizers, and educational institutions communicate with people indoors. In this paper, we aim to provide a more accurate, cost efficient approach to indoor positioning of mobile devices using iBeacon.

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Journal ArticleDOI

Internet of Things (IoT), mobile cloud, cloudlet, mobile IoT, IoT cloud, fog, mobile edge, and edge emerging computing paradigms: Disambiguation and research directions

TL;DR: This paper attempts to disambiguate emerging computing paradigms and explain how and where they fit in the above three areas of research and/or their intersections before it becomes a serious problem.
Journal ArticleDOI

A Precise Dead Reckoning Algorithm Based on Bluetooth and Multiple Sensors

TL;DR: This paper improves the traditional Bluetooth propagation model and calculate the steps and step lengths for different users in the process of multisensor track calculation and proposes a precise dead reckoning algorithm based on Bluetooth and multiple sensors (DRBMs).
Journal ArticleDOI

Fusing Bluetooth Beacon Data with Wi-Fi Radiomaps for Improved Indoor Localization

TL;DR: A hybrid method that combines the simplicity (and low cost) of Bluetooth Low Energy (BLE) and the popular 802.11 infrastructure is introduced, to improve the accuracy of indoor localization platforms.
Journal ArticleDOI

A Measurement Study of BLE iBeacon and Geometric Adjustment Scheme for Indoor Location-Based Mobile Applications

TL;DR: The lessons on the limitations of iBeacon technique lead us to design a simple class attendance checking application by performing a simple form of geometric adjustments to compensate for the natural variations in beacon signal strength readings.
Journal ArticleDOI

Multi-Phase Fingerprint Map Based on Interpolation for Indoor Localization Using iBeacons

TL;DR: A time-variant multi-phase fingerprint map, with specific fingerprint databases constructed for different time periods, thereby automatically employing the most appropriate fingerprint map according to the time period is proposed.
References
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Proceedings ArticleDOI

On indoor position location with wireless LANs

TL;DR: Some experimental results towards a systematic study of the performance tradeoff and deployment issues of location fingerprinting schemes are presented and some issues related to the indoor positioning problem are discussed.
Journal ArticleDOI

Method for yielding a database of location fingerprints in WLAN

TL;DR: A new method based on kriging is presented which can not only achieve more accurate estimation, but can also greatly reduce the workload and save training time and make the fingerprinting technique more flexible and easier to implement.
Journal ArticleDOI

Location Fingerprinting In A Decorrelated Space

TL;DR: By projecting the measured signal into a decorrelated signal space, the positioning accuracy is improved, since the cross correlation between each AP is reduced, and experimental results show that the size of training samples can be greatly reduced in the decorrelated space.
Proceedings ArticleDOI

Location Fingerprint Analyses Toward Efficient Indoor Positioning

TL;DR: A new analytical model that employs proximity graphs for predicting performance of indoor positioning systems based on location fingerprinting and employs the analysis of the internal structure to identify and eliminate unnecessary location fingerprints stored in the database, thereby saving on computation while performing location estimation.
Proceedings ArticleDOI

Hybrid algorithm for indoor positioning using wireless LAN

TL;DR: A hybrid method is developed that balances the flexibility and accuracy of the two traditional methods, makes intelligent use of missing values, produces error bounds, and can be made dynamic.
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