Kyung Kook Son

Bio: Kyung Kook Son is an academic researcher from Memorial University of Newfoundland. The author has contributed to research in topics: Mobile device & Hybrid positioning system. The author has an hindex of 1, co-authored 1 publications receiving 67 citations.

Indoor positioning of mobile devices with agile iBeacon deployment

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.

A Precise Dead Reckoning Algorithm Based on Bluetooth and Multiple Sensors

Abstract: More and more applications of location-based services lead to the development of indoor positioning technology. As a part of the Internet of Things ecosystem, low-power Bluetooth technology provides a new direction for indoor positioning. Most existing indoor positioning algorithms are applied to specific situations. Thus, they are difficult to adapt to actually complex environments and different users. To solve this problem, this paper proposes a precise dead reckoning algorithm based on Bluetooth and multiple sensors (DRBMs). To address positioning accuracy, 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. In addition, this paper fuses the localization results of Bluetooth propagation model and multiple sensors through the Kalman filter. The experiment results show that the proposed DRBM algorithm can obtain accurate positions. The localization accuracy is within 1 m, and the best can be controlled within 0.5 m. Compared with the traditional Bluetooth positioning methods and the traditional dead reckoning methods, the proposed algorithm greatly improves positioning accuracy and universality.

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

Abstract: Indoor user localization and tracking are instrumental to a broad range of services and applications in the Internet of Things (IoT) and particularly in Body Sensor Networks (BSN) and Ambient Assisted Living (AAL) scenarios. Due to the widespread availability of IEEE 802.11, many localization platforms have been proposed, based on the Wi-Fi Received Signal Strength (RSS) indicator, using algorithms such as K-Nearest Neighbour (KNN), Maximum A Posteriori (MAP) and Minimum Mean Square Error (MMSE). In this paper, we introduce a hybrid method that combines the simplicity (and low cost) of Bluetooth Low Energy (BLE) and the popular 802.11 infrastructure, to improve the accuracy of indoor localization platforms. Building on KNN, we propose a new positioning algorithm (dubbed i-KNN) which is able to filter the initial fingerprint dataset (i.e., the radiomap), after considering the proximity of RSS fingerprints with respect to the BLE devices. In this way, i-KNN provides an optimised small subset of possible user locations, based on which it finally estimates the user position. The proposed methodology achieves fast positioning estimation due to the utilization of a fragment of the initial fingerprint dataset, while at the same time improves positioning accuracy by minimizing any calculation errors.

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

Abstract: Bluetooth Low Energy (BLE) and the iBeacons have recently gained large interest for enabling various proximity-based application services. Given the ubiquitously deployed nature of Bluetooth devices including mobile smartphones, using BLE and iBeacon technologies seemed to be a promising future to come. This work started off with the belief that this was true: iBeacons could provide us with the accuracy in proximity and distance estimation to enable and simplify the development of many previously difficult applications. However, our empirical studies with three different iBeacon devices from various vendors and two types of smartphone platforms prove that this is not the case. Signal strength readings vary significantly over different iBeacon vendors, mobile platforms, environmental or deployment factors, and usage scenarios. This variability in signal strength naturally complicates the process of extracting an accurate location/proximity estimation in real environments. Our 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. We believe that the negative observations made in this work can provide future researchers with a reference on how well of a performance to expect from iBeacon devices as they enter their system design phases.

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

Abstract: It has traditionally been laborious and problematic to construct and maintain fingerprint maps for indoor positioning. In this paper, a multi-phase fingerprint map indoor localization method based on interpolation was proposed. The method addresses two needs: (1) to construct an efficient fingerprint map, and (2) to provide high-accuracy indoor positioning. For the effective collection of the real-time received signal strength indicator (RSSI), we proposed a mobile data-collection cart, equipped with a laser rangefinder and a smartphone, which can rapidly collect environmental data in indoor spaces. In practice, as some regions might be inaccessible, we adopted a kriging-based interpolation method that exploits the spatial autocorrelation of the RSSI to efficiently generate and update the fingerprint database. To overcome the instability of RSSI and improve positioning accuracy, we proposed 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. Indoor experiments verified that the proposed method has practical value, with sufficient accuracy for general indoor applications. We believe that the proposed method provides a feasible and low-cost indoor positioning solution.