As a key technology that is widely adopted in location-based services (LBS), indoor localization has received considerable attention in both research and industrial areas. Despite the huge efforts made for localization using smartphone inertial sensors, its performance is still unsatisfactory in large open areas, such as halls, supermarkets, and museums, due to accumulated errors arising from the uncertainty of users’ mobility and fluctuations of magnetic field. Regarding that, this paper presents iBILL, an indoor localization approach that jointly uses iBeacon and inertial sensors in large open areas. With users’ real-time locations estimated by inertial sensors through an improved particle filter, we revise the algorithm of augmented particle filter to cope with fluctuations of magnetic field. When users enter vicinity of iBeacon devices clusters, their locations are accurately determined based on received signal strength of iBeacon devices, and accumulated errors can, therefore, be corrected. Proposed by Apple Inc. for developing LBS market, iBeacon is a type of Bluetooth low energy, and we characterize both the advantages and limitations of localization when it is utilized. Moreover, with the help of iBeacon devices, we also provide solutions of two localization problems that have long remained tough due to the increasingly large computational overhead and arbitrarily placed smartphones. Through extensive experiments in the library on our campus, we demonstrate that iBILL exhibits 90% errors within 3.5 m in large open areas.

iBILL: Using iBeacon and Inertial Sensors for Accurate Indoor Localization in Large Open Areas

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.

/pdf/a-measurement-study-of-ble-ibeacon-and-geometric-adjustment-lwqb1tzb5v.pdf

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

The Handbook of Personal Area Networking Technologies and Protocols: Bluetooth low energy

Wi-Fi-based localization using received signal strength (RSS) with pedestrian dead reckoning (PDR) algorithm is widely used to track pedestrians in indoor environments. However, the unsatisfactory deployment of Wi-Fi access points (APs) in buildings and the unstable performance of PDR are still key problems that lead to low localization accuracy. In this paper, we make contributions on proposing a hybrid Wi-Fi and Bluetooth Low Energy (BLE) indoor localization system (ILS) based on an efficient BLE deployment strategy and hierarchical topological fingerprinting (HTF). For the BLE deployment strategy, we deploy BLE beacons in places that do not have clear Wi-Fi signals for localization. This efficiently increases the localization accuracy. For HTF, we hierarchically localize targets based on a topological fingerprint (TF) map. First of all, we quickly localize the room in which the target is located by Dendogram-based support vector machine (DSVM). Then, the specific position of the target is estimated by fusing Wi-Fi and BLE signals with the TF map. The new BLE-based fingerprinting algorithm is used to localize targets in environments sparsely populated by BLE beacons. We conduct physical experiments in a real building. The experimental results demonstrate that the beacons deployed based on our proposed deployment strategy results in greater localization accuracy. Furthermore, the HTF approach performs better than the other commonly used localization methods.

Indoor Localization System Based on Hybrid Wi-Fi/BLE and Hierarchical Topological Fingerprinting Approach

Ubiquitous connectivity among objects is the future of the coming Internet of Things era. Technologies are competing fiercely to fulfill this goal, but none of them can fit into all application scenarios. However, efforts are still made to expand application ranges of certain technologies. Shortly after the adoption of its newest version, Bluetooth 5.0, the Bluetooth Special Interest Group released another new specification on network topology: Bluetooth Mesh. Combined together, those two bring Bluetooth to a brand new stage. However, current works related to it only focus on part of the new Bluetooth, and discussion over the entire one is lacking. Therefore, in this survey, we conduct an investigation toward the new Bluetooth from a comprehensive perspective. Through this, we show that the new Bluetooth not only consolidates its strengths in original application fields but also brings alterations and opportunities to new ones, making it a strong competitor in the future for providing complete solutions to meet the demands of seamless communications in the Internet of Things area.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=5543&context=sis_research

A Survey on Bluetooth 5.0 and Mesh: New Milestones of IoT

iBeacons are a new way to interact with hardware. An iBeacon is a Bluetooth Low Energy device that only sends a signal in a specific format. They are like a lighthouse that sends light signals to boats. This paper explains what an iBeacon is, how it works and how it can simplifiy your daily life, what restriction comes with iBeacon and how to improve this restriction., as well as, how to use Location-based Services to track items. E.g., every time you touchdown at an airport and wait for your suitcase at the luggage reclaim, you have no information when your luggage will arrive at the conveyor belt. With an iBeacon inside your suitcase, it is possible to track the luggage and to receive a push notification about it even before you can see it. This is just one possible solution to use them. Ibeacon can create a completely new shopping experience or make your home smarter. This paper demonstrates the luggage tracking use case and evaluates its possibilities and restrictions.

Markus Kouhne

Papers

Location-Based Services with iBeacon Technology