Building Applications with iBeacon: Proximity and Location Services with Bluetooth Low Energy PDF/EPUb by Matthew S. Gast. Building Applications with iBeacon: Proximity and Location Services with Bluetooth Low Energy PDF/EPUb Livre par Matthew S. Gast. Building Applications with iBeacon. Proximity and Location Services with Bluetooth Low Energy ebook: mobi (kindle), epub (ipad).
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Book: Building Applications with iBeacon: Proximity and Location Services with Bluetooth Low Energy. ISBN: Publisher: O'. A curated list of awesome Bluetooth beacon software and tools. Eddystone A platform for marking up the world to make your apps and BLE beacons using a REST interface; Nearby - Build simple interactions between nearby devices and people iBeacon for Developers · Getting Started with iBeacon (PDF) · iBeacon . The application space to which iBeacons and BLE proximity corridor of an office building to ensure line-of-sight (LOS) signal propagation.
An Estimote iBeacon was placed vertically below an WiFi AP that uses channel 5, and the receiving Android mobile smartphone was placed 5 meters away. Both the iBeacon and the mobile device were 1. We make two important observations from this experiment.
Second, even when an iBeacon advertisement was successfully received, the RSSI readings showed significantly lower values e. Effect of Obstacles In this experiment, we examine the effect of physical obstacles on the iBeacon signal reception and compare against the LOS case.
Specifically, here, we considered six different cases: three cases with obstacles of an iron door, a wooden door and a window each, one case for signal amplification using a sheet of aluminum foil, and two cases where the mobile phone is covered by hand or paper. Again, the height was 1. We present the results from this study in Figure As a final note, we were able to amplify the beacon signal by using a sheet of aluminum foil behind the iBeacon which resulted in an RSSI increase of 6 dBm.
While it is unlikely that iBeacon deployment will intentionally be wrapped in a sheet of aluminum foil, this result suggests that some environmental artifact or ornament can also unexpectedly cause such an effect: implying that obstacles not only reduce the RSSI levels but can also amplify the signal strength.
Distance Estimation Using the Curve-Fitted Model Finally, we now take all the measurement data from the four iBeacon-mobile device pairs, combination of Estimote and Wizturn Pebble beacons with Android and iOS phones, and fit the data on to the model in 1 using the least-mean square method to calculate the estimated distance based on the RSSI.
We note that all data are for the outdoors, LOS, 1. We then use this derived model to compare the real distance to the estimated distance as in Figure One point to take away from this plot is the fact that distance estimation can show significant errors even under LOS conditions due to high signal strength variations.
Furthermore, the errors increase as distance increases e. While we omit additional figures for brevity, we note that we can see similar plots even with data from a single pair of iBeacon-mobile device.
Quantitatively, the errors can increase from tens of meters to even hundreds of meters. Unfortunately, as per our experimental results, neither is satisfactory. Figure Real distance versus estimated distance plot where the previously collected RSSI data was used to curve fit the model in 1 and empirically determine the model parameter.
Application Case Study: Automatic Attendance Checker System As our experimental results show, the main challenge in using iBeacon signals for accurate indoor positioning is the variability of RSSI readings and its sensitivity to environment changes which result in drastic changes in signal propagation.
Our findings in Section 4 show that the RSSI value and the corresponding signal propagation model for estimating distances varies significantly among iBeacons from different vendors e.
Overall, these experiences suggest that, with such iBeacon devices, we should take these limitations and performance characteristics into consideration when designing applications and apply improvement schemes with respect to the intuitions collected from such real-world pilot deployment experiences. In this case study, we extend the line of possible iBeacon applications by proposing an automatic attendance checker system that automatically checks the attendance of a college student to her classes in a university.
However, simple proximity is not enough to accurately determine whether a student is inside the classroom or not since the beacon signal can be received behind the walls of the classrooms as well.
Thus, we use the trilateration-based position estimation to make a decision on whether the student is attending the class in the room or not. To compensate for RSSI reading errors due to unexpected obstacles e.
On a system-level perspective, our system is composed of not only iBeacons and mobile devices, but also a database server which maintains and handles information about students, classes, classrooms, time-table, and attendance information. System Architecture Figure 12 shows the overview of our system architecture.
Three iBeacons are deployed in each classroom with unique identification numbers major-minor pair for each iBeacon, and the relative x-y coordinate of each classroom is preconfigured. The server maintains all the necessary information to compute and confirm attendance in the database.
Figure Overall system architecture of our iBeacon-based automatic attendance checker system. Specifically, we used an Oracle DB to maintain information regarding students, professors, classes, time tables, and, most importantly, deployed iBeacons including their x-y coordinates relative, within each classroom and identification numbers.
To store the actual attendance information the class, date, time, and x-y coordinate within classroom for each student, we used the MongoDB to handle the frequently updated data. Figure 13 shows our database structure. Figure Databased structure in our iBeacon-based automatic attendance checker system server. Using the aforementioned system, the automatic attendance check process operates as follows. When a student enters a classroom, the smartphone application will receive beacon advertisements from three or more iBeacons there may be signals from nearby classrooms.
Given this information, the server will first check the classroom information of each iBeacon and validates it against the classroom at which the student should be attending at the given time instance. If the classroom information indicates that the student is in a wrong classroom or if there are only two or less number of beacons detected from the target classroom, then the server returns FALSE for attendance.
For all other cases, the server uses the relative x-y coordinates within the target classroom of the iBeacons to estimate a position of the mobile device and checks whether the device is within the boundaries of the classroom.
If so, the server returns TRUE for attendance and records it in the database. This process is repeated periodically so that attendance can be checked during the class as well. This is to account for students who are a few minutes late, as well as those students who leave early during class.
However, to reduce the unnecessary power consumption from the use of BLE for the duration when a student does not have a class to attend, her time-table can be locally cached on the mobile device to enable attendance checking only when needed. Note that the x-y coordinates stored in the database are only relative and local to the target classroom. This is in contrast to other indoor positioning systems where global x-y coordinate within the entire target area e.
This greatly simplifies the system architecture not only in terms of estimating the position of the mobile device, but also in terms of iBeacon deployment, replacement, and reconfiguration.
In other words, if systems are mostly concerned for a small geographical region, iBeacon placements can be independently and better customized and optimized. We provide detailed descriptions on such calibration procedures in the following section. Geometric Adjustment for Improved Accuracy The main challenge in using iBeacon signals for accurate indoor positioning is the variability of RSSI readings and their sensitivity to environment changes such as obstacles or user handling of the mobile device.
Our findings earlier show that RSSI values can drop significantly when a beacon signal is received behind an obstacle, and the amount of signal attenuation varies depending on the obstacle types e. Furthermore, the height of the mobile device from the ground also affects the RSSI height of the iBeacon also introduces a high impact, but iBeacons are usually wall-mounted and its height is fixed in typical scenarios.
Such a high variation is a significant challenge when using trilateration for position estimation. However, we see one commonality from the aforementioned cases; the signal attenuates RSSI is lower than the model for a given distance , and it is extremely rare to see higher RSSI than the best-case line-of-sight environment. Using this intuition, we use a simple yet novel geometric adjustment scheme to improve accuracy rather than trying to calibrate the model based on highly varying RSSI values.
For example, Figure 14 shows one example of an exception case where the distance estimation from three iBeacons results in three RSSI-coverage distance circles circles drawn by the estimated distance as radii for trilateration without any intersection; one circle is completely enclosed by the biggest circle, and a third circle is completely outside of the biggest one. In this case, standard trilateration calculation is not possible.
However, our intuition is that the distance of the larger circle larger distance from higher RSSI value is closer to the estimation model, and the signals of the two smaller circles have been attenuated due to some reason such as obstacles. Based on this intuition, our approach is as follows. For each pair of circles out of three pairs from three iBeacons , we first check whether [Case 1] two circles have no intersection points upper figure of Figure 15 or whether [Case 2] one circle is completely enclosed by another circle left figure of Figure If neither is true, then there is nothing else to consider for that specific pair of RSSI-coverage distance circles.
Figure An example of possible exception scenario where trilateration calculation will fail due to high variation in RSSI reading, thus causing error. Figure [Case 1] where two distance circles circles representing the estimated distance using RSSI from two iBeacons are nonintersecting because the sum of two distance estimations are smaller than the distance between the two iBeacons.
This results in no intersection point for trilateration calculation. Thus, we gradually increase the sizes of the circles until they intersect. Figure [Case 2] where a distance circle a circle representing the estimated distance using RSSI from one iBeacon is completely enclosed by another distance circle from another iBeacon , resulting in no intersection point for trilateration calculation. In this case, we gradually increase the size of the inner circle until the two circles intersect.
If a pair of circles have no intersection points i.
If no intersection points are created even after two circles are of the same sizes, then we increase the size of both circles with an increment of 1 meter until there are two intersection points c. Figure Again, this adjustment comes from the assumption that the indoor RSSI levels are disrupted by obstacles, which make the RSSI levels degrade more rapidly.
In the second case where, for a pair of circles, one circle is completely enclosed by another circle i. Figures 17 and 18 show the position estimation results from the two exception cases where the estimated distances from three iBeacons did not provide sufficient coverage for trilateration.
After the geometric adjustment based on our prior observations, the resulting estimations were detected to be very close to the actual positions. As a final note, the entire position estimation process, including the geometric adjustment and trilateration, takes place at the server. The mobile device simply detects iBeacon signals during the period of classes for the student, sends the list of iBeacon advertisement data to the server, and queries for attendance check.
Thus, there is practically no computation burden on the mobile device, and this leads to minimizing the energy usage as well. Figure An example of position estimation from an exception case where the original distance circles from distances estimated by RSSI had two circles completely enclosed in a third circle.
Figure An example of position estimation from the exception case in Figure 14 where the original distance circles from distances estimated by RSSI had one circle enclosed in another and a third circle is nonintersecting with the other two circles. Evaluation We have evaluated our automatic attendance checker system with the help of four undergraduate students attending classes in three classrooms.
While this case study was done in a limited scale due to challenges in getting individual consent for accessing students personal information, the system reported no false reports neither false positive nor false negatives for attendance for our student volunteers during the course of 1 month.
We plan to scale the experiment to a larger number of students and classes in the future. Related Work There are several pieces of prior work that proposes applications and services using iBeacon devices. The work in [ 9 ] uses iBeacons for tracking luggage at the airport, and the work in [ 10 ] provides interactive experience to visitors in museums.
Furthermore, the work in [ 13 ] proposes an indoor route guidance system, and the work in [ 15 , 16 ] uses iBeacons for occupancy detection within buildings.
These applications all use proximity-based on RSSI as the source of information rather than trying to pinpoint the exact and absolute location of the mobile device. There are also a number of prior works that focus on using iBeacon devices for precise indoor positioning [ 6 , 7 , 12 , 23 , 24 ]. However, none of these efforts provide an in-depth and extensive measurement study of iBeacon RSSI measurements and its variability with different environmental factors.
The work in [ 30 ] implements an Android application that collects statistics of RSSI values from nearby iBeacons and provides some measurement results but only at a fixed distance. The work in [ 5 ] provides some measurement data regarding the transmission power and reception ratio along with the estimated distance, and the work in [ 31 ] attempts to calibrate the distance estimation model using measurement data and perform error analysis.
Furthermore, we combine the experiences from the RSSI measurements to design a well-suited and practically applicable system and propose a case study application for automatic attendance checking, which uses localized trilateration techniques along with geometric adjustments to limit the scope of error due to RSSI variability and meet the target accuracy.
Conclusion The original goal of this research was to develop an improved indoor positioning system using iBeacon technologies. However, after numerous and extensive experiments, we realized that the signal variation was too high to retrieve accurate distance estimation for designing a reliable and robust localization system.
Instead we decided to report on this variation and inaccuracy to clarify the misunderstanding caused by numerous sugar-coated news articles on how accurate iBeacon technology can be.
It is obvious that the accuracy and efficiency of location estimation depend heavily on the accuracy of the measured RSSI measurements and the model used to estimate the distance, not to mention the surrounding environmental factors. We believe that our work provides evidence on the challenges for designing an indoor localization system using commercial-off-the-shelf COTS iBeacons devices and dismantles the misunderstanding of its overestimated accuracy. Furthermore, based on the observations made in this work, our future work is to find a way to approach these errors differently and develop an iBeacon-based system that is resilient and robust to such RSSI dynamics.
Competing Interests The authors declare that there is no conflict of interests regarding the publication of this paper.
For Hyungsik Shin, this work was supported by the Hongik University new faculty research support fund. Gomez, J. Oller, and J. Martin, B. Ho, N. Grupen, S. Chen, Q. Zhu, H. Jiang, and Y. Fard, Y. Chen, and K. Kouhne and J. He, B. Cui, W. Zhou, and S. Chen, J. Chung, and J. Lin, T. Ho, C.
Fang, Z. Yen, B. Yang, and F. Fujihara and T. Cheng, W. Hong, J. Wang, and K. Conte, M. De Marchi, A.
Nacci, V. Rana, and D. Corna, L. Fontana, A. Nacci, and D. Bottacioli [ 18 ] integrates data from several sources: environmental sensors temperature, humidity, and electricity consumption , historical and real-time meteorological and BIM-based building energy modelling, to assess building expected and real thermal behavior.
University campuses present excellent test-beds because of their centralized management, intensive utilization, and openness to research activities: the authors of [ 12 ] integrate sensors in a campus-wide, web-based system based on IFC and open messaging standards to gather energy, occupancy, and user feedback comfort while also providing other services to users such as room booking, location, and navigation assistance.
On the specific topic of this paper, an indoor location associated with BIM [ 20 ] employ multimodal sensors for indoor location and use building geometry extracted from BIM model restricted to one building level to improve location precision. Shayeganfar [ 21 ] combines semantic and geometric information from BIM models and propose a navigation solution to be implemented on Android smartphones for people with special needs, but do not detail indoor location methods or implement the solution.
More specific research on the utilization of BIM for detailed navigation around obstacles structural, openings, and furniture with the goal of assisting in autonomous navigation [ 14 , 22 , 23 ] is being pursued, but only go so far as to demonstrate that BIM models contain the necessary information and that information can be extracted and used, but do not actually develop and test a complete navigation and indoor location system.
The authors of [ 24 ] implement a hybrid BIM-Bluetooth System to locate workers and dangerous locations in construction sites. Proposed Approach The primary goal of this project is the development of an indoor guidance app that uses Bluetooth beacons for location and BIM for physical context information. The secondary goal is to obtain information about building utilization patterns, which is not covered in the present research work.
The mobile app then calculates the shortest path between the current location and the destination, using a Path Finding Algorithm.
The calculated path is shown on the map and is updated step-by-step when a new beacon is intersected until the destination room is reached. If the user chooses not to follow the proposed path, the App will recalculate the way to the chosen destination. Each user role has a defined role as described in the list below and in Figure 1. End User: any facility user that installed the Find Me! App on an Android smartphone.
System and Database Administrator: builds or adapts a 3D BIM the model of the building; generates maps and input data to the mobile App, e.