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2019 International Conference on Advanced Technologies for Communications (ATC) Smartphone Indoor Positioning System based on BLE iBeacon and Reliable region-based position correction algorithm Thai-Mai Dinh and Ngoc-Son Duong Department of Telecommunications Systems, Faculty of Electronics and Telecommunications University of Engineering and Technology, Vietnam National University Ha Noi, Viet Nam Email: dttmai@vnu.edu.vn, duongson.vnu@gmail.com Abstract—In the near future, indoor localization will become one of the essential parts of the IoT ecosystem Accuracy in indoor positioning is always a big challenge that requires the endeavor of researchers Without any extra cost, Trilateration is considered a feasible method to be able to build an indoor positioning system In this paper, we introduce an iBeaconbased smartphone indoor positioning system using Pedestrian Dead Reckoning (PDR) and Trilateration method We propose a Reliable region-based position correction method to reduce the cumulative error caused by PDR The proposed system is implemented on an iPhone as an application In order to evaluate the performance of the proposed method, we perform some real experiments in two cases: only use PDR, use our proposed method The results showed that the accuracy improved markedly Index Terms—Bluetooth Low Energy, trilateration, iBeacon, indoor positioning system, iOS, pedestrian dead reckoning, smartphone sensor I I NTRODUCTION Since its inception, Global Positioning System (GPS) technology [1] has really changed the way people pinpoint the location and find their path on the planet The ideal condition for GPS to achieve the highest accuracy is the Line-ofSight (LOS) or less obstructive object However, the rapid development of the construction architecture somewhat hinders the reception of GPS signals, especially in the basement or deep hall Position errors in these cases can be up to hundreds of meters Therefore, Indoor Positioning System (IPS) was put into the study to be able to locate objects or people in the indoor environment Basically, the concept of Indoor Positioning System inherits the characteristics of the Global Positioning System Receivers gathering information from the transmitter to get the location The thing makes IPS different from GPS is the transmitter technology Instead of use satellites, IPS use other technology based on radio frequency such as Wifi [2], Radio Frequency Identification (RFID) [3] or Ultra-wideband (UWB) [4] To yield the benefits as much as possible, not only for indoor localization, a protocol running on Bluetooth Low Energy (BLE) platform was introduced by Apple called iBeacon Compared to previous technologies, BLE brings back significant benefits such as low energy consumption, lowcost 978-1-7281-2392-9/19/$31.00 ©2019 IEEE hardware, and ease of deployment We can make it possible to perform indoor positioning through received signal strength (RSS) based approach of Bluetooth signals By using iBeacon technology, researchers around the world have made concerted efforts to reduce the position error to a minimum An extensive study using iBeacon and Fingerprinting as the main approach is presented in [5] The study looked at most of the factors that could affect the system such as beacon density, transmit power, and BLE frequency A good idea in the study is present in [6] Group of author constructed an efficient fingerprint map that can generate and update the fingerprint database for the inaccessible region by adopting a Kriging-based interpolation method and build a multi-fingerprint database for different time periods To achieve higher accuracy, the hardware in the phone is fully utilized to combine with iBeacon technology Sensors embedded in phones are now available to compute the position of the object when knowing the initialization point A framework for combining smartphone sensors and iBeacons is presented in [7] In this work, particle filter is applied as the fusion algorithm In the prediction phase, the user position is anticipated based on PDR In the observation phase, iBeacon coordinates are used to correct the drifting position caused by PDR The error correction procedure occurs when the user enters the 4m-calibration ranges of iBeacon Another favored iBeacon-based approach is Trilateration It can help implement indoor localization without any extra requirements However, Trilateration itself has its difficulties The dilemma of this technique is the fluctuation of RSSI and distance estimation To cope with this problem, we propose an algorithm called ”Reliable region-based position correction” Principally, we adopt a smart phone sensor-based positioning method to enhance system accuracy In the short travel distance, PDR shows a quite high accuracy We exploit the advantage of this feature to identify a reliable region around users Whenever Trilateration returns the position in this zone, that position will be used to correct the PDR error The remainder of paper is organized as follows After this short introduction, we describe the model of the proposed indoor tracking system in Section II Section III will present in details the method and positioning algorithms Section IV 264 2019 International Conference on Advanced Technologies for Communications (ATC) provides system parameters and experimental results Finally, Section V concludes this paper II S YSTEM D ESIGN A Proposed System The proposed model is described in detail in Fig All data sources are provided by the modules and sensors built in the phone In this system, the information of interest is the received signal strength read from the BLE chip, acceleration on three phone axes taken from accelerometer and orientation taken from magnetometer At first, the RSS information from the three nearest beacons that read by BLE chip is used in Trilateration module to estimate the position At every step, both the step length and heading angle that obtained from the built-in sensor are used to calculate the user displacement in prediction phase of particle filter Then, if the conditions of “Region-based position correction” module is satisfied, the error correction procedure will take place in correction/observation phase of particle filter Finally, the estimated position will continue to be used to calculate the position in the next step verticals, from proximity marketing to museums (Brooklyn Museum), airlines (Luton Airport) or zoos (Los Angeles Zoo) Moreover, BLE signals from iBeacon can broadcast in an area with a radius of 100m [9] That makes it ideal for indoor positioning An indispensable thing to bring iBeacon into reality is the smartphone application Currently, iBeacon compatible with both Android and iOS OS In this study, we build an app that runs on the iOS platform To collect iBeacon signals, we use the CoreLocation framework [10] provided by Apple III P OSITIONING A LGORITHMS A Trilateration In our study case, Trilateration is defined as a method for obtaining the position of an object or people under an indoor environment based on RSSI information of at least beacons Received signal strengths from these beacons are calculated via the formula: P L (d) = P L (d0 ) + 10η log d d0 (1) where, P L (d) and P L (d0 ) are RSSI at Euclidean distance d and reference distance d0 , respectively (in dBm) d0 is usually chosen equal to meter for the indoor environment η represents the path loss exponent and χ ∼ N (0, σ ) The distance from the smart phone to the ith beacon can be expressed as: P L(di )−P L(d0 ) 10η di = d0 (2) Unlike fingerprinting, Trilateration method does not have an offline phase However, it still requires a database of the beacons coordinate Let (ui , vi ) be the coordinates of ith beacon The equation for each circle is represented by (z = 0): 2 (u − ui ) + (v − vi ) = d2i (3) Then, the user position is determined by the intersection of the three circles Fig System design B iBeacon and iOS indoor positioning application iBeacon is an Apple trademark that was introduced at WWDC in June 2013 By using Bluetooth Low Energy (BLE),iBeacon opens up many opportunities for the Internet of Things (IoT) Messages from iBeacon are divided into nested classes including UUID, Major, and Minor We can take advantage of these classes to identify real objects such as product groups, shops, building floors, etc With smaller size and cheaper price, it makes more advantage for LocationBased Services [8] than Wifi, NFC or RFID There are a number of beacon manufacturer has deployed their beacon across various 978-1-7281-2392-9/19/$31.00 ©2019 IEEE +χ 265 B3(u3,v3) B1(u1,v1) B2(u2,v2) Fig Trilateration Technique 2019 International Conference on Advanced Technologies for Communications (ATC) B Smartphone-Based Pedestrian Dead-Reckoning T where, [xk , yk ] is the coordinates of the position in twodimensional space, L is the step length and θk the direction of the user at time k The factors that directly affect the accuracy of this method will be presented below 2) Step Length Estimation: In order to predict the next step, information of the step length must be known In this paper, step length is defined as the distance from the top of the foot to the other In practice, the length of a step definitely depends on many factors Walking speed and personal height is the most impact The step length will change if the user changes speed This problem can be solved by integrating the acceleration to update speed, thereby updating footsteps Other factors influence to step length is gender and personal height The solution to this problem is to set the average step length According to statistics, the average step length of women is 0.67m, this number is 0.75m for men [11] However, adding this factor to calculate the length of footstep will make the system more complicated To reduce the complexity of calculations, we propose to set the value of footsteps to average value between men and women that fixed and equals to 0.71m 3) Real-time Step Event Dectection: Errors due to overcounting or undercounting a step will be worse than accurately determining the length of a step Detecting a step can be determined by information from the accelerometer Basically, the sign to detect a step is the change in acceleration on the vertical axis Accordingly, in a one-step cycle, when the user lifts the foot and moves forward, this acceleration will reach the maximum and decrease to a minimum when the foot hit to the ground So, a step is detected when: Pp − Pn ≥ δ (5) 0.2 VerticalAcc (m/s2) 1) Sensor–based positioning method: The development of Micro-Electro-Mechanical Systems (MEMS) allows the fabrication and embedding of very small sensors into smartphones including accelerometers, gyroscopes and magnetometers, etc Information from these sensors is used to detect the human movement The solution only requires these inertial sensors called is Pedestrian Dead-Reckoning or PDR This method computes the user’s displacement using the previous position, step length, and the current direction through the equation as follow: xk xk−1 cos θk = + Lk (4) yk yk−1 sin θk Based on actual testing and previous studies, when knowing the initial point, PDR technology shows very high accuracy in short-range movement The accuracy of this technique will be diminished when the user moves in a long distance This phenomenon occurs due to noise from the sensor, so the error correction procedure needs to be done to bring the 978-1-7281-2392-9/19/$31.00 ©2019 IEEE -0.1 -0.2 -0.3 100 200 300 400 500 Time (1/50s) 600 700 800 900 1000 Fig Change of vertical acceleration user position to the right trajectory In order to solve this problem, we introduce a method that corrects the error by using Trilateration We determined that, after a short time of moving, the position error caused by PDR is not too much, so a region with a radius equal to D is drawn to indicate the area where the user is likely to be in there The error correction procedure occurs when the position calculated by the Trilateration technique located inside the circle Fig describes in detail the proposed correction method Normally, D 2L with the reason that the PDR module can count missing or redundant one or two steps T True path P Drifted path Fig Visualized view of proposed correction method D Particle filter – based position fusion Due to the drift caused by the noise in the sensors, PDR only achieves high accuracy in short distances This error will be very serious if not corrected To ensure accuracy for the entire system, we decided to utilize particle filter [12] as the fusion algorithm Assume each particle is defined by: where, Pp and Pn are maximum and minimum peak at time t, respectively and δ is the threshold level C Region-based position correction method 0.1 xi yi pi = , wi , i = 1, 2, , N (6) where [xi , y i ]T is the coordinates in the two-dimensional space of the ith particle, wi is the weight and N is the number of particles 1) Initialization: At time t = 0, the position of the particles are randomly selected around the point [x0 , y0 ]T : 266 xi0 y0i = N (x0 , σ ) N (y0 , σ ) (7) 2019 International Conference on Advanced Technologies for Communications (ATC) 2) Prediction: In each step, movement state and heading direction observed from the sensor is used to predict the position of the particles in the next step: xik yki = xik−1 i yk−1 + Lk cos θk sin θk TABLE I S YSTEM PARAMETERS Device Opera System Beacon Bluetooth Interface Advertising Interval Broadcasting Power Broadcasting Range Path loss exponent η Sigma σ (8) 3) Correction: Suppose at time t, Trilateration returns position T = [ut , vt ]T Then, the distance dt between current position and T can be calculated If dt > T , this mean the estimated position of Trilateration is not reliable enough to correct errors for PDR On the contrary, T will be used to correct errors for PDR Distance from particles to T is determined by: dit = (ut − xit )2 + (vt − yti )2 iPhone SE iOS 12.1.2 Proximity Estimote Beacons BLE v4.0/ 2.4 GHz 100 ms dBm 50 m 2.42 [ 8S 8S 6WDUW3RLQW 8S 8S (9) The weight of particles is then updated via the SIR method: i w ˆti = w ˆt−1 · P dt |dit (10) 3 where P dt |dit is important density and: P dt |dit = √ (dt − dit )2 exp − 2σ 2πσ \ (11) Fig The position of beacons and true path on the testbed Then, the weight of each particle is normalized as follows: wit = w ˆit N i=1 w ˆti (12) 4) Position Updating: Finally, the user position is calculated by: N i i xt i=1 xt wt = (13) N i i yt i=1 yt wt 5) Sampling: The generality of the particle filter will be lost due to degradation of samples This phenomenon occurs when the variance of the weights increase over time Then, weight distribution becomes progressively more skewed So, effort in updating particles whose contribution to final estimate is almost and the position after updating depends only on the sample with the largest weight Therefore, the re-sampling procedure should be performed when the total weight of the particles is less than a certain threshold The re-sampling procedure replaces the entire old sample with the new N weighted samples and equals N1 IV E VALUATION A Experiment Setup The experiment was carried out on the 1st floor of G2 building, University of Engineering and Technology (UETVNU), that is a semi-open indoor environment with three open entrances, rooms and some of the tree The size of the test area is 25m x 15m The location of the experiment and the position of the beacons are shown in Fig In this work, we use beacon produced by Estimote Beacon is designed as a miniature computer that builds on a 32-bit ARM Cortex M0 CPU with an nRF51822 BLE chip The distance between beacons is approximately m, the beacon was mounted at a 978-1-7281-2392-9/19/$31.00 ©2019 IEEE height of 1.4 m above the ground The detailed parameters of the system are given by Table I Then, the user holds the phone close to the body and moves around the experimental area following the true path in order to the application to record the position This app applies the Root Mean Square Error criterion to evaluate the performance of the proposed method Suppose (xtp , ytp ) and (xest , yest ) are the true positions and the estimated position by the proposed method at time t, respectively Then the error is defined by: RM SE = N i=1 (xtp − xest )2 + (ytp − yest )2 N (14) B Experiment Results In Fig 6, we easily see the drift of PDR trajectory The cause of this situation is due to sensor noise or over-counting or under-counting a step In the case of using only PDR, since no error correction method is applied, the cumulative error increases sharply after the PDR module count excess a step As a result, the final position is far from the starting point Our proposal shows the effectiveness when occasionally dragging the wrong to the near true position Errors in two cases is shown in Fig.7 V C ONCLUSION AND FUTURE WORK In this paper, an indoor positioning system based on iBeacon and phone sensors was presented In particular, the embedded sensor is utilized to calculate user displacement In order to correct the trajectory drift from PDR, we propose a Trilateration based error correction method Due to the instability of RSS, the estimated position by Trilateration is not completely reliable To be able to correct the error of PDR by Trilateration 267 2019 International Conference on Advanced Technologies for Communications (ATC) take place whenever the user position returned by Trilateration lie in this circle In addition, we choose particle filter as the fusion algorithm to cope with the non-linearity of the system We have built an application to test the correctness of the system The results show that the accuracy is clearly improved compared to PDR 11 Overcounting 10 y-Axis ACKNOWLEDGMENT Correct Point This work has been supported/partly supported by Vietnam National University, Hanoi (VNU), under Project No QG.19.25 R EFERENCES Proposed Method PDR True Path 2 10 x-Axis 12 14 16 18 20 Fig Trajectories of true path and tracked path in two experiments & & & % & & & & & #( & & & & '" ( ) #*+,- Fig Cumulative error of proposed method and PDR method, we use a limit of a circle with the center is the user to indicate the reliable region The error correction procedure will [3] Gharat, V., Colin, E., Baudoin, G and Richard, D “Indoor performance analysis of LF-RFID based positioning system: Comparison with UHFRFID and UWB,” 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN), Sapporo, 2017 978-1-7281-2392-9/19/$31.00 ©2019 IEEE [1] B Hofmann-Wellenhof, James Collins, Herbert Lichtenegger Global Positioning System: Theory and Practice Springer-Verlag GmbH 2000 [2] Zhuang, Y., Li, Y., Qi, L., Lan, H., Yang, J and El-Sheimy, N “A TwoFilter Integration of MEMS Sensors and WiFi Fingerprinting for Indoor Positioning,” IEEE Sensors Journal, vol 16, pp 5125-5126, Jul 2016 [4] Ren, A., Zhou, F., Rahman, A., Wang, X., Zhao, N and Yang, X “A study of indoor positioning based on UWB base-station configurations,” 2017 IEEE 2nd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), China, 2017 [5] Faragher, R and Harle, R “Location Fingerprinting With Bluetooth Low Energy Beacons.” IEEE Journal on Selected Areas in Communications, vol 33, pp 2418-2428, Nov 2016 [6] Zuo, J., Liu, S., Xia, H and Qiao, Y “Multi-Phase Fingerprint Map Based on Interpolation for Indoor Localization Using iBeacons,” IEEE Sensors Journal, vol 18, pp 3351-3359, Apr 2018 [7] Chen, Z., Zhu, Q., Jiang, H and Soh, Y “Indoor localization using smartphone sensors and iBeacons,” 2015 IEEE 10th Conference on Industrial Electronics and Applications (ICIEA), New Zealand, 2015 [8] Dey, A., Hightower, J., de Lara, E and Davies, N “Location-Based Services,” IEEE Pervasive Computing, vol 9, pp 11-12, Mar 2010 [9] Estimote, Inc (2019) Estimote (Version 2.42.5) [Mobile application software] Retrieved from https://apps.apple.com/us/app/estimote/id686915066 [10] Developer.apple.com (2019) Core Location — Apple Developer Documentation [online] Available at: https://developer.apple.com/documentation/corelocation [Accessed 17 Jul 2019] [11] Tianjian Ji, “Frequency and velocity of people walking,” Institution of Structural Engineers, vol.83, pp 36 - 40, Mar 2005 [12] Gustafsson, F., Gunnarsson, F., Bergman, N., Forssell, U., Jansson, J., Karlsson, R and Nordlund, P “Particle filters for positioning, navigation, and tracking,” IEEE Transactions on Signal Processing, vol 50, pp 425 - 437, Feb 2002 268 ... Richard, D ? ?Indoor performance analysis of LF-RFID based positioning system: Comparison with UHFRFID and UWB,” 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN),... That makes it ideal for indoor positioning An indispensable thing to bring iBeacon into reality is the smartphone application Currently, iBeacon compatible with both Android and iOS OS In this study,... (3) Then, the user position is determined by the intersection of the three circles Fig System design B iBeacon and iOS indoor positioning application iBeacon is an Apple trademark
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