Performance Evaluation of Ibeacon Deployment for Location-Based Services in Physical Learning Spaces

applied sciences

Article Performance Evaluation of iBeacon Deployment for Location-Based Services in Physical Learning Spaces

Coco Yin Tung Kwok 1, Man Sing Wong 1,* , Sion Griﬃths 1 , Fiona Yan Yan Wong 1, Roy Kam 2, David C.W. Chin 1 , Guanjing Xiong 1 and Esmond Mok 1

1 Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong; [email protected] (C.Y.T.K.); [email protected] (S.G.); ﬁ[email protected] (F.Y.Y.W.); [email protected] (D.C.W.C.); [email protected] (G.X.); [email protected] (E.M.) 2 Educational Development Centre, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong; [email protected] * Correspondence: [email protected]

Received: 10 September 2020; Accepted: 9 October 2020; Published: 13 October 2020

Abstract: Bluetooth Low Energy (BLE) is a wireless network technology used for transmitting data over short distances. BLE maintains a data transmission range comparable to the regular Bluetooth transmission, but consumes less energy and cost. iBeacon technology refers to BLE mobile devices, which allow mobile applications to receive signals from iBeacons in both indoor and outdoor environments. It is commonly used nowadays for positioning, location services, navigation and marketing, for the sustainable development of smart cities. The applications, however, can be further enhanced for use in many disciplines, such as education, health sector, and exhibitions for disseminating information. This study performed a set of robustness and performance tests on BLE-based iBeacons in the teaching and learning environments to evaluate the performance of iBeacon signals for positioning. During robustness testing, positioning accuracy, signal availability and stability were assessed under different environmental conditions, and the findings suggested pedestrian traffic blocking the line of sight between iBeacon and receiver, causing the most signal attenuations and variation in RSSI. In performance testing, a series of tests was conducted to evaluate the deployment of the iBeacons for positioning; leading to recommendations of iBeacon deployment location, density, transmission interval, fingerprint space interval and collection time in physical learning spaces for sustainable eLearning environments.

Keywords: Bluetooth Low Energy; iBeacons; positioning accuracy; physical learning space

1. Introduction

1.1. Background Bluetooth is a wireless communication technology under the standard of Wireless Personal Area Networks (WPAN), which is managed by the Bluetooth Special Interest Group (Bluetooth SIG) [1]. It aims at providing short-range communication and data exchange in 2.4 GHz channel as a replacement of the connection between devices with wires [2]. This technology has been commonly used in our daily life, such as in hands-free headsets, wireless mouse and keyboards, and data transfers between smart mobile devices, as well as the wireless control applications in smart homes. Bluetooth Low Energy (BLE, which started from Bluetooth version 4.0) is a low-cost, low-power protocol that is an extension of the general Bluetooth standard used for wireless communication. It allows short-range wireless communication and signiﬁcantly reduces power consumption when compared to regular

Appl. Sci. 2020, 10, 7126; doi:10.3390/app10207126 www.mdpi.com/journal/applsci Appl. Sci. 2020, 10, 7126 2 of 22

Bluetooth transmission [3]. The latest development of Bluetooth version 5 allows communication within a few hundred meters distance, increases transmission speed, enhances direction-finding and it is optimized for Internet of Things (IoT) applications [4,5]. iBeacon is a framework proposed by Apple [6] based on BLE. The iBeacon device operates in the 2.4 GHz unlicensed band, which is the same as the Bluetooth standard channel, and emits Bluetooth signals in advertising mode with general information using major and minor identifiers, media access control (MAC) address and universally unique identifier (UUID) [6]. These identifiers allow a BLE-enabled device to identify which specific area is the user, and they are useful in the development of mobile applications which interact with users. For example, when a user gets close to an item on exhibit in a museum, an app will pop up with information about the exhibition based on the distance and identification information broadcasted from iBeacon installed near the exhibit. iBeacon devices using BLE can now operate for a period of several months to a couple of years on a single coin battery, which support sustainable practices in broadcasting signals. Bluetooth-equipped mobile devices are able to detect their proximity to an iBeacon transmitter based on the Bluetooth signals. Although iBeacon was originally specified by Apple, the BLE proximity features are compatible with Android and other BLE-equipped devices. With the development of smart devices, location-based services can be used in many areas, for example, positioning and navigation, range monitoring and trackers, entertainment, business advertisement and information dissemination. Nowadays, real-time positioning is available for most smart devices with an embedded Global Positioning System (GPS) receiver, and outdoor positioning is no longer a complicated task. However, there are some limitations in GPS positioning, as this method relies on satellite visibility and receiver-satellite geometry. Due to the inaccessible GPS signals in indoor environments, indoor positioning becomes difficult, and researchers have attempted to use radio networks to tackle this issue. To provide location-based services in indoor environments, Wi-Fi fingerprinting has been used for indoor positioning [7,8] down to a few meters of accuracy, given a highly surveyed environment and Wi-Fi coverage. However, Wi-Fi is power-demanding compared to BLE. As well, Wi-Fi-based indoor positioning systems generally lack ideal density and geometry of access points for positioning purposes, as the Wi-Fi capabilities were not originally designed for this purpose. iBeacon, therefore, offers the potential of unrestricted indoor positioning, as it is available in almost every possible location via the placement of iBeacons [9]. The original goal of BLE iBeacons was to provide proximity-based application services, such as targeted advertising and checkpoint services. By detecting the range to an iBeacon from the mobile devices, location-based notification and information can be disseminated to the user devices. TX power and the current received signal strength indicator (RSSI), are used to determine the distance from the iBeacons to the mobile devices. The TX power is the calibrated strength of the signal measured at 1 m from the source device (iBeacons), whereas RSSI is a measure of the strength of the iBeacon’s signal from the receiving mobile devices. Extensive research has been done on the methods in locating a mobile device with the iBeacon, given the potential interference from environmental factors on radio signals and the accuracy of RSSI, as well as the variations of radio frequency (RF) in indoor environments.

1.2. Objectives The deployment of iBeacons is challenging work, which can be affected by numerous factors, e.g., the number of iBeacons, spacing between iBeacons, interference with electronic devices, algorithms used for positioning and environmental conditions. Despite the amount of research on the accuracy of indoor positioning, the literature has only focused on limited aspects of the deployment, such as placement or TX power, approaching issues from the point of view of an approximate function or general solution to a problem. While some educational institutions have used iBeacons in physical learning spaces for teaching and learning purposes [10], for example, attendance monitoring [11,12], dissemination of electronic teaching material and information [13,14], combining augmented reality contents [15,16] and for smart facility management purposes [17,18], there is no research paper that Appl. Sci. 2020, 10, 7126 3 of 22 has conducted a comprehensive study to analyse the various environmental factors and deployment factors on iBeacon positioning accuracy for physical learning spaces. For the sustainable deployment of iBeacons in the future, therefore, this paper aims to: (a) evaluate the influences of different environmental factors to iBeacon signals with a series of robustness testing, and (b) evaluate the performance of different deployment settings of iBeacon, i.e., deployment position, deployment density, space interval of fingerprint method, collection time interval of fingerprint method, iBeacon signal transmission interval, venue and crowd condition, with a series of performance testing under the same control environment.

2. Related Work

2.1. Related Work of iBeacon Positioning Methods There have been many attempts in testing and improving indoor BLE positioning systems using RSSI [19–21]. They are mainly categorised into two groups of method, i.e., trilateration and fingerprinting.

2.1.1. Positioning Using Trilateration Method Trilateration locates the device by intersecting the distances between the transmitter and the device estimated from RSSI. Experimental results from Paek, Ko and Shin [22] showed that while iBeacon has promising accuracy in distance estimation and proximity, signal strength readings vary greatly, depending on the manufacturer, mobile platform (Android/iOS), environmental and deployment factors, and use cases. Using maximum TX power of 4 dBm at 1.2 m height, they found that RSSI readings for Android and iOS platforms decreased at different rates with increasing distances and temporary variation as well. Accuracy analysis of BLE for indoor positioning applications [23] clarifies that low bandwidth of BLE signals is the cause of significant measurement error when using the three designated BLE advertising channels. To eliminate the measurement error in received signals, some researchers enhanced the positioning accuracy using particle filtering [24], Kalman, Gaussian, and hybrid filters [25]. Fard, Chen and Son [26] demonstrated that errors could increase significantly with increased distance when the RSSI signals varied over time. To improve positioning accuracy, previous studies conducted experimental tests in different indoor situations, with various signal database matching and signal-to-distance estimation methods. Ng [27] revealed an average error of 0.43 m when measuring the distance from 0 m to 3 m with a constant speed of 0.01 m/s, and the average percentage error of 41.47%. This indicated that using iBeacon for precise positioning with consideration of height, localisation, geometry and TX power, could lead to fast fading and RSSI fluctuations, because of the logarithmic nature of power-based distance prediction and the low bandwidth of BLE. Martin, Ho, Grupen, Muñoz and Srivastava [28] achieved positioning accuracy of 0.53 m in a 9 m 10 m area × by modelling the distance with exponential function of RSSI and TX power. Castillo-Cara et al. [29] used two classification algorithms to improve the TX power settings for each iBeacon, and discovered a custom TX power level for each BLE Beacon could greatly improve positioning accuracy.

2.1.2. Positioning Using Fingerprinting Method The fingerprinting method is another approach for radio-based positioning. This method requires the collection of signal strengths (RSSI) from each of the transmitters at every evenly spaced grid, with true location recorded to establish a signal database. With the database collected, positioning can be achieved by matching the instantaneous signals collected by a mobile device, with the database using deterministic or probabilistic methods to determine the location of the device. Generally, fingerprint-based approaches are superior in terms of accuracy to trilateration techniques [30]. Kriz, Maly and Kozel [31] proposed a radio-based, indoor stationary positioning system, by comparing the Wi-Fi and BLE signals with the database using the k-nearest neighbour algorithm. Accuracy was improved and variance was reduced moderately when using the system. In the study conducted by Takahashi and Kondo [32], location estimation algorithms were also compared, and the nearest Appl. Sci. 2020, 10, 7126 4 of 22 neighbour method had a 1-meter lower average error than the k-nearest neighbour. Peng, Fan, Dong and Zhang [33] used an iterative weighted k-nearest neighbour method to improve the traditional k-nearest neighbour method, and reduced the mean error by 1.5 m to 2.7 m in their experimental environment. Similar work [34] reported a 1.2 m accuracy using a fingerprinting indoor positioning scheme in an ideal test space of 47.4 m 15.9 m. × 2.2. Related Work of iBeacon Deployment The effectiveness and accuracy of a standalone implementation of BLE systems are clearly highly dependent on the environmental conditions [22] and devices [35]. Research on improving the maximum accuracy and efficiency of indoor positioning [36], as well as discussions of placement techniques for positioning such as trilateration and cell-based positioning, has been conducted [35]. The linkages between positioning accuracy, iBeacon arrangement and the number of iBeacons were discussed by Aman, Jiang, Quint, Yelamarthi and Abdelgawad [37], who found additional iBeacons improved positioning accuracy. The lowest average errors were found by the authors of [37], with a hexagonal iBeacon placement pattern relative to pentagonal, square and triangular layouts. Ji, Kim, Jeon and Cho [38] analysed the signal attenuation of iBeacon and simulated the deployment of iBeacons in random-based and grid-based approaches. The results showed that more iBeacons always led to higher positioning accuracy, and the grid-based approach outperformed random-based approach when the grid interval was small enough. Supporting the idea that additional iBeacons improve accuracy, a three-dimensional positioning experiment conducted by Yang, Wang and Wang [39] resulted in maximum errors of 1.2 m in a 7 m 5 m 3 m room with four iBeacons, and a decreased maximum error of 0.77 m with × × an additional four iBeacons. However, receiving six to eight iBeacons at each fingerprint point was suggested by the authors of [23], which was found to be sufficient, as there was minimal incremental benefit and iBeacon coverage beyond that point to further improve the accuracy of positioning [23]. The iBeacons were typically placed in the room in a high position on the wall or the ceiling for most studies, and the test sites were devoid of obstacles such as furniture and computers [39]. As the installation height affects signal quality, Paek, Ko and Shin [22] compared the effects of placing the iBeacons on the ground with a height of 1.2 m. The placement of iBeacons on the ground yielded significant drops in RSSI with a maximum distance of only 12 m, as opposed to the 100 m recorded when the iBeacons were installed at a height of 1.2 m. To reduce the time cost of positioning error calculations, Yuan, Li, Xu and Zhao [40] formulated an approximate function to optimise the BLE iBeacon placement method. They found that in various indoor settings, the approximate function was comparably effective with regards to other node placement methods. A more specific exploration of iBeacon placement was studied by Rezazadeh et al. [41], who formulated the Crystal-based iBeacon Placement (CiP) method for placing iBeacons indoors, in order to deploy the smallest number of iBeacons to cover the maximum possible area. To ensure this would happen, user devices must be able to receive sufficient iBeacon messages for positioning. This was achieved by initially deploying three iBeacons in an equilateral triangle, and then extending the coverage by placing iBeacons adjacent to the equilateral triangle, creating a “crystal shape.” CiP ensures the shortest possible distance between any three iBeacons, which improves positioning accuracy. CiP was analysed both vertically and horizontally; this method provided a 21.16% improvement in accuracy over random placement and Z-placement, as described by Rezazadeh, Moradi, Ismail and Dutkiewicz [42]. The CiP method resulted in a relatively lower location error due to the higher quality of the signals. A novel approach to the Beacon placement problem by Schaff, Yunis, Chakrabati and Walter [43] aimed at searching for placement and inference strategies that, when combined, would be optimal for any given environment. They achieved this using a neural network formulation of inference, in conjunction with a differential neural layer that encoded Beacon distribution. The inference network and Beacon layer were then trained together to learn an optimal design for any given environment. In doing so, Beacon placement decisions could be made automatically and without expert supervision. Appl. Sci. 2020, 10, 7126 5 of 22

Appl. Sci. 2020, 10, x FOR PEER REVIEW 5 of 22 Whereas iBeacon/Beacon placements have been explored through a number of attenuations and 3. Methods experimental studies, examinations and testing in other settings and conditions, which possibly influenceIn order the to accuracy evaluate of the positioning performance using of BLE, iBeaco aren also positioning necessary. under different environments and deployment settings, two sets of testing were conducted in this study, namely, robustness testing and performance3. Methods testing, with the overall framework shown in Figure 1. Robustness testing focuses on the effectsIn order of todistance evaluate and the environmental performance of factors iBeacon (e.g., positioning temperature, under wind different, trees, environments vehicle traffic, and pedestriandeployment traffic settings, with twoand setswithout of testing blocking were line conducted of sight) inon thisthe study,stability namely, of the iBeacon robustness signals testing in outdoorand performance environment. testing, The with details the of overall the setup framework are described shown in in Se Figurection 4.1. Performance Robustness testingtesting focusesfocuses onon the the effects effects of of various distance deployment and environmental plans and envi factorsronmental (e.g., temperature, factors on indoor wind, positioning trees, vehicle accuracy, traffic, includingpedestrian the tra ffiiBeaconc with anddeployment without blockingposition, line deployment of sight) ondensity, the stability fingerprint of the iBeaconspace interval, signals fingerprintin outdoor collection environment. time, The transmission details of theinterval, setup influence are described of venue in Section and the4. Performancecrowd analysis. testing The detailsfocuses of on the the tests effects can ofbe various found in deployment Section 5. plans and environmental factors on indoor positioning accuracy,The two including sets of the testing iBeacon were deployment designed position,for indoor deployment and outdoor density, learning fingerprint spaces, space respectively. interval, Thesefingerprint tests collectionwere conducted time, transmission using BrightBeacon, interval, influencewhich is ofa BLE venue 4.0 and iBeacon. the crowd The analysis.data were The collected details withof the two tests Android can be foundphones, in Samsung Section5. SM-C7100 (hereafter device 1) and Sony G3426 (hereafter device 2).

Signal Coverage 1. Signal coverage (1 – 133 m)

Robustness Testing 1. High temperature 2. Strong wind 3. Obstruction of trees Environmental 4. High vehicle traffic Conditions 5. Pedestrian traffic 6. Pedestrian traffic blocking line of sight

Deployment 1. Ceiling (8 & 10 iBeacons) Position 2. Wall (8 & 10 iBeacons)

Performance Evaluation of iBeacon Deployment Deployment Density 1. Number of iBeacon (3 - 12)

Fingerprint Space 1. Space interval of iBeacons Interval (1 - 10 m)

Fingerprint 1. Fingerprint collection time Collection Time (1 – 10 s) Performance Testing

1. 100 ms Transmission 2. 417 ms Interval 3. 800 ms

1. Typical classroom 2. Computer laboratory Venue 3. Laboratory 4. Typical lecture theatre 5. Small lecture theatre

1. Empty room Crowd Analysis 2. Occupied with 22 students

FigureFigure 1 1.. OverallOverall framework framework of of the the study. study.

4. RobustnessThe two sets Testing of testing were designed for indoor and outdoor learning spaces, respectively. These tests were conducted using BrightBeacon, which is a BLE 4.0 iBeacon. The data were collected with two AndroidTwo phones,robustness Samsung tests were SM-C7100 conducted (hereafter outdoors device usin 1)g andtwo SonyiBeacons G3426 and (hereafter the two mobile device 2).devices, including (1) the signal coverage test and (2) the environmental conditions test. The iBeacon settings were defined as default (i.e., TX power of 4 dBm and 417 ms transmission interval) and a standard deployment height of 1.5 m was implemented for both the iBeacons and the mobile devices. The tests were conducted in the outdoor environments of the Hong Kong Polytechnic University. Figure 2 shows the site photos of the experiment. Appl. Sci. 2020, 10, 7126 6 of 22

4. Robustness Testing Two robustness tests were conducted outdoors using two iBeacons and the two mobile devices, including (1) the signal coverage test and (2) the environmental conditions test. The iBeacon settings were deﬁned as default (i.e., TX power of 4 dBm and 417 ms transmission interval) and a standard deployment height of 1.5 m was implemented for both the iBeacons and the mobile devices. The tests were conducted in the outdoor environments of the Hong Kong Polytechnic University. Figure2 shows theAppl. site Sci. photos2020, 10, x of FOR the PEER experiment. REVIEW 6 of 22

Figure 2. Experimental setup of signal coverage test and environmental conditions test where the redFigure circles 2. Experimental indicate the locationsetup of ofsignal iBeacons coverage installed. test and (a) environmental Signal coverage conditions test. (b) Obstruction test where the of tree. red (circlesc) High indicate vehicle the tra ffilocationc. (d) Pedestrian of iBeacons tra installed.ffic. (e) Control (a) Signal dataset. coverage test. (b) Obstruction of tree. (c) High vehicle traffic. (d) Pedestrian traffic. (e) Control dataset. 4.1. Signal Coverage Test 4.1. SignalTwo iBeacons Coverage were Test deployed on a column with tape, with a long line of sight, to test the maximum signalTwo coverage iBeacons and were the stability deployed of signals;on a column the two with mobile tape, devices with a were long fixed line onof camerasight, to tripods test the at 1.5maximum m height signal as shown coverage in Figure and 2thea. Thestability RSSI of was signal measureds; the two using mobile the two devices mobile were devices fixed from on camera 5 m to 133tripods m distance at 1.5 m ranges, height as at 5shown m intervals in Figure from 2a. 5 The m to RSSI 120 was m, and measured at 1 m intervalusing the from two 121mobile m to devices 133 m. Atfrom each 5 m of to these 133 distances,m distance 1 ranges, min of dataat 5 m was intervals collected. from 5 m to 120 m, and at 1 m interval from 121 m to The133 m. outdoor At each maximum of these distan coverageces, 1 test min demonstrated of data was collected. that the iBeacons could provide outdoor coverageThe outdoor for up to maximum 133 m with coverage the data test from demonstrat two iBeacons.ed that The the signal iBeacons packet could loss rate, provide RSSI outdoor and the standardcoverage deviationfor up to of133 RSSI m with from the the data two devicesfrom two at 5iBeacons. m intervals The from signal 5 m packet to 120 loss m, and rate, at RSSI 1 m intervals and the fromstandard 121 mdeviation to 133 m, of were RSSI recorded from the and two plotted devices in Figureat 5 m3 .intervals From Figure from3a, 5 them packetto 120 lossm, and rate at from 1 m deviceintervals 1 becamefrom 121 unstable m to 133 after m, were 120 m.recorded For device and plotted 2, it started in Figure to fluctuate 3. From at Figure 80 m. 3a, The the graph packet shows loss thatrate from signals device were 1 attainablebecame unstable and stable after when 120 m. the For distance device 2, was it started short, to but fluctuate different at results80 m. The obtained graph shows that signals were attainable and stable when the distance was short, but different results obtained by the two devices after 80 m suggest that differing wireless capabilities of the devices could lead to different sensitivities to wireless signals. However, implications for long-range outdoor use- cases are probably unusual, as any deployment of iBeacons should entail more than one iBeacon for a distance of over 100 m. In terms of the RSSI, a logarithmic nature was found in both Figure 3b and Figure 3c. The RSSI collected by the two devices (Figure 3b) and their standard deviations (Figure 3c) were very similar, with an average RSSI of −88.02 dBm (for device 1) and −87.94 dBm (for device 2), respectively, and a standard deviation of 3.56 dBm (for device 1) and 3.50 dBm (for device 2) from the range of 5 m to Appl. Sci. 2020, 10, 7126 7 of 22

byAppl. the Sci. two 2020 devices, 10, x FOR after PEER 80 REVIEW m suggest that diﬀering wireless capabilities of the devices could lead7 of to22 diﬀerent sensitivities to wireless signals. However, implications for long-range outdoor use-cases are probably120 m. The unusual, result assuggests any deployment the RSSI ofwas iBeacons stable shouldwhen entailcollected more by than different one iBeacon devices for at a different distance ofdistances, over 100 and m. the estimation of distances from RSSI was practical.

Figure 3. Analysis of maximum coverage test on (a) signal packet loss rate, (b) received received signal Figure 3. Analysis of maximum coverage test on (a) signal packet loss rate, (b) received received signal strength indicator (RSSI) and (c) standard deviation of RSSI. strength indicator (RSSI) and (c) standard deviation of RSSI. In terms of the RSSI, a logarithmic nature was found in both Figures3b,c. The RSSI collected by the two4.2. devicesEnvironmental (Figure 3Conditionsb) and their Test standard deviations (Figure3c) were very similar, with an average RSSI of 88.02The environmental dBm (for device conditions 1) and 87.94 test was dBm conducted (for device in 2), the respectively, summer of and2018. a standardAll experiments deviation were of − − 3.56performed dBm (for on device cloudy 1) anddates 3.50 withou dBm (fort direct device sunshine 2) from theor rangeconducted of 5 m at to locations 120 m. The with result cover; suggests the therecorded RSSI was air temperatures stable when collected ranged from by di 27fferent °C to devices 32 °C. The at di signalsfferent were distances, tested and under the estimationthe following of distancesconditions: from (a) RSSIhigh-temperature was practical. environment (i.e., air temperature of 32 °C and iBeacon surface temperature of 45 °C recorded by thermometer); (b) strong wind, with speeds of 5.7 m/s recorded by 4.2. Environmental Conditions Test nearby weather station; (c) obstruction of trees (Figure 2b); (d) high vehicle traffic (Figure 2c); (e) pedestrianThe environmental traffic (Figure conditions 2d); (f) pedestrian test was traffic conducted blocking in the the summer line of sigh of 2018.t between All experiments the iBeacon were and performedmobile devices; on cloudy and dates(g) control without environment. direct sunshine In expe or conductedriments (a), at locations(b), (e), (f) with and cover; (g), two the recordediBeacons airwere temperatures installed on ranged a camera from tripod 27 ◦C at to 1.5 32 ◦mC. height The signals, and the were iBeacons testedunder were stuck the following on tree trunks conditions: and (a)fences high-temperature with plastic tape environment at 1.5 m height (i.e., airfor temperature experiments of (c) 32 and◦C and(d), respectively. iBeacon surface As temperaturewith the signal of 45coverage◦C recorded test, the by two thermometer); mobile devices (b) strong were wind,fixed on with camera speeds tripods of 5.7 during m/s recorded the data by collection. nearby weather station;To (c)ensure obstruction the stability of trees of iBeaco (Figuren 2signals,b); (d) highthe tests vehicle started tra ffiafterc (Figure the iBeacons2c); (e) were pedestrian deployed tra ffiforc (Figureat least2 d);two (f) hours pedestrian under tra eachffic environmental blocking the line condit of sightion. betweenFor experimental the iBeacon conditions and mobile (a)–(e), devices; the andsignals (g) controlwere measured environment. from distances In experiments 1 m to (a), 20 (b),m at (e), 1 m (f) intervals and (g), twofor 1 iBeacons minute. wereFor experimental installed on acondition camera tripod(f), the at signals 1.5 m were height, measured and the from iBeacons distances were 2 stuck m to on20 treem with trunks same and interval fences and with duration plastic as (a)–(e). The signal data collected were compared with the control data collected in a typical outdoor environment with fewer environmental factors, as shown in Figure 2e (i.e., air temperature at 28 °C and iBeacon installed under the shadow of a tree crown with an internal temperature of 34 °C, wind speed of 3.3 m/s recorded by nearby weather station and no pedestrian and vehicle in the experimental environment). Appl. Sci. 2020, 10, 7126 8 of 22 tape at 1.5 m height for experiments (c) and (d), respectively. As with the signal coverage test, the two mobile devices were fixed on camera tripods during the data collection. To ensure the stability of iBeacon signals, the tests started after the iBeacons were deployed for at least two hours under each environmental condition. For experimental conditions (a)–(e), the signals were measured from distances 1 m to 20 m at 1 m intervals for 1 minute. For experimental condition (f), the signals were measured from distances 2 m to 20 m with same interval and duration as (a)–(e). The signal data collected were compared with the control data collected in a typical outdoor environment with fewer environmental factors, as shown in Figure2e (i.e., air temperature at 28 ◦C and iBeacons installed under the shadow of a tree crown with an internal temperature of 34 ◦C, wind speed of 3.3 m/s recorded by nearby weather station and no pedestrian and vehicle in the experimental environment). Appl. Sci. 2020, 10, x FOR PEER REVIEW 8 of 22 Figure4 and Table1 show the results of the environmental conditions test using two iBeacons where FigureFigure4 presents 4 and Table the 1 (a) show packet the lossresults rate, of the (b) en receivedvironmental RSSI conditions and (c) standard test using deviation two iBeacons of RSSI; and Tablewhere1 comparesFigure 4 presents the overall the (a) packetpacket loss loss rate, rate, (b average) received and RSSI standard and (c) standard deviation deviation of RSSI of RSSI; of 0.5 m to 20 mand measurements. Table 1 compares Comparisons the overall packet were loss made rate, withaverage the and control standard set data,deviation although of RSSI fluctuations of 0.5 m to of the RSSI20 m and measurements. signal stability Comparisons existed were under made normal with conditions.the control set Figure data, although4a indicates fluctuations that the of packetthe loss rateRSSI di andffered signal significantly stability existed between under thenormal control conditions. and some Figure testing 4a indicates environments, that the packet including loss ratehigh temperature,differed pedestriansignificantly tra betweenffic near the iBeaconscontrol and and some mobile testing devices, environments, and pedestrian including traffic blockinghigh temperature, pedestrian traffic near the iBeacons and mobile devices and pedestrian traffic blocking the line of sight between iBeacons and the devices. Table1 also suggests the obstruction by pedestrians the line of sight between iBeacons and the devices. Table 1 also suggests the obstruction by and high temperature resulted in a relatively higher packet loss rate, while the effects of other factors pedestrians and high temperature resulted in a relatively higher packet loss rate, while the effects of were limited.other factors were limited.

Figure 4. Analysis of environmental conditions test on (a) signal packet loss rate, (b) received RSSI and Figure 4. Analysis of environmental conditions test on (a) signal packet loss rate, (b) received RSSI (c) standardand (c) standard deviation deviation of RSSI. of RSSI.

Table 1. Average packet loss rate, RSSI and standard deviation of RSSI.

Environmental Average Packet Loss Standard Deviation Average RSSI (dBm) Condition Rate (%) of RSSI (dBm) Temperature 4.1 75.51 4.94 − Wind 2.2 77.67 3.84 − Tree 1.8 71.87 4.01 − Vehicle traﬃc 2.4 72.87 4.19 − Pedestrian traﬃc 2.8 73.83 4.70 − Blocked by pedestrian 2.9 75.52 6.08 − Control 2.3 73.35 4.70 −

By comparing the RSSI between the environmental conditions in Figure4b and Table1, the high temperature, strong wind and blocking of pedestrians lowered the signal strength when compared with the control set. The standard deviation of the RSSI also indicates the adverse effects at the high temperature and the blocking of the signal by pedestrians. Table1 highlights that the signal stability was more severely affected if pedestrians were blocking the line of sight between the mobile device and the iBeacon in comparison to when the line of sight was not blocked. Our findings showed that signal fluctuation was present in the control data, which were collected from ideal outdoor settings, regardless of environmental conditions. The results suggest the presence of trees and nearby vehicle traffic did not have any negative effects on the signals. The high air temperature led to significant increase of packet loss rate and this slightly affected the signals in terms of RSSI and standard deviation of RSSI. The strong wind reduced the signal strength but did not vary the stability of signals. Pedestrian traffic without blocking the line of sight, led to mild increases of packet loss rate, and did not impact the stability of the iBeacon signals. Pedestrian traffic blocking the line of sight, caused the largest adverse impact on both packet loss rate, RSSI and the standard deviation of RSSI. The effects of the line of sight were far more significant than other environmental factors. When accounting for ways to provide stable and accurate positioning, the results recommend that the deployment of signal-based positioning systems should consider the line of sight as the first priority, as the signal quality and stability are most easily impacted by this factor.

5. Performance Testing Various implementation factors that could affect the BLE signals and the overall positioning accuracy were identified in performance testing. A series of tests was performed, including (1) a deployment position test (ceiling vs. wall), (2) a deployment density test, (3) a fingerprint space interval test, (4) a fingerprint collection time test, (5) a transmission interval test, (6) a venue test and (7) a crowd analysis test; to evaluate the performance of the iBeacon deployments in indoor positioning. Tests were conducted in sequences that allowed findings from earlier tests to be incorporated into later tests. For each test, we collected sets of fingerprint data using the mobile devices as the signal database, with handheld posture and sets of random samples spread across the room as test points. The true positions of the test points were surveyed by steel tape, and these true positions were compared with the positioning results obtained from the tests for evaluation and analysis. The weighted k-nearest neighbour method was employed for all performance tests in this study, for matching the signals collected at test points to the fingerprint database. This method assigns the weighting to the k-nearest neighbour points based on the signal distance, and calculates the coordinates of the test points (x, y) using (1): Pk = wi (xi, yi) (x, y) = i 1 · (1) Pk i=1 wi Appl. Sci. 2020, 10, 7126 10 of 22

where k is the selected number of nearest neighbour, (xi, yi) are the coordinates of the points having the shortest distance with the test point and wi is the weighting calculated by distance:

1 wi = (2) di where di is the Euclidean distance of signals. Based on the findings from our preliminary study, the positioning accuracy is the highest when selecting k equals to 4 in the weighted k-nearest neighbour method. Thus, we used this value for all the performance tests. The fingerprint grid interval was 1 m and the fingerprint collection time was 10 s, except when testing the effects of fingerprint grid interval and collection time on positioning accuracy. The fingerprint data were collected with static status to one direction, facing the blackboard/whiteboard at the front only, because mostAppl. Sci. students 2020, 10, x wouldFOR PEER sitREVIEW in this direction in normal teaching and learning spaces.10 of 22 The testing data wereconducted collected after with the the first same test, i.e., configurations the deploymentas po thesition training test, used data. the signal Tests data conducted collected from after the first test, i.e., theceiling-mounted deployment iBeacons position due test, to the used superior the signalaccuracy data of that collected placement from method. ceiling-mounted The tests were iBeacons due to theconducted superior in accuracy the classrooms of that of placementthe Hong Kong method. Polytechnic The Universi tests werety as conducteddescribed in Table in the 2 classroomsand of shown in Figure 5. the Hong KongRoom Polytechnic ZB214, which University is a typical as classroom described with in general Table 2classroom and shown furniture in Figure and less5. known factors affecting iBeacon signals, was selected as a testbed to evaluate the general influence of deployment configurationsTable to 2. thClassroome positioning used accuracy. for performance Other classrooms testing. (i.e., Rooms ZS1010, V721, Z205 and Z414), with characteristics listed in Table 2, were used for the venue test and crowd analysis test. Room Size Descriptions Test With chairs, long tables Typical classroom (Room Table16.0 m2. Classroom9.5 m used for andperformance walls without testing. (1), (2), (3), (4), (5), (6) ZB214) × windows Room Size Descriptions Test With chairs, long tables, (1), (2), ComputerTypical laboratory classroom Withtwo windowschairs, long on tables one and walls 16.016.0 m m ×14.5 9.5 m (6)(3), (4), (Room(Room ZS1010) ZB214) × wall andwithout 60 desktop windows computers (5), (6) Computer laboratory WithWith chairs, basic long furniture, tables, a two windows 16.0 m × 14.5 m (6) Laboratory(Room (Room ZS1010) V721) 13.0 m 11.7 m onhanging one wall light and fixture 60 desktop and computers (6) × Witha full-height basic furniture, glass panel a hanging light TypicalLaboratory lecture (Room theatre V721) 13.0 m × 11.7 m 248 auditorium seats (6) 15.0 m 19.0 m fixture and a full-height glass panel (6) Typical(Room Z205)lecture theatre × with tablet arms 15.0 m × 19.0 m 24879 auditoriumauditorium seatsseats withwith tablet arms (6) Small lecture(Room theatre Z205) 9.5 m 5.7 m tablet arms with one (6), (7) (RoomSmall lecture Z414) theatre × 79 auditorium seats with tablet arms 9.5 m × 5.7 m window on wall (6), (7) (Room Z414) with one window on wall

(a) (b) (c)

(d) (e)

Figure 5. ExperimentalFigure 5. Experimental environments. environments. (a )(a Typical) Typical classroom (Room (Room ZB214). ZB214). (b) Computer (b) Computer laboratory laboratory (Room ZS1010).(Room ZS1010). (c) Laboratory (c) Laboratory (Room (Room V721). V721). ( d(d)) Typical lecture lecture theatre theatre (Room (Room Z205). ( Z205).e) Small (lecturee) Small lecture theatre (Roomtheatre Z414). (Room Z414).

Room ZB214, which is a typical classroom with general classroom furniture and less known factors affecting iBeacon signals, was selected as a testbed to evaluate the general influence of deployment configurations to the positioning accuracy. Other classrooms (i.e., Rooms ZS1010, V721, Z205 and Z414), with characteristics listed in Table2, were used for the venue test and crowd analysis test.

5.1. Deployment Position Test (Ceiling vs. Wall) The deployment position test was conducted in Room ZB214, and the wall and ceiling placement of iBeacon were deployed and compared, in order to study whether simple placement choices could provide substantial increases in positioning accuracy at little to no extra cost and labour. For the wall deployment,Appl. Sci. 2020 the, 10 iBeacons, x FOR PEER were REVIEW stuck on the wall with tape at 2 m height, while the ceiling deployment11 of 22 involved installing the iBeacons to the ceiling with the location shown in Figure6. The test compared deployment involved installing the iBeacons to the ceiling with the location shown in Figure 6. The the positioningtest compared accuracy the positioning of deployment accuracy heights of deployment of 2 m up heights the wall of 2 versusm up the on wall the ceiling,versus on which the was 2.38 mceiling, high. which Eight was and 2.38 ten m iBeacons high. Eight were and tested ten iBeacons for each were deployment tested for each scheme deployment with the scheme locations with shown in Figurethe locations6. The discrepancy shown in Figure between 6. The thediscrepancy two placement between methodsthe two placement was the methods e ﬀect of was geometry, the effect as the iBeaconsof geometry, wereAppl. located Sci. as 2020 the, 10 iBeacons at, x FOR the PEER edge were REVIEW of located classroom at the for edge wall of classroom placement for because wall placement of the physical because11 of 22 of limitation, the whereasphysical the iBeaconslimitation, could whereas be the evenly iBeacons distributed could be in evenly the classroom distributed for in the classroom ceiling placement. for the ceiling placement.deployment involved installing the iBeacons to the ceiling with the location shown in Figure 6. The test compared the positioning accuracy of deployment heights of 2 m up the wall versus on the ceiling, which was 2.38 m high. Eight and ten iBeacons were tested for each deployment scheme with the locations shown in Figure 6. The discrepancy between the two placement methods was the effect of geometry, as the iBeacons were located at the edge of classroom for wall placement because of the physical limitation, whereas the iBeacons could be evenly distributed in the classroom for the ceiling placement.

(a) (b)

Figure 6. Deployment plans. (a) 8 iBeacons; (b) 10 iBeacons. Figure 6. Deployment plans. (a) 8 iBeacons; (b) 10 iBeacons. (a) (b) The results of deploying iBeacons to the ceiling or wall with 8 or 10 iBeacons, respectively, The results of deploying iBeacons to the ceiling or wall with 8 or 10 iBeacons, respectively, are Figure 6. Deployment plans. (a) 8 iBeacons; (b) 10 iBeacons. are presentedpresented inin FigureFigure 77.. AsAs shown in in Figure Figure 7,7, the the positioning positioning accuracies accuracies from from the theceiling ceiling and andwall wall deploymentdeployment schemes Theschemes results were were of compareddeploying compared iBeacons with with to the thethe cumulative ceilingcumulative or wall distributionwith 8 or 10 iBeacons, function function respectively, (CDF). (CDF). From are From Figure Figure 7, we observed7, we observedpresented a lower a lowerin error Figure error rate 7. As rate when shown when 8in iBeacons 8Figure iBeacons 7, the were were positioning deployeddeployed accuracies on on the thefrom ceiling ceiling the ceiling compared compared and wall with with being being deployeddeployed ondeployment theon the wall, wall, schemes and and the werethe superior supe comparedrior accuracy accuracy with the cumulative of the ceiling ceiling distribution depl deploymentoyment function scheme(CDF). scheme From with Figure with 10 iBeacons 10 iBeacons echoed the7, we results observed obtained a lower error with rate 8 when iBeacons. 8 iBeacons Th weree root-mean-square deployed on the ceiling error compared (RMSE) with for being the ceiling echoed the resultsdeployed obtainedon the wall, withand the 8 supe iBeacons.rior accuracy The of the root-mean-square ceiling deployment scheme error with (RMSE) 10 iBeacons for the ceiling deploymentdeployment wasechoed was also the also lowerresults lower obtained than than that with that of 8 theof iBeacons. the wall wall placementTh placemente root-mean-square method method error for for the(RMSE)the deploymentsdeployments for the ceiling of of both both 8 8 and 10 iBeacons.and 10 iBeacons.deployment was also lower than that of the wall placement method for the deployments of both 8 and 10 iBeacons.

Figure 7. CumulativeFigure 7. Cumulative distribution distribution function func (CDF)tion (CDF) comparison comparison of of the the two two deployment deployment methods methods using 8 and 10 iBeacons, respectively. using 8 and 10 iBeacons, respectively. Figure 7. Cumulative distribution function (CDF) comparison of the two deployment methods The positioning accuracyusing improved 8 and with 10 iBeacons,ceiling placement respectively. due to the reduction of obstructions between the iBeacons and the mobile device. By mounting iBeacons on the ceiling, there were fewer obstructions like people, furniture, hanging light fixtures and computers, etc. This mounting method The positioning accuracy improved with ceiling placement due to the reduction of obstructions allows a more consistent line of sight and enables flexibility in placement geometry, as iBeacons can between bethe placed iBeacons anywhere and theon themobile ceiling device. to ensure By mounbalancedting coverage, iBeacons while on wall-basedthe ceiling, schemes there wereare fewer obstructions like people, furniture, hanging light fixtures and computers, etc. This mounting method allows a more consistent line of sight and enables flexibility in placement geometry, as iBeacons can be placed anywhere on the ceiling to ensure balanced coverage, while wall-based schemes are Appl. Sci. 2020, 10, 7126 12 of 22

The positioning accuracy improved with ceiling placement due to the reduction of obstructions between the iBeacons and the mobile device. By mounting iBeacons on the ceiling, there were fewer obstructions like people, furniture, hanging light fixtures and computers, etc. This mounting method allows a more consistent line of sight and enables flexibility in placement geometry, as iBeacons can be placedAppl. Sci. anywhere 2020, 10, x FOR on PEER the ceiling REVIEW to ensure balanced coverage, while wall-based schemes are confined12 of 22 to the edges of the space. The results confirmed a drop in the positioning accuracy when using wall-basedconfined to schemes, the edges in of particular, the space. poorer The results accuracy co innfirmed larger areas,a drop as in poor the geometrypositioning results accuracy in the when lack ofusing signal wall-based in the centre schemes, as well in as particular, higher susceptibility poorer accuracy to obstructions. in larger areas, If ceilings as poor are geometry too high, orresults ceiling in deploymentthe lack of signal is impracticable in the centre because as well of as the higher environment, susceptibility wall-mounted to obstructions. iBeacons If ceilings should are be placedtoo high, at aor height ceiling higher deployment than the is average impracticable person, because to ensure of thethe lineenvironment, of sight is notwall-mounted blocked as suggestediBeacons should in the environmentalbe placed at a conditionsheight higher test. than the average person, to ensure the line of sight is not blocked as suggested in the environmental conditions test. 5.2. Deployment Density Test 5.2. Deployment Density Test To identify a balance between iBeacon number and accuracy of positioning, different numbers of placementTo identify were a balance tested. between Twelve iBeacons iBeacon werenumber deployed and accuracy in Room of ZB214positioning, on the different ceiling andnumbers their positionsof placement were were recorded. tested. Fingerprint Twelve iBeacons data were were then deployed collected, in Room and the ZB214 test wason the repeated, ceiling withand their one iBeaconpositions removed were recorded. each time Fingerprint until there data were were only threethen iBeaconscollected, left. and The the initialtest was setup repeated, of the iBeacon with one is presentediBeacon removed in Figure each8. time until there were only three iBeacons left. The initial setup of the iBeacon is presented in Figure 8.

FigureFigure 8.8. Deployment plan of 12 iBeaconsiBeacons andand thethe locationslocations ofof fingerprintfingerprint collection.collection. Using more iBeacons can result in greater coverage, with the following caveats. Deployment density Using more iBeacons can result in greater coverage, with the following caveats. Deployment can be increased by walls, furniture, distance and other obstacles, as they could attenuate the signals. density can be increased by walls, furniture, distance and other obstacles, as they could attenuate the With the deployment of different numbers of iBeacons, ranging from 12 to 3 iBeacons in this test, signals. With the deployment of different numbers of iBeacons, ranging from 12 to 3 iBeacons in this the CDF of RMSEs were plotted in Figure9. The results show that positioning accuracy was enhanced test, the CDF of RMSEs were plotted in Figure 9. The results show that positioning accuracy was by increasing the number of iBeacons from 3 to 12 (Figure9). The RMSE was highest with three iBeacons enhanced by increasing the number of iBeacons from 3 to 12 (Figure 9). The RMSE was highest with and diminished gradually until eight iBeacons were deployed. The CDFs of the RMSE in using more three iBeacons and diminished gradually until eight iBeacons were deployed. The CDFs of the RMSE than eight iBeacons were very similar in Figure9. Figure 10 presents the di fference in RMSE using of using more than eight iBeacons were very similar in Figure 9. Figure 10 presents the difference in different numbers of iBeacons. The results indicate that there was a significant improvement in accuracy RMSE using different numbers of iBeacons. The results indicate that there was a significant from 3.44 m to 1.80 m, when the number of iBeacons was increased from three to eight. Using more than improvement in accuracy from 3.44 m to 1.80 m, when the number of iBeacons was increased from eight iBeacons resulted in a minimal increase in accuracy. The improvement of RMSE from 8 iBeacons three to eight. Using more than eight iBeacons resulted in a minimal increase in accuracy. The to 12 iBeacons was 0.26 m, which was reduced from 1.80 m to 1.54 m. Dalkilic et al. [44] explained that improvement of RMSE from 8 iBeacons to 12 iBeacons was 0.26 m, which was reduced from 1.80 m when iBeacons are close to each other (for instance, when there are too many iBeacons), the receiving to 1.54 m. Dalkilic et al. [44] explained that when iBeacons are close to each other (for instance, when mobile phones have difficulties detecting the exact location of the signal sources. Our results suggest there are too many iBeacons), the receiving mobile phones have difficulties detecting the exact that having too many iBeacons not only fails to improve the positioning accuracy significantly, but could location of the signal sources. Our results suggest that having too many iBeacons not only fails to also result in unnecessary overlaps of coverage areas. improve the positioning accuracy significantly, but could also result in unnecessary overlaps of coverage areas. Appl. Sci. Sci. 20202020,, 1010,, x 7126 FOR PEER REVIEW 13 of 22 Appl. Sci. 2020, 10, x FOR PEER REVIEW 13 of 22

Figure 9. CDF comparison of deploying different number of iBeacons. FigureFigure 9 9.. CDFCDF comparison comparison of of deploying deploying different diﬀerent number number of of iBeacons. iBeacons.

FigureFigure 10 10.. Root-mean-squareRoot-mean-square error error (RMSE) (RMSE) of of deploying deploying different different number of iBeacons. Figure 10. Root-mean-square error (RMSE) of deploying different number of iBeacons. From the experimental results, eight iBeacons were appropriate for the deployment in balancing From the experimental results, eight iBeacons were appropriate for the deployment in balancing the iBeaconFrom the hardware experimental required results, and eight the RMSE iBeacons error, were but appropriate this number for was the dependent deployment upon in balancing the room the iBeacon hardware required and the RMSE error, but this number was dependent upon the room thesize. iBeacon In considering hardware the required testing environment and the RMSE of aerror, classroom but this with number an area was of 152dependent m2, the averageupon the spacing room size. In considering the testing environment of a classroom with an area of 152 m2, the average size.between In considering iBeacons was the 4.4 testing m, the environmen context wherebyt of a this classroom spacing waswithrecommended an area of 152 in m consideration2, the average of spacing between iBeacons was 4.4 m, the context whereby this spacing was recommended in spacingdeployment between density. iBeacons was 4.4 m, the context whereby this spacing was recommended in consideration of deployment density. consideration of deployment density. 5.3. Fingerprint Space Interval Test 5.3. Fingerprint Space Interval Test 5.3. Fingerprint Space Interval Test The viability of increasing fingerprint grid spacing to reduce labour at a negligible cost to accuracy The viability of increasing fingerprint grid spacing to reduce labour at a negligible cost to was alsoThe studied.viability Theof increasing same configuration fingerprint with grid the spacing deployment to reduce density labour test, usingat a 12negligible iBeacons cost on theto accuracy was also studied. The same configuration with the deployment density test, using 12 accuracyceiling, was was conducted also studied. in Room The ZB214, same andconfigur the fingerprintation with datathe weredeployment collected density at 1 m gridstest, asusing shown 12 iBeacons on the ceiling, was conducted in Room ZB214, and the fingerprint data were collected at 1 iBeaconsin Figure 8on. Grid the ceiling, spacing was was conducted compared, in ranging Room ZB from214, 1 mand to the 10 mfingerprint at 1 m intervals. data were collected at 1 m grids as shown in Figure 8. Grid spacing was compared, ranging from 1 m to 10 m at 1 m intervals. m gridsExperimenting as shown in Figure with fingerprint 8. Grid spacing grid widthswas compared, represents ranging an opportunity from 1 m to for 10 those m at implementing1 m intervals. Experimenting with fingerprint grid widths represents an opportunity for those implementing BLE-basedExperimenting systems towith save fingerprint time and manpower.grid widths The repr resultsesents ofan collecting opportunity fingerprint for those at implementing different space BLE-based systems to save time and manpower. The results of collecting fingerprint at different space BLE-basedintervals with systems 12 iBeacons to save installedtime and on manpower. the ceiling The are resu shownlts of in collecting Figure 11. fingerp The CDFrint from at different 9 m and space 10 m intervals with 12 iBeacons installed on the ceiling are shown in Figure 11. The CDF from 9 m and 10 intervalsgrids were with very 12 similar, iBeacons having installed the lowest on the accuracy, ceiling are which shown was in over Figure 5 m 11. in overallThe CDF RMSE. from This 9 m wasand the10 m grids were very similar, having the lowest accuracy, which was over 5 m in overall RMSE. This mresult grids of were a lack very of fingerprint similar, having collection the pointslowest in accuracy, the room, which since thesewas over two intervals5 m in overall collected RMSE. signals This at was the result of a lack of fingerprint collection points in the room, since these two intervals collected wasone the point result as database of a lack only.of fingerprint collection points in the room, since these two intervals collected signals at one point as database only. signals at one point as database only. When the fingerprint became denser by reducing the interval from 7 m to 2 m, the accuracy When the fingerprint became denser by reducing the interval from 7 m to 2 m, the accuracy improved significantly. The 1 m grid barely outperformed the 2 m grid in terms of positioning improved significantly. The 1 m grid barely outperformed the 2 m grid in terms of positioning accuracy. In the 2 m grid, the RMSE was 1.70 m, which was very close to the result of a 1 m grid at accuracy. In the 2 m grid, the RMSE was 1.70 m, which was very close to the result of a 1 m grid at 1.54 m RMSE. The 1 m fingerprint grid had the advantage of producing the highest accuracy, making 1.54 m RMSE. The 1 m fingerprint grid had the advantage of producing the highest accuracy, making Appl. Sci. 2020, 10, x FOR PEER REVIEW 14 of 22

it a preferred choice when time and manpower are not limiting factors. However, this setting required double effort in collecting the fingerprints compared to the 2 m grid. The 2 m grid was the most balanced grid width choice with regards to accuracy-labour-time trade-offs. If a large-scale deployment was to take place, this might allow significant savings of time and manpower. Figure 11 also suggests that even-number intervals performed better than odd-number intervals for most cases, e.g., 8 m was better than 7 m and 6 m was better than 5 m. The 3 m grid result was substantially less Appl.accurate Sci. 2020 than, 10 ,2 7126 m and 4 m grid. If the 2 m interval is unachievable because of the limited time14 of or 22 labour resources, selecting even-number intervals can slightly improve the positioning accuracy.

Figure 11. CDF comparison of collecting fingerprint at different space intervals. Figure 11. CDF comparison of collecting fingerprint at different space intervals. When the fingerprint became denser by reducing the interval from 7 m to 2 m, the accuracy 5.4. Fingerprint Collection Time Test improved significantly. The 1 m grid barely outperformed the 2 m grid in terms of positioning accuracy. In theThe 2 m effect grid, theof shorter RMSE wasfingerprint 1.70 m, whichcollection was time very closeon positioning to the result accuracy of a 1 m and grid time at 1.54 saved m RMSE. was Theevaluated. 1 m fingerprint Similar to grid the hadfingerprint the advantage spacing of test, producing 12 iBeacons the highest were deployed accuracy, on making the ceiling it a preferred and the choicefingerprints when were time andcollected manpower at 1 m are spacing not limiting in Room factors. ZB214. However, iBeacon this signals setting were required collected double at every effort inpoint collecting for 10 thes and fingerprints the data were compared trimmed to the progressiv 2 m grid.ely The to 2 mshorter grid wascollection the most times, balanced at 1 s grid intervals, width choiceuntil 1 with s. The regards positioning to accuracy-labour-time result, the relationsh trade-oipff s.between If a large-scale fingerprint deployment collection was time to take and place, the thispositioning might allow accuracy significant were analysed. savings of time and manpower. Figure 11 also suggests that even-number intervalsTesting performed of fingerprint better thancollection odd-number time was intervals conducted for mostwith cases,the same e.g., 8configuration, m was better thanusing 7 12 m andiBeacons 6 m was installed better on than ceiling. 5 m. The The 3 CDF m grid comparison result was of substantially the collection less time accurate by RMSE, than 2 munder and different 4 m grid. Ifcollection the 2 m intervaltimes, is unachievableshown in Figure because 12. ofAs the the limited collection time ortime labour increased, resources, the selectingRMSE ofeven-number positioning intervalsaccuracy candecreased. slightly The improve RMSE the decreased positioning substantially accuracy. from a collection time of 1 s to 3 s, suggesting that very short collection times cause large uncertainties in positioning. There was a significant 5.4.reduction Fingerprint in variance Collection at a Timecollection Test time of 4 s, and similar positioning errors were found in collection time Theof 4 esff toect 10 of s. shorter Although fingerprint improvements collection were time observed on positioning in the error accuracy rate with and every time savedadditional was evaluated.second of collection Similar to time, the fingerprintimprovements spacing in RMSE test, 12 from iBeacons 6 s to were10 s were deployed extremely on the marginal, ceiling and with the a fingerprintsdrop from 1.53 were m to collected 1.49 m. at 1 m spacing in Room ZB214. iBeacon signals were collected at every point for 10 s and the data were trimmed progressively to shorter collection times, at 1 s intervals, until 1 s. The positioning result, the relationship between fingerprint collection time and the positioning accuracy were analysed. Testing of fingerprint collection time was conducted with the same configuration, using 12 iBeacons installed on ceiling. The CDF comparison of the collection time by RMSE, under different collection times, is shown in Figure 12. As the collection time increased, the RMSE of positioning accuracy decreased. The RMSE decreased substantially from a collection time of 1 s to 3 s, suggesting that very short collection times cause large uncertainties in positioning. There was a significant reduction in variance at a collection time of 4 s, and similar positioning errors were found in collection time of 4 s to 10 s. Although improvements were observed in the error rate with every additional second of collection time, improvements in RMSE from 6 s to 10 s were extremely marginal, with a drop from 1.53 m to 1.49 m. Appl. Sci. 2020, 10, 7126 15 of 22 Appl. Sci. 2020, 10, x FOR PEER REVIEW 15 of 22

Figure 12. CDF comparison of the collection time by RMSE under different collection times (in seconds). Figure 12. CDF comparison of the collection time by RMSE under different collection times (in seconds).The fingerprint collection time is also a parameter that can be altered to either save time or increase accuracy, depending on the limitations of the deployment. Our results demonstrated that The fingerprint collection time is also a parameter that can be altered to either save time or a fingerprint collection time of at least 6 s was required for satisfactory accuracy, for use in indoor increase accuracy, depending on the limitations of the deployment. Our results demonstrated that a deployments. Whereas improvement in accuracy was observed beyond 6 s, it was almost imperceptible fingerprint collection time of at least 6 s was required for satisfactory accuracy, for use in indoor compared to significant leaps in accuracy when moving from 1 s collection time to 2 s, or from 2 s to deployments. Whereas improvement in accuracy was observed beyond 6 s, it was almost 3 s. When deploying iBeacon positioning systems, our recommendation is a collection time of at least imperceptible compared to significant leaps in accuracy when moving from 1 s collection time to 2 s, 6 s at each grid point, as no discernible positive effect was seen for collection times over 10 s. or from 2 s to 3 s. When deploying iBeacon positioning systems, our recommendation is a collection time5.5. Transmissionof at least 6 Intervals at each Test grid point, as no discernible positive effect was seen for collection times over 10 s. We aimed at determining the accuracy performance differences between different transmission 5.5.intervals Transmission and balance Interval accuracy Test with battery usage. An identical setup in Room ZB214 with a 10 s collection time was used in this test (Figure8), and the transmission interval e ffects on accuracy were testedWe by aimed altering to thedetermine settings the of accuracy the iBeacons. performanc Three transmissione differences intervalsbetween ofdifferent 100 ms, transmission 417 ms and intervals800 ms were and tested.balance Transmissionaccuracy with intervals battery usage. of over An 1000 identical ms were setup not in used Room because ZB214 of with potentially a 10 s collectionunacceptable time delays was used for real-timein this test location-based (Figure 8), and service the transmission applications, interval such aseffects adaptive on accuracy learning were and testedexhibitions. by altering For thethe settings iBeacon of used, the iBeacons. 417 ms was Thre thee transmission default transmission intervals of interval, 100 ms, and 417 100ms and ms was800 msselected were as tested. a potential Transmission improvement intervals over the over default 1000 in ms terms were of positioningnot used accuracy.because of potentially unacceptableAs the increase delays offor the real-time transmission location-based interval reduces service the applications, power consumption such as adaptive of iBeacons, learning we aimed and exhibitions.to locate a balance For the between iBeacon accuracy used, 417 and ms power was consumption.the default transmis Thus, thesion experiment interval, wasand conducted100 ms was in selectedthe same as room a potential (ZB214) improvement with 12 iBeacons. over The the hypothesisdefault in terms in this of test positioning was that reducing accuracy. the transmission intervalAs the would increase improve of the the transmission positioning accuracy,interval reduces due to the a greater power number consumption of Bluetooth of iBeacons, signals we in aimeda fixed to period locate of a time. balance We expectedbetween thataccuracy a short and transmission power consumption. interval (i.e., Thus, 100 ms) the would experiment have better was conductedperformance in thanthe same long room transmission (ZB214) intervalswith 12 iBeacons (i.e., 417. ms The and hypothesis 800 ms)because in this test ofthe was high that number reducing of thesignal transmission packets. Yet, interval the CDF would of RMSE improve did notthe supportpositioning this hypothesisaccuracy, due (Figure to a 13 greater). The CDFnumber curves of Bluetoothof 100 ms signals and 800 in ms a fixed interval period were of very time. similar, We expected and most that of a the short parts transmission overlapped. interval The di ff(i.e.,erences 100 ms)between would their have overall better RMSE performance were only than 0.01 long m, transmi where thession RMSE intervals of 100 (i.e., ms 417 interval ms and was 800 1.81 ms) m because and the ofRMSE the high of 800 number ms was of 1.80 signal m. Aspackets. shown Yet, in Figurethe CD 13F ,of the RMSE interval did ofnot 417 support ms outperformed this hypothesis significantly (Figure 13).and The the overallCDF curves RMSE wasof 100 only ms 1.54 and m. 800 ms interval were very similar, and most of the parts overlapped. The differences between their overall RMSE were only 0.01 m, where the RMSE of 100 ms interval was 1.81 m and the RMSE of 800 ms was 1.80 m. As shown in Figure 13, the interval of 417 ms outperformed significantly and the overall RMSE was only 1.54 m. Appl. Sci. 2020, 10, 7126 16 of 22 Appl. Sci. 2020, 10, x FOR PEER REVIEW 16 of 22

Figure 13 13.. CDFCDF comparison comparison of of the the three three di fferentﬀerent transmission intervals.

One of the main advantages of the BLE protocol over standard Bluetooth is its lower power consumption over the same range, en enablingabling battery life extensions of months to years. When altering the transmission interval of the iBeacons, the battery life is aaffected.ffected. Frequent transmissions lead to shorter battery life, whereas less frequent transmissions prolong it. Our results show that decreasing the transmission intervalinterval toto 100 100 ms ms was was actually actually detrimental detrimental to to the the positioning positioning accuracy, accuracy, relative relative to the to thedefault default setting setting of 417of 417 ms. ms. Furthermore, Furthermore, this this resulted resulted in in significantly significantly faster faster depletion depletion ofof thethe iBeacon battery. We recommendrecommend that that the the transmission transmission interval interval be be set set at at 417 417 ms, ms, as as this this ensures ensures a long a long battery battery life lifeand and provides provides two signaltwo signal packets packets per second, per second, which canwhich fulfil can the fulfil requirements the requirements of mostusage of most scenarios. usage scenarios. 5.6. Venue Test

5.6. VenueDue toTest the potential influences of environmental factors (e.g., electronic equipment, furniture and glass walls) on the quality and availability of the iBeacon signal, testing in various learning space venues Due to the potential influences of environmental factors (e.g., electronic equipment, furniture was conducted to ensure ample coverage and performance. The purposes of the test was to assess the and glass walls) on the quality and availability of the iBeacon signal, testing in various learning space accuracy differences between different classrooms and to acquire useful data on implementing indoor venues was conducted to ensure ample coverage and performance. The purposes of the tests were to location-based systems for different venues. Classrooms with characteristics (i.e., Rooms ZS1010, V721, assess the accuracy differences between different classrooms and to acquire useful data on Z205 and Z414) were used for the venue test. Room ZS1010 is a computer laboratory with 60 desktop implementing indoor location-based systems for different venues. Classrooms with characteristics computers, which was used to investigate the effects of electronic interference on the iBeacon signals. (i.e., Rooms ZS1010, V721, Z205 and Z414) were used for the venue test. Room ZS1010 is a computer Room V721 is a laboratory with a full-height glass panel which is known to induce multipath effects. laboratory with 60 desktop computers, which was used to investigate the effects of electronic In addition, two types of common lecture theatres were also tested (Rooms Z205 and Z414) for better interference on the iBeacon signals. Room V721 is a laboratory with a full-height glass panel which is evaluation of positioning accuracy in learning spaces. The data collected from each classroom were known to induce multipath effects. In addition, two types of common lecture theatres were also tested compared with the results from a typical classroom in Room ZB214 to investigate the detrimental effect (Rooms Z205 and Z414) for better evaluation of positioning accuracy in learning spaces. The data on positioning accuracy. collected from each classroom were compared with the results from a typical classroom in Room A typical classroom (Room ZB214), a computer laboratory with 60 desktop computers (Room ZS1010), ZB214 to investigate the detrimental effect on positioning accuracy. a laboratory with a full-height glass panel (Room V721), a typical lecture theatre (Room Z205) and A typical classroom (Room ZB214), a computer laboratory with 60 desktop computers (Room a small lecture theatre with one window on the wall (Room Z414) were chosen to assess the differences ZS1010), a laboratory with a full-height glass panel (Room V721), a typical lecture theatre (Room in positioning accuracy caused by indoor environmental factors. Since the rooms are in different Z205) and a small lecture theatre with one window on the wall (Room Z414) were chosen to assess dimensions, the percentage errors are compared in Table3 rather than comparing the CDF of the RMSE. the differences in positioning accuracy caused by indoor environmental factors. Since the rooms are in different dimensions, the percentage errors are compared in Table 3 rather than comparing the CDF of the RMSE. Table 3 shows that Rooms Z205, ZB214 and Z414, a typical lecture theatre, a typical classroom, and a small lecture theatre, respectively, which did not contain an abundance of computers, windows, and electronic equipment, had the lowest error rates of 7.81%, 8.26% and 9.93%, respectively. The computer laboratory (Room ZS1010) had a higher percentage error than the three rooms mentioned above. The difference between the computer laboratory and the others was the existence of 60 desktop computers. Computers and other electronic equipment generate an electromagnetic field which can affect the BLE signals. In the study conducted by Dalkilic et al. [44], iBeacons were affected by the computer, and the estimated distance from the mobile device to the Appl. Sci. 2020, 10, 7126 17 of 22

Table 3. RMSE and percentage error results of the venue test.

Longest Diagonal of Room RMSE (m) Percentage Error (%) Room (m) Typical classroom 18.6 1.54 8.26 (Room ZB214) Computer laboratory 21.6 2.23 10.35 (Room ZS1010) Laboratory (Room V721) 17.5 2.17 12.41 Typical lecture theatre 24.2 1.89 7.81 (Room Z205) Small lecture theatre 11.1 1.10 9.93 (Room Z414)

Table3 shows that Rooms Z205, ZB214 and Z414, a typical lecture theatre, a typical classroom, and a small lecture theatre, respectively, which did not contain an abundance of computers, windows, and electronic equipment, had the lowest error rates of 7.81%, 8.26% and 9.93%, respectively. The computer laboratory (Room ZS1010) had a higher percentage error than the three rooms mentioned above. The difference between the computer laboratory and the others was the existence of 60 desktop computers. Computers and other electronic equipment generate an electromagnetic field which can affect the BLE signals. In the study conducted by Dalkilic et al. [44], iBeacons were affected by the computer, and the estimated distance from the mobile device to the iBeacons became more accurate if the computers were removed. Our results also support the finding that the presence of computer equipment lowers iBeacon positioning accuracy. The laboratory with the full-height glass panel (Room V721) provided the worst positioning error, 12.41%. The reflective materials, e.g., the coatings on a glass panel, can cause a multipath effect where the iBeacons could be easily reflected by the glass coatings. As stated by Huh and Seo [45], Bluetooth Beacon signals have a multipath problem, as the signals are reflected several times before reaching the receiver, causing uncertainty in distance estimation and positioning. Our results demonstrated that the wall materials of the room could contribute to the error on measurement, and the existence of reflective materials adversely affected the positioning when compared with other indoor environment settings. The findings from this test support that positioning accuracy varies greatly according to the equipment, obstructions, and amount of reflective materials present in the indoor environment. The installation location of iBeacons should be tested to reduce the effects of interference from electromagnetic signals and the multipath effects.

5.7. Crowd Analysis Test As our usage scenario involves deployment in university lectures, labs and classrooms, it is important to determine the effects of crowds of people on the availability, stability and quality of the iBeacon signal. Since a human body is composed of approximately 70% water, it interferes with radio signals in the 2.4 GHz spectrum in which iBeacon operates [46]. Crowd analysis test was conducted in Room Z414 by comparing positioning results for the case with and without students in the room. The tests followed the same procedures for fingerprint collection, but two sets of random test points were collected. One set was collected in a classroom with 22 students, and another set was tested in an empty room. Two sets of data were then analysed for accuracy. The negative impact of the pedestrian was evaluated in the previous outdoor environmental conditions test, and the results suggest the human body attenuates wireless signals. The influence of a crowd in a room was tested in this experiment by mounting the iBeacons on the ceiling to enhance the line of sight for better signal transmission. Figure 14 compares the CDF of RMSE results from the classroom with 22 students and the classroom without students. The positioning performance of the empty room was better than that of the occupied classroom. The overall RMSE increased from 1.10 m to 1.23 m, with an increment error rate of 1.17% when the room was occupied with students. Appl. Sci. 2020, 10, 7126 18 of 22 Appl. Sci. 2020, 10, x FOR PEER REVIEW 18 of 22

Figure 14. CDF comparison of classroom with/without students. Figure 14. CDF comparison of classroom with/without students. We anticipated that the density of students in classrooms would attenuate the signal to a certain 6.extent. Discussion However, the iBeacon placement tests confirmed the superiority of ceiling placement scheme overThis wall-based study presented scheme, and the demonstratedresults of experiments that the negativeconducted eff ectfor ofthe the performance human body evaluation on positioning of the deploymentaccuracy performance of iBeacons could for posi be mostlytioning negated purposes. by increasingDuring robustne the liness of testing, sight propagation, positioning gainedaccuracy, by signalceiling availability placement ofand the stability iBeacons. were We assessed recommend unde thatr different any concerns environmental regarding conditions. the attenuation The results of the ofsignal the outdoor by a crowd robustness be allayed test by demonstrated adopting a ceiling that the placement maximum scheme. signal coverage of up to 133 m was relatively consistent, despite different mobile devices were used, and the signal was stable until 80 m.6. DiscussionThis test also evaluated the stability of the iBeacon signals under different environmental conditions,This study indicating presented the thepresence results of of trees experiments and near conductedby vehicle for traffic the performancecaused an ignorable evaluation effect of the to thedeployment iBeacons. of The iBeacons conditions for positioning of high temperatur purposes.e, During strong robustness wind and testing, pedestrian positioning traffic accuracy,without blockingsignal availability the line of and sight stability induced were minor assessed discrepancy under ditoff theerent signal. environmental A significant conditions. adverse effect The resultson the signalsof the outdoor was found robustness when the test line demonstrated of sight between that the a smart maximum device signal and coveragethe iBeacon of upwas to blocked 133 m was by pedestrians.relatively consistent, Thus, preserving despite di thefferent line mobileof sight devicesbetween were the iBeacons used, and and the the signal receiver was stable is the untilkey factor 80 m. inThis enabling test also steady evaluated signal the availability. stability of the iBeacon signals under different environmental conditions, indicatingThe performance the presence testing of trees examined and nearby various vehicle para traffimeters,c caused determined an ignorable the e fftrade-offsect to the iBeacons.between accuracyThe conditions and other of high factors temperature, and developed strong winda series and of pedestrian recommendations traffic without for indoor blocking BLE the iBeacon line of deploymentsight induced (Table minor 4). discrepancy The results to suggest the signal. installing A significant iBeacons adverse to the eceilingffect on instead the signals of the was wall, found as ceilingwhen the placement line of sight has better between geometry a smart and device maximises and the the iBeacon line of wassight blocked by avoiding by pedestrians. the obstructions. Thus, Althoughpreserving more the line iBeacons of sight deployed between would the iBeacons provide and high theer positioning receiver is the accuracy, key factor there in is enabling still a concern steady ofsignal cost. availability. Our results recommend an average spacing of 4.4 m for iBeacon deployment, which can balanceThe the performance positioning testingaccuracy examined and the number various of parameters, iBeacons. Appropriate determined transmission the trade-o fftimes between setting ofaccuracy iBeacon and can otheralso reduce factors the and cost developed of iBeacons, a seriesby extending of recommendations the battery life, for as indoorthe setting BLE of iBeacon 417 ms intervaldeployment outperformed (Table4). 100 The ms results and 800 suggest ms in installing this study. iBeacons The training on the phase ceiling of insteadiBeacon ofpositioning the wall, alwaysas ceiling involves placement the has collection better geometry of fingerprints, and maximises which the is linea labo of sightur and by avoidingtime-intensive the obstructions. task. By consideringAlthough more the iBeaconscollection deployed time and would outcome, provide collecting higher positioningthe signal database accuracy, at there the is2 stillm grid a concern interval of withcost. 6 Our s to results 10 s at recommendeach point is an recommended. average spacing The of venue 4.4 m test for iBeaconsuggests deployment, that the existence which of can electronic balance devices/equipmentthe positioning accuracy and the and reflective the number materials of iBeacons. would Appropriate cause unfavourable transmission positioning time setting performance of iBeacon becausecan also ofe reduce interference the cost and of iBeacons, the multipath by extending effects, but the these battery negative life, as effects the setting are unavoidable of 417 ms intervaland the installationoutperformed location 100 ms of andiBeacons 800 msshould in this be study.evaluated The empirically. training phase of iBeacon positioning always involves the collection of fingerprints, which is a labour and time-intensive task. By considering the collection time and outcome, collecting the signal database at the 2 m grid interval with 6 s to 10 s at each point is recommended. The venue test suggests that the existence of electronic devices/equipment and the reflective materials would cause unfavourable positioning performance because of the interference

Appl. Sci. 2020, 10, 7126 19 of 22 and multipath eﬀects, but these negative eﬀects are unavoidable and the installation location of iBeacons should be evaluated empirically.

Table 4. Recommendations for iBeacon deployment.

Performance Test Trade-off Recommendations Ease of deployment/ Deployment position (ceiling vs. wall) Ceiling maintenance-accuracy Deployment density Cost vs. accuracy Average spacing of 4.4 m Fingerprint space interval Time vs. accuracy 2 m Fingerprint collection time (1–10 s) Time vs. accuracy 6–10 s Transmission interval (100, 417, Battery life vs. accuracy 417 ms 800 ms) Select suitable location for Different venues (equipment, building Generalisability vs. accuracy installation to reduce interference materials, etc., leading to attenuation) and multipath effects Crowd analysis (effect of human body) N/A Maximise line of sight propagation

7. Conclusions In order to deploy iBeacons in physical learning spaces sustainably, this study performed robustness testing and performance testing to evaluate the signal stability and positioning accuracy under different deployment settings. The robustness testing indicated the iBeacon signals are relatively stable when the distance between the transmitter and receiver is shorter than 80 m, with the preservation of the line of sight. The results from performance testing recommend future deployment on the ceiling, with a grid density of 4.4 m, configurated at a transmission interval of 417 ms. A time for each of the fingerprint collection point, from 6 s to 10 s, is preferred, and the results suggest that the spacing should be 2 m. Although recommendations are made in this study, there are still some limitations. First, the experiments conducted in this study used only two Android phones for data collection, and the smart devices with different BLE chips had different sensitivities on the signal receptions. The performance of iBeacons from different brands is also another consideration, as only Brightbeacon was employed in this study. The signal transmission power and interval, stability of signals and the battery life of other brand products may differ from the iBeacons we used, and the recommendations may deviate from the current results. In addition, the number of testing sites was limited. Ideally, the tests should be conducted in various room sizes, with variations in ceiling height, different building materials and diverse densities. However, these would have caused unlimited combinations of situations for testing. Exceptions may arise where a certain parameter leads to better positioning performances. Signal attenuation is present, even in ideal, controlled environments. There are also negative impacts as a result of obstructions, geometry, and iBeacon numbers in other environments. Furthermore, the introduction of the BLE 5.1 standard enhances the location-based services with the information of the angle of arrival and angle of departure in addition to the RSSI values [47], and the positioning accuracy can achieve submeter level [48]. The results from this study provide a fundamental basis for iBeacon deployment and positioning with a combination of angle of arrival and RSSI in the near future. For future study, experiments will be conducted with diverse combinations of parameters. Both iOS and Android devices will be used in the data collection phase with different brands of iBeacons, which are expected to have different characteristics and performances than the one used in this study. The same set of performance tests will be conducted in different types of classrooms with variations in size, ceiling height and density of people. In addition, the interrelationship between the deployment factors will be investigated and analysed, since the factors are posited to be intercorrelated. These experiments will optimise iBeacon deployment in physical learning spaces and offer a general guideline for iBeacon deployment in the future. Appl. Sci. 2020, 10, 7126 20 of 22

Author Contributions: Conceptualization, C.Y.T.K., M.S.W., R.K. and E.M.; Data curation, G.X.; Formal analysis, C.Y.T.K., M.S.W. and G.X.; Funding acquisition, M.S.W., R.K. and E.M.; Investigation, C.Y.T.K., M.S.W. and S.G.; Methodology, C.Y.T.K., M.S.W. and E.M.; Project administration, M.S.W.; Software, G.X.; Visualization, C.Y.T.K. and G.X.; Writing—Original draft, C.Y.T.K., M.S.W., S.G. and F.Y.Y.W.; Writing—Review & editing, R.K., D.C.W.C., G.X. and E.M. All authors have read and agreed to the published version of the manuscript. Funding: This project was supported in part by “New Mobile Learning Scenarios Enabler—Development of Location-based Driven Application for Innovative and Technology-assisted Teaching and Learning Using iBeacon Technology” from the eLearning and Blended Learning Development Fund 2014–17, the Hong Kong Polytechnic University, Hong Kong; and “Augmenting Physical Learning Spaces with Location-based Services Using iBeacon Technology for Engaging Learning Experiences”, UGC Funding Scheme for Teaching and Learning Related Proposals (2016–19 Triennium), University Grants Committee, Hong Kong. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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