=Paper=
{{Paper
|id=Vol-3919/short8
|storemode=property
|title=Multisource-based Cloud-Native Hybrid Positioning Platform for Emergency Location Service
|pdfUrl=https://ceur-ws.org/Vol-3919/short8.pdf
|volume=Vol-3919
|authors=Dongwook Choi,Chanyeong Park,Chaewon Lee
|dblpUrl=https://dblp.org/rec/conf/ipin/Choi1PL24
}}
==Multisource-based Cloud-Native Hybrid Positioning Platform for Emergency Location Service==
https://ceur-ws.org/Vol-3919/short8.pdf
Multisource-based Cloud-Native Hybrid Positioning
Platform for Emergency Location Service
Dongwook Choi1,∗, Chanyeong Park1 and Chaewon Lee1
1
Korea Telecom, Geumto-ro 32, Sujeong-gu, Seongnam-si, Gyeonggi-do, Republic of Korea
Abstract
In this paper, we present work in progress of the multisource-based cloud-native hybrid positioning
platform to improve the quality of emergency location service by Korea Telecom. Since 2019, the
Korea Communications Commission has publicly announced the test results of the MNOs emergency
location service’s positioning quality, which are classified by three positioning methods, GPS, Wi-Fi,
and base station, in terms of accuracy and time. The main guideline for positioning accuracy is as
follows: 50 metres horizontal location accuracy and 30 seconds total positioning time limit. Recently,
followed by the Federal Communications Commission guideline of vertical positioning requirements,
3 metres z-axis with 90% probability is challenged by many stakeholders. To achieve these quality
requirements for emergency location service, Korea Telecom is setting a new challenging accuracy
target and working to provide enhanced emergency location services from the perspective of an end-
to-end location platform using a cloud-native hybrid positioning platform.
Keywords
emergency location service, smartphone, hybrid positioning, cloud-native, platform 1
1. Introduction
To obtain faster and more accurate location information for emergency rescue, Assisted-Global
Navigation Satellite System (A-GNSS) positioning from the caller's mobile phone [1] and
Wireless-Fidelity (Wi-Fi) access point signals are widely used indoors and outdoors [2, 3]. In
the US, the Federal Communications Commission (FCC) has proposed enhanced regulations for
911 emergency location-based services, requiring a horizontal positioning accuracy of 50 metres
with a total positioning time of 30 seconds and a vertical positioning accuracy of 3 metres
indoors [4, 5]. As a technical alternative to meet the enhanced vertical positioning accuracy
requirements, a method for estimating vertical location information using the barometric
pressure sensor of mobile phones has been studied [6, 7]. Korea Telecom (KT) is developing a
hybrid positioning platform based on multisource of smartphone to provide emergency
location-based services. KT's application modernisation with a cloud-native hybrid positioning
platform can be a catalyst for the rapid commercialisation of more diverse positioning methods
for emergency location services.
Proceedings of the Work-in-Progress Papers at the 14th International Conference on Indoor Positioning and Indoor
Navigation (IPIN-WiP 2024)
∗
Corresponding author.
matthew.choi@kt.com (D. Choi); chanyeong.park@kt.com (C. Park); lee.chaewon@kt.com (C. Lee)
0000-0003-2020-9883 (D. Choi); 0009-0003-2312-1868 (C. Park); 0009-0009-1455-8498 (C. Lee)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
2. Emergency Location-based Service in South Korea
Unlike the US and EU approaches to emergency location service, in South Korea, the request
for emergency location service is initiated by the public safety answering point. The location
information of the requestor's device is obtained through Mobile Network Operator (MNO) in
what is commonly referred to as the Network Initiated (NI) method in the Open Mobile Alliance
(OMA) Secure User Plane Location (SUPL) and 3rd Generation Partnership Project (3GPP) LTE
Positioning Protocol (LPP) standard [8, 9]. In addition, the Korea Communications Commission
(KCC) has been conducting its own annual evaluation of the quality of emergency location
service since 2019 and disclosing the results to the media.
2.1. Emergency Location Positioning Procedures
Emergency location services are involved in many system entities. To make a brief description
of currently provided emergency location service in South Korea, figure 1 shows conceptual
architecture of emergency location service configuration [10]. The architecture is consisted of
four entities: Public Safety Answering Point (PSAP), Location Manager, Positioning System and
device. PSAP is operated by government, for instance, police department or fire department.
PSAP performs answering emergency request calls and dispatches. To make efficient dispatches,
location information of caller is required. PSAP is interfaced with MNO to acquire the caller’s
immediate location information. Location manager and positioning system as shown in figure
1 are operated by MNO to make user’s immediate location information.
Figure 1: End to end emergency location service entities.
In brief demonstration, emergency location service is delivered as follows:
1. PSAP requests user’s location information to MNO
2. MNO receives PSAP request
3. Location manager triggers positioning data
4. Positioning system wakes User Equipment (UE)
5. UE performs raw data measurement for positioning
6. Measured raw data is delivered to positioning system
7. Positioning system estimates user’s location
8. Location manager response user’s location to PSAP
2.2. Positioning Quality Assessment for Emergency Location Service
In order to support rapid and accurate rescue activities of first responders, the KCC has been
measuring the quality of emergency rescue location information since 2019 and disclosing the
results to encourage mobile network operators to invest in emergency rescue precision
positioning technology and improve its quality.
The evaluation items are as follows:
1. Location standard satisfaction rate: The percentage of the provided location information
that meets the distance error standard (within 50m) and location response time standard
(within 30 seconds)
2. Location accuracy: The distance error between the actual location of the rescue point
and the location information provided by measuring it
3. Location response time: The time it takes to receive location information from the time
the rescue organisation requests it from the carrier.
In 2024, the KCC publicly announced that, as a result of this quality measurement, the
distance (within 50 metres) and response time (within 30 seconds) standards of the three mobile
operators were met at 98.2% to 97.7% for GPS and 93.6% to 96.8% for Wi-Fi, which showed an
increase from the previous year, but overall at a good level. In addition, the location accuracy,
which refers to the distance error between the actual location of the measurement point and
the positioned location, was significantly improved from 21.6m to 11.3m for GPS, 34.2m to
20.1m for Wi-Fi, and 107m to 52.3m for base station [11].
3. Barometric Assist Data based Hybrid Positioning Technique
Multisource-based hybrid positioning technologies are being researched and developed to
improve the accuracy of emergency rescue location information. For rapid emergency rescue
operations, the FCC guides regulations on the use of vertical location information [5].
Technologies using Wi-Fi access points, beacons, etc. have been examined to provide vertical
location information [12, 13]. Recently, various experiments have been made to combine and
utilise barometric sensor data from smartphones [6, 7, 13, 14]. KT proposes a hybrid positioning
technique using barometric correction data to reduce the error range of smartphone barometric
sensors and ease of use.
3.1. Hexagonal Cluster Virtual Automatic Weather Station Assisted Data
Height above sea level is a measure of the vertical distance between the earth's surface and
mean sea level. This mean sea level, on which height above sea level is based, is set as a long-
term average value for the entire planet. However, different regions have different heights
above sea level, and accurate elevation measurements need to account for these regional
differences in real-world applications [15, 16]. It is important to refer to national or international
datum points for accurate elevation measurements.
To provide vertical positioning information based on smartphone barometric pressure
sensors in the Emergency Location Service efficiently, KT suggests utilising hexagonal cluster
based the virtual Automatic Weather Station (AWS) data that is estimated from the collected
data sets in real time through automatic weather stations as shown in figure 2. The virtual AWS
data is estimated by microsphere interpolation technique [17] to provide a particular hexagonal
cluster’s barometer, temperature etc. immediately.
Figure 2: Generation of hexagonal cluster’s virtual AWS from real time AWS data collection.
To conduct hexagonal cluster’s virtual AWS data, determine AWS data sets to interpolate in
the order of near distance,
𝑑! = ‖𝑥 − 𝑥! ‖ (1)
where x is centre coordinate of hexagonal cluster, xi is ith AWS’s coordinates di is ith
Euclidean distance.
To set a weight factor of di,
1 (2)
𝑤! = "
𝑑!
where wi is ith AWS’s weight factor that is proportional to the reciprocal of di. p is typically
works as a power factor, but due to the characteristics of AWS data (temperature, altitude,
barometer, etc.) it is not distance dependent, so set to 1.
To obtain interpolated data of the virtual AWS,
𝑤! (3)
𝑤
(# = % ,
∑$&' 𝑤$
% (4)
(# 𝑓(𝑥! )
𝑓(𝑥) = . 𝑤
!&(
where N is number of data sets of AWS, to perform a fast acquisition, 𝑤
(# is normalised wi
and 𝑓(𝑥! ) is the value of the function at each data point 𝑥! .
3.2. Barometric sensor-based Vertical Positioning
When Emergency Location Services provide elevation information to first responders, it is
common to use Mean Sea Level (MSL) for elevation information. However, using MSL contains
errors because the height of sea level varies from region to region [15]. Also, since rescue
operations are conducted at ground level, it is more efficient from the user's perspective to
provide elevation information based on ground level.
To estimate vertical positioning data, smartphone provides its raw measurements data,
latitude, longitude and Porg. latitude and longitude are smartphone’s horizontal positioning result
of GNSS technique, Porg is its barometric sensor data, respectively. Since smartphone’s
barometric sensor contains errors, it is possible to calibrate those errors, Pcal by utilising crowd-
sourcing technique that is not discussed more details in this paper [6]. Sea level elevation of the
smartphone hb is calculated using Geographical Information System (GIS) that uses Digital
Elevation Model (DEM) based on horizontal coordinate of smartphone’s GNSS positioning
result,
ℎ) = 𝐺𝐼𝑆[𝐺𝑁𝑆𝑆*+, , 𝐺𝑁𝑆𝑆*-. ] (5)
where GNSSlat and GNSSlon are smartphone’s GNSS positioning result latitude and longitude,
respectively. To estimate virtual AWS data set, pressure Pb and temperature Tb are calculated
by microsphere interpolation in equation (4).
Figure 3 shows overall parameters of estimate ground height of smartphone based on GNSS
and barometric sensor utilising virtual AWS. The correlation between air pressure and elevation
has been the subject of extensive research and is defined as follows:
/0! ∙2 (6)
𝐿) 3∙4"
𝑃 = 𝑃) ∙ 81 + ∙ − ℎ)(ℎ )<
𝑇)
where Lb is standard temperature lapse rate, h is height about sea level, R is universal gas
constant, g0 is gravitational acceleration constant and M is molar mass of Earth’s air. To
calculate elevation, equation (6) is inversed as follows:
/0! ∙2 (7)
𝑇) 𝑃 3∙4"
ℎ = ℎ) + ∙ => ? − 1@
𝐿) 𝑃)
where P is smartphone’s barometric sensor data where we calibration factor Pcal is applied
so that Porg=P+Pcal. Finally, smartphone’s height from the ground, hg is estimated by hg=h-hb.
Figure 3: Smartphone’s ground height estimation using GNSS, barometric sensor and virtual
AWS.
3.3. Field Test Result
To verify the vertical positioning performance of the proposed smartphone barometric sensor,
we chose test points in Seocho-gu, Seoul and Gwangmyeong-si, Gyeonggi-do, and conducted
tests. A total of 107 points were selected in Seocho-gu and 95 points were selected in
Gwangmyeong-si. For the performance analysis, we tested four Samsung Galaxy S series
devices that has barometric sensors. To compare the vertical positioning performance, the
comparison groups were selected and tested in the same building, such as outdoor, indoor lower
floors, indoor middle floors, and indoor upper floors. In the performance analysis, 30 vertical
positioning tests per smartphone were conducted at each test point to eliminate statistical
outliers.
Figure 4 shows the test results to verify the performance of the smartphone barometric
sensor-based hybrid positioning platform. The test results were grouped into ground level, low
level, mid level and high level to identify the specificity of each test site and test altitude, and
the performance was analysed for the ground floor, first to second floor, third to sixth floor,
and upper seventh floor.
Table 1
Test Environment
Seocho-gu Gwangmyeong-si
Buildings 27 26
Test Points 107 95
- Ground Level 27 26
- Low Level 41 36
- Mid Level 25 27
- High Level 14 6
The left side of figure 4 indicates the performance of the vertical positioning CEP 80 tested
in Seocho-gu, showing an accuracy of about 2.4 metres on the ground floor, 2.7 metres on the
lower floors, 2.1 metres on the middle floors, and 1.9 metres on the upper floors. The right side
of figure 3 represents the performance of the CEP 80 in Gwangmyeong-si, with an accuracy of
approximately 2.4 metres on the ground floor, 2.1 metres on the lower floors, 2.4 metres on the
middle floors, and 1.1 metres on the upper floors.
Figure 4: Test result (left) Seocho-gu, (right) Gwangmyeong-si.
4. Cloud-Native Hybrid Positioning Platform
In addition to the conventional positioning techniques such as GNSS, Wi-Fi, and base station
positioning in smartphone, alternative positioning resources such as Bluetooth Low Energy
(BLE) and barometric sensors are continuously researched and developed [18, 19]. The
commercialisation of multisource-based hybrid positioning techniques, which combine more
than one specific positioning resources, is rapidly enhancing the quality of emergency location
service. KT is researching and developing various positioning techniques to commercialise and
accelerate multi-signal and multisensor-based hybrid positioning platform.
4.1. Cloud-Native Application Modernisation
Cloud-based application modernisation enhances a number of advantages over conventional
siloed systems [20, 21]. The key advantages in cloud-native hybrid positioning platform include:
1. Scalability: In a cloud environment, you have the flexibility to adjust resources based
on the demand of your applications. You can add more servers when you need them, or
reduce resources when you don't, which is cost-effective.
2. Flexibility: Cloud-based systems can integrate a variety of services and tools, making it
easy to add new features or expand existing systems. This gives you the ability to
respond quickly to business needs.
3. Automation and DevOps Support: Cloud platforms support continuous
integration/continuous deployment (CI/CD) pipelines, automated testing, and
deployment capabilities to speed up development and deployment.
4. Ease of Implementing New Technologies: The cloud provides an environment that
makes it easy to adopt the latest technologies such as artificial intelligence (AI), machine
learning (ML), and big data analytics.
These advantages help emergency location service respond quickly to technical changes,
provide an improved quality of service in positioning, and increase operational efficiency in
commercialisation. In the second half of 2023, KT started the development of application
modernisation of the location-based service platform include positioning engines, that has been
researched and commercialised in silo from 2010 to 2022 over a decade, with the aim of
providing better location information. In particular, the transition to a cloud-native hybrid
positioning platform using multisensor-based information is expected to enhance the speed of
quality improvement that is conducive to practical rescue activities in a commercial
environment through CI/CD of precision location technology for emergency rescue, which is
being continuously researched and developed.
Figure 5: Silo to cloud-native hybrid positioning platform architecture.
Figure 5 describes architecture of the Silo platform, and the Cloud-native Hybrid Positioning
Platform developed by KT. The left side of Figure 5 is the Monolithic Architecture (MA), which
consists of the Location Manager, base stations and Wi-Fi positioning, GNSS positioning. In
order to commercialise products with recently researched and developed to achieve faster and
more accurate location information, independent engine or platform must be newly built and
directly interfaced with existing platforms in ongoing operation environments. This
commercialisation is time-consuming and costly, making it difficult to respond to rapid
technological changes. The right side of Figure 5 describes Micro Service Architecture (MSA),
which includes all existing siloed functions of one platform, and has the advantage of ensuring
CI/CD that can immediately commercialise and manage various positioning technologies by
adding or modifying only software modules, especially the CORE-PDE part. In example,
barometric assist data-based hybrid positioning technique introduced in Section 3 is also applied
as a software module called Barometric PDE, which is configured to operate as a hybrid with
the existing positioning function.
5. Conclusion
In this paper, we present a multisource-based cloud-native hybrid positioning platform work in
progress that KT is developing for emergency location service. The performance of the cloud-
native hybrid positioning platform using the smartphone’s barometric sensor-based positioning
software architecture, and the platform-based barometric correction information can satisfy the
quality requirements for emergency location service proposed by the KCC. As various
positioning technologies are developing at a rapid pace, the application modernisation of KT's
cloud-native hybrid positioning platform can be one of the ways to accelerate the
commercialisation of positioning technologies and quality improvement for emergency location
service.
Acknowledgements
This work was supported by Institute of information & communications Technology Planning
& Evaluation (IITP) grant funded by the Korea government (MSIT) (No. RS-2024-00337857,
Development of Hybrid Positioning Technology for Emergency Rescue based on 5G and Multi-
GNSS).
References
[1] F. V. Diggelen, A-GPS: Assisted GPS, GNSS, and SBAS, Artech, Norwood, MA, USA, 2009.
[2] C. Yang, H. Shao, WiFi-based indoor positioning, IEEE Communications Magazine 53
(2015), 150-157, doi: 10.1109/MCOM.2015.7060497.
[3] Y. Cho, M. Ji, Y. Lee, S. Park, WiFi AP position estimation using contribution from
heterogeneous mobile devices, in: Proceedings of the 2012 IEEE/ION Position, Location and
Navigation Symposium, Myrtle Beach, SC, USA, 2012, pp. 562-567, doi:
10.1109/PLANS.2012.6236928.
[4] FCC, Wireless E911 Location Accuracy Requirements: FOURTH REPORT AND ORDER,
FCC 15-9 PS Docket No. 07-114, 2015, URL: https://www.fcc.gov/document/fcc-adopts-
new-wireless-indoor-e911-location-accuracy-requirements.
[5] FCC, Wireless E911 Location Accuracy Requirements: FOURTH FURTHER NOTICE OF
PROPOSED RULEMAKING, FCC 19-20 PS Docket No. 07-114, 2019, URL:
https://docs.fcc.gov/public/attachments/FCC-19-20A1.pdf.
[6] D. Shin, J. Lee, B. Shin, C. Yu, H. Kyung, D. Choi, Y. Kim, T. Lee, A Study on Altitude
Estimation using Smartphone Pressure Sensor for Emergency Positioning, Journal of
Positioning, Navigation, and Timing 9 (2020), 175–182, doi: 10.11003/JPNT.2020.9.3.175.
[7] G. Liu, M. Iwai, Y. Tobe, D. Matekenya, K. M. A. Hossain, M. Ito, K. Sezaki, Beyond
horizontal location context: measuring elevation using smartphone's barometer, in:
Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous
Computing: Adjunct Publication (UbiComp '14 Adjunct), Association for Computing
Machinery, New York, NY, USA, pp. 459–468, doi: 10.1145/2638728.2641670.
[8] OMA, User Plane Location Protocol (Approved Version 2.0.6 - 04 Aug 2020), 2020,
https://www.openmobilealliance.org/release/SUPL/V2_0_6-20200804-A/OMA-TS-ULP-
V2_0_6-20200804-A.pdf.
[9] 3GPP, Evolved Universal Terrestrial Radio Access (E-UTRA); LTE Positioning Protocol
(LPP) (3GPP TS 36.355 version 15.6.0 Release 15), 2020, URL:
https://www.etsi.org/deliver/etsi_ts/136300_136399/136355/15.06.00_60/ts_136355v150600
p.pdf.
[10] J. Choi, M. Ji, J. Kim, Y. Cho, Requirement and Architecture of Positioning System Standard
for Public Safety, in: 2017 19th International Conference on Advanced Communication
Technology (ICACT), PyeongChang, Korea (South), 2017, pp. 382-387, doi:
10.23919/ICACT.2017.7890118.
[11] KCC, Results of the Positioning Quality Assessment for Emergency Location Service, URL:
https://kcc.go.kr/user.do?boardId=1113&page=A05030000&dc=K00000200&boardSeq=602
72&mode=view.
[12] Y. Cho, M. Ji, J. Kim, J. Jeon, High-Scalable 3D Indoor Positioning Algorithm Using Loosely-
Coupled Wi-Fi/Sensor Integration, in: 2015 17th International Conference on Advanced
Communication Technology (ICACT), PyeongChang, Korea (South), 2015, pp. 96-99, doi:
10.1109/ICACT.2015.7224765.
[13] I. Bisio, A. Sciarrone, L. Bedogni, L. Bononi, WiFi Meets Barometer: Smartphone-Based 3D
Indoor Positioning Method, in: 2018 IEEE International Conference on Communications
(ICC), Kansas City, MO, USA, 2018, pp. 1-6, doi: 10.1109/ICC.2018.8422989.
[14] C. Hajiyev, U. Hacizade, D. Cilden-Guler, Data Fusion for Integrated Baro/GPS Altimeter,
2019 9th International Conference on Recent Advances in Space Technologies (RAST),
Istanbul, Turkey, 2019, pp. 881-885, doi: 10.1109/RAST.2019.8767791.
[15] K. F. Aleem, Geoid modeling, 2012, URL: https://mycoordinates.org/geoid-modelling/.
[16] R. V. Mises, Theory of Flight, Dover Publications, New York, NY, USA, 1959.
[17] W. Dudziak, PRESENTATION AND ANALYSIS OF A MULTI-DIMENSIONAL
INTERPOLATION FUNCTION FOR NON-UNIFORM DATA: MICROSPHERE
PROJECTION, Master’s thesis, University of Akron, Ohio, USA, 2007.
[18] R. Faragher, R. Harle, Location Fingerprinting With Bluetooth Low Energy Beacons, IEEE
Journal on Selected Areas in Communications 33 (2015), 2418-2428, doi:
10.1109/JSAC.2015.2430281.
[19] L. Bai, F. Ciravegna, R. Bond, M. Mulvenna, A Low Cost Indoor Positioning System Using
Bluetooth Low Energy, IEEE Access 8 (2020), 136858-136871, doi:
10.1109/ACCESS.2020.3012342.
[20] Amazon Web Services, What is Cloud Native?, 2024, URL: https://aws.amazon.com/what-
is/cloud-native/?nc1=h_ls.
[21] Microsoft Learn, What is Cloud Native?, 2023, URL: https://learn.microsoft.com/en-
us/dotnet/architecture/cloud-native/definition.