WSEAS TRANSACTIONS on INFORMATION SCIENCE and APPLICATIONSDOI: 10.37394/23209.2021.18.11

Luis Carlos Manrique, Anthony Weiss,
Sandra Puentes

Simulating Wave Propagation Distribution Through GIS
Integration
LUIS CARLOS MANRIQUE, ANTHONY WEISS, SANDRA PUENTES
Data scientist, Engineer, Assistant professor
Credit Engine Inc., Moneytree Inc., The University of Tsukuba
Tokyo Minato-ku Roppongi 7-15-7, Tokyo Minato-ku Castalia 2F, 3-13-3 Nishiazabu
Ibaraki Prefecture Tsukuba 1-1-1 Tennodai
JAPAN
Abstract: The use of electronic devices designed for user location estimation has become widely popular in
the last decade. This is thanks to emergent technologies such as Bluetooth Low Energy, Radio-Frequency
Identification, and Ultra-WideBand (UWB) among others. In the present study; the authors provide
a method for using a Geographic Information System (GIS) to define spatial constraints, in order tosimulate the lines of sight of anchors to make an informed selection of adequate locations for installation.
By leveraging GIS, researchers or enterprises can improve the installation process by reducing costs whilesetting up arrangements that will ensure reliable data collection. We include a scenario illustrating thepossibility of budget reduction of around 30% related to the orientation and survey of the devices.
Key-Words: Geographic Information Systems, Bluetooth Low Energy (BLE), indoor simulation,
Ultra-Wideband (UWB), indoor location detection.

Received: January 19, 2021. Revised: May 30, 2021. Accepted: June17, 2021. Published: July 7, 2021.

1 Introduction

serves as absolute reference positions that can beused to calculate the location of pedestrians.
We will describe UWB very briefly here. Pleaserefer to [1] for further details. UWB technology
allows the mobility of wireless communications athigh speeds. These devices are specified to havea -10 dB fractional bandwidth greater than 20%
or at least 500 MHz of the occupancy spectrum[2]. The US Federal Communications Commission(FCC) specifies that the power emission levels of
UWB must be within the spectrum of 3.1 - 10.6
GHz to assure coexistence with other signals inthe same band [1].
UWB is nowadays one of the most accurate indoor systems. The accuracy is within centimeters[3]. However, some smoothing or correcting methods such as a Kalman filter or Loess regression
may be applied to reduce the noise in the estimated path [4].

Tracking information collected from different objects as well as users has become a valuable resource for companies in multiple sectors. Regarding outdoor tracking, the Global Positioning System (GPS) is the most commonly used resource.
However, for indoor tracking, the GPS doesn’t
work well since mechanical barriers such as roofs
and walls may block or reflect signals. For thisreason, other technologies must be used to identify an individual or product in a specific place ata specific time indoors.
In location-based services, devices such as
Bluetooth Low Energy (BLE), Ultra-Wideband(UWB), and Radio Frequency Identification(RFID) tags provide tracking information bybroadcasting or receiving signals that can be used
to estimate the proximity of another device withina certain area. In an ideal situation, the devices
should be installed in an open space with no interference. However, indoor structures as walls orlarge objects may generate shadows or blind spots
where the system signal is unable to reach.
From the technology mentioned above, UWB
is the most expensive. Therefore, finding a way to
install the fewest UWB anchors while getting the
maximum visibility possible will reduce costs andimprove the data collection process as well.

1.2 Related work
Different methods have been studied for optimizing the positioning and reducing the number ofused UWB anchors. Genetic algorithms have been
a popular approach; Barret [5] studied the problem by simulating the sensor readings while using
a probabilistic model where the chance of detection was directly proportional to 1/Range2 . The
study attempted to place sensors in the Hudson
River in a random way to detect intruders. The algorithm found the Pareto frontier and the curve of
optimal trade-offs between the objectives. Olagueand Mohr [6] optimized the placement of cameras

1.1 Indoor proximity services
The placement of BLE beacons, RFID tags, UWB
devices, and so forth in an indoor environment

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to minimize the error in sensing. They analyzed
the problem in two steps; in the first step, theychose a criterion with the maximum element in
the diagonal of the covariance matrix which depends on the error propagation. And in the second
one, they created a heuristic to minimize the previous criterion. Xia and collaborators [7] measured
the distance from the tag to the base stations andproposed a positioning equation that is solved by
the genetic algorithm. The authors realized theimportance of considering other variables rather
than only the line of sight.
Monica and Ferrari [8] introduced an analytical approach to optimize anchor nodes placement
using time- difference-of-arrival UWB-based localization of a moving target node. The authors developed a deeper analysis based on the hypothesisthat the target will move in a straight line in the
middle of a corridor. Other methods of positioning have been studied by using Taylor series expansion [9] and Levenberg-Marquardt algorithm [10].
Particularly for improvement of indoors positioning, some papers have been introduced such as theone by Hol and colleagues implementing UWB calibration [11] and the study by Ridolfi et al., using
particle filters as well as Kalman filter to estimate
the localization of UWB for analyzing sports postures [12]. The study by Arsan et al. analyzed
behavior mapping (BM) by using three different
technologies Wi-Fi, BLE Beacon, and UWB sensors. They found the best performance by using
the UWB sensors for BM [13]. Suwatthikul et al
experimented by comparing the performance of a
UWB system in different scenarios. They demonstrated that configuring the UWB with the maximum range did not outperform when there was aclear communication path [14].
Regarding GIS, ArcGIS indoors appears to be
an interesting approach. Introduced by ESRI; oneof the largest companies that work on GIS, thistool allows to place different spatial features suchas layouts, sensors, routes, among others in a simulated space. However, it does not have features
for simulating signals in indoor locations. To ourknowledge, there are no investigations related tothe integration of GIS and signal broadcasting.
For the present study, we propose a virtual approach based on simulations of the physical properties of a space designed for placing UWB anchors, followed by a simulation of the anchors’ signal propagation to support the setup process.
Additionally, we added to the simulation, thepossibility to be transferable to different locations.
In order to approach this issue, we explored thepossibility of implementing a Coordinate Reference System (CRS). The Geographic Information

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Luis Carlos Manrique, Anthony Weiss,
Sandra Puentes

System (GIS) was useful for gathering and displaying geographic information. This system structures and gives format to the spatial informationby using a CRS, a set of rules to identify points
in the real world as data[15]. Furthermore, it canalso be integrated with economic, demographic, social, or mobility data. The relevance of this investigation relies upon the integration of GIS withsignal propagations in indoor environments. The
implementation of such simulations may provide
companies and researchers with useful information
for anchor positioning and its optimal distribution indoors, also providing the ability to estimate
and minimize costs while gaining the best distribution for the acquisition of quality data. Although
we propose the implementation of UWB for thepresent paper, we consider that the same analysis
may be also usable for BLE beacons positioning,
Received Signal Strength Indicators (RSSI) evaluation, among other technologies.

2 Materials and Methods
2.1 Geographic Information System (GIS)
A GIS combines hardware and software to manipulate, store, retrieve, view, and analyze large spatial databases [16][17]. For this study, we will use
QGIS, since it is freeware and can be connected toother software and tools straightforwardly. QGIS
is an oﬀicial project of the Open Source Geospatial Foundation (OSGeo). It runs on Linux, Unix,
MacOS, Windows, and Android, and supports numerous vector, raster, and database formats andfunctionalities [18].
QGIS allows the user to connect the softwarewith other programs through a toolbox named“processing.” It also contains several algorithms
grouped in different blocks, and allows running
single or batch processes. By using spatial functions, new objects can be imported into QGIS. The
toolbox can be downloaded and integrated into
QGIS by installing the plugin called “Processing
R provider”.
A screenshot of the processing toolbox is shownin figure 1. For this study, we will connect QGIS
3.16 with Microsoft R Open 4.0.2 to create spatial
features with a CRS by running a batch file. R wasused because of the complexity of calculations.
There are many CRSs such as the Universal
Transverse Mercator (UTM), World Geodetic System 84 (WGS 84), a global standard for latitude
and longitude positions, among others. For thisstudy, the authors will use the World Mercator:
54004 CRS using meters instead of angles to locate the spatial features relatively. Later they canbe projected if required.

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Luis Carlos Manrique, Anthony Weiss,
Sandra Puentes(
P athLoss(d) = C0 + 10n log10

λ
4∗π∗d

)
+ xσ

(1)

Where the path loss is given in decibels (dB).
C0 is the loss at the reference distance of 1 meter.
λ is the center of the wavelength in meters. d isthe distance in meters. xσ is the shadow fading
and n is the path loss exponent, equal to 2, foropen spaces [20].
UWBs are used for positioning by using thetime difference from arrival, angle of arrival,time of arrival, received signal strength indication
among others. Some of them are explained as follows:a. Time difference from arrival (TDOA): Measures the time difference between the UWB
signal from the tag to multiple UWB anchors.
This calculation allows the system to identify
the location of a moving tag. Since the power
consumption of the tag is low, it is easier to
identify several tags.
b. Angle of arrival (AOA): The position of the
tag is calculated by using the signal incidence
angle to the anchors. When the whole system
includes multiple anchors, the angle of arrivalis calculated by using a minimum of 3 of them.
c. Time of arrival (TOA): The position of the
tag is calculated by recording the time it takes
from the tag to more than 3 anchors. The system then estimates the location of the tag byusing the distances from the tag to each anchor through the triangulation principle. The
tags, as well as the anchors, must be synchronized to avoid problems.
d. Received signal strength indication: This application is similar to the BLEs and Wi-Fi. It
uses the RSSI value to estimate the location of
the tag. However, methods such as TOA provide better accuracy given that the location isestimated by the pulse of the signal.

Figure 1: QGIS Processing Toolbox. Through thistoolbox, QGIS allows to connect the software withother programs easily.

2.2 Simulations through R software
R is ‘GNU S,’ a freely available language and environment for statistical computing and graphics
which provides a wide variety of statistical andgraphical techniques: linear and nonlinear modeling, statistical tests, time-series analysis, classification, clustering, among others. [19]The libraries used in the current manuscript
are: maptools, sp, rgeos and rgdal. They are available for free on the R-project website.
The spatial files were generated from R, thenread and imported into QGIS. A special characteristic of the simulation provided and tested by
the authors is that the CRS of the layout (walls,
columns, and other polygons) must be the sameas the generated tag to generate the object at thedesired place.

2.3.2 Broadcasting signals simulation on GIS
To simulate the signal propagation based on spatial constraints and distance from the origin, a layout was created using a GIS. For generating thecoverage area of the UWB anchor, the following
parameters were defined before generating the spatial feature:a. Walls: spatial constraints such as scaffolds,
columns, rooms, and any physical object thatmight interfere with the signal propagation.
b. nSegments: The total number of dots that will
be generated between the origin of the signal
and the defined propagated distance.

2.3 Methods
2.3.1 Path loss identification
The radio signals broadcast by the UWB tag are
emitted through space and are affected by spatial restrictions such as walls, doors, or windows,
among others. These restrictions reduce the powerof the wave in the space; this phenomenon is called‘path loss’.
The path loss for UWB devices can be described as follows:

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Luis Carlos Manrique, Anthony Weiss,
Sandra Puentes

Figure 3: Simulated environment: The blacksquares making a reticle represent the columns in
the area. The polygons with diagonal red lines
correspond to scaffolds, shelves, or other physicalobstacles.
propagated points with the GIS diagram. Fromeach line of sight, the algorithm identifies if thesignal is blocked by a polygon and assigns a specific value at a specific point. Since this processuses geographical features, it is possible to add
even more information to x and y coordinates. In
this case, we add information related to obstacles
that are overlaying or blocking the line of sight.

Figure 2: Tool for simulating wave propagation.
c. nDivisions: Represents the number of “rays”
that will be emitted in total. This value will
provide the angle between rays, it is definedas: angle = nDivisions/360.
d. xOrig: Location at x.
e. yOrig: Location at y .
f. uwbRange: The distance where the UWB can
get accurate data. In theory, it can be between 45 to 60 m from the origin.

Once the process is finished, we define theshadow as follows:
startingShadowia = min(lineSightia ̸= 0)

where i refers to the line of sight i, and a refers
to the position where a polygon is interfering withthe line of sight i.
Later, we iterated through all the lines of sight
as follows.

The used tool also allows changing the parameters regarding the conditions of the proposed simulation. A snapshot of this feature can be seen in
figure 2.

Algorithm 1 Pseudocode
1: procedure Shadows identification2:
while i ≤ max(i) do
3:lineSighti = 1∀i4:
lineSighti :length(lineSighti(a−1) ) = 1
5:
startingShadowia :length(lineSighti ) = 0

2.3.3 Simulated environment
After broadcasting the signal simulation, a simulated mesh layout was obtained. Figure 3 shows
an example where each mesh area corresponds toone square meter and the black squares distributed
homogeneously through the plane represent thecolumns in the area. The polygons with diagonal
red lines correspond to scaffolds, shelves, or otherphysical obstacles.
The algorithm uses the parameters describedin section 2.3.2 to simulate the area coverage froma UWB anchor until some distance where thesignal is no longer reliable. Next, we overlaid the

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Regarding details of the algorithm 1:• Line 3: Define the status of all points in a lineof sight i as 1. The initial assumption wasthat there was no obstruction.
• Line 4: Once a point a is overlaid with a physical constraint, it changes the status of thatpoint to 0. It means that a shadow starts at
the point a.

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Figure 4: Generated signal: The radius of coverage of the UWB anchor is shown by the yellow
circle.

Figure 5: Detail of Shapefile: Spatial points are
generated by modifying parameters included in
the tool.

• Line 5: Define the status of the subsequent
points in the line of sight i as 0.
We applied this process to all the lines of sightto test if they were obstructed by physical constraints. Given that the UWB antenna will bemoving given specific activities such as tracking
raw materials, tracking staff, etc., the interferencewith objects will reduce the positioning accuracy[22].

3.2 Costs
The deployment of indoor location tracking systems generates costs including the configuration and installation, support and maintenance,monthly fees, loss, and firmware updates, among
others.
One of the most challenging steps during theimplementation is the setup of the devices due tothe small tag antenna location and the interaction
with the physical environment. Ideally, the anchors should be placed appropriately, since changing their position brings other costs related to cable extension and re-positioning [23]. In the investigation conducted by Wang and Pan, it wasevident the diﬀiculties of placing the anchors byusing a total station or laser range finder. Given
that in most of the cases the antenna will move,other systems such as Light Detection and Ranging (LiDAR) could be implemented to calibrate
the system to reduce the errors and thus improve
the accuracy [24].
The price of buying 5 Ubisense UWB compact
tags, suitable for mounting into objects and vehicles is about 18,500 USD (2,000,000 JPY). Other
devices, such as the “Ubisense series 9000 IP sensor” that consists of an array antenna and an ultraUWB RF receiver of 4 x 4 x 1.7 cm are useful for
mounting on people or objects[25]. Just 4 sensors
cover an area of 200m2 [26].
If a company wants to cover an area of about
10,570 m2 (as the simulated area used for thisstudy), 22 sets of sensors are required. If they
test the installation for 3 weeks, the total cost
per hour is about $2,775 USD. The average timespent for experienced workers with this technology
could be around 22 hours, this covers bipods place-

3 Results
3.1 GIS and signal simulation
In Figure 4, the signal propagation from certain
coordinates during a simulation is displayed. We
used the radius that a UWB can broadcast withhigh accuracy (60 m).
In Figure 5, a detailed diagram of the simulated signal is shown. The black squares represent
columns, and the polygon in the center represents
an obstruction such as a shelf.
Figure 6 shows that it is possible to see the simulated signal in two dimensions. The red area represents the direction that the UWB is not broadcasting. The line of sight is detailed in green,
and the areas not covered by the signal due toobstruction by physical objects are generated inblue, columns and scaffolds are also shown.
By integrating GIS it is easier to calculate theeffective area coverage for the UWB anchors considering all the parameters included in the tool for
broadcasting signals. This calculation may help
genetic algorithms or other methods for optimization to deal with multiple objective functions suchas maximize the area coverage and reducing thenumber of UWB anchors.

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ever, not all companies or industries are familiar
with them. Thus, a simulation in situ could beeasier to implement and help reducing setup costs,
since users will be able to see how the signal ispropagated.
Genetic algorithms have been applied to optimize the number and placement of the anchors
indoors. However, they have some disadvantages
like the formulation of the fitness function, the parameters, the rate of mutation and crossover andthe selection criteria of the new population [28].
One of the main issues is that the algorithm may
not converge to an optimal solution or global optima [29]. Also, most of the applications on this
topic have not considered spatial constraints that
affect the optimization process. The investigations
have been conducted in open spaces or clear corridors without considering physical limitations.
Renting UWB equipment is a cost that a company should consider because this technology canachieve high accuracy, but it requires an important economic investment. Other technologies
such as BLE may be cheaper, but the area of coverage is smaller, accounting just for a few metersfrom the BLE placement in contrast to the coverage provided by a UWB anchor which may coverabout 60 m (or an area of 11,310 m2 ). If a company or researchers want to define an effective area
where the RSSI is bigger than -60 dBm, it meansthat the BLE beacon will cover just a range ofabout 2.5 m [30].
As for limitations, the 2-dimensional analysis
does not account for all the spatial features thatcan exist in an indoor environment. If the device
is in a high place (usual condition), our approachcannot fully represent how the signal is obstructed.
However, this proposal does allow the workers andresearchers to have an idea of the signal behavior
before implementation.
Further, it is possible to include additional information inside each feature of the shapefiles,
such as signal loss, type of obstruction, among others. This also helps to estimate and conduct a kernel density method in two dimensions by using thesignal properties. For the spatial constraints suchas walls, doors, etc., it is possible to integrate theinformation related to the obstruction to estimate
the signal loss or power at some point (x,y).

Figure 6: Projecting shadows and line of sight:
The upper figure shows shadows in blue, line ofsight in green and non-visible area in red. The
coordinates are given in meters. The lower figure
shows the line of sight in green with an effective
area coverage of 2,288 m2 .
ment, sensor placements, layout and cabling, orientation and survey, and system calibration. Following Umer et al, the total time related to theorientation and survey is about 27% - 34% of thetotal time of the deployment [27]. Translated into
man-hours, this is about 8 to 10 hours. By using
our proposed method, it is possible to reduce thistime to less than 2 - 3 hours since the setup isdetermined beforehand via simulations.

5 Conclusion
The tool introduced in this document can be defined using coordinate reference systems. By implementing this tool, companies, as well as researchers, will be able to estimate signal propagation in specific indoor environments. This feature may be helpful to control several parameters

4 Discussion
One of the most diﬀicult decisions for setting up
broadcast devices is how to place them for maximum effectiveness and cost reduction. Some algorithms have been proposed for this problem. How-

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to get the required information and thus perform
simulations related to the location of the devices.
Another advantage of this tool is that by integrating GIS, it would be possible to incorporate itwith different algorithms towards an optimization
model. Finding the best parameters for the different variables will help companies and researchers
to place the UWB anchors in the best way to minimize the number of tests before the experiments.
Our tool considers spatial features loaded in
GIS as range and location. So, it is possible to use
it for simulating other types of technologies such
as BLE beacons or Wi-Fi Antennas as well. Also,
since the tool projects several spatial points froman origin, more properties can be given to each ofthem. Properties such as the strength of a radio
signal in a clear environment or its attenuation
across materials[31].
While enterprise systems such as TruNET
Wireless can accomplish similar results, a commercial license is required. Another reason theauthors worked with R and Q-GIS specifically isthat these tools allow companies to reduce costs
since no commercial software or license is requiredfor its implementation.
Finally, this paper contributes to electronic devices given the advantages of integrating not only
spatial features into the signal broadcast but also
other intrinsic properties such as power strength(RSSI), power loss, among others; and the parameters of the spatial features can be adjusted under
the optimization models.

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Based on Genetic Algorithm., Journal of Computer and Communications, Vol. 9, Issue 04,
2021, pp 110-118.
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[14] Suwatthikul, C., Chantaweesomboon, W.,
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6 Contribution of individual authors
to the creation of a scientific article(ghostwriting policy)
Luis Carlos Manrique carried out the writing of
this manuscript, research and simulations.
Anthony Weiss carried out the writing and checking the logic of this manuscript.
Sandra Puentes carried out the writing of thismanuscript.

[24] Wang, J. and Pan, R., Research and Evaluation of the Precision and Accuracy of
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[26] Elkhouly,
Essam Abdelkadir,PreciseIndoorLocalization

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7 Sources of funding for researchpresented in a scientific article orscientific article itself

UWB
SystemPersonal funding.

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WSEAS TRANSACTIONS on INFORMATION SCIENCE and APPLICATIONSDOI: 10.37394/23209.2021.18.11

Luis Carlos Manrique, Anthony Weiss,
Sandra Puentes

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