Simulating Wave Propagation Distribution Through GIS

LUIS CARLOS MANRIQUE, ANTHONY WEISS, SANDRA PUENTESData scientist, Engineer, Assistant professor

Credit Engine Inc., Moneytree Inc., The University of TsukubaTokyo Minato-ku Roppongi 7-15-7, Tokyo Minato-ku Castalia 2F, 3-13-3 Nishiazabu

Ibaraki Prefecture Tsukuba 1-1-1 TennodaiJAPAN

Abstract: The use of electronic devices designed for user location estimation has become widely popular inthe last decade. This is thanks to emergent technologies such as Bluetooth Low Energy, Radio-FrequencyIdentification, and Ultra-WideBand (UWB) among others. In the present study; the authors providea 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.

Tracking information collected from different ob-jects as well as users has become a valuable re-source for companies in multiple sectors. Regard-ing outdoor tracking, the Global Positioning Sys-tem (GPS) is the most commonly used resource.However, for indoor tracking, the GPS doesn’twork well since mechanical barriers such as roofsand walls may block or reflect signals. For thisreason, other technologies must be used to iden-tify an individual or product in a specific place ata specific time indoors.

In location-based services, devices such asBluetooth Low Energy (BLE), Ultra-Wideband(UWB), and Radio Frequency Identification(RFID) tags provide tracking information bybroadcasting or receiving signals that can be usedto estimate the proximity of another device withina certain area. In an ideal situation, the devicesshould be installed in an open space with no in-terference. However, indoor structures as walls orlarge objects may generate shadows or blind spotswhere the system signal is unable to reach.

From the technology mentioned above, UWBis the most expensive. Therefore, finding a way toinstall the fewest UWB anchors while getting themaximum visibility possible will reduce costs andimprove the data collection process as well.

The placement of BLE beacons, RFID tags, UWBdevices, and so forth in an indoor environment

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 technologyallows 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 ofUWB must be within the spectrum of 3.1 - 10.6GHz to assure coexistence with other signals inthe same band [1].

UWB is nowadays one of the most accurate in-door systems. The accuracy is within centimeters[3]. However, some smoothing or correcting meth-ods such as a Kalman filter or Loess regressionmay be applied to reduce the noise in the esti-mated path [4].

Different methods have been studied for optimiz-ing the positioning and reducing the number ofused UWB anchors. Genetic algorithms have beena popular approach; Barret [5] studied the prob-lem by simulating the sensor readings while usinga probabilistic model where the chance of detec-tion was directly proportional to 1/Range2. Thestudy attempted to place sensors in the HudsonRiver in a random way to detect intruders. The al-gorithm found the Pareto frontier and the curve ofoptimal trade-offs between the objectives. Olagueand Mohr [6] optimized the placement of cameras

Simulating Wave Propagation Distribution Through GIS

Integration

1 Introduction

1.1 Indoor proximity services

1.2 Related work

Received: May 10, 2021. Revised: June 30, 2021. Accepted: July 2, 2021. Published: July 7, 2021.

WSEAS TRANSACTIONS on INFORMATION SCIENCE and APPLICATIONS DOI: 10.37394/23209.2021.18.11

Luis Carlos Manrique, Anthony Weiss, Sandra Puentes

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to minimize the error in sensing. They analyzedthe problem in two steps; in the first step, theychose a criterion with the maximum element inthe diagonal of the covariance matrix which de-pends on the error propagation. And in the secondone, they created a heuristic to minimize the previ-ous criterion. Xia and collaborators [7] measuredthe distance from the tag to the base stations andproposed a positioning equation that is solved bythe genetic algorithm. The authors realized theimportance of considering other variables ratherthan only the line of sight.

Monica and Ferrari [8] introduced an analyti-cal approach to optimize anchor nodes placementusing time- difference-of-arrival UWB-based local-ization of a moving target node. The authors de-veloped a deeper analysis based on the hypothesisthat the target will move in a straight line in themiddle of a corridor. Other methods of position-ing have been studied by using Taylor series expan-sion [9] and Levenberg-Marquardt algorithm [10].Particularly for improvement of indoors position-ing, some papers have been introduced such as theone by Hol and colleagues implementing UWB cal-ibration [11] and the study by Ridolfi et al., usingparticle filters as well as Kalman filter to estimatethe localization of UWB for analyzing sports pos-tures [12]. The study by Arsan et al. analyzedbehavior mapping (BM) by using three differenttechnologies Wi-Fi, BLE Beacon, and UWB sen-sors. They found the best performance by usingthe UWB sensors for BM [13]. Suwatthikul et alexperimented by comparing the performance of aUWB system in different scenarios. They demon-strated that configuring the UWB with the maxi-mum range did not outperform when there was aclear communication path [14].

Regarding GIS, ArcGIS indoors appears to bean 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 sim-ulated space. However, it does not have featuresfor 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 ap-proach based on simulations of the physical prop-erties of a space designed for placing UWB an-chors, followed by a simulation of the anchors’ sig-nal 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 Refer-ence System (CRS). The Geographic Information

System (GIS) was useful for gathering and display-ing geographic information. This system struc-tures and gives format to the spatial informationby using a CRS, a set of rules to identify pointsin the real world as data[15]. Furthermore, it canalso be integrated with economic, demographic, so-cial, or mobility data. The relevance of this inves-tigation relies upon the integration of GIS withsignal propagations in indoor environments. Theimplementation of such simulations may providecompanies and researchers with useful informationfor anchor positioning and its optimal distribu-tion indoors, also providing the ability to estimateand minimize costs while gaining the best distribu-tion for the acquisition of quality data. Althoughwe propose the implementation of UWB for thepresent paper, we consider that the same analysismay be also usable for BLE beacons positioning,Received Signal Strength Indicators (RSSI) evalu-ation, among other technologies.

A GIS combines hardware and software to manip-ulate, store, retrieve, view, and analyze large spa-tial databases [16][17]. For this study, we will useQGIS, since it is freeware and can be connected toother software and tools straightforwardly. QGISis an official project of the Open Source Geospa-tial Foundation (OSGeo). It runs on Linux, Unix,MacOS, Windows, and Android, and supports nu-merous 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 algorithmsgrouped in different blocks, and allows runningsingle or batch processes. By using spatial func-tions, new objects can be imported into QGIS. Thetoolbox can be downloaded and integrated intoQGIS by installing the plugin called “ProcessingR provider”.

A screenshot of the processing toolbox is shownin figure 1. For this study, we will connect QGIS3.16 with Microsoft R Open 4.0.2 to create spatialfeatures with a CRS by running a batch file. R wasused because of the complexity of calculations.

There are many CRSs such as the UniversalTransverse Mercator (UTM), World Geodetic Sys-tem 84 (WGS 84), a global standard for latitudeand longitude positions, among others. For thisstudy, the authors will use the World Mercator:54004 CRS using meters instead of angles to lo-cate the spatial features relatively. Later they canbe projected if required.

2 Materials and Methods 2.1 Geographic Information System (GIS)



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

R is ‘GNU S,’ a freely available language and en-vironment for statistical computing and graphicswhich provides a wide variety of statistical andgraphical techniques: linear and nonlinear model-ing, statistical tests, time-series analysis, classifi-cation, clustering, among others. [19]

The libraries used in the current manuscriptare: maptools, sp, rgeos and rgdal. They are avail-able for free on the R-project website.

The spatial files were generated from R, thenread and imported into QGIS. A special charac-teristic of the simulation provided and tested bythe 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.

The radio signals broadcast by the UWB tag areemitted through space and are affected by spa-tial 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 de-scribed as follows:

PathLoss(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 fadingand 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 indicationamong others. Some of them are explained as fol-lows:

a. Time difference from arrival (TDOA): Mea-sures the time difference between the UWBsignal from the tag to multiple UWB anchors.This calculation allows the system to identifythe location of a moving tag. Since the powerconsumption of the tag is low, it is easier toidentify several tags.

b. Angle of arrival (AOA): The position of thetag is calculated by using the signal incidenceangle to the anchors. When the whole systemincludes multiple anchors, the angle of arrivalis calculated by using a minimum of 3 of them.

c. Time of arrival (TOA): The position of thetag is calculated by recording the time it takesfrom the tag to more than 3 anchors. The sys-tem then estimates the location of the tag byusing the distances from the tag to each an-chor through the triangulation principle. Thetags, as well as the anchors, must be synchro-nized to avoid problems.

d. Received signal strength indication: This ap-plication is similar to the BLEs and Wi-Fi. Ituses the RSSI value to estimate the location ofthe tag. However, methods such as TOA pro-vide better accuracy given that the location isestimated by the pulse of the signal.

To simulate the signal propagation based on spa-tial constraints and distance from the origin, a lay-out was created using a GIS. For generating thecoverage area of the UWB anchor, the followingparameters were defined before generating the spa-tial 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 willbe generated between the origin of the signaland the defined propagated distance.

2.2 Simulations through R software

2.3 Methods 2.3.1 Path loss identification

2.3.2 Broadcasting signals simulation on GIS



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Figure 2: Tool for simulating wave propagation.

c. nDivisions: Represents the number of “rays”that will be emitted in total. This value willprovide 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 be-tween 45 to 60 m from the origin.

The used tool also allows changing the parame-ters regarding the conditions of the proposed sim-ulation. A snapshot of this feature can be seen infigure 2.

After broadcasting the signal simulation, a simu-lated mesh layout was obtained. Figure 3 showsan example where each mesh area corresponds toone square meter and the black squares distributedhomogeneously through the plane represent thecolumns in the area. The polygons with diagonalred 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

Figure 3: Simulated environment: The blacksquares making a reticle represent the columns inthe area. The polygons with diagonal red linescorrespond 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 spe-cific value at a specific point. Since this processuses geographical features, it is possible to addeven more information to x and y coordinates. Inthis case, we add information related to obstaclesthat are overlaying or blocking the line of sight.

Once the process is finished, we define theshadow as follows:

startingShadowia = min(lineSightia ̸= 0) (2)

where i refers to the line of sight i, and a refersto the position where a polygon is interfering withthe line of sight i.

Later, we iterated through all the lines of sightas follows.

Algorithm 1 Pseudocode1: procedure Shadows identification2: while i ≤ max(i) do3: lineSighti = 1∀i4: lineSighti:length(lineSighti(a−1)) = 15: startingShadowia:length(lineSighti) = 0

Regarding details of the algorithm 1:• Line 3: Define the status of all points in a line

of sight i as 1. The initial assumption wasthat there was no obstruction.

• Line 4: Once a point a is overlaid with a phys-ical constraint, it changes the status of thatpoint to 0. It means that a shadow starts atthe point a.

2.3.3 Simulated environment



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Figure 4: Generated signal: The radius of cover-age of the UWB anchor is shown by the yellowcircle.

• Line 5: Define the status of the subsequentpoints 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 con-straints. Given that the UWB antenna will bemoving given specific activities such as trackingraw materials, tracking staff, etc., the interferencewith objects will reduce the positioning accuracy[22].

In Figure 4, the signal propagation from certaincoordinates during a simulation is displayed. Weused the radius that a UWB can broadcast withhigh accuracy (60 m).

In Figure 5, a detailed diagram of the simu-lated signal is shown. The black squares representcolumns, and the polygon in the center representsan obstruction such as a shelf.

Figure 6 shows that it is possible to see the sim-ulated signal in two dimensions. The red area rep-resents the direction that the UWB is not broad-casting. 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 con-sidering all the parameters included in the tool forbroadcasting signals. This calculation may helpgenetic algorithms or other methods for optimiza-tion to deal with multiple objective functions suchas maximize the area coverage and reducing thenumber of UWB anchors.

Figure 5: Detail of Shapefile: Spatial points aregenerated by modifying parameters included inthe tool.

The deployment of indoor location tracking sys-tems generates costs including the configura-tion and installation, support and maintenance,monthly fees, loss, and firmware updates, amongothers.

One of the most challenging steps during theimplementation is the setup of the devices due tothe small tag antenna location and the interactionwith the physical environment. Ideally, the an-chors should be placed appropriately, since chang-ing their position brings other costs related to ca-ble extension and re-positioning [23]. In the in-vestigation conducted by Wang and Pan, it wasevident the difficulties of placing the anchors byusing a total station or laser range finder. Giventhat in most of the cases the antenna will move,other systems such as Light Detection and Rang-ing (LiDAR) could be implemented to calibratethe system to reduce the errors and thus improvethe accuracy [24].

The price of buying 5 Ubisense UWB compacttags, suitable for mounting into objects and vehi-cles is about 18,500 USD (2,000,000 JPY). Otherdevices, such as the “Ubisense series 9000 IP sen-sor” that consists of an array antenna and an ultra-UWB RF receiver of 4 x 4 x 1.7 cm are useful formounting on people or objects[25]. Just 4 sensorscover an area of 200m2 [26].

If a company wants to cover an area of about10,570 m2 (as the simulated area used for thisstudy), 22 sets of sensors are required. If theytest the installation for 3 weeks, the total costper hour is about $2,775 USD. The average timespent for experienced workers with this technologycould be around 22 hours, this covers bipods place-

3 Results 3.1 GIS and signal simulation

3.2 Costs



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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. Thecoordinates are given in meters. The lower figureshows the line of sight in green with an effectivearea coverage of 2,288 m2.

ment, sensor placements, layout and cabling, ori-entation and survey, and system calibration. Fol-lowing Umer et al, the total time related to theorientation and survey is about 27% - 34% of thetotal time of the deployment [27]. Translated intoman-hours, this is about 8 to 10 hours. By usingour proposed method, it is possible to reduce thistime to less than 2 - 3 hours since the setup isdetermined beforehand via simulations.

One of the most difficult decisions for setting upbroadcast devices is how to place them for maxi-mum effectiveness and cost reduction. Some algo-rithms have been proposed for this problem. How-

ever, not all companies or industries are familiarwith 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 opti-mize the number and placement of the anchorsindoors. However, they have some disadvantageslike the formulation of the fitness function, the pa-rameters, the rate of mutation and crossover andthe selection criteria of the new population [28].One of the main issues is that the algorithm maynot converge to an optimal solution or global op-tima [29]. Also, most of the applications on thistopic have not considered spatial constraints thataffect the optimization process. The investigationshave been conducted in open spaces or clear corri-dors without considering physical limitations.

Renting UWB equipment is a cost that a com-pany should consider because this technology canachieve high accuracy, but it requires an impor-tant economic investment. Other technologiessuch as BLE may be cheaper, but the area of cov-erage is smaller, accounting just for a few metersfrom the BLE placement in contrast to the cover-age provided by a UWB anchor which may coverabout 60 m (or an area of 11,310 m2). If a com-pany or researchers want to define an effective areawhere 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 analysisdoes not account for all the spatial features thatcan exist in an indoor environment. If the deviceis 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 behaviorbefore implementation.

Further, it is possible to include additional in-formation inside each feature of the shapefiles,such as signal loss, type of obstruction, among oth-ers. This also helps to estimate and conduct a ker-nel 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 estimatethe signal loss or power at some point (x,y).

The tool introduced in this document can be de-fined using coordinate reference systems. By im-plementing this tool, companies, as well as re-searchers, will be able to estimate signal propa-gation in specific indoor environments. This fea-ture may be helpful to control several parameters

4 Discussion

5 Conclusion



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to get the required information and thus performsimulations related to the location of the devices.

Another advantage of this tool is that by inte-grating GIS, it would be possible to incorporate itwith different algorithms towards an optimizationmodel. Finding the best parameters for the differ-ent variables will help companies and researchersto place the UWB anchors in the best way to min-imize the number of tests before the experiments.

Our tool considers spatial features loaded inGIS as range and location. So, it is possible to useit for simulating other types of technologies suchas 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 radiosignal in a clear environment or its attenuationacross materials[31].

While enterprise systems such as TruNETWireless can accomplish similar results, a com-mercial license is required. Another reason theauthors worked with R and Q-GIS specifically isthat these tools allow companies to reduce costssince no commercial software or license is requiredfor its implementation.

Finally, this paper contributes to electronic de-vices given the advantages of integrating not onlyspatial features into the signal broadcast but alsoother intrinsic properties such as power strength(RSSI), power loss, among others; and the param-eters of the spatial features can be adjusted underthe optimization models.

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Luis Carlos Manrique carried out the writing ofthis manuscript, research and simulations.Anthony Weiss carried out the writing and check-ing the logic of this manuscript.Sandra Puentes carried out the writing of thismanuscript.

Personal funding.

6 Contribution of individual authors

to the creation of a scientific article

(ghostwriting policy)

7 Sources of funding for research

presented in a scientific article or

scientific article itself



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http://qgis.osgeo.org

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Simulating Wave Propagation Distribution Through GIS