Propagation Model Evaluation for LoRaWAN:Planning Tool Versus Real Case Scenario

Nıbia Souza Bezerra, Christer Ahlund and Saguna SagunaDepartment of Computer Science, Electrical and Space Engineering

Lulea University of TechnologySkelleftea, Sweden

{nibia.souza.bezerra, christer.ahlund, saguna.saguna}@ltu.se

Vicente A. de Sousa Jr.Communication Engineering Department (DCO)

Federal University of Rio Grande do Norte (UFRN)Rio Grande do Norte, Brazil

vicente.sousa@ufrn.edu.br

Abstract—LoRa has emerged as a prominent technology forthe Internet of Things (IoT), with LoRa Wide Area Network(LoRaWAN) emerging as a suitable connection solution for smartthings. The choice of the best location for the installation ofgateways, as well as a robust network server configuration,are key to the deployment of a LoRaWAN. In this paper, wepresent an evaluation of Received Signal Strength Indication(RSSI) values collected from the real-life LoRaWAN deployed inSkelleftea, Sweden, when compared with the values calculatedby a Radio Frequency (RF) planning tool for the IrregularTerrain Model (ITM), Irregular Terrain with Obstructions Model(ITWOM) and Okumura-Hata propagation models. Five sensorsare configured and deployed along a wooden bridge, withdifferent Spreading Factors (SFs), such as SF 7, 10 and 12.Our results show that the RSSI values calculated using the RFplanning tool for ITWOM are closest to the values obtained fromthe real-life LoRaWAN. Moreover, we also show evidence thatthe choice of a propagation model in an RF planning tool has tobe made with care, mainly due to the terrain conditions of thearea where the network and the sensors are deployed.

Index Terms—LoRa, LoRaWAN, IoT, propagation, ITM,ITMWO, Okumura-Hata, smart city.

I. INTRODUCTION

With the increasing need for IoT technologies, a new rangeof Low Power Wide Area Networks (LPWANs) has emerged.LPWANs usually trade high bit rates for low power and longrange. End devices normally used in LPWANs are cheap, senddata at low rates (in the order of kbps) and have several yearsof battery life. All those characteristics make LPWANs theoptimal choice for several scenarios, like IoT applications insmart cities, smart agriculture and Industry 4.0.

One of the more prominent LPWANs discussed nowadays isLoRa [1]. LoRa is a long range, low power wireless technologywhich uses a spread spectrum based on Chirp SpreadSpectrum (CSS) modulation. It has a flexible configuration,offering options for increasing/decreasing the bit rate, whiledecreasing/increasing the range. As LoRa is a Physical (PHY)layer specification, it requires a suitable protocol to allow theuse of all its features, especially in a multi-device fashion.LoRaWAN [2] is a network intended for wireless devices

This work is funded by the Societal development through Secure IoT andOpen Data (SSiO) project (https://en.ssio.se/).

in which battery is a constraint and uses LoRa as its PHYlayer. It is typically developed in a star-of-stars topology,where gateways are the entities responsible for forwarding themessages from the end-devices to the network server.

For research purposes, Lulea University of Technologydecided to deploy a LoRaWAN in the smart region ofSkelleftea, where there are numerous sensors being installedfor different types of IoT applications to improve the qualityof life for the citizens. For example, there is a requirement tohave pollution sensors monitoring the air in some areas of thecity, as well as dust bin sensors which measure and report thelevel of trash in bins for smart and efficient garbage collection.As the bins and the pollution sensors are widely spread acrossthis smart region, LoRaWAN is the best choice for the overallconnection infrastructure.

In order to better understand how the city landscapeinfluences the signal propagation, we compare real RSSIvalues obtained from our LoRaWAN with RSSI valuesobtained from an RF planning tool. We compare realmeasurements to three different propagation models,Okumura-Hata [3], ITM [4] and ITWOM [5], [6]. Wechose those models because Okumura-Hata is a well knownmodel suitable for large cell mobile systems, while ITMand ITWOM are the models recommended by the planningtool used during our experiments. Before the deploymentof a LoRaWAN, it is recommended to use a planning toolto address the network range and capacity, mainly in termsof received signal strength. Most of the planning toolsrequire the selection of a propagation model, which allowsan accurate prediction of the radio conditions for a specificarea. This paper shows a comparison of real life resultsacquired from the LoRaWAN in Skelleftea and the resultsfrom an RF planning tool in terms of RSSI values. The aimof this paper is to compare the propagation models in theplanning tool which best reflect the values obtained from ourLoRaWAN. The results showed herein reflect the scenarioin the city of Skelleftea, Sweden, which is a suburban/ruralcity with approximately 35.000 inhabitants in the center, andapproximately 35.000 outside the city center. Skelleftea is atypical small town, where there are not so many high risebuildings.

In this paper, our key contributions are:978-1-5386-4980-0/19/$31.00 © 2019 IEEE

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• LoRaWAN deployment and comparison of real-life RSSIvalues collected from the network with propagationmodels from an RF planning tool.

• Our results show that ITWOM performance is closest tothe values from our real-life LoRaWAN.

• Based on our results we can state that terrain profile,environment and the transmitter-receive distance areimportant factors while choosing a particular propagationmodel in a planning tool.

This paper is organized as follows. Section II discussessome relevant literature about LoRa and LoRaWAN.Section III describes the network setup and the propagationmodels used during the experiments. The results are presentedin Section IV, and a further discussion is shown in Section V.Section VI concludes the paper and brings a summary of themain results.

II. RELATED WORK

Due to the attention gained in the latest years, LoRaWANanalysis are becoming of interest among the researchcommunity. Authors in [7] present an overview of LoRacharacteristics, using mathematical models to discuss thenetwork capacity and scalability. A performance evaluation isalso performed, with tests done to verify how LoRa is affectedby the Doppler effect, as well as measurements to evaluate theeffects of linear and angular velocity, also including an outdoorcoverage measurement. Regarding the network capacity andscalability, results show that, depending on the frequencyin which a node sends data, a single gateway can supportup to few millions devices. They also show a less reliablecommunication due to Doppler for speeds exceeding 750RPM, when using SF 12 and 125 kHz of bandwidth. However,the authors did not test any other SF than 12.

In [8] and [9], the authors have evaluated LoRaWANsunder different conditions. While in [8] the authors conductedtheir experiments in a LoRa testbed, where a Cisco 910industrial router was used as a gateway, in [9] the authorshad a LoRaWAN with three gateways from Kerlink [10]. Theauthors on [8] found that SF and Data Rate (DR) are criticalfactors regarding the network coverage, while the authorsin [9] found LoRa to be a reliable technology for smart sensingapplications.

The LoRa FABIAN implementation is studied in [11].The LoRa FABIAN protocol stack is configured with theIEEE 802.15.4 as Layer-2 instead of LoRaWAN. Their drivetests show that the height of the gateways antenna plays animportant role in the reception. As they focused their workin the LoRa FABIAN, a LoRaWAN comparison might bringsome insights for their model.

The evaluation of LoRa in different propagation scenarios ispresented in [12]. The authors evaluated how LoRa performsin urban, suburban and rural areas, using a real network. Theirresults show the need of a proper evaluation of the propagationconditions before the network deployment, in order to obtainthe highest reliability possible from a LoRaWAN. Althoughtheir experiments are done in a real network, they used a

controlled environment, using different frequency channelsthan default ones used for LoRa in Europe, and alwayspointing the gateway antenna to the end-device direction. Allthose configurations might have lead to optimistic results.

III. EXPERIMENTAL SETUP AND PROPAGATION MODELS

While LoRa is a PHY layer specification, LoRaWANis a Medium Access Control (MAC) protocol intended tobe used with LoRa as its PHY layer. Fig. 1 presents theLoRaWAN topology adopted in our experiments. The dottedlines represent wireless connections, while the continuouslines represent cabled connections (although, it is possible toconnect the gateways and the network server via radio).

A LoRaWAN is built as a star-of-stars topology, wherethe devices send packets to a gateway, which is thenresponsible for forwarding those packages to a networkserver. LoRaWANs differ from traditional cellular networksin a way that the devices are not associated to a particulargateway. Instead, each device broadcasts its messages to allthe gateways in its range, and the gateway simply relaysthose messages to the network server. In fact, the sensorsare associated to a specific network server, which is thenresponsible for the collection/storage of the received messages.

Fig. 1: LoRaWAN topology.

We have a LoRaWAN deployed in the city of Skelleftea,Sweden. The network is comprised of three Kerlink WirnetiBTS Compact gateways [13] and one network server. Thegateways are outdoor LoRaWAN gateways for smart IoTnetworks. They are light and simple to install, and canbe remotely monitored and managed with Kerlink networkoperations solutions. The network server is also from Kerlink,and it is their network operations solutions, known as theWanesy Management Center [14], where we have accessto all the information about the equipment, configurationand received messages. All the gateways are equipped withvertically polarized omnidirectional antennas with a maximumgain of 3 dBi. One of the gateways is equipped with twoantennas for spatial diversity, and the other two gateways areequipped with one antenna each. The LoRaWAN in Skellefteaoperates in the 868 MHz frequency band.

The devices used for the data collection are five ElsysLoRa® ELT-1 [15] stationary sensors, which are commercialLoRaWAN devices from Elsys. Each sensor is equipped withone LoRa antenna with a maximum gain of 3 dBi, and are

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powered by battery. The sensors were mounted along theLejonstrom bridge in Skelleftea (the oldest wooden bridge inSweden), and their configuration is shown in Table I. All thesensors are located at approximately 2 m above the groundlevel.

TABLE I: Sensors Configuration

Sensor SpreadingFactor

ReportingInterval

CodingRate

Lat,Long

E1 7 1 min 4/5 64.748983,20.912189

E2 and E3 10 3 min 4/5 64.749404,20.912538

E4 and E5 12 5 min 4/5 64.750306,20.914273

As aforementioned, the sensors were placed at a woodenbridge (Lejonstrom bridge). The sensors locations, as wellas the gateway sites, are shown in Fig. 2. Sensors E1 andE5 are indicated in the figure by the red and green markers,respectively. The figure also shows the distance in straight linefrom the bridge to each gateway.

Fig. 2: Sensors and gateways location (source: Google Maps).

We use a radio planing tool [16] to estimate the coveragearea for the three LoRa gateways we have in place. This toolallows us to configure the receiver and the transmitter, creatinga simulated environment that considers the characteristicsof the real equipment. It also allows us to configure thepropagation model, and it counts with topographic maps tobetter estimate the terrain impact on the signal propagation.For this work we used the Okumura-Hata [3], ITM [4], andITWOM [5], [6] propagation models. Fig. 3 presents thecoverage map generated with the radio planning tool [16]when considering the ITWOM propagation model. For everygateway, the radius of the coverage area is 10 km.

The Okumura-Hata model is based on data collected in thecity of Tokyo, Japan, in 1968. It combines both the Okumuraand Hata models, where the equations of the Hata model areused to fit the original curves from the Okumura model. Thepath loss equation for the Okumura-Hata is written as [3]:

Pl = A+B log(d) + C, (1)

Fig. 3: Theoretical coverage map when considering ITWOMpropagation model created with the radio planing tool [16],where the city of Skelleftea is in the center of the coveragecircles.

where

A = 69.55 + 26.16 log(fc)− 13.82 log(hb)− a(hm), (2a)

andB = 44.9− 6.55 log(hb). (2b)

The parameters C and a(hm) depend on the environment,fc is the carrier frequency (150 MHz < fc < 1500 MHz),hb is the base station/gateway antenna height (30 m < hb <200 m), hm is the device’s antenna height (1 m < hm <10 m), and d is the transmitter-receiver distance in kilometers.One disadvantage of this model is not to consider the terrainprofile in its calculation.

The ITM (also known as Longley-Rice model) is a modelused for broadcast coverage for frequencies between 20 MHzand 20 GHz. It is defined as a two-part system containingin the first part the ITM core and, in the second part,an input-output package. This model is generally used forpoint-to-point communications, and in the point-to-point modeit uses terrain data to calculate the path loss. However, thismodel is based on the classical diffraction theory, whichdoes not provide precise calculations of radio waves overirregular terrain. Also, it does not provide corrections due toenvironmental factors in areas closer to the receiver.

The ITWOM is an improved version of ITM. Accordingto [6], the ITWOM adds high location accuracy predictionto the ITM core, making it more accurate than itspredecessor. It counts with the Radiative Transfer Engine(RTE) functions implementation described in the ITU-RP.1546-2 recommendation [17]. It also takes in consideration

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more than one obstruction in its diffraction calculations (ITMonly considers one obstruction).

IV. RESULTS

This section presents the results obtained from ourLoRaWAN deployed in Skelleftea, Sweden, versus the resultsobtained from the RF planning tool.

As previously stated, we have five sensors deployed at theLejonstrom bridge, which send data at specific time intervals,and are configured with different SFs, as seen in Table I. Forsake of brevity, and due to similar results between sensorsthat are close to each other (less than 50 m apart), we onlyshow the results from two sensors, each one with a differentSF (E1 and E5). It is worth mentioning that sensors E2 andE4, located in between E1 and E5, and configured with SF 10and SF 12, respectively, present results similar to the resultsof sensor E1 (SF 7), while sensor E3 (configured with SF 10)present results similar to the results of sensor E5 (SF 12).

Fig. 4 shows the RSSI values collected from our LoRaWANfor the E1 sensor, together with the values for ITWOM,ITM and Okumura-Hata models calculated using the RFplanning tool spanned on time, per gateway. The word ‘data’in the figure legend refers to the values from our LoRaWAN.Firstly, we can see that the ITM value calculated by theplanning tool is overestimated for all the gateways whencomparing to the network values. For Klockarhojdan and OdenSkrapan gateways (Figs. 4b and 4c), only few RSSI valuescollected from our LoRaWAN are in the vicinity of the ITMvalue calculated with the RF planning tool. For the Vitbergetgateway (fig. 4a), none of the network values are close tothe ITM calculated value. As the ITM does not take intoaccount the effects of irregular terrains (which is the case inour scenario), the RSSI calculated values are far from the realones.

The Okumura-Hata is at the opposite end: its calculatedvalue is underestimated when compared with the values fromour LoRaWAN. This is because the model does not considerany terrain data in its calculations, and it only uses the distancetransmitter-receiver and the type of propagation environment(small city, in our case) for the path loss calculation. Asexpected, the distance parameter shows its relevance whenwe compare the three gateways, as seen in Fig. 4: theKlockarhojdan gateway, which is the closest one from thesensor (as shown in Fig. 2), shows the highest calculated RSSIvalue for this model; the Oden Skrapan gateway, the secondclosest one from the sensor, presents the second highest valuefor the model, while for the furthest gateway (Vitberget), thecalculated value is the lowest one.

The ITWOM is the propagation model which gives thebest results, because the RSSI values from our LoRaWANare located around the ITWOM calculated value. Theimprovements made in this new model, like the adoptionof RTE instead of the classical diffraction theory, makes itmore accurate in comparison to the two previously mentionedmodels.

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Fig. 4: Network RSSI x RSSI from RF planning tool - sensorE1 with SF = 7.

We also evaluate the variations in the transmitter-receiverdistance and the adoption of different SFs among the sensors.Fig. 5 shows the values for the E5 sensor. This sensor islocated at a distance of approximately 172 m from the E1sensor, closer to all the gateways compared to E1. For theVitberget gateway (Fig. 5a), the ITWOM presents a higherRSSI value than the ITM. In this case, the distance fromthe sensor to the gateway is the only factor causing thedifference, once the planning tool does not consider the SF inits calculation. This might be related to the terrain resolutionin the planning tool, as the best terrain resolution for thelocation where we carried the experiments is 90 m, andthe distance from E1 to E5 is approximately 172 m, whichmeans that the terrain profile used for the calculation of E5

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values is probably different from the one used to calculateE1. Klockarhojdan gateway, shown in Fig. 5b, presents thesame behaviour as the same gateway for E1 (Fig. 4b), anotherindication that the distance between the sensor and the gatewayis a relevant parameter when choosing propagation models andits configurations.

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Fig. 5: Network RSSI x RSSI from RF planning tool - sensorE5 with SF = 12.

Fig. 5c shows the results for the Oden Skrapan gateway.The first noticeable difference from the other results (Fig. 4c)is that, for this gateway, the calculated RSSI value for theOkumura-Hata model is lower than the one calculated forthe ITWOM. There are two reasons for this behaviour. First,this sensor is 172 m closer to this gateway than the E1,and, as previously stated, the distance transmitter-receiver

has a meaningful impact in this model. Second, the OdenSkrapan gateway is located in the center of Skelleftea, andthe Okumura-Hata model works better for urban scenarios, asthe curves used to fit this model were collected in the cityof Tokyo in 1968. In summary, the combination of proximityand a more urban scenario in relation to this gateway leadsto the RSSI value calculated for the Okumura-Hata model.Regarding the other two models for this gateway, we can seethat they follow the same trend as the results presented forthe E1 sensor in Fig. 4c. From the graphics, we can clearlysee that the location of the sensors has a considerable impactin the RSSI values calculated for the propagation models. Wecan also see that both the environment and the terrain profileinfluence the calculations. This means that the choice of apropagation model when using an RF planning tool must becarried with caution.

In order to allow a better understanding of our comparisons,Table II presents the numerical comparison between theaverage of the RSSI values from our LoRaWAN, and the onesobtained with the RF planning tool for the models evaluated inthis work. As the collected RSSI values are in dBm units, wefirst converted them to linear and then we took the average.After that, we converted the averaged value back to dBm.The error column in the table is the difference between thecalculated values for each propagation model, and the averageRSSI from our LoRaWAN for each sensor, in each gateway.These values show how far the network collected values arein comparison to the values calculated with the planning tool.

The model that shows the lowest values of error for allthe sensors in all the gateways is the ITWOM. Thus, for ourscenario, this is the propagation model that better capturesthe environment and the terrain profiles. From the overallresults, Okumura-Hata is the model that shows the highesterrors when compared to the RSSI values from our LoRaWAN.This is expected, as the Okumura-Hata Model does notconsider the terrain profile, and it is dependent mainly in thetransmitter-receiver distance. The performance of ITM laysbetween the two aforementioned models for most scenarios.In fact, this is the model recommended by the planning toolto be use with LoRa. However, if the goal of the planningis to have the best configuration possible for a LoRaWAN,then according to our results, the adoption of ITWOM fora suburban/rural scenario is recommended, as this modelpresented values closer to the values acquired from the ourLoRaWAN.

V. DISCUSSION

During our experiments, we expected that all the sensorswould show the same trend in their results, mainly regardingthe values calculated by the RF planning tool. However, aswe can see from Figs. 4 and 5, the RSSI values calculatedby the planning tool differ between the two sensors used inthis study. This is caused mainly by the distance betweenthe sensors and the gateways, as well as due to the terrainprofile of the sensor’s location. Our results show that, inaverage, the RSSI values collected from our LoRaWAN are

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TABLE II: Numerical comparison between RSSI average from our LoRaWAN (Network value) and the propagation models.

dBm Error (dB)Gateway Sensor Network value (avg.) ITWOM ITM Okumura-Hata Network vs.

ITWOMNetwork vs.

ITMNetwork vs.

Okumura-HataE1 -90.17 -86.7 -61.7 -114.0 3.47 28.47 -23.83Vitberget E5 -89.71 -78.4 -82.8 -121.4 11.31 6.91 -31.69E1 -90.15 -93.0 -79.7 -111.1 -2.85 10.45 -20.95Oden Skrapan E5 -90.37 -91.1 -79.9 -85.7 -0.73 11.37 4.67E1 -90.15 -87.6 -81.2 -102.8 2.55 8.95 -12.65Klockarhojdan E5 -89.90 -87.0 -63.7 -100.6 2.9 26.2 -10.7

very close to the RSSI calculated values for the ITWOM formost of our scenarios. They also show that the longer thedistance between the sensor and the gateway, the higher is thedifference between the values collected from our LoRaWANand the calculated ones from the planning tool, as shown inTable II.

We can also emphasize that while other works haveperformed evaluations on LoRaWANs, most of those worksuse a single gateway and a single SF in their experiments,while we use a LoRaWAN comprising of three gateways,and with sensors configured with three different SFs. Thegateways positioning was decided based on the availabilityof fiber connection around the city of Skelleftea.

Based on our results, and for a scenario with the sameconditions as the ones used in our experiments (small city, norough terrain), the ITWOM is the propagation model whichgives similar RSSI values to the ones acquired from theLoRaWAN assembled in Skelleftea.

VI. CONCLUSION AND FUTURE WORK

In this paper, we present the comparison of RSSI valuescollected from a real-life deployment of a LoRaWANwith three different propagation models: ITWOM, ITM andOkumura-Hata in the city of Skelleftea, Sweden. Our resultsshow that, for the specific conditions and terrain presentedin this paper, the RSSI value calculated by an RF planningtool for the ITWOM is closest to the average of theRSSI values collected from the real-life LoRaWAN. Further,the RSSI results for the Okumura-Hata model are farthestaway from the averaged real-life LoRaWAN collected RSSIvalues, while the ITM lies between the other two models.In conclusion, the performance of the models was in thefollowing order: ITWOM performs the best, followed byITM and then Okumura-Hata. Even though ITWOM is thepropagation model that best captures the characteristics of ourLoRaWAN, our conclusion is that, the environment, the terrainprofile and the distance transmitter-receiver must be taken intoconsideration when selecting a propagation model in an RFplanning tool.

For future work, we intend to perform a larger study, withthe deployment of more sensors in different locations aroundthe city. We are particularly interested in the performance ofindoor sensors located at the border of the coverage area shownin Fig. 3. The study will not only focus on the signal strength,

but will also consider Signal-to-Noise Ratio (SNR) values toinfer the channel conditions.

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