Gateway Placement in LoRaWAN Enabled Sensor Networks

Batuhan Can, Halit Uyanık and Tolga Ovatman

Department of Computer Engineering, Istanbul Technical University, Istanbul, Turkey

Keywords:

Gateway Placement, LoRaWAN Networks, Environmental Monitoring.

Abstract:

This paper proposes two different approaches to be applied in gateway placement problem in LoRaWAN sen-

sor networks. The ﬁrst approach is based on ﬁnding the minimal set to contain all the coverage intersections

of the sensors and the second approach is based on optimization via integer programming over the distance

between the gateways and sensors. Our results show that using automated gateway placement provides signif-

icantly less number of gateways to be used.

1 INTRODUCTION

Placing gateways in a sensor network has always been

a challenging problem. There is vast amount of re-

search about this problem and different types of ap-

proaches have been considered to solve it efﬁciently.

Gateway placement has a critical role in real-world

applications of sensor networks, since it is necessary

for a system to be energy-efﬁcient and to have high

quality data communication. A smart way of placing

gateways can highly affect the total energy consump-

tion of sensors.

In this paper, we propose two different approaches

to solve the problem of gateway placement by group-

ing the sensors in a LoRaWAN enabled sensor net-

work. One of these approaches relies on exploring

the intersection of sensor coverage ranges and plac-

ing gateways on these intersections to cover the most

intersection with the least number of gateways. Other

approach uses mixed-integer programming (MIP) on

an optimization model that try to minimize the num-

ber of gateways that fully cover the provided set of

sensors.

We validate the proposed model not by using

hypothetical sensor locations but by simulating the

model with actual sensor positions obtained from a

real-world case study: Ergene river (which is a major

left tributary of the Maritsa river, ﬂowing entirely in

the East Thrace region of Turkey). All the sensor po-

sitions from the Ergene river has been pre-determined

by a previous project to collect the most information-

rich data about micro-pollutants in the river (see Fig-

ure 2). We run our experiments over two different

simulators: ns-3 (Riley and Henderson, 2010) and Lo-

RaPlan (Loh et al., 2021b). In ns-3, we have used

LoRaWAN module to tune the low level communi-

cation properties between the gateways and the sen-

sors. We collected transmission failure rates over ns-3

simulations. Also by running LoRaPlan simulations,

we obtain RSSI levels throughout the sensor network

and other basic signal properties for certain gateway

placements.

Our results show that using MIP signiﬁcantly de-

creases the number of gateways that can provide a full

coverage even though it results in a degradation in

communication quality. Both of these differences in-

crease as the coverage of the gateways increase. Our

study provides novel results over a realistic sensor dis-

tribution scenario in a rural environment. Proposed

approaches have been validated by two different sim-

ulation environments ns-3 and LoRaPlan, and the re-

sults of these approaches are compared with manual

placement.

2 RELATED WORK

Even though the underlying concepts such as inter-

net of things (IoT), wireless sensor networks (WSN)

and wireless mesh networks (WMN) may change

through time, optimal gateway placement in wire-

less networks have been taking the attention of many

researchers. Also the problem of coverage and

clustering devices in wireless networks, have been

considered in various studies (Abbasi and Younis,

2007)(Younis and Akkaya, 2008). Gateway place-

ment as a cluster assignment problem can be deﬁned

as the division of nodes in a wireless network into

Can, B., Uyanık, H. and Ovatman, T.

Gateway Placement in LoRaWAN Enabled Sensor Networks.

DOI: 10.5220/0011777000003399

In Proceedings of the 12th International Conference on Sensor Networks (SENSORNETS 2023), pages 93-100

ISBN: 978-989-758-635-4; ISSN: 2184-4380

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

clusters and ﬁnding the optimal location for the gate-

way in each group, so that the requirements of the net-

work are satisﬁed by dividing the network into mini-

mum number of clusters.

Various methods can be considered to ﬁnd a so-

lution to the problem of clustering. An example to

such methods is a hybrid model which is the combi-

nation of three algorithms based on LEACH and K-

Means (Bholowalia and Kumar, 2014). Clustering in

the proposed model is done by K-Means algorithm.

Another study presents an integer linear programming

(ILP) formulation and a polynomial time recursive al-

gorithm (Aoun et al., 2006), aiming to divide a WMN

into minimum of number of clusters.

In gateway placement, it is crucial to satisfy the

quality-of-service (QoS) requirements. These re-

quirements can differ in different scenarios. In order

to provide the bandwith requirements of the clients,

three different models have been developed (Qiu

et al., 2004) to ﬁnd the best locations for internet acc-

sess points (ITAP) and ILP is used to formulate the

problem. Another study focuses on clustering the sen-

sors in a way that the least transmission latency is sat-

isﬁed (Youssef and Younis, 2007). For this purpose

two genetic algorithms for the optimal gateway place-

ment have been introduced and validated by simulat-

ing WSN. There is also a study where a MIP formu-

lation is used in order to optimize the throughput in a

WMN, and a greedy algorithm to solve the problem

of gateway placement (Li et al., 2008). A formula-

tion based on ILP is proposed to lower the cost of the

network deployment by dividing the IoT devices into

two groups such as gateways and lower-end tranmis-

sion devices, according to their power consumptions

(Gravalos et al., 2016).

Communication of devices over long distances is

costly. In order to overcome this problem LoRa,

which is a Low-Power Wide-Area Network (LP-

WAN) technology, can be used. Ability of commu-

nicating over long distances at low power satisﬁed by

LoRa increases the sustainability of the IoT applica-

tions immensely. A star-to-star topology is introduced

in Long-Range Wide-Area Networks (LoRaWAN) ar-

chitecture. With LoRaWAN, the necessity of building

and sustaining a multi-hop network is discarded, since

IoT devices are able to communicate in a single-hop,

in LoRaWAN (Haxhibeqiri et al., 2018).

Pace of the studies on planning and setting up

efﬁcient LoRa networks has been increasing in the

last few years. For example, a recent study dis-

cusses efﬁcient gateway placement, where a formula-

tion based on mixed-integer non-linear programming

(MINLP) is presented to solve the problem for rela-

tively small LoRa networks, whereas an approximate

algorithm is proposed to set up wider networks (Ousat

and Ghaderi, 2019). An approach to the problem of

gateway placement as a clustering challenge is also

presented, studying on a dynamic IoT scenario and

aiming to ﬁnd the optimal number of clusters so that

the number of devices which are not able to transmit

data will be minimized (Matni et al., 2020). Coefﬁ-

cients in a propagation model for Low-Power Wide-

Area Networks (LPWANs) in a speciﬁc scenario, in

which a wooded area is considered, are adjusted in

(Cruz et al., 2022) in order to optimize the coverage

area and minimize the number of gateways by using

Evolutionary Particle Swarm Optimization (EPSO).

In (Loh et al., 2021a) the authors introduced an

ILP based approach in order to satisfy the most com-

mon requirement of gateway placement - minimizing

the total number of gateways considering the famous

set cover problem while guaranteeing the robustness

of the network and the proposed method has been

simulated using real-world data of an urban environ-

ment. Aim of the o optimization model in this paper

is to cover all the sensors and cluster them in a way

that each group has an overall quality of data trans-

mission above a determined threshold. Additionally

this approach is simulated by using real-world data of

a rural environment, in which data transmit ranges of

gateway devices differs due to the topography of the

terrain. Results are also evaluated and compared by

using different tools and considering several metrics.

3 PROPOSED APPROACHES

Gateway placement is handled as an application of

the set cover problem in most of the cases, because

the common aim in placing gateways is to cover all

the sensors in a network. It is obvious that a sensor

network can be fully covered by assigning a gateway

to each single device. However, this approach is not

cost-efﬁcient, since a gateway can cover more than

one sensor. Thus, an optimization is needed to ﬁnd

these proper placement locations of gateways so that

network can be divided into clusters, in where there is

a single gateway device can transmit data with all the

sensors in the cluster.

3.1 Placement by Coverage

Figure 1 shows the ﬁrst approach on ﬁnding appro-

priate gateway locations for the distributed sensors

across the Ergene river. First, a sphere which en-

capsulates the maximum assumed signal range of the

sensors is drawn around the sensors. Second, the in-

tersection of these spheres are taken, because if any

SENSORNETS 2023 - 12th International Conference on Sensor Networks

Figure 1: Intersection of signal ranges (dashed circles) are

used for potential gateway (green shaded) regions.

gateway is placed within these intersections, then it

is guaranteed that the communication can be estab-

lished at least once. However, the intersection area

is wide and therefore another step is required to ﬁnd

exact three-dimensional geographical location of the

gateway. In this step, if there exists any river points

along the intersection area, which the rivers are repre-

sented as small vector lines, then one of these points

are chosen randomly. If there are no river points avail-

able, then the intersection of vertical and horizontal

center of the intersection is chosen.

Number of chosen gateways can widely vary ac-

cording to the coverage distance of the gateways. Due

to possible inclusion of some end-devices within mul-

tiple gateway ranges, it is also possible for a sensor to

broadcast its packet to multiple gateway units which

can further affect the network performance. Another

issue to resolve is the selection of most optimal gate-

way within possible optimal locations so that all sen-

sors can be covered with minimum number of gate-

way units. To resolve this issue, a basic set-cover

approach is applied. First the sensors and their cor-

responding gateways are listed in a hash-map. Then

these maps are sorted in decreasing order. Lastly, sets

are taken one by one until all sensors are taken at least

once.

3.2 Gateway Placement by MIP

In order to ﬁnd optimal locations for placing gate-

ways, coverage distances of the devices must be con-

sidered. Transmission range of a gateway might dif-

fer due to the topology of the terrain. Also a sensor

should not be in more than one cluster in a network

divided into independent sets of sensors, so that each

data transmitted to a gateway is unique. Especially in

rural environments it is hard to estimate the range of a

gateway, therefore it is difﬁcult to determine optimum

locations for gateways.

In order to formulate the problem as an MIP in-

stance, all the deﬁnitions done in the statement of the

problem must be expressed mathematically. Table 1

shows variables used throughout the development of

the proposed model.

In the proposed technique, a grid based approach

Table 1: Variables used in MIP formulation.

variable description

G grid representation of the terrain

S set of sensors in the network

N number of sensors in the network

R gateway coverage range

grid

scale of the grid (resolution)

lon

, lat

projected coordinates of the i

, y

grid coordinates of the i

node

, s

grid coordinates of sensor s ∈ S

is used to model the environment. Scale of the grid is

provided as a parameter, which indicates the number

of potential placement location in a single axis. Ac-

cording to the grid scale, resolution of the grid can be

determined. Grid resolution indicates the total num-

ber of potential placement locations in the terrain.

Grid resolution has a signiﬁcant role in the perfor-

mance of the optimization model. A change in the res-

olution of the grid effects the run-time performance of

the optimization model.

= r

grid

lon

− lon

min

lon

max

− lon

min

(1)

= r

grid

lat

− lat

min

lat

max

− lat

min

(2)

lon

(lon

max

− lon

min

) · x

grid

+ lon

min

(3)

lat

(lon

max

− lon

min

) · x

grid

+ lon

min

(4)

Sensor location data should be provided following

the format of projected coordinate system. Conver-

sion from projected coordinate system to grid coor-

dinate system is performed by using the Equations 1

and 2. The conversion is performed by scaling each

coordinate between the minimum and maximum val-

ues among the coordinates following the resolution

of the grid. Even though the conversion is straightfor-

ward (for instance it does not take earth’s curvature

into account), it still produces admissible results as

seen in the section 4.2.

After the optimization, selected gateway coordi-

nates than converted back to the real coordinates us-

ing the Equations 3 and 4 which are the inverse

functions of the conversion functions Equation 1 and

Equation 2.

Main purpose of the MIP based optimization

model is to deploy least gateway device as possible

while guaranteeing the sensor network is fully cov-

ered by these gateways. The model divides the wire-

less sensor network into clusters such that in each

cluster there is only one gateway and all the sensors

Gateway Placement in LoRaWAN Enabled Sensor Networks

in the cluster are connected only to this gateway de-

vice. In other words, in the end it is expected to see

that there is a one-to-many relation between sensors

and gateways.

Equation 5 shows the objective function for the

optimization model. A 2-dimensional boolean array

(G) is used to represent the grid. Rows and columns

of the array indicate the placement location of a gate-

way. According to the representation, if the gateway

is placed at a point, corresponding value in the array

G will be True (1), otherwise False (0). Thus, we can

formulate the objective of minimizing the number of

gateway as minimizing the number of 1s in G. To be

more precise, summation of the entities in the array

must be reduced as much as possible.

minimize

N−1

∑

i=0

N−1

∑

j=0

i j

(5)

During the optimization, the aim is to cover all the

sensors in the network by the planted gateways. Also

each sensor should be connected to only one gateway

device. Therefore a constraint for each sensor is de-

ﬁned in the optimization model.

In the expression of the constraint shown in Equa-

tion 6, 2D binary array G, is used with a function f (in

Equation 7), where the distance between the sensor

and the potential gateway location is calculated. Ac-

cording to the calculated distance a Boolean value is

returned, which indicates if the sensor is in the range

of a gateway device or not. Euclidean Distance for-

mula in Equation 8 is used in order to calculate the

distance between devices.

∀(s ∈ S)

N−1

∑

i=0

N−1

∑

j=0

i j

· f (i, j, s

, s

) ≤ 1

(6)

f (x

, y

, x

, y

) =

(

1 E(x

, y

, x

, y

) ≤ R

0 otherwise

(7)

E(x

, y

, x

, y

) =

− x

)

+ (y

− y

)

(8)

Implementations related to MIP formulation are

done by using Python programming language. A li-

brary called Python-MIP (Santos and Toffolo, 2020)

is used to built an optimization model. Python-MIP is

a tool, in which a mixed-integer programming based

optimization model is used to formulate the problem

and solve it. To visualize the outcomes of the op-

timization, matplotlib (Hunter, 2007), a commonly

used tool to visualize data, is used. Codes of the

model deﬁnition and visualizations are shared within

a repository

https://github.com/canbatuhan/gp-mip

Figure 2: Initial collection of measurements from 75 sample

locations on the Ergene watershed. (The image is a courtesy

of TUBITAK project 115Y064.

4 EVALUATION

4.1 Experimental Environment

Simulation of the introduced model is performed by

using data obtained from a real-world study case. Lo-

cation of the sensors placed in Ergene river are taken

as inputs of the optimization model, in the form of

projected coordinate system. Later, the river is mod-

elled as a grid by converting the input data to grid

coordinate system, as mentioned in Section 3.2.

Sensor network locations in Ergene River contains

75 sensors spread on a 12000 km

area (see Figure 2).

Number of gateways to cover all the sensors differs re-

lated to the coverage distance, which is the maximum

distance for a device to transmit data successfully.

In our simulations, we use Network Simulator 3

(ns-3) (Riley and Henderson, 2010). ns-3 supports

third-party modules and for this study, a LoRaWAN

module developed by researchers in a previous study

is utilized (Magrin et al., 2017). In our simula-

tions, we use a scheduling approach similar to slot-

ted ALOHA to distribute sensors across duty-cycles

with as little collision as possible. ns-3 also supports

simulating the network under realistic path-loss con-

ditions which are also taken from previous studies and

fed into the simulation (Phaiboon and Somkuarnpanit,

2006). For 100 meter, path-loss exponent is found

around 2.2 with path-loss as 78 dB.

We also use another simulator, LoRaPlan (Loh

et al., 2021b), which is a gateway placement planning

software for LoRa enabled sensor networks. In Lo-

RaPlan, a user is able to run simulations with sensor

location and gateway location data. Received Sig-

nal Strength Indication (RSSI) level of each reach-

able sensor can be extracted. In LoRaPlan interface,

thresholds for RSSI levels can be set and they are set

to -120 dBm (lower threshold) and -100 dBm (upper

SENSORNETS 2023 - 12th International Conference on Sensor Networks

threshold) in default, therefore these threshold values

are used in our experiments.

4.2 Evaluation and Discussion

Evaluation of the proposed methods are performed

using different measures. Manual placement of gate-

ways is used as a baseline during the comparison

of the proposed approaches. Firstly, the number of

gateways is compared. Secondly, a measure called

Failed Packet Attempt (FPA) ratio (as percentage to

all packet transmissions attempts of a sensor) of each

gateway placement scenario is extracted from the re-

sults of ns-3. Thirdly, by using LoRaPlan, RSSI

(dBm) value of each reachable sensor is calculated

for different coverage distances ranging from 1000

meters to 10000 meters. Finally, in order to see the

differences between proposed approaches, gateway

placements are shown on map.

Figure 3a shows how many gateways are used in

order to cover all the sensors located in Ergene River.

In all methods, the number of gateways decreases as

the range increases. Specially in MIP formulation the

decline of gateway count is immense and constant.

This is because the main objective of the optimization

model is to minimize the number of gateways and the

formulation in Equation 5. Also in coverage intersec-

tion method, gateway count declined in short and long

ranges. However, it did not change when the coverage

distance is 6000 to 8000 meters. Also as expected,

the comparison results shows that placing gateways

manually is inefﬁcient in term of the total of number

of gateways, since it can be seen that even when the

range is 10000 meters, 40 gateway locations are se-

lected in manual placement, whereas 27 gateways are

used in coverage intersection method and 14 gateway

location is proposed in MIP formulation.

Figure 3b indicates the probability of a packet loss

during a transmission considering the proposed gate-

way placement. FPA percentage for every method

increase, while the coverage distance increases. As

expected, the manual placement is the best approach

to have a network with less transmission failures.

Nonetheless, it is important to remember that within

the manual placement, the number of gateways is the

highest among proposed approaches. In small ranges

such as 1000 to 3000 meters; MIP method ends up

with ﬁner FPA percentages than coverage intersection

method. For an example in the scenario, in which the

range is 2000 meters, FPA percentage of MIP formu-

lation is approximately 25,63%, whereas the FPA per-

centage of coverage intersection methods is 30,83%.

In medium ranges, FPA percentage of Coverage Inter-

section method is less than the proposed optimization

model. For long ranges; gateway placement proposal

of the MIP method performs better than coverage in-

tersection approach.

In ﬁgures 4a and 4b; distribution of RSSI values

of the reachable sensors for each gateway placement

is compared. Comparisons are done for coverage dis-

tances of 2000 meters and 8000 meters, respectively.

In other words, behaviour of the introduced methods

and the base case are analyzed in terms of short and

long ranges of coverage. In all comparisons -100 dBm

is selected as an upper threshold, whereas the -120

dBm is selected as a lower threshold as they set as

default in simulation software, LoRaPlan.

Figure 4a shows the distributions of RSSI values

for reachable sensors in a short range of coverage,

where the coverage is 2000 meters. All the distri-

butions indicate that after each gateway placement

provided by the proposed methods, strength of the

sensors are above the upper threshold. Moreover, it

is noticeable that coverage intersection method ends

up with RSSI values which are similar to the manual

placement, whereas the number of gateways for cov-

erage intersection much less than the manual place-

ment for 2000 meters as shown in Figure 3a.

Distributions of RSSI values in long range sce-

nario, where the coverage distance is 8000 meters, are

presented in Figure 4b. In terms of MIP formulation,

results show that signals strengths are between upper

and lower thresholds. Also in this scenario, MIP for-

mulation ends up with signals that their strengths are

below the lower threshold which means there are no

signal. As it mentioned previously, this is because

that the number of gateways is reduced signiﬁcantly

in MIP formulation, specially in long range coverage

cases. Figure3a shows that for 8000 meters range,

the number of gateways used in the MIP formulation

is less than half the gateways used in the coverage

intersection method and approximately one third the

gateways used in manual deployment. Therefore the

distances between gateway and sensors are greater in

MIP formulation, thus signal strengths are low.

On the other hand, distribution of RSSI values

with gateway deployment by coverage intersection

differ from the one with manual placement. In cov-

erage intersection method, RSSI values are mostly

between lower threshold and a bit above the upper

threshold. Also there is a group of sensors receiving

signals that their strengths are very high, in coverage

intersection method. However, in manual placement

of gateways, some of the signals are below the lower

threshold. Also in manual placement, there is a bigger

group of sensors that their signal strengths are very

high like in coverage intersection method. In overall,

coverage intersection method protects its stability in

Gateway Placement in LoRaWAN Enabled Sensor Networks

(a) Comparison for full coverage. (b) Comparison of failed packet attempt ratios.

Figure 3: Comparison among placement methods.

(a) 2000 meters. (b) 8000 meters.

Figure 4: Distribution of RSSI values for reachable sensors.

terms of RSSI values, even though the distance is in-

creased. It is also remarkable that the number of gate-

ways used in coverage intersection method is much

less than manual placement.

Finally, in order to make a comparison between

the results of MIP formulation and coverage inter-

section method, the sensor network and the proposed

gateway locations are visualized on a grid. Also the

coverage distances and connections between devices

are shown within the network. In Figures 5a and 5b,

gateway placements for 8000 meters of coverage dis-

tance are visualized.

After the gateway placement by coverage intersec-

tion for 8000 meters of coverage distance, 34 gate-

ways are used to cover all the sensors in the network

as shown in Figure 5a. However, there are some loca-

tions in the network that a single gateway might be de-

ployed, so that multiple sensors can be covered. Thus,

the model does not reduce the number gateways as

possible. Moreover, a sensor might be in the range of

more than one gateways. On the other hand, perfor-

mance of the model is better than MIP formulation in

term of overall signal strength as it shown in Figures

4a and 4b.

Gateway placement done by MIP formulation is

focused on minimizing the number of gateways to

fully cover the network. Figure 5b shows that the pro-

posed optimization model, selects 15 gateway loca-

tions to cover all the sensors. This means that MIP

formulation reduces the number of gateways more

than half of the gateways used in coverage intersec-

tion method. Also it is noticeable that the model re-

sults with gateway placements, so that all the sensors

in the network are connected to a single gateway. Re-

ducing the number of gateways causes a decline in

signal strengths as indicated in Figures 4a and 4b,

since distances between devices increase. However,

Figure 3b shows that the FPA percentages of place-

ment by MIP formulation are similar to Coverage In-

tersection method.

While forming the clusters, transmission range

of gateway devices is the ﬁrst consideration of the

proposed approach. Also minimizing the number of

gateways is the main objective of the optimization

model. In the end, the offered model provides a set

of placement locations, which covers the whole net-

SENSORNETS 2023 - 12th International Conference on Sensor Networks

(a) Placement by coverage intersection. (b) Placement by MIP formulation.

Figure 5: Gateway placement examples.

work while the number of gateway devices decreases

and partition the network into clusters having a cer-

tain level of quality of data measurement.

A caveat for the introduced technique is that the

experimental environment is a rural area. Topology of

the terrain has a crucial role in data transmission be-

tween devices. Even though LoRa provides a higher

distance of coverage compared to other techniques,

due to the geographical obstacles, range of a signal

might differ. For example, if there is a hill between

two devices, data transmission might not be com-

pleted successfully, despite to the fact that these two

devices are very close to each other.

5 CONCLUSION

This paper presents two different gateway placement

approaches and evaluates these approaches over a

real-world sensor positions in Ergene River. We run

our simulations in ns-3 and LoRaPlan simulators to

investigate the communication quality obtained by the

network. Our results provide numerical guides to ex-

amine the trade-off between using less gateways to

cover the full set of sensors versus communication

quality.

This work can be extended by experimenting on

different sensor position distributions to further in-

vestigate the points where each of the proposed ap-

proaches’ perform poorly. Moreover, MIP formula-

tion and optimization presented in the paper can be

investigated under different grid sizes to discover the

effect of using ﬁner resolutions in the optimization

versus time requirements. Finally the proposed place-

ment strategies may also have some additional con-

straints such as ease of transportation to gateways or

terrain information such as vegetation, etc.

ACKNOWLEDGEMENTS

This work was supported by the CHIST-ERA

grant CHIST-ERA-19-CES-005, by The Scientiﬁc

and Technological Research Council of Turkey

(TUBITAK) under grant id 120N679.

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