Performance Analysis of LoRaWAN Technology

for Optimum Deployment of Jakarta Smart City

Anisa Dewi P

Faculty of Electrical Engineering

Universitas Indonesia

Depok 16242

anisa.dewi72@ui.ac.id

Bambang Wahyuaji

Faculty of Electrical Engineering

Universitas Indonesia

Depok 16242

bambang.wahyuaji@ui.ac.id

Fandhy Bayu R

Faculty of Electrical Engineering

Universitas Indonesia

Depok 16242

fandhy.bayu@ui.ac.id

Ruki Harwahyu

Faculty of Electrical Engineering

Universitas Indonesia

Depok 16242

ruki.h@ui.ac.id

Riri Fitri Sari

Faculty of Electrical Engineering

Universitas Indonesia

Depok 16242

riri@ui.ac.id

Abstract––The integration of LoRA modulation methods in the

LPWAN wireless network and the LoRaWAN protocol

network is utilized in LoRa technology, which provides long

range coverage to end devices in license-free frequency bands.

Implementation of this technology covers a broad range of

fields, from smart house and Internet of Things (IoT) to

industry and Smart Cities. In this article, we conducted

simulation of LoRaWAN deployment in Jakarta area using

NS3 simulator. We calculated the total number of end devices

for Jakarta area coverage that gives the best performance.

Jakarta has 662,33 km2 area with many buildings, especially

high rise buildings. Our simulation results with various

gateway radiuses indicate that best performances are reached

in 3000 m for smaller and 6500 m for larger radius. Optimum

number of end devices that covered by each gateway is 3000.

Estimated number of gateways is 26, so it takes up to 78.000

end devices for entire Jakarta. From economic consideration,

service provider will optimize its capital expenditure in about

five years by deployment of 1750 end devices which will grow

up to 3049 on the next fifth year while maintain its Packet

Success Rate above 91%.

Keywords––LoRa; LoRaWAN, NS3, simulation, performance,

throughput, end device, radius, Packet Success Rate, Smart City,

Jakarta

I. INTRODUCTION

Large cities have a large population. Many activities

happening there, ranging from education, recreation, business, and government. This creates complexity and problems such as decreased environmental quality, pollution and noise, energy supply, food and work, and others. The government that manages cities anywhere in the world must solve this problem efficiently. Lately, the use of Internet of Things (IoT) technology to solve urban problems has been widely researched and implemented. IoT technology can be implemented to monitor facilities and environmental quality (such as air, water and noise levels), help monitor and predict traffic conditions, monitor and control the distribution of energy (such as electricity, gas and water), and others.

Internet of things scale, there are always be an end-node,

a gateway and a server. Device for wireless data

transmission is called end-node. It includes of a receiver and

transmitter module, microcontroller and set of periphery,

such as sensors [1]. Wireless data transmission between

end-node and gateway can uses many wireless technologies.

These technologies are commonly referred as Low-Power

Wide-Area network (LPWAN) since the current IoT trends

demanding longer transmission distance for small battery-

powered end-node. Recently, LPWANs have providing

wireless connectivity using a star topology and long range

transmission in the unlicensed sub-GHz frequency bands.

There are many IoT technologies such as LoRa, SigFox,

RPMA, NB-IoT, and IEEE 802.11ah. In this paper, LoRa is

chosen as currently it is open , low-cost, and can be

considered as the most widely adopted technology for

industry as well as DIY individuals.

Davide Margrin et al performed a LoRa simulation

experiment using NS-3 with link-level and system-level

assumptions [2] [3]. Magrin [3], simulates throughput

performance, the probability of packet success being

received, and gateway coverage with scenarios in urban

areas. Using a circular shape with a radius of 7500 m. The

result of the simulation throughput with LoRaWAN scheme

similar to ALOHA. Packets received by the gateway also

deliver results of over 95% with a gateway that can serve

more than 15000 end devices. In increasing the number of

gateways will also increase the coverage area and the

reliability of uplink. K. H. Pung[4], LoRaWAN is extremely

sensitive to the traffic load. Base on the average traffic

request of each node, the optimal number of nodes per cell

depends on the operation mode of devices and the trade-off

of data delivery quality and energy consumption. Nolan [7],

LoRA offers a wider range of payload sizes, for example

from 19 to 250 bytes while SigFox's uplink payload size is

limited to 12 bytes. In addition, SigFox technology is

proprietary and LoRaWAN can be used and further

developed through the open LoRa alliance consortium

initiative. Pasolini in [10] indicated that maximum coverage

in a dense urban environment is in the range of 1–2 km,

which is well under the 15 km stated by LoRa

manufacturers and vendors. SF=10 is the best, providing

optimal condition between collision issues and connectivity,

and allowing to reach a PSR larger then 90% for up to 400

devices.

From [2][3][4][7][10], we conducted micro research

using NS3 simulations to measure Lorawan's performance if

implemented in Jakarta. Jakarta is the capital of Indonesia,

the 4th

most populous developing country in the world.

Jakarta has 28 million residents. As a megapolitan city that

has many economic opportunities, every day Jakarta visits

by over 1 million peoples around Jakarta in the morning,

and returns at night [17]. Like other big cities in the world,

Jakarta has various challenges to be solved with IoT

technology. The application of IoT technology requires

investment. This paper describes a case study for the

implementation of LoRaWAN in Jakarta. The goal of this

paper is to analyse and review the performance and

efficiency of LoRaWAN in Jakarta area. The aim of

education in this study is to support researchers,

governments and developers in studying and implementing

LoRaWAN in Jakarta as an approach of industry 4.0.

We provide the background of LPWAN and several

technologies in Section II. In Section III, we show our

system design and simulation scenario. Section IV, we

present the analysis and overview of the LoRaWAN

network simulations in details and provides evaluation

performance studies hereof. In Section V, we present the

conclusion.

II. LORAWAN AND OPTIMUM DEPLOYMENT

A. LoRaWAN Protocol and LoRaWAN Frequency Bands

The LoRaWAN network is laid out in a star-of-stars

topology, where End Devices (EDs) wirelessly send /

receive messages to / from one or more Gateways (GWs),

which in turn transmit them to a centralized Network Server

(NS) through high throughput and reliable links. Sending

one ED message to more than one gateway is possible in

this topology. In fact, ED is not clearly explained to be

attached to one gateway: assuming that at least one gateway

will receive messages from the wireless channel device and

forward it to NS. The responsibility in this centralize system

is filtering out duplicates and selecting the most suitable

gateway for sending downlink message to that device. Some

logical channels are defined for the whole network to

elevate the network more sturdily for interference. Then, to

pick channels in pseudo-random, the devices needs to

dispatch necessary packets [3]. However, since processing

similar packet in two or more gateways may waste the

energy and removing duplicated packets may increase the

processing load, an optimal gateway location for

deployment is important. This is firstly studied in [13].

Additionally, LoRa gateway can also provide location

information using the same basic concept (with slightly

different mechanism) as the one used by cellular

networks[14].

The wireless sensor network LoRaWAN has been

developed in light of energy efficiency considerations, as

most devices are battery-powered. The lifespan of wireless

devices that should be in sequence of years since battery

replacement is not a decent solution. Figure 1 shows the

LoRaWAN communication stack. This figure shows that,

LoRa modulation technique has been patented by Semtech.

The LoRa Alliance has defined the specification of the

LoRaWAN communication protocol over LoRaTM

modulation located at the physical level. [8].

Fig. 1. LoRaWAN communication stack [8]

Three regions in the world, e.g., Europe, China and the

United States are expected to operate LoRaWANs at fixed

frequencies, based on local regulations. Each region has the

standard mandates customized parameters that defines the

preamble, channel frequencies, allowed spreading factors,

maximum payload size, receive windows and join

procedures to make sure that LoRaWAN always complies

with the local regulations. Europe has frequency band

(MHz) at 868-870, US at 902-928, and China at 779-787[2].

B. DKI Jakarta (Jakarta) As An Area of Lora Simulation

Deployment

The Special Capital Region of Jakarta (DKI Jakarta) is

the capital city of the country and largest city in Indonesia.

Jakarta is the only city in Indonesia that has a provincial

level status. Jakarta is a province with rapid technological

developments. Economic growth, building infrastructure,

telecommunication infrastructure, and residential housing

continues to grow over time. The mode of transportation and

expansion of the road continues to be built to compensate

for the growth of this urban city. Formerly once known by

several names among them Sunda Kelapa, Jayakarta, and

Batavia.

Jakarta has an area of about 661.52 km², with a

population of 28 million inhabitants. Jakarta is located in the

lowlands at an average height of 8 meters above sea level.

This resulted in Jakarta often flooded. Jakarta is located on

the northwest coast of Java Island. To the south of Jakarta is

a mountainous area with high rainfall. Jakarta is passed by

13 rivers which all empty into Jakarta Bay. The most

important river is Ciliwung, which divides the city into two.

East and south of Jakarta borders the province of West Java

and in the west by the province of Banten.

C. LoRa Network Simulation Using NS3

The Network Simulator 3 (NS3) software, a Discrete

Event Simulation tool (DES), has been used to simulate

networks of LoRa systems. The simulator has been

expanded with the creation of a Lora module that

implements various models. NS3 is a network simulation

software dedicated to research and educational use, licensed

under the GNU General Public License (GPL) and

developed by the user community. NS3 is able to simulate

complex networks in a specified and realistic way, by

utilising multiple C ++ objects, with each class modelling

aspects of the network. A new Lora module is built to

model LoRaWAN behavior. This module is principally an

aggregate of classes that run together to describe the

behavior of LoRa ED and GW at several levels, from PHY

to the Application layer. [3].

Muratchaev et al in [1], checked the efficiency of the

LoRaWan protocol for a number of different devices using

NS3. Wireless data transmission between end-node and

User Defined Layer

Semtech

LoRa Allience

gateway implementation by creating LPWAN (Low-Power

Wide-area-network). LPWAN is a network with low power

usage, which apply LoRa modulation on layer 1 OSI, and

open-source LoRaWAN protocol on layer 2 OSI.

Transmission is carried out at frequencies of 433 MHz and

868 MHz with data rates up to 50 kbps with a range of 20

km, with data rate and long distance rates possible in the

case of low frequency channel loads, low data size and air /

radio visibility between transmitter devices and receiver. In

this paper discusses the effect of the number of sensors on

the performance of wireless sensor networks. The paper

shows that the larger the number of sensor devices the data

transmission ratio will decrease, and in the picture on the

right shows packet-loss with respect to interference between

devices against the number of devices.

D. Limitation of LoRaWAN

Potsch et al discussed that LoRaWAN technology and

LoRa modulation takes into account the limitations of

Machine-to-Machine (M2M) data exchanges on the LoRa

gateway connection to the cellular network [9]. Theoretical

estimation of the maximum communications range that may

occur in accordance with the power output and spreading

factor (SF) and illustrated the opposite nature of LoRa

transceiver to energy consumption and range to the data rate

obtained from different combinations of modulation

parameters. The number of traffic produce by the network

backhaul can cause a large cost to the IoT service provider

in the case of gateways connected to the cellular network or

limit the number of supported sensor devices, which is

identified from an analysis of overhead data in the LoRa

gateway. From the packet overhead analysis evoke by the

LoRa gateway, it is shown that the low resource and low

power concept of LoRaWAN take place only between

gateways and end-devices. There is no economical use of

data, after the LoRa transmission to the gateway. The

number of sensor devices becomes limited due to the limited

volume of data on M2M contracts, where in scenarios when

the IOT service provider relies on a large amount of LoRa

gateways connected to the cellular network. [9].

E. Lightweight Scheduling in LoRaWAN

Brecht et al in [11] addresses the problem of scalability

when there are thousands of devices accessing the same

channel. This paper proposes a new MAC layer - RS LoRa -

to enhance the reliability and scalability of the LoRa Wide-

Area Network (LoRaWANs). A key renewal is a two-step

scheduling:

•Scheduled Gateway nodes, through dynamically

permitted transmission permissions and dispersion

factors in each channel;

• Based on the scheduling information, a node

determines its own transmitting power, the dispersion

factor, and when and on which channel to transmit.

Nodes are also guided to select different deployment

factors to enhance network reliability and scalability. They

applied RS-LoRa in NS-3 and evaluated its performance

through extensive simulations. The results show the RS-

LoRa benefits to LoRaWAN legacy, in terms of packet error

ratios, throughput, and performance sharing. For example in

a single-cell scenario with 1000 devices, RS-LoRa can

lower the error rate of packets from LoRaWAN legacy by

nearly 20%[11].

III. LORAWAN SIMULATION SCHEME AND

EXPERIMENTAL SET UP

Jakarta has an area of 662,33 km2 and skyscrapers in the

city center. Jakarta is divided into 6 administrative city,

namely Central Jakarta, East Jakarta, South Jakarta, West

Jakarta, North Jakarta and Seribu Island. With this area the

average radius of each administrative city about 7500 m. A

study for Flexi Radio Base Station has been conducted in

West Jakarta area for its position, coverage, and throughput

in serving the spread-out customer [15]. A study for

WiMAX coverage in Jakarta has also been conducted in

[16], discussing the link budget for the area.

The node spreading scenario will be divided into bigger

and smaller radius. Bigger radius is an area to cover the

administrative city, while small radius is to cover the district

of each administrative city. Simulations used hexagonal

area. and provided a series of scenarios to test the best

performance of end devices placement in Central Jakarta. In

the simulation, there is an assumption of propagation with

the formula using by [2][3]. All simulations used lorawan

module developed by [2][3].

Each scenario is done by testing the number of devices

as n1, n2, n3, n4, and n5. The simulation program has been

configured with frequency 868.1 MHz up to 868.3 MHz.

The simulation are conduct of 6 SF, e.g. SF7, SF8, SF9,

SF10, SF11, and SF12 will match the number of devices

and spacing that is simulated. Simulation scenario shows in

Table 1.

TABLE I. SIMULATION CENARIO

Location Packet Success of

Radius R1 N1 N2 N3 N4 Nj

Radius R2 N1 N2 N3 N4 Nj

Radius Ri N1 N2 N3 N4 Nj

Radius r1 N1 N2 N3 N4 Nj

Radius r2 N1 N2 N3 N4 Nj

Radius rn N1 N2 N3 N4 Nj

Ri = R is bigger radius, i is range from 6500 m to 7500 m

rn = r is smaller radius, n is range from 2500 m to 3500 m

Nj = N is number of end devices, j is range from 250 to 5000

This simulation works in the same value of simulation

time and the periods second. The large area of Jakarta is

shows in Table 2:

TABLE II. JAKARTA AREA

Area Area in km2

Central Jakarta 48,13

West Jakarta 129,54

South Jakarta 141,27

North Jakarta 146,66

East Jakarta 188,03

Kep. Seribu 8,7

Fig 2. The Map of Jakarta Area[18]

Figure 2 shows the map of Jakarta area. The range of

simulation of the area will be displayed with a circle area

model with a radius closer to the location area. In Figure 3,

shows the radius of coverage and the gateway location.

Fig. 3. Gateway Alternatives

The big circle is scenario for each Jakarta administrative

city and the small circle is scenario for each district in

Jakarta.

For the sample of end devices distribution in Jakarta area

are shows in Fig. 4 below

Fig. 4. End devices distribution sample

IV. ANALYSIS AND DISCUSSION

We used Lora module by [2][3] to simulate the

experiment scenario. Lora modules in [2][3] compute the

network traffic as described in [12]:

∑

(1)

For a given value of G in (1), throughput S is then got as

S = G x Psucc , where the probability of success of a given

packet Psucc is the ratio between the total number of sent

packets and the number of successfully received packets.

We called Psucc as Packet Success Rate (PSR).

A. Analysis

Our simulation results ran under scenario in section IV

are shown in Table 3. For a small radius range of 2500 m -

3500 m, simulations deliver same PSR values. Maximum

number of End Devices generated with PSR critical values

above 0.9 [11] is 3000. Larger radius gave smaller number

of End Devices which better PSR.

TABLE III. PSR RESULT OF THE SIMULATION

The comparison of PSR in a large radius with a range of

6500 m - 7500 m can be seen in the Figure 5.

Fig. 5. PSR of bigger radius area

The range at a radius of 7500 m decreased sharply on

PSR compared to the range at radius of 6500 m and 7500 m.

Where the PSR value at radius 6500 still shows a stable

result of 0.9 to the number of devices as much as 3000.

As shows in Fig 6 and 7, that the movement of PSR

graph in radius of 2500 m to 3500 m range it almost the

same result as radius of span of 6500 m.

Fig. 6. Packet Success of all simulations

Number

of ED

Ri

7500 (m)

Ri

7000 (m)

Ri

6500 (m)

rj

2500 -

3000 (m)

250 0,992 0,996 1 0,996

500 0,976 0,986 0,982 0,994

1000 0,961 0,963 0,978 0,975

1500 0,923333 0,949333 0,958667 0,946

2000 0,9105 0,919 0,93 0,9385

2500 0,8872 0,9148 0,9268 0,93

3000 0,847667 0,883333 0,900667 0,901333

3500 0,824857 0,872857 0,893429 0,885143

4000 0,8175 0,86425 0,8695 0,87775

4500 0,776222 0,847556 0,866222 0,866

5000 0,7562 0,8184 0,8442 0,84

Radius (in meters)

Fig. 7. Packet Success of R=6500 and smaller r

Referring to Table 3, we take the minimum PSR value of

0.9, as we get the number of end devices for each radius

shows in Table 4. The Ratio is taken by dividing the number

of optimum end device with the radius.

Referring to table 3, we take the minimum PSR value is

0.9. with equation (2):

(2)

we get the number of end devices for each radius is as

follow:

TABLE IV. COMPARISON OF OPTIMUMED FOR EACH RADIUS

Fig. 8. The Ratio of Optimum End Devices & Radius

B. Discussion

According to Figure 8 which shows that optimal radius

is 3000 m, then for each area (district) of Jakarta we

calculate that the number of gateways as listed in Table 5.

The total gateways number for entire Jakarta area are 26.

TABLE V. NUMBER OF GATEWAYS FOR EACH AREA OF JAKARTA

Area Area in Km2 Number of

Gateway

Central Jakarta 48,13 2

West Jakarta 129,54 5

South Jakarta 141,27 5

North Jakarta 146,66 6

East Jakarta 188,03 7

Seribu Island 8,7 1

Total Gateway 26

Table 5 denotes that by using a gateway placement with

radius of 3000 m with coverage area reaches approximately

28 km2 of 3000 devices, then each node can be installed in

an area of about 9000 m2. By this way, placement position

of one node with another can be set according to the

requirement of sensor data to be retrieved.

Our next step is to analyze economical consideration,

that is, which number of end devices deliver the best capital

expenditure for a service provider. We calculate the growth

of end devices for the next five 5 years using formula (3)

∑

(3)

where N represents the growth of end devices of current

year and ⍺ is the value of growth speed per year. ⍺ = 0.12 is

taken from the vehicles annual growth speed in Jakarta. The

calculation result is shown in Table 6.

TABLE VI. END DEVICE GROWTH AND THE PACKET SUCCESS

RECEIVED IN 5-YEARS LOW

As we see in Table 6, maximum growth of end devices

with PSR greater than 0.9 is reached at about 3000 of end

devices. The fastest growth starts at 2500 number of end

devices to the next second year become 3136 end devices.

The growth of end devices from 2000 to the next third year

become 2810 of end devices. While the initial end devices

growth of 1500 to the fifth year become 2644 of end devices

in the PSR is 0.901286. From this growth table can be

calculated the average initial number of best performance

end device is between 1500 and 2000, that is about 1750 end

devices that will grow to 3049 in the fifth year and the PSR

is 0.91571. The optimum growth results from initial 1750

end device to next 5 years is described in Figure 9.

Fig. 9. Optimum Growth of End Devices

V. CONCLUSION

Simulations were conducted using LoRaWAN module

developed in [2] [3], with a predetermined scenario. The

results show that according to PSR value, for smaller range

of radius, number of End Devices will increase. The more

amounts of end devices, the PSR will be gradually

decrease. PSR optimum value is 90% that was reached at

radius range of 3000 m to 3500 m with the number of end

devices is 3000. The total average gateways number can be

placed in Jakarta is 26, so totally can reach an average of

78,000 end devices spreaded in entire Jakarta area

(districts).

Meanwhile, from economic consideration, service

provider will optimize its capital expenditure in five years

by deployment of 1750 end devices which will grow up to

3049 on the next fifth year while maintain its Packet

Success Rate above 91%. Thus, the use of end devices to

capture the necessary sensors in Jakarta can be distibuted as

required to create Smart City. These can be applied in public

transport to monitor pollution levels, in watersheds to

monitor water conditions, in buildings (high rise) to monitor

air humidity.

ACKNOWLEDGEMENT

This research/article’s publication is supported by the

United States Agency for International Development

(USAID) through the Sustainable Higher Education

Research Alliance (SHERA) Program for Universitas

Indonesia’s Scientific Modelling, Application, Research and

Training for City-centred Innovation and Technology

(SMART CITY) Project, Grant #AID-497-A-1600004, Sub

Grant #IIE- 00000078-UI-1.

REFERENCES

[1]. S. Muratchaev, A. Bakhtin, A. Volkov, V .Ivanov, and Aleksandr

Baskakov. “Modeling the Process of Network Scaling for LoRaWan Basen on NS3”, pp. 1309-1312, 2018.

[2]. D. Magrin, M. Centenaro, and L. Vangelista, “Performance

Evaluation of LoRa Networks in a Smart City Scenario”, IEEE ICC SAC Symposium IoT Track, 2017.

[3]. D. Margin, “Network level performances of a LoRa System”, 2017.

[4]. K. H. Pung, H. Tran, Q. Nguyen, T. Huong, and T. L Nguyen. “Analysis and Assessment of LoRaWAN”, 2nd ICRASP SigTelCom.

pp. 241-246, 2018.

[5]. N. Sornin, M. Luis, T. Eirich, T. Kramp, and O. Hersent, “LoRaWAN Specifications,” LoRa Alliance Tech. Rep., 2015.

[6]. F. Adelantado, X.Vilajosana, P. Tuset-Peiro, B. Martinez, J. Melià-

Seguí, and T. Watteyne. “Understanding the Limits of LoRaWAN”, IEEE Communications Magazine, Jan. 2017.

[7]. K. E. Nolan, W. Guibene, M. Y. Kelly, “An Evaluation Of Low

Power Wide Area Network Technologies For The Internet Of Things”, Wireless Communications and Mobile Computing

Conference (IWCMC), 2016.

[8]. A. Lavric, V. Popa, “A LoRaWAN: Long Range Wide Area Networks”, Study International Conference on Electromechanical

and Power Systems (SIELMEN), 2017.

[9]. A. Potsch, F. Haslhofer, “Practical Limitations for Deployment of LoRa Gateways,” IEEE Instrumentation and Measurement Society”,

Oct. 2017.

[10]. G. Pasolini,. “Smart City Project using LoRa and IEEE802.15.4 Technologies”, 2018.

[11]. R. Brecht, W. Q. Wang, T.S Pere, V. Xavier, P. Sofie, “Improving

Reliability and Scalability of LoRaWANs Through Lightweight Scheduling”, IEEE Internet of Things Journal, 2018. pp. 1-12.

[12]. N. Benvenuto, M. Zorzi, “Principles of communications Networks and Systems”, Wiley Online Library, 2011.

[13]. Harwahyu, R., Presekal, A., & Sari, R. F. “LoRaWAN Performance

Evaluation with Optimized Configuration”, International Journal of

Future Generation Communication and Networking 11(4), pp. 51-68,

2018.

[14]. Harwahyu, R., Misbahuddin, M., & Sari, R. F. Performance evaluation of LTE Random Access procedure under distributed

location data mining for road traffic monitoring. International

Journal of Communication Networks and Information Security (IJCNIS), 8(3), 2016.

[15]. Ferdian, M., & Zulkifli, F. Flexi Radio Base Station (RBS)

Performance Analysis of Voice and Trafic on Kota 2 Jakarta Barat Base Station Control (BSC). Makara Journal Of Technology, 9(1), 6-

12. doi:10.7454/mst.v9i1.318, 2010.

[16]. Sirat, D., Asvial, M., & Marpaung, L. WiMAX Implementation as Existing Hotspot Network Backhaul in Jakarta. Makara Journal Of

Technology,14(2),8591.doi:10.7454/mst.v14i2.697, 2011.

[17]. BPS. “Statistik Mobilitas Penduduk dan Tenaga Kerja”. ISBN 978-602-438-094-6, 2017.

[18]. Map of City/Kotamadya of Jakarta with its Subdistrict/Kecamatan

(online). Available: https://commons.wikimedia.org/wiki/File:Jakarta_districts.png,

accessed: 15 August 2018.