Performance Analysis of LoRaWAN Technology
for Optimum Deployment of Jakarta Smart City
Anisa Dewi P
Faculty of Electrical Engineering
Universitas Indonesia
Depok 16242
Bambang Wahyuaji
Faculty of Electrical Engineering
Universitas Indonesia
Depok 16242
Fandhy Bayu R
Faculty of Electrical Engineering
Universitas Indonesia
Depok 16242
Ruki Harwahyu
Faculty of Electrical Engineering
Universitas Indonesia
Depok 16242
Riri Fitri Sari
Faculty of Electrical Engineering
Universitas Indonesia
Depok 16242
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.
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