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LoRa-based IoT Network Planning for Advanced Metering Infrastructure in
Urban, Suburban and Rural Scenario
Conference Paper · December 2019
DOI: 10.1109/ISRITI48646.2019.9034583
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2019 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)
LoRa-based IoT Network Planning for Advanced
Metering Infrastructure in Urban, Suburban and
Rural Scenario
Yosia Bagariang
Center for Regulation &
Telecommunication Management
School of Electrical Engineering,
Telkom University
Bandung, Indonesia
yosiasibagariang@student.telkomunive
rsity.ac.id
Muhammad Imam Nashiruddin
Center for Regulation &
Telecommunication Management
School of Electrical Engineering,
Telkom University
Bandung, Indonesia
imamnashir@telkomuniversity.ac.id
Abstract—Internet of Things is predicted to be the future of
digital business in Indonesia. The trend of IoT implements on a
large scale as more sensors and devices are connected. LoRa is
one of the promising applications of the Internet of Things with
long-range wireless communication in broad areas. LoRa
network deployment in Advanced Metering Infrastructure
(AMI) is one of the most significant components in smart
metering which measure and collect data from smart meters to
be analyzed utilities distribution and consumption. In the IoT,
the scenario must achieve two main goals: efficient
communication like massive connectivity and defense against
environmental conditions. In this paper, we propose the LoRa
for Advanced Metering Infrastructure in three utilities (electric,
gas, and water meter) to implement in urban, suburban, and
rural areas in case of Medan and surroundings by calculating
capacity and coverage planning to find out the optimum number
of LoRa gateways. Use the method of forecasting IoT connected
devices then predict the amount of LoRa gateways need with
two scenarios. The first scenario requires the capacity planning
by volume demand of the customer's supply of the system.
Meanwhile, the second scenario needs coverage planning mostly
depends on the link budget and geographical. The simulation
results in Forsk Atoll 3.3.2 indicate that the whole determined
areas can serve by the number of gateways that have to obtain
within acceptable the mean of best signal levels is -76,28 dBm, 75,7 dBm, -81,67 dBm for the urban, suburban and rural
scenario, respectively.
Keywords— Internet of Things, LPWAN, LoRa, Advanced
Metering Infrastructure
I. INTRODUCTION
Several communication technologies and standards have
emerged to fulfill the communication requirements of the
Internet of Things (IoT) propose for Low-Power Wide Area
Network (LPWANs). Examples of such LPWAN are LoRa
(Long Range). LoRa is one such LPWA protocol and to be
targets deployments where end-devices have limited energy,
where end-devices have not to need to transmit more than a
few bytes at the time [1]. However, the management of LoRa
networks for IoT faces several challenges. Including a large
and highly- varying number of devices, many wireless
scenarios characterized by demanding environmental factors,
with many buildings, especially high-rise buildings,
interference due to other co-located networks operating on the
same license-free frequency bands, It called industrial
scientific and medical (ISM) radio band.
LoRa communication technology on IoT supports
connectivity operating sensors to connect people and
businesses using data rates ranging from 0.3 Kbps to 50 Kbps
for each channel to save battery life. This new type of
978-1-7281-4518-1/19/Print ©2019 IEEE
Nachwan Mufti Adriansyah
Center for Regulation &
Telecommunication Management
School of Electrical Engineering,
Telkom University
Bandung, Indonesia
nachwan@telkomuniversity.ac.id
transceiver in Advanced Metering Infrastructure (AMI)
allows utility ( electric, water, and gas meter) to remotely and
timely obtain the customer consumption, and fault detection
in real-time without unnecessary waste[2]. Data will be
processed by MDMS (Meter Data Management System)
before making it available for billing and analysis. MDMS
carries out long term data storage and management for large
amounts of data delivered by a smart meter.
Fig. 1. The Components of Advanced Meter Infrastructure[3]
The application of AMI is expected to increase the
efficiency of monitoring and improving leak detection to
reduce the losses due to the leakage. In 2018, national power
utility in Indonesia (PLN) recorded the total value of losses
due to electricity theft both in residential areas and industry
every year around Rp. 10 trillion. At national water utility in
Indonesia (PDAM) reports that the level of water loss or NonRevenue Water (NRW) is 32.80%. This non-billed water not
only causes PDAM financial losses but also causes water
volume and pressure to flow into house connections to be
reduced[4]. While in 2018, recorded carbon emissions of gas
leakage are 20.259,01m3[5].
Therefore, AMI in LoRa is expected to be a solution to
prevent and loss reduction and information to enable
automated. It is in line with the research by [6] that the
majority of the Indonesian market is interested in IoT
applications that will provide benefits in terms of their work
affairs.
This paper aims to provide the number of LoRa gateways
by calculating the capacity and coverage planning in three
scenarios of areas (urban, suburban, and rural) to handle and
cover communication between utilities and costumers
188
II. LORA AND ADVANCED METERING
INFRASTRUCTURE
A. LoRa Physical Layer
The Long Range (LoRa) network consists of two
components, namely, LoRa and Long-Range Wide Area
Network (LoRaWAN), based on different layers of the
protocol stack. LoRa is a wireless communication proprietary
technology of the LPWA to provide the low power, low rate,
with long-range communication that uses the Chirp Spread
Spectrum (CSS) radio modulation technique developed by
Semtech Corporation. LoRaWAN is introducing by the LoRa
Alliance [8]. Facilitates the interconnection among the end
nodes and the base stations that collect and forward messages.
It uses the unlicensed ISM band (The Industrial, Scientific,
and Medical radio band), which varies following government
regulations [9]. Indonesia already has a regulation of LPWA
technical equipment; its frequency spectrum allocation is 920923 MHz[10].
Each LoRa modulation has several parameters in the
physical layer to optimization:
a) Bandwidth (BW): The range frequency of chirp is used to
modulate the LoRa data signal (
to
). Bandwidth
also represents the chip rate of the LoRa signal
modulation. It has three programmable bandwidth
settings 500 kHz, 250 kHz, and 125 kHz.
b) Spreading Factor (SF): the ratio between the data symbol
rate and chirp rate or many chips used to represent one
symbol, with an exponential factor is 2. When SF is
higher at a lower data rate, and coverage is higher[8] —
usually taking SF values in 7, 8, 9, 10, 11, 12.
c) Code Rate (CR): the forward error correction (FEC) rate,
and it affects the airtime of packet transmissions[8]. This
implementation is carried out by encoding 4 bits of data
with redundancy to 5 bits (4/5), 6 bits (4/6), 7 bits (4/7),
or 8 (4/8) bits. Using this redundancy makes LoRa signals
more resistant to interference, CR values increase if there
is much interference in the channel, but the transmission
with n: 1 – 4.
time is longer. The CR is equal to
B. Advanced Metering Infrastructure
Advanced Metering Infrastructure (AMI) is the basic
building block for the development of Smart Grid in the
distribution system to calculate, gather, store, analyze, and
process the data regarding the consumption of customers. The
AMI system provides information on the use of granular
energy for utilities and customers to remotely read usage
reading in real-time. Advanced metering infrastructure has
functionalities such as power quality monitoring and
management, improving energy efficiency, adaptive power
pricing, etc. [11]
AMI system has three major components: (1) smart meters
(and associated communication modules), (2) a
communication network, and (3) AMI back-office
information technology (IT) systems to manage the two-way
communications enabled by AMI. An overview of AMI
system components is shown in Fig. 2[12].
Fig. 2. AMI Overview[12]
Designing an AMI system is essential to have a common
platform for monitoring as well as utilization of critical
features in the AMI system. They are smart energy meter,
Data Concentrator Unit (DCU), and Smart Grid Control
Centre (SGCC)[13]. The main feature of Machine Type
Communication can handle massive connectivity. Smart
meter device on water, electricity, and gas does not need
handover and sends different small data of each service that
concentrate on the uplink side.
Smart Meter is a digital electronic device to collects
information. The most challenging applications of the smart
distribution grid because of the massive number of endsnodes. Energy management to improve efficiency that is
predicted to be implemented massively in the future. The
smart meter will replace exiting metering and offer scheduled
or on-demand remote reading. It can be used both prepaid and
postpaid. Fig. 3 provides a list of features and the indicators
need in the smart meters regarding electric energy,
communication, data real-time alarm also cost, and
maintenance[15].
Fig. 3. Features of Smart Meters [15]
III. METHODOLOGY AND SCENARIO OF LORA
To determine the number of gateways needs to be
analyzed: number of customers, LoRa capacity, and
geographic area
A. Research Area and Density of Connected Devices
The area of this paper divide into three scenarios based on
the number of population (customers) per kilometers. The
high density, which represents by urban areas will apply in
Medan city is 256 Km2. The low density which represents by
suburban areas at Pematang Siantar city is 55,66 Km2. The last
is rural as the land area at Padang Sidempuan city is 114,66
Km2. Based on these three areas, the AMI network for
communication will design with the same services (electric,
water, and gas providing). Based on data from the Central
189
Bureau of Statistics (Badan Pusat Statistik) stating the number
of devices in 2018, this data can forecast IoT connected
devices for ten years. Table 1 shows the number of customers
in 2018 and the prediction number in 2029:
TABLE I. NUMBER OF CUSTOMERS [16]
Year
Utility
Medan
P. Siantar
P. Sidempuan
2018
Electricity
702151
639369
306607
Water
487251
66604
13438
Gas
21498
23694
17674
Electricity
1432977
1304849
625736
Water
694915
560512
19166
Gas
53927
59435
44335
2181819
1924796
689237
2029
Total in 2029
B. Network Dimensioning
Technically, dimensioning of LoRa objective is to
determine the total required capacity to carry the aggregated
traffic with data communication needs. The capacity of the
planned LoRa network must comply with the requirements of
traffic to handle, demand ≤ supply. To manage the network
system can estimate from Time on-Air (ToA) or data rate
distribution and receiver sensitivity. For calculation of the
ToA, it defines symbol duration, Tsym. The formula is below:
Tsym =
(1)
The element of the LoRa Packet, as shown in the following
image.
C. Network Planning
An estimation of the resources needed to provide service
in the deployment area with the given system parameters
(Txpower, Receive Sensitivity, Tx gain, Rx gain) mostly
dependent on geographical and environmental factors. It isn’t
about coverage but also loads. Design network planning in
radio communication systems end to end needs a link budget.
The point of calculating the link budget is to get the estimated
maximum path loss from Tx to Rx, namely is Maximum
Allowable Path Loss (MAPL).
Okumura-Hata model is the simplest propagation and
gives the best accuracy in path loss (PL) estimation. This
model used for determining the path loss in the frequency
range of 150MHz to 1920MHz, and this is the LPWA band.
Most of the people used it to predict path loss in an urban,
suburban and rural area, because of its performance in
accuracy and simplicity[18]
a) Medan City: Scenario of an urban area that has a high
level of population density and human activity compared
to the other areas. The path loss, PLoss equation for the
Hata model (in dB) for urban areas is given below [2]:
PL = 69.55 + 26.16log (f) – 13.82log hb – a(hm) +
(44.9 – 6.55loghb)log10 d
(6)
Where a(hm) = 3.2log(11.75hm)2 – 4.97, for f ≥ 400MHz
(7)
b) P. Siantar: An area that has a lower population density
than an urban area. The path attenuation equation is[19] :
PL = PL (urban) – {log (f /28)}2 -5.4
(8)
2
Where a(hm) = {1.1log(f)} – 0.7) hm – (1.56log(f) –
0.8)4.97, for small and medium
(9)
c)
PL = PL (urban) – 4.78{log(f)}2 + 18.33log(f) – 40.98
(10)
Fig. 4. LoRa Modem Packet formatting[17]
configurations is a sequence of preamble Where npreamble is the
number of programmed preamble symbols., it is given by:
Tpreamble = (npreamble + 4.25) Tsym
PayloadSymbNb= 8 + max (ceil (
(
(2)
)
P. Sidempuan: This area is still close to the city and is
usually called rural because there are few houses or other
buildings and there are mountains and farms and below
for rural area:
)(CR+4),0)
(3)
With the following dependencies[17]:
•
•
•
•
•
PL is the number of payload bytes
SF is Spreading Factor
H = 0; the header is enabled, H = 1; no header.
CR is the coding rate from 1 to 4
DE = 1; the low data rate optimization is enabled,
DE = 0; for disabled.
The payload duration is the symbol period multiplied by
the number of payload symbols. The ToA, (packet duration),
is sketchily then the sum of the preamble and payload
duration:
Tpayload = payloadSymbNb Tsym
(4)
Tpacket = Tpreamble + Tpayload
(5)
190
Where a(hm) suburban = a(hm) rural
Note:
•
•
•
•
•
F
hb
hm
a(hm)
d
: transmit frequency (MHz)
: height gateway (m)
: height of device (m)
: correction factor
: cell radius (Km)
There is also a widely used logarithmic calculation
formula for MAPL
MAPL = Tx power + Rx gain + Tx gain + Rx-sensitivity
(11)
To find the MAPL the parameters are:
TABLE II. CONFIGURATION PARAMETERS[20]
Rx – Sensitivity (dBm)
SF
7
SF
8
SF
9
SF
10
SF
11
SF
12
123
126
129
132
134.5
137
Tx
power
(dBm)
Tx
gain
(dBi)
Rx
Gain
(dBi)
14
5
2
IV. RESULTS AND ANALYSIS
Gateways in P. Siantar
A. Capacity Result
a) Required Capacity: Calculation of demand based on
traffic characteristic of LoRa and technical
requirement of each utility in providing services.
Table III shows the total required packet per day after
the calculation feature (scheduled meter reading,
firmware update, on-demand mater reading) and
technical requirement of the smart metering based on
the density of devices
40
30
20
10
0
SF 7
CR = 4/5
SF 11
CR = 4/7
SF 12
CR = 4/8
15
TABLE IV. SINGLE LORA GATEWAY CAPACITY
Spreading
Coding Rate (CR)
Factor (SF)
CR = 4/5
CR = 4/6
CR = 4/7
CR = 4/8
SF 7
14,285,714.
14,594,594.5
14,594,5
14,594,59
32
6
94.56
4.56
SF 8
7,803,468.2
7,803,468.24
7,988,16
7,988,165
4
5.68
.68
SF 9
4,272,546.5
4,192,546.56
4,299,36
4,299,363
6
3.04
,04
SF 10
2,205,882.3
2,265,100.64
2,265,10
2,265,100
2
0.64
.64
SF 11
1,074,840.8
1,074,840.80
1,102,94
1,102,941
0
1.20
.20
SF 12
566,275.20
581,896.56
581,896.
581,896.5
56
6
10
5
0
SF 7
SF 8
CR = 4/5
SF 9
CR = 4/6
SF 10
SF 11
CR = 4/7
SF 12
CR = 4/8
Fig. 7. The initial number of gateways in P. Sidempuan
B. Coverage Results
According to (11) is used to a propagation prediction model
based on SF (7-12), Tx power (14dBm), Tx gain (5dBi and
Rx gain (2dBi) can be used to determine the path loss from
the gateway to the device which is the required path loss for
three scenarios. Table V shows the Path loss value
SF
Path Loss
TABLE V. PATH LOSS VALUE
7
8
9
10
11
144
147
150
153
155.5
12
158
To find the total number of gateways need are: Single√
Prediction of Total Gateway: A number of the
gateway can obtain by comparing the Required
Capacity (demand) to Gateway Capacity (supply).
Axis x is Code Rate for each SF, and axis y is the
number of LoRa gateways
40
CR = 4/6
SF 10
Gateways in P. Sidempuan
b) Gateway Capacity: Numerical evaluation of the
gateway’s capability to manage the traffic. The
number is the maximum level of agent load.
Configure bandwidth (BW), Spreading Factor (SF),
the last Code Rate (CR). Calculation of the number
of packets per day based on the equation. The packets
of the single gateway in eight channels [21]. Table IV
shows the individual LoRa gateway capacity
Gateways in Medan
SF 9
Fig. 6. The initial number of gateways in P. Siantar
TABLE III. TOTAL REQUIRED PACKET FOR MEDAN,
P. SIANTAR, AND P. SIDEMPUAN AREA
Area
Number of a required packet per day
Medan
20,563,668
P. Siantar
18,141,230
P. Sidempuan
6,496159
c)
SF 8
(12)
gateway coverage (hexagonal area) =
Total gateways = area-wide / single- gateway coverage
(13)
a) Urban scenario: According to equation (6) is used to
determine cell radius (d) of the urban gateway in Medan
and use path loss value base on table VI for every SF,
and the result is used to find total gateways by using
equation (12)
TABLE VI. RADIUS CELL AND TOTAL GATEWAYS IN MEDAN
SF
7
8
9
10
11
12
Radius (Km)
4.172 5.022 6.047 7.282 8.501
9.923
Total
24
20
17
14
12
10
gateways
30
20
10
0
SF 7
SF 8
CR = 4/5
SF 9
CR = 4/6
SF 10
CR = 4/7
SF 11
SF 12
CR = 4/8
Fig. 5. The initial number of gateways in Medan
b) Urban scenario: Okumura-Hata suburban according to
equation (8) to find the distance. Table VII shows the
radius cell and total gateways in P. Siantar
191
TABLE VII. RADIUS CELL AND TOTAL GATEWAYS IN
P. SIANTAR
SF
7
8
9
10
11
12
Radius (Km)
4.832 5.842 7.064 8.541 10.01
11.72
Total gateways
5
4
4
3
3
2
c)
b) Suburban: P. Siantar
PEMATANG SIANTAR
Coverage Siantar
Siantar Capacity CR2
Siantar Capacity CR4
Rural scenario: Same as the previous scenario, by using
equation (10), the distance can be generated for a rural
area.
Siantar Capacity CR1
Siantar Capacity CR3
40
TABLE VIII. RADIUS CELL AND TOTAL GATEWAYS IN
P. SIDEMPUAN
SF
7
8
9
10
11
12
Radius (Km)
18.01 21.84 26.50 32.15 37.77 44.37
Total gateways
3
3
2
2
2
1
20
0
SF7
C. Network Planning Analysis
The total gateway is known based on capacity and
coverage. These results will be compared to determine the
optimal gateway would expect.
a) Urban: Medan
MEDAN AREA
40
Medan Coverage
Medan Capacity CR1
Medan Capacity CR2
Medan Capacity CR3
SF8
SF9
SF10
SF11
SF12
Fig. 9. Number of gateways in P. Siantar
The results can conclude that in the suburban scenario has
two intersection points, SF 8 and SF 9. Table X shows the
comparison. The final gateway version is four gateways. But
the area has unstable ground contours, adds one gateway to
cover the blank spot. The mean of best signal level is -75,7
dBm Fig. 10 shows the coverage prediction of LoRa in P.
Siantar
TABLE X. COMPARISION OF TOTAL GATEWAYS
SF
Capacity Planning
Coverage Planning
8
3
4
9
5
4
Medan Capacity CR6
20
0
SF7
SF8
SF9
SF10
SF11
SF12
Fig. 8. Number of gateways in Medan
Fig. 8 shows the intersection point of SF 10 in the urban
scenario. The final gateway version is 14 gateways with a
mean of best signal level is -76,28 dBm. Fig. 9 shows the
coverage prediction of LoRa in Medan
TABLE IX. COMPARISION OF TOTAL GATEWAYS
SF
Capacity Planning
Coverage planning
10
10
14
Fig. 10. Coverage prediction in P. Siantar
c)
Rural: P. Sidempuan
P. SIDEMPUAN
Coverage P.Sidempuan
P. Sidempuan CR1
P. Sidempuan CR2
P. Sidempuan CR3
P. Sidempuan CR4
15
10
5
0
SF7
Fig. 9. Coverage prediction in Medan
SF8
SF9
SF10
SF11
SF12
Fig. 10. Number of gateways in P. Sidempuan
The intersection point of SF 9 in Fig. 10 has the same
gateways of capacity and coverage planning. The final
gateway version is two gateways with a mean of best signal
level is -81,67 dBm. Fig. 11 shows the coverage prediction of
LoRa in P. Sidempuan
192
Advanced Metering Infrastructure, 2016.
TABLE XI. COMPARISION OF GATEWAYS
Capacity Planning
Coverage Planning
2
2
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ACKNOWLEDGMENT
[17]
The author expressed appreciation to the Telkom
University on their support in this research.
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SF
9
Fig. 11. Coverage prediction in P. Sidempuan
V. CONCLUSION
This study contributes to the internet of things research
in the following ways. First, the integrated IoT architecture
based on LoRa for Advanced Metering Infrastructure for
electricity, water, and gas can apply in urban, suburban, and
rural areas. Second, following the scenarios and calculations
based on capacity planning and coverage planning show that
to determine the number of gateways needed is the number of
customers, the LoRa capacity system, and the geographical
area.
Third, from the analysis of capacity and coverage, it
found that the gateway requirements from each region were
fourteen gateways for Medan (Urban Scenario), five
gateways for Pematang Siantar (Sub Urban Scenario), and
two gateways for Padang Sidempuan (Rural Scenario).
Fourth, from the simulations carried out, it can also
conclude that for the Medan City that is specified can be
served at an acceptable level with a value > -132 dBm,
Pematang Siantar with an adequate level > -129 dBm, while
for the Padang Sidempuan City it is > -129 dBm. The best
signal levels on average are -76,28 dBm, -75,7 dBm, and 81,67 dBm for the Medan, Pematang Siantar, and Padang
Sidempuan.
The number of gateways that calculated in this paper
needs to adjust with the actual environment to find an
accurate total gateway to serve customers optimally. For
further research, it is necessary to use BTS existing to design
LoRa gateways laying to decrease investment costs.
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