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Understanding the Limits of LoRaWAN
Ferran Adelantado, Xavier Vilajosana, Pere Tuset-Peiro, Borja Martinez, Joan Melià-Seguí, Thomas Watteyne,
Low-Power Wide Area Networking (LPWAN) technology
offers long-range communication, which enables new types
of services. Several solutions exist; LoRaWAN is arguable
the most adopted. It promises ubiquitous connectivity in
outdoor IoT applications, while keeping network structures,
and management, simple. This technology has received a lot
of attention in recent months from network operators and
solution providers. Yet, the technology has limitations that
need to be clearly understood to avoid inflated expectations
and disillusionment. This article provides an impartial and
fair overview of what the capabilities and the limitations of
LoRaWAN are. We discuss those in the context of use cases,
and list open research and development questions.
Network operators are starting to deploy horizontal M2M
solutions to cover a wide set of large scale verticals, using Low
Power Wide Area Networking (LPWAN) technologies [1],
[2]. Application domains include smart city, metering, on-
street lighting control or precision agriculture. LPWAN tech-
nologies combine low data rate and robust modulation to
achieve multi-km communication range. This enables simple
star network topologies that simplify network deployment and
maintenance [3]. While the benefits of these technologies are
known and are often considered as the key enablers for some
applications, their limitations are still not well understood [4],
In this article we aim to provide an impartial overview of
the limitations of LoRaWAN [6], one of the most successful
technologies in the LPWAN space. LoRaWAN is a network
stack rooted in the LoRa physical layer. LoRaWAN features a
raw maximum data rate of 27 kbps (50 kbps when using FSK
instead of LoRa), and claims that a single gateway can collect
data from thousands of nodes deployed kilometers away. These
capabilities have really resonated with some solution providers
and network operators, who have created a large momentum
behind LoRaWAN to the point that it is sometimes touted as
the connectivity enabler for any IoT use case [7].
The goal of this article is to bring some sanity to these
statements, by providing a comprehensive, fair and inde-
pendent analysis of what the capabilities and limitations of
LoRaWAN are. We adopt a pragmatic approach, and identify
in which use cases the technology works, and in which use
cases it doesn’t work. Section II provides an overview of
LPWAN technologies, including cellular. Section III describes
F. Adelantado, P. Tuset-Peiro, B. Martinez and J. Melià-Seguí are with IN3
at the Universitat Oberta de Catalunya.
X. Vilajosana is with IN3 at Universitat Oberta de Catalunya and World-
T. Watteyne is with Inria, EVA-team, Paris, France.
LoRaWAN technology in details. Section IV analyzes the
network capacity and scale limitations of the technology. Sec-
tion V discusses the use cases where LoRaWAN works/doesn’t
work. Section VI lists open research and development chal-
lenges for the technology. Section VII concludes.
A. Low-Power Wide-Area Alternatives
Although LoRaWAN is one of the most adopted technolo-
gies for IoT, there is a wide range of LPWAN technologies
in the market, such as Ingenu, Weightless W, N and P or
SigFox [8].
Ingenu developed a proprietary LPWAN technology in the
2.4 GHz band, based on Random Phase Multiple Access
(RPMA) to provide M2M industry solutions and private
networks. The main asset of Ingenu in comparison with
alternative solutions is high data rate up to 624 kbps in the
uplink, and 156 kbps in the downlink. On the contrary, the
energy consumption is higher and the range is shorter (a range
around 5-6 km) due to the high spectrum band used.
The Weightless Special Interest Group developed a set of
three open standards for LPWAN: Weightless-W, Weightless-N
and Weightless-P. Weightless-W was developed as a bidirec-
tional (uplink/downlink) solution to operate in TV whitespaces
(470-790 MHz). It is based on narrowband FDMA channels
with Time Division Duplex between uplink and downlink; data
rate ranges from 1 kbps to 1 Mbps and battery lifetime is
around 3-5 years. Weightless-N was designed to expand the
range of Weightless-W and reduce the power consumption (a
battery lifetime up to 10 years) at the expense of data rate
decrease (from up to 1 Mbps in Weightless-W to 100 kbps in
Weightless-N). Unlike Weightless-W, Weightless-N is based
on the Ultra Narrow Band (UNB) technology and operates
in the UHF 800-900 MHz band; it provides only uplink
communication. Finally, Weightless-P is proposed as a high-
performance two-way communication solution that can operate
over 169, 433, 470, 780, 868, 915 and 923 MHz bands.
However, cost of the terminals and power consumption are
higher than in Weightless-N, with a battery lifetime of 3-8
Together with LoRaWAN, SigFox is one of the most
adopted LPWAN solutions. It is a proprietary UNB solution
that operates in the 869 MHz (Europe) and 915 MHz (North
America) bands. Its signal is extremely narrowband (100 Hz
bandwidth). It is based on Random Frequency and Time
Division Multiple Access (RFTDMA) and achieves a data rate
around 100 bps in the uplink, with a maximum packet payload
of 12 Bytes, and a number of packets per device that cannot
exceed 14 packets/day. These tough restrictions, together with
arXiv:1607.08011v2 [cs.NI] 13 Feb 2017
a business model where SigFox owns the network, have some-
what shifted the interest to LoRaWAN, which is considered
more flexible and open.
B. Cellular solutions for IoT
The 3rd Generation Partnership Project (3GPP) standard-
ized a set of low cost and low complexity devices target-
ing Machine-Type-Communications (MTC) in Release 13.
In particular, 3GPP addresses the IoT market from a three-
fold approach by standardizing the enhanced Machine Type
Communications (eMTC), the Narrow Band IoT (NB-IoT) and
the EC-GSM-IoT [9].
eMTC is an evolution of the work developed in Release
12 that can reach up to 1 Mbps in the uplink and downlink,
and operates in LTE bands with a 1.08 MHz bandwidth. NB-
IoT is an alternative that, thanks to the reduced complexity,
has a lower cost at the expense of decreasing data rate (up
to 250 kbps in both directions). Finally, EC-GSM-IoT is an
evolution of EGPRS towards IoT, with data rate between 70
and 240 kbps.
Although the approaches proposed by 3GPP reduce the
energy consumption and the cost of the devices, they have
not yet caught up their non-3GPP counterparts. For instance,
module cost for LoRaWAN and SigFox is around $2-5 and for
eMTC is still around $8-12. Despite the expected broad adop-
tion of cellular IoT solutions supported by 3GPP, LoRaWAN
presents some assets to prevail against these technologies in
specific market niches. Current assets are: i) the number of
LoRaWAN network deployments is increasing continuously
while, on the other hand, few initial NB-IoT deployments have
been already deployed; ii) LoRaWAN operates in the ISM
band whereas cellular IoT operates in licensed bands; this
fact favours the deployment of private LoRaWAN networks
without the involvement of mobile operators; iii) LoRaWAN
has backing from industry, e.g. CISCO, IBM or HP, among
others. In the future, both technologies will probably coexist
when 3GPP solutions will be backed up by large volumes.
LoRa is the physical layer used in LoRaWAN. It features
low power operation (around 10 years of battery lifetime),
low data rate (27 kbps with spreading factor 7 and 500 kHz
channel or 50 kbps with FSK) and long communication range
(2-5 km in urban areas and 15 km in suburban areas). It
was developed by Cycleo (a French company acquired by
Semtech). LoRaWAN networks are organized in a star-of-stars
topology, in which gateway nodes relay messages between
end-devices and a central network server. End-devices send
data to gateways over a single wireless hop and gateways are
connected to the network server through a non-LoRaWAN
network (e.g. IP over Cellular or Ethernet). Communication
is bi-directional, although uplink communication from end-
devices to the network server is strongly favoured, as it will
be explained in the following [6].
LoRaWAN defines three types of devices (Class A,Band
C) with different capabilities [6]. Class A devices use pure
ALOHA access for the uplink. After sending a frame, a Class
Adevice listens for a response during two downlink receive
windows. Each receive window is defined by the duration,
an offset time and a data rate. Although offset time can be
configured, the recommended value for each receive window
is 1 sec and 2 sec, respectively. Downlink transmission is only
allowed after a successful uplink transmission. The data rate
used in the first downlink window is calculated as a function
of the uplink data rate and the receive window offset. In the
second window the data rate is fixed to the minimum, 0.3
kbps. Therefore, downlink traffic cannot be transmitted until
a successful uplink transmission is decoded by the gateway.
The second receive window is disabled when downlink traffic
is received by the end-device in the first window. Class A
is the class of LoRaWAN devices with the lowest power
consumption. Class B devices are designed for applications
with additional downlink traffic needs. These devices are syn-
chronized using periodic beacons sent by the gateway to allow
the schedule of additional receive windows for downlink traffic
without prior successful uplink transmissions. Obviously, a
trade-off between downlink traffic and power consumption
arises. Finally, Class C devices are always listening to the
channel except when they are transmitting. Only class A must
be implemented in all end-devices, and the rest of classes must
remain compatible with Class A. In turn, Class C devices
cannot implement Class B. The three classes can coexist in
the same network and devices can switch from one class to
another. However, there is not a specific message defined by
LoRaWAN to inform the gateway about the class of a device
and this is up to the application.
The underlying PHY of the three classes is the same.
Communication between end-devices and gateways start with
aJoin procedure that can occur on multiple frequency channels
(e.g. in EU863-870 ISM Band there are 3 channels of 125 kHz
that must be supported by all end-devices and 3 additional
125 kHz channels) by implementing pseudo-random channel
hopping. Each frame is transmitted with a specific Spreading
Factor (SF), defined as SF =log2(Rc/Rs), where Rsis
the symbol rate and Rcis the chip rate. Accordingly, there
is a trade-off between SF and communication range. The
higher the SF (i.e. the slower the transmission), the longer
the communication range. The codes used in the different
SFs are orthogonal. This means that multiple frames can be
exchanged in the network at the same time, as long as each
one is sent with one of the six different SFs (from SF=7 to
SF=12). Depending on the SF in use, LoRaWAN data rate
ranges from 0.3 kbps to 27 kbps.
The maximum duty-cycle, defined as the maximum percent-
age of time during which an end-device can occupy a channel,
is a key constraint for networks operating in unlicensed
bands. Therefore, the selection of the channel must implement
pseudo-random channel hopping at each transmission and be
compliant with the maximum duty-cycle. For instance, the
duty-cycle is 1% in EU 868 for end-devices.
The LoRa physical layer uses Chirp Spread Spectrum (CSS)
modulation, a spread spectrum technique where the signal
is modulated by chirp pulses (frequency varying sinusoidal
pulses) hence improving resilience and robustness against
interference, Doppler effect and multipath. Packets contain a
preamble (typically with 8 symbols), a header (mandatory in
explicit mode), the payload (with a maximum size between
51 Bytes and 222 Bytes, depending on the SF) and a Cyclic
Redundancy Check (CRC) field (with configurations that pro-
vide a coding rate from 4/5 to 4/8). Typical bandwidth (BW)
values are 125, 250 and 500 kHz in the HF ISM 868 and
915 MHz band, while they are 7.8, 10.4, 15.6, 20.8, 31.2,
41.7 and 62.5 kHz in the LF 160 and 480 MHz bands. The
raw data rate varies according to the SF and the bandwidth,
and ranges between 22 bps (BW = 7.8 kHz and SF = 12) to
27 kbps (BW = 500 kHz and SF = 7) [2]. Frequency hopping
is exploited at each transmission in order to mitigate external
interference [10].
In this section we study the LoRaWAN network scale with
respect to data rate, duty-cycle regulations, etc.
A. Network size limited by duty-cycle
Although the performance of LoRaWAN is determined by
PHY/MAC overviewed in Section III, the duty-cycle regula-
tions in the ISM bands [11], [12] arise as a key limiting factor.
If the maximum duty-cycle in a sub-band is denoted by d
and the packet transmission time, known as Time On Air, is
denoted by Ta, each device must be silent in the sub-band
for a minimum off-period Ts=Ta(1
d1). For instance, the
maximum duty-cycle of the EU 868 ISM band is 1% and it
results in a maximum transmission time of 36 sec/hour in each
sub-band for each end-device. Fig. 1 shows the Time on Air
of a packet transmission with coding rate 4/5 over a 125 kHz
bandwidth channel. It is known that large SFs allow longer
communication range. However, as observed in Fig. 1, large
SFs also increase the time on air and, consequently, the off-
period duration. This problem is exacerbated by the fact that
large SFs are used more often than small SFs. For instance,
considering a simple scenario with end-devices distributed
uniformly within a round-shaped area centred at the gateway,
and a path loss calculated with the Okumura-Hata model for
urban cells [13], the probability that an end-device uses a SF
i,pi, would be p12 =0.28,p11 =0.20,p10 =0.14,p9=0.10,
p8=0.08 and p7=0.19.
Although Listen Before Talk is not precluded in LoRaWAN,
only ALOHA access is mandatory. Accordingly, the Lo-
RaWAN capacity can be calculated roughly as the superposi-
tion of independent ALOHA-based networks (one independent
network for each channel and for each SF, since simultaneous
transmissions only cause a collision if they both select the
same SF and channel; no capture effect is considered). How-
ever, and in contrast to pure ALOHA, a LoRaWAN device
using SF icannot exceed a transmitted packet rate given by
nd/Tai, where nis the number of channels, dis the duty-cycle
and Taiis the Time On Air with SF i.
In the simple scenario described above, if all end-devices
transmit packets at the maximum packet rate nd/Tai, the num-
ber of packets successfully received by the gateway decreases
as shown in Fig. 2, where a network with n=3channels has
10 20 30 40 50
Time on Air (sec)
MAC payload size (Bytes)
Fig. 1. Time on Air of LoRaWAN with code rate 4/5 and a 125 kHz
0 500 1000 1500 2000 2500 3000
Num. received packets/hour per no de
Num. end-devices
Payload: 10 Bytes
Payload: 30 Bytes
Payload: 50 Bytes
Fig. 2. Number of packets received per hour when end-devices attempt
transmission at nd /Taipackets/sec with coding rate 4/5 and n=3channels
with 125 kHz bandwidth.
been analyzed. The number of received packets drops due to
the effect of collisions.
In Fig. 3 the number of packets received successfully
per hour and end-device is shown for deployments with
{250,500,1000,5000}end-devices and n=3channels. For
low transmission rate values (in packets/hour), throughput is
limited by collisions; for high values, the maximum duty-cycle
prevents end-devices from increasing the packet transmission
rate and stabilizes the throughput. For deployments with
a “small” number of end-devices, the duty-cycle constraint
limits the maximum throughput.
Table I summarizes the maximum throughput per end-
device and the probability of successful reception for a set
of different deployments. The maximum throughput falls as
0 500 1000 1500 2000 2500 3000 3500
Num. received packets/hour per node
Generated Packets/hour per no de (λ)
N=500 N=1000
by Duty
Fig. 3. Number of 10 Bytes payload packets received per hour and node for
{250,500,1000,5000}end-devices and n=3channels as a function of the
packet generation.
the number of end-devices grows.
B. Reliability and Densification drain Network Capacity
In LoRaWAN, reliability is achieved through the acknowl-
edgment of frames in the downlink. For class A end-devices,
the acknowledgment can be transmitted in one of the two
available receive windows; for class B end-devices it is trans-
mitted in one of the two receive windows or in an additional
time-synchronized window; for class C end-devices it can be
transmitted at any time .
In LoRaWAN the capacity of the network is reduced not
only due to transmissions in the downlink, but also due to
the off-period time following those transmissions (gateways
must be compliant with duty-cycle regulation). Therefore, the
design of the network and the applications that run on it
must minimize the number of acknowledged frames to avoid
the capacity drain. This side-effect calls into question the
feasibility of deploying ultra-reliable services over large-scale
LoRaWAN networks.
At this point of development of the technology, LoRaWAN
faces deployment trends that can result in future inefficiencies.
Specifically, LoRaWAN networks are being deployed follow-
ing the cellular network model, that is, network operators
provide connectivity as a service. This model is making
gateways to become base stations covering large areas. The
increase in the number of end-devices running applications
from different vendors over the same shared infrastructure
poses new challenges to coordinate the applications. In par-
ticular, each application has specific constraints in terms of
reliability, maximum latency, transmission pattern, etc. The
coordination of the diverse requirements over a single shared
infrastructure using an ALOHA-based access is one of the
main future challenges for the technology. Therefore, a fair
spectrum sharing is required beyond the existing duty-cycle
regulations. Finally, the unplanned and uncoordinated deploy-
ment of LoRaWAN gateways in urban regions, along with
the deployment of alternative LPWAN solutions (e.g. SigFox),
could cause a decrease of the capacity due to collisions and
due to the use of larger SFs (to cope with higher interference
Several application use cases are considered in order to
analyze the suitability of LoRaWAN and complement the
understanding of the advantages and limitations of the tech-
nology when applied to different types of data transmission
patterns, latency requirements, scale and geographic dispersion
among others.
A. Real Time Monitoring
Agriculture, leak detection or environment control are appli-
cations with a reduced number of periodic/aperiodic messages
and relaxed delay constraints. In contrast, the communication
range must be long enough to cope with dispersed location
of end-devices. LoRaWAN has been designed to handle the
traffic generated by this type of applications and meets their
requirements as long as the deployment of the gateways is
enough to cover all end-devices.
On the other hand, industrial automation, critical infrastruc-
ture monitoring and actuation require some sort of real time
operation. Real time is understood in general by low latency,
and bounded jitter and depends on the specific application. Lo-
RaWAN technology cannot claim to be a candidate solution for
industrial automation, considering for example that industrial
control loops may require response times around 1ms to 100
ms and that, even for small packets of 10 Bytes, the time on
air with SF=7 is around 40 ms. As presented in the previous
section, due to the MAC nature of LoRaWAN, deterministic
operation cannot be guaranteed despite of application specific
periodicity as ALOHA access is subject to contention which
impacts network jitter. Despite that, small LoRaWAN networks
can deliver proper service to applications that require, for
instance, sampling data every second. To do that, two main
design considerations should be taken into account:
The spreading factor should be as small as possible to
limit both the time on air and the subsequent off-period.
In other words, the gateway must be close enough to the
The number of channels must be carefully designed
and must be enough to i) minimize the probability
of collisions (tightly coupled with the number of end-
devices) and ii) offer quick alternative channels for nodes
to retransmit collided packets thereby diminishing the
impact of the duty-cycle.
Despite the two aforementioned aspects, latency will not be
B. Metering
The LoRa Alliance is working on standard encapsulation
profiles for popular M2M and metering protocols. Keeping an
250 end-devices 500 end-devices 1000 end-devices 5000 end-devices
Payload (Bytes) 10 30 50 10 30 50 10 30 50 10 30 50
Max. throughput per node (Packets/hour) 367 217 157 198 117 84 89 53 38 18 10 7.3
Max. throughput per node (Bytes/hour) 3670 6510 7850 1980 3510 4200 890 1590 1900 180 300 365
λof the max. throughput (Packets/hour) 2620 1500 1090 1500 870 620 670 390 280 130 70 50
Prob. of successful transmission (%) 14.01 14.47 10.73 13.20 13.45 13.55 13.28 13.59 13.57 13.85 14.29 14.60
existing application layer allows to keep intact most of the
firmware and ecosystem, facilitating migration to LPWAN.
These protocols include Wireless M-Bus for water or gas
metering, KNX for building automation, and ModBus for
industrial automation. It is important to understand that those
scenarios range from time sensitive operation to best effort
monitoring. Therefore, it is key to identify in such a diverse
ecosystem what the requirements of each application are and
if LoRaWAN is the appropriate technology to address them.
C. Smart City Applications
LoRaWAN has shown key success stories with smart light-
ing, smart parking and smart waste collection thanks to their
scale and the nature of the data generated by those applica-
tions. These encompass periodic messaging with certain delay
tolerance. For example, smart parking applications report the
status of the parking spots upon a change is detected [14].
Parking events are slow and therefore network signaling is
limited to few tens of messages per day. Analogously smart
waste collection systems and smart lighting actuate or report
information in response to a measure with large variation
periods. Although latency and jitter are not major issues in
these applications, in some of them the triggering factor is
simultaneous for a huge number of end-devices. For instance,
sunset and down trigger the lighting elements around the
whole city, thereby causing an avalanche of messages. Lo-
RaWAN is an appropriate technology for this use case since
it handles the wide coverage area and the significant number
of users at the expense of increasing number of collisions,
latency and jitter.
D. Smart Transportation and Logistics
Transportation and logistics are seen as two major pillars
of the expected IoT growth over the next few years thanks
to their impact on the global economy. Most applications are
targeting efficiency in areas such as public transportation or
transport of goods. However, some applications are tolerant to
delay, jitter or unreliability and some others are not.
Different standards have been developed in the 5.9 GHz
band for Intelligent Transportation Systems (ITS) based on the
IEEE 802.11p standard. The constraints on delay are diverse
for different applications, but LoRaWAN, being a LPWAN
solution, is not suitable for these applications. On the contrary,
solutions such as fleet control and management can be sup-
ported by LoRaWAN. Roaming is one of the developments
under definition within LoRa Alliance to enhance mobility.
Specifically, future roaming solution is expected to support
back-end to back-end secure connections, clearing and billing
between operators, location of end-devices (pointed out as an
open research challenge in Section VI) and transparent device
provisioning across networks.
E. Video Surveillance
The most common digital video formats for IP-based video
systems are MJPEG, MPEG-4 and H.264. The bit rate rec-
ommended for IP surveillance cameras ranges from 130 kbps
with low quality MJPEG coding to 4 Mbps for 1920x1080
resolution and 30 fps MPEG-4/H.264 coding. Given that
LoRaWAN data rate ranges from 0.3 kbps to 50 kbps per
channel, LoRaWAN will not support these applications.
The effect of the duty-cycle stated in Section IV jeopardizes
the actual capacity of large-scale deployments. This has been
initially addressed by TheThingsNetwork [15], an interesting
global, open, crowd-sourced initiative to create an Internet
of Things data network over LoRaWAN technology. The
proposed solution defines an access policy, known as the
TTN Fair Access Policy, that limits the Time on Air of each
end-device to a maximum of 30 sec per day. This policy is
simple to implement and guarantees pre-defined end-device
requirements for a large-scale network (more than 1000 end-
devices per gateway). However, it fails to provide the network
with enough flexibility to adapt to environment and network
conditions (i.e. link budget of each end-device, number of end-
devices, number of gateways, etc), as well as to applications
with tight latency or capacity requirements.
At this stage, the optimization of the capacity of the Lo-
RaWAN network, as well as the possibility to perform traffic
slicing for guaranteeing specific requirements in a service
basis, remain as open research issues. From the authors’ point
of view, the research community will have to address the
following open research challenges during the next years:
Explore new channel hopping methods: A pseudo-
random channel hopping method is natively used in Lo-
RaWAN to distribute transmissions over the pool of avail-
able channels, thereby reducing the collision probability.
However, this method cannot meet traffic requirements
when there are latency, jitter or reliability constraints (i.e.
downlink ACKs for all packets), and it is not able to get
adapted according to the noise level of each channel.
The design of pre-defined and adaptive hopping se-
quences arises as an open research issue. From the
authors’ point of view, the proposed channel hopping
sequences should be able to reserve a set of channels
for retransmissions of critical packets, both in the uplink
and in the downlink (ACK).
The design of feasible feedback mechanisms between
gateways and end-devices must be a key part of the
approach in a system where uplink traffic is strongly
Time Division Multiple Access (TDMA) over Lo-
RaWAN: The random nature of ALOHA-based access is
not optimal to serve deterministic traffic, which is gaining
importance in the IoT ecosystem. Building a complete
or hybrid TDMA access on top of LoRaWAN opens up
new use cases for this technology and provides additional
The TDMA scheduler should be able to allocate resources
for ALOHA-based access and schedule deterministic traf-
fic along time and over the set of available channels. The
proposed schedulers should manage the trade-off between
resources devoted for deterministic and non-deterministic
traffic, meet the regional duty-cycle constraints and guar-
antee fairness with co-existing LoRaWAN networks.
Geolocation of end-devices: The location of end-devices
is a mandatory requirement for specific use cases, partic-
ularly in industry 4.0. However, GPS-based solutions are
not feasible due to cost, and CPU and energy consump-
tion. Currently, interesting works have been initiated to
develop TDOA-based (Time Difference Of Arrival) trian-
gulation techniques for LoRaWAN. It has been shown that
this approach benefits from large SFs and dense gateway
Cognitive Radio: As pointed out in Section IV-A, reg-
ulation in ISM bands concerning maximum duty-cycle
has a significant impact on the capacity of the network.
One of the most promising future directions could be the
inclusion of cognitive radio into the LoRaWAN standard.
In contrast to Weightless-W, LoRaWAN has not been
designed to operate in TV whitespaces. In the future, the
inclusion of cognitive radio into the LoRaWAN standard
would be subject to a significant reduction of the energy
consumption associated with cognitive radio techniques.
Power reduction for multi-hop solutions: LoRaWAN is
organized with a single-hop star topology for simplicity.
As discussed in Section IV, the impact of high SFs on the
capacity of the network is two-fold, since it increases both
the Time on Air and the off-period. A two-hop strategy
for LoRaWAN networks should be investigated to figure
out its potential.
Proposals in this direction should consider the reduction
of transmitted power and the decrease of the SFs. On
the other hand, also negative effects such as complexity,
synchronization, and increasing power consumption of
relays should be analyzed to thoroughly characterize the
Densification of LoRaWAN networks: The proliferation
of LPWAN technologies, and particularly LoRaWAN,
poses co-existence challenges as the deployment of gate-
ways populate urban areas. Given the random-based ac-
cess in unlicensed bands of LoRaWAN and its inher-
ent unplanned deployment, the performance achieved in
isolated networks is put into question in scenarios with
co-existing gateways and limited number of available
It is essential to devise coordination mechanisms between
gateways from the same or different operators to limit
interference and collisions. The co-existence mechanisms
encompass coordination and reconfiguration protocols for
gateways and end-devices.
This article is aimed to clarify the scope of LoRaWAN
by exploring the limits of the technology, matching them to
application use cases and stating the open research challenges.
In the low power M2M fragmented connectivity space there
is not a single solution for all the possible connectivity needs
and LoRaWAN is not an exception. A LoRaWAN gateway,
covering a range of tens of kilometers and able to serve up
to thousands of end-devices, must be carefully dimensioned to
meet the requirements of each use case. Thus, the combination
of the number of end-devices, the selected SFs and the number
of channels will determine if the LoRaWAN ALOHA based
access and the maximum duty-cycle regulation fit each use
case. For instance, we have seen that deterministic monitoring
and real time operation cannot be guaranteed with current
LoRaWAN state of the art.
This work is partially supported by the Spanish Ministry
of Economy and the FEDER regional development fund
under SINERGIA project (TEC2015-71303-R), and by the
European Commission through projects H2020 F-Interop and
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Ferran Adelantado ( received the Engineering
degree in Telecommunications (2007) and the PhD degree in Telecommu-
nications (2007) from UPC, and the BSc in Business Sciences (2012) from
UOC. Currently, he is associate professor at Universitat Oberta de Catalunya
(UOC) and researcher at the Wireless Networks Research Group (WINE).
His research interests are wireless networks, particularly 5G, LPWAN and
IoT technologies.
Xavier Vilajosana is Principal Investigator of the Wireless Networks Re-
search Lab at the Open University of Catalonia. Xavier is also co-founder
of Worldsensing and OpenMote Technologies. Xavier is an active member of
the IETF 6TiSCH WG where he authored different standard proposals. Xavier
also holds 30 patents. Xavier has been visiting professor at the Prof. Pister
UC Berkeley lab. He co-leads Berkeley’s OpenWSN project. Xavier has been
Senior Researcher at the HP R&D labs (2014-2016) and visiting researcher
at the France Telecom R&D Labs Paris (2008). He holds a PhD(2009), MSc
and MEng (2004) from UPC, Barcelona, Spain.
Pere Tuset-Peiro [M’12] ( is Assistant Professor at the
Department of Computer Science, Multimedia and Telecommunications and
researcher at the Internet Interdisciplinary Institute (IN3), both of Universitat
Oberta de Catalunya (UOC). He received the BSc and MSc in Telecommuni-
cations Engineering from Universitat Politècnica de Catalunya (UPC) in 2007
and 2011 respectively, and the PhD in Network and Information Technologies
from UOC in 2015. Currently, he holds more than 20 high-impact publications
and 7 international patents.
Borja Martinez received the B.Sc. in Physics and Electronics Engineering,
the M.Sc. in Microelectronics and the Ph.D. in Computer Science from the
Universidad Autónoma de Barcelona (UAB), Spain. From 2005 to 2015 he
was assistant professor at the Department of Microelectronics and Electronic
Systems of the UAB. He is currently a research fellow at the Internet
Interdisciplinary Institute (IN3-UOC). His research interests include low-
power techniques for smart wireless devices, energy efficiency and algorithms.
Joan Melià-Seguí (, Ph.D. (2011), is a lecturer at the Estudis
de Informàtica, Multimèdia i Telecomunicació and a researcher at the Internet
Interdisciplinary Institute, both at Universitat Oberta de Catalunya. Before, he
was a postdoctoral researcher at Universitat Pompeu Fabra and the Palo Alto
Research Centre (Xerox PARC). He has published more than 30 papers and
one patent in the areas of the Internet of Things, intelligent systems, security,
and privacy.
Thomas Watteyne ( is a researcher in the EVA
team at Inria in Paris, and a Senior Networking Design Engineer at Linear
Technology/Dust Networks in Silicon Valley. He co-chairs the IETF 6TiSCH
working group. Thomas did his postdoctoral research with Prof. Pister at UC
Berkeley. He co-leads Berkeley’s OpenWSN project. In 2005-2008, he was a
research engineer at France Telecom, Orange Labs. He holds a PhD (2008),
MSc and MEng (2005) from INSA Lyon, France.
... Στα πλαίσια αυτής της διπλωματικής εργασίας, θα δοθεί ιδιαίτερη έμφαση στην τεχνολογία ασύρματης δικτύωσης LoRa, που παρέχεται από τη LoRa Alliance, η οποία επιλέχθηκε μεταξύ των υπολοίπων long range τεχνολογιών χαμηλής κατανάλωσης ενέργειας (LPWAN) για τους σκοπούς αυτής της διπλωματικής εργασίας. Μερικά από τα χαρακτηριστικά αυτής της τεχνολογίας είναι η αυξημένη εμβέλεια του σήματος που μπορεί να φθάσει τα 15 χιλιόμετρα σε περιοχές εκτός του αστικού ιστού και η χαμηλή κατανάλωση ενέργειας που είναι βασική προϋπόθεση σε δίκτυα αισθητήρων των οποίων οι συσκευές τροφοδοτούνται από μπαταρίες [16]. Συνεπώς, για τους παραπάνω λόγους και επειδή η τεχνολογία LoRa δεν απαιτεί άδεια εκπομπής σήματος στη συχνότητα που χρησιμοποιεί, αποτελεί την ιδανική επιλογή για εφαρμογές ασύρματων δικτύων αισθητήρων που λειτουργούν σε απομακρυσμένες περιοχές. ...
... Η τεχνολογία LoRa ανήκει στην κατηγορία LPWAN τεχνολογιών ασύρματης δικτύωσης και επιτρέπει χαμηλή Ανάπτυξη ευφυούς δομημένου P2P συστήματος αισθητήρων για υποστήριξη εφαρμογής του διαδικτύου των πραγμάτων (IoT) σε αγροτικό περιβάλλον με τεχνολογίες υπολογιστικού νέφους (Cloud Computing) ΧΡΗΣΤΟΣ-ΠΑΝΑΓΙΩΤΗΣ ΜΠΑΛΑΤΣΟΥΡΑΣ, ΑΜ: 1054335 Σελίδα 37 από 108 κατανάλωση ενέργειας, καθιστώντας την ιδανική για συσκευές του δικτύου αισθητήρων που βασίζονται σε μπαταρίες για τροφοδοσία ενέργειας. Επίσης, η τεχνολογία LoRa παρουσιάζει εμβέλεια σήματος της τάξης των 20 χιλιομέτρων σε μη-αστικές περιοχές [16], καθιστώντας την ιδανική για την κάλυψη μιας μεγάλης περιοχής. ...
In the modern era of Industry 4.0, new technologies and interconnected devices are utilized to optimize various human activities. A notable example is Smart Agriculture, in which Internet of Things (IoT) technologies such as Wireless Sensor Networks (WSNs) are used in agriculture to improve farming produce. In the context of this diploma thesis, an applications ecosystem is developed to support smart agriculture in vineyards and wineries. First of all, a modified version of the Chord protocol from peer-to-peer (P2P) systems, is proposed for efficient support of LoRa Wireless Sensor Networks, named “WiCHORD”. Next, wireless sensor networks are implemented for vineyard and winery monitoring, by considering the constraints for each case. Also, the proposed “WiCHORD” algorithm can be applied in the LoRa wireless sensor network implemented in the vineyard. Furthermore, all the collected sensor readings from these networks are stored in the application’s database supported by Cloud Computing technologies. The dataset collected from the sensors is used to train the implemented Machine Learning models for plant disease detection in the vineyard. Finally, WEB and mobile applications are implemented for agricultural process monitoring and sensor networks management in real time. The results from this proposed methodology are encouraging in terms of WiCHORD algorithmic complexity, sensor networks energy consumption and machine learning models prediction accuracy. Future directions of this work include the study of the implemented application in terms of scalability and running machine learning models on the edge directly in sensor devices.
... Semtech SX1276 modules using LoRa modulation were selected for wireless communication between the Gateway and DSN nodes. These modules are characterized by low energy consumption when working in both sleep and active modes and long (up to 10 km) transmission distances [41]. When choosing a microcontroller, the power consumption in sleep mode, price, and availability in the market were considered. ...
... active modes and long (up to 10 km) transmission distances [41]. When choosing a microcontroller, the power consumption in sleep mode, price, and availability in the market were considered. ...
Full-text available
The monitoring and detection of wild animals is a significant topic for researchers who study the behavior, lifestyle, and environment of wild animals, as well as for people who encounter wildlife both in residential areas and near roads while traveling. An innovative wild-animal detection internet-of-things (IoT) sensor network running on harvested solar energy and detection methodology is described in this article. The sensor-networks node is implemented via the principle of an embedded system incorporating passive infrared sensors, a long-range (LoRa) module, and a solar panel for energy harvesting. For experimental purposes, a small IoT sensor network was implemented near the road. The network consists of eight nodes placed near the road with a distance of 50 m between nodes, a gateway for gathering detection data from the nodes, and a thermo-vision camera for verification of the received data.
... As a result, the topology of a wireless sensor network based on the WiCHORD protocol is hybrid as well. On the one hand, when a sensor node sends its sensor data readings to the gateway sensor node of the LoRa wireless sensor network, the network topology follows the "star" network topology, in which all the simple sensor nodes send data packets to a single gateway node of the network [87,88]. On the other hand, when running WiCHORD queries, the network topology follows the "ring" network topology from the WiCHORD protocol, which is based on the Chord protocol from [9]. ...
Full-text available
In the evolving landscape of Industry 4.0, the convergence of peer-to-peer (P2P) systems, LoRa-enabled wireless sensor networks (WSNs), and distributed hash tables (DHTs) represents a major advancement that enhances sustainability in the modern agriculture framework and its applications. In this study, we propose a P2P Chord-based ecosystem for sustainable and smart agriculture applications, inspired by the inner workings of the Chord protocol. The node-centric approach of WiCHORD+ is a standout feature, streamlining operations in WSNs and leading to more energy-efficient and straightforward system interactions. Instead of traditional key-centric methods, WiCHORD+ is a node-centric protocol that is compatible with the inherent characteristics of WSNs. This unique design integrates seamlessly with distributed hash tables (DHTs), providing an efficient mechanism to locate nodes and ensure robust data retrieval while reducing energy consumption. Additionally, by utilizing the MAC address of each node in data routing, WiCHORD+ offers a more direct and efficient data lookup mechanism, essential for the timely and energy-efficient operation of WSNs. While the increasing dependence of smart agriculture on cloud computing environments for data storage and machine learning techniques for real-time prediction and analytics continues, frameworks like the proposed WiCHORD+ appear promising for future IoT applications due to their compatibility with modern devices and peripherals. Ultimately, the proposed approach aims to effectively incorporate LoRa, WSNs, DHTs, cloud computing, and machine learning, by providing practical solutions to the ongoing challenges in the current smart agriculture landscape and IoT applications.
... To reduce collisions, a brand-new prerecruitment hopping technique was implemented. The proposed channel utilized time division multiple access (TDMA), with ACKs being used to sequence both the uplink (UL) and the DL, where the probability of successful transmission for 500 EDs was 14.6 % for a payload of 50 bytes [33]. Using an iterative competitive user estimating model and his random-access approach, Ruki Harwahyu created a multi-channel slotted ALOHA for the IoT [34]. ...
Full-text available
Several technologies provide cellular connectivity to transmit data to end devices (EDs) in the indoor environment. These technologies have short-range; complex network architecture; short lifespan; and high power consumption. In this paper, LoRa is utilized to design the Building Internet of Things (BIoT) system for electronic shelf labels (ESL) to provide long-range connectivity. To minify the transmission losses, the concurrent data exploits data parallelism for updating transmission parameters at the network server (NS) for non-interruptive communication between the centroid gateways (GWs) and allocated EDs. The EDs are deployed by K-means at different locations in each cluster based on spreading factor (SF) and data rate (DR) to avoid data congestion and intra-SF interference for the SF and hybrid (DR/SF) models. The varying bandwidths (BWs) and duty cycles (DCs) enhance the lifespan of the EDs, while the Bayesian game parameter selection (BGPS) method minimizes the power losses among the EDs. The one-slope estimates medium losses; the adjusted R-square predicts variance; and Pearson finds a correlation among the measured values. The hybrid model improves the network's performance to a spiraling average efficiency of 90.48 % and 89.64 %, with network saturation of 10.185 % and 10.337 % for pure and slotted ALOHA, respectively. The overall results illustrate a packet loss ratio (PLR) of 20.11 % for the SF model and 4.861 % for the hybrid model. The energy dissipation plunged to 0.06032 J per day, prolonging the EDs' life span to 14265.34 days.
... A tecnologia LPWAN combina uma transferência de dados em baixas taxas com uma modulação robusta para obter um maior alcance de comunicação, na ordem de quilômetros (ADELANTADO et al., 2017). Devido a essas características, podem ser amplamente aplicadas em tecnologias de ambientes inteligentes, cidades inteligentes, agricultura inteligente, dentre outras. ...
Full-text available
A emissão de biogás proveniente de aterros sanitários para a atmosfera constitui importantes contribuições antropogênicas de gases causadores do efeito estufa, sendo necessário o seu adequado monitoramento para subsidiar propostas de medidas mitigadoras ao impacto ambiental. O uso de dispositivos eletrônicos, conhecidos como nariz eletrônico, associados à internet das coisas (IoT) permite a automatização do monitoramento ambiental de aterros sanitários por meio do desenvolvimento de sistemas de medição com redes de sensores sem fio, de baixo custo e baixo consumo de energia, frente às técnicas amplamente adotadas no mundo, bem como permite a coleta de um maior volume e qualidade de dados, e sua transmissão em tempo real. O objetivo deste estudo é apresentar uma revisão da literatura acerca das pesquisas que vêm sendo desenvolvidas nos últimos anos sobre a aplicação da tecnologia de nariz eletrônico e redes de sensores sem fio para medição de gases, apresentando os principais desafios encontrados pelos autores e as perspectivas de estudos futuros. Esta revisão foi realizada por meio da busca de artigos nas plataformas Science Direct, IEEE Xplore e Scielo, considerando os últimos dez anos. Os principais desafios encontrados pelos autores em seus estudos foram a calibração dos dispositivos e a sua aplicação em escala real. De acordo com a revisão apresentada, considera-se promissora a realização de estudos de aplicação de narizes eletrônicos para medição de gases em aterros sanitários, o que pode impactar positivamente os custos com implantação, operação e monitoramento desses empreendimentos.
Remote patient monitoring is a critical component of digital medicine, and the COVID-19 pandemic has further highlighted its importance. Wearable sensors aimed at noninvasive extraction and transmission of high-fidelity physiological data provide an avenue toward at-home diagnostics and therapeutics; however, the infrastructure requirements for such devices limit their use to areas with well-established connectivity. This accentuates the socioeconomic and geopolitical gap in digital health technology and points toward a need to provide access in areas that have limited resources. Low-power wide area network (LPWAN) protocols, such as LoRa, may provide an avenue toward connectivity in these settings; however, there has been limited work on realizing wearable devices with this functionality because of power and electromagnetic constraints. In this work, we introduce wearables with electromagnetic, electronic, and mechanical features provided by a biosymbiotic platform to realize high-fidelity biosignals transmission of 15 miles without the need for satellite infrastructure. The platform implements wireless power transfer for interaction-free recharging, enabling long-term and uninterrupted use over weeks without the need for the user to interact with the devices. This work presents demonstration of a continuously wearable device with this long-range capability that has the potential to serve resource-constrained and remote areas, providing equitable access to digital health.
Designing and implementing telecommunications and broadcasting networks in the Amazon Region becomes a major challenge due to the morphological characteristics. In the low-power wide-area networks (LPWANs) context, this work proposes large-scale modeling of propagation loss channels for the LoRaWAN protocol at a frequency of 915 MHz. The proposed study aims to assist in planning IoT networks, considering the mobility of end nodes to serve riverside communities with precarious or non-existent telecommunications systems. Extensive measurement campaigns were carried out on the Federal University of Pará banks to collect values of Received Signal Strength Indication, Signal-to-Noise Ratio, and geolocated points, considering different spreading factors. Empirical Close-In and Floating Intercept propagation models for path loss prediction have been investigated in uplink. Okumura-Hata and a new proposed model were used to compare with empirical data. Additionally, received packet rate statistics between communication links are calculated to check channel quality for the LoRa PHY layer. The CI and FI models had approximately the same behavior, and the proposed model estimates radio loss in dense vegetation scenarios with lower RMSE values than the Okumura-Hata model for SF9. On average, the radius coverage threshold, which considers mobility, was 945 meters.
Full-text available
The emergence of low-cost connected devices is enabling a new wave of sensorization services. These services can be highly leveraged in industrial applications. However, the technologies employed so far for managing this kind of system do not fully cover the strict requirements of industrial networks, especially those regarding energy efficiency. In this article a novel paradigm, called Low-Power Wide Area Networking (LP-WAN), is explored. By means of a cellular-type architecture, LP-WAN–based solutions aim at fulfilling the reliability and efficiency challenges posed by long-term industrial networks. Thus, the most prominent LP-WAN solutions are reviewed, identifying and discussing the pros and cons of each of them. The focus is also on examining the current deployment state of these platforms in Spain. Although LP-WAN systems are at early stages of development, they represent a promising alternative for boosting future industrial IIoT (Industrial Internet of Things) networks and services.
Full-text available
This paper focuses on the the emerging transmission technologies dedicated to IoT networks. We first analyze the classical cellular network technologies when taking into account the IoT requirements, and point out the need of dedicated technologies for IoT. Then, we present the PHY and MAC layers of the technologies that are already deployed, or likely to be deployed: UNB by SigFox, CSS by LoRa T M , Weighless, and RPMA by Ingenu. We then compare their performances to highlight their pros and cons. Finally, we discuss on the open research challenges that still need to be addressed.
Full-text available
Cyber-physical technologies enable event-driven applications which monitor in real-time the occurrence of certain, inherently stochastic, incidents. Those technologies are being widely deployed in cities around the world and one of their critical aspects is energy consumption, as they are mostly battery powered. The most representative examples of such applications today is Smart Parking. Since parking sensors are devoted to detect parking events in almost-real time, strategies like data aggregation are not well suited to optimize energy consumption. Furthermore, data compression is pointless, as events are essentially binary entities. Therefore, this article introduces the concept of Lean Sensing, which enables the relaxation of sensing accuracy at the benefit of improved operational costs. To this end, the article departs from the concept of instantaneous randomness and it explores the correlation structure that emerges from it in complex systems. Then, it examines the use of this system-wide, aggregated, contextual information to optimize power consumption, thus going in the opposite way: from the system-level representation to individual device power consumption. The discussed techniques include customizing the data acquisition to temporal correlations (i.e, to adapt sensor behavior to the expected activity) and inferring the system-state from incomplete information based on spatial correlations. These techniques are applied to real-world smart parking application deployments, aiming to evaluate the impact that a number of system-level optimization strategies have on devices power consumption.
Conference Paper
Full-text available
Wireless sensor networks (WSNs) face the challenge of ensuring end-to-end communication while operating over individually unreliable wireless links. This paper addresses channel hopping, a class of frequency diverse communication protocols in which subsequent packets are sent over different frequency channels. Channel hopping combats external interference and persistent multipath fading, two of the main causes of failure along a communication link. This paper is, to our knowledge, the first to address the impact of channel hopping on routing. We simulate the performance of channel hopping and single channel solutions on connectivity traces gathered from a real-world office WSN deployment. Results indicate that the most basic channel hopping protocol increases connectivity along communication links, improving network efficiency (measured by the expected transmission count ETX) by 56% and network stability (measured by the average churn) by 38%. Further improvement can be achieved through the use of whitelisting - selective channel hopping over a subset of the available frequencies.
As one of the fastest growing technologies, machine-to-machine (M2M) communications are expected to provide ubiquitous connectivity. M2M devices can be used for a wide range of emerging applications that have various communications requirements. While M2M communications have been developed for many years, major challenges still remain with their efficient implementation from the perspective of low energy consumption and wide coverage. To address these challenges, low power wide area (LPWA) technology is investigated as one of the potential candidate solutions. In this article, we first introduce some typical LPWA M2M application scenarios. Given their requirements, we highlight key techniques and standards that are explicitly designed for LPWA M2M communications. Finally, we present an LPWA prototype system to evaluate its performance and demonstrate its potential in bridging a technological gap for future Internet-of-Things (IoT) applications.
A Comprehensive Look At Low Power, Wide Area Networks For Internet of Things Engineers and Decision Makers
  • L Labs
L. Labs, "A Comprehensive Look At Low Power, Wide Area Networks For Internet of Things Engineers and Decision Makers," White Paper, 2016. [Online, Accessed on 2016-12-19]. Available at:
LoRa Specification 1.0," Lora Alliance Standard specification
  • N Sornin
  • M Luis
  • T Eirich
  • T Kramp
  • O Hersent
N. Sornin, M. Luis, T. Eirich, T. Kramp, O.Hersent, "LoRa Specification 1.0," Lora Alliance Standard specification., 2016. [Online]. Available:
LoRa Device Developer Guide
  • N Ducrot
  • D Ray
  • A Saadani
  • O Hersent
  • G Pop
  • G Remond
N. Ducrot, D. Ray, A. Saadani, O. Hersent, G. Pop, and G. Remond, "LoRa Device Developer Guide," Orange, Connected Objects and Partnership. Technical Document., Apr. 2016. [Online, Accessed on 2016-12-19]. Available at: uploads/2016/04/LoRa-Device-Developer-Guide-Orange.pdf