Content uploaded by Victor Sarker
Author content
All content in this area was uploaded by Victor Sarker on Apr 22, 2019
Content may be subject to copyright.
A Survey on LoRa for IoT:
Integrating Edge Computing
V. K. Sarker1, J. Pe ˜
na Queralta1, T. N. Gia1, H. Tenhunen2and T. Westerlund1
1Department of Future Technologies, University of Turku, Turku, Finland
2Department of Electronics, KTH Royal Institute of Technology, Stockholm, Sweden
Email: 1{vikasar, jopequ, tunggi, tovewe}@utu.fi, 2hannu@kth.se
Abstract—Increased automation and intelligence in computer
systems have revealed limitations of Cloud-based computing
such as unpredicted latency in safety-critical and performance-
sensitive applications. The amount of data generated from ubiq-
uitous sensors has reached a degree where it becomes impractical
to always store and process in the Cloud. Edge computing brings
computation and storage to the Edge of the network near to
where the data originates yielding reduced network load and
better performance of services. In parallel, new wireless commu-
nication technologies have appeared to facilitate the expansion
of Internet of Things (IoT). Instead of seeking higher data rates,
low-power wide-area network aims at battery-powered sensor
nodes and devices which require reliable communication for a
prolonged period of time. Recently, Long Range (LoRa) has
become a popular choice for IoT-based solutions. In this paper,
we explore and analyze different application fields and related
works which use LoRa and investigate potential improvement
opportunities and considerations. Furthermore, we propose a
generic architecture to integrate Edge computation capability
in IoT-based applications for enhanced performance.
Index Terms—IoT, Edge Computing, Fog Computing, Smart
Cities, Smart Agriculture, Farming, Animal Tracking, IIoT,
Smart Metering, LoRa, LoRaWAN, LPWAN, Survey.
I. INTRODUCTION
The number of Internet of Things (IoT) applications has
exponentially increased in recent years. Often these require
low-power operation and long-range communication which
cannot be provided by traditional communication protocols
such as Wi-Fi and Bluetooth. Low power wide area network
(LPWAN) becomes one of the most prominent candidates
satisfying the requirements. LPWAN have been extensively
studied over the last decade and multiple protocols related to
LPWAN have been developed [1]–[5]. LoRa/LoRaWAN and
Sigfox are the mostly used among LPWAN related protocols
as they provide very low power consumption and long-range
transmission. The main drawback of these technologies is
that the data rate is significantly lower compared to Wi-Fi
or Bluetooth.
The big data generated by an exponentially increasing
number of connected devices has yielded system architec-
tures based on new computational paradigms such as Edge
computing [6]. Unlike traditional IoT-based architecture [7]
where the collected raw data is sent from end-devices to
gateways and then forwarded to Cloud servers, Edge-assisted
IoT architecture consists of an extra layer between end-devices
and gateways. The Edge layer refers to the action of data
processing and analysis capabilities near where the data origi-
nates. We emphasize that by leveraging the advantages of Edge
computing, end-devices can provide rich information with a
limited amount of transmitted data. This lowers the burden
of the Cloud servers through a more distributed computing
approach, and simultaneously reduces network load.
Low-Power Wide Area Networks (LPWANs) have emerged
to overcome some disadvantages of short-range communi-
cation protocols (i.e., Bluetooth and Wi-Fi). For example,
LPWANs help maintain low-power operation which cannot
be achieved with Wi-Fi [8]–[11]. LPWANs also offer a long-
range communication up to kilometers. The two prominent
protocols of the LPWAN family are LoRa (Long Range) and
Sigfox [12]. Sigfox uses narrow-band modulation while LoRa
is a modulation scheme which uses chirp spread spectrum
(CSS). Different open and proprietary standards for the link
and network layers have been designed and some of them can
be independently deployed by end-users.
Although there are different protocols in LPWAN family
such as Weightless,Ingenu RPMA,Symphony Link, we have
conducted a study of recent use cases where LoRa has
been applied. We have chosen LoRa, among other LPWAN
technologies, because of its market penetration and wide use
in the industrial, educational and amateur community. LoRa
is a technology that defines a physical layer for low-power
and long-range communication, and can be operated on sub-
gigahertz unlicensed radio bands. LoRaWAN is the standard
protocol for the link and network layers over LoRa backed
by the LoRa Alliance. Private LoRaWAN networks can be
deployed by individuals or organizations. Among several open
and public ones existing across the globe, the Things Network
is one of the largest public LoRaWAN networks [13].
Due to the open standard, LoRaWAN has been deployed in
many public networks such as the ones in Amsterdam and
Bristol [14]. However, from another viewpoint, LoRaWAN
cannot be considered as a fully open standard since the LoRa
patents are property of Semtech Corporation and only devices
manufactured by them can be used for data transmission with
LoRa. Fortunately, the LoRa network is open for everyone for
professional and non-professional usage. This has boosted the
popularity of LoRa in general and particularly LoRaWAN for
a myriad of applications related to the IoT.
Multiple studies on LoRa and LoRaWAN have been pub-
lished over the past few years [1] [9] [10]. However, the fast
adoption of the technology and its increasing adoption by
industry and academia demonstrate its applicability in new
scenarios every day. Moreover, to the best of our knowledge,
previous surveys and studies on LoRa has been focused on
its applicability for the IoT in general. This mostly includes
applications where lower volume of data are acquired and
transmitted over the network. In this paper, in addition to an
overview of up-to-date LoRa-based applications, we provide
a proof-of-concept (PoC) of an Edge-assisted IoT architec-
ture suiting to high data rate LoRa-based applications while
maintaining LoRa and LoRaWAN’s advantages of low power
consumption and long-range communication. The architecture
can open the door to a myriad of new possibilities and
scenarios.
The rest of the paper is structured as follows. Section II
discusses different applications based on LoRa and LPWAN
technologies which would benefit from Edge computing. Sec-
tion III presents the advantages of leveraging Edge computing
in applications that rely on LoRa communication. Section
IV discusses aspects which should be taken into account for
LoRa-based solutions. Finally, Section V concludes the work.
II. APP LICATIO N SCENARI OS
In this section, we discuss multiple recent IoT applications
based on LoRa and LPWAN and analyze possible enhance-
ment possibilities.
A. Smart Cities
IoT has been closely related to the concept of smart cities
[15]–[19]. Mitton et al. proposed integration with Cloud ser-
vices and software as a service (SaaS) platforms. They defined
a high-level modular architecture that offers adaptability to a
wide variety of sensor data. A variety of sensors is since being
deployed in cities across the world to provide city admin-
istrators with more in-depth information of the environment
and the interaction of citizens with a city’s infrastructure [20]
[21]. In earlier applications, Wi-Fi, Bluetooth or GSM/3G/LTE
were the mainly used wireless technologies, but more recent
solutions have been presented that use LoRa for low-rate, low-
power, long battery life applications in Smart Cities [12], [22]–
[26]. Regarding the reliability of LoRa, Pasolini et al. have
shown that the range of LoRa in a dense urban environment
is about 1 to 2 km, with the gateway deployed in a favorable
position at 71 m above average ground level [23]. The authors
have run different simulations to estimate the bit error rate, and
the percentage of packets successfully received at the gateway
for different configurations.
B. Industrial IoT
With the increased variety of applications in industrial envi-
ronments where connectivity, monitoring, tracking and control
are necessary, there has always been a need of a cost-effective
solution [27]. In such scenario, LoRa technology has gained
popularity due to its small hardware footprint, low-power
operation and long range. Addabbo et al. [28] proposed a
system based on LoRa LPWAN to monitor chemical emissions
in industrial plants. The system consists of sensor nodes in
which an array of humidity, temperature and electrochemical
gas sensors are managed and thus compensates temperature
sensor data dependency when monitoring gases such as CO,
NOxand O2. In addition, a network architecture for acquiring
and managing data is presented with tests ensuring that in
noisy urban areas it is possible to achieve a communication
range of 3 km.
The latest industry standard encourages digitization, compu-
tation and use of multi-nodal data exchange. With a time-based
channel hopping mechanism of LoRa and specific planning of
the parameters such as time, frequency and spreading factor,
it is possible to access up to 6000 individual nodes in a
minute cycle, as investigated by Rizzi et al. [29]. Huang et
al. [30] shown a simulation that constitutes a multi-hop long-
chain topology to analyze and find the optimal path for power
lines in smart grids, resulting in greater performance in long-
distance transmission.
Waste management from growing industries is a chal-
lenge particularly for manufacturing plants and consumer
production. Mdukaza et al. [31] proposed an IoT-based smart
waste management system using LoRa and LPWAN with
improvement in usability and performance due to problems
caused by weather conditions, unauthorized access, range.
Another approach by Chung et al. [32] presented a system
for intelligent classification and environment monitoring with
LoRa. The system focuses on automatic classification, easier
monitoring and actuation based on a wide range of sensors for
the trash containers and provides historical data from different
locations for enhanced garbage collection management.
C. Animal Tracking and Farming
Yim et al. deployed an IoT-based crop monitoring system
and recognized the LoRa LPWAN as appropriate technology
for using in agriculture [33]. They noted that there were incon-
sistency in RSSI and data reliability during the communication
with respect to distance and that LoRa technology is affected
by Fresnel Zone. However, they have not shown a clear way
of solving those and to improve performance in rural areas
where there is lot of interference.
In another work, Ali et al. [34] proposed a precision agricul-
ture monitoring system with Green IoT in focus for observing
different aspects such as weather, soil, water, pesticide, fire and
intrusion from anywhere. The authors considered LoRa as a
better alternative compared to other communication protocols
such as ZigBee, Wi-Fi and GSM for periodic updating of data
while using lower energy. The system is supposed to reduce
emission of greenhouse gases and lower the time it takes to
deploy such infrastructure in a cost-effective way.
Wireless Sensor Networks (WSN) paved the way to system-
atic surveillance of wildlife. An interesting attempt was made
by Ibrahim et al. [35] to better understand the life of Swiftlet
birds by using LoRa communication and video analytics with
a simulation of optimum condition inside a bird’s nest. The
proposed system uses LoRa alongside 3G GSM connectivity
to monitor temperature, humidity, oxygen level and number of
birds going in and out of a particular nest.
Li et al. presented a LoRa LPWAN based data acquisition
collar for monitoring the vital signs of grassland roaming
cows [36]. The system enhances husbandry of animals with
the use of sensors such as GPS sensor and accelerometers
taking the advantage of long-distance communication of LoRa
technology to reduce human labor and related resources.
The proposed system consists of different sensor-equipped
nodes communicating over SX1278-based LoRaWAN besides
gateways and severs.
D. Smart Metering
In an extensive analysis, Tome et al. illustrated how event-
based monitoring can be used to reproduce a user’s electricity
consumption profile rather than using the traditional time
based monitoring [37]. They used LoRa communication to
send the data of which the interval of transmission is reduced
because data is sent only when a change in the monitored
load is detected rather than continuously sending. The authors
have focused on LoRa based meters to send average power
requirement to a gateway during a given period. Being event-
driven the system is inherently able to detect peak consumption
and could reach a 90% similarity with respect to when
monitored continuously.
Li et al. analyzed water consumption metering system for
consumers and presented a multilevel remote meter reading
architecture based on LoRa and GPRS [38]. The system
relies on three components- a meter node, a repeater and a
concentrator which stores the acquired data and then sends it to
the server for remote management and monitoring. They also
defined an uplink communication data packet format to ensure
data integrity and system reliability. However, their proposed
system uses multiple communication technologies at the lower
level of the hierarchy which could be simplified by using the
LoRa as it has longer range and lower power requirement.
Monitoring electricity consumption in a secure and properly
managed way is another challenge in developing countries
where the digital infrastructure is just emerging and there
are a lot of customers. Dalpiaz et al. presented an impressive
low-cost battery-free power meter based on energy harvesting
and LoRa wireless transmission [39]. The authors claim that
it can drastically reduce energy consumption due to zero
standby energy requirement at zero-load situation as the device
harvests energy from the load it monitors and transmits data
only when there is a threshold voltage present. This approach
reduces the cost of installation, maintenance and eliminates
need of replacing batteries.
In another work, Wibisono et al. has presented an advanced
metering infrastructure based on LoRaWAN for eco-friendly
smart grid [40]. As the single phase meters do not require
lot of data to be transferred, the authors preferred LoRa
communication technology over GSM-based solutions due
to its low-power operation, cost-effectiveness and unlicensed
frequency band availability. They claimed that the proposed
system achieved full control accuracy of the meters while
reducing investment and operational cost to 60% and 90%,
respectively. The authors discussed how LoRaWAN can still
be used under special IoT category in countries where the
exact frequency is not available through sub-licensing and
compliance certification. However, the system sends all data to
a central base transceiver station (BTS) without any processing
in terms of compression, encryption and data integrity check
which can cause problem when the number of customer
increases or when covering large area.
E. Environment Monitoring
Wu et al. presented a wearable sensor node named WE-Safe
based on LoRa for monitoring levels of carbon monoxide,
carbon dioxide, ultra-violet rays and other general environ-
mental parameters [41]. The low-power sensor node connects
to a gateway for sending data to the Cloud. However, the
proposed system is not feasible when the number of nodes
increases. In addition, the authors did not mentioned if any
kind of processing is done after receiving the data and how it
is handled in situations when the network is not available. The
system would benefit from data pre-analysis before uploading
the data to the Cloud considering the fact that numerous nodes
can generate a huge amount of data resulting in a unpredicted
latency and possible bottleneck in the network.
Guibene et al. has presented a PoC for monitoring river
environment by deploying a buoy with multiple sensors,
LoRa LPWAN-based transceiver and a 3G modem [42]. They
measured water depth, temperature, velocity and GPS location.
The performed experiment validated that it was possible to
cover a large area with near-line-of-sight LoRa transceivers. A
boundary condition was set to prevent erroneous sensor data
when the data bytes got changed due to long distance but
were received with a valid CRC. Introducing Edge computing
capable gateways before the central gateways and applying
compression and appropriate error handling mechanism would
improve the system.
In a similar work, Nordin et al. has implemented a narrow-
band IoT-based hydrological monitoring system for a rural
lake marked as UNESCO biosphere [43]. The authors have in-
vestigated network performance predictability, limitations and
reliability of wireless networks in rural area and with 2G and
LoRa. They concluded that GSM-based data communication is
not reliable in rural areas due to irregular terrain and non line
of sight operation and decided that LoRa is a better alternative
in terms of RSSI with high altitude antenna placement.
In summary, although the mentioned LoRa and LoRaWAN-
based applications show benefits of long-range communica-
tion, these cannot be considered the most appropriate solutions
as those are not scalable. When the number of LoRa-based
sensor nodes increases significantly and the collected data is
large, the system will collapse. Consequently, high latency
and data errors may occur due to bandwidth overload or
LoRaWAN regulation cannot be fulfilled. Therefore, a new
architecture is required which can both harvest advantages of
LoRa and provide scalability.
Sensor Nodes Edge Gateway Cloud
Node 1
•Pre-processing
•Analysis
•Compression
•Encryption
*
Node 2
*
Node N
*
•Global Storage
•Power Computing
•Applications
LoRa Gateways
Fig. 1. The proposed system architecture with integration of Edge computing
III. LEVERAGING EDGE COMPUTING WITH LORA
Edge and Fog computing is a paradigm which refers to the
concept of distributed computing by bringing advantages of
Cloud paradigm to the Edge of the network [44]. With this
approach, benefits including load-shedding in the Cloud and
efficient bandwidth utilization can be achieved. Moreover, by
bringing services such as advanced analytics, artificial intel-
ligence and distributed storage closer to end-devices, overall
system latency can be significantly reduced.
While Cloud computing has been playing a remarkable role
in IoT applications for smart cities, more recently Fog and
Edge computing have been leveraged to reduce the network
load and the amount of unprocessed data transmission while
enabling a more balanced and intelligent solution with dis-
tribution of computational tasks across different layers in the
network. Perera et al. presented the integration of Cloud and
Fog computing as a sustainable solution for a smart city to
minimize the waste of resources such as network capacity,
Cloud storage and computational capability of Cloud servers
[45]. A mobile Edge computing architecture has been tested by
Chen et al. using unmanned aerial vehicles (UAVs) to gather
environmental data from different points of a city [24]. In this
case, sensors are not static and the drones move towards points
of interest to obtain data.
Giordano et al. introduced a platform that enables Edge
computing for tasks carried out by multi-agent systems [46].
Tang et al. presented a pipeline monitoring system with
sequential learning algorithms and a prototype for a PoC [47].
The authors also introduced a hierarchical Fog Computing
architecture for big data analysis in smart cities [48]. The
advantages of adopting Edge computing in a smart city en-
vironment have been studied by He et al. in an extensive
framework that enables organization of computing, networking
and caching resources to enhance the performance of different
applications [49]. Improving the quality of service (QoS) with
mobile Edge computing (MEC) by realizing the ’Follow Me
Edge’ concept has been addressed by Taleb et al. [50]. The
proposed MEC network architecture has ultra-short latency.
Suresh et al. [51] presented an architecture based on LoRa for
monitoring animal health. Machine learning is applied at Edge
device for data compression and feature-extraction and then
sent via LoRa to gateway for forwarding to Cloud servers. The
experimental results show that the proposed approach helps to
extend the battery life of sensor nodes from 13 to up to 39
days.
As illustrated in Figure 1, we show a PoC of an Edge-
assisted IoT applications using LoRa and LoRaWAN. The
proposed architecture is different from the Edge-assisted or
Fog-based papers mentioned above. In addition to traditional
IoT architecture, the proposed architecture consists of an
extra layer of Edge-assisted gateways. In detail, sensor nodes
can collect different information and transmit the data over
Bluetooth Low Energy (BLE), nRF, or IPv6 over Low-Power
Wireless Personal Area Networks (6LoWPAN) to a Edge-
assisted gateway. The Edge-assisted gateway is often equipped
with a high-computation capable hardware, fixed in a place
and uses socket power or a large battery. Therefore, the Edge-
assisted gateway is able to perform complex algorithms while
maintaining its operation for a long period of time. These
protocols can transmit data with a data rate up to 250 kbps in
theory and 150 kbps in practice [52], [53] while they consume
approximately 70 mW of power. Correspondingly, these are
suitable for high data rate applications such as real-time
ECG monitoring. When the number of sensor nodes increases
significantly, extra hardware modules (i.e., nRF, 6LoWPAN or
BLE) can be added into one or several Edge-assisted gateways
depending on the application. In case when an application
requires extremely high data rate such as video streaming, Wi-
Fi can be used as the main wireless communication between
sensor nodes and Edge gateways. However, this results in an
increase in energy consumption of sensor nodes.
At Edge-assisted gateways, data can be compressed by
lossy or lossless algorithms depending on the application. The
lossless compression does not provide high compression rate
as possible in lossy ones such as approximately 10:1, but
the data can be correctly decoded without compromising any
information. It is suitable for critical applications such as real-
time health monitoring whilst lossy one suits to non-critical
applications such as video streaming. The compressed and
summarized data is sent from an Edge-assisted gateways to
a LoRa gateway which helps to utilize bandwidth efficiently
[54]. In addition, data can be processed at Edge-assisted
gateways for extracting useful information which will be
transmitted to LoRa gateways for saving bandwidth. In this
TABLE I
DUR ATI ON OF EN CRYP TIO N AND DE CRYP TIO N IN 8-BIT MCU AT 8 MH Z
Scheme Encrypt Decrypt
(mS) (mS)
AES-128 0.984 1.312
AES-192 1.176 1.592
AES-256 1.384 1.864
Scheme Encrypt Decrypt
(mS) (mS)
SECP160R1 1323 1317
SECP192R1 2158 2165
SECP224R1 3213 3222
paper, we demonstrate a few features such as data compression
and data encryption at both sensor nodes and Edge-assisted
gateways.
In our experiments, data is encrypted at a sensor node based
on an 8-bit AVR mirco-controller. Then, the encrypted data
is decrypted at an Edge-assisted gateway for processing. The
processed data is compressed with a lossless LZW algorithm
at an Edge-assisted gateway implemented upon a Raspberry
Pi 3 before being sent to a LoRa-based gateway. As seen in
Table I, latency varies depending on the encrypted algorithm.
Depending on the application, a specific encryption algorithm
should be chosen. At our Edge-assisted gateway, it took
approximately 13.2 ms for compressing 3800 bytes of data and
the data compression rate is about 4 times. The results show
that Edge computing helps to save bandwidth significantly at
the cost of slight increase in latency.
IV. CONSIDERATIONS
Development of traditional IoT solutions widely rely on
wireless technologies such as Wi-Fi or Bluetooth, and have
client-server models with a strong dependency on Cloud
servers. When using low-power, low data-rate solutions such
as LoRa, some aspects need to be taken care of which are not
commonly addressed.
A. Regulatory Issues
Most of the LPWAN technologies relying on unlicensed
free-to-use ISM radio bands have regional or country-specific
regulatory limitations. Although bandwidth and spreading
factor can be configured to achieve different modulation,
LoRaWAN is still limited to a single carrier frequency for
a given transmission. For European Union, section 7.2.3 of
the ETSI EN300.220 standard states the maximum radiated
power limit, channel spacing, spectrum access and mitigation
requirements for unlicensed frequency bands [55]. In Europe,
868.0 MHz frequency is commonly used for LoRa. The 868.0
MHz to 868.6 MHz band has a maximum radiated power limit
of 25 mW and a maximum duty cycle of 1%. It significantly
limits real-time transferable amount of data. This ensures a
fair network usage and hence a large number of devices can
connect to a single access point. The modulation directly
affects range and thus the distance to the access point limits
maximum attainable data rate.
B. Security and Reliability
Reliability is an important feature of wireless communica-
tion protocols. A reliable protocol needs to fulfill requirements
of low error rate and robustness against interference and packet
collision. QoS ensures that the communication is predictable,
i.e. delays and variation in delay during data transfer are
managed properly. Besides, environmental factors and spatial
location can affect the performance of the communication. For
example, interference from nearby overhead power-line and
high-rise buildings can reduce the range and increase packet
error rate (PER) [56]. Therefore, a set of situation-aware
QoS management rules should be implemented and proper
authorization, data integrity verification and recovery plan be
applied in cases where errors cannot be avoided completely.
In LoRaWAN protocol, the security needs to satisfy the
criteria of LoRaWAN such as low cost, low installation and
implementation complexity and low power consumption [57].
Therefore, AES-128 [58] is used in LoRaWAN. Although
LoRaWAN does not support originally, it is recommended
that AES-192, AES-256 or other lightweight cryptography
algorithm based on elliptic curve digital signature (ECDSA)
be considered for applications requiring secure end-to-end
data transfer. These provide a stronger data authentication
compared to an error-detection code or simple check-sum.
However, using these algorithms can cause some extra over-
head of latency and energy consumption.
V. CONCLUSION AND FUTURE WORK
In this paper, we presented a general discussion of LoRa
wireless communication technology and its advantages and
limitations. We enumerated different application scenarios
which use LoRa to achieve agile, low-power and cost-effective
communication to adopt newer practices and standards rang-
ing from Industrial operation networks to simpler IoT-based
infrastructure. In addition, important aspects such as regulatory
bindings, communication range, security and power optimiza-
tion were also considered for optimizing the parameters while
keeping reliable performance as the goal. Furthermore, we
have investigated how different applications could benefit
from implementing Edge and Fog computing paradigm which
currently use LoRa. In addition, we provided a PoC of an
Edge-assisted IoT architecture using LoRa. Data compression
and encryption demonstrated via the experiment accredits that
the proposed architecture can provide a robust solution for
overcoming some of the drawbacks of the existing LoRa-based
IoT applications. In future, more services will be applied,
demonstrated and analyzed via the experiments in specific
applications such as smart home monitoring systems. In sum-
mary, LoRa communication technology together with Edge
computing can bring a promisingly positive influence on IoT-
based applications in boosting operational performance and
energy efficiency whilst maintaining reliability and security of
the system.
REFERENCES
[1] U. Raza et al. Low power wide area networks: An overview. IEEE
Communications Surveys Tutorials, 19(2), Secondquarter 2017.
[2] D. Ismail et al. Low-power wide-area networks: Opportunities, chal-
lenges, and directions. In Proceedings of the Workshop Program of the
19th ICDCN. ACM, 2018.
[3] A. J. Wixted et al. Evaluation of lora and lorawan for wireless sensor
networks. In 2016 IEEE SENSORS, Oct 2016.
[4] K. Mekki et al. A comparative study of lpwan technologies for large-
scale iot deployment. ICT Express, 2018.
[5] Link Labs. A comprehensive look at low power, wide area networks. ,
”www.link-labs.com/lpwan”, 2016.
[6] W. Shi et al. The promise of edge computing. Computer, 49(5), 2016.
[7] V. K. Sarker et al. Portable multipurpose bio-signal acquisition and wire-
less streaming device for wearables. In 2017 IEEE Sensors Applications
Symposium, pages 1–6, March 2017.
[8] P. Neumann et al. Indoor deployment of low-power wide area networks
(lpwan): A lorawan case study. In 12th WiMob, Oct 2016.
[9] R. S. Sinha et al. A survey on lpwa technology: Lora and nb-iot. ICT
Express, 3(1):14 – 21, 2017.
[10] A. Lavric and V. Popa. Internet of things and lora™ low-power wide-
area networks: A survey. In 2017 International Symposium on Signals,
Circuits and Systems (ISSCS), pages 1–5, July 2017.
[11] K. E. Nolan et al. An evaluation of low power wide area network
technologies for the internet of things. In 2016 IWCMC, Sep. 2016.
[12] M. Centenaro et al. Long-range communications in unlicensed bands:
the rising stars in the iot and smart city scenarios. IEEE Wireless
Communications, 23(5):60–67, October 2016.
[13] The things network. [Online] Available: https://www.thethingsnetwork.
org/. Accessed: Apr. 15, 2019.
[14] S. Battle et al. Lorawan bristol. In Proceedings of the 21st IDEAS, NY,
USA, 2017. ACM.
[15] N. Mitton et al. Combining cloud and sensors in a smart city environ-
ment. EURASIP Journal on Wireless Communications and Networking,
2012(1):247, Aug 2012.
[16] A. Caragliu et al. Smart cities in europe. Journal of Urban Technology,
18(2):65–82, 2011.
[17] H. Ahvenniemi et al. What are the differences between sustainable and
smart cities? Cities, 60:234 – 245, 2017.
[18] S. Musa. Smart cities-a road map for development. IEEE Potentials,
37(2):19–23, March 2018.
[19] K. Su et al. Smart city and the applications. In 2011 International
Conference on Electronics, Communications and Control, pages 1028–
1031, Sep. 2011.
[20] M. Pla-Castells et al. Use of ict in smart cities. a practical case applied
to traffic management in the city of valencia. In 2015 Smart Cities
Symposium Prague, pages 1–4, June 2015.
[21] A. Zanella et al. Internet of things for smart cities. IEEE Internet of
Things Journal, 1(1):22–32, Feb 2014.
[22] V. A. Stan et al. Overview of high reliable radio data infrastructures
for public automation applications: Lora networks. In 2016 8th ECAI,
pages 1–4, June 2016.
[23] G. Pasolini et al. Smart city pilot project using lora. In European
Wireless; 24th European Wireless Conference, pages 1–6, May 2018.
[24] L. Chen et al. A lora-based air quality monitor on unmanned aerial
vehicle for smart city. In 2018 International Conference on System
Science and Engineering (ICSSE), pages 1–5, June 2018.
[25] J. J. Chen et al. A viable lora framework for smart cities. In 22nd Pacific
Asia Conference on Information Systems (PACIS 2018), Yokohama,
Japan, June 2018. AIS Electronic Library (AISeL).
[26] J. G. James et al. Efficient, real-time tracking of public transport, using
lorawan and rf transceivers. In Region 10 Conference, TENCON 2017-
2017 IEEE, pages 2258–2261. IEEE, 2017.
[27] T. Lennvall et al. Challenges when bringing iot into industrial automa-
tion. In 2017 IEEE AFRICON, pages 905–910, Sep. 2017.
[28] T. Addabbo et al. An iot framework for the pervasive monitoring of
chemical emissions in industrial plants. In 2018 Workshop on Metrology
for Industry 4.0 and IoT, pages 269–273, April 2018.
[29] M. Rizzi et al. Using lora for industrial wireless networks. In 2017
IEEE 13th International Workshop on Factory Communication Systems
(WFCS), pages 1–4, May 2017.
[30] H. Huang et al. A lora-based optimal path routing algorithm for smart
grid. In 2018 12th International Conference on Sensing Technology
(ICST), pages 71–76, Dec 2018.
[31] S. Mdukaza et al. Analysis of iot-enabled solutions in smart waste
management. In IECON 2018 - 44th Annual Conference of the IEEE
Industrial Electronics Society, pages 4639–4644, Oct 2018.
[32] C. Chung et al. Intelligent classification and environment monitoring
system combined with lora wireless transmission technology. In 2018
IS3C, pages 173–176, Dec 2018.
[33] D. Yim et al. An experimental lora performance evaluation in tree farm.
In 2018 IEEE Sensors Applications Symposium (SAS), March 2018.
[34] T. A. A. Ali et al. Precision agriculture monitoring system using green
internet of things (g-iot). In 2018 2nd International Conference on
Trends in Electronics and Informatics (ICOEI), May 2018.
[35] A. R. Ibrahim et al. Automated monitoring and lorawan control
mechanism for swiftlet bird house. In 2018 International Conference
on Intelligent and Advanced System (ICIAS), pages 1–5, Aug 2018.
[36] Q. Li et al. A data collection collar for vital signs of cows on the
grassland based on lora. In 2018 IEEE 15th International Conference
on e-Business Engineering (ICEBE), pages 213–217, Oct 2018.
[37] M. de Castro Tom´
eet al. Long-range low-power wireless networks
and sampling strategies in electricity metering. IEEE Transactions on
Industrial Electronics, 66(2):1629–1637, Feb 2019.
[38] Y. Li et al. Research on water meter reading system based on lora
communication. In 2017 IEEE International Conference on Smart Grid
and Smart Cities, pages 248–251, July 2017.
[39] G. Dalpiaz et al. A battery-free non-intrusive power meter for low-cost
energy monitoring. In 2018 IEEE Industrial Cyber-Physical Systems
(ICPS), pages 653–658, May 2018.
[40] G. Wibisono et al. Development of advanced metering infrastructure
based on lora wan in pln bali toward bali eco smart grid. In 2017 Saudi
Arabia Smart Grid (SASG), pages 1–4, Dec 2017.
[41] F. Wu et al. We-safe: A wearable iot sensor node for safety applications
via lora. In 2018 IEEE 4th World Forum on Internet of Things (WF-IoT),
pages 144–148, Feb 2018.
[42] W. Guibene et al. Evaluation of lpwan technologies for smart cities:
River monitoring use-case. In IEEE WCNCW, pages 1–5, March 2017.
[43] R. Nordin et al. The world-first deployment of narrowband iot for rural
hydrological monitoring in unesco biosphere environment. In 2017 IEEE
ICSIMA, pages 1–5, Nov 2017.
[44] General Electric. What is edge computing? [Online] Available:
https://www.ge.com/digital/blog/what-edge-computing. Accessed: Apr.
18, 2019.
[45] C. Perera et al. Fog computing for sustainable smart cities: A survey.
ACM Comput. Surv., 50(3):32:1–32:43, June 2017.
[46] A. Giordano et al. Smart agents and fog computing for smart city
applications. In Smart Cities, pages 137–146, Cham, 2016. Springer
International Publishing.
[47] B. Tang et al. Incorporating intelligence in fog computing for big data
analysis in smart cities. IEEE Transactions on Industrial Informatics,
13(5):2140–2150, Oct 2017.
[48] B. Tang et al. A hierarchical distributed fog computing architecture for
big data analysis in smart cities. In Proceedings of the ASE BigData
#38; SocialInformatics 2015, pages 28:1–28:6. ACM, 2015.
[49] Y. He et al. Software-defined networks with mobile edge computing
and caching for smart cities: A big data deep reinforcement learning
approach. IEEE Communications Magazine, 55(12):31–37, Dec 2017.
[50] T. Taleb et al. Mobile edge computing potential in making cities smarter.
IEEE Communications Magazine, 55(3), March 2017.
[51] V. M. Suresh et al. Powering the iot through embedded machine learning
and lora. In 2018 IEEE 4th World Forum on Internet of Things (WF-
IoT), pages 349–354. IEEE, 2018.
[52] T. N. Gia et al. Customizing 6lowpan networks towards internet-of-
things based ubiquitous healthcare systems. In NORCHIP, 2014, pages
1–6. IEEE, 2014.
[53] T. N. Gia et al. Energy efficient wearable sensor node for iot-based fall
detection systems. Microprocessors and Microsystems, 56:34–46, 2018.
[54] A. Rahmani et al. Exploiting smart e-health gateways at the edge
of healthcare internet-of-things: A fog computing approach. Future
Generation Computer Systems, 78:641–658, 2018.
[55] ERM TG28 ETSI. Electromagnetic compatibility and radio spectrum
matters (erm); short range devices (srd); radio equipment to be used in
the 25 mhz to 1000 mhz frequency range with power levels ranging up
to 500 mw. European harmonized standard EN, 300(220), v2.
[56] J. C. Liando et al. Known and unknown facts of lora: Experiences from
a large-scale measurement study. ACM Trans. Sen. Netw., 15(2):16:1–
16:35, February 2019.
[57] White Paper. Lorawan security full end-to-end encryption for iot
application providers. , LoRa Alliance, 2017.
[58] I. B. Dhaou et al. Low-latency hardware architecture for cipher-based
message authentication code. In 2017 IEEE International Symposium
on Circuits and Systems (ISCAS), pages 1–4. IEEE, 2017.
