LoRa Structural Monitoring Wireless Sensor Networks

Mattia Ragnoli

, Alfiero Leoni

, Gianluca Barile

1,2 c

,Vincenzo Stornelli

1,2 d

and Giuseppe Ferri

Department of Industrial and Information Engineering, University of L’Aquila, 67100 L’Aquila, Italy

DEWS, University of L’Aquila, 67100 L’Aquila, Italy

Keywords: Structural Health Monitoring, Wireless Sensor Network, LoRa, Sensor Networks Applications,

Accelerometer, MEMS, Energy Harvesting, Internet of Things, Remote Monitoring.

Abstract: The demand for sensor-based information has seen a rapidly increasing demand due to the massive

deployment of Structural Health Monitoring (SHM) systems. SHM allows for the analysis aimed to the

prediction of forthcoming incidents and enables the evaluation of a structure's status. The advances in the

Internet of Things (IoT) structures to retrieve data anytime, everywhere through the internet represents a

promising paradigm for SHM. Among the various technologies and topologies that are now evolving,

Wireless Sensor Networks (WSNs) have become well suited for the implementation of monitoring systems,

especially in low power wide area network (LPWAN) structures. LoRa modulation technology is a suitable

technical solution for sensor node communication. In this study, two LoRa-based systems for Structural

Health Monitoring (SHM) are presented, located in Sicily and Calabria, Italy. Accelerometric sensors are

encapsulated into solar harvesting powered sensor nodes and are used to monitor the variation of inclinations

of the mounting location. LoRaWAN gateways interface the nodes towards the internet, enabling the Internet

of Things (IoT) paradigm for the monitoring solution. In this article, an overview of the system structure is

given, with nodes and gateways' hardware features provided. Inclination monitoring using accelerometric data

is explained, and real scenario recorded data are given. Brief power analysis for the sensor nodes is also

reported.

1 INTRODUCTION

Structural health monitoring (SHM) is the process of

integrity estimation of structures at every stage of

their life based on suitable analysis of measured data.

Using this technique it is possible to increase safety

and decrease costs of maintenance, performing

detection, localization and assessment of the damage

at earlier stages. This allows an anticipated

knowledge of the damage for maintenance planning

and future organization. By applying the

aforementioned strategies, an economic benefit can

be achieved (Martinez-Luengo et al., 2019; Orcesi et

al., 2011). In general, an SHM system contains three

main elements: a sensor system, a data processing

https://orcid.org/0000-0002-1536-3969

https://orcid.org/0000-0002-0066-4216

https://orcid.org/0000-0003-4937-0398

https://orcid.org/0000-0001-7082-9429

https://orcid.org/0000-0002-8060-9558

structure, and a health evaluation system. The sensor

elements monitor the physical phenomenon,

producing a signal which is acquired by the

processing sub-system. Different kinds of sensors can

be integrated into one SHM block, allowing data to

be merged. The processing block allows the raw data

to be presented to the health evaluation system in

order to allow the analysis of the current state. SHM

can be applied to a wide variety of elements and

structures, from small-scale installations to big civil

constructions (Amafabia et al., n.d.; Catbas, 2009; Ni

et al., 2009; Ragnoli et al., 2022; Sohn et al., 2003).

In the past, engineers have collected usable data for

structural monitoring using wired and single-hop

wireless data-collecting devices (Ishikawa et al.,

2008; Paolucci et al., 2020). The location and quantity

Ragnoli, M., Leoni, A., Barile, G., Stornelli, V. and Ferri, G.

LoRa Structural Monitoring Wireless Sensor Networks.

DOI: 10.5220/0011692100003399

In Proceedings of the 12th International Conference on Sensor Networks (SENSORNETS 2023), pages 79-86

ISBN: 978-989-758-635-4; ISSN: 2184-4380

 2023 by SCITEPRESS – Science and Technology Publications, Lda. Under CC license (CC BY-NC-ND 4.0)

of sensor nodes may be constrained by power

limitations and wiring restrictions, possibly

increasing the cost and overall complexity of the

system. Employing WSNs, these problems are

frequently assisted, especially by adopting Internet of

Things (IoT) principles and using interconnected

nodes to develop flexible infrastructures for data

collection and analysis. In IoT applications, the

physical elements which contain sensing equipment

are connected to the internet, thus allowing data to be

exchanged universally between various kinds of

platforms. This separates the physical system

implementation technology for the acquisition

process from the sequent sub-systems, allowing

system modularity. New wireless communication

technologies are demonstrating effectiveness in WSN

implementations through the development of

innovative ad-hoc hardware; thus, significant

academic and corporate research efforts are being

reported in this area. WSNs have seen a rapid

expansion in recent years (Mainetti et al., 2011),

which report those approaches being used for a

variety of unique applications (Jino Ramson et al.,

2017), among those monitoring the environment

(Oliveira et al., 2011), industrial systems (Gungor et

al., 2009), health-related and wearable applications

(Awotunde et al., 2022), early warning system

(Ragnoli et al., 2020). The implementation of optimal

SHM solutions is a difficult task for the large number

of variables involved; thus, finding the best one from

the current options or creating a new system is not

trivial. The implementation technology, network

topology, and costs of the structures are various, and

finding a method that fits all parameters is difficult.

In this work, the application of a LoRa-based

monitoring system for structural health monitoring in

two real scenarios is presented. The system is

installed in two different locations: the first is in a

construction site in Calabria, Italy, while the second

is located in a rockfall protection barrier in Sicily,

Italy. Monitoring the movements of housings caused

by soil drift due to landslides is the interest in the first

case. In the second case, rockfall barriers are being

monitored. The sensor nodes are standalone elements

in a LoRaWAN (Haxhibeqiri et al., 2018), (LoRa and

LoRaWAN: A Technical Overview, 2020) star

network where the centre is the gateway. Access to

the internet layer is provided using a portable solar-

powered gateway implementation. A user interface is

provided by a remote dashboard panel for data

analysis and warning implementation. This paper is

structured as follows. In section two, different

solutions for SHM based on wireless networks will be

briefly reviewed to give an overview of state of the

art. In section three a high-level system overview is

given, with a description of functionality. A hardware

description of the nodes and gateway assembly is given

in section four, with electrical measurements of power

performances, and considerations on the long-term

reliability. In section five real scenario data are shown.

2 RELATED WORK

In (Zhiguo et al., 2022), the authors review the current

process and future trends of SHM applied to bridge

monitoring, focusing on transmission and analytics

methods of the sensing data, prediction, and early-

warning models. Some extensively applied sensing

technologies are reviewed and compared. Some

wireless data transmission technologies are discussed

(i.e., ZigBee, Bluetooth, NB-IoT, Wi-Fi, LoRa) along

with artificial intelligence (AI) data processing

methods. Composite structures are widely employed

in building scenarios; however, those are prone to

impact damage. In (Amafabia et al., 2017), the

authors review SHM monitoring techniques applied

to the aforementioned structures. In (Mascarenas et

al., 2007), the authors present a wireless impedance

sensor node equipped with an integrated circuit chip

for measuring and recording the electrical impedance

of a piezoelectric transducer. A microcontroller

performs local computing, and a wireless Xbee

module (Vaibhav et al., 2016) transmits the structural

information to a base station.

A network of sensor nodes for structural

monitoring is implemented by (Pakzad et al., 2008).

The Golden Gate Bridge's linear mounting of the

devices makes the multi-hop transmission topology

particularly appropriate for the investigation. In order

to prevent data loss, significant effort is made to

investigate the data transmission reliability in

pipeline mode. Remote Structural Health Monitoring

(RSHM) is a perfect solution for tracking critical

damage to urban structures. In (Sidorov et al., 2019),

the authors propose a LoRa-based IoT sensor for

monitoring the health of bolted joints (Vangelista,

2017). The management of risk associated with

landslides has gained significant attention in SHM as

a result of growing awareness of the economic and

social effects of these catastrophes (Rossi et al.,

2019). Movements on slopes can be detected using

wireless sensor nodes equipped with

microelectromechanical system (MEMS) inertial

measurement unit (IMU) sensors. In (Fekih

Romdhane et al., 2017), in order to test the viability

of a LoRa-based network in hostile conditions, the

authors deploy a WSN on an uninhabited hillside

SENSORNETS 2023 - 12th International Conference on Sensor Networks

landslide, concentrating on the communication zone

coverage. The authors (Ramesh, 2009) present the

design and deployment of a landslide detection system

at Anthoniar Colony, India. There have been a lot of

studies done to explain and separate various types of

structural movement. Different patterns of

accelerometer data in the behaviour of physical

structures have been shown in the literature (Sabato et

al., 2017). The experiments conducted by the authors,

which used information from the accelerometer to

categorize movements, made use of the idea of pattern

recognition. In addition to assisting in the analysis of

the structural stability and the deployment of safety and

mitigation devices, this information serves as

fundamental knowledge in civil engineering.

3 SYSTEM DESCRIPTION AND

FUNCTIONALITY

WSNs benefit from the employment of Low Power

Wide Area Network (LPWAN) technologies as the

state of the art that reports, allowing to development

of IoT systems. LPWANs (Bernardo et al., 2020) can

be oriented to obtain sensor nodes with particularly

good energetic performances, which makes a perfect

fit for energy-harvesting powered devices (Ruan et

al., 2017). These technologies are employed in

applications where a small amount of data is

transferred with intermittent behaviour (B. S.

Chaudhari et al., 2020), resulting in less complicated

transceiver circuits. The Media Access Control

(MAC) layer deployment often occupies the highest

portion of the costs of the whole system (B. S. and Z.

M. Chaudhari, 2020). The LoRaWAN MAC layer is

regulated by the LoRa Alliance (LoRa Alliance,

2022) and provides a solution for MAC access by

providing free interfacing of the network by only

deploying dedicated LoRaWAN gateways.

Interfacing LoRaWAN gateways with web services

allows the establishment of a reliable and low-cost

network, as will be shown following. In Figure 1, we

find the general architecture of a LoRaWAN-based

WSN network. The sensor nodes use LoRa

modulation by Semtech, which is a Chirp Spreading

Spectrum (CSS)-based method. This physical layer is

employed to communicate with the gateway over a

single hop link.

Figure 1: LoRaWAN IoT network general architecture.

A comparison of the energetic efficiency of LPWAN

IoT devices based on different technologies is

presented in (Finnegan et al., 2018). LoRa

modulation stacks up against other IoT technologies

(Lauridsen et al., 2017), reporting good coverage

even when operating in poor link conditions.

According to the system proposed in this work,

sensor nodes are equipped with MEMS

accelerometers. Other elements, such as GPS tracking

devices and environmental sensors for relative

humidity, barometric pressure, and ambient

temperature, are fitted onboard. The nodes send

packets every 60 minutes to the gateway using LoRa

transmissions. This component is battery-powered,

charged by solar energy harvesting using a solar

panel. It is connected to the internet using Long Term

Evolution (LTE) and a Subscriber Identity Module

(SIM), which enables cellular network

communication. The Things Network (The Things

Network, 2022) exchange service manages the

packets received at the gateway and employs a

JavaScript payload decoding function to encapsulate

the incoming bytes into a JavaScript Object Notation

(JSON) element: the measured quantities are

represented by a numerical value and a key-value

field in the object. The packets are sent via MQ

Telemetry Transport (MQTT) integration to a Node-

RED (OpenJS Fundation, 2022) server instance,

which enables a workflow for data analysis and user

interface, moreover, as an alarm triggering service.

The collected data is also stored in a database. The

application-specific block scheme is displayed in

Figure 2, where the first block represents the

mounting scenario in the two respective cases,

respectively.

Figure 2: System architecture scheme.

In Calabria location, the WSN's nodes are

mounted on the housing walls in specific positions

where it is necessary to monitor the variation of

inclination in the structure (Figure. 3). In Sicily

location, the nodes are positioned on the rockfall

barrier's steel cables and nets, in positions particularly

prone to movement in case of an event. The LoRa

gateways are placed at locations to allow all sensing

devices to have radio communication despite the

nodes being located at different heights and positions

LoRa Structural Monitoring Wireless Sensor Networks

and facing sunlight to ensure optimal battery charge

accumulation.

Figure 3: Sensor node on a steel braid cable-protected rock

formation.

4 HARDWARE DESCRIPTION

The nodes are electronic systems that include a

microcontroller, motion, and environmental sensors,

a GPS modem, and a battery management unit

working with a compact 5 V solar panel.

Programming and debugging are available using a

USB connection available as a port of Silicon Labs

CP2102 (SiliconLabs, 2017) UART to USB circuit.

The microcontroller is an STM32L

(STMicroelectronics, 2016b) from

STMicroelectronics, 32-bit ARM Cortex M3-based,

designed for low-power systems. The low-power

operating mode with current absorption down to a

few microamperes makes this MicroController Unit

well suited to battery-powered applications,

especially if harvesting-based. A 3700 mAh lithium

polymer (LiPo) battery powers the device, and its

charge state is readable by the MCU analog-to-digital

converter (ADC). Texas Instruments BQ21040

(Texas Instruments, 2019) single cell charging

integrated circuit charges it via sun harvesting or from

the USB connection. Supply voltage at 3.3 V is

available thanks to a Nisshinbo RP104N331

(Nisshinbo, 2018) Low Drop-out (LDO) regulator.

Ublox MAX-7Q (u-blox, 2021) is the GPS modem

installed on the device. The movement sensor is an

STMicroelectronics MEMS-based digital output 3-

axis accelerometer mode LIS3DH

(STMicroelectronics, 2016). The chip is powered at

3.3 V, connected to the microcontroller via I

C, and

reports a 2 µA sleep current. The Semtech SX1276

(Semtech, 2020) LoRa unit is a transceiver based on

the spread spectrum communication technique,

connected using SPI to the microcontroller. SX1276

can attain a sensitivity of over -148dBm. Moreover,

this integrated circuit is also reasonably priced. The

transmission (TX) power of the transceiver is 13

dBm. An 868 MHz ISM band planar dipole antenna

model connects the wireless module to enable radio

communication. The node operating temperature

range is from -40 to +85 °C, and a valve ensures the

correct barometric pressure distribution inside the

enclosure. In Figure 4 a block scheme of the node is

reported.

Figure 4: Hardware block scheme of the sensor node.

The node operates sequentially as follows: it first

operates in low power mode, which means the

circuitry is in a standby state to achieve lower current

absorption. Standby is deactivated at user-chosen

intervals. The data collecting process starts with GPS

position retrieving. If a GPS response signal cannot

be received until a timeout of 30 seconds during

satellite connection attempts, the node continues with

sensor reading. If the link is successful, latitude and

longitude are obtained. The LoRa transmission starts

after ending the retrieval of data from the

environmental unit and accelerometer.

If the node cannot access LoRaWAN, successive

retries until a total of 8 attempts will be performed. If

the connection is not successful after those attempts,

the node will enter standby mode until next

transmission interval.

At complete data transferring the node switches

back to low power mode until the next send interval.

The nodes employ the Adaptive Data Rate (ADR) (Li

et al., 2018) feature of LoRaWAN. In Figure 5 a flow

diagram of the node operating procedure is reported.

Figure 5: Sensor node's operating diagram.

The Milesight UG65 (Xiamen Milesight IoT Co.,

2022) LoRaWAN gateways allow data transfer

SENSORNETS 2023 - 12th International Conference on Sensor Networks

towards the internet layer through the Things

Network service. These LTE-enabled devices are by

a lead-acid battery, recharged through solar

harvesting by a polycrystalline panel. The rated

typical total power dissipation of the unit is 2.9 W. A

waterproof IP65 enclosure is used to encapsulate the

gateways and battery system, which includes the

battery charge regulator circuit.

5 RESULTS

Achieving low power requirements for nodes in a

WSN is fundamental. In the presented SHM system,

this has been addressed by employing standby phases

between each data-sending interval. The nodes report

a current absorption of an average of 16 µA in sleep

mode and 36 mA average in active mode. Once the

initial start-up is done, where there is the first GPS

satellite connection, therefore a longer activity time

span is necessary. The next active period lasts an

average of 5 seconds. Estimating the battery duration

with a sending interval of 1 hour with the

aforementioned active mode duration gives a battery

life of more than five years in case of total darkness.

This is estimated without taking into consideration

the degradation of the LiPo cell. Solar harvesting

gives a sufficient level of energy to keep the battery

charged at full state, as will be shown from the sample

data following in this paper. Figure 6 shows a current

absorption measurement of a node in the initial start-

up phase, where it is possible to see current peaks due

to GPS and LoRa communications.

Figure 6: Sensor nodes measured current absorption during

an initial start-up and data transmission cycle.

A remote monitoring web platform has been

implemented using the NodeRED web service to

allow the management personnel to observe the status

of the structural elements. The dashboard instance

holds a monitoring panel for each sensor node. To

access data, the user must have an enabled email

address and password. The structural nodes report

location as GPS position, inclination data along three

axes as tilt in degrees, battery voltage, temperature in

Celsius degrees, relative humidity, and barometric

pressure in Hectopascal. The inclination in degrees is

calculated from accelerometric data along the three

frame body axis, with respect to a reference system

(x,y,z), where the z direction is opposite to the

direction of the gravitational acceleration vector g,

see Figure 7.

Figure 7: Axis reference for inclination measurement.

Using the following formula is possible to

calculate the inclinations, where I

represents the

inclination angle with respect to reference x axis in

this case. Applying different acceleration values at

the numerator is possible to obtain the inclination of

the other frame axis.





𝑐𝑜𝑠





𝑎





𝑎





𝑎





𝑎





 ∙ 180

𝜋

(1)

During a normal state of a sensor node, the

reported inclinations are stable, this mean that no

structural variation has been reported, as the example

reported in the following Figure 8, relative to the

three nodes in the Calabria location.

Figure 8: measured inclination sampled data from three

sensor nodes in Calabria during the period 05 – 30

September 2022.

When a structural movement happens, the system

senses and quantifies a variation in the inclination of

a sensor node, allowing a warning to be triggered. In

the following Figure 9, a displacement relative to an

axial rotation has been recorded.

Figure 9: Sensor nodes operating diagram.

LoRa Structural Monitoring Wireless Sensor Networks

The system is continuously operating, and using

the GPS unit is possible to track the nodes and execute

checks in case of warnings. This is useful for

maintenance and enables the fastest risk-reduction

technique and hazard plan in case of events, also in

case of vandalism. In the following Figures 10 – 13,

some reported data collected in the period of 05 – 30

September 2022 are reported. The reported

temperature and humidity are considered inside the

waterproof enclosure of each sensor node.

Figure 10: Recorded temperature in the period 05-30

September 2022.

Figure 11: Recorded barometric pressure in the period 05-

30 September 2022.

Figure 12: Recorded humidity in the period 05-30

September 2022.

Figure 13: Recorded nodes battery voltage in the period 05-

30 September 2022.

Data exchange and post-processing can be

implemented with various solutions, but in a low-cost

WSN, this should be addressed in an economical

manner: the LoRaWAN implementation, with TTN

structure employment, allows obtaining a good

performance in terms of deployment cost and

complexity. The system presented in this study allows

for ﬂexibility thanks to its fully portable characteristic,

which allows quicker installation with respect to other

mentioned solutions.

The modularity of the system is another key

factor; it can be equipped with different sensing

hardware despite the LoRa blocks remaining the same.

The system can be employed in many applications of

SHM: safety-enhancing mechanisms, industrial

production, and Building Information Modelling

(Tang et al., 2019) are some examples.

6 CONCLUSIONS

In this paper, a LoRa-based structural health

monitoring system was presented. The paper reports

the description of functionality, hardware, and real

scenario results. The electronic system was developed

on low-cost elements, allowing the deployment of a

modular WSN. The nodes are accelerometer-based

but also report values of temperature, barometric

pressure, and relative humidity, which is also useful

for environmental monitoring. The nodes

communicate with portable LoRaWAN gateways

using LoRa technology, allowing an IoT structure

thanks to the integration of web services enabled by

interfacing the gateways with the internet using LTE.

The WSN was installed in real scenarios, in Sicily,

Italy, on rockfall barriers and in Calabria, Italy, on

housings subjected to landslide. Measurement of

current absorption of the nodes is reported along with

a consideration of battery life in the worst-case

scenario. Real scenario data is reported, showing how

the inclinations along three special axes can vary in

case of structural movement events. The integration of

the presented system with other IoT-enabled structures

and services is used by the local authorities to ensure

inhabitants' safety, viability management and optimize

the on-field operations. Future developments of this

work will be in the optimization of packet transmission

to reduce the packet loss and a possible implementation

of better resource allocation algorithms. The

integration of AI frameworks for preventive

maintenance will also be evaluated for integration.

ACKNOWLEDGEMENTS

The authors acknowledge Frutti Carlo M+M,

WeMonitoring brand, FATA COSTRUZIONI SRL

and FALVO COSTRUZIONI SRL.

SENSORNETS 2023 - 12th International Conference on Sensor Networks

REFERENCES

Amafabia, D. M., Montalvão, D., David-West, O., &

Haritos, G. (n.d.). A Review of Structural Health

Monitoring Techniques as Applied to Composite

Structures. Structural Durability & Health Monitoring

2017, 11(2), 91-147. https://doi.org/10.3970/sdhm.20

17.011.091.

Amafabia, D. M., Montalvão, D., David-West, O., &

Haritos, G. (2017). A Review of Structural Health

Monitoring Techniques as Applied to Composite

Structures. Structural Durability & Health Monitoring,

11.

Amphenol. (n.d.). 868MHz ISM Band PCB Antenna

PIOV008NRA.

Awotunde, J. B., Jimoh, R. G., AbdulRaheem, M., Oladipo,

I. D., Folorunso, S. O., & Ajamu, G. J. (2022). IoT-

Based Wearable Body Sensor Network for COVID-19

Pandemic (pp. 253–275). doi: 10.1007/978-3-030-

77302-1_14

Bernardo, G. di, Narayana, A., & Hazarika, R. (2020).

Choice of effective LPWAN protocol for IoT System:

Sigfox and LoRa. International Journal of Engineering

Research and Applications Www.Ijera.Com, 10(11),

53–57. doi: 10.9790/9622-1011015357

Catbas, F. N. (2009). Structural health monitoring:

applications and data analysis. In Structural Health

Monitoring of Civil Infrastructure Systems (pp. 1–39).

Elsevier. doi: 10.1533/9781845696825.1

Chaudhari, B. S. and Z. M. (2020). LPWAN Technologies

for IoT and M2M Applications. Academic Press.

Chaudhari, B. S., Zennaro, M., & Borkar, S. (2020).

LPWAN Technologies: Emerging Application

Characteristics, Requirements, and Design

Considerations. Future Internet, 12(3), 46. doi:

10.3390/fi12030046

Fekih Romdhane, R., Lami, Y., Genon-Catalot, D., Fourty,

N., Lagreze, A., Jongmans, D., & Baillet, L. (2017).

Wireless sensors network for landslides prevention.

2017 IEEE International Conference on Computational

Intelligence and Virtual Environments for

Measurement Systems and Applications (CIVEMSA),

222–227. doi: 10.1109/CIVEMSA.2017.7995330

Finnegan, J., & Brown, S. (2018). An Analysis of the

Energy Consumption of LPWA-based IoT Devices.

2018 International Symposium on Networks,

Computers and Communications (ISNCC), 1–6. doi:

10.1109/ISNCC.2018.8531068

Gungor, V. C., & Hancke, G. P. (2009). Industrial Wireless

Sensor Networks: Challenges, Design Principles, and

Technical Approaches. IEEE Transactions on Industrial

Electronics, 56(10), 4258–4265. doi:

10.1109/TIE.2009.2015754

Haxhibeqiri, J., de Poorter, E., Moerman, I., & Hoebeke, J.

(2018). A Survey of LoRaWAN for IoT: From

Technology to Application. Sensors, 18(11), 3995. doi:

10.3390/s18113995

Ishikawa, K. I., & Mita, A. (2008). Time synchronization

of a wired sensor network for structural health

monitoring. Smart Materials and Structures, 17(1). doi:

10.1088/0964-1726/17/01/015016

Jino Ramson, S. R., & Moni, D. J. (2017). Applications of

wireless sensor networks — A survey. 2017

International Conference on Innovations in Electrical,

Electronics, Instrumentation and Media Technology

(ICEEIMT), 325–329. doi:

10.1109/ICIEEIMT.2017.8116858

Lauridsen, M., Nguyen, H., Vejlgaard, B., Kovacs, I. Z.,

Mogensen, P., & Sorensen, M. (2017). Coverage

Comparison of GPRS, NB-IoT, LoRa, and SigFox in a

7800 km2 Area. 2017 IEEE 85th Vehicular Technology

Conference (VTC Spring), 1–5. doi:

10.1109/VTCSpring.2017.8108182

Li, S., Raza, U., & Khan, A. (2018). How Agile is the

Adaptive Data Rate Mechanism of LoRaWAN? 2018

IEEE Global Communications Conference

(GLOBECOM), 206–212. doi: 10.1109/GLOCOM.20

18.8647469

LoRa Alliance. (2022). LoRa Alliance Website.

https://lora-alliance.org/.

LoRa and LoRaWAN: A Technical Overview LoRa® and

LoRaWAN®: A Technical Overview. (2020).

Mainetti, L., Patrono, L., & Vilei, A. (2011). Evolution of

Wireless Sensor Networks towards the Internet of

Things: a Survey. In SoftCOM 2011, 19th International

Conference on Software, Telecommunications and

Computer Networks.

Martinez-Luengo, M., & Shafiee, M. (2019). Guidelines

and Cost-Benefit Analysis of the Structural Health

Monitoring Implementation in Offshore Wind Turbine

Support Structures. Energies, 12(6), 1176. doi:

10.3390/en12061176

Mascarenas, D. L., D Todd, M., Park, G., & Farrar, C. R.

(2007). Development of an impedance-based wireless

sensor node for structural health monitoring. Smart

Materials and Structures, 16(6).

Ni, Y. Q., Xia, Y., Liao, W. Y., & Ko, J. M. (2009).

Technology innovation in developing the structural

health monitoring system for Guangzhou New TV

Tower. Structural Control and Health Monitoring,

16(1), 73–98. doi: 10.1002/stc.303

Nisshinbo. (2018). Datasheet RP104x series 150mA ultra

low supply current ldo regulator.

Oliveira, L. M. L., & Rodrigues, J. J. P. C. (2011). Wireless

sensor networks: A survey on environmental

monitoring. In Journal of Communications (Vol. 6,

Issue 2, pp. 143–151). doi: 10.4304/jcm.6.2.143-151

OpenJS Fundation. (2022). Node-RED website,

https://nodered.org/.

Orcesi, A. D., & Frangopol, D. M. (2011). Optimization of

bridge maintenance strategies based on structural health

monitoring information. Structural Safety, 33(1), 26–

41. doi: 10.1016/j.strusafe.2010.05.002

Pakzad, S. N., Fenves, G. L., Sukun, K., & Culler, D. E.

(2008). Design and Implementation of Scalable

Wireless Sensor Network for Structural Monitoring.

JOURNAL OF INFRASTRUCTURE SYSTEMS. doi:

10.1061/ASCE1076-0342200814:189

LoRa Structural Monitoring Wireless Sensor Networks

Paolucci, R., Muttillo, M., di Luzio, M., Alaggio, R., &

Ferri, G. (2020, September 23). Electronic Sensory

System for Structural Health Monitoring Applications.

2020 5th International Conference on Smart and

Sustainable Technologies, SpliTech 2020. doi:

10.23919/SpliTech49282.2020.9243798

Ragnoli, M., Barile, G., Leoni, A., Ferri, G., & Stornelli, V.

(2020). An autonomous low-power lora-based flood-

monitoring system. Journal of Low Power Electronics

and Applications, 10(2). doi: 10.3390/jlpea10020015

Ragnoli, M., Leoni, A., Barile, G., Ferri, G., & Stornelli, V.

(2022). LoRa-Based Wireless Sensors Network for

Rockfall and Landslide Monitoring: A Case Study in

Pantelleria Island with Portable LoRaWAN Access.

Journal of Low Power Electronics and Applications,

12(3), 47. doi: 10.3390/jlpea12030047

Ramesh, M. v. (2009). Real-time wireless sensor network

for landslide detection. Proceedings - 2009 3rd

International Conference on Sensor Technologies and

Applications, SENSORCOMM 2009, 405–409. doi:

10.1109/SENSORCOMM.2009.67

Rossi, M., Guzzetti, F., Salvati, P., Donnini, M.,

Napolitano, E., & Bianchi, C. (2019). A predictive

model of societal landslide risk in Italy. In Earth-

Science Reviews (Vol. 196). Elsevier B.V. doi:

10.1016/j.earscirev.2019.04.021

Ruan, T., Chew, Z. J., & Zhu, M. (2017). Energy-Aware

Approaches for Energy Harvesting Powered Wireless

Sensor Nodes. IEEE Sensors Journal, 17(7), 2165–

2173. doi: 10.1109/JSEN.2017.2665680

Sabato, A., Niezrecki, C., & Fortino, G. (2017). Wireless

MEMS-Based Accelerometer Sensor Boards for

Structural Vibration Monitoring: A Review. IEEE

Sensors Journal, 17(2).

Semtech. (2020). Datasheet SX1276/77/78/79 - 137 MHz

to 1020 MHz Low Power Long Range transceiver.

Sidorov, M., Nhut, P. V., Matsumoto, Y., & Ohmura, R.

(2019). LoRa-Based Precision Wireless Structural

Health Monitoring System for Bolted Joints in a Smart

City Environment. IEEE Access, 7, 179235–179251.

doi: 10.1109/ACCESS.2019.2958835

SiliconLabs. (2017). Datasheet CP2102/9 single-chip USB-

TO-UART bridge.

Sohn, H., Charles R. Farrar, Francois M. Hemez, Devin D.

Shunk, Daniel W. Stinemates, Brett R. Nadler, & Jerry

J. Czarnecki. (2003). A review of structural health

monitoring literature: 1996–2001. Los Alamos

National Laboratory.

STMicroelectronics. (2016a). LIS3DH MEMS digital

output motion sensor: ultra-low-power high-

performance 3-axis “nano” accelerometer. Retrieved

from www.st.com

STMicroelectronics. (2016b). STM32L151x6/8/B

STM32L152x6/8/B Ultra-low-power 32-bit MCU

ARM®-based Cortex®-M3, 128KB Flash, 16KB

SRAM, 4KB EEPROM, LCD, USB, ADC, AC.

Retrieved from www.st.com

Tang, S., Shelden, D. R., Eastman, C. M., Pishdad-Bozorgi,

P., & Gao, X. (2019). A review of building information

modeling (BIM) and the internet of things (IoT) devices

integration: Present status and future trends.

Automation in Construction, 101, 127–139. doi:

10.1016/j.autcon.2019.01.020

Texas Instruments. (2019). Datasheet bq21040 0.8-A,

Single-Input, Single Cell Li-Ion and Li-Pol Battery

Charger.

The Things Network. TTN Website (2022).

https://www.thethingsnetwork.org/ .

u-blox. (2021). MAX-7 u-blox 7 GNSS modules Datasheet.

Vaibhav, K., Sonal, M., & Ankush, K. (2016). A Review

on XBEE Technology. International Journal of

Emerging Technologies in Engineering Research

(IJETER), 4(4).

Vangelista, L. (2017). Frequency Shift Chirp Modulation:

The LoRa Modulation. IEEE Signal Processing Letters,

24(12), 1818–1821. doi: 10.1109/LSP.2017.2762960

Xiamen Milesight IoT Co., Ltd. (2022). UG65 Gateway

Datasheet.

Zhiguo, H., Wentao, L., Hadi, S., Hao, Z., Haiyi, Z., &

Pengcheng, J. (2022). Integrated structural health

monitoring in bridge engineering. Automation in

Construction, 136.

SENSORNETS 2023 - 12th International Conference on Sensor Networks