Designing and Building a Low-Cost IoT Solution for Natural Disaster

Monitoring and Mitigation: An Experience Report

Anselmo P. R. Costa

1,3 a

, Claudia L. R. Motta

2,3 b

and Daniel Schneider

2,3 c

Polytechnic Institute UFRJ - Macaé, Rio de Janeiro, Brazil

Tércio Pacitti Institute of Computer Applications and Research, NCE/UFRJ, Rio de Janeiro, Brazil

Postgraduate Program in Informatics, PPGI/UFRJ, Rio de Janeiro, Brazil

Keywords: Low-Cost IoT System, Natural Disaster Monitoring, Crowdsourcing, Design Science, Problem-Based

Learning.

Abstract: Mitigating natural disasters is a global concern and a social challenge, and new technologies and social

inclusion mechanisms have been applied to improve prevention and response to these events. Several

initiatives seek to mitigate natural disasters and rely on technologies such as the Internet of Things,

crowdsourcing, volunteer training, society involvement, among others. Although there are many initiatives in

this regard, building a model enabling real mitigation of natural disasters remains a major challenge. This

study aims to present a Natural Disaster Mitigation model, which combines different technologies through

the construction of a multidisciplinary group composed of researchers, students, civil defense technicians, and

the municipal school network. The goal is to develop and implement a meteorological monitoring network

using low-cost technological artifacts installed in school systems in the target cities.

1 INTRODUCTION

Digital technologies like the Internet of Things (IoT)

and environmental education have been explored to

improve prevention and response to natural disasters.

However, to the best of our knowledge, no proposals

were found in the literature leveraging such a myriad

of strategies and technologies like Project-Based

Learning (PBL), IoT, crowdsourcing, meteorological

education, and environmental education, applied in

the context of protection and defense actions.

The goal of this work is to present a natural

disaster mitigation model that combines the

construction of IT artifacts with training for the public

involved. The idea is to build a set of low-cost

meteorological stations leveraging IoT technology,

crowdsourcing, robotics training, notions of

meteorology, and civil defense techniques. The

proposed model was prototyped and implemented by

an interdisciplinary group made up of researchers and

students from four universities, civil defense

technicians, and a group of teachers and students from

https://orcid.org/0000-0002-9041-3989

https://orcid.org/0000-0002-4069-1462

https://orcid.org/0000-0003-2987-4732

schools in the municipal education network, using the

PBL strategy and the Design Science Research (DSR)

method. This paper also describes the methodology

used to build and implement the proposed model, the

actions taken, and the applied technology, in addition

to presenting the results achieved so far and future

works.

This article is organized as follows. Section II

presents the theoretical foundation of digital

technologies and strategies applied in disaster

mitigation. Section III depicts the related works, and

Section IV describes the proposed model. Section V

presents the conclusions, and section VI describes our

future work.

2 BACKGROUND

Emerging technologies like IoT have been explored

to improve prevention and response to these events.

The literature highlights the importance of using the

crowdsourcing strategy in collecting voluntary

Costa, A., Motta, C. and Schneider, D.

Designing and Building a Low-Cost IoT Solution for Natural Disaster Monitoring and Mitigation: An Experience Report.

DOI: 10.5220/0012703900003690

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 26th International Conference on Enterprise Information Systems (ICEIS 2024) - Volume 1, pages 1005-1012

ISBN: 978-989-758-692-7; ISSN: 2184-4992

1005

geographic information to better understand natural

disasters (Tulloch, 2014), especially leveraging the

participatory sensing paradigm (Oliveira et al., 2015).

Furthermore, studies have analyzed socio-

environmental vulnerability and disaster risk

reduction, recognizing that natural threats are not

unexpected events and that preparation is essential

(Freitas et al., 2012). The analysis of the situation of

natural disasters in Brazil has also been the subject of

study, highlighting the need to understand the

interactions between natural disasters and public

health (Freitas et al., 2014).

Literature reviews on natural disaster information

and surveillance systems are relevant to

understanding how technologies can be applied in this

context (Sobral et al., 2010). Studies have sought to

help plan preventive and mitigating actions, as in the

case of meteorological diagnosis of natural disasters

(Barcellos et al., 2016).

On the other hand, the theoretical analysis of

natural disasters has been explored in several areas of

knowledge, emphasizing the importance of risk and

disaster management due to the increase in the

magnitude and frequency of these events (Machado

& Machado, 2019; Kobiyama et al., 2018). The use

of IoT technologies for humanitarian logistics and for

search and rescue in natural disasters has also been

the subject of a literature review (Monteiro et al.,

2018). Vulnerability to natural disasters and their

nursing implications have also been addressed,

highlighting the importance of preparation to face

these events (Bandeira et al., 2014).

Meteorological monitoring in Brazilian cities is

carried out through a network of meteorological

stations distributed throughout the country. These

stations collect data on atmospheric conditions, such

as temperature, air humidity, atmospheric pressure,

wind speed, and direction, among other

meteorological parameters. The main institution

responsible for this monitoring in Brazil is the

National Institute of Meteorology (INMET), which is

linked to the Ministry of Agriculture, Livestock and

Food Supply.

INMET operates an extensive network of

automatic and conventional meteorological stations

located in urban and rural areas. Automatic stations

collect data continuously and in real time,

transmitting the information to processing centers. In

addition, there are conventional stations where human

observers manually record meteorological data. In

addition to INMET, other institutions, such as

universities and research centers, also contribute to

meteorological monitoring in Brazil. The information

collected is used to produce weather forecasts,

weather alerts, and climate studies, providing data for

various areas, such as agriculture, aviation, water

resources management, and natural disaster

prevention (INMET. 2024).

Considering INMET's limited resources and the

country’s territorial extension, the involvement of

various institutions and the very civil society through

transversal, collaborative and low-cost alternatives is

crucial, aiming to address the challenges related to

meteorological monitoring. One of these alternatives

lies in the proposal of a natural disaster mitigation

model leveraging IoT technology and crowdsourcing,

with the use of low-cost meteorological sensors,

cloud applications with various collaborative

resources, and the direct involvement of the school

community and its surroundings, supported by the

offer of robotics courses, notions of meteorology and

civil defense. With this methodology, microregions

can be equipped with low-cost stations, and a clear

picture of the meteorological response can be

obtained.

Regarding the inclusion of crowdsourcing in our

mitigation model, we were inspired by the work of

Chaves et al. (2019), who highlight that citizen

involvement has been increasingly used in the

prevention and management of urban disasters, in

addition to the fact that current networked citizens are

in direct contact with institutions and companies

(Schneider and de Souza, 2015). This scenario thus

creates a unique opportunity to leverage the work of

crowd volunteers.

3 RELATED WORK

An example that uses the DSR approach and values

integrating IoT resources with education is presented

by Costa et al. (2022). It describes a proposal to

integrate IoT concepts and technologies into the

curriculum of a high school technical course in

Agriculture, using a technological artifact based on

IoT as a teaching tool. In an evaluation with course

teachers, interest in applying the proposal and the

great potential of the artifact for improving teaching-

learning processes was demonstrated.

Several works seek to mitigate natural disasters

using software and hardware artifacts. Simoes and De

Souza (2016) demonstrate technologies for

implementing low-cost stations using the MQTT

protocol and the ESP8266 microcontroller. On the

other hand, Math and Dharwadkar (2018) propose a

cloud-based intelligent system weather station. The

storage and processing of data obtained to predict the

effect of climate change is done in the cloud, and the

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system is designed to effectively monitor

environmental weather conditions such as

temperature, humidity, wind speed, pressure and

precipitation. The authors aimed to design a low-cost

system requiring less maintenance and minimal

human intervention.

Kodali and Mandal (2016) present the way in

which a weather station can be described as an

instrument or device that provides us with

information about the weather in our neighboring

environment. The authors also show how it can

provide details about ambient temperature,

barometric pressure, humidity, etc. Therefore, this

device basically detects temperature, pressure,

humidity, light intensity and rainfall amount. They

further describe that several types of sensors are

present in the prototype, through which all the

aforementioned parameters can be measured. With

the help of temperature and humidity, we can

calculate other data parameters such as dew point.

In addition to the resources mentioned above, the

study presents the possibility of monitoring the light

intensity of the location, the atmospheric pressure in

the room, and the amount of rain. The core of the

presented prototype is the Nodemcu (12E) WiFi

module based on ESP8266 with connected sensors.

Such sensors are parameterized so that whenever

values exceed a chosen limit, warnings are sent via

email and a tweet alerting the device owner to take

the necessary measures.

Banara, Singh and Chauhan (2022) present a

bibliographical review of an existing meteorological

monitoring system, including its sensors,

microcontrollers, and means of communication. The

chronological review of meteorological monitoring

system literature presented by the authors provides a

chronological table with publication date, sensors

used, parameters used, description, and platform used

of fourteen studies.

As previously stated, in the literature, we did not

find a model similar to the one proposed by this study,

where it was decided to: a) combine the competence

of researchers and university students in a set of

activities that involved different actors in the

mitigation control process; b) bring the municipal

school network into the mitigation control process; c)

implement meteorological stations in schools; d)

include in the project a teaching and training model

(for teachers, technicians, students and volunteers

involved) addressing fundamental concepts related to

the problem of mitigating natural disasters as well as

notions of meteorology and civil defense.

4 PROPOSED MODEL

The methodology used in this work was DSR because

it aims to build innovative artifacts proposed to solve

real societal problems based on the production of

scientific knowledge (Dresch et al., 2015). The DSR

process is divided into highly interconnected steps

that may overlap, allowing the results of some steps

to influence the review of a previous step.

Development in cycles allows new functionalities to

be developed and integrated into previous cycles,

according to the evaluation of the results (Goecks et

al., 2021).

DSR begins with the stages of identification and

awareness of the problem, where the possibility of

integrating universities, civil defense and municipal

school networks through a disaster mitigation model

was discussed. The goal is to reduce the effects of

natural disasters and build more participatory,

resilient societies with a preventive culture.

From the problems encountered to the final

proposal of the model, collective intelligence

concepts were used both in generating ideas and in

decision support, enabling the aggregation of

information, decision-making, the improvement of

scientific practice, and work management (Pentland,

2006). Initially, the model was proposed to include

only a couple of actors, emphasizing developing a

station prototype and the necessary tools for data

collection and analysis. However, it became clear that

building such a model would require more actors,

with a structure being built that encompassed several

entities acting through an iterative DSR process.

Although the goal of the proposed model was to

serve the school networks of the target cities, it was

crucial to give the starting point by building a pilot

project. This paper describes the first pilot application

of our natural disaster mitigation model, designed for

the city of Macaé, in the state of Rio de Janeiro,

Brazil. Several activities were carried out

collaboratively using an iterative DSR process to

develop and implement this network of stations.

Figure 1 depicts the core of our proposed model,

showing key tasks covering the various entities

involved.

● Creation of a multidisciplinary working group

involving students and researchers from four

higher education institutions with Educational

management, Hardware Development Team,

Software Development Team, Marketing Team,

Robotics Training Team, BI Data Analysis

Training Team, Deployment Team, Management

and quality team;

Designing and Building a Low-Cost IoT Solution for Natural Disaster Monitoring and Mitigation: An Experience Report

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● Strategic definition of the points where

meteorological stations would be located;

● Low-cost, real-time, Internet-enabled station

construction/assembly with IOT technology;

● Development of a tool for collaborative use for the

analysis and control of measurements carried out

by stations;

● Field testing of the station prototype and

computational tools;

● Building a collaborative partnership with

interested parties, including Macaé City Hall –

(Secretary of education and civil defense), the

Universidade Norte Fluminense (UENF) through

its meteorology laboratory (LAMET)

● Development of a relationship with the municipal

school network providing robotics training for

staff and students (construction of a

meteorological station), notions of meteorology

and notions of civil defense;

● Training civil defense employees in a BI tool to

analyze data collected by stations.

Before understanding the process, we describe the

entities involved and their relationships (Figure 1).

4.1 Multidisciplinary Working Group

A multidisciplinary group was created with

researchers and students from different courses from

four higher education institutions distributed in

teams, as shown in Figure 3.

The courses involved were Mechanical

Engineering, Production Engineering, Pharmacy,

Nutrition, Chemistry, Medicine, Information

Systems, Mathematics, Electrical Engineering and

Meteorological Engineering. The purpose was to use

free robotics with the application of active

methodologies in solving and testing practical

problems (PBL), seeking to contribute significantly

to the improvement of learning, in addition to

enabling the construction of robotic devices that assist

in the learning process and construction of

knowledge. Integrating the university with partner

institutions will allow it to respond to social,

environmental, and organizational challenges in the

context of the continued training of professionals in

the school network and civil defense.

4.2 Strategic Definition of

Meteorological Stations

Macaé is located at latitude S -22º22'33" and

longitude W -41º46'30", with 23 kilometers of

coastline. The climate is hot and humid most of the

year, with temperatures varying between 18ºC and

30ºC, with considerable thermal amplitude caused

due to the exchange of winds between the coast and

the mountains, which are relatively close. The schools

were defined through a study carried out by civil

defense looking for important areas for

meteorological studies or vulnerable to natural

disasters, taking into account that the city has

characteristics of mountains and sea. The school

community plays a fundamental role in the model, as

it functions as a center for collecting and

disseminating information.

Figure 1: Relationship of the entities that comprise the process (multidisciplinary working group).

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Teachers and students are trained to understand and

interpret a weather station’s construction mechanism

and the alerts issued by the IoT system, transforming

schools into crucial points of contact for communica-

tion with civil defense and the wider community.

Figure 2: Teams that make up the project.

4.3 Low-Cost Station Assembly with

IoT Technology

A low-cost real-time station prototype was

developed. It was crucial that the station had the

following characteristics: easy to build, weather

resistant, insectproof, lightweight, easy to install, and

sustainable. We also aimed that the sensors had the

following technical capabilities: internet-enabled,

consuming little energy, auxiliary power,

computationally efficient, easily programmable, with

over-the-air (OTA ) updates.

Figure 3: Part of the multidisciplinary group

Thinking about functionality and ease of

maintenance, which should be carried out in

partnership with teams from schools, civil defense

and employees, it was decided to build the stations in

modules, which were categorized into four groups

that include: microcontroller, circuit, sensors for

humidity, temperature, wind speed, wind direction,

rainfall volume, atmospheric pressure and auxiliary

outputs for new sensors, solar power supply and

hardware peripherals.

A prototype station was developed based on the

ESP32 microcontroller platform with Wi-Fi functio-

nality and using OTA updates. The microcontroller is

connected to a custom circuit board, as seen in Figure

4. It acts as the central location to which all electronic

and electrical components and sensors are connected,

as shown in Figure 5.

Figure 4: Weather station central.

Figure 5: Station components, including sensors.

4.4 A Tool for Collaboratively

Analyzing Measurements

The application was developed according to the needs

established by civil defense, using a set of

programming languages and technologies (such as

Designing and Building a Low-Cost IoT Solution for Natural Disaster Monitoring and Mitigation: An Experience Report

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PHP, apache web server, Codeigniter framework, and

MySQL database). Figures 6-8 depict various

features of the application, such as control panel

functions displaying general statistics and event

control; visualization of station status and monitoring

with color scale on georeferencing; statistical

visualization of readings through graphs, control of

incidents throughout the city reported by users;

registrations for station management.

Figure 6: Real-time application main screen.

Figure 7: Station map screen per alert.

Figure 8: Station graphics screen.

4.5 Field Testing

After prototyping, the stations were produced and

programmed with embedded software in C language.

A service analysis and configuration tool was also

developed using Bluetooth Low Energy (BLE)

technology to facilitate the maintenance and

installation of stations in schools (Figure 9).

4.6 Collaborative Partnership

A collaborative partnership was developed with

interested parties, including Macaé City Hall (civil

defense department), municipal education

department, and UENF through its meteorology

laboratory (LAMET), where we have several

researchers and students from different courses.

Figure 9: Service analysis and configuration tool using

Bluetooth Low Energy (BLE) technology.

Figure 10: Part of the team of project collaborators.

Figure 11: Civil defense monitoring center.

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Such partnership has become fundamental to building

knowledge and the feeling of belonging. A

monitoring center (Figure 11) was created to support

the analysis of collected data and civil defense

decision-making with real-time readings.

4.7 Development of a Relationship with

the School Network

The participation of school representatives in

robotics, meteorology, and civil defense courses is

essential for disseminating information and building

a sense of belonging with each station being

implemented as well as the project as a whole, aiming

to mitigate natural disaster actions.

Training in robotics, notions of meteorology, and

civil defense techniques was offered to thirty-five

schools in the municipal network. At this stage, it was

possible to serve a number of forty-nine employees

and students from sixteen schools.

In the robotics course, theoretical and practical

classes were offered with an ESP32 microcontroller

and a set of sensors, enabling the student to obtain

robotics concepts through the construction of

experiments and the development of a station similar

to the one implemented in their school. The goal was

to involve the school group in the use and

maintenance of it.

In meteorology training, relevant topics were

presented, such as the World Meteorological

Organization (WMO), global observation system,

meteorological radars, frontal systems and

extratropical cyclones, meteorological systems,

mesoscale convective complexes, extreme events,

cloud observation, how to monitor season data,

changes climate, renewable energy, weather forecast,

air pollution, climate and arboviruses.

The civil defense training was composed of

notions of civil defense, disasters and monitoring, a

set of prevention, mitigation, preparation, response

and recovery actions, national defense system,

concepts of prevention, mitigation, preparation,

response, recovery and restoration, what is a disaster,

types of disasters, Brazilian classification and coding

of disasters (cobrade), differentiating floods from

overflow, risks in flooded or overflow streets, storms

and hangovers.

4.8 Training Civil Defense Employees

in a BI Tool

The training aimed to train civil defense employees in

data analysis in order to allow the generation of

relevant information from data generated by

meteorological stations. Google Looker Studio was

used as a data analysis and business intelligence tool

emphasizing meteorological data. Training was

offered in a virtual learning environment due to the

dynamics of the technical activity of civil defense

agents. Participation in the training was voluntary and

we had significant participation, with a 65%

completion rate. Training of technicians in the

analysis of meteorological data using the Google

Looker tool was considered to be positive, where 83%

of students gave grades nine and ten after being

questioned with the following question: Considering

all aspects evaluated, globally, what grade would you

give the Course on a scale of 1 to 10?

5 CONCLUSIONS

The strategy of combining the expertise of

researchers and university students in a set of

activities that involve several actors in the mitigation

control process is neither simple nor trivial, and the

results related to the challenge of disaster mitigation

must be evaluated in a medium and long term. Part of

the challenge was to bring the municipal school

network into the mitigation control process and offer

the possibility of training in addition to implementing

meteorological stations in schools. However,

evidence of positive results has already been

collected, despite the very recent implementation of

the project, highlighting the integration of

universities, through their various courses, with the

local government and the education department.

In addition, a set of results was achieved in the

context of the project in 2023:

● Savings of 4 million Brazilian reais in

implementing a low-cost station monitoring

system.

● 16 meteorological stations installed.

● 16 schools in the municipal school network had

groups trained in robotics, meteorology

concepts and civil defense.

● Integration of 42 students from 4 universities in

the knowledge construction process, with the

exchange of skills between different courses.

● 36 civil defense employees participated in BI

training, using the Google Looker Studio tool

emphasizing meteorological data.

● Organization of the First Meeting on Climate,

Technologies and Accident Risk Reduction,

involving the academic community, local

government, and society.

Designing and Building a Low-Cost IoT Solution for Natural Disaster Monitoring and Mitigation: An Experience Report

1011

6 FUTURE WORK

Future work will include creating sustainability

indicators for monitoring and prevention of natural

disasters, contributing to achieving the goals of the

2030 Agenda, the Sendai Framework for Disaster

Risk Reduction 2015-2030, the National Civil

Protection and Defense Policy through Law

12608/2012; promoting actions within the scope of

the Innovation Ecosystem established in Macaé; and

supporting the development of projects with the

potential to generate technology-based businesses.

It is also planned to analyze the collected data,

geoprocess it, and adapt or resize the models

according to the needs of the Deputy Secretariat for

Civil Defense. In the educational area, an adjustment

of technical and scientific cooperation is planned and

carried out through teaching and/or research, and the

development of projects in the area of Protection and

Civil Defense aligned with Civil Defense projects in

schools.

In a second development cycle, prediction

mechanisms will be implemented through the

analysis of data stored on servers. BI tools (such as

Google Looker) have been applied in the training of

Civil Defense technicians, who, in the second

development cycle, will be advised by

Meteorological Engineering specialists belonging to

the multidisciplinary group in the analysis and

construction of predictability patterns.

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