Considerations for a cloud-based system for
IoT data acquisition from heterogeneous
sensors
Daniel Coutinho1, Pedro Salgueiro1, Jos´e Saias1, Lu´ıs
Rato1, Vitor Nogueira1, Jorge Silva2, Pedro Lucas2,
Filipe Ara´ujo2, Catarina Silva2, Paulo Gil2, Alberto
Cardoso2, Joel Arrais2, Bernadete Ribeiro2
1LISP - Laboratory of Informatics, Systems and Parallelism, University of
´
Evora, Portugal
2CISUC - Center for Informatics and Systems, Dep. of Informatics
Engineering, University of Coimbra, Portugal
Corresponding/Presenting author: m43354@alunos.uevora.pt
Poster Abstract
Air pollution is a rising concern, demanding for low cost air quality mon-
itoring systems. In this paper, we describe the back-end of an air quality
monitoring system, developed in the context of the NanoSen-AQM [3, 4]
project, a project with the goal of creating a real-time system that allows
for a cost-effective, distributed and ubiquitous air quality monitoring. In
particular, we describe the Data Aggregation Module.
The NanoSen-AQM [3, 4] project is focused in the air quality monitoring
using low-cost nanosensors, developed in the context of the project. The
system will have at its core a cloud system that supports a mobile application,
a web application and third party platforms.
The cloud system starts from receiving the data in cloud system and
adding its database, so the data can be monitored by the web application.
The cloud system can be divided in two modules: 1) the Central System; and
2) the Data Aggregation Modules.
The Data Aggregation Modules collect data from the sensors, acting as a
buffer for the messages to be inserted in central system database.
The Central System is responsible for storage, processing and data access.
The web, mobile and third party applications fetch data from the aforemen-
tioned module.
The Data Aggregation Modules receives data from the sensors, which then
sends it to Central System to be stored. This module can be further divided
in two sub-modules: 1) the Data Input; and 2) the Data Publishing Service.
The Data Input Module receives data from the RESTFul[1] and MQTT[2]
protocols.
1
MQTT is a protocol developed for sensors and IoT thinking how the
sensors are exposed to low quality connections. In cases where sensors have
low resources and can’t handle the MQTT library, RESTFul is considered
the best alternative.
In both protocols, there is a Message Authentication Code (MAC) that
validates each message integrity. The application that receives the message
from the sensors, will also receive an hash. After the message is received in
the application, the message is processed and accepted only if the hash is
valid. The message then reaches the data publishing service that serves as a
buffer to hold the messages before being inserted in the database. Meanwhile,
in data publishing service, the messages need to be processed so they can be
inserted in the database. The data publishing service uses Apache Kafka with
Kafka Streams in order to serve as a buffer and data processing, respectively.
Keywords: cloud systems, MQTT, REST, apache kafka
Acknowledgements
This work was supported by the Fundo Europeu de Desenvolvimento Regional
(FEDER) through the project NanoSen-AQM (SOE2/P1/E0569).
References
[1] Richardson, Leonard, and Sam Ruby. RESTful web services. ” O’Reilly
Media, Inc.”, 2008.
[2] A. Stanford-Clark and H. L. Truong, MQTT for sensor networks
(MQTTs), http://www.mqtt.org/MQTTs Specification V1.0.pdf, Oct.
2007.
[3] Jorge Silva, Pedro Lucas, Filipe Araujo, Catarina Silva, Alberto Cardoso,
Joel Arrais, Bernardete Ribeiro, Daniel Coutinho, Pedro Salgueiro, Lu´ıs
Rato, Jos´e Saias, and V´ıtor Nogueira. An online platform for real-time air
quality monitoring. In 2019 5th Experiment@ International Conference
(exp. at’19). IEEE, 2019.
[4] Jorge Silva, Pedro Lucas, Filipe Araujo, Catarina Silva, Alberto Cardoso,
Joel Arrais, Bernardete Ribeiro, Daniel Coutinho, Pedro Salgueiro, Lu´ıs
Rato, Jos´e Saias, and V´ıtor Nogueira. A hybrid application for real-time
air quality monitoring. In 2019 5th Experiment@ International Confer-
ence (exp. at’19). IEEE, 2019.
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