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A Standard-Based Internet of Things Platform and Data Flow Modeling for Smart Environmental Monitoring.

Authors :
Filho, Tércio
Fernando, Luiz
Rabelo, Marcos
Silva, Sérgio
Santos, Carlos
Ribeiro, Maria
Grout, Ian A.
Moreira, Waldir
Oliveira-Jr, Antonio
Source :
Sensors (14248220); Jun2021, Vol. 21 Issue 12, p4228, 1p
Publication Year :
2021

Abstract

The environment consists of the interaction between the physical, biotic, and anthropic means. As this interaction is dynamic, environmental characteristics tend to change naturally over time, requiring continuous monitoring. In this scenario, the internet of things (IoT), together with traditional sensor networks, allows for the monitoring of various environmental aspects such as air, water, atmospheric, and soil conditions, and sending data to different users and remote applications. This paper proposes a Standard-based Internet of Things Platform and Data Flow Modeling for Smart Environmental Monitoring. The platform consists of an IoT network based on the IEEE 1451 standard which has the network capable application processor (NCAP) node (coordinator) and multiple wireless transducers interface module (WTIM) nodes. A WTIM node consists of one or more transducers, a data transfer interface and a processing unit. Thus, with the developed network, it is possible to collect environmental data at different points within a city landscape, to perform analysis of the communication distance between the WTIM nodes, and monitor the number of bytes transferred according to each network node. In addition, a dynamic model of data flow is proposed where the performance of the NCAP and WTIM nodes are described through state variables, relating directly to the information exchange dynamics between the communicating nodes in the mesh network. The modeling results showed stability in the network. Such stability means that the network has capacity of preserve its flow of information, for a long period of time, without loss frames or packets due to congestion. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
14248220
Volume :
21
Issue :
12
Database :
Complementary Index
Journal :
Sensors (14248220)
Publication Type :
Academic Journal
Accession number :
151145638
Full Text :
https://doi.org/10.3390/s21124228