Data Acquisition System Using Hybrid Network Based on LoRa for Hydraulic Plants

Medeiros, Emmanuel Leite de; Carvalho, Fabrício Braga Soares de; Villanueva, Juan Moisés Maurício; Moreira, Cleumar da Silva; Filho, Carlos Alberto de Souza

doi:10.1109/tim.2021.3068169

Cited by 5 publications

(2 citation statements)

References 29 publications

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“…The sector is increasingly committed to promoting LoRa-based solutions that could be better for human civilization [18] [26]. The LoRa-based IoT ecosystem offers a multifaceted applicability across several industries [27]. A sensor node, which is referred to as an end device, and a gateway, which creates communication linkages between end devices and servers, make up the LoRa architecture.…”

Section: Introductionmentioning

confidence: 99%

Long range based effective field monitoring system

Rajani Kumari,

Suneetha,

Sri Lakshmi

et al. 2024

IJEECS

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<p><span>Adoption of the internet of things (IoT) is moving forward quickly because of the developments in communication protocols and technology involving sensors. The IoT is promoting real-time agricultural field monitoring from any distant place. For the IoT to be implemented effectively there are a number of agricultural issues related to less power usage and long-distance transfer of data are to be addressed. By using LoRa, which is a wireless communication system for IoT applications, these difficulties can be avoided when sending information from fields of crops to a web server. Acustomized sensor node and LoRa are used in this work to transmit continuously updated information to a remote server. Monitoring the quality of water, and reducing wasteful use of water are the main goals.</span></p>

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Section: Introductionmentioning

confidence: 99%

Long range based effective field monitoring system

Rajani Kumari,

Suneetha,

Sri Lakshmi

et al. 2024

IJEECS

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“…In this scenario, actions aimed at minimizing the losses related to inefficient water transport management are fundamental and urgent, especially in the application and modernization of control and monitoring systems capable of optimizing operational decisions [ 5 ]. Implementation and expansion of supervisory, control, and data acquisition (SCADA) systems, low-cost Internet of Things (IoT) technologies, system modeling via artificial intelligence (AI), and modern controls are some of the actions that can provide greater operational efficiency.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Control of Pressure in a Water Supply Network Based on Neural Network System Modeling and IoT Measurements

Araújo

Villanueva

Cordula³

et al. 2022

Sensors

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As hydroenergetic losses are inherent to water supply systems, they are a frequent issue which water utilities deal with every day. The control of network pressure is essential to reducing these losses, providing a quality supply to consumers, saving electricity and preserving piping from excess pressure. However, to obtain these benefits, it is necessary to overcome some difficulties such as sensing the pressure of geographically distant consumer units and developing a control logic that is capable of making use of the data from these sensors and, at the same time, a good solution in terms of cost benefit. Therefore, this work has the purpose of developing a pressure monitoring and control system for water supply networks, using the ESP8266 microcontroller to collect data from pressure sensors for the integrated ScadaLTS supervisory system via the REST API. The modeling of the plant was developed using artificial neural networks together with fuzzy pressure control, both designed using the Python language. The proposed method was tested by considering a pumping station and two reference units located in the city of João Pessoa, Brazil, in which there was an excess of pressure in the supply network and low performance from the old controls, during the night period from 12:00 a.m. to 6:00 a.m. The field results estimated 2.9% energy saving in relation to the previous form of control and a guarantee that the pressure in the network was at a healthy level.

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