Water Quality Monitoring with Fuzzy Logic Control based on Graphical Programming

Ichsan, Mochammad Hannats Hanafi; Kurniawan, Wijaya; Huda, Miftahul

doi:10.12928/telkomnika.v14i4.3505

Cited by 9 publications

(5 citation statements)

References 8 publications

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“…The QoS used is a delay, jitter, and throughput [27]. Therefore the system function is to monitor the environment such as a smart home environment and it could measure well [28].…”

Section: Figure 2 Research Methodsmentioning

confidence: 99%

WSN performance based on node placement by genetic algorithm at smart home environment

et al. 2019

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Wireless sensor connectivity is one of several factors that determines the communication reliability of each node. The placement of the node depends on the area that covered by wireless coverage area, so the node placement should be optimally placed. But the other aspect is the sensor coverage area. Sensor coverage area sometimes could be different with wireless sensor coverage area. Based on that situation, it needs to optimize that situation. Genetic Algorithm is an algorithm that utilizes a heuristic approach that uses biological mechanism evolution. It used to evolution the best position of Sensor Node based on Wireless and Sensor coverage area. After the position of each node generated by Genetic Algorithm, it still needs to evaluate the wireless sensor node performance. The performance indicates that the genetic algorithm can be used to determine sensor node placement in the smart home environment. The smart home environment used to monitor event at the house such as wildfire. In this research used Quality of Services (QoS) to measure wireless sensor performance. The experimental testing scenario will be used to place several nodes that generated. The QoS performed systems reliability that produced based on 3, 4 and 5 testing nodes, the minimum and maximum of each: delay is 6.21 and 8.74 milliseconds, jitter is 0.11 and 1.59 Hz and throughput is 68.83 and 90.49 bps. Based on ETSI classification, the performance of sensor node placement is Good and acceptable in real-time systems.

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“…The QoS used is a delay, jitter, and throughput [27]. Therefore the system function is to monitor the environment such as a smart home environment and it could measure well [28].…”

Section: Figure 2 Research Methodsmentioning

confidence: 99%

WSN performance based on node placement by genetic algorithm at smart home environment

et al. 2019

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“…In the field of fish farming, artificial intelligence has been used in real-time environmental monitoring and control systems. Several research and studies that use soft-computing tools such as fuzzy logic [21], [22], and artificial neural networks [23] to control water quality, track fish size in ponds, and diagnose fish diseases [23], [24]. This work presents an IoT-based approach to automating fish farm management and enabling remote monitoring of fish ponds.…”

Section: Introductionmentioning

confidence: 99%

IoT-based smart monitoring and management system for fish farming

Al-Mutairi¹,

Al-Aubidy²

2023

Bulletin EEI

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Fish farming is still controlled and managed in the traditional way where water quality and fish feeding are manually controlled. There is a need to use computer and communication technology in fish farms for remote monitoring and control. This paper deals with the design and implementation of an internet of things (IoT) based system for real-time monitoring, control and management of fish farming. The design of such a system is based on measuring different types of variables and using the information to control fish growth and increase productivity. Each fish pond is a node in a wireless sensor network. The node contains an embedded microcontroller connected to a set of sensors and actuators and a wireless communication module. Two fuzzy controllers are designed to control the water quality in the ponds as well as the environment using five sensors in each pond plus three environmental sensors. Practical results indicate the accuracy of the measurement system compared to the results obtained from commercial devices used on the farm. These results also showed that the proposed approach achieves the best performance of the real-time monitoring and control system in fish ponds.

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“…Rana and Rani [17] conducted a MATLAB simulation of a fuzzy logic system to determine the percentage of fish health in freshwater using temperature, dissolved oxygen, and www.ijacsa.thesai.org conductivity. In the study of Ichsan, Kurniawan and Huda [18], they designed a fuzzy logic control based on graphical programming to monitor the water quality in shrimp pond by considering the salinity and turbidity. Hiyunissa, Alam and Salim [19] designed fuzzy logic control system to control microbubble aeration to maintain a desirable value of dissolved oxygen.…”

Section: Introductionmentioning

confidence: 99%

AquaStat: An Arduino-based Water Quality Monitoring Device for Fish Kill Prevention in Tilapia Aquaculture using Fuzzy Logic

Molato¹

2022

IJACSA

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In the Philippines, Tilapia fish farming sector is vital to the economy in providing substantial employment, income and meeting local demand for protein sources of the Filipinos. However, the possible benefits that can be derived from this industry are at stake because of the sudden occurrences of fish kill events. This can be attributed to a wide variety of natural and unnatural causes such as old age, starvation, body injury, stress, suffocation, water pollution, diseases, parasites, predation, toxic algae, severe weather, and other reasons. With the identified severe effects of fish kill events to the fish farmers, consumers and the fisheries industry, advanced measures and methods must be established to alleviate the adverse effects of this phenomenon. To solve the underlying problem on water quality monitoring system to improve freshwater aquaculture, various studies were already conducted. However, these studies merely focused on the reading and gathering of water parameters. In this paper, fuzzy logic was used to come up with a model that can analyze and generate result regarding the overall quality of the water being used in Tilapia aquaculture. The water parameters considered in this paper were temperature, dissolved oxygen, and pH level. The results of the water parameter readings using the conventional method were compared to the data that were gathered by AquaStat to test its accuracy and showed no significant difference. Also, the overall water quality obtained using the conventional method was compared to the overall water quality generated by AquaStat and obtained an accurate result.

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Water Quality Monitoring with Fuzzy Logic Control based on Graphical Programming

Abstract: Abstract

Cited by 9 publications

References 8 publications

WSN performance based on node placement by genetic algorithm at smart home environment

WSN performance based on node placement by genetic algorithm at smart home environment

IoT-based smart monitoring and management system for fish farming

AquaStat: An Arduino-based Water Quality Monitoring Device for Fish Kill Prevention in Tilapia Aquaculture using Fuzzy Logic

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