Congestion Control in Cognitive IoT-Based WSN Network for Smart Agriculture

Alghazzawi, Daniyal; Bamasaq, Omaima; Bhatia, Surbhi; Kumar, Ankit; Dadheech, Pankaj; Albeshri, Aiiad

doi:10.1109/access.2021.3124791

Cited by 26 publications

(12 citation statements)

References 34 publications

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“…The detection of proposed work can be evaluated by considering the attacks such as DoS11, DoS53s, DoS53a, Scan11, Scan44, Botnet, IP in Blacklist, UDP Scan, SSH Scan, SPAM with the existing works such as SVM [27], MLP [28], Faster RCNN [29], CNN [30], and DCGAN approaches [31]. Fig.…”

Section: Classification Of Attacks Analysismentioning

confidence: 99%

Optimized Generative Adversarial Networks for Adversarial Sample Generation

Alghazzawi¹,

Hasan²,

Bhatia³

2022

Computers, Materials &Amp; Continua

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Detecting the anomalous entity in real-time network traffic is a popular area of research in recent times. Very few researches have focused on creating malware that fools the intrusion detection system and this paper focuses on this topic. We are using Deep Convolutional Generative Adversarial Networks (DCGAN) to trick the malware classifier to believe it is a normal entity. In this work, a new dataset is created to fool the Artificial Intelligence (AI) based malware detectors, and it consists of different types of attacks such as Denial of Service (DoS), scan 11, scan 44, botnet, spam, User Datagram Portal (UDP) scan, and ssh scan. The discriminator used in the DCGAN discriminates two different attack classes (anomaly and synthetic) and one normal class. The model collapse, instability, and vanishing gradient issues associated with the DCGAN are overcome using the proposed hybrid Aquila optimizer-based Mine blast harmony search algorithm (AO-MBHS). This algorithm helps the generator to create realistic malware samples to be undetected by the discriminator. The performance of the proposed methodology is evaluated using different performance metrics such as training time, detection rate, F-Score, loss function, Accuracy, False alarm rate, etc. The superiority of the hybrid AO-MBHS based DCGAN model is noticed when the detection rate is changed to 0 after the retraining method to make the defensive technique hard to be noticed by the malware detection system. The support vector machines (SVM) is used as the malicious traffic detection application and its True positive rate (TPR) goes from 80% to 0% after retraining the proposed model which shows the efficiency of the proposed model in hiding the samples.

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Section: Classification Of Attacks Analysismentioning

confidence: 99%

Optimized Generative Adversarial Networks for Adversarial Sample Generation

Alghazzawi¹,

Hasan²,

Bhatia³

2022

Computers, Materials &Amp; Continua

Self Cite

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“…Water in the soil increases its electrical conductivity, reducing the amount of resistance. As a result, the relative humidity will be greater [19]. Soil with less water conducts electricity less efficiently, resulting in higher resistance to current flow.…”

Section: Control System Of Indoor Plantation Systemmentioning

confidence: 99%

“…A signal from the soil moisture sensor is sent to the controller unit to determine if the plant needs water [6] [8]. [15]- [18] Humidity levels will be shown on the LCD panel in order to inform the user of soil humidity [19]. When the soil is at a low humidity level, the water pump will switch on automatically to water the plants [20].…”

Section: Introduction 1project Backgroundmentioning

confidence: 99%

Smart Indoor Plantation System Using Soil Moisture Sensor and Light Dependent Resistor Sensor

Abu

Bukhari

Firdaus

et al. 2022

IJRCS

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Plantation methods, including hydroponics, have been extensively used in agriculture. They also employed a time-based irrigation system for the plant. The goal of this project was to create a self-sustaining indoor plantation system that uses soil moisture sensor data to control the flow of water when the sensor detects that the soil is almost dry. Soil conditions are monitored, and crops are irrigated more efficiently with the help of this new technology. Water is conserved by just watering the plants when they absolutely need it, rather than watering them continually all the time as the traditional method would require. Light-dependent resistors are used to measure the brightness of the surroundings in this project. As a result, the grow light will be activated when the ambient light level drops. With the help of a soil moisture sensor and a light-dependent resistor (LDR), one can create a system that automatically waters and lights plants. Finally, the soil moisture sensor collects data for the sprinkler system and displays it on the LCD screen, and then the appropriate measures are taken. When the soil's humidity level is high, the water that flows will be stopped.

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“…Greenhouse design considers greenhouse structures, ventilation, and lighting systems [27]. Wireless Sensor Networks have been often used in the context of Greenhouse architectures.…”

Section: Introductionmentioning

confidence: 99%

Monitoring and Controlling System of Smart Mini Greenhouse Based on Internet of Things (IoT) for Spinach Plant (Amaranthus sp.)

Putri,

Lestari,

Ifmalinda

et al. 2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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Greenhouses, often composed of plastic or glass structures, play a fundamental role in optimizing the environmental conditions essential for successful plant cultivation, ultimately resulting in higher-quality crop yields. The study aims to develop a real-time monitoring and control system for a smart mini greenhouse utilizing the Internet of Things (IoT), with a specific focus on cultivating spinach. The monitoring system is constructed around an ESP32 microcontroller, complemented by a DHT22 sensor for accurate air temperature and humidity measurements, and an RTC DS3231 timer for scheduling tasks. The DHT22 sensor's set point values trigger the fan and misting system operations. The fan activates when the air temperature reaches 32°C, while the misting system turns on when humidity levels (RH) reach 55%. The ESP32 is the central processing unit, enabling internet connectivity through Wi-Fi for real-time data monitoring via the Blynk application. Sensor calibration confirms the system's precision, with regression analyses producing impressive R2 values of 0.989 and 0.952. The shading net control system operates four hours daily, from 11:00 to 15:00 WIB (Western Indonesian Time), optimizing the greenhouse environment. This well-executed system leads to the robust growth of spinach plants, reaching a height of 23.02 cm, sprouting nine leaves, and attaining a weight of 10 grams. The findings from this study highlight the efficiency and effectiveness of the smart mini greenhouse's monitoring and control system, offering promising applications in broader greenhouse management and crop cultivation practices.

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Congestion Control in Cognitive IoT-Based WSN Network for Smart Agriculture

Cited by 26 publications

References 34 publications

Optimized Generative Adversarial Networks for Adversarial Sample Generation

Optimized Generative Adversarial Networks for Adversarial Sample Generation

Smart Indoor Plantation System Using Soil Moisture Sensor and Light Dependent Resistor Sensor

Monitoring and Controlling System of Smart Mini Greenhouse Based on Internet of Things (IoT) for Spinach Plant (Amaranthus sp.)

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