Optimizing Federated Learning With Deep Reinforcement Learning for Digital Twin Empowered Industrial IoT

Yang, Wei; Wang, Xiang; Yang, Yuan; Cheng, Peng

doi:10.1109/tii.2022.3183465

Cited by 90 publications

(25 citation statements)

References 26 publications

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“…A multi-objective integrated optimization framework is proposed to manipulate the whole operation of the smart greenhouse [22]. automation using the Internet of Things (IoT) in a greenhouse environment [23].…”

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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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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“…For instance, in gaming, an agent can learn to play complex video games directly from pixel inputs, making it more versatile and adaptive. In robotics, it enables intelligent machines to navigate and interact with their environment, making them suitable for real-world applications [15]. The integration of Q Learning with CNNs represents a promising approach for enhancing decision-making accuracy in dynamic, high-dimensional environments.…”

Section: Introductionmentioning

confidence: 99%

Penetration Testing Framework using the Q Learning Ensemble Deep CNN Discriminator Framework

Railkar,

Joshi

2024

IJACSA

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Penetration testing (PT) serves as an effective tool for examining networks and identifying vulnerabilities by simulating a hacker's attack to uncover valuable information, such as details about the host's operating and database systems. Strong penetration testing is crucial for assessing system vulnerabilities in the constantly changing world of cyber security. Existing methods often struggle with adapting to dynamic threats, providing limited automation, and lacking the ability to discern subtle security weaknesses. In comparison to manual PT, intelligent PT has gained widespread popularity due to its efficiency, resulting in reduced time consumption and lower labor costs. Considering this, the effective penetration testing framework is developed using prairie natural swarm (PNS) optimized Q-learning ensemble deep CNN. Initially, the penetration testing environment (Shodan search engine) is simulated, and along with that expert knowledge base is also generated. Subsequently, the Nmap script engine and Metasploit are deployed, providing robust tools for network investigation and vulnerability assessment. The system state is then relayed to the Q-learning ensemble deep convolutional neural network (Qlearning ensemble deep CNN) classifier. This unique ensemble combines the strengths of Q-learning and deep CNNs, enabling optimal policy learning for decision-making. The prairie natural swarm optimization algorithm is developed through the hybridization of coyote and particle swarm characteristics to fine-tune classifier parameters, enhancing performance. Additionally, the discriminator is trained to maximize standard action rewards while minimizing discounted action rewards, distinguishing valuable from less valuable information. By evaluating the advantage function, successful penetration likelihood is determined, informing situational decision-making through the Q-learning ensemble deep CNN classifier. Accuracy, sensitivity, and specificity as well as the proposed PNS-optimized Q-learning ensemble deep model are used to evaluate the output. In comparison to other approaches currently in use, CNN achieves values of 94.54%, 94.40%, 94.90% for TP, 94.64%, 94.69%, and 94.52% for k-fold.

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“…The concept of a DT can be explored to effectively enable the properties of B5G wireless systems. Enhancing the quality of applications and the user experience of services such as autonomous vehicles and smart cities, in practice, depends on evaluating and mining data from the edge network by allocating limited resources and optimizing the network to deliver high-quality services [ 1 , 2 ]. The DT paradigm is one of the most exciting technologies, which can offer instantaneous wireless connectivity and very reliable wireless communication in a B5G network [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Sustainable Resource Allocation and Reduce Latency Based on Federated-Learning-Enabled Digital Twin in IoT Devices

Alhartomi,

Salh,

Audah

et al. 2023

Sensors

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In this article, we utilize Digital Twins (DT) with edge networks using blockchain technology for reliable real-time data processing and provide a secure, scalable solution to bridge the gap between physical edge networks and digital systems. Then, we suggest a Federated Learning (FL) framework for collaborative computing that runs on a blockchain and is powered by the DT edge network. This framework increases data privacy while enhancing system security and reliability. The provision of sustainable Resource Allocation (RA) and ensure real-time data-processing interaction between Internet of Things (IoT) devices and edge servers depends on a balance between system latency and Energy Consumption (EC) based on the proposed DT-empowered Deep Reinforcement Learning (Deep-RL) agent. The Deep-RL agent evaluates the performance action based on RA actions in DT to distribute its bandwidth resources to IoT devices based on iteration and the actions taken to generate the best policy and enhance learning efficiency at every step. The simulation results show that the proposed Deep-RL-agent-based DT is able to exploit the best policy, select 47.5% of computing activities that are to be carried out locally with 1 MHz bandwidth and minimize the weighted cost of the transmission policy of edge-computing strategies.

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Optimizing Federated Learning With Deep Reinforcement Learning for Digital Twin Empowered Industrial IoT

Cited by 90 publications

References 26 publications

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

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

Penetration Testing Framework using the Q Learning Ensemble Deep CNN Discriminator Framework

Sustainable Resource Allocation and Reduce Latency Based on Federated-Learning-Enabled Digital Twin in IoT Devices

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