[Retracted] Dynamic Wavelength Scheduling by Multiobjectives in OBS Networks

Kumar, V. Kishen Ajay; Kumar, Madapuri Rudra; Shribala, N.; Singh, Ninni; Gunjan, Vinit Kumar; Siddiquee, Kazy Noor-e-Alam; Arif, Muhammad

doi:10.1155/2022/3806018

Cited by 15 publications

(4 citation statements)

References 37 publications

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“…The suggested batch scheduling strategy is an unsupervised clustering technique that partitions the data frames to be transmitted over fog enabled IoT networks in to multiple clusters and then performs QEESM scheduling model on each cluster in the priority order of these clusters defined under delay sensitivity. The subsequent sections explore the methods and materials used in scheduling process portrayed [30], [31].…”

Section: Methodsmentioning

confidence: 99%

Optimum Batch Scheduling Model for Quality Aware Delay Sensitive Data Transmission over Fog Enabled IOT Network

Potu,

Jatoth,

Parvataneni

2024

SCPE

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The emerging fog networks in the internet of things (IoT) applications provide flexibility and agility for service providers. The combination of fog nodes and edge nodes enable them to deliver a given network service. However, the selection of suitable edge and fog nodes and their scheduling still remain a research challenge. Finding a globally optimal scheduling of oversized data transmission over IoT applications for industrial requirements is crucial. Optimal batch scheduling has been regarded as a viable way to achieve optimal scheduling in other contemporary network models. This manuscript has projected an Optimum Batch Scheduling Model (OBSM) for Quality aware Delay Sensitive Data Transmission over Fog Enabled IoT Networks. A novel clustering technique has been proposed in this manuscript to group the transmission nodes (fog or edge nodes) and data packets, which further pairs each group of data with one of the corresponding node group to achieve delay sensitivity and other quality factors such as energy efficiency. The data scheduling between data and node group is drawn from the previous contribution -"Quality aware Energy Efficient Scheduling Model (QEESM) for Fog Enabled IoT Network". The simulation results have shown that, in terms of average make span rate, average round trip time, and energy consumption, the batch scheduling model OBSM performs noticeably better than the contemporary scheduling models. The OBSM scheduling model's average make-span rate, roundtrip time, as well as energy consumption per make span are 23.3 7.03, 17.8 5.2, and 11.33 6.9 joules, respectively, which conclusively demonstrate that the OBSM model outperforms the existing models. A novel batch scheduling algorithm has been proposed using a unique unsupervised learning approach that suggested to cluster the transmission requests and transmission channels in to multiple clusters.

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Section: Methodsmentioning

confidence: 99%

Optimum Batch Scheduling Model for Quality Aware Delay Sensitive Data Transmission over Fog Enabled IOT Network

Potu,

Jatoth,

Parvataneni

2024

SCPE

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“…: (7) where s and t are the width and height of the Mask, and where Mask is derived by the overage pooling of the reference map. Backpropagation with weights started with the Golorot initializer [31], [32], [33] is used to train the WO-DRL architecture via an Adaptive Moment optimizer [30], [29]. WOA (Section 3.…”

Section: Convolution Layersmentioning

confidence: 99%

Whale Optimization based Deep Residual Learning Network for Early Rice Disease Prediction in IoT

Lakshmi,

Kashyap,

Fazal Khan

et al. 2023

ICST Transactions on Scalable Information Systems

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Disease detection on a farm requires laborious and time-consuming observation of individual plants, which is made more difficult when the farm is large and many different plants are farmed. To address these problems, cutting-edge technologies, AI, and Deep Learning (DL) are employed to provide more accurate illness predictions. When it comes to smart farming and precision agriculture, IoT opens up exciting new possibilities. To a certain extent, the goal-mouth of "smart farming" is to upsurge productivity and efficiency in agricultural processes. Smart farming is an approach to agriculture in which Internet of Things devices are interconnected and new technologies are used to optimize existing methods. Utilizing Internet of Things (IoT) devices, smart farming aids in more informed decision making. In many parts of the world, rice is the staple diet. This means that early detection of rice plant diseases using automated techniques and IoT devices is essential. Growing rice yields and profits may be helped along by DL model creation and deployment in agriculture. Here we introduce DRL, a deep residual learning framework that has been trained using photos of rice leaves to recognize one of four classes. The suggested model is called WO-DRL, and the hyper-parameter tuning procedure of DRL is executed with the help of the Whale Optimization algorithm. The outcomes demonstrate the efficacy of our suggested approach in directing the WO-DRL model to learn important characteristics. The findings of this study will pave the way for the agriculture sector to more quickly diagnose and treat plant diseases using AI.

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“…The diversity in data characteristics makes them challenging yet valuable for research in data generation and manipulation. Finally, UWB and UHF-RFID sensors are extensively used in real-world scenarios, ranging from smart manufacturing and logistics to healthcare and retail [34], [35], [38], [39] and [40]. Therefore, the synthetic data generated from these sensors has direct applications in these domains, allowing for the development and validation of algorithms and methodologies that are highly relevant to practical industrial and commercial settings.…”

Section: Introductionmentioning

confidence: 99%

Synthetic Sensor Measurement Generation With Noise Learning and Multi-Modal Information

Romanelli,

Martinelli

2023

IEEE Access

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Deep learning has transformed data generation, particularly in creating synthetic sensor data. This capability is invaluable in fields like autonomous driving, robotics, and computer science. To achieve this, we train models using real data, enabling them to replicate sensor data closely. These models introduce variations and noise, enhancing diversity and realism. Prominent techniques, including generative adversarial networks (GANs), variational autoencoders (VAEs), and recurrent neural networks (RNNs), excel in generating synthetic sensor data. Our paper focuses on Autoregressive Convolutional Recurrent Neural Networks (CRNN) for Multivariate Time Series Prediction. We incorporate Denoising Autoencoders (DAE) to mimic real-world noise characteristics. Our model is trained and validated using Ultra Wide Band (UWB) and Ultra High-Frequency Radio Frequency Identification (UHF-RFID) sensor data. It integrates sensor measurements and diverse information sources to produce synthetic data complementing realworld data. While demonstrated with UHF-RFID and UWB sensors, these techniques extend to industrial automation, healthcare and environmental monitoring. While our methodology exhibits broad potential, we present practical demonstrations with UHF-RFID and UWB sensors. Our deep neural network model allows researchers to construct datasets for algorithm validation, eliminating the need for costly and time-consuming data collection.

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[Retracted] Dynamic Wavelength Scheduling by Multiobjectives in OBS Networks

Cited by 15 publications

References 37 publications

Optimum Batch Scheduling Model for Quality Aware Delay Sensitive Data Transmission over Fog Enabled IOT Network

Optimum Batch Scheduling Model for Quality Aware Delay Sensitive Data Transmission over Fog Enabled IOT Network

Whale Optimization based Deep Residual Learning Network for Early Rice Disease Prediction in IoT

Synthetic Sensor Measurement Generation With Noise Learning and Multi-Modal Information

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