Analysis and Prospect of the Application of Wireless Sensor Networks in Ubiquitous Power Internet of Things

Cao, Li; Wang, Zhengzong; Yue, Yinggao

doi:10.1155/2022/9004942

Cited by 12 publications

(8 citation statements)

References 82 publications

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“…It is also capable of processing vast amounts of data from a variety of terminals, smart devices, and end users of the power terminal layer. thousands of sensor nodes and power terminal devices have a variety of alternatives for connecting to the smart grid to sense or control the power grid [38].…”

Section: H Ubiquitous Power Internet Of Things (Upiot)mentioning

confidence: 99%

Deep Learning for Edge Computing Applications: A Comprehensive Survey

Sanjeetha

Kanagaraj

Herath

et al. 2022

AJCST

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Edge computing is a modern computer architecture that processes data quickly and efficiently close to its point of origin, hence avoiding slowdowns caused by network latency and capacity limitations. By moving processing power to the network’s perimeter, edge computing decreases the load on central data centers and reduces the time it takes for users to submit data. Therefore, access latency may become a barrier, potentially negating the benefits of edge computing, particularly for applications that need a great deal of data.. Edge computing has some challenges, such as security, incomplete data, investment costs, and maintenance costs. In this research, we undertake a thorough analysis of edge computing, how edge device placement improves performance in IoT networks, compare various edge computing implementations, and explain various difficulties encountered during edge computing implementation. This study aims to promote creative edge-based Internet of Things security design by thoroughly examining existing Internet of Things security solutions at the edge layer and facilitate the dynamic deployment of edge devices.

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Section: H Ubiquitous Power Internet Of Things (Upiot)mentioning

confidence: 99%

Deep Learning for Edge Computing Applications: A Comprehensive Survey

Sanjeetha

Kanagaraj

Herath

et al. 2022

AJCST

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“…These algorithms are deeply inspired by the laws of nature, species evolution, and the behavior of biological populations. In particular, swarm intelligence algorithms have attracted much attention from the academic community because of their simulation of the intelligent behavior among biological populations [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Objective Optimization Problem Solving Method Based on Improved Golden Jackal Optimization Algorithm and Its Application

Jiang,

Yue,

Chen

et al. 2024

Biomimetics

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The traditional golden jackal optimization algorithm (GJO) has slow convergence speed, insufficient accuracy, and weakened optimization ability in the process of finding the optimal solution. At the same time, it is easy to fall into local extremes and other limitations. In this paper, a novel golden jackal optimization algorithm (SCMGJO) combining sine–cosine and Cauchy mutation is proposed. On one hand, tent mapping reverse learning is introduced in population initialization, and sine and cosine strategies are introduced in the update of prey positions, which enhances the global exploration ability of the algorithm. On the other hand, the introduction of Cauchy mutation for perturbation and update of the optimal solution effectively improves the algorithm’s ability to obtain the optimal solution. Through the optimization experiment of 23 benchmark test functions, the results show that the SCMGJO algorithm performs well in convergence speed and accuracy. In addition, the stretching/compression spring design problem, three-bar truss design problem, and unmanned aerial vehicle path planning problem are introduced for verification. The experimental results prove that the SCMGJO algorithm has superior performance compared with other intelligent optimization algorithms and verify its application ability in engineering applications.

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“…In essence, the two cooperate and support one another. The best model cannot be assured if they are optimized individually [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Towards an Optimal KELM Using the PSO-BOA Optimization Strategy with Applications in Data Classification

et al. 2023

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The features of the kernel extreme learning machine—efficient processing, improved performance, and less human parameter setting—have allowed it to be effectively used to batch multi-label classification tasks. These classic classification algorithms must at present contend with accuracy and space–time issues as a result of the vast and quick, multi-label, and concept drift features of the developing data streams in the practical application sector. The KELM training procedure still has a difficulty in that it has to be repeated numerous times independently in order to maximize the model’s generalization performance or the number of nodes in the hidden layer. In this paper, a kernel extreme learning machine multi-label data classification method based on the butterfly algorithm optimized by particle swarm optimization is proposed. The proposed algorithm, which fully accounts for the optimization of the model generalization ability and the number of hidden layer nodes, can train multiple KELM hidden layer networks at once while maintaining the algorithm’s current time complexity and avoiding a significant number of repeated calculations. The simulation results demonstrate that, in comparison to the PSO-KELM, BBA-KELM, and BOA-KELM algorithms, the PSOBOA-KELM algorithm proposed in this paper can more effectively search the kernel extreme learning machine parameters and more effectively balance the global and local performance, resulting in a KELM prediction model with a higher prediction accuracy.

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Analysis and Prospect of the Application of Wireless Sensor Networks in Ubiquitous Power Internet of Things

Cited by 12 publications

References 82 publications

Deep Learning for Edge Computing Applications: A Comprehensive Survey

Deep Learning for Edge Computing Applications: A Comprehensive Survey

A Multi-Objective Optimization Problem Solving Method Based on Improved Golden Jackal Optimization Algorithm and Its Application

Towards an Optimal KELM Using the PSO-BOA Optimization Strategy with Applications in Data Classification

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