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A low-power consumption security data fusion model for Industrial Internet of Things

International Journal of Computers and Applications 2025年第3期47卷 312-322页

作者： Zeng, Mengjia Cheng, Zhaolin Huang, Xu Xu, Huibin School of Electronic Information Huzhou College Huzhou China Huzhou City Key Laboratory of Urban Multidimensional Perception and Intelligent Computing Huzhou China School of Economic and Management Huzhou University Huzhou China School of Information Engineering Huzhou University Huzhou China

Industrial Internet of Things (IIOT) is an important part of manufacturing industry in the future, and the front-end sensor data acquisition network is the core component of it. Due to the limited energy of IIOT, reducing transmission interaction, energy consumption, and computational complexity in the process of data transmission is the key and difficult point in designing model of IIOT. In this paper, a lightweight green security data fusion model for IIOT is proposed, called LCSDF model, which aims to achieve data security and integrity verifiability by using less energy consumption. The simulation results show that the proposed model can ensure data security and verifiability, and meet the energy consumption requirements of the system. The performance of the algorithm is also satisfactory, reflecting the lightweight, green, and security of the model. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： Sensor data fusion

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Locally Linear Embedding Based on Scrambled Halton Sequence Initialized Frilled Lizard Optimization

Locally Linear Embedding Based on Scrambled Halton Sequence ...

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IEEE Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Qihua Guo Jiashu Wang Longhui Hou Nengwei Lei Dan Li Wei Nai Zan Yang School of Electronic Information Huzhou College Huzhou China Huzhou Key Laboratory for Urban Multidimensional Perception and Intelligent Computing Huzhou College Huzhou China Public Teaching and Research Department Huzhou College Huzhou China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

In the era of big data, the scale and complexity of data are ever increasing, traditional data dimensionality reduction methods can no longer meet the requirements of data users. In this context, manifold learning has now become an important research direction, which mainly achieves dimensionality reduction and mapping of nonlinear structural data in high-dimensional space to low-dimensional space, reduces data noise, and improves low-dimensional data representation ability. Locally linear embedding (LLE) is an important algorithm in manifold learning that projects data from high-dimensional space onto low-dimensional space while maintaining the linear relationship between data. However, the optimization objective function of LLE uses L2 norm measurement to approximate the error, which can easily amplify or reduce the error and cause oscillation in the error measurement. Therefore, in this paper, L1 norm instead of L2 norm is used to overcome this drawback, but introduces a new problem of the objective function becoming a non-smooth function. To solve this problem, in this paper, a derivative-free optimization method, which is also a swarm intelligence method called scrambled Halton sequence initialized frilled lizard optimization has been proposed, and via numerical experiment, its reliability and effectiveness has been verified.

关键词： Mechatronics Measurement uncertainty Noise Optimization methods Linear programming Manifold learning Reliability Particle swarm optimization Information technology Oscillators

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t-SNE Based on Hammersley Sequence Initialized Genghis Khan Shark Optimizer

t-SNE Based on Hammersley Sequence Initialized Genghis Khan ...

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IEEE Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Longhui Hou Jiashu Wang Qihua Guo Zan Yang Dan Li Wei Nai School of Electronic Information Huzhou College Huzhou China Huzhou Key Laboratory for Urban Multidimensional Perception and Intelligent Computing Huzhou College Huzhou China Public Teaching and Research Department Huzhou College Huzhou China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

Manifold learning is a nonlinear dimensionality reduction technique that preserves the original data features and obtains the intrinsic probability features and distribution structure of high-dimensional data through dimensionality reduction and simplification processing. t-distribution stochastic neighbor embedding (t-SNE) is an important algorithm in manifold learning that can visualize high-dimensional data by preserving the local probability distribution structure of the data in a low-dimensional space. However, the loss function of t-SNE is a strongly nonlinear non-convex function, and parameter optimization often faces difficulties in global optimization and high computational complexity, which limits the efficiency and scalability of the algorithm. To address these issues, in this paper, a derivative-free global optimization algorithm as well as a swarm intelligence method, namely Hammersley sequence initialized Genghis Khan shark optimizer (HSGKSO) has been proposed. The proposed algorithm can achieve stronger global search capability and faster convergence speed, and its effectiveness in finding the optimal solution of t-SNE loss function has been verified through numerical experiments.

关键词： Mechatronics High dimensional data Numerical analysis Scalability Noise reduction Sharks Probability distribution Manifold learning Particle swarm optimization Optimization

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Optimizing Charging Strategies for Electric Vehicles in Residential Areas Based on Leaped Halton Sequence Initialized Flood Algorithm

Optimizing Charging Strategies for Electric Vehicles in Resi...

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IEEE Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Qingsong Li Daili Hu Yongjin Yu Wei Nai Zan Yang Dan Li School of Electronic Information Huzhou College Huzhou China Huzhou Key Laboratory for Urban Multidimensional Perception and Intelligent Computing Huzhou College Huzhou China School of Intelligent Manufactoring Huzhou College Huzhou China Public Teaching and Research Department Huzhou College Huzhou China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

The rapid growth of electric vehicles (EVs) has led to a surge in charging demand, resulting in power grid overload and insufficient charging facilities, thus, it is urgent to optimize models to solve the problem of supply-demand imbalance. At present, relevant research has focused on proposing valuable strategies to solve the problem of charging overload during peak hours. However, these models are based on static prediction and fail to effectively consider the various factors of real-time changes in EV charging, resulting in inflexible system scheduling, congestion during charging peak hours, and poor performance when charging demand fluctuates. And for many charging optimization problems, they are equivalent to non-smooth and non-convex optimization problems, and traditional subgradient descent (SubGD) and coordinate descent (CD) methods are generally prone to getting stuck in local optimal solutions. In order to overcome the above shortcomings, in this paper, a derivative-free optimization method called leaped Halton sequence initialize flood algorithm (LHSFLA) has been proposed, which can break out of the trap of local optimal solutions and find global optimal solution, so as to balance power load fluctuations and user charging costs, and effectively solve the problems of power grid overload and insufficient charging facilities.

关键词： Fluctuations Processor scheduling Optimization methods Predictive models Electric vehicle charging Power grids Scheduling Real-time systems Floods Surges

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Energy-Aware Task Offloading for Rotatable STAR-RIS-Enhanced Mobile Edge computing Systems

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IEEE Internet of Things Journal 2025年

作者： Yang, Dongdong Li, Bin Niyato, Dusit Nanjing University of Information Science and Technology School of Computer Science Nanjing210044 China Huzhou Key Laboratory of Urban Multidimensional Perception and Intelligent Computing Huzhou313000 China Nanyang Technological University College of Computing and Data Science Singapore Singapore

Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) can expand the coverage of mobile edge computing (MEC) services by reflecting and transmitting signals simultaneously, enabling full-space coverage. The orientation of the STAR-RIS plays a crucial role in optimizing the gain of received and transmitted signals, and a rotatable STAR-RIS offers potential enhancement for MEC systems. This paper investigates a rotatable STAR-RIS-assisted MEC system, operated under three protocols, namely energy splitting, mode switching, and time switching. The goal is to minimize energy consumption for multiple moving user devices through the joint optimization of STAR-RIS configurations, orientation, computation resource allocation, transmission power, and task offloading strategies. Considering the mobility of user devices, we model the original optimization problem as a sequential decision-making process across multiple time slots. The high-dimensional, highly coupled, and nonlinear nature makes it a challenging non-convex decision-making problem for traditional optimization algorithms. Therefore, a deep reinforcement learning (DRL) approach is employed, specifically utilizing soft actor-critic algorithm to train the DRL model. Simulation results demonstrate that the proposed algorithm outperforms the benchmarks in both convergence speed and energy efficiency, while reducing energy consumption by up to 52.7% compared to the fixed STAR-RIS scheme. Among three operating protocols, the energy splitting yields the best performance. © 2014 IEEE.

关键词： Mobile edge computing

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Energy-Aware Task Offloading for Rotatable STAR-RIS-Enhanced Mobile Edge computing Systems

arXiv

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arXiv 2025年

作者： Yang, Dongdong Li, Bin Niyato, Dusit School of Computer Science Nanjing University of Information Science and Technology Nanjing210044 China Huzhou Key Laboratory of Urban Multidimensional Perception and Intelligent Computing Huzhou313000 China College of Computing and Data Science Nanyang Technological University Singapore

关键词： Mobile edge computing

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Index t-SNE Based on Sobol Sequence Initialized Harris Hawk Optimization Algorithm 15

Index t-SNE Based on Sobol Sequence Initialized Harris Hawk ...

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15th International Conference on intelligent Human-Machine Systems and Cybernetics, IHMSC 2023

作者： Zhang, Yuanzhao Wei, Linsong Nai, Wei Li, Dan Yang, Zan School of Electronic Information Huzhou College Huzhou China Huzhou College Huzhou Key Laboratory for Urban Multidimensional Perception and Intelligent Computing Huzhou China Huzhou College Department of Public Education Huzhou China

ISBN: (纸本)9798350326178

It is of no doubt that artificial intelligence (AI) is a cutting-edge technological science, in which manifold learning methods have become a hot research topic in recent years. t-distribution stochastic neighbor embedding (t-SNE) is a representative nonlinear dimensionality reduction method in manifold learning. The 2 main advantages of t-SNE are its ability to maintain structural characteristics and solve congestion problems. However, t-SNE is not parametric, so the 2 initializations of the algorithm will result in 2 different embeddings, thus, a guiding index is added to t-SNE, and thus index t-SNE algorithm has been formed. Nevertheless, like traditional t-SNE, the optimization method of index t-SNE is still prone to fall into the trap of local minimum, resulting in no global optimal solution. And the closer to the global optimal solution, the more oscillatory the parameter optimization path will be, which means that the optimization efficiency of index t-SNE is not high. The root of causing the above problems is that the optimization method of index t-SNE relies on gradient or derivative information. In this paper, a derivative-free swarm intelligence method-Sobol sequence initialized Harris hawk optimization (HHO) has been utilized to overcome the above shortcomings, and the advantages of the proposed method have been proved via numerical experiments. © 2023 IEEE.

关键词： Optimization

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Logistic Regression Based on Faure Sequence Initialized Fruit Fly Optimization Algorithm 15

Logistic Regression Based on Faure Sequence Initialized Frui...

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15th International Conference on intelligent Human-Machine Systems and Cybernetics, IHMSC 2023

作者： Sun, Xinsheng Wang, Yingjie Yang, Zan Nai, Wei Li, Dan School of Electronic Information Huzhou College Huzhou China Huzhou College Huzhou Key Laboratory for Urban Multidimensional Perception and Intelligent Computing Huzhou China Huzhou College Department of Public Education Huzhou China

ISBN: (纸本)9798350326178

As a regression algorithm, logistic regression can be used for not only regression prediction, but also classification. By employing Sigmod function of nonlinear transformation to obtain a value within (0, 1), it can be given the characteristic of probability in logistic regression. According to the rule 'value is 1 when p > 0.5, and value is 0 when p © 2023 IEEE.

关键词： Fruits

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Index t-SNE Based on Neiderreit Sequence Initialized White Shark Optimizer 6

Index t-SNE Based on Neiderreit Sequence Initialized White S...

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6th IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference, IMCEC 2024

作者： Lei, Nengwei Tang, Yue Yang, Zan Li, Dan Nai, Wei Huzhou College School of Electronic Information Huzhou China Huzhou College Huzhou Key Laboratory for Urban Multidimensional Perception and Intelligent Computing Huzhou China Huzhou College Public Teaching and Research Department Huzhou China

ISBN: (纸本)9798350316520

t-distributed stochastic neighbor embedding (t-SNE) is an unsupervised manifold learning method in the field of machine learning (ML). It mainly achieves noise removal and feature extraction through nonlinear data dimensionality reduction. However, t-SNE is a type of non parametric algorithm, the two initialization steps in its algorithm process may result in two different embeddings, leading to a decrease in computational efficiency. Index t-SNE method can solve the problem of t-SNE to some extent, but its loss function still relies on gradient line search methods to solve, such as gradient descent (GD) method and stochastic gradient descent (SGD) method which are prone to fall into the trap of local optima and cannot achieve global optimization. To overcome this drawback, in this paper, a derivative-free optimization method, which is called Neiderreit sequence initialized white shark optimizer (NSWSO) has been proposed. Via numerical experiment, its effectiveness and superiority comparing with gradient dependent methods are verified. © 2024 IEEE.

关键词： Global optimization

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Locally Linear Embedding Based on Gannet Optimization Algorithm 4

Locally Linear Embedding Based on Gannet Optimization Algori...

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4th IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2024

作者： Zhu, Yiqi Yang, Yang Fang, Yilin Nai, Wei Yang, Zan Li, Dan Huzhou College School of Electronic Information Huzhou China Huzhou College Huzhou Key Laboratory for Urban Multidimensional Perception and Intelligent Computing Huzhou China Huzhou College Public Teaching and Research Department Huzhou China

ISBN: (纸本)9798350363586

The analysis and feature extraction of high-dimensional data can help people solve various problems in current industry applications, and manifold learning is a type of data dimensionality reduction method developed based on the concept of topological manifolds. Locally linear embedding (LLE) algorithm, as a classic manifold learning algorithm, has attracted much attention due to its low computational complexity and strong scalability. However, the objective function of LLE algorithm may contain absolute value terms and cannot be guaranteed to be convex. Traditional derivative dependent solving methods are difficult to obtain its optimal solution, making it impossible to achieve effective feature extraction of data. In this paper, an LLE based on gannet optimization algorithm (GOA) has been proposed, and it is supposed to be used as a derivative-free optimization method to effectively solve the problems of non-differentiable of non-convex objective function, premature convergence, and easy to fall into local optimum in the process of solving the objective function of LLE. © 2024 IEEE.

关键词： Optimization algorithms

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