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An Asynchronous Clustering and Mobile Data Gathering Schema Based on Timer Mechanism in Wireless Sensor Networks

Computers, Materials & Continua 2019年第3期58卷 711-725页

作者： Jin Wang Yu Gao Wei Liu Wenbing Wu Se-Jung Lim Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation School of Computer&Communication EngineeringChangsha University of Science&TechnologyChangsha410114China School of Information Science and Engineering Fujian University of TechnologyFuzhou350000China College of Information Engineering Yangzhou UniversityYangzhou225000China Department of Computer Engineering Chonnam National UniversityYeosu596597Korea.

Recently,Wireless sensor networks(WSNs)have become very popular research topics which are applied to many *** provide pervasive computing services and techniques in various potential applications for the Internet of Things(IoT).An Asynchronous Clustering and Mobile Data Gathering based on Timer Mechanism(ACMDGTM)algorithm is proposed which would mitigate the problem of“hot spots”among sensors to enhance the lifetime of *** clustering process takes sensors’location and residual energy into consideration to elect suitable cluster ***,one mobile sink node is employed to access cluster heads in accordance with the data overflow time and moving time from cluster heads to *** experimental results display that the presented method can avoid long distance communicate between sensor ***,this algorithm reduces energy consumption effectively and improves package delivery rate.

关键词： Internet of things wireless sensor networks clustering mobile data collection timer.

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Implementation of PRINCE with resource-efficient structures based on FPGAs

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Frontiers of information Technology & Electronic Engineering 2021年第11期22卷 1505-1516页

作者： Lang LI Jingya FENG Botao LIU Ying GUO Qiuping LI Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal UniversityHengyang421002China College of Information Science and Engineering Hunan Normal UniversityChangsha410081China College of Computer Science and Technology Hengyang Normal UniversityHengyang421002China

In this era of pervasive computing, low-resource devices have been deployed in various fields. PRINCE is a lightweight block cipher designed for low latency, and is suitable for pervasive computing applications. In this paper, we propose new circuit structures for PRINCE components by sharing and simplifying logic circuits, to achieve the goal of using a smaller number of logic gates to obtain the same result. Based on the new circuit structures of components and the best sharing among components,we propose three new hardware architectures for PRINCE. The architectures are simulated and synthesized on different programmable gate array devices. The results on Virtex-6 show that compared with existing architectures, the resource consumption of the unrolled, low-cost, and two-cycle architectures is reduced by 73, 119, and 380 slices, respectively. The low-cost architecture costs only 137 slices. The unrolled architecture costs 409 slices and has a throughput of 5.34 Gb/s. To our knowledge, for the hardware implementation of PRINCE, the new low-cost architecture sets new area records, and the new unrolled architecture sets new throughput records. Therefore, the newly proposed architectures are more resource-efficient and suitable for lightweight,latency-critical applications.

关键词： Lightweight block cipher Field-programmable gate array(FPGA) Low-cost PRINCE Embedded security

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A Novel Image Encryption Scheme Based on Poker Cross-Shuffling and Fractional Order Hyperchaotic System 10th

A Novel Image Encryption Scheme Based on Poker Cross-Shuffli...

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10th International Conference on Computer Engineering and Networks, CENet 2020

作者： Chen, Zhong Zhao, Huihuang Chen, Junyao College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China Department of Computer Engineering AjouUniversity Suwon16499 Korea Republic of

ISBN: (纸本)9789811584619

A novel digital image encryption scheme based on Poker shuffling and fractional order hyperchaotic system is proposed. Poker intercrossing operation has nonlinearity and periodicity, and can form permutation group. Firstly, the plain image is transformed into block image, and each block is confused by Poker shuffling operation with key. Secondly, bit plane of every pixel in each block is confused. Lastly, the scrambled image is encrypted by fractional order hyperchaotic sequence. This encryption scheme provides a secure and efficient key stream, and guarantees a large key space. The experimental results and security analysis imply that the new encryption method has secure encryption effect and property of resisting common attacks. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cryptography

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Social Emergency Event Judgement based on BiLSTM-CRF 19

Social Emergency Event Judgement based on BiLSTM-CRF

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19th Chinese National Conference on Computational Linguistic, CCL 2020

作者： Hu, Huijun Wang, Cong Dai, Jianhua Liu, Maofu School of Computer Science and Technology Wuhan University of Science and Technology Wuhan430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan430065 China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha410081 China

In recent years, classification and rating of social emergency event have attracted more and more attentions in emergency management. However, most of the current studies adopt the rule-based methods to identify the evidences for event judgement, and have troubles in event judgement due to the complexity of social emergency event composition and the flexibility of language description. Inspired by the idea of event extraction, this paper has proposed the event judgement method via BiLSTM (Bidirectional Long-Short Term Memory) and CRF (Conditional Radom Fields) based on event classification and evidence extraction. The social emergency event classification is carried out firstly, and then the event evidences are extracted based on event type. In the end, the rating of social emergency event is judged with the attention mechanism and the combination of event type and evidence. Experimental results show that the proposed method is more robust than rule-based ones, and effective in the social emergency event judgement. © 2020 China National Conference on Computational Linguistics Published under Creative Commons Attribution 4.0 International License

关键词： Extraction

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Joint Optimization of Deployment and Trajectory in UAV and IRS-Assisted IoT Data Collection System

arXiv

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arXiv 2022年

作者： Dong, Li Liu, Zhibin Jiang, Feibo Wang, Kezhi Changsha Social Laboratory of Artificial Intelligence Hunan University of Technology and Business Changsha China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China The Department of Computer and Information Sciences Northumbria University United Kingdom

Unmanned aerial vehicles (UAV) can be applied in many Internet of Things (IoT) systems, e.g., smart farms, as a data collection platform. However, the UAV-IoT wireless channels may be occasionally blocked by trees or high-rise buildings. intelligent reflecting surface (IRS) can be applied to improve the wireless channel quality by smartly reflecting the signal via a large number of low-cost passive reflective elements. This paper aims to minimize the energy consumption of the system by jointly optimizing the deployment and trajectory of the UAV. The problem can be formulated as a mixed-integer- and-nonlinear-programming (MINLP), which is difficult to be addressed by the traditional solution, which may be easily fall into the local optimal. To address this issue, we propose a Joint Optimization framework of depLoyment and Trajectory (JOLT), where an adaptive whale optimization algorithm (AWOA) is applied to optimize the deployment of the UAV, and an elastic ring self-organizing map (ERSOM) is introduced to optimize the trajectory of the UAV. Specifically, in AWOA, a variable-length population strategy is applied to find the optimal number of stop points, and a nonlinear parameter a and a partial mutation rule are introduced to balance the exploration and exploitation. In ERSOM, a competitive neural network is also introduced to learn the trajectory of the UAV by competitive learning, and a ring structure is presented to avoid the trajectory intersection. Extensive experiments are carried out to show the effectiveness of the proposed JOLT framework. Copyright © 2022, The Authors. All rights reserved.

关键词： Trajectories

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Outer Synchronization Between Complex Delayed Networks with Both Uncertain Parameters and Unknown Topological Structure 9th

Outer Synchronization Between Complex Delayed Networks with ...

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9th International Conference on Computer Engineering and Networks, CENet2019

作者： Chen, Zhong Tian, Xiaomei Lei, Tianqi Chen, Junyao College of Computer Science and Technology Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China Department of Computer Engineering Ajou University Suwon16499 Korea Republic of

ISBN: (纸本)9789811537523

In this paper, synchronization of both non-identical unknown network and identical known network can be considered. Based on Lyapunov stability theory, for the case of non-identical or identical network, synchronization criteria between drive-response networks are obtained, and both the uncertain parameters and unknown coupling configuration matrix are be identified or constructed. Meanwhile, the coupling matrix may be free. The proposed synchronization scheme is simple and easy to realize. Finally, three illustrative examples show the effectiveness of presented control schemes. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Synchronization

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A parallel algorithm based on CPU/GPU dynamic coordination control and scheduling1

Acta Technica CSAV (Ceskoslovensk Akademie Ved)

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Acta Technica CSAV (Ceskoslovensk Akademie Ved) 2017年第1期62卷 361-369页

作者： Jiang, Hanyang Wang, Danhua Hengyang Normal University Hengyang421002 China Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang421002 China

CPU/GPU dynamic coordination technology is gradually applied to the development of parallel algorithms, but the current algorithm has some shortcomings in terms of efficiency and power consumption. Therefore, it is necessary to further study the parallel algorithm based on dynamic coordination control and scheduling. Based on the power cap technology, this paper sets up the device frequency and the single computing node of the synchronization system by studying the synchronization system of the computer, controlling DVFS and mapping mode. In order to avoid power failure and load imbalance, this paper has developed a new empirical model of performance and synchronization system, these models make the most of the power of the system to reduce the power consumption to a predetermined level, Good settings can be done. © 2017 Institute of Thermomechanics CAS, v.v.i.

关键词： Graphics processing unit

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Online Fault Monitoring Based on Moving Window Recursive Sparse Principal Component Analysis

Online Fault Monitoring Based on Moving Window Recursive Spa...

引用

第30届中国过程控制会议（CPCC 2019）

作者： Jinping Liu Jie Wang Zhaohui Tang Junbin He Jingjing Zhang Jean Paul Niyoyita Hunan Provincial Key Laboratory of Intelligent Computing and Language Processing Hunan Normal University School of Automation Central South University Hunan Institute of Metrology and Test College of Science and Technology University of Rwanda

This paper presents a moving window-recursive sparse principal component analysis(M WRSPCA)-based online fault monitoring method, aim at providing effective fau lt monitoring solution for the large-scale complex industrial processes with time varying and dynamic changing *** establishes a sparse principal component model with a sliding window block matrix to monitor the industrial data sample set online and re-adds the normal industrial sample data to the training set during the fault monitoring process, so that the process monitoring model can be adaptive to the time varying *** the window is sliding, the covariance information of window data block matrix is updated recursively, and the sparse loading matrix is also updated recursively by using the modified algorithm of the rank 1 matrix, so that the computational complexity of the adaptive process monitoring model is greatly reduced and the model real-time monitoring capability is *** proposed method is experimentally verified in the Tennessee-Eastman(TE) *** with the traditional fault monitoring methods, the proposed method can effectively improve the accuracy of fault detection and adapt to fault monitoring of long process and complex industrial processes.

关键词： Time varying industrial process Sparse principal component analysis(SPCA) Recursive SPCA M oving window model Online fault detection

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Semi-Supervised Feature Selection Based on Fuzzy Related Family

SSRN

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SSRN 2023年

作者： Yang, Tian Guo, Zhijun Li, Yuan-Jiang Deng, Yanfang Qian, Yuhua Hunan Provincial Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Hunan Changsha410081 China Institute of Big Data Science and Industry Shanxi University Shanxi Taiyuan030006 China

Existing machine learning algorithms face the problems of label-missing and high dimensionality. Feature selection is an effective dimensionality reduction method that can improve the efficiency and accuracy of subsequent machine learning tasks by removing irrelevant and redundant features. Related family is an efficient and rough set-based feature selection method, but is difficult to be used in simisupervised learning tasks. However, due to the difficulty of obtaining all labeled data, partially labeled data has become an important object that machine learning models must deal with. Therefore, this paper proposes a semi-supervised fuzzy related family-based feature selection method for partially labeled data. This method first calculates the fuzzy label values of unlabeled samples through fuzzy similarity relationships to establish a new fuzzy covering system. Then, based on the fuzzy covering system, it constructs a fuzzy related family based on consistent fuzzy set. Finally, based on the constructed feature significance measurement, a semi-supervised feature selection algorithm based on fuzzy related family (SFRF) is established. Compared with existing semi-supervised feature selection algorithms, SFRF can significantly improve the efficiency of feature selection while maintaining classification accuracy, with the average reduction efficiency of nine datasets increased by up to 201 times. © 2023, The Authors. All rights reserved.

关键词： Feature Selection

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Deep progressive reinforcement learning-based flexible resource scheduling framework for IRS and UAV-assisted MEC system

arXiv

引用

arXiv 2024年

作者： Dong, Li Jiang, Feibo Wang, Minjie Peng, Yubo Li, Xiaolong School of Computer Science Hunan University of Technology and Business Changsha China Xiangjiang Laboratory Changsha China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University Changsha China College of Information Science and Engineering Hunan Normal University Changsha China

The intelligent reflection surface (IRS) and unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system is widely used in temporary and emergency scenarios. Our goal is to minimize the energy consumption of the MEC system by jointly optimizing UAV locations, IRS phase shift, task offloading, and resource allocation with a variable number of UAVs. To this end, we propose a Flexible REsource Scheduling (FRES) framework by employing a novel deep progressive reinforcement learning which includes the following innovations: Firstly, a novel multi-task agent is presented to deal with the mixed integer nonlinear programming (MINLP) problem. The multi-task agent has two output heads designed for different tasks, in which a classified head is employed to make offloading decisions with integer variables while a fitting head is applied to solve resource allocation with continuous variables. Secondly, a progressive scheduler is introduced to adapt the agent to the varying number of UAVs by progressively adjusting a part of neurons in the agent. This structure can naturally accumulate experiences and be immune to catastrophic forgetting. Finally, a light taboo search (LTS) is introduced to enhance the global search of the FRES. The numerical results demonstrate the superiority of the FRES framework which can make real-time and optimal resource scheduling even in dynamic MEC systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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