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Energy Efficiency Maximization Strategy for Sink Node in SWIPT-Enabled Sensor-Cloud Based on Optimal Stopping Rules

China Communications 2021年第1期18卷 222-236页

作者： Zhe Wang Lina Ge Taoshen Li Guifen Zhang Min Wu Institute of Artificial Intelligence Guangxi University for NationalitiesNanning 530004China Guangxi Key Laboratory of Hybrid Computation and IC Design Analysis Nanning 530006China school of Computer Electronics and InformationGuangxi UniversityNanning 530006China Guangxi Power Grid Co. LtdNanning 530023China

Leveraging energy harvesting abilities in wireless network devices has emerged as an effective way to prolong the lifetime of energy constrained *** system gains are usually optimized by designing resource allocation algorithm ***,few works focus on the interaction that channel’s time-vary characters make the energy transfer *** address this,we propose a novel system operation sequence for sensor-cloud system where the Sinks provide SWIPT for sensor nodes opportunistically during downlink phase and collect the data transmitted from sensor nodes in uplink ***,the energy-efficiency maximization problem of the Sinks is presented by considering the time costs and energy consumption of channel *** is proved that the formulated problem is an optimal stopping process with optimal stopping *** optimal energy-efficiency(OEE)algorithm is designed to obtain the optimal stopping rules for ***,the simulations are performed based on the OEE algorithm compared with the other two strategies to verify the effectiveness and gains in improving the system efficiency.

关键词： sensor-cloud SWIPT optimal stopping theory energy efficiency channel quality

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Hybrid Sooty Tern Optimization and Differential Evolution for Feature Selection

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Computer Systems Science & Engineering 2021年第12期39卷 321-335页

作者： Heming Jia Yao Li Kangjian Sun Ning Cao Helen Min Zhou College of Information Engineering Sanming UniversitySanming365004China College of Mechanical and Electrical Engineering Northeast Forestry UniversityHarbin150040China school of Engineering Manukau Institute of TechnologyAuckland2241New Zealand

In this paper,a hybrid model based on sooty tern optimization algo-rithm(STOA)is proposed to optimize the parameters of the support vector machine(SVM)and identify the best feature sets *** selec-tion is an essential process of data preprocessing,and it aims to find the most rele-vant subset of *** recent years,it has been applied in many practical domains of intelligent *** application of SVM in many fields has proved its effectiveness in classification tasks of various *** performance is mainly determined by the kernel type and its *** of the most challenging process in machine learning is feature selection,intending to select effective and representative *** main disadvantages of feature selection processes included in classical optimization algorithm are local optimal stagnation and slow ***,the hybrid model proposed in this paper merges the STOA and differential evolution(DE)to improve the search efficiency and con-vergence rate.A series of experiments are conducted on 12 datasets from the UCI repository to comprehensively and objectively evaluate the performance of the proposed *** superiority of the proposed method is illustrated from dif-ferent aspects,such as the classification accuracy,convergence performance,reduced feature dimensionality,standard deviation(STD),and computation time.

关键词： sooty tern optimization algorithm hybrid optimization feature selection support vector machine

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