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Distributed Weighted Data Aggregation Algorithm in End-to-Edge Communication Networks Based on multi-armed bandit

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计算机科学 2023年第2期50卷 13-22页

作者： Yifei ZOU Senmao QI Cong'an XU Dongxiao YU School of Computer Science and Technology Shandong UniversityQingdao 266237China Naval Aviation University Yantai 264000China Advanced Technology Research Institute Beijing Institute of TechnologyJinan 250300China

As a combination of edge computing and artificial intelligence,edge intelligence has become a promising technique and provided its users with a series of fast,precise,and customized *** edge intelligence,when learning agents are deployed on the edge side,the data aggregation from the end side to the designated edge devices is an important research *** the various importance of end devices,this paper studies the weighted data aggregation problem in a single hop end-to-edge communication ***,to make sure all the end devices with various weights are fairly treated in data aggregation,a distributed end-to-edge cooperative scheme is ***,to handle the massive contention on the wireless channel caused by end devices,a multi-armed bandit(MAB)algorithm is designed to help the end devices find their most appropriate update ***-rent from the traditional data aggregation works,combining the MAB enables our algorithm a higher efficiency in data *** a theoretical analysis,we show that the efficiency of our algorithm is asymptotically *** experiments with previous works are also conducted to show the strength of our algorithm.

关键词： Weighted data aggregation End-to-edge communication multi-armed bandit Edge intelligence

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Residential HVAC Aggregation Based on Risk-averse multi-armed bandit Learning for Secondary Frequency Regulation

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Journal of Modern Power Systems and Clean Energy 2020年第6期8卷 1160-1167页

作者： Xinyi Chen Qinran Hu Qingxin Shi Xiangjun Quan Zaijun Wu Fangxing Li the School of Electrical Engineering Southeast UniversityNanjingChina Jiangsu Provincial Key Laboratory of Smart Grid Technology and Equipment NanjingChina the School of Electrical Engineering and Computer Science University of TennesseeKnoxvilleUSA

As the penetration of renewable energy continues to increase,stochastic and intermittent generation resources gradually replace the conventional generators,bringing significant challenges in stabilizing power system ***,aggregating demand-side resources for frequency regulation attracts attentions from both academia and ***,in practice,conventional aggregation approaches suffer from random and uncertain behaviors of the users such as opting out control *** risk-averse multi-armed bandit learning approach is adopted to learn the behaviors of the users and a novel aggregation strategy is developed for residential heating,ventilation,and air conditioning(HVAC)to provide reliable secondary frequency *** with the conventional approach,the simulation results show that the risk-averse multiarmed bandit learning approach performs better in secondary frequency regulation with fewer users being selected and opting out of the ***,the proposed approach is more robust to random and changing behaviors of the users.

关键词： Heating ventilation and air conditioning(HVAC) load control multi-armed bandit online learning secondary frequency regulation

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带有讨厌参数的multi-armed bandit问题

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中国科学(A辑数学物理学天文学技术科学) 1986年第2期 124-134页

作者：孙嘉阳北京大学数学系

这篇文章讨论了带有讨厌参数的multi-armed bandit问题。给出了关于该问题的所有ISS法则(不变的序贯抽样法则)之“遗憾”的渐近下界;找出了渐近最优的法则,即使“遗憾”达到上述下界的法则;并给出了构造渐近最优法则中要求的点估计和置... 详细信息

这篇文章讨论了带有讨厌参数的multi-armed bandit问题。给出了关于该问题的所有ISS法则(不变的序贯抽样法则)之“遗憾”的渐近下界;找出了渐近最优的法则,即使“遗憾”达到上述下界的法则;并给出了构造渐近最优法则中要求的点估计和置信序列的一般性方法。还着重考虑了讨厌参数为刻度参数的情形。最后用一些技巧对几个常见的总体进行了具体计算。

关键词：孙嘉所有渐近最优 multi-armed bandit 定理概率测度序贯抽样法定义正态总体

Matching while Learning: Wireless Scheduling for Age of Information Optimization at the Edge

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China Communications 2023年第3期20卷 347-360页

作者： Kun Guo Hao Yang Peng Yang Wei Feng Tony Q.S.Quek School of Communications and Electronics Engineering East China Normal UniversityShanghai 200241China Chongqing Key Laboratory of Precision Optics Chongqing Institute of East China Normal UniversityChongqing 401120China Zhejiang University/University of Illinois at Urbana-Champaign Institute Zhejiang UniversityHaining 314400China School of Electronic and Information Engineering Beihang UniversityBeijing 100083China Beijing National Research Center for Information Science and Technology Department of Electronic EngineeringTsinghua UniversityBeijing 100084China Information Systems Technology and Design(ISTD)Pillar Singapore University of Technology and DesignSingapore 487372

In this paper,we investigate the minimization of age of information(AoI),a metric that measures the information freshness,at the network edge with unreliable wireless ***,we consider a set of users transmitting status updates,which are collected by the user randomly over time,to an edge server through unreliable orthogonal *** begs a natural question:with random status update arrivals and obscure channel conditions,can we devise an intelligent scheduling policy that matches the users and channels to stabilize the queues of all users while minimizing the average AoI?To give an adequate answer,we define a bipartite graph and formulate a dynamic edge activation problem with stability ***,we propose an online matching while learning algorithm(MatL)and discuss its implementation for wireless ***,simulation results demonstrate that the MatL is reliable to learn the channel states and manage the users’buffers for fresher information at the edge.

关键词： information freshness Lyapunov opti-mization multi-armed bandit wireless scheduling

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数字图书馆基于用户行为分析的自适应众包技术

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数字图书馆基于用户行为分析的自适应众包技术

作者：刘军浙江大学

学位级别：硕士

随着互联网的快速发展,人们在生活中需要传播和存储的信息越来越多,信息的形式和特性也越来越多样化,人们接受信息的渠道也越来越广泛,传统的图书馆模式已无法满足互联网信息时代人们的需求。数字图书馆是传统图书馆在互联网信息时代的... 详细信息

随着互联网的快速发展,人们在生活中需要传播和存储的信息越来越多,信息的形式和特性也越来越多样化,人们接受信息的渠道也越来越广泛,传统的图书馆模式已无法满足互联网信息时代人们的需求。数字图书馆是传统图书馆在互联网信息时代的发展和延续,能存储海量的数字资源,维护成本低且不易损坏,信息检索方便,用户可以方便地通过互联网来访问数字图书馆的电子资源。众包技术也是互联网技术带来的新的生产组织形式。众包利用网络将计算机较难处理的工作分解并分配给众包工人去完成,并给予众包工人适当的奖励,将人们的空闲生产力利用起来,从而提升解决问题的效率,节约成本。本文的研究涉及到数字图书馆与众包技术,其主要工作如下：第一、提出了数字图书馆应用场景下基于用户行为分析的自适应众包方法。首先,该方法通过分析用户行为,预测最有兴趣成为众包工人的注册用户,减少随机选择工人的盲目性;其次,利用multi-armed bandit模型自适应地选择众包工人,以高质量快速地完成众包任务。第二、实现了基于用户行为分析的CADAL数字图书馆自适应众包系统,该系统包括潜在众包工人预测、邮件邀请、反馈收集、基于multi-armed bandit的众包工人选择等功能模块。在该系统之上,我们实验对比了多种众包工人选择方法,并分析了实验结果。总的来说,本文所要研究的是在数字图书馆的背景下应用众包技术的方法,提出了基于用户行为分析,结合multi-armed bandit模型与个性化推荐技术来创造性的提升众包性能的方法,从而实现自适应的众包模型。另外,在CADAL平台上实现了自适应众包系统。本文的研究可以较好的帮助解决CADAL数字图书馆上修订民国期刊元数据信息的问题。

关键词：数字图书馆众包 multi-armed bandit 用户行为分析推荐