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Privacy-preserving top-k query in two-tiered wireless sensor networks

International Journal of Advancements in Computing Technology 2012年第6期4卷 226-235页

作者： Yao, Yonglei Ma, Li Liu, Jingfa Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China

In a two-tiered wireless sensor network, storage nodes act as an intermediate tier between sensors and the sink for storing data and processing queries. This architecture has been widely adopted because of the benefits of power and storage saving for sensors as well as the efficiency of query processing. On the other hand, the importance of storage nodes also makes them attractive targets for compromise. A compromised storage node may leak sensitive information and breach data privacy. In this paper, we propose a privacy-preserving protocol specializing for top-k queries that prevents attackers from gaining sensitive information from sensor collected data. To preserve privacy, an orderpreserving encryption is employed to encrypt sensor data such that a storage node can correctly process top-k queries over encrypted data without knowing their actual values. Detailed theoretical and quantitative results confirm the high efficacy and efficiency of the proposed scheme.

关键词： Efficiency

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An ACO-LB algorithm for task scheduling in the cloud environment

Journal of Software

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Journal of software 2014年第2期9卷 466-473页

作者： Xue, Shengjun Li, Mengying Xu, Xiaolong Chen, Jingyi Nanjing University of Information Science and Technology School of Computer and Software Nanjing China Nanjing University of Information Science and Technology Jiangsu Engineering Center of Network Monitoring Nanjing China

In the face of a large number of task requests which are submitted by users, the cloud data centers need not only to finish these massive tasks but also to satisfy the user's service demand. How to allocate virtual machine reasonably and schedule the tasks efficiently becomes a key problem to be solved in the cloud environment. This paper proposes a ACO-LB(Load balancing optimization algorithm based on ant colony algorithm) algorithm to solve the load imbalance of virtual machine in the process of task scheduling The ACO-LB algorithm can adapt to the dynamic cloud environment. It will not only shorten the makespan of task scheduling, but also maintain the load balance of virtual machines in the data center. In this paper, the workflow scheduling is simulated in CloudSim. The results show that the proposed ACO-LB algorithm has better performance and load balancing ability. © 2014 ACADEMY PUBLISHER.

关键词： Ant colony optimization

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An improved algorithm based on NSGA-II for cloud PDTs scheduling

Journal of Software

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Journal of software 2014年第2期9卷 443-450页

作者： Xue, Shengjun Liu, Fei Xu, Xiaolong Nanjing University of Information Science and Technology School of Computer and Software Nanjing China Nanjing University of Information Science and Technology Jiangsu Engineering Center of Network Monitoring Nanjing China

Partly dependent tasks (PDTs) scheduling with multi-objective optimization in cloud computing is an NP-hard problem. Taking the quality of service (QoS) requirements of users that use cloud computing into account, we set the cost and time requirements of handling the PDTs as the multiple objectives and present an improved algorithm based on the non-dominated sorting genetic algorithm-II (NSGA-II) to find the Pareto optimal set of the PDTs scheduling. In this paper, the similar task order crossover (STOX) operator is applied to make the evolution more efficient while the shift mutation operator is applied in the process of evolution to avoid the premature convergence. In addition, we present a new method named self-adapting crowding distance (SCD) operator, which can improve the diversity of individuals in the Pareto-optimal front. The simulation results and analysis show that the proposed algorithm performs better than NSGA-II in maintaining the diversity and the distribution of the Pareto-optimal solutions in the cloud PDTs scheduling. © 2014 ACADEMY PUBLISHER.

关键词： Multiobjective optimization

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Quantum Blockchain: A Decentralized, Encrypted and Distributed Database Based on Quantum Mechanics

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Journal of Quantum Computing 2019年第2期1卷 49-63页

作者： Chuntang Li Yinsong Xu Jiahao Tang Wenjie Liu School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and TechnologyNanjing210044China

Quantum blockchain can be understood as a decentralized, encrypted anddistributed database based on quantum computation and quantum information *** the data is recorded in the quantum blockchain, it will not be maliciously tamperedwith. In recent years, the development of quantum computation and quantum informationtheory makes more and more researchers focus on the research of quantum blockchain. Inthis paper, we review the developments in the field of quantum blockchain, and brieflyanalyze its advantages compared with the classical blockchain. The construction and theframework of the quantum blockchain are introduced. Then we introduce the method ofapplying quantum technology to a certain part of the general blockchain. In addition, theadvantages of quantum blockchain compared with classical blockchain and itsdevelopment prospects are summarized.

关键词： Quantum computation quantum blockchain digital currency

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A comparative study of quantum evolutionary algorithm and particle swarm optimization for numerical optimization problems

International Journal of Digital Content Technology and its ...

引用

International Journal of Digital Content Technology and its Applications 2011年第7期5卷 182-190页

作者： Ma, Tinghuai Yan, Qiaoqiao Xuan, Wenxia Wang, Bin Jiangsu Engineering Center of Network Monitoring School of Computer and Software Nanjing University of Information Science and Technology China National Meteorological Information Centre Chinese Meteorological Administration China

Quantum evolutionary algorithm (QEA) and particle swarm optimization (PSO) are two different types of intelligent optimization algorithm. Many efforts on these two algorithms have progressed actively in recent years. In this paper, six typical and complex benchmark testing functions are applied to verify their abilities for dealing with numerical optimization problems. The results show that QEA has better global search capability, which is manifested very outstandingly for the multi peak function optimization, but its convergence speed is slower. Contrarily, PSO has faster convergence speed, and it can solve certain optimization problem rapidly, especially for single peak functions.

关键词： Particle swarm optimization (PSO)

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Privacy-preserving MAX/MIN aggregation in wireless sensor networks

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Advances in Information Sciences and Service Sciences 2012年第6期4卷 272-280页

In-network data aggregation has been adopted widely to reduce communication overhead and improve efficiency in wireless sensor networks. On the other hand, security is another basic requirement for wireless sensor network, and providing privacy without disrupting in-network data aggregation is a challenging problem. Although there have been extensive studies on privacy-preserving data aggregation for additive and multiplicative aggregation functions, nonlinear aggregation functions, such as maximum and minimum, have not been well addressed. In this paper, we present a privacy-preserving aggregation method specialized for MAX/MIN aggregation functions. To preserve privacy, encryption and the prefix membership verification scheme are employed, allowing an intermediate aggregator to correctly compare data without access to plaintext data, thus protect data privacy. Extensive simulations are conducted to evaluate the proposed schemes. The results show that our scheme provides privacy protection for both raw and aggregated data items, while achieving high efficiency in terms of energy consumption and time delay.

关键词： Wireless sensor networks

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An intelligent agent-based method for task allocation in competitive cloud environments

An intelligent agent-based method for task allocation in com...

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作者： Kong, Yan Zhang, Minjie Ye, Dayong Zhu, Jinxiu Choi, Junho Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing210044 China School of Computer Science and Software Engineering University of Wollongong Wollongong2522 Australia Research Institute of Ocean and offshore engineering Hohai Universtiy Nantong226300 China Division of Undeclared Majors Chosun University Gwangju61452 Korea Republic of

In market-based cloud environments, both resource consumers and providers are self-interested;additionally, they can come and leave the environment freely. Therefore, the environment is competitive and uncertain. Because of the competition, participants may cheat in making deals, and this represents that the environment is insecure to resource providers who intend to earn profits through renting their resources to the tasks of resource consumers. Against this, in this paper, intelligent agents are designed to strategically quote for the tasks that they are interested in, on behalf of resource providers. Agents could quote according to the messages it obtained and the information learnt and predicted from the messages, to minimize the influence of insecure factors, such as cheating, competition, and dynamism. The experimental evaluation shows that the proposed method outperforms both a well-known multiresource negotiation-based task allocation method and a max-sum belief propagation–based method. Copyright © 2017 John Wiley & Sons, Ltd.

关键词： Intelligent agents

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A Balanced Cloudlet Management Method for Wireless Metropolitan Area networks 13th

A Balanced Cloudlet Management Method for Wireless Metropoli...

引用

13th EAI International Conference on Testbeds and Research Infrastructures for the Development of networks and Communities, TridentCom 2018

作者： Xu, Xiaolong Chen, Yuhao Qi, Lianyong He, Jing Zhang, Xuyun School of Computer and Software Nanjing University of Information Science and Technology Nanjing China Jiangsu Engineering Centre of Network Monitoring Nanjing University of Information Science and Technology Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Information Science and Engineering Qufu Normal University Jining China Department of Electrical and Computer Engineering University of Auckland Auckland New Zealand

ISBN: (纸本)9783030129705

With the rapid development of wireless communication technology, cloudlet-based wireless metropolitan area network, which provides people with more convenient network services, has become an effiective paradigm to meet the growing demand for requirements of wireless cloud computing. Currently, the energy consumption of cloudlets can be reduced by migrating tasks, but how to jointly optimize the time consumption and energy consumption in the process of migrations is still a significant problem. In this paper, a balanced cloudlet management method, named BCM, is proposed to address the above challenge. Technically, the Simple Additive Weighting (SAW) and Multiple Criteria Decision Making (MCDM) techniques are applied to optimize virtual machine scheduling strategy. Finally, simulation results demonstrate the effectiveness of our proposed method. © 2019, ICST Institute for computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Energy utilization

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Kernel-based motion-blurred target tracking

Kernel-based motion-blurred target tracking

引用

7th International Symposium on Visual Computing, ISVC 2011

作者： Wu, Yi Hu, Jing Li, Feng Cheng, Erkang Yu, Jingyi Ling, Haibin Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing 210044 China School of Computer and Software Nanjing University of Information Science and Technology Nanjing 210044 China Center for Information Science and Technology Computer and Information Science Department Temple University Philadelphia PA United States Department of Computer and Information Sciences University of Delaware Newark DE 19716 United States Network Center Nanjing University of Information Science and Technology Nanjing 210044 China

Motion blurs are pervasive in real captured video data, especially for hand-held cameras and smartphone cameras because of their low frame rate and material quality. This paper presents a novel Kernel-based motion-Blurred target Tracking (KBT) approach to accurately locate objects in motion blurred video sequence, without explicitly performing deblurring. To model the underlying motion blurs, we first augment the target model by synthesizing a set of blurred templates from the target with different blur directions and strengths. These templates are then represented by color histograms regularized by an isotropic kernel. To locate the optimal position for each template, we choose to use the mean shift method for iterative optimization. Finally, the optimal region with maximum similarity to its corresponding template is considered as the target. To demonstrate the effectiveness and efficiency of our method, we collect several video sequences with severe motion blurs and compare KBT with other traditional trackers. Experimental results show that our KBT method can robustly and reliably track strong motion blurred targets. © 2011 Springer-Verlag.

关键词： Target tracking

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Short text classification algorithm based on semi-supervised learning and SVM

International Journal of Multimedia and Ubiquitous Engineeri...

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International Journal of Multimedia and Ubiquitous engineering 2015年第12期10卷 195-206页

作者： Yin, Chunyong Xiang, Jun Zhang, Hui Yin, Zhichao Wang, Jin Jiangsu Key Laboratory of Meteorological Observation and Information Processing School of Computer and Software Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing China Nanjing No.1 Middle School NanjingJiangsu China

Short text is a popular text form, which is widely used in real-time network news, short commentary, micro-blog and many other fields. With the development of the application such as QQ, mobile phone text messages and movie websites, the size of data is also becoming larger and larger. Most data is useless for us while other data is significant for us. Therefore, it is necessary for us to extract the useful short text from the big data. However, there are many problems with the short text classification, such as fewer features, irregularity and so on. To solve these problems, we should pretreat the short text set first, and then choose the significant features. This paper use semi-supervised learning method and SVM classifier to improve the traditional methods and it can classify a large number of short texts to mining the useful massage from the short text. The experimental results in this paper also show a good promotion. © 2015 SERSC.

关键词： Supervised learning

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