一种面向服务的WSN与网格的集成方法研究

按面向服务的思想组建无线传感器网络WSN系统,透过规范统一的服务接口与网格互联是WSN与网格集成的一种参照现有SOA架构的直观方式。本文给出了面向服务的WSN解构和WSN服务组合和扩展的形式化定义和验证方法,并提出一个包含接口转换和服务中间件的集成方案。面向服务的WSN与网格集成,不仅能以一个友好接口与网格协同工作,而且能灵活地保证WSN在网格中的服务质量。     

A flexible layered control policy for resource allocation in a sensor grid

The paper proposes a flexible layered control policy for sensor resource allocation in a sensor grid. In order to allocate sensor resources in the system to maximize the sensor grid utility, different controllers are deployed at three levels: a job-level controller, an application group controller, and a sensor grid system controller. At the lowest levels, job-level controllers perform fast, frequent, local adaptation for optimizing a single sensor grid application at a time, while, at the highest levels, sensor grid system controllers perform less frequent control actions to optimize all applications. Sensor grid system control considers all sensor grid applications in response to large system changes at coarse time granularity. Sensor grid system control exploits the interlayer coupling of the resource layer and the application layer to achieve a system-wide optimization based on the sensor grid users’ preferences. Job-level control adapts a single application to small changes at fine granularity. The layered control system uses a set of utility functions to evaluate the performance of sensor grid applications and groups. The control system chooses control actions that would result in a higher level of utility. In the simulation, a performance evaluation of the algorithm is carried out.     

服务网格环境下一种基于整体QoS的服务匹配策略

研究服务网格中的服务质量(QoS)保障问题.定义基本QoS参数及量化要求,阐述QoS参数归一计算方法,提出一种基于整体QoS的服务匹配策略.该策略利用QoS约束的一致性和整体QoS特性进行网格服务发现与选择,从而减小满足最小服务要求的解空间.实验结果表明该策略有效提高了匹配服务的执行效率,满足了用户的QoS需求.     

Joint contexts optimization for mobile grid

This paper presents joint contexts optimization in mobile grid. The paper describes device context information for context-aware services in the mobile device collaboration. The objective of the paper is to dynamically deliver services to mobile grid users according to current context of mobile grid environment. A utility function is used as objective function that expresses values for the current contexts. The optimization is carried out by the joint context parameter optimizer with respect to an objective function. A joint contexts optimization algorithm is proposed which decomposes mobile grid system optimization problem into sub-problems. In the experiment, the performance evaluation of joint contexts optimization algorithm is conducted.     

面向服务网格的访问控制模型研究

建立以网格服务为中心的访问控制模型,通过集中式的管理来降低网格环境中访问控制复杂性,将角色网格服务的分配和服务的访问授权分离管理,能够有效地保护资源拥有者的权限.在中国科学院e-science科学数据网格项目中的初步应用实施表明了该模型的灵活性和有效性.     

Dependency-aware maintenance for highly available service-oriented grid

When the scale of computational system grow from a single machine to a Grid with potentially thousands of heterogeneous nodes, the interdependencies among the resources and software components make management and maintenance activities much more complicated. One of the most important challenges to overcome is how to balance maintenance of the system and the global system availability. In this paper, a novel mechanism is proposed, the Cobweb Guardian, which provides solutions not only to reduce the effects of maintenance but to remove the effects of dependencies on system availability due to deployment dependencies, invocation dependencies, and environment dependencies. By using the Cobweb Guardian, Grid administrators can execute the maintenance tasks safely at runtime whilst ensuring high system availability. The results of our evaluations show that our proposed dependency-aware maintenance mechanism can significantly increase the throughput and the availability of the whole system at runtime.     

Cross-layer optimization policy for QoS scheduling in computational grid

This paper presents a cross-layer quality of service (QoS) optimization policy for computational grid. Efficient QoS management is critical for computational grid to meet heterogeneity and dynamics of resources and users’ requirements. There are different QoS metrics at different layers of computational grid. To improve perceived QoS by end users over computational grid, QoS supports can be addressed in different layers, including application layer, collective layer, fabric layer and so forth. The paper tackles cross-layer grid QoS optimization as optimization decomposition, each layer corresponds to a decomposed subproblem. The proposed policy produces an optimal set of grid resources, service compositions and user's payments at the fabric layer, collective layer and application layer respectively to maximize global grid QoS. The cross-layer optimization problem decomposes into three subproblems: grid resource allocation problem, service composing and user satisfaction degree maximization problem, all of which interact through the optimal variables for capacities of grid resources and service demand. In order to coordinate the subproblems, cross-layer QoS feedback mechanism is established to ensure different layer interactions. The simulations are conducted to validate the efficiency of the proposed policy.     

基于微粒群算法和网格模型的参数优化方法

针对在半导体制造工艺参数优化过程中缺乏直观参考的问题,在微粒群优化算法(PSO)和等值线理论分析的基础上,将PSO与等值线矩形网格模型相结合,提出一种全新的工艺参数窗口选择方法,在二维标准多峰函数上验证了所提出方法的有效性,同时对所提出的方法进行了实际生产验证,对于双输入参数问题,该方法可以直接输出所有满足工艺要求的二维区域,从而为参数优化和范围选取提供直观参考,仿真测试结果和生产验证数据均表明了所提出的算法是一种有效的参数优化方法。     

基于微粒群算法的网格工作流优化调度问题的研究

网格工作流中的调度问题是一个复杂且具有挑战性的问题,它影响着网格工作流执行成功与否及效率的高低.针对具有时序和因果约束关系的网格工作流优化调度问题进行了研究,建立了网格工作流的任务调度模型和调度问题的目标模型,并应用微粒群算法来优化网格工作流中任务的调度.实验结果证明该算法优于传统的调度算法.     

Joint QoS optimization for layered computational grid

Many existing grid resource allocation and scheduling algorithms mainly focus on isolated layers of the grid architecture. The inflexibility of the strict layering structure results in an inefficient utilization of the grid resources. This paper takes a system view of the computational grid and aims to jointly optimize global QoS by adopting cross-layer design. Cross-layer design is based on information exchange and joint optimization among multiple grid layers. Parameters from different layers are provided to a cross-layer optimizer, which selects the values of the layer specific parameters maximizing joint global QoS. The objective of the paper is to jointly optimize the parameters of all layers in a decentralized optimization problem and decompose joint QoS optimization into three sub problems at fabric layer, collective layer and application layer. In simulation part, we compare the performance of the global joint QoS optimization approach with application layer local optimization and resource layer local optimization approach, respectively.     

多目标蚁群优化网格调度算法

提出基于蚁群算法的网格调度算法,优化作业完成时间。同时局部升级和全局升级采用不同策略,解决资源负载均衡问题,满足网格的多目标优化。最后通过Gridsim仿真环境和其他算法进行比较分析。     

A service-oriented framework for agent-based simulations of collaborative supply chains

Current collaborative practices of supply chain management are limited to some known configurations where a dominant member sets the pace for the collaboration extent. Extending collaborative models to supply chains without a dominant member requires defining a fair assessment of costs and benefits and how they are distributed among members. To understand collaborative models and their mechanisms, simulation-based approaches are recommended as they can afford the complexity of real scenarios. However, building ad-hoc simulation models for studying complex supply chain interactions can be prohibitive in terms of both cost and time. Therefore, the availability of simulation frameworks, to be used easily by business managers and facilitating the development of those models, has a strong incentive in the quest of current business efforts to increase their supply chain performance. The objective of this work is to present a systematic and reusable serviceoriented framework for agent based simulation to support the analysis of collaborative interactions in supply chains. Results of a requirement analysis performed to this aim are described, and the fulfillment of identified requirements by the proposed framework, and capabilities thereof, are discussed.     

基于网格近邻优化的密度峰值聚类算法

密度峰值聚类(DPC)将数据样本点的局部密度和相对距离进行结合,能对任意形状数据集进行聚类处理,但密度峰值聚类算法存在主观选择截断距离、简单分配策略和较高时间复杂度等问题。为此,提出了一种基于网格近邻优化的密度峰值聚类算法(KG-DPC算法)。首先对数据空间进行网格化,减少了样本数据点之间距离的计算量;在计算局部密度时不仅考虑了网格自身的密度值,而且考虑了周围k个近邻的网格密度值,降低了主观选择截断距离对聚类结果的影响,提高了聚类准确率,设定网格密度阈值,保证了聚类结果的稳定性。通过实验结果表明,KG-DPC算法比DBSCAN、DPC和SDPC算法在聚类准确率上有很大提升,在聚类平均消耗时间上DPC、SNN-DPC和DPC-NN算法分别降低38%、44%和44%。在保证基本聚类准确率的基础上,KG-DPC算法在聚类效率上有特定优势。     

A sensor array optimization method for electronic noses with sub-arrays

Sensor array configuration needs to be optimized in developing an application-specific instrument of electronic noses. Currently, sensor array optimization is commonly done by feature selection techniques. These methods could solve how to optimize a sensor array. However, they could not figure out what are the unique functions that each sensor plays in the optimized sensor array. The method proposed in this paper could solve this problem by sensor clustering and dividing the whole sample classification mission into several small recognition tasks. A measurement with a six Taguchi Gas Sensors (TGS sensor hereinafter) sensors array to classify 11 gas sorts was used in the data validation. The sensor array was optimized to three sensors with the proposed method. Each sensor in the optimized array had unique functions to solve different recognition tasks. TGS2600 had the unique functions to discriminate butanone and acetaldehyde. TGS2602 had the unique functions to discriminate benzene and cyclohexane, methanol and ethanol. TGS813 had the unique functions to discriminate cyclohexane and pentane. The combination of TGS2600 and TGS2602 had the unique functions to discriminate acetone and butanone, acetone and acetaldehyde. The proposed method might be a new generation of sensor array optimization methods.     

基于数据网格的分布式查询优化模型

智能交通系统（ITS）中多个异构的、地理位置分散的数据源能使用像数据网格这样的分布式计算技术进行集成,这种集成所面临的真正挑战是分布式查询处理引擎的设计和开发。一般而言,分布式查询优化按照以下三个阶段进行：查询所涉及节点的确定,并行执行方案的生成,执行查询的最佳节点选择。由于这三个阶段的相互隔离可能会导致得到的查询方案并不是最佳的,提出了一个新的分布式查询优化模型,该模型集成了查询优化的三个阶段,综合考虑了查询优化各个阶段所涉及的参数,如节点的有效内存、处理速度、数据传输容错能力等。     

基于投影栅格扫描的无线传感器网络三维定位算法

针对现有无线传感器网络(WSN)三维定位算法在精度和复杂度方面的不足,提出了一种改进的三维空间定位算法。利用栅格扫描分别求解邻居锚节点在两个坐标平面的投影交域,得出未知节点在两坐标平面的对应位置,最终实现三维位置估计。仿真结果表明：在100m×100m×100m的空间里,随机投放200个传感器节点,锚节点数为45时,其覆盖率达到了99.1%,相对定位误差仅为0.5533。且平面投影的引入,有效地降低了算法复杂度。     

无线传感器网络中基于关联度的多查询优化

无线传感器网络是一种以数据为中心的网络,用户通过基站向网络提出查询请求获取所需数据。如何通过多查询的优化来减少传感器节点的能耗以延长网络生命期是无线传感器网络中需要解决的关键问题之一。提出了基于关联度的多查询优化算法,其基本思想是节点通过节点与候选父亲节点之间的关联度来选择父节点,从而被相同查询覆盖的节点聚集成一个组,多个查询间共享组中节点的数据,在网络中对查询数据进行有效的融合,充分减少了网络的数据传输量,延长了网络的生命期。理论分析和模拟实验表明该算法可以充分减少数据传输量,从而达到节能的目的。     

可信禁忌粒子群优化网格任务调度算法

网格任务调度是典型的NP（Non-Polynomial）难题,基于粒子群优化智能算法,提出一种新的任务调度算法,目标是使所有任务整体完成时间最小。算法首先随机产生一群粒子,然后对粒子的位置和速度不断迭代,获得可以接受的任务调度方案,并从中选择一组信任度最高的作为较优解,再利用禁忌搜索算法由较优解获得最优解。仿真实验结果表明,与遗传算法相比更适合于求解规模较大的网格任务调度问题。     

考虑不确定性因素的微电网经济运行的优化

本文针对微电网经济运行优化问题,考虑分布式可再生能源的输出功率具有随机性特点,提出了一种基于场景分解及场景策略聚合机制的优化方法.该方法针对所提出的优化问题中,统计信息不足以建立一个随机规划模型的难点,首先进行了场景分析,将原优化问题分解为多个场景子优化问题;其次引入场景束约束,将场景策略分为可容许策略、可实现策略和偶然策略,依照可用信息的进化机制,迭代修正每个场景子优化问题,并且采用动态规划方法求解每个场景子优化问题.然后,统计分析子优化问题最优解的主要特征和趋势,最终得到微电网经济运行优化问题的最优解.通过仿真验证了所提出方法的有效性.     

Multi-objective optimization of sensor array using genetic algorithm

A multi-objective optimization method using genetic algorithm was proposed for sensor array optimization. Based on information theory, selectivity and diversity were used as the criteria for constructing two objective functions. A statistic measurement of resolving power, general resolution factor, and visual inspection were used to evaluate the optimization results with the aid of principal component analysis. In each Pareto set, most nondominated solutions had better statistics than the combination using all potential sensors. Also the principal component plots showed that different vapor classes were generally better separated after optimization. The experiment results indicated that the proposed method could successfully identify a set of Pareto optimal solutions of small size; and most optimized sensor arrays provided input with improved quality, i.e. better separation of target analytes. The running time for implementing the multi-objective optimization was satisfactory.