无线传感网络中通用传感器节点硬件结构设计

无线传感网络是当前国内外传感器技术领域的热点研究课题。提出了一种构建无线传感网络节点实验平台的方法;基于该设计方案实现的无线传感网络节点可以有效地组建网络拓扑结构及多种传感数据采集传输。它还可以用于实现声音空间定位系统。关键词:无线传感网络;节点;拓扑结构;数据采集;声音空间定位     

一种非结构化P2P的随机有向图拓扑模型

在非结构化P2P系统中,P2P拓扑直接影响了网络的性能,如何构建P2P拓扑是一件重要的问题.采用一个简单的映射将非结构化P2P抽象成一个G(V,E)图,在G(V,E)及其非结构化P2P拓扑的特点基础上,提出了一种非结构化P2P的随机有向图拓扑模型,并讨论了该拓扑模型的度分布、连通性和直径,为研究非结构化P2P系统的性能和算法协议提供了仿真的理论基础.     

嵌套NEMO中的路由优化方案研究

针对嵌套NEMO中存在的乒乓路由和数据包多层封装问题进行深入分析,提出一种嵌套NEMO的优化方案,改变NEMO中传统的MR嵌套转发报文的做法,使得MR的HA能够将发送到MR的数据直接重定向到整个嵌套NEMO所连接的外地网络,减少了嵌套的次数,并使得小范围的切换更新报文能控制在NEMO内部,减少了切换时延。分析表明,与NEMO基本支持协议相比,方案有效地解决了NEMO路由优化问题,提高了数据包的传输效率。     

航班着陆调度的多目标优化研究

航班着陆调度问题是多目标优化问题,难以使用最优化方法求解。为了解决这一难题,以减少航班延迟时间和降低飞行延误成本为目标,提出一种整合的启发式方法。该方法使用吱呀轮算法SWO(Squeaky-Wheel Optimization)进行导向式搜索,并利用改进的GA充分扩展SWO的搜索空间,最后通过合理整合GA和SWO,取得求解效率和求解质量的提高。通过实验仿真对比表明该算法能高效求解该问题,满足了实时调度的需求,同时求解质量也优于其他启发式算法,节省了更多降落时间和成本。     

桥接局域网第2层拓扑结构的自动发现

With the rapid development and the wide use of Bridged Local Area Networks, the importance of below-OSI-Layer-3 topology comes to be realized. Recent research work has demonstrated that active layer-2 topology can be determined using Address Forwarding Information. The large quantities of address forwarding entries in a typical BLAN, however, make their algorithms both time-consuming and bandwidth-consuming. In view of the above and the fact that transparent bridges are used preponderantly in real networks, we base our algorithm on information supported by Spanning Tree Protocol M1B group, which reduces the cost of acquiring and processing information, and improves the accuracy. Our algorithm relies on standard MIB information and can be used in heterogeneous environments     

基于GAPSO混合算法的网格工作流调度研究

网格工作流调度关注大规模的资源和任务调度,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以及效率的高低。提出了基于遗传粒子群(GAPSO)的混合算法,引用了特殊的适应度函数,设定了动态的交叉和变异概率,并提出了动态切换算法的方法。结合各自算法的优势,在算法运行初期利用遗传算法的全局搜索能力进行优化搜索,在后期利用粒子群较强的局部搜索能力加快收敛速度。仿真结果表明该算法在执行时间方面有一定的优越性,能更有效地解决网格工作流调度问题。     

A robust underwater navigation method fusing data of gravity anomaly and magnetic anomaly

Navigation using geophysical information is often implemented on underwater vehicles to correct the position errors of inertial navigation system. In this paper, we fuse the information of gravity anomaly and magnetic anomaly by developing an improved elitism based multi-objective artificial bee colony algorithm. In the proposed algorithm, multi-objective matching strategy and sequence matching strategy are adopted for the purpose of reducing the possibility of mismatching. Specifically, the mean absolute difference (MAD) of gravity anomaly and the MAD of magnetic anomaly are selected as two matching objective. Single-point matching is changed to sequence matching by introducing affine transformation. To keep diversity without losing too much convergence speed, two elites chosen by different strategies are used to generate new food source. Finally, in order to avoid premature convergence, a new mechanism of scout bee phase is explored. The maximum capacity of external archive is regarded as the monitoring target. Simulations based on the actual gravity anomaly base map and the actual magnetic anomaly base map have been performed for the validation of the proposed algorithm.     

基于粒子群优化建模的贝叶斯网络结构学习方法

贝叶斯网络(BN)在不确定性的条件下表示信息和推理论证具有良好的性能,但由于其结构搜索空间的复杂性,通常将从一个数据集合中学习贝叶斯网络的结构认为是一种NP-hard的问题。基于此,提出一种新的基于粒子群优化算法建模的贝叶斯网络结构学习方法。为了学习一个贝叶斯网络的结构,该方法先使用粒子群优化算法在排序空间中进行搜索,然后运行K2算法计算每个排序的吻合度。每个排序都会有一个网络结构与之一致,该方法会返回这个网络的计分。仿真结果表明,在不同规模的数据集中,该算法相对于其他贝叶斯网络结构学习算法对不同类型的网络都具有更好的网络稳定性。     

一种基于蚁群优化的图像分类算法

现有图像降维方法中特征信息被过多压缩,从而影响图像分类效果。提出IC-ACO算法,利用蚁群算法来解决图像分类问题。算法充分提取并保留图像的各种形态特征。利用蚁群优化算法在特征集中自动挖掘有效特征和特征值,构建各类分类规则,从而实现图像的分类识别。在真实的车标图像数据集上的实验结果表明,IC-ACO算法比其他类似算法具有更高的分类识别率。     

Exploring networks with traceroute-like probes: Theory and simulations

Mapping the Internet generally consists in sampling the network from a limited set of sources by using traceroute-like probes. This methodology, akin to the merging of different spanning trees to a set of destination, has been argued to introduce uncontrolled sampling biases that might produce statistical properties of the sampled graph which sharply differ from the original ones. In this paper, we explore these biases and provide a statistical analysis of their origin. We derive an analytical approximation for the probability of edge and vertex detection that exploits the role of the number of sources and targets and allows us to relate the global topological properties of the underlying network with the statistical accuracy of the sampled graph. In particular, we find that the edge and vertex detection probability depends on the betweenness centrality of each element. This allows us to show that shortest path routed sampling provides a better characterization of underlying graphs with broad distributions of connectivity. We complement the analytical discussion with a throughout numerical investigation of simulated mapping strategies in network models with different topologies. We show that sampled graphs provide a fair qualitative characterization of the statistical properties of the original networks in a fair range of different strategies and exploration parameters. Moreover, we characterize the level of redundancy and completeness of the exploration process as a function of the topological properties of the network. Finally, we study numerically how the fraction of vertices and edges discovered in the sampled graph depends on the particular deployements of probing sources. The results might hint the steps toward more efficient mapping strategies.