Attribute Allocation and Retrieval Scheme for Large-Scale Sensor Networks

Wireless sensor network is an emerging technology that enables remote monitoring of large geographical regions. In this paper, we address the problem of distributing attributes over such a large-scale sensor network so that the cost of data retrieval is minimized. The proposed scheme is a data-centric storage scheme where the attributes are distributed over the network depending on the correlations between them. The problem addressed here is similar to the Allocation Problem of distributed databases. In this paper, we have defined the Allocation Problem in the context of sensor networks and have proposed a scheme for finding a good distribution of attributes to the sensor network. We also propose an architecture for query processing given such a distribution of attributes. We analytically determine the conditions under which the proposed architecture is beneficial and present simulation results to demonstrate the same. To the best of our knowledge, this is the first attempt to determine an allocation of attributes over a sensor network based on the correlations between attributes.     

Research and Design of Network Management Construction Based on Mobile AGENT

The technical characters of mobile agent （MA） originated in the distributional artificial intelligence domain is introduced. A network management construction based on agent （NMCA） is then proposed. The NMCA structure features are elaborated in detail. A prototype design of NMCA is given by using the jKQML programming. The establishment of NMCA platform will be helpful to reduce the correspondence load of network management and improves the efficiency and the expansion ability of network management systems.     

Level of nodes in increasing trees revisited

Simply generated families of trees are described by the equation T(z) = ϕ(T(z)) for their generating function. If a tree has n nodes, we say that it is increasing if each node has a label ∈ { 1,…,n}, no label occurs twice, and whenever we proceed from the root to a leaf, the labels are increasing. This leads to the concept of simple families of increasing trees. Three such families are especially important: recursive trees, heap ordered trees, and binary increasing trees. They belong to the subclass of very simple families of increasing trees, which can be characterized in 3 different ways. This paper contains results about these families as well as about polynomial families (the function ϕ(u) is just a polynomial). The random variable of interest is the level of the node (labelled) j, in random trees of size n ≥ j. For very simple families, this is independent of n, and the limiting distribution is Gaussian. For polynomial families, we can prove this as well for j,n → ∞ such that n − j is fixed. Additional results are also given. These results follow from the study of certain trivariate generating functions and Hwang's quasi power theorem. They unify and extend earlier results by Devroye, Mahmoud, and others. © 2007 Wiley Periodicals, Inc. Random Struct. Alg., 2007     

移动IP的关键技术

本详细描述了移动IP的工作过程，并介绍了移动IP的隧道技术和IPSec，最后对移动IP技术中存在几个热点问题进行了讨论。     

有限元节点编号优化

从分析最简单最优的矩形单元节点编号着手，分析其特点，得出各个节点的相邻单元节点编号总和除以该节点相邻单元数所得的节点商以及相邻单元最小最大节点编号和都为升序排列。总结出一种新型的有限元节点重编号方法，并举例说明该算法能减少有限元的带宽。     

一种快速低开销的移动节点分布式寻路RPL路由协议

针对低功耗有损网络(Low Power and Lossy Network, LLN)中移动节点作为中继节点时,存在无线链路易断开、数据传输成功率较低和控制开销较高的问题,提出了一种快速低开销的移动节点分布式寻路RPL路由协议(Mobile Node Distributed Pathfinding RPL Routing Protocol, MNA-RPL)。首先,提出一种邻居表自适应更新机制,依据邻居表快速切换父节点;其次,提出一种父子节点双断开机制,能够有效减少数据包丢失;最后,提出一种基于速度动态管理的Trickle计时器,根据自身速度动态调整DIO广播周期。理论分析和仿真结果表明,与主流路由协议相比,该协议在数据传输和控制开销等方面的性能均有一定的提升。     

道路交通网络节点分配优化策略研究进展

为了进一步提高城市道路交通网络的通行效率,粒子群优化和神经网络等多种智能优化算法受到越来越多的关注。近年来,深度学习技术的普及与应用大幅提升了城市交通网络的节点识别效率,而交通网络的节点调度又扩展了深度学习技术的应用。文中详细分析了交通节点调度所面临的关键问题,归纳并总结了相关网络节点分配的研究现状。在此基础上,深入研讨了城市交通网络节点调度与深度学习的应用前景,并对交通网络节点分配优化策略的未来研究方向进行了展望。     

基于背景先验与中心先验的显著性目标检测北大核心CSCD

针对传统的图流行排序显著性目标检测算法存在先验信息单一,显著目标检测不完整的问题,提出一种新的基于背景先验与中心先验的显著性目标检测算法。首先将图像边界节点作为背景种子进行流行排序获得粗略的前景区域,将其再次流行排序得到初步显著图;然后利用Harris角点检测、聚类实现中心先验显著性检测,捕获中心显著信息;最后在初步显著图上融合图像中心显著性,得到最终显著图。本文对综合指标、精确率-召回率曲线、F-measure值以及平均绝对误差(mean absolute error,MAE)值进行实验评估,在公开数据集MSRA-10K和ECSSD上进行的实验结果表明:对比10种主流算法,本文算法在不同的评估指标上都具有较好的表现,且能准确地突出显著目标,提升背景抑制效果。     

A path planning model based on nested regular hexagons using weighted centroid localization

In many wireless sensor network (WSN) applications, the location of a sensor node is crucial for determining where the event or situation of interest occurred. Therefore, localization is one of the critical challenges in WSNs. Mobile anchor node assisted localization (MANAL) is one of the promising solutions for the localization of statically deployed sensors. The main problem in MANAL localization is that the path planning of the mobile anchor (MA) node should be done so that the localization error in the network will be minimal and that all unknown nodes in the network are covered. This paper proposes a new path planning approach called nested hexagons curves (NHexCurves) for MANAL. NHexCurves guarantees that it will receive messages from at least three non-collinear anchors to locate all unknown nodes in the network. The proposed model has compared six different path planning schemes in the literature using weighted centroid localization (WCL). In these comparisons, first of all, localization errors of the models are compared using some statistical concepts. Second, the variation of the localization error according to parameters such as resolution (R) and the standard deviation of noise (σ) is observed. Then, with similar approaches, the standard deviation of errors, localization ratio, scalability performances, and finally, path lengths of the models are examined. The simulation results show that the NHexCurves static path planning model proposed in this study stands out compared to other models with high localization error and localization ratio performance, especially at low resolutions, due to its path design. At the same time, the lowest error values according to σ are obtained with the proposed model among all models considered.