Location and calculation-free node-scheduling schemes in large wireless sensor networks

In wireless sensor networks that consist of a large number of low-power, short-lived, unreliable sensors, one of the main design challenges is to obtain long system lifetime without sacrificing system original performance (sensing coverage and sensing reliability). To solve this problem, one of the potential approaches is to identify redundant nodes at the sensing interface and then assign them an off-duty operation mode that has lower energy consumption than the normal on-duty mode. In our previous work [J. Wireless Commun. Mobile Comput. 3 (2003) 271; Processing of ACM Wireless Sensor Network and Application Workshop 2002, September 2002], we proposed a node-scheduling scheme, which can provide a 100% sensing coverage preservation capability. This, however, requires each node to be aware of its own and its neighbors’ location information. Also, in that scheme, each node has to do accurate geometrical calculation to determine whether to take an off-duty status. In this paper, we propose and study several alternative node-scheduling schemes, which cannot completely preserve the original system coverage, but are nonetheless light-weighted and flexible compared with the previous one. Our simulation results compare these schemes with the previous one and demonstrate their effectiveness.     

Strong asymptotics in Lagrange interpolation with equidistant nodes

In this paper we prove three conjectures of Revers on Lagrange interpolation for f_λ(t)=|t|^λ,λ>0, at equidistant nodes. In particular, we describe the rate of divergence of the Lagrange interpolants L_N( f_λ,t) for 0<|t|<1, and discuss their convergence at t=0. We also establish an asymptotic relation for max_|t|1| |t|^λ−L_N( f_λ,t)|. The proofs are based on strong asymptotics for |t|^λ−L_N( f_λ,t), 0|t|<1.     

基于C8051F040的CAN总线通讯系统设计

作为一种很有前途的现场总线,CAN总线通信相对于一般的串行通信总线,他的实时性、可靠性和灵活性的特点更加突出。介绍了C8051F040内部集成的CAN控制器的使用方法,给出了以此芯片为核心的CAN总线智能节点通讯接口的硬件和软件设计,实际的运行验证了设计的正确性。其设计通用性好、可靠性高、性价比高,具有广泛的应用前景。     

Markovian network processes: Congestion-dependent routing and processing

A Markovian network process describes the movement of discrete units among a set of nodes that process the units. There is considerable knowledge of such networks, often called queueing networks, in which the nodes operate independently and the routes of the units are independent. The focus of this study, in contrast, is on networks with dependent nodes and routings. Examples of dependencies are parallel processing across several nodes, blocking of transitions because of capacity constraints on nodes, alternate routing of units to avoid congestion, and accelerating or decelerating the processing rate at a node depending on downstream congestion. We introduce a general network process representing the numbers of units at the nodes and derive its equilibrium distribution. This distribution takes the form of a product of functions of vectors in which the arguments of the functions satisfy an interchangeability property. This new type of distribution may apply to other multi-variate processes as well. A basic idea in our approach is a linking of certain micro-level balance properties of the network routing to the processing rates at the nodes. The link is via routing-balance partitions of nodes that are inherent in any network. A byproduct of this approach is a general characterization of blocking of transitions without the restriction that the process is reversible, which had been a standard assumption. We also give necessary and sufficient conditions under which a unit moving in the network sees a time average for the unmoved units (called the MUSTA property). Finally, we discuss when certain flows between nodes in an open network are Poisson processes.This research was sponsored in part by Air Force Office of Scientific Research contract 84-0367.     

面向鲁棒频谱感知的模糊K-means++算法

恶意用户通过向数据融合中心发送伪造的频谱感知数据,解决自身频谱资源短缺问题,但会极大地降低频谱感知系统的检测概率。为了解决此问题,提出了基于模糊K means++的数据融合算法。该算法首先引入模糊处理机制处理样本的数据特征值,以此来增加样本间的差异性;然后将模糊处理后的数据发送到融合中心,融合中心采用离群点挖掘的方法排除恶意用户,并对保留下来的用户进行融合,使样本向量具有鲁棒性;最后运用K means++算法对样本向量进行聚类。该算法利用轮盘法选择聚类中心,可有效抵御恶意用户的攻击,提高系统感知性能;无需知晓信号与噪声的分布等一些先验信息,也避免了繁杂的门限推导。从仿真结果可以看出,该算法对抵御恶意用户攻击具有突出的效果,有效提升了协同频谱感知系统的稳定性和鲁棒性。     

传感器网络节点随机调度算法的覆盖度分析

节点随机调度算法,是一种研究网络局部覆盖的重要方法,它无需知道节点位置信息、简单易用并有较高的实用价值。但是目前对该算法的研究成果均是基于布尔感知模型设计。布尔感知模型尽管简单,但不能真实地反映节点的感知能力。文中首次在概率感知模型下,分析了随机调度算法中网络覆盖度与节点数之间的关系,给出了节点数的下界值,并通过仿真验证了分析的正确性。     

高效的基于段模式的恶意URL检测方法

提出一种高效的基于段模式的检测恶意URL的方法,该方法首先解析已标注的恶意URL中的域名、路径名和文件名3个语义段,然后通过建立以三元组为词项的倒排索引快速计算恶意URL每个语义段的模式,最后基于倒排索引查找到的段模式来判定给定的URL是否是恶意URL。不仅如此,该方法还支持基于Jaccard的随机域名识别技术来判定包含随机域名的恶意URL。实验结果表明,与当前先进的基准方法相比,该方法具有较好的性能和可扩展性。     

WSANs中基于蜂巢结构的移动容错恢复算法

旨在研究无线传感器与执行器网络(WSANs)中节点失效情况下恢复执行器(actor)节点服务的算法. 首先说明了WSANs中的实时覆盖模型, 证明WSANs覆盖恢复问题是NP难问题, 给出了近似求解方案. 在此基础上, 提出了一种基于六边形蜂巢结构的移动容错算法HMFR用于恢复失效actor节点, HMFR 算法在限制网络初始部署的条件下拥有很好的性能. 通过实验与现有的恢复算法进行比较, 发现HMFR算法在actor覆盖sensor节点数和移动距离方面有更好的性能.     

粒子群算法优化神经网络结构的研究

针对BP神经网络初始权阈值确定的随机性和隐含层节点数的不确定性,通过利用十进制粒子群优化算法（DePSO）和二进制粒子群优化算法（BiPSO）,同时优化神经网络的初始权阈值和结构。通过粒子群优化算法首先确定一个较好的搜索空间,然后在这个解空间里利用BP算法对网络进行训练和学习,搜索出最优解。通过函数拟合数值实验对该模型来进行训练和测试,相比其他算法,该模型可以获得较高的预测精度,结果表明该方法是可行的。