合作性DTN的基于系统效益的内容分发

近年来在时延容忍网络(delay tolerant network,DTN)中的数据分发成为研究热点.由于DTN节点之间不存在持续的端到端连接,节点通常采用"存储-搬运-转发"的方式进行数据递交.当两个节点相接触时,节点如何为空间有限的缓存选择存储内容是一个关键问题,这关系到整个DTN网络的分发性能.但在分布式动态的DTN环境下,每个节点难以找到全局最优的存储选择.基于这个原因,全局效益最大化问题被转变为每次接触时的效益增益最大化问题,然后将转化后的问题形式化为0-1背包问题,并设计了一种启发式贪婪算法来进行求解,使得每个节点在与其他节点发生接触时,能够依据自身维护的局部网络信息来选取转发内容,从而最大化系统分发效益的增益.此外进一步详细分析了节点维护的网络信息的范围与节点对转发内容选择之间的关系.基于Trace的仿真结果表明,与SocialCast算法相比,启发式算法可以有效地提高节点对订阅内容的接收率和降低接收时延,并且随着节点维护的网络信息范围的增大系统效益也不断增大.     

多个无线传感器网络中节能MAC协议设计

在无线传感器网络中,媒体访问控制(MAC)层协议影响着整个网络的性能。根据无线传感器网络对节点能耗和时延的要求,本文提出了一种基于跨层设计的节能MAC协议。利用物理层、MAC层和路由层之间的信息交互,在保证可靠通信的基础上,实现在一个监听/睡眠周期内数据多跳传输,缩短数据传输时延,并且有效控制网络数据传输的冗余度,降低冗余节点能量消耗。性能分析和仿真结果表明,节能MAC协议能够有效地降低网络时延并且减少节点能耗。     

基于最短时延的认知无线电网络安全路由算法

针对认知无线电网络可用频谱动态变化和路由过程中出现的安全问题,提出将最短数据传输时延和高安全等级路由作为选路标准,设计了一种跨层路由选择算法。算法通过优化转发节点个数和各转发节点接收、发送数据信道来减少数据传输时延。通过选择具有高安全级别的转发节点来保证路由的安全性。理论分析表明算法是高效和可行的。它的时间复杂度是O(N2),其中N是拓扑图中节点的个数。     

Ad Hoc网络中具有远距离通信能力的方向性MAC协议

针对在使用方向性天线的AdHoc网络中没有合理利用远距离传输优势的问题,提出了一种支持远距离通信的方向性MAC协议.在该方向性MAC协议中,中间节点会利用一种远距离定向算法帮助发送节点获取位于两跳远的目的节点的定向信息从而建立远距离链路,这种链路的建立有助于解决局部拥塞,同时降低传输延时.本文中根据MAC层能否获得信号接收功率信息提出了两种LRO算法:基本LRO算法不使用信号接收功率信息,仅利用几何关系获得近似的远距离节点定向信息;而LRO-PI(LRO with power in for mation)算法在利用信号接收功率信息后可以进行更加准确的远距离节点定向.性能分析表明,在合理地选择相关参数后,LRO算法有着很高的定位准确性.并且仿真结果表明远距离链路的建立可以有效地提高网络的吞吐量,并且降低端到端延时.     

基于接收信号强度的无线传感器网络组网协议

受应用场合和节点结构的限制,无线传感器网络的算法应具有低功耗和低复杂度等特点,为此提出了一种基于接收信号强度的分布式组网协议(RSSIOP)。节点不需要位置信息,通过接收基站定向天线扫描时发送的信息完成定位和分簇过程;根据剩余能量和接收信号强度竞选簇头,形成直接链路;根据接收信号强度,成员节点选择转发节点,形成转发链路;按照网络用户的时延要求成员节点自动选择直接链路或转发链路向簇头传输数据。此外,RSSIOP设计了簇头轮换和失效节点的处理机制。该算法可以减小节点的发射功率,分担簇头在数据接收和融合两方面的工作负荷。针对不同场景进行了仿真,结果证明了算法的有效性。     

应用于桥梁监控的无线传感器网络MAC协议

为保证监控桥梁任务的实时性,提出了一种多优先级多信道的无线传感器网络MAC(MPMC-MAC)协议。MPMC-MAC协议结合节点接收的数据类型和节点发送信息频率为其分配优先级,再根据优先级及信道状况对节点进行信道分配,以确保优先级高的节点优先发送数据。当信道冲突或节点通信受到干扰导致信息需要重传时,采用信道再分配技术。该技术主要根据节点的优先级、节点的剩余能量及其重传次数重新对节点分配信道,保证了信道分配的公平性。另外,该协议通过动态调整节点的活跃周期和睡眠周期的时长以节省能量,并减少传播时延。仿真结果表明,MPMC-MAC协议在网络吞吐量、平均传播时延以及节点的能量消耗等性能方面均优于Hybrid MAC(HyMAC)、Zebra MAC(ZMAC)及IEEE802.15.4 MAC协议。     

一种改进的井下无线传感器网络广播算法

针对现有的井下无线传感器网络广播算法能耗较高的问题,提出了一种改进的无线传感器网络前向广播算法。该算法中,转发节点可根据自身位置,选择优先节点转发信息,大大减少了无线传感器网络中需要转发的冗余信息量,从而降低了网络能耗。仿真结果表明,该算法在网络节点密度达到一定值时,满足网络接收率要求,且能够限制转发的信息总量,有效降低了无线传感器网络能耗。     

机会网络中基于社区的高效消息传输算法

针对机会社会网络中现有基于社区的消息传输算法(OSNCMTS)在社区内存在节点积压消息分发任务、在社区间存在盲目选择活跃节点转发信息的问题,提出一种基于社区的高效机会网络路由算法(HECMTS)。在HECMTS算法中,首先,采用极值优先(EO)算法划分社区并将划分的社区矩阵分发给社区节点;其次,采用社区矩阵和消息到目的节点成功率相结合的方法分配社区内消息副本数;最后,利用活跃节点往返不同社区的机会收集活跃节点信息,然后查询这些活跃节点信息从中选取合适的活跃节点完成社区间消息传输。仿真结果表明,与OSNCMTS算法相比,HECMTS算法在路由开销性能和平均端到端时延性能上分别至少下降了19%和16%。     

一种基于跨层设计的无线传感器网络自适应概率广播策略

针对盲目泛洪带来的广播风暴问题,提出了一种基于跨层设计的无线传感器网络自适应概率广播策略(APBCL).APBCL通过MAC层接收信号的强度估算发送节点和接收节点的距离,根据距离计算出转发分组的概率.APBCL减少了分组在网络中的重复传输,降低节点间的竞争和碰撞.仿真实验表明:相比泛洪和概率泛洪,APBCL在不牺牲网络吞吐率的前提下,提高了转播节省率,降低了平均端对端延迟和节点平均丢包率.     

移动传感网中基于密度和距离的概率广播算法

广播是移动传感器网络(mobile wireless sensor networks)中最基本的信息传播方式,但现有的广播算法在广播时需要大量中间转发节点,造成大量消息冗余转发,从而导致能量浪费.因此提出一种基于节点密度和距离的概率(broadcasting algorithm named node density and distance-based probability,NDDP)广播算法.该算法平均转发率为5S/(Nπr~2),这里S为网络区域面积,N为网络节点总数,r为通信半径.理论分析得出该算法的平均广播接收率超过95%.ns-2模拟结果表明平均广播接收率达到92%以上,并且网络节点密度越大算法的转发率越低,越节能.模拟实验结果表明NDDP算法无论在稳定性方面还是在节能性方面均优于Smite和Sidewinder中的广播算法.     

Rethinking the architecture design of data center networks

In the rising tide of the Internet of things, more and more things in the world are connected to the Internet. Recently, data have kept growing at a rate more than four times of that expected in Moore’s law. This explosion of data comes from various sources such as mobile phones, video cameras and sensor networks, which often present multidimensional characteristics. The huge amount of data brings many challenges on the management, transportation, and processing IT infrastructures. To address these challenges, the state-of-art large scale data center networks have begun to provide cloud services that are increasingly prevalent. However, how to build a good data center remains an open challenge. Concurrently, the architecture design, which significantly affects the total performance, is of great research interest. This paper surveys advances in data center network design. In this paper we first introduce the upcoming trends in the data center industry. Then we review some popular design principles for today’s data center network architectures. In the third part, we present some up-to-date data center frameworks and make a comprehensive comparison of them. During the comparison, we observe that there is no so-called optimal data center and the design should be different referring to the data placement, replication, processing, and query processing. After that, several existing challenges and limitations are discussed. According to these observations, we point out some possible future research directions.     

Currents in connectionism

This paper reviews four significant advances on the feedforward architecture that has dominated discussions of connectionism. The first involves introducing modularity into networks by employing procedures whereby different networks learn to perform different components of a task, and a Gating Network determines which network is best equiped to respond to a given input. The second consists in the use of recurrent inputs whereby information from a previous cycle of processing is made available on later cycles. The third development involves developing compressed representations of strings in which there is no longer an explicit encoding of the components but where information about the structure of the original string can be recovered and so is present functionally. The final advance entails using connectionist learning procedures not just to change weights in networks but to change the patterns used as inputs to the network. These advances significantly increase the usefulness of connectionist networks for modeling human cognitive performance by, among other things, providing tools for explaining the productivity and systematicity of some mental activities, and developing representations that are sensitive to the content they are to represent.A version of this paper was the invited address at the first annual conference of the Society for Machines and Mentality in December, 1991. A related version was presented at the Computers and Philosophy Conference in August, 1992. I am extremely grateful to Adele Abrahamsen for her numerous suggestions and comments on various drafts of this paper.     

复杂网络聚类算法在生物网络中的应用

复杂网络在现实世界中普遍存在,具有小世界性和无标度性等统计特性,网络簇结构是复杂网络重要的拓扑属性之一。在复杂生物网络中使用聚类算法揭示生物网络中的簇结构对分析生物网络的拓扑结构、预测其功能都具有重要意义。对复杂网络聚类方法在蛋白质-蛋白质相互作用网络和新陈代谢网络中的应用及其进展情况进行了综述,分析了几种聚类算法的评价函数和适用条件,并对生物网络聚类算法研究所面临的主要问题进行了讨论。     

因子分解二终端网络可靠度近似计算

因子分解算法可以用来计算网络可靠度精确值,但对于大型网络,可靠度精确值的计算非常困难。基于时间和精确度的双重考虑,在精确算法的基础上通过改进得出一种近似算法。实验结果证明,该算法得到的近似值接近精确值,而且计算时间要低于精确算法。     

A middleware for industry

This paper describes an innovative distributed framework for monitoring and control of large-scale systems by integrating heterogeneous smart objects, the world of physical devices, sensors and actuators, legacy devices and sub-systems, cooperating to support holistic management [1]. Its featured Service Oriented Architecture (SOA) exposes objects’ capabilities by means of web services, thus supporting syntactic and semantic interoperability among different technologies, including SCADA systems [23]. Wireless Sensor and Actuator Network (WSAN) devices and legacy subsystems cooperate while orchestrated by a manager in charge of enforcing a distributed logic. Particularly crafted for industrial networks are new middleware services such as dynamic spectrum management, distributed control logic, object virtualization, WSANs gateways, a SCADA gateway service, and data fusion transport capability. In addition, new application oriented objects such as shop floor, manufacturing line, welding station, robots, and cells have been introduced in the middleware. The combination of such objects and previous modules offers a new and flexible industry oriented middleware. A second contribution is in the form of traffic analysis conducted at the floor level. It shows the dominance of some end systems such as PLCs, the presence well behaved almost constant traffic made up of small packets.     

A new routing algorithm for multirate rearrangeable Clos networks

In this paper, we study the problem of finding routing algorithms on the multirate rearrangeable Clos networks which use as few number of middle-stage switches as possible. We propose a new routing algorithm called the “grouping algorithm”. This is a simple algorithm which uses fewer middle-stage switches than all known strategies, given that the number of input-stage switches and output-stage switches are relatively small compared to the size of input and output switches. In particular, the grouping algorithm implies that m = 2n+(n−1)/2^k is a sufficient number of middle-stage switches for the symmetric three-stage Clos network C(n,m,r) to be multirate rearrangeable, where k is any positive integer and rn/(2^k−1).     

无线有组织网络

无线有组织网络是网络节点间有内在关系的无基础设施的无线网络,其除具有无基础设施无线网络的特征外,网络有中心节点,且节点位置关系相对固定。这种特殊的网络应用于野外救援、军事活动等临时性通信。首先回顾了对讲机系统、无线分组网络和无线自组织网络等无基础设施无线网络的结构和特点,它们都不能包涵无线有组织网络特征。详细定义了无线有组织网的相关模型,分析了无线有组织网络的特点和研究重点。     

神经网络控制结构及所用神经网络

本文阐述了神经网络控制所能解决的控制难题,若干种控制结构及常用的动态神经网络。讨论了神经网络控制研究的重点,在实时控制中的物理可实现性及其发展等问题。     

多层前向小世界神经网络及其函数逼近

借鉴复杂网络的研究成果, 探讨一种在结构上处于规则和随机连接型神经网络之间的网络模型—-多层前向小世界神经网络. 首先对多层前向规则神经网络中的连接依重连概率p进行重连, 构建新的网络模型, 对其特征参数的分析表明, 当0 < p < 1时, 该网络在聚类系数上不同于Watts-Strogatz 模型; 其次用六元组模型对网络进行描述; 最后, 将不同p值下的小世界神经网络用于函数逼近, 仿真结果表明, 当p = 0:1时, 网络具有最优的逼近性能, 收敛性能对比试验也表明, 此时网络在收敛性能、逼近速度等指标上要优于同规模的规则网络和随机网络.     

Banet — a local area network for distributed data processing

A broadcast architecture network (Banet) suitable for distributed data processing is proposed. One feature of Banet is that the broadcast-within-a-group function is supported not only by the datalink level but by the transport or session-level network structure. The commitment control scheme is included in the network protocol. Design goals, physical structure and protocols are discussed.