WiNoC中无线节点和无线链路级拥塞避免的高效路由器设计

无线片上网络中,无线网络拥塞可以分为无线节点级的拥塞和无线链路级的拥塞,这两种拥塞都会造成网络性能下降.针对无线节点和无线链路级拥塞,本文设计了一种拥塞避免的高效无线路由器,首先提出了节点级的拥塞避免机制,无线节点通过广播本地拥塞信息相互感知拥塞程度,避免向拥塞程度较高的无线节点发送数据包;其次提出了链路级拥塞避免机制,在无线接口中设计了并行FIFO,允许无线接口以流水的方式,在单个时钟周期内传输一个拥塞信息数据包和三个数据微片,数学建模证明使用并行FIFO至少降低50%无线信道竞争频率,从而避免了无线链路级拥塞,提高了无线资源利用率.实验表明本文方案相较普通无线路由器增加了少量的面积,但是在网络整体性能、无线路由器性能以及功耗方面都取得了不错的优势.     

三维片上网络故障及拥塞感知的容错路由器设计

 三维片上网络中路由器的输入端口和交叉开关出现故障,将严重影响整个网络的性能,因此文章提出了一种故障及拥塞感知的容错路由器.通过增加一个冗余的输入端口和旁路总线,不仅能实现对输入端口和交叉开关容错的目的,而且还能在没有端口故障的情况下使用冗余端口有效地解决拥塞问题.实验表明此容错机制能够使得网络在故障路由器多、拥塞严重的情况下,仍然保持良好的性能.     

针对路径故障与局部拥塞的NoC容错路由算法

片上网络作为一种新型片上互连架构,克服了片上系统在发展中遭遇的瓶颈问题。然而,片上网络中的路由器故障以及路由器之间的链路故障都会造成网络性能损失。对此,文章提出一种针对路径故障与局部拥塞的NoC容错路由算法。首先,设计了一种相隔节点间路径故障模型,该模型下的路由器以较小的开销为代价,动态感知两跳以内的路径故障状态。其次,提出了一种新颖的更能准确反映局部网络拥塞状态的拥塞模型来均衡网络流量。最后,当网络无故障时,算法保证走最优路径;有故障时,算法不仅可以实现容错还能保证网络具有良好的性能。实验表明,在无故障的情况下,本文方案相较于对比对象延迟降低了10％～20％,吞吐率提高了25％左右。在有故障的情况下,本文方案较对比对象的优势更加明显。     

基于Q学习的无线传感网络自愈算法

无线传感网络存在关键区域节点能量消耗过快,节点能量供应有限以及通信链路拥塞等问题,容易造成节点故障和路由破坏。为减小上述问题对网络传输造成的影响,提出一种基于Q学习的无线传感网络自愈算法,通过引入Q学习的反馈机制,动态感知网络的状态信息,当故障发生时,自适应地选择恢复路径,保证数据实时顺利传输。仿真结果表明,该算法降低了错误选择故障或拥塞路径的概率,在故障感知、故障恢复和延长网络寿命等方面,表现出了良好的性能。     

基于网络监控器的专用片上网络动态容错路由

针对专用片上网络（Network on Chip,NoC）全局通信事务管理和可靠性设计问题,提出片上网络监控器的概念,用于获取全局网络实时状态信息及执行路径分配算法,基于此提出一种动态路由机制DyRS-NM.该机制能检测和定位NoC中的拥塞和故障链路,并能区分瞬时和永久性链路故障,采用重传方式避免瞬时故障,通过重新路由计算绕开拥塞和永久性故障.设计实现了RTL级网络监控器和与之通信的容错路由器模块,并将MPEG4解码器应用映射至基于网络监控器的4×4Mesh结构NoC体系结构中,验证了系统性能以及面积功耗开销.相比静态XY路由和容错动态路由FADR,DyRS-NM机制在可接受的开销代价下获得了更优的性能.     

WiNoC中EF-ACK容错无线接口设计

无线片上网络中的无线信道面临着严重的可靠性挑战,无线路由器的容错设计对整个片上网络的传输效率有着较大的影响.本文提出一种EF-ACK容错无线接口设计,将多条确认信息配置在一个数据包内,通过无线信道传递确认信息数据包;在无线接口处设立重传数据缓冲区,以更高效的方式确认数据以及控制错误数据包的重传;另外,提出了基于网络状态的编解码控制,在网络情况较差时用BCH编码的方式提高数据的鲁棒性.实验表明,本文方案使用了较小的额外面积和功耗开销,高效地完成了对于数据的无线确认反馈,且在错误率较高时,可以保证网络中较低的网络延迟和较高的饱和吞吐量,大大提高了网络的性能.     

WiNoC中交叉开关仲裁及路由算法设计

WiNoC(Wireless Network-on-Chip)中的无线路由器面临着比传统片上路由器更加严峻的拥塞问题,平衡有线/无线链路负载是当前无线片上网络的研究热点之一.为此本文提出并设计了一种基于优先级的交叉开关仲裁方案PbSA(Priority based Switch allocator),其将优先级更高的无...     

Zigbee无线组网的技术与设计

随着科学技术的飞速发展,无线通信技术在通信领域中的应用越来越广泛,与传统的通信技术相比具有传输速度快、传输效果好、信息存储量大等优势,受到了各个领域的高度重视。Zigbee无线组网技术是一种新型的通信技术,在网络设计的过程中采用了无线自组织网络的概念,即使在使用的过程中出现故障问题,也能够及时的进行自我修复和自我组织,不会对无线通信系统的功能造成影响。本文将针对Zigbee无线组网技术的应用进行深入分析。     

基于片上网络的容错通信方法

本文提出了一种基于片上网络的容错通信算法。若NoC中出现路由器或者链路故障,将导致NoC不能有效地进行通信。本方案为每个路由器的输出端口配置输出状态寄存器,标识出输出端口所连接的路由器或链路的故障状态,从而建立起一个新的容错模型,在路由时采用新的可重构路由算法避免这些故障的路由器和链路,从而达到保证NoC有效通信的目的。本文在5×52D-Mesh结构上仿真了所提出的方案,统计了数据传输时延,实验结果表明,与现有方案相比,这种方法能够在保证容忍NoC中路由器和链路故障的前提下,获得较低的通信时延。     

采用无线自组网技术的远程苔藓微雾灌溉系统

设计了一种适用于高寒干燥坡地开展苔藓人工培育的智能微雾灌溉系统,通过ZigBee节点进行无线数据传输、以蛛网架构实施通信故障中断后的自主连接,实现了节水、节能、无人化远程管控的目标。系统底层的信息采集单元所获取的多种实时环境参数信息经ZigBee无线通信网络传递到信息处理节点,形成智能化的微雾灌溉模式指令,控制雾化喷灌的强度、时间,并将当前水窖的水量信息传输至远程控制端。当某个底层终端或路由器受外界影响而出现通信故障时,系统能够自行搜寻附近的路由器或协调器,确保网络数据传输的实时性和准确性。     

CPCA: An efficient wireless routing algorithm in WiNoC for cross path congestion awareness

Wireless network-on-chip (WiNoC) is a new paradigm to mitigate the long-distance transmission latency for conventional wired network-on-chip. The wireless routers in WiNoC have to handle a large number of packets which could cause data congestion, thus reducing the network performance. In this paper, we propose a novel wireless routing algorithm, called CPCA, which exploits the cross path congestion information as hints to route the packets. Under CPCA, the whole network is partitioned into sub-networks. In each subnet, the congestion information of the wireless router is propagated along the cross path. As a result, the routers in the same dimension can get the congestion degree of wireless router within the subnet. Based on the congestion information, CPCA can compute the suitable path for packets routing, which can prominently avoid the congestion aggravation in the wireless router. Experimental results show that our proposed method can effectively improve performance in terms of packets transmission latency and network throughput.     

基于DV-Hop的RSSI-Hop算法

在无线传感器网络中,监测到时间之后关心的一个重要问题就是该事件发生的位置。传感器节点能量有限、可靠性差、节点规模大且随机布放、无线模块通信距离有限,对定位算法和定位技术提出了很高的要求。针对随机布放、节点配置低的无线传感器网络,提出一种新的RSSI-Hop定位方法,该方法可以在不增加硬件开销的基础上,有效降低节点能量消耗,较准确地估算未知节点到参考节点之间的距离,减少累积误差,提高定位的准确性。其主要思想是,节点信息根据RSSI强弱,估算各节点到信标节点之间的距离。实验表明,新算法比以前的算法定位更准确。     

Online Distributed Fault Diagnosis in Wireless Sensor Networks

This paper presents an online fault diagnosis algorithm for wireless sensor networks. This work explicitly takes into account the possibility of faults in different sections of sensor networks and communication channel. The diagnostic local view is obtained by exploiting the spatially correlated sensor measurements. These local views are then disseminated using a spanning tree of cluster heads. Our algorithm is shown to be energy efficient as it works in conjunction with the normal network activities and requires minimum additional diagnostic messages to be exchanged. Simulation results show that the performance of our algorithm is less sensitive to the average node degree for a wide range of fault rates.     

基于无线传感网的公交拥挤指数实时播报系统

公交拥挤指数实时播报属于城市交通环境实时播报系统范畴。公交剩余座位、车内CO2浓度、车厢底部单位面积受力等信息可有效的反映当前公交内部的拥挤情况。针对目前交通系统中没有技术相对成熟的拥挤指数实时播报系统,提出了一种基于无线传感网的公交拥挤指数实时播报系统。系统分为无线传感器网络节点、SINK节点、终端分析评估系统和站台显示平台4个子模块,传感器网络节点实时采集公交内部运营环境数据,通过射频和GPRS无线通信模式将数据传递给终端评估系统分析并向该条公交线路的所有站台做出播报,方便出行市民选择最佳的交通线路。文章给出了系统的总体结构和4个子系统的软硬件设计,实现了城市公交内部运营环境的实时播报。     

专用无线IP城域网的网络层设计与实现

无线数据业务具有广泛的应用需求。基于有线基础网络,提出了可以覆盖全市范围的无线IP网络的网络层设计方案。首先介绍了系统结构和各组成部分,详细介绍了系统控制中心(SCC)和业务处理中心(SPC)的功能,并给出了SPC详细的业务处理流程。随后,分析了移动终端切换时的切换过程,并通过模拟实验证实了该方案的可行性。该方案是对原有有线网络进行了扩展,支持无线IP终端在全网内的移动接入,它对现有网络的改动较小,支持多部门共享专用无线资源等特点。     

基于规则推理的输电网络故障诊断方法

当输电网络有故障发生,如果处理不当,或者报警信息错误,就会产生难以预计的损失和后果,故电网故障诊断成了重要的研究对象?针对上述问题,文中提出了一种基于规则推理的输电网络故障诊断方法,通过建立电力网络的Petri网模型,分析故障变迁的可诊断子网结构信息和内部托肯的流动形式,经过相关故障规则推理来定位故障元件位置,并运用IEEE-14局部电路图进行了故障仿真运算。仿真结果表明,规则推理故障诊断方法能够对输电网络故障进行精确迅速的故障定位与分析,是一种有效可行的故障诊断方法?     

基于博弈论的井下监测网QoS路由算法研究

构建煤矿井下无线视频监控系统可有效弥补有线监控系统的不足,以无线多媒体传感器为网络节点设计了煤矿井下无线视频监控系统,针对传感器存在潜在的自私性特点,引入博弈理论思想,建立一种无线多媒体传感器网络QOS路由的博弈模型,并将其融入到路由协议中,提出了一种博弈路由协议,仿真实验表明能够有效均衡节点能量消耗,克服传感器自私性...     

Fault diagnosis and its prediction in wireless sensor networks using regressional learning to achieve fault tolerance

Due to the wide range of critical applications and resource constraints, sensor node gives unexpected responses, which leads to various kind of faults in sensor node and failure in wireless sensor networks. Many research studies focus only on fault diagnosis, and comparatively limited studies have been conducted on fault diagnosis along with fault tolerance in sensor networks. This paper reports a complete study on both 2 aspects and presents a fault tolerance approach using regressional learning with fault diagnosis in wireless sensor networks. The proposed method diagnose the different types of faulty nodes such as hard permanent, soft permanent, intermittent, and transient faults with better detection accuracy. The proposed method follows a fault tolerance phase where faulty sensor node values would be predicted by using the data sensed by the fault free neighbors. The experimental evaluation of the fault tolerance module shows promising results with R² of more than 0.99. For the periodic fault such as intermittent fault, the proposed method also predict the possible occurrence time and its duration of the faulty node, so that fault tolerance can be achieved at that particular time period for better performance of the network.     

Multifault diagnosis in WSN using a hybrid metaheuristic trained neural network

Wireless sensor networks are susceptible to failures of nodes and links due to various physical or computational reasons. Some physical reasons include a very high temperature, a heavy load over a node, and heavy rain. Computational reasons could be a third-party intrusive attack, communication conflicts, or congestion. Automated fault diagnosis has been a well-studied problem in the research community. In this paper, we present an automated fault diagnosis model that can diagnose multiple types of faults in the category of hard faults and soft faults. Our proposed model implements a feed-forward neural network trained with a hybrid metaheuristic algorithm that combines the principles of exploration and exploitation of the search space. The proposed methodology consists of different phases, such as a clustering phase, a fault detection and classification phase, and a decision and diagnosis phase. The implemented methodology can diagnose composite faults, such as hard permanent, soft permanent, intermittent, and transient faults for sensor nodes as well as for links. The proposed implementation can also classify different types of faulty behavior for both sensor nodes and links in the network. We present the obtained theoretical results and computational complexity of the implemented model for this particular study on automated fault diagnosis. The performance of the model is evaluated using simulations and experiments conducted using indoor and outdoor testbeds.