传感反应网络中单反应节点任务分配问题的研究

无线传感反应网络由大量资源受限的传感节点和少量资源丰富的反应节点通过无线通信自主形成的。其中,传感节点负责收集外界的事件信息,而反应节点依据传感节点探测到的信息做出决策,并对外界做出响应。本文主要研究WSANs中单反应节点任务分配的问题,并提出集中式和分布式两种算法,最后用J-Sim评估两种算法的性能。     

多移动汇聚节点的无线传感网中基于服务质量的能耗

针对无线传感网（WSN）中传感节点能耗过高、传输时延过长、数据完整性过差的问题,提出一种基于多移动汇聚节点考虑服务质量（QoS）的路由算法——时延敏感和数据完整性（MSTSDI）算法。首先,通过从基站接收信号的强度判断节点的密度,用K-means聚类算法将传感网划分成自治区域;其次,给每个自治区域分配一个移动汇聚节点,利用支持向量回归（SVR）的方法确定移动汇聚节点的轨迹;最后,引入深度引力域和队列引力域,通过Improved-IDDR算法对时延敏感数据包和数据完整性要求高的数据包进行传输。理论分析和仿真表明,与GLRM算法和LEACH算法相比,Improved-IDDR算法的路由策略的能耗下降幅度分别为21.2%和23.7%;而该算法的时延分别降低了15.23%和17.93%;该算法的所传输数据包的完整性也更好。实验结果表明,MSTSDI在传感网络中能够有效提高系统的性能。     

有向传感网络中通信节点分类方法研究与仿真

研究有向传感网络通信节点的分类优化问题.网络通信节点众多,在很多属性上有着较强的相似性.传统的通信节点分类方法多是运用属性上的相似性进行分类,没有考虑通信节点的传播方向,有向传感网络中的通信节点分类忽略了传递方向,造成分类过程较为粗糙,结果不准.为了避免上述传统算法的弊端,提出了一种基于密度自适应粒子群算法的有向传感网络通信节点分类方法.利用信息熵方法能够建立自适应粒子群,并且对粒子群中的粒子进行更新,保证粒子群的多样性,提高通信节点分类算法的局部寻优能力,从而完成有向传感网络的通信节点分类.实验结果表明,利用改进算法进行有向传感网络的通信节点分类,能够提高分类的准确性,从而满足实际有向传感网络的需求.     

一种无线传感网的Sink节点移动路径规划算法研究

为寻找传感节点均匀分布时Sink节点的最优移动路径和最大网络生存时间,提出一种无线传感网的Sink节点移动路径规划算法(MPOA).在MPOA算法中,将Sink节点的数据收集范围分解成多个圆环,将监测区域分解成多个网格.根据Sink节点的停留位置和多跳通信方式,采用数学公式表示每一个网格的单位节点能耗,从而获得Sink节点移动的网络生存时间优化模型.采用修正的混合粒子群算法求解该优化模型,获得网络生存时间、Sink节点的停留位置和移动路径的最优方案.仿真结果表明:MPOA算法可寻找到Sink节点的最优移动路径,从而平衡网络能耗,提高网络生存时间.在一定的条件下,MPOA算法比Circle,Rect和Rand算法更优.     

无线传感网络移动节点位置并行微粒群优化策略

网络节点位置优化是无线传感网络研究的核心问题之一.无线传感网络通常由固定节点和少量移动节点构成,传统的虚拟力导向算法无法解决固定节点对移动节点优化的约束.该文针对这一问题,提出了基于并行微粒群算法的优化策略.微粒群算法具有适于解决连续空间多维函数优化问题、能快速收敛至全局最优解的特点.并行框架提高了算法的运行效率,降低了算法的运算复杂度,使算法能够满足无线传感网络的需求.通过并行微粒群算法搜索不同状态下无线传感节点的最优位置,使无线传感网络能够利用移动节点实现网络结构的动态重组,最大化网络覆盖范围,提高网络测量可靠性.实验证明,并行微粒群优化策略能快速有效地实现无线传感网络移动节点位置优化.     

多智能体蝙蝠算法在无线传感器中的应用

针对无线传感器网络(WSNs)节点的定位误差较大的问题,提出了一种新的具有局部搜索能力强的多智能体蝙蝠算法.改进算法中对寻优蝙蝠个体融入多智能体技术,通过邻域竞争合作算子以及自学习过程提高了算法全局搜索能力,避免算法陷入局部最优,加快算法的收敛速度.通过对标准测试函数的仿真,改进算法相比于其他算法,寻优精度和进化效率得到了较大的提高.随后采用多智能体蝙蝠算法求解无线传感节点定位问题,仿真结果表明改进算法减少了测距误差对定位精度的影响,提高了未知节点定位的精度,为无线传感网络节点定位的实际应用提供理论参考.     

面向智慧教室的无线传感网边缘节点智能部署方法

摘 要: 为降低部署后的通信时延,提高智慧教室的数据发送与网络使用效率,提出面向智慧教室的无线传感网边缘节点智能部署方法。以智慧教室场景中良好的通信、最大限度降低部署边缘节点成本为优化目标,构建边缘节点智能部署的目标函数。针对目标函数设定流量约束条件、无线传感网数据流约束条件、节点计算能力约束条件。自适应调整粒子群优化算法的惯性权重、粒子更新速度、Pareto最优解保存策略,设计多目标改进粒子群优化算法求解目标函数,实现面向智慧教室的无线传感网边缘节点智能部署。测试结果表明,该方法的时延较低,网络计算能力较高,保证了智慧教室无线传感网通信和传输质量。     

基于概率感知模型的多节点联合网络覆盖算法

针对传感网在对关注目标节点进行覆盖过程中出现的非连续性覆盖，将会产生覆盖盲区以及大量冗余数据迫使通信链路出现瓶颈现象，提出了一种基于概率感知模型的多节点联合覆盖算法（Multi-Node Joint Network Coverage Algorithm，MNJC）。该算法借助于概率相关知识，给出了多节点联合时覆盖质量期望值计算方法，以减少覆盖盲区的产生，提高了多节点联合网络覆盖率；利用节点动态转换机制对节点部署进行重新优化，抵制了网络能量的快速消耗，延长网络生存周期；该算法与其他三种算法覆盖率、生存周期以及节点存活数量等方面进行了比对实验，其性能指标平均提升了18.11%，15.57%和8.26%，从而验证该算法的有效性和实效性。     

基于Q-Learning反馈机制的无线传感网络通信节点自愈算法

针对目前无线网络通信节点自愈能力差,以及自愈后网络流量出口带宽低的问题,提出基于Q-learning反馈机制的无线传感网络通信节点自愈算法。通过计算网路节点的RSSI值建立节点衰减模型,通过质心算法完成节点定位;应用Q-learning学习算法获取链路选取策略,完成节点传输过程路径时延、吞吐量以及丢包率的计算,建立网络节点模型提取链路反馈机制,利用Q-learning学习算法进行迭代计算,实现无线传感网络的通信节点自愈。仿真分析表明,运用该算法自愈网络通信节点时,当检测次数为100时,检测出的节点自愈数量为280个,节点拓扑移动距离平均值为175 m,网络流量出口带宽平均值为550 Mbyte/s,证明该算法的节点自愈能力高。     

多汇聚节点无线传感网络充电路径多目标优化方法

针对多汇聚节点无线传感网络中因汇聚节点位置固定、能量消耗过快而导致的网络生存周期短的问题,提出了一种考虑路径总距离和消耗总能量的双重目标下的节点充电路径优化方法。该方法充分考虑了节点剩余能量以及节点与节点间的距离,通过等价目标转化和加权处理,建立了多目标优化问题模型,并使用改进的遗传算法来求解该多目标优化问题模型。仿真结果表明,该方法可以持续地为汇聚节点补充能量,有效的延长网络生存周期。与传统的遗传算法相比,该算法最优解精度更高,收敛速度更快,种群进化效果更好。     

无线感知执行网的智能协作机制研究

针对无线感知执行网中的节点协作问题,提出了多目标智能协作策略。在感知节点不完全连通的WSANs中,设计局部感知节点成簇算法,实现感知节点间的通信协作;以网络能耗均衡和信息传输时延为目标,应用离散多目标自由搜索算法对WSANs进行实时路径规划,实现簇头节点和执行节点间的协作,并通过路径惩罚策略和路径均衡策略,保证寻优结果的有效性、均衡执行节点之间的能耗。仿真实验表明:无线感知执行网智能协作机制能有效地选择多执行节点路径,实现网络协作的实时需求与能量消耗之间的平衡。     

多传感器数据融合在飞机地面防撞中的应用

针对我国目前对于飞机在地面经常发生的相撞或刮蹭方面的研究较少的现实情况,研究用多传感器数据融合的方法实现飞机的地面防撞。根据现有的多传感器数据融合理论,在分析数据融合过程的基础上,建立混合式数据融合模型,结合模糊神经网络方法实现面向目标的数据融合算法。同时,选用毫米波雷达、红外传感器等多类进行地面多目标的身份识别和威胁评估,从理论上验证了多传感器数据融合思想在飞机地面防撞理论中的可行性,为进一步建立飞机地面防撞系统提供理论基础。     

Clustering of wireless sensor and actor networks based on sensor distribution and connectivity

Wireless Sensor and Actor Networks (WSANs) employ significantly more capable actor nodes that can collect data from sensors and perform application specific actions. To take these actions collaboratively at any spot in the monitored regions, maximal actor coverage along with inter-actor connectivity is desirable. In this paper, we propose a distributed actor positioning and clustering algorithm which employs actors as cluster-heads and places them in such a way that the coverage of actors is maximized and the data gathering and acting times are minimized. Such placement of actors is done by determining the k-hop Independent Dominating Set (IDS) of the underlying sensor network. Basically, before the actors are placed, the sensors pick the cluster-heads based on IDS. The actors are then placed at the locations of such cluster-heads. We further derive conditions to guarantee inter-actor connectivity after the clustering is performed. If inter-connectivity does not exist, the actors coordinate through the underlying sensors in their clusters to adjust their locations so that connectivity can be established. The performances of the proposed approaches are validated through simulations.     

Maximizing connected coverage via controlled actor relocation in wireless sensor and actor networks

Wireless sensor and actor networks (WSANs) have recently emerged with the idea of combining wireless sensor networks (WSNs) and mobile ad hoc networks (MANETs). In addition to resource constrained sensors, resource rich and mobile actor nodes are employed in WSANs. These actors can collect data from the sensors and perform appropriate actions as a result of processing such data. To perform the actions at all parts of the region in a timely manner, the actors should be deployed in such a way that they might be able to communicate with each other and cover the whole monitored area. This requires that the actors should be placed carefully prior to network operation in order to maximize the coverage and maintain the inter-actor connectivity. In this paper, we propose a distributed actor deployment algorithm that strives to maximize the coverage of actors without violating the connectivity requirement. The approach applies repelling forces between neighboring actors and from the sensors that sit on the boundaries in order to spread them in the region. The spreading of the nodes is done using a tree of actors which can provide more freedom for the movement of the nodes but at the same time maintain the required connectivity among the nodes. We present two techniques for creation of such an actor tree which are based on local pruning of the actor links and spanning tree of the inter-actor network. The performance of our approach is validated both analytically and experimentally.     

带执行器节点的无线传感器网络的分簇算法

带执行器节点的无线传感器网络(WSAN)是指在无线传感器网络中加入执行器,传感器用于检测物理环境信息,执行器收集和处理这些检测数据,并作出适当的执行任务。传感器和执行器的协作是WSAN研究的一个重要内容,就此提出了一个动态分簇算法,根据事件发生的实际情况,仅仅对该事件区域分簇,每个簇包括一个执行器节点以及传送数据到该执行器节点的传感器节点。通过这种分簇,可以决定传感器与哪个执行器通信以及路由方式。     

What is the effective key length for a block cipher: an attack on every practical block cipher

Recently,several important block ciphers are considered to be broken by the brute-force-like cryptanalysis,with a time complexity faster than the exhaustive key search by going over the entire key space but performing less than a full encryption for each possible key.Motivated by this observation,we describe a meetin-the-middle attack that can always be successfully mounted against any practical block ciphers with success probability one.The data complexity of this attack is the smallest according to the unicity distance.The time complexity can be written as 2k(1-),where>0 for all practical block ciphers.Previously,the security bound that is commonly accepted is the length k of the given master key.From our result we point out that actually this k-bit security is always overestimated and can never be reached because of the inevitable loss of the key bits.No amount of clever design can prevent it,but increments of the number of rounds can reduce this key loss as much as possible.We give more insight into the problem of the upper bound of effective key bits in block ciphers,and show a more accurate bound.A suggestion about the relationship between the key size and block size is given.That is,when the number of rounds is fixed,it is better to take a key size equal to the block size.Also,effective key bits of many well-known block ciphers are calculated and analyzed,which also confirms their lower security margins than thought before.The results in this article motivate us to reconsider the real complexity that a valid attack should compare to.     

基于容量约束的WSANs路由算法的研究

在无线传感反应网络通过引入具有丰富资源、甚至可以移动的反应节点,极大地增强了现有的无线传感器网络的功能和应用范围.针对距离反应节点越近传感节点能量消耗越快的问题,研究了基于容量约束的最大跳数最小化的问题,并在此基础上提出了一种全局近似算法.仿真实验表明,该算法能通过设置节点的容量约束,限制节点每轮转发某个事件的最大报文数,以降低单个节点的最大能耗,达到延长网络生存时间的目的.     

An Energy Efficient Clustering Scheme for Data Aggregation in Wireless Sensor Networks

In wireless sensor networks, a clustering scheme is helpful in reducing the energy consumption by aggregating data at intermediate sensors. This paper discusses the important issue of energy optimization in hierarchically-clustered wireless sensor networks to minimize the total energy consumption required to collect data. We propose a comprehensive energy consumption model for multi-tier clustered sensor networks, in which all the energy consumptions not only in the phase of data transmissions but also in the phase of cluster head rotations are taken into account. By using this new model, we are able to obtain the solutions of optimal tier number and the resulted optimal clustering scheme on how to group all the sensors into tiers by the suggested numerical method. This then enables us to propose an energy-efficiency optimized distributed multi-tier clustering algorithm for wireless sensor networks. This algorithm is theoretically analyzed in terms of time complexity. Simulation results are provided to show that, the theoretically calculated energy consumption by the new model matches very well with the simulation results, and the energy consumption is indeed minimized at the optimal number of tiers in the multi-tier clustered wireless sensor networks.     

MIMO网络控制系统的稳定性与控制器设计

对于具有多个独立传感器与执行器的多输入多输出网络控制系统,在具有多重时滞的情况下,建立一类网络控制系统的连续时间模型。通过构造一个新的李雅普诺夫函数,给出一个具有较低保守性的稳定性判据。基于该稳定性判据,给出输出反馈控制器的设计方法,数值仿真结果表明了该方法的有效性。     

无线传感器网络中移动协助的数据收集策略

利用移动数据收集器(mobile data collector,简称MDC)进行传感器网络中感知数据的收集,可以有效地减少传感器将数据发送到静止基站的传输跳数,节约网络的能量,延长网络寿命.此外,MDC通过循环收集传感器数据或承担数据转发的功能,避免节点间由于多跳传输引起的能量空洞(energy hole)以及节点失效造成的传输链路中断等问题.MDC的移动性也为无线传感器网络的研究带来新的挑战.研究基于移动协助数据收集的无线传感器网络结构,分类总结了近年来提出的一些典型的基于MDC的算法和协议,着重讨论了MDC在网络能量、延迟、路由和传输等方面带来的性能变化.最后,进行了各种算法的比较性总结,针对传感器网络中MDC的研究提出了亟待解决的问题,并展望了其未来的发展方向.