动态组簇的协同定位算法

提出一种动态组簇的协同定位方法,用于基于传感器网络的目标定位和跟踪.该方法包括数据融合算法和虚拟簇漂移(virtual cluster shift,VCS)机制两部分.数据融合算法部分采用均值漂移(mean shift)算法.虚拟簇漂移机制分布式地在组织目标周围的锚节点建立临时簇.簇首管理簇成员,收集感知数据,执行融合算法.当虚拟簇无法锁定目标时,簇首指定离目标最近的簇成员担任新簇首,簇的成员也进行更替,由此将虚拟簇移动(shift)到合适的位置.分析和仿真结果显示,采用动态组簇的协同定位方法跟踪目标可以大幅度降低通信开销,产生的通信量仅为以往集中式定位算法开销的1/3.     

无线传感网中基于LEACH的节能方案

节能技术是目前无线传感网研究的热点问题。文中基于LEACH提出了一个新的无线传感网的节能方案MIMOLEACH。在方案中,簇与簇之间,簇与源节点之间传递信息是通过MIMO协作通信来完成的。与传统现有的方案相比,文中设计提出的方案的区别在于：在无线传感网选择簇头节点和协作节点的过程中,每个节点的位置和剩余能量都会被考虑进来。综合信道情况和目标分组差错率等多种因素,将最终确定参与协作通信的节点个数。将MIMO-LEACH与传统方案比较后发现,新方案在节能方面有所提高。     

PEACH: Power-efficient and adaptive clustering hierarchy protocol for wireless sensor networks

The main goal of this research is concerning clustering protocols to minimize the energy consumption of each node, and maximize the network lifetime of wireless sensor networks. However, most existing clustering protocols consume large amounts of energy, incurred by cluster formation overhead and fixed-level clustering, particularly when sensor nodes are densely deployed in wireless sensor networks. In this paper, we propose PEACH protocol, which is a power-efficient and adaptive clustering hierarchy protocol for wireless sensor networks. By using overhearing characteristics of wireless communication, PEACH forms clusters without additional overhead and supports adaptive multi-level clustering. In addition, PEACH can be used for both location-unaware and location-aware wireless sensor networks. The simulation results demonstrate that PEACH significantly minimizes energy consumption of each node and extends the network lifetime, compared with existing clustering protocols. The performance of PEACH is less affected by the distribution of sensor nodes than other clustering protocols.     

基于分簇的无线传感器网络节能路由算法

针对传感器网络能量有限的特点,提出一种基于分簇的节能路由算法—CESR算法,算法采用逐步求解的方式,将整个网络分成若干个交叠簇,整个网络有簇间和簇内两级拓扑。节点在选择路由时先根据簇间拓扑选择簇路由,当数据经过某个簇进行转发时,边界节点根据簇内拓扑产生簇内路由,算法尽量选择消耗能量值最小的路径转发数据,并且能够避免低能量节点,算法存储开销和控制消息相对较少。     

Ellipsoidal neighbourhood outlier factor for distributed anomaly detection in resource constrained networks

Anomaly detection in resource constrained wireless networks is an important challenge for tasks such as intrusion detection, quality assurance and event monitoring applications. The challenge is to detect these interesting events or anomalies in a timely manner, while minimising energy consumption in the network. We propose a distributed anomaly detection architecture, which uses multiple hyperellipsoidal clusters to model the data at each sensor node, and identify global and local anomalies in the network. In particular, a novel anomaly scoring method is proposed to provide a score for each hyperellipsoidal model, based on how remote the ellipsoid is relative to their neighbours. We demonstrate using several synthetic and real datasets that our proposed scheme achieves a higher detection performance with a significant reduction in communication overhead in the network compared to centralised and existing schemes.     

基于粒子滤波的移动物体定位和追踪算法

提出一种基于粒子滤波的目标定位算法PFTL(particle filter based target localization)以及一种基于网络覆盖问题的节点组织策略SAC(sampling aware tracking cluster formation).PFTL 的基本思想是,采用一系列带权粒子(weighted particles)来预测移动物体位置的后验分布空间,每个新时刻根据传感器的测量数据来权衡和定位目标.PFTL 通过引入误差容忍(error tolerant)的方式来存储和发送目标位置数据,使汇聚点关于物体位置信息的数据误差在一个可控的范围内,进而极大地减少网络通信负荷.SAC基于传感器采样离散化的特点来制订数据融合策略,并以最大化覆盖物体运动轨的方式动态地选取节点和进行节点簇的有效组织.模拟实验结果表明,与现有的几种定位算法和追踪协议相比,结合PFTL 算法和SAC 策略能够以较小的代价取得更好的定位效果和网络负载均衡,进而延长网络寿命.     

一种无线传感器网络中自主选择簇头的聚类算法

在无线传感网络中,传感器节点要定期向基站发送收集的数据。为了支持数据汇总,通过高效的网络组织将节点划分成若干簇。在这种类型的系统中,随着簇头的轮转,每个簇中的簇头选择方法是最具有挑战性的问题,有效的簇头选择算法可以提高网络的续航时间,并减少在WSN中的节点之间的通信开销。提出一个簇内民主方式选举算法来选择簇中的节点作为簇头,用MatLab对算法进行仿真,证明该算法的性能可以有效改善网络的性能。     

基于逻辑密钥树的无线传感网络密钥管理方案

针对层次型无线传感网络（HSN）中的安全通信问题，提出了一种基于逻辑密钥树（LKH++）的组密钥管理方案——W-LKH++。针对无线传感器节点的低配置特点，首先，对LKH++树的组密钥初始化计算方法进行修改，降低传感器节点的计算消耗；其次，对LKH++的组密钥持有方式进行改进，减少传感节点的存储消耗；最后，提出适用于簇头节点的动态密钥更新方法，在降低通信消耗的基础上增强簇头节点的抗捕获能力，提高无线通信网络的安全性。性能分析和仿真实验结果表明，W-LKH++在保证低计算、存储和通信消耗的基础上，进一步提高了网络安全性。     

基于分布式粒子滤波的二进制无线传感器网络目标跟踪

为了提高二进制无线传感器网络跟踪算法的精度和实时性,降低传感器节点能耗,将分布式粒子滤波运用到二进制无线传感器网络中进行目标跟踪。选择信号强度最大的节点作为簇头节点,在簇头单跳通信范围内的所有节点和簇头组成对目标跟踪的动态分簇,在簇头节点进行粒子采样和状态估计,在簇头之间传递粒子及其权值,从而得到了二进制无线传感器网络的分布式粒子滤波跟踪算法。研究了粒子数和网络节点数量对跟踪精度的影响。仿真结果表明,传感器的节点数量会影 响跟踪精度,但是粒子数对跟踪精度的影响更大。同时分布式粒子滤波比集中式粒子滤波具有更好的实时性和更低的能耗。     

基于IEEE1451的靶场分布式无线传感器网络构建方案

摘要：为了解决兵器试验靶场“异常弹”和散布大的弹箭的弹着点定位问题，提出了基于IEEE1451标准和ZigBee、WiFi通信协议构建一种异构分簇网络的方案。论文首先介绍了IEEE 1451 协议族，阐述了系统总体结构，对比了三中无线通讯协议，优选出ZigBee、WiFi通信协议构建无线传感器网络。针对靶场分布式测试结构，给出了簇头资源分配方案和簇头节点、网关节点硬件设计方案及结构设计方案。最后，分析了IEEE1451协议的实现形式，并详细讨论了服务器端的软件设计、功能模块划分。实验结果表明：该方案实现了靶场上多节点多通信方式的无线传感器网络构建，网络具有良好的稳定性、扩展性及远程控制能力，为其他无线传感器网络构建提供了很好的应用案例     

A dynamic programming approach: Improving the performance of wireless networks

Traditional wireless networks focus on transparent data transmission where the data are processed at either the source or destination nodes. In contrast, the proposed approach aims at distributing data processing among the nodes in the network thus providing a higher processing capability than a single device. Moreover, energy consumption is balanced in the proposed scheme since the energy intensive processing will be distributed among the nodes. The performance of a wireless network is dependent on a number of factors including the available energy, energy–efficiency, data processing delay, transmission delay, routing decisions, security architecture etc. Typical existing distributed processing schemes have a fixed node or node type assigned to the processing at the design phase, for example a cluster head in wireless sensor networks aggregating the data. In contrast, the proposed approach aims to virtualize the processing, energy, and communication resources of the entire heterogeneous network and dynamically distribute processing steps along the communication path while optimizing performance. Moreover, the security of the communication is considered an important factor in the decision to either process or forward the data. Overall, the proposed scheme creates a wireless “computing cloud” where the processing tasks are dynamically assigned to the nodes using the Dynamic Programming (DP) methodology. The processing and transmission decisions are analytically derived from network models in order to optimize the utilization of the network resources including: available energy, processing capacity, security overhead, bandwidth etc. The proposed DP-based scheme is mathematically derived thus guaranteeing performance. Moreover, the scheme is verified through network simulations.     

基于单位元的无线传感器网络密钥管理方案

针对现有无线传感器网络密钥管理方案的网络可扩展性差、连通率低和存储开销大等问题,在分簇结构无线传感器网络基础上,根据单位设计原理,提出了一种具有较强网络可扩展性同时具有较高网络连通率的新的密钥预分配方案。方案中,簇头间采用了以基站作为第三方服务节点来完成通信,簇头与簇内节点间通信采用新方案。在相同网络规模的情况下,与现有方案相比,分析和计算结果验证了新方案具有较好的可扩展性同时有较高的连通性。     

基于RFID的传感器网络合作式充电和数据收集策略

能量限制是制约无线传感器网络发展的主要瓶颈之一,可充电无线传感器网络的出现对其发展起了巨大的作用.提出了一种基于RFID标签的无线传感器网络合作式无线充电和数据收集策略,根据通信方式的不同,具体提出了TBR和TDC两种方案,通过将网络中的节点进行分簇,并在单个簇内部署簇内移动读取器进行路径移动,对簇内的各个节点进行充电和数据收集;在簇间部署簇间移动读取器收集簇内读取器内的数据,并将数据传输给汇聚节点进行数据处理,通过分簇完成对节点充电和数据收集任务的分层处理.通过仿真验证,其结果表明合作式充电策略可应用在大型区域内部署的网络,并且保证所需的移动读取器数量最少,数据传输至汇聚节点的时延最短,TBR方案与TDC方案有效.     

基于分簇的节点复制攻击入侵检测方法

部署于敌对环境的传感器网络,其节点可能被敌方俘获解析并构成恶意节点。再重新布放于网络,对网络进行攻击。针对上述问题,提出一种新的无线传感器网络节点复制攻击检测方法。这种方法将集中检测和分布检测相结合,在分簇传感器网络中分别利用簇头和基站进行复制攻击检测。仿真结果表明:该法克服了单独采用分布检测或集中检测的缺点,其检测率和通信成本均优于分布式方法,其网络生命周期优于集中式方法。     

基于簇的能量均衡无线传感器网络时间同步算法

针对现有时间同步算法应用于多跳无线传感器网络时存在的误差累计和能耗不均衡问题,提出一种基于簇的能量均衡时间同步算法。该算法基于簇状分层的网络拓扑,簇首之间采用双向监听机制代替双向交换机制,以减小通信开销和发送时延带来的同步误差;簇成员节点利用双向交换和单向广播相结合的机制与簇首同步,并通过最优剩余能量选取回应节点,均衡簇内节点能耗。对提出的新方法和传统的同步算法在精度和能耗方面进行理论分析和仿真验证,结果表明,该算法在保证较高同步精度的前提下可以减小通信开销,均衡网内节点能量消耗,延长网络生命周期。     

无线传感器网络中基于分簇的节点定位异常检测*

在引入WSN分簇结构基础上,提出一种分布式的节点定位异常检测方法,利用聚类拓扑减少通信量,同时降低以往集中式检测存在的单点风险。该方法不需要任何已知的部署知识或额外的硬件,每个簇的簇头节点只需根据该簇节点报告的位置和邻居表信息进行过滤计算,更新权值,即可确定和撤销定位异常的节点。通过理论分析和仿真模拟验证了这种基于分簇的节点定位异常检测方法的正确性和有效性。     

Data aggregation in wireless sensor network using SVM-based failure detection and loss recovery

Abstract

In wireless sensor network, data aggregation can cause increased transmission overhead, failures, data loss and security-related issues. Earlier works did not concentrate on both fault management and loss recovery issues. In order to overcome these drawbacks, in this paper, a reliable data aggregation scheme is proposed that uses support vector machine (SVM) for performing failure detection and loss recovery. Initially, a group head, selected based on node connectivity, splits the nodes into clusters based on their location information. In each cluster, the cluster member with maximum node connectivity is chosen as the cluster head. When the aggregator receives data from the source, it identifies node failures in the received data by classifying the faulty data using SVM. Furthermore, a reserve node-based fault recovery mechanism is developed to prevent data loss. Through simulations, we show that the proposed technique minimises the transmission overhead and increases reliability.     

基于动态压缩的无线传感网数据重构模型研究

由于动态数据的节点分布处于动态变化的状态,极具不稳定性,无法为无线传感网络实时提供可靠信息,需要进行数据重构处理,提升无线传感网数据稳定性,提出基于动态数据压缩的无线传感网数据重构模型.通过构建多维节点组网模型,分析节点间位置关系,并得到其多模状态重组结果,利用重组结果融合并调度无线传感数据.在融合结果中提取无线传感网...     

Resilience of sink filtering scheme in wireless sensor networks

One of severe security threats in wireless sensor network is node compromise. A compromised node can easily inject false data reports on the events that do not occur. The existing approaches in which each forwarding sensor along a path probabilistically filters out injected false data may not be adequate because such protection may break down when more than a threshold number of nodes are compromised. To solve this problem, we present a sink filtering scheme in clusters of heterogeneous sensor networks. In addition to basic sensors, some powerful data gathering sensors termed as cluster heads (CHs) are added. Each aggregation report generated by a CH must carry multiple keyed message authentication codes (MACs); each MAC is generated by a basic sensor that senses the event. The sink node checks the validity of the carried MACs in an aggregation report and filters out the forged report. We analyze the resilience and overhead of the sink filtering scheme. Both analytical and simulation results show that the scheme is resilient to an increasing number of compromised nodes, with graceful performance degradation. Particularly, we adopt Poisson Approximation to investigate the performance tradeoff between resilience and overall cost, and give some suggestions on how to choose the parameters. The scheme is also scalable and efficient in communication, computation and storage.     

一种基于簇头选择模型的无线传感器网络分簇算法

针对分层结构的无线传感器网络,本文提出了一种分簇算法.节点通过簇头选择模型来决定自己是否被选为簇头.簇头选择模型由节点度(ND)和剩余能量(RE)共同决定.从而提高了网络体系结构的稳定性,减少计算和通信开销.所述分簇算法不仅优化了簇头选择,还均衡整个网络节点的能量消耗,进而延长网络的生命周期.最后通过理论分析和仿真进一步验证该分簇算法的性能.