基于改进蚁群算法的WSN分簇路由机制研究

针对现有的用于无线传感器网络(WSN)的分簇路由协议,存在着所有簇头直接与汇聚节点通信、远离汇聚节点的簇头能量消耗过快等一系列的问题,根据蚁群算法(ACA)及WSN分簇路由算法的特点,对ACA进行改进并引入到WSN分簇路由机制中,提出一种基于改进蚁群算法的WSN分簇路由算法;该算法将到汇聚节点的距离设定为启发函数以找到簇头下沉的最佳路径和提高蚁群算法的效率,同时,在选择节点概率公式时将该节点的剩余能量考虑在内,在数据传输过程中,减少了簇头节点的能量消耗,进而实现节点能量的高效利用,增强网络的使用寿命,以实现网络通信的高效;通过仿真,结果表明,该算法是可行的、有效的。     

无线传感器网络中基于最小延时的数据汇集树构建与传输调度算法

无线传感器网络的数据通信模式问题是目前的研究热点,针对现有的无线传感器网络数据汇集算法延时较大这一不足,对最小延时数据汇集树和传输调度问题进行了研究。提出一种基于度约束的汇集树构建算法(DCAT)。该算法按照 BFS 方式遍历图,当遍历到每个节点时,通过确定哪些节点与汇点更近来确定潜在母节点集合。然后,选择图中度数最小的潜在母节点作为当前被遍历节点的母节点。此外,为了在给定的汇集树上进行高效地数据汇集,还提出两种新的基于贪婪的TDMA传输调度算法：WIRES-G 和 DCAT-Greedy。利用随机生成的不同规模的传感器网络,参照当前最新算法,对文中方法的性能进行了全面评估。结果表明,与当前最优算法相比,文中调度算法与文中汇集树构建算法结合起来,可显著降低数据汇集的延时。     

基于灰色模型和改进Chan的海洋移动传感器定位

为了实现海洋水下传感器网络中的移动节点定位,并改善传统节点定位方法在应用于无人值守和复杂环境的海洋生态监控中具有的定位误差大的问题,提出了一种基于灰色模型预测和改进Chan算法的海洋移动传感器节点定位方法;首先,采用灰色模型对节点在下一时刻的采集数据进行预测,然后将预测值与实际采集值进行比较从而判断出移动节点的状态是否正常;在此基础上,采用改进的Chan算法对处于正常状态的移动节点进行定位,从而提高水下移动传感器节点的定位精度;在Matlab中进行仿真实验,实验结果表明:文中方法能在节点运动速度增加、通信半径变大和锚节点密度增加的情况下,均具有比其它方法更低的节点定位误差,具有一定的优越性。      

基于无线传感器网络的混合混沌新分组加密算法

针对无线传感器网络(WSNS)中节点配备的能源少、节点计算能力低、存储资源 有限以及传统的加密方法不适用于WSNS中等问题, 提出了一种新的基于动态迭代的混合混沌方程及其整型数值化方法, 并结合Feistel网络结构设计了一种快速、安全且资源消耗低的适用于WSNS节点的分组加密算法. 通过对混合混沌分组加密算法进行了大量的实验测试之后, 发现该算法具有密钥空间大、严格的雪崩效应、扩散及扰乱性高以及均等的统计平衡性等优点, 同时该算法还成功地通过了SP800-22的严格测试; 算法经过仿真器平台上运行的速度、时间及所占存储空间的测试分析, 结果表明设计的混合混沌分组加密算法是完全能够适用于WSNS节点的数据加密.     

运载火箭类周期测试数据特征预测方法研究

鉴于我国运载火箭测试数据判读工作现状,研究测试数据的预测算法,有助于预判故障趋势,提前采取措施。分析了运载火箭测试数据,提出测试数据依时间序列的分类方法;针对类周期型数据,设计了相应的特征提取算法,得出数据特征时间序列;应用滚动自回归预测算法,并将历史实际值与预测值的加权值作为当前时刻的建模数据,实现了类周期数据特征的趋势预测。该方法有助于改进运载火箭类周期型数据判读方法。     

基于核独立成分分析和聚类中值的水下传感器故障诊断

水下传感器所处环境的开放性以及其长期处于无人值守状态,极易收到环境作用而发生破坏,传统分簇式的分布式故障诊断方法无法有效对其进行故障诊断,因此,提出了一种基于核主成分分析和聚类中值的故障诊断方法。首先,设计了水下传感器节点故障诊断的模型,然后采用核主成分分析方法对节点采集的数据和来自邻居节点发送的数据进行数据降维,得到具有最小属性集的数据集,然后对节点的邻居节点集进行聚类,选出具有最多元素的聚类,并将聚类的中值作为参考数据,将各节点与其对应的参考数据进行比较从而确定节点是否发生故障,最后定义了基于核独立成分分析和聚类中值的传感器节点故障诊断算法。仿真实验证明了文中方法能有效地对水下传感器进行故障诊断,且与其它方法相比,具有误差检测率高和误检率低的优点。     

基于RSSI和改进MCL算法的移动节点定位跟踪设计

由于传统节点定位方法大多针对静止传感器网络,不能适用于网络结构和节点位置动态变化的移动传感器网络,提出了一种基于RSSI测距和改进的MCL (Monte Carlo Localization)算法的移动传感器节点定位跟踪方法;首先描述了经典MCL算法和接收信号强度RSSI测距方法,然后设计了一种改进的MCL算法,将传统的MCL方法预测粒子位置的过程即预测和滤波两个阶段,更新为锚节点TTL受控泛洪方式广播自身位置、采用拉格朗日插值法预测节点下一时刻的位置和速度、求取锚盒采样区域、k 跳锚节点粒子滤波和根据预测下一时刻的节点位置和速度与当前时刻的位置信息确定各粒子权重的5个阶段;采用仿真器MCL-Simulator进行仿真,结果证明:文中方法能有效实现移动节点的定位,与其它方法相比,具有较小的平均定位误差,具有很强的可行性。     

大数据背景下的数据通信调度方法研究

大数据背景下,传统数据通信调度方法研究由于预测要素引入面窄、预测算法逻辑同意性差,导致数据调度出现数据调度断层现象;针对上述问题,提出大数据背景下数据通信调度3+1集成法设计;采用3+1集成法,通过CPU信号强度波动算法、处理器数据节点动态数检测技术、电频信号转换算法与快速执行代码,解决传统的数据通信调度过程中的数据响应慢、数据调度断层的问题;通过仿真实验证明,提出的大数据背景下数据通信调度3+1集成法具有数据调度速度快、资源开销小、调度数据准确度高的特点。     

基于分布式视频编码的低能耗视频传感器节点研究

在无线多媒体传感器网络中,如何提高节点能源的利用率,延长整个网络的生命周期是当前迫切需要解决的关键问题。从视频数据处理和传输能耗的角度出发,对分布式视频编码(DVC)基础理论及视频传感器节点的能耗模型进行分析和研究。通过实验仿真的方式将H.264帧内编码方案同DVC方案进行比较,实验结果表明了DISCOVER-DVC方案在节点节能方面的优势。最后基于DISCOVER-DVC算法,在S3C6410硬件平台和嵌入式Linux操作系统平台之上完成视频传感器节点设计,来降低节点能耗,提高节点生命周期。     

基于Gocator的多传感器数据拼接方法研究

点云数据拼接在众多科研领域有着十分广泛的应用。为完整、精确地得到复杂物体的点云数据,提出一种基于Gocator的多传感器数据拼接方法。该方法需要对多传感器系统进行两两校准以获取各传感器坐标系与基准坐标系之间的空间变换关系,进而将各传感器自身坐标系下的数据转换到基准坐标系下,实现多传感器数据的拼接。对于双传感器数据拼接,首先通过两只传感器同时拍摄单孔标定块,利用最大距离法提取标定块轮廓坡口特征点,根据坐标转换原理,初步确定了两传感器间的旋转平移关系;在此基础上采用迭代最近点(ICP)算法进一步优化确定两传感器之间的最优变换矩阵,以得到精确的拼接关系。实验室搭建双传感器钢轨廓形检测平台对该算法进行验证,实验结果表明,多次拼接得到的钢轨廓形与标准模板误差不超过0.2mm,完全符合钢轨廓形允许误差要求,该算法具有较高精度和稳定性。     

一种基于势博弈的无线传感器网络拓扑控制算法

在实际的应用中,无线传感器网络常常由大量电池资源有限的传感器节点组成.如何降低网络功耗,最大化网络生存时间,是传感器网络拓扑控制技术的重要研究目标.随着传感节点的运行,节点的能量分布可能越来越不均衡,需要在考虑该因素的情况下,动态地调整节点的网络负载以均衡节点的能耗,达到延长网络生存时间的目的.该文引入博弈理论和势博弈的概念,综合考虑节点的剩余能量和节点发射功率等因素,设计了一种基于势博弈的拓扑控制模型,并证明了该模型纳什均衡的存在性.通过构造兼顾节点连通性和能耗均衡性的收益函数,以确保降低节点功耗的同时维持网络的连通性.通过提高邻居节点的平均剩余能量值以实现将剩余能量多的节点选择作为自身的邻居节点,提高节点能耗的均衡性.在此基础上,提出了一种分布式的能耗均衡拓扑控制算法.理论分析证明了该算法能保持网络的连通性.与现有基于博弈理论的DIA算法和MLPT算法相比,本算法形成的拓扑负载较重、剩余能量较小的瓶颈节点数量较少,节点剩余能量的方差较小,网络生存时间更长.     

Modeling wireless sensor networks using random graph theory

A critical issue in wireless sensor networks (WSNs) is represented by limited availability of energy within network nodes. Therefore, making good use of energy is necessary in modeling sensor networks. In this paper we proposed a new model of WSNs on a two-dimensional plane using site percolation model, a kind of random graph in which edges are formed only between neighbouring nodes. Then we investigated WSNs connectivity and energy consumption at percolation threshold when a so-called phase transition phenomena happen. Furthermore, we proposed an algorithm to improve the model; as a result the lifetime of networks is prolonged. We analyzed the energy consumption with Markov process and applied these results to simulation.     

Power control and channel allocation optimization game algorithm with low energy consumption for wireless sensor network

In a wireless sensor network(WSN), the energy of nodes is limited and cannot be charged. Hence, it is necessary to reduce energy consumption. Both the transmission power of nodes and the interference among nodes influence energy consumption. In this paper, we design a power control and channel allocation game model with low energy consumption(PCCAGM). This model contains transmission power, node interference, and residual energy. Besides, the interaction between power and channel is considered. The Nash equilibrium has been proved to exist. Based on this model, a power control and channel allocation optimization algorithm with low energy consumption(PCCAA) is proposed. Theoretical analysis shows that PCCAA can converge to the Pareto Optimal. Simulation results demonstrate that this algorithm can reduce transmission power and interference effectively. Therefore, this algorithm can reduce energy consumption and prolong the network lifetime.     

Experimental studies of using wireless energy transmission for powering embedded sensor nodes

A major challenge impeding the deployment of wireless sensor networks for structural health monitoring (SHM) is developing a means to supply power to the sensor nodes in an efficient manner. In this paper, we explore possible solutions to this challenge by using a mobile-host based wireless energy transmission system to provide both power and data interrogation commands to sensor nodes. The mobile host features the capability of wirelessly transmitting energy to sensor nodes on an as-needed basis. In addition, it serves as a central data repository and processing center for the data collected from the sensing network. The wirelessly transmitted microwave energy is captured by a receiving antenna, transformed into DC power by a rectifying circuit, and stored in a storage medium to provide the required energy to the sensor node. The application of wireless energy transmission is targeted toward SHM sensor nodes that have been recently developed by the authors, which can be used to collect peak mechanical displacements or piezoelectric impedance measurements. This paper will describe considerations needed to design such energy transmission systems, experimental procedure and results, method of increasing the efficiency, energy conditioning circuits and storage medium, and target applications. Experimental results from a field test on the Alamosa Canyon Bridge in southern New Mexico will also be presented.     

Adaptive multimode transmission in wireless sensor networks with energy harvesting

Limited energy has always been an important factor restricting the development of wireless sensor networks. The unbalanced energy consumption of nodes will accelerate the death of some nodes. To solve the above problems, an adaptive routing algorithm for energy collection sensor networks based on distributed energy saving clustering (DEEC) is proposed. In each hop of data transmission, the optimal mode is adaptively selected from four transmission modes: single-hop cooperative, multi-hop cooperative, single-hop non-cooperative and multi-hop non-cooperative, so as to reduce and balance the energy consumption of nodes. The performance of the proposed adaptive multi-mode transmission method and several benchmark schemes are evaluated and compared by computer simulation, where a few performance metrics such as the network lifetime and throughput are adopted. The results show that, the proposed method can effectively reduce the energy consumption of the network and prolong the network lifetime; it is superior to various benchmark schemes.     

一种能耗均衡的无线传感器网络加权无标度拓扑研究

针对无线传感器网络节点能耗不均衡问题,通过对节点生命期建模,得出节点生命期受节点剩余能量和通信距离的影响,进而将两端节点生命期作为构建拓扑时边权重的影响因子,通过边权重控制节点权重,最终得出了一种能耗均衡的无线传感器网络加权无标度拓扑模型,并理论证明了该模型的点权、边权和节点度均服从幂律分布.实验结果表明,该模型具有无标度拓扑的强容错性,并有效的均衡了网络中的节点能耗,延长了网络的生命期.     

Ricci Curvature-Based Semi-Supervised Learning on an Attributed Network

In recent years, on the basis of drawing lessons from traditional neural network models, people have been paying more and more attention to the design of neural network architectures for processing graph structure data, which are called graph neural networks (GNN). GCN, namely, graph convolution networks, are neural network models in GNN. GCN extends the convolution operation from traditional data (such as images) to graph data, and it is essentially a feature extractor, which aggregates the features of neighborhood nodes into those of target nodes. In the process of aggregating features, GCN uses the Laplacian matrix to assign different importance to the nodes in the neighborhood of the target nodes. Since graph-structured data are inherently non-Euclidean, we seek to use a non-Euclidean mathematical tool, namely, Riemannian geometry, to analyze graphs (networks). In this paper, we present a novel model for semi-supervised learning called the Ricci curvature-based graph convolutional neural network, i.e., RCGCN. The aggregation pattern of RCGCN is inspired by that of GCN. We regard the network as a discrete manifold, and then use Ricci curvature to assign different importance to the nodes within the neighborhood of the target nodes. Ricci curvature is related to the optimal transport distance, which can well reflect the geometric structure of the underlying space of the network. The node importance given by Ricci curvature can better reflect the relationships between the target node and the nodes in the neighborhood. The proposed model scales linearly with the number of edges in the network. Experiments demonstrated that RCGCN achieves a significant performance gain over baseline methods on benchmark datasets.     

一种无线传感器网络健壮性可调的能量均衡拓扑控制算法

无线传感器网络中, 应用环境的干扰导致节点间距不能被准确度量. 所以利用以节点间距作为权重的闭包图(EG)模型构建的拓扑没有考虑环境的干扰, 忽略了这部分干扰带来的能耗, 缩短了网络生存时间. 针对无线传感器网络拓扑能量不均的特点和EG模型的缺陷, 首先引入节点度调节因子, 建立通信度量模型和节点实际生存时间模型; 其次量化网络节点度, 从而获取满足能量均衡和网络生命期最大化需求的节点度的取值规律; 然后利用该取值规律和函数极值充分条件解析推导出网络最大能量消耗值和最长生存时间, 并获得最优节点度; 最后基于以上模型提出一种健壮性可调的能量均衡拓扑控制算法. 理论证明该拓扑连通且为双向连通. 仿真结果说明网络能利用最优节点度达到较高的健壮性, 保证信息可靠传输, 且算法能有效平衡节点能耗, 提高网络健壮性, 延长网络生命周期.