基于空间相关性的事件驱动无线传感器网络分簇算法

分簇算法是传感器网络中减少能量消耗的一种关键技术,它能够增强网络的扩展性和延长网络的生存时间。针对传感器节点数据的空间相关性,该文提出了一种新的基于空间相关性的事件驱动传感器网络分簇算法。算法根据用户要求的误差门限及结合节点数据的空间相关性马尔可夫模型,将事件感知区域划分成虚拟极坐标等价层。每个等价层选取层内当前剩余能量最大的节点作为簇头,网络通过移动代理收集簇头感知信息,该方法减少了传输数据量,有效节省了网络能量。     

基于位置感知的无线传感器网络聚类算法

针对以能量有效的方式收集传感器网络空间相关性数据的问题,本文提出了一种新的基于位置感知的无线传感器网络聚类算法。算法根据用户查询误差门限和基于位置信息的节点感知数据相异度矩阵,进行无监督数据挖掘,将监测区域划分成信息等价域。每个等价域选取城内当前剩余能量最大的节点作为簇头,网络通过移动代理收集簇头感知信息,从而减少了传输数据量,有效节省了网络能量。     

传感器网络中基于时空相关性的压缩感知算法

传感器网络节点的能量有限,为节省传感器节点的能耗,提出了利用节点内及节点间的时空相关性的压缩感知模型及算法,减少了通信的数据量,进一步节省了能耗,延长了网络的生命周期。算法在分簇协议和多跳路由优化的基础上,在簇头节点运用较为简单的压缩感知压缩测量方法,降低了计算复杂度。通过对实测数据的误差分析及能耗仿真,验证了该模型及算法的有效性和实用性。     

WSN中一种基于数据融合的能量高效分簇路由协议

基于无线传感器网络中监测数据具有较高时空相关性的应用场景。提出了一种基于数据融合的局部能量高效汇聚分簇协议LEEAC，该协议通过反映局部空间相关性的数据相异度对节点剩余能量进行约束，并使用约束后的预测能量作为竞选簇头的主要依据，被选举的簇头在传感器网络中具有良好的分布性。同时通过引入数据鉴定码，减少了簇内数据传输阶段的通信量以及簇头数据融合的工作量，从而大大节约了能量消耗。实验结果表明，LEEAC协议能够有效均衡网络能量消耗。延长网络生存时间。     

能量感知非均匀成簇路由优化算法

无线传感器网络(WSN)具有的能量有限,其能量利用效率的高低直接影响着网络的生命周期.为了提高无线传感器网络的能量利用效率,提出了一种能量感知非均匀成簇路由优化算法(Energy Awareness Unequal Clustering Routing Optimization Algorithm,EUCR).该算法通过节点在网络中所处的位置确定各节点的邻居节点,并以局部能量选举簇头,各簇头根据其邻居节点构建非均匀分簇网络.同时该算法在路由阶段考虑了簇头的剩余能量和转发代价.仿真结果表明,EUCR算法能有效提高网络的能量利用效率,并延长网络的生命周期.     

无线传感器网络中基于能量的成簇协议

无线传感器网络采用能量有效方式传输数据对于延长传感器网络寿命十分重要。LEACH是一种基于簇的协议,它采用本地簇头随机轮转机制将能量负载均匀分布到网络中的所有传感器节点,簇头节点将收集到数据进行融合后发送给基站。提出一种改进的方案,采用随机成簇算法让网络中传感器节点成簇,成簇的过程考虑传感器节点剩余能量和簇头与非簇头结点之间的距离。通过分析评价和仿真结果,说明新算法比LEACH更能有效利用能量且发送更多的数据。     

基于DCS的无线传感器网络数据压缩算法研究

针对无线传感器网络节点能量有限的特点,基于网络节点间感知数据在空间上具有相关性,提出一种适用于无线传感器网络的基于边信息的分布式压缩感知算法。该算法在簇头接收的多个信号中,选择其中一个信号作为边信息,来优化其他信号的压缩以及解压过程,使得其他信号能够得到最大程度的压缩。仿真结果表明,该算法能够有效地压缩信号,并且能减少整个网络的能量消耗。     

WSN中基于节点地理位置信息分簇算法的研究

针对无线传感器网络的特点,提出了一种新的分簇算法.在选择簇头时,节点根据自己的地理位置与能量决定是否成为簇头,使得簇头在网络中均匀分布;在数据传输阶段根据节点的权重来选择中间节点进行数据传输,减少网络消耗的能量从而延长网络的生存时间.仿真结果表明该算法可以显著延长网络寿命,提高网络的业务容量.     

基于最优阶估计与分布式分簇的传感器网络数据压缩方法研究

在无线传感器网络的诸多应用中,被监测区域发生异常情况的概率通常较小,正常情况下,同一传感器节点在前后连续时刻所采集的数据具有时间相关性,处于相邻区域的不同传感器节点在同一时刻所采集的数据具有空间相关性,发送存在时间、空间冗余的数据至基站必将耗费节点大量的能量。该文提出了基于最优阶估计和分布式分簇的传感器网络数据压缩方法,利用节点采集数据的时空相关性,基于最优阶估计在基站处建立相关系数,经分布式分簇,节点仅需传送少量数据,基站根据时空相关性恢复原始数据。仿真结果表明应用该算法,可以有效减少传感器网络中冗余数据传输量和节点能耗,进而延长系统寿命。     

有效降低无线网络节点能量的LEACH新算法

LEACH是一种低功耗自适应按簇分层路由算法.为了降低节点能耗,在LEACH协议的基础上提出了在选举簇头时,改变阈值T(n)的大小以降低节点成为簇头的概率,从而节省网络因分簇而消耗的能量.同时又提出了一种基于节点剩余能量的二层簇头的算法,该算法能使节点减少将冗余信息传输到基站,从而达到降低节点消耗能量的目的.通过实验仿真,表明这些方法能使网络节点能量的消耗减少,达到了延长网络生命周期的目的.     

Energy harvesting,cooperative spectrum sensing,and data transmission improvement in energy‐efficient multichannel cognitive sensor networks

In this paper, we investigate the energy harvesting capability in a multichannel wireless cognitive sensor networks for energy‐efficient cooperative spectrum sensing and data transmission. Spectrum sensors can cooperatively scan the spectrum for available channels, whereas data sensors transmit data to the fusion center (FC) over those channels. We select the sensing, data transmission, and harvesting sensors by setting the sensing time, data transmission time, and also harvesting time to maximize the network data transmission rate and improve the total energy consumption in the multichannel network under global probability of false alarm and global probability of detection constraints. We formulate our optimization problem and employ the convex optimization method to obtain the optimal times and nodes for spectrum sensing, data transmission, and harvesting energy in each subchannel for multiband cognitive sensor networks. Simulation results show that in our proposed algorithm, the network data transmission rate is improved while more energy is saved compared with the baseline methods with equal sensing time in all subchannels.     

基于分层的层内无线传感网络数据融合

无线传感网络数据融合能够有效减少传感节点的数据通信量,减少节点的能量消耗,延长了网络的寿命。本文提出了节点分层算法,在层内传感节点加入了具体的数据融合算法,利用拉依达准则对节点收到的数据进行异常数据检测,在上层节点利用主成分分析对剩余数据进行数据融合。通过仿真实验得出该算法数据融合结果准确率好。     

A collaborative node management scheme for energy-efficient monitoring in wireless multimedia sensor networks

Clustering in sensor networks provides energy conservation, network scalability, topology stability, reducing overhead and also allows data aggregation and cooperation in data sensing and processing. Wireless Multimedia Sensor Networks are characterized for directional sensing, the Field of View (FoV), in contrast to scalar sensors in which the sensing area usually is more uniform. In this paper, we first group multimedia sensor nodes in clusters with a novel cluster formation approach that associates nodes based on their common sensing area. The proposed cluster formation algorithm, called Multi-Cluster Membership (MCM), establishes clusters with nodes that their FoVs overlap at least in a minimum threshold area. The name of Multi-Cluster Membership comes from the fact that a node may belong to multiple clusters, if its FoV intersects more than one cluster-head and satisfies the threshold area. Comparing with Single-Cluster Membership (SCM) schemes, in which each node belongs to exactly one cluster, because of the capability of coordination between intersected clusters, MCM is more efficient in terms of energy conservation in sensing and processing subsystems at the cost of adding complexity in the node/cluster coordination. The main imposed difficulty by MCM, is the coordination of nodes and clusters for collaborative monitoring; SCMs usually assign tasks in a round-robin manner. Then, as second contribution, we define a node selection and scheduling algorithm for monitoring the environment that introduces intra and inter-cluster coordination and collaboration, showing how the network lifetime is prolonged with high lifetime prolongation factors particularly in dense deployments.     

Spatial correlation-based collaborative medium access control in wireless sensor networks

Wireless Sensor Networks (WSN) are mainly characterized by dense deployment of sensor nodes which collectively transmit information about sensed events to the sink. Due to the spatial correlation between sensor nodes subject to observed events, it may not be necessary for every sensor node to transmit its data. This paper shows how the spatial correlation can be exploited on the Medium Access Control (MAC) layer. To the best of our knowledge, this is the first effort which exploits spatial correlation in WSN on the MAC layer. A theoretical framework is developed for transmission regulation of sensor nodes under a distortion constraint. It is shown that a sensor node can act as a representative node for several other sensor nodes observing the correlated data. Based on the theoretical framework, a distributed, spatial Correlation-based Collaborative Medium Access Control (CC-MAC) protocol is then designed which has two components: Event MAC (E-MAC) and Network MAC (N-MAC). E-MAC filters out the correlation in sensor records while N-MAC prioritizes the transmission of route-thru packets. Simulation results show that CC-MAC achieves high performance in terms energy, packet drop rate, and latency.     

传感器网络中基于域的分布式自动成簇算法研究

 在传感器网络中,分簇是其他应用的基础,本文结合传感器网络的节点特性和位置信息,提出一种基于域的分布式自动成簇算法DCAM(Distributed Clustering Auto Model),DCAM把传感器网络按域划分来构建簇,簇之间是相互连通并且可以覆盖网络中所有的传感节点,簇头节点与簇中的网关节点就构建成网络的连通核,当传送数据时,传感节点只需在连通核中寻径,因而能明显减少寻径时间复杂度并且具有更好的分布性;然后在DCAM的基础上提出了簇的自愈和更新算法, 更新和自愈算法可更大程度地延长整个网络的生命周期.     

A comparison study on node clustering techniques used in target tracking WSNs for efficient data aggregation

Wireless sensor applications are susceptible to energy constraints. Most of the energy is consumed in communication between wireless nodes. Clustering and data aggregation are the two widely used strategies for reducing energy usage and increasing the lifetime of wireless sensor networks. In target tracking applications, large amount of redundant data is produced regularly. Hence, deployment of effective data aggregation schemes is vital to eliminate data redundancy. This work aims to conduct a comparative study of various research approaches that employ clustering techniques for efficiently aggregating data in target tracking applications as selection of an appropriate clustering algorithm may reflect positive results in the data aggregation process. In this paper, we have highlighted the gains of the existing schemes for node clustering‐based data aggregation along with a detailed discussion on their advantages and issues that may degrade the performance. Also, the boundary issues in each type of clustering technique have been analyzed. Simulation results reveal that the efficacy and validity of these clustering‐based data aggregation algorithms are limited to specific sensing situations only, while failing to exhibit adaptive behavior in various other environmental conditions. Copyright © 2016 John Wiley & Sons, Ltd.     

基于协同学习的频谱智能感知方法

目前无线通信网络频谱环境时空分布复杂多变，现有多用户协同感知方法数据预处理繁琐，感知效率低下。为此，在由用户感知层和边缘融合层构成的系统架构下，提出了一种基于协同学习的频谱智能感知算法。用户感知层采用多分支卷积循环门控神经网络，利用原始归一化能量信号的底层结构信息，实现本地感知。边缘融合层基于自注意力机制进行消息传播，融合用户感知层中各个非授权用户的感知结果得出最终决策。实验表明，在信噪比为-20 dB以及5个用户协同感知的情况下，该方法能在虚警概率为1.91%时达到18.3%的检测概率，相比对比模型提升了6.1%,且不需要对原始数据额外预处理，降低了算法的复杂度。     

Energy-Efficient Routing Algorithm Based on Unequal Clustering and Connected Graph in Wireless Sensor Networks

Clustering and multi-hop routing algorithms substantially prolong the lifetime of wireless sensor networks (WSNs). However, they also result in the energy hole and network partition problems. In order to balance the load between multiple cluster heads, save the energy consumption of the inter-cluster routing, in this paper, we propose an energy-efficient routing algorithm based on Unequal Clustering Theory and Connected Graph Theory for WSN. The new algorithm optimizes and innovates in two aspects: cluster head election and clusters routing. In cluster head election, we take into consideration the vote-based measure and the transmission power of sensor nodes when to sectionalize these nodes into different unequal clusters. Then we introduce the connected graph theory for inter-cluster data communication in clusters routing. Eventually, a connected graph is constituted by the based station and all cluster heads. Simulation results show that, this new algorithm balances the energy consumption among sensor nodes, relieves the influence of energy-hole problem, improve the link quality, achieves a substantial improvement on reliability and efficiency of data transmission, and significantly prolongs the network lifetime.