共查询到20条相似文献,搜索用时 218 毫秒
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针对某些特定场合无线传感器网络中传感器产生的数据时间和空间上的冗余和高度相关性,提出了一种面向数据相关性及权重的传感器网络采样优化算法DCACW。它基于聚合树结构连接整个网络,在各节点根据样本的相关性和节点权重进行数据融合。仿真实验结果表明,本算法采集的样本覆盖度更广,而且在聚合树中去除了冗余和相关的数据,保证了最终收集的样本差异性较强。 相似文献
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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。 相似文献
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针对无线传感器网络中节点通信能力及能量有限的情况,该文提出基于动态分簇路由优化和分布式粒子滤波的传感器网络目标跟踪方法。该方法以动态分簇的方式将监测区域内随机部署的传感器节点划分为若干个簇,并对簇内成员节点与簇首节点之间、簇首节点与基站之间的通信路由进行优化,确保网络能耗的均衡分布,在此基础上,被激活的簇内成员节点并行地执行分布式粒子滤波算法实现目标跟踪。仿真结果表明,该方法能有效地降低传感器网络中节点的总能耗,能在实现跟踪的同时保证目标跟踪的精度。 相似文献
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糜昊 《智能计算机与应用》2021,11(9):108-112
LEACH协议可延长无线传感器网络的使用寿命,提高信息传输量.但是研究发现基站距离网络区域愈远,LEACH协议的效果愈差,网络价值愈小.故本文提出了一种基于最优簇头数和三段路由的改进型LEACH算法,以克服基站位置对网络寿命和信息传输量的影响.该算法依据不同WSN的传感器节点数目,预先计算出理论上最优的簇头数目,残余能量最高的簇头将被选举为唯一的高层簇头,形成节点—簇头—高层簇头—基站的三段数据路由.实验结果表明,与LEACH协议相比,当传输距离小于距离阈值时,该算法有效提升了节点能耗的均衡性,推迟首节点死亡时间,从而提高信息传输量;当距离超过阈值后,网络寿命和信息传输量显著提高,算法优势更为明显. 相似文献
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为加快无线传感器网络(WSN)路径搜索速度,减少了路径寻优能量消耗,提出了基于最优-最差蚂蚁系统(BWAS)算法的无线传感器网络动态分簇路由算法。该算法是基于WSN动态分簇能量管理模式,在簇头节点间运用BWAS算法搜寻从簇头节点到汇聚节点的多跳最优路径,以多跳接力方式将数据发送至汇聚节点。BWAS算法在路径搜寻过程中评价出最优-最差蚂蚁,引入奖惩机制,加强搜寻过程的指导性。结合动态分簇能量管理,避免网络连续过度使用某个节点,均衡了网络节点能量消耗。通过与基于蚁群算法(ACS)路由算法仿真比较,本算法减缓了网络节点的能量消耗,延长了网络寿命,在相同时间里具有较少的死亡节点,具有较强的鲁棒性。 相似文献
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为平衡无线传感器网络中的簇头负载并进一步降低多跳传输能耗,文中提出了一种改进的基于时间竞争成簇的路由算法。该算法通过限制近基站节点成簇入簇,以防止近基站节点成簇入簇的节能收益无法补偿成簇入簇能耗;利用基站广播公共信息和基于时间机制成簇,以减少节点基本信息交换能耗;通过候选簇头中继来平衡簇头负载。候选簇头的评价函数综合考虑了剩余能量和最优跳数的理想路径,以期在保持中继负载平衡的基础上尽量降低多跳能耗。仿真结果显示,该算法较LEACH和DEBUC算法延长了以30%节点死亡为网络失效的网络生存周期,表明该算法在降低节点能耗和平衡负载方面是有效的。 相似文献
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数据存储是无线传感器网络中数据管理的基础操作.在移动低占空比传感网中,由于节点的移动性,每个节点需要频繁更新邻居节点集合,使得节点能量消耗过大;同时,节点大部分时间处于睡眠状态,仅在少部分时间内苏醒工作,造成数据备份的通信延迟过大.提出一种快速的低能耗数据保存机制.首先,源节点基于连续时间序列对感知数据进行分段线性拟合压缩;接着,节点根据预估故障概率和存储空间大小,计算出合理的压缩数据备份数量.在此基础上,设计一种动态自适应传输协议.实验仿真表明,与已有存储算法比较,该机制具有更低的传输能耗和通信延迟. 相似文献
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在无线传感器网络通信中,物理层同步捕获是制约节点复杂度和功耗的重要环节.为了进一步减少标签节点的同步捕获时长和反馈功耗,在脉冲主机同步算法的基础上,提出了相位误差估计同步捕获算法.标签节点采用对称双极性锯齿相关波形,锯齿信号相关输出由于具备与相位误差间的线性关系,所以可获得更好的稳定性和捕获速度.对这种基于发射端主机(锚节点)的同步捕获性能进行了理论分析,并通过仿真验证了理论分析的正确性,同时比较了与原算法在非理想信道下的捕获性能. 相似文献
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The phenomenon of missing sensor data is very common in wireless sensor networks (WSN). It has a dramatic effect on the usability, stability and efficiency of the WSN-based applications. There exist many methods for the missing sensor data estimation. However, the accurate and efficient consequent estimation of missing sensor data remains a challenging problem. To solve this problem, we propose a new method named consecutive sensor data deep neural network (CSDNN). In this method, firstly, we analyze the correlation coefficients among different types of sensor data and choose a certain number of nearest neighbors of the target sensor nodes. Secondly, to estimate a certain type of sensor data from a target sensor node, we utilize the different types of sensor data that are from the same target sensor node and have strong correlation with the missing ones, and the same type of sensor data from the aforementioned nearest neighbors. We treat these data as the input of the deep neural networks (DNN). Thirdly, we construct the DNN model, discuss the optimized DNN structure for the missing data problem, and test the accuracy of CSDNN for different types of environmental sensor data. The results show that the CSDNN method allows to accurately estimate the consecutively missing sensor data. 相似文献
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密钥管理作为传感器网络安全中最为基本的环节,在认证和加密过程中起着重要作用。针对无线传感器网络(Wireless Sensor Network,WSN)的通信密钥易被破解的缺点以及为建立安全信道而增加密钥会造成网络的连通率低的问题,提出了一种改进的无线传感器网络密钥管理方案,通过定位算法得到网络中的坐标,利用所得到的位置信息对所存储的密钥空间进行优化,可以增大2个邻居节点拥有相同密钥空间的概率。实验结果表明:该方法占用较小密钥存储空间,能明显改善网络连通性和网络的安全性等性能,提高安全性。 相似文献
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In this paper, a clustering algorithm is proposed based on the high correlation among the overlapped field of views for the wireless multimedia sensor networks. Firstly, by calculating the area of the overlapped field of views (FoVs) based on the gird method, node correlations have been obtained. Then, the algorithm utilizes the node correlations to partition the network region in which there are high correlation multimedia sensor nodes. Meanwhile, in order to minimize the energy consumption for transmitting images, the strategy of the cluster heads election is proposed based on the cost estimation, which consists of signal strength and residual energy as well as the node correlation. Simulation results show that the proposed algorithm can balance the energy consumption and extend the network lifetime effectively. 相似文献
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针对现有无线传感器网络(Wireless Sensor Networks,WSNs)节点片上RAM(随机存储器)利用率低的特点,设计了一种基于链表的改进型内存管理方案。该方案以事件驱动开发模式为程序运行的前提,在将RAM划分为静态内存空间和动态内存空间之后,通过内存隔离技术,实现内存管理结构与内存空间在实体内存中的分离,从而达到提高节点内存利用率的目的。经测试,写内存的平均速率能够达到500kb/s,而在开启内存交换功能时,实际内存的使用率接近80%。最终为提高节点内存利用率提供了一种良好的解决方案。 相似文献
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基于D-S证据理论的组合数据融合算法 总被引:4,自引:1,他引:3
针对在无线传感器网络中传感器节点本身能量有限的特性,提出一种基于D-S证据理论的组合数据融合算法.先对传感器网络的当前值依据各组数据的标准差进行聚类,然后对每一类数据组,用D-S证据推理算法进行融合,将其结果看成一个虚拟传感器节点数据,最后通过计算马哈诺比斯距离得出虚拟节点数据向量的异常值,把它作为加权权重进行加权融合.仿真试验表明:该算法识别目标的可信度高于D-S推理法,且在计算复杂度上也有明显优势. 相似文献
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Data aggregation in sensor networks using learning automata 总被引:1,自引:0,他引:1
One way to reduce energy consumption in wireless sensor networks is to reduce the number of packets being transmitted in the
network. As sensor networks are usually deployed with a number of redundant nodes (to overcome the problem of node failures
which is common in such networks), many nodes may have almost the same information which can be aggregated in intermediate
nodes, and hence reduce the number of transmitted packets. Aggregation ratio is maximized if data packets of all nodes having
almost the same information are aggregated together. For this to occur, each node should forward its packets along a path
on which maximum number of nodes with almost the same information as the information of the sending node exist. In many real
scenarios, such a path has not been remained the same for the overall network lifetime and is changed from time to time. These
changes may result from changes occurred in the environment in which the sensor network resides and usually cannot be predicted
beforehand. In this paper, a learning automata-based data aggregation method in sensor networks when the environment’s changes
cannot be predicted beforehand will be proposed. In the proposed method, each node in the network is equipped with a learning
automaton. These learning automata in the network collectively learn the path of aggregation with maximum aggregation ratio
for each node for transmitting its packets toward the sink. To evaluate the performance of the proposed method computer simulations
have been conducted and the results are compared with the results of three existing methods. The results have shown that the
proposed method outperforms all these methods, especially when the environment is highly dynamic. 相似文献
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Energy constraints have a significant impact on the design and operation of wireless sensor networks. This paper investigates the base station (BS) selection (or anycast) problem in wireless sensor networks. A wireless sensor network having multiple BSs (data sink nodes) is considered. Each source node must send all its locally generated data to only one of the BSs. To maximize network lifetime, it is essential to optimally match each source node to a particular BS and find an optimal routing solution. A polynomial time heuristic is proposed for optimal BS selection and anycast via a sequential fixing procedure. Through extensive simulation results, it is shown that this algorithm has excellent performance behavior and provides a near-optimal solution. 相似文献