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Wireless Personal Communications - Nowadays, Wireless Sensor Networks (WSNs) is enhancing for different applications. Simultaneously, energy consumption for processing the tasks in most of the... 相似文献
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针对无线传感器网络数据汇聚与传输过程面临的安全威胁,文中提出一种基于安全数据汇聚与信息重构的多路由数据传输算法——MDT.该算法允许网络中存在妥协节点,也允许丢失部分数据.通过理论分析与仿真实验,证实该算法在不增加网络整体能耗的前提下,能有效地抵御侦听、数据篡改和拒绝服务攻击,全面提高系统的安全性和可靠性. 相似文献
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针对分簇网络拓扑结构中簇头节点能量消耗过快,综合考虑了节点的密集程度和剩余能量,采用节点自适应的簇头选择算法,选择部署越集中和剩余能量越大的节点作为簇头节点.同时节点引入了新鲜性信息熵模型,通过比较前后两次接收到的数据的差别程度,设置一个参考阈值来判断是否转发数据.这种数据汇聚算法有效地降低了数据的冗余,减少了能量消耗,增加了带宽利用率,延长了网络的生存期. 相似文献
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Wireless Personal Communications - In wireless sensor network (WSN) redundant data gathering and transmission occurs due to dense deployment. Recently compressive sensing (CS) has attracted... 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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This paper studies energy efficient routing for data aggregation in wireless sensor networks. Our goal is to maximize the
lifetime of the network, given the energy constraint on each sensor node. Using linear programming (LP) formulation, we model
this problem as a multicommodity flow problem, where a commodity represents the data generated from a sensor node and delivered
to a base station. A fast approximate algorithm is presented, which is able to compute (1−ε)-approximation to the optimal
lifetime for any ε > 0. Then along this baseline, we further study several advanced topics. First, we design an algorithm,
which utilizes the unique characteristic of data aggregation, and is proved to reduce the running time of the fastest existing
algorithm by a factor of K, K being the number of commodities. Second, we extend our algorithm to accommodate the same problem in the setting of multiple
base stations, and study its impact on network lifetime improvement. All algorithms are evaluated through both solid theoretical
analysis and extensive simulation results.
Yuan Xue received her B.S. in Computer Science from Harbin Institute of Technology, China in 1994 and her M.S. and Ph.D. in Computer
Science from the University of Illinois at Urbana-Champaign in 2002, and 2005. Currently she is an assistant professor at
the Department of Electrical Engineering and Computer Science of Vanderbilt University. Her research interests include wireless
and sensor networks, mobile systems, and network security.
Yi Cui received his B.S. and M.S. degrees in 1997 and 1999, from Department of Computer Science, Tsinghua University, China, and
his Ph.D. degree in 2005 from the Department of Computer Science, University of Illinois at Urbana-Champaign. Since then,
he has been with the Department of Electrical Engineering and Computer Science at Vanderbilt University, where he is currently
an assistant professor. His research interests include overlay network, peer-to-peer system, multimedia system, and wireless
sensor network.
Klara Nahrstedt (M ' 94) received her A.B., M.Sc degrees in mathematics from the Humboldt University, Berlin, Germany, and Ph.D in computer
science from the University of Pennsylvania. She is an associate professor at the University of Illinois at Urbana-Champaign,
Computer Science Department where she does research on Quality of Service(QoS)-aware systems with emphasis on end-to-end resource
management, routing and middleware issues for distributed multimedia systems. She is the coauthor of the widely used multimedia
book ‘Multimedia:Computing, Communications and Applications’ published by Prentice Hall, and the recipient of the Early NSF
Career Award, the Junior Xerox Award and the IEEE Communication Society Leonard Abraham Award for Research Achievements, and
the Ralph and Catherine Fisher Professorship Chair. Since June 2001 she serves as the editor-in-chief of the ACM/Springer
Multimedia System Journal.
An erratum to this article is available at . 相似文献
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Communication overhead is a major concern in wireless sensor networks because of inherent behavior of resource constrained sensors. To degrade the communication overhead, a technique called data aggregation is employed. The data aggregation results are used to make crucial decisions. Certain applications apply approximate data aggregation in order to reduce communication overhead and energy levels. Specifically, we propose a technique called semantic correlation tree, which divides a sensor network into ring-like structure. Each ring in sensor network is divided into sectors, and each sector consists of collection of sensor nodes. For each sector, there will be a sector head that is aggregator node, the aggregation will be performed at sector head and determines data association on each sector head to approximate data on sink node. We propose a doorway algorithm to approximate the sensor node readings in sector head instead of sending all sensed data. The main idea of doorway algorithm is to reduce the congestion and also the communication cost among sensor nodes and sector head. This novel approach will avoid congestion by controlling the size of the queue and marking packets. Specifically, we propose a local estimation model to generate a new sensor reading from historic data. The sensor node sends each one of its parameter to sector head, instead of raw data. The doorway algorithm is utilized to approximate data with minimum and maximum bound value. This novel approach, aggregate the data approximately and efficiently with limited energy. The results demonstrate accuracy and efficiency improvement in data aggregation. 相似文献
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Data aggregation algorithms play a primary role in WSN, as it collects and aggregates the data in an energy efficient manner so that the life expectancy of the network is extended. This paper intends to develop a query-based aggregation model for WSN using the advanced optimization algorithm called group search optimization (GSO). The proposed model is constructed in such a way that the querying order (QO) can be ranked based on latency and throughput. Accordingly, the main objective of the proposed GSO-based QO is to minimize the latency and maximize the throughput of WSN. The proposed data aggregation model facilitates the network administrator to understand the best queries so that the performance of the base station can be improved. After framing the model, it compares the performance of GSO-based QO with the traditional PSO-based QO, FF-based QO, GA-based QO, ABC-based QO and GSO-based QO in terms of idle time and throughput. Thus the data aggregation performance of proposed GSO-based QO is superior to the traditional algorithms by attaining high throughput and low latency. 相似文献
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Wenzhong Guo Naixue Xiong Athanasios V. Vasilakos Guolong Chen Hongju Cheng 《Wireless Personal Communications》2011,56(3):359-370
Data aggregation has been emerged as a basic approach in wireless sensor networks (WSNs) in order to reduce the number of
transmissions of sensor nodes.This paper proposes an energy-efficient multi-source temporal data aggregation model called
MSTDA in WSNs. In MSTDA model, a feature selection algorithm using particle swarm optimization (PSO) is presented to simplify
the historical data source firstly. And then a data prediction algorithm based on improved BP neural network with PSO (PSO-BPNN)
is proposed. This MSTDA model, which helps to find out potential laws according to historical data sets, is deployed at both
the base station (BS) and the node. Only when the deviation between the actual and the predicted value at the node exceeds
a certain threshold, the sampling value and new model are sent to BS. The experiments on the dataset which comes from the
actual data collected from 54 sensors deployed in the Intel Berkeley Research lab made a satisfied performance. When the error
threshold greater than 0.15, it can decrease more than 80% data transmissions. 相似文献
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当前基于压缩感知的传感器网络数据融合方案中,不论数据字段有何特征,均假设网络具有固定而均匀的压缩阈值,从而导致数据通信量过高,能耗浪费较大。提出一种基于多分辨率和压缩感知的数据融合方案。首先,对传感器网络进行配置,以生成多个层次类型不同的簇结构,用于过渡式数据收集,在该结构上,最低层的叶节点只传输原始数据,其他层的数据收集簇进行压缩采样;然后将其测量值向上发送,当母数据收集簇收到测量值时,利用基于反向DCT和DCT模型的CoSaMP算法恢复原始数据;最后,在SIDnet-SWANS平台上部署了该方案,并在不同的二维随机部署传感器网络规模下进行了测试。实验结果表明,随着分层位置的变化,大部分节点的能耗均显著降低,与NCS方案相比,能耗下降50%~77%,与HCS方案相比,能耗下降37%~70%。 相似文献
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基于无线网状网络体系结构的传感器网络已演变成远程传感器管理一种强大而又经济实用的方法,有可能让器件实现自组建,无需人为干预,为新一类机对机应用铺平了道路.作为互连传感器的增长链,它们能收集到大量的以往无法获取的信息. 相似文献
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该文提出了一种基于分簇的无线多媒体传感器网络(WMSNs)数据聚合方案(Cluster-based Data Aggregation Algorithm, CDAA)。利用新的分簇方法和数据聚合策略,CDAA可以有效延长网络生命期。根据多媒体节点数据采集的方向性和节点剩余能耗,该文提出新的无线多媒体传感器网络的分簇方法,并基于该分簇方法进行网内多媒体数据聚合。仿真结果表明,该方法能够有效减少冗余数据的传送,与LEACH, PEGASIS等传统WSNs路由协议和针对WMSNs的AntSensNet协议相比,在能耗均衡和节能方面表现出更好的性能。 相似文献