摘要
无线传感器网络在水下生态环境的监测方面有着广泛的应用,但是常常会碰到能量缺乏和生命周期较短的问题。为了克服这些问题,基于压缩感知理论,提出了一种网络节点调度和稀疏采样的优化方法,与传统的无线传感器网络节点调度方法不同,它采用随机概率采样模型,只对整个网络的一个子集进行采样,这是一种"物理静止,逻辑动态"的随机调度方案,节省了有限的带宽和能量。同时该方法在节点调度过程中,引入了状态转换图,对节点的状态进行分类控制,增加了网络覆盖要求和最小观测数等约束条件,使得设计的稀疏选择矩阵更为合理。同时研究了在满足最小l1范数的条件下,基于组合算法对稀疏信号进行重构的方法。实验的结果表明,与传统的无线传感器水下监测网络相比,基于上述节点调度方法所构建的无线传感器水下监测网络,具有较低的能耗和较长的网络生存时间,并且能对稀疏采样后的信号以较大的概率进行近似重构,使得重构信号逼近原始监测信号。
Wireless sensor networks have a wide range of applications in underwater ecological environment monitoring, but often encoun- ter the problems of lacking energy and short life cycle. To overcome these problems ,this paper proposes an optimization method of network node scheduling and sparse sampling based on compressed sensing theory. Unlike traditional wireless sensor network node scheduling, the method adopts a random probability sampling model and only samples a subset of the entire network, which is a＂ physical static,logic dy- namic＂ stochastic scheduling scheme, and saves limited bandwidth and energy. Furthermore, in the node scheduling process, the method introduces a state transition diagram to classify and control the states of the node, and adds the constraint conditions of network coverage requirements and minimum observation number, which makes the design of the sparse selection matrix more reasonable. At the same time, the sparse signal reconstruction method based on the combination algorithm is studied under the condition of meeting the minimum It norm. The experiment results show that, compared with traditional underwater monitoring network of wireless sensors, the underwater monitoring network of wireless sensors based on the proposed node scheduling method possesses lower energy consumption and longer net- work lifetime,can approximately reconstruct the sparse sampled signal with a larger probability and make the reconstructed signal approach the original monitored signal.
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2012年第12期2728-2734,共7页
Chinese Journal of Scientific Instrument
基金
国家自然科学基金(60973139
61170065
61171053
61003039
61003236
61103195
6160903181)
江苏省自然科学基金(BK2011755)
江苏省科技支撑计划项目(BE2010197
BE2010198
BE2011844
BE2011189)
东大实验室开放基金项目(K93-9-2010-06)
省属高校自然科学研究重大项目(11KJA520001)
江苏省高校自然科学基础研究项目(11KJB520016)
高校科研成果产业化推进工程项目(JH2010-14
JHB2011-9)
国家博士后基金(20100480048
20100471356)
江苏高校科技创新计划项目(CXZZ11-0405
CXZZ11-0406)
教育部博士点基金(20103223120007
20113223110002)
江苏省计算机信息处理技术重点实验室基金(KJS1022)
江苏高校优势学科建设工程(yx002001)资助项目
关键词
无线传感器网络
压缩感知
稀疏表示
能量有效
水下监测
wireless sensor networks （WSNs）
compressed sensing （CS）
sparse representation
energy efficient
underwater monitoring
