【摘要】 面向海洋环境监测的水下传感器网络是典型的能量和带宽受限系统,大规模、高分辨率的信息获取需求和受限的网络资源之间的严重不对称性,是制约该网络发展的瓶颈问题。鉴于此,以压缩感知为理论基础,提出了基于传感器节点随机稀疏部署的信息获取方案,充分利用被测对象在相关处理域上的稀疏性,以较小的网络成本实现对监测区域的高分辨率信息重构。对实际海洋环境数据的仿真实验表明,该方案与传统传感器网络工作方式相比,在不降低监测分辨率的情况下,能够显著减少所需部署的节点数量,并降低对带宽、能耗等网络资源的需求。该方案对二维、三维海洋环境监测传感器网络均具有适用性。更多还原

【Abstract】 The ocean environment monitoring underwater sensor network is a typically energy-limited and bandwidthlimited system,in which the serious asymmetry between the demand for large-scale and high-resolution information acquisition and the limited network resources is its technical bottleneck restricting the network development. In order to solve this problem,a novel information acquisition scheme with random and sparse sensor node deployment is proposed based on compressed sensing. By making full use of the sparsity of the monitored objects in related transform domains,the proposed scheme can realize high-resolution information reconstruction with lower network cost. The simulation experiments with real ocean environment data show that compared with traditional sensor network operation mode,the proposed scheme requires much less sensor nodes in deployment,and decreases the requirements for bandwidth and energy consumption as well,without degradation in monitoring resolution. The scheme is suitable for both two-dimensional and three-dimensional ocean environment monitoring sensor networks.更多还原