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Bayesian Compressive Sensing Based Optimized Node Selection Scheme in Underwater Sensor Networks

机译：水下传感器网络中基于贝叶斯压缩感知的优化节点选择方案

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摘要

Information acquisition in underwater sensor networks is usually limited by energy and bandwidth. Fortunately, the received signal can be represented sparsely on some basis. Therefore, a compressed sensing method can be used to collect the information by selecting a subset of the total sensor nodes. The conventional compressed sensing scheme is to select some sensor nodes randomly. The network lifetime and the correlation of sensor nodes are not considered. Therefore, it is significant to adjust the sensor node selection scheme according to these factors for the superior performance. In this paper, an optimized sensor node selection scheme is given based on Bayesian estimation theory. The advantage of Bayesian estimation is to give the closed-form expression of posterior density function and error covariance matrix. The proposed optimization problem first aims at minimizing the mean square error (MSE) of Bayesian estimation based on a given error covariance matrix. Then, the non-convex optimization problem is transformed as a convex semidefinite programming problem by relaxing the constraints. Finally, the residual energy of each sensor node is taken into account as a constraint in the optimization problem. Simulation results demonstrate that the proposed scheme has better performance than a conventional compressed sensing scheme.
机译：水下传感器网络中的信息获取通常受到能量和带宽的限制。幸运的是，可以在某种基础上稀疏表示接收到的信号。因此，可以通过选择全部传感器节点的子集来使用压缩的传感方法来收集信息。传统的压缩传感方案是随机选择一些传感器节点。不考虑网络寿命和传感器节点的相关性。因此，根据这些因素调整传感器节点选择方案对于提高性能具有重要意义。基于贝叶斯估计理论，给出了一种优化的传感器节点选择方案。贝叶斯估计的优点是给出后验密度函数和误差协方差矩阵的闭式表达式。提出的优化问题首先旨在基于给定的误差协方差矩阵来最小化贝叶斯估计的均方误差（MSE）。然后，通过放松约束将非凸优化问题转换为凸半定规划问题。最后，将每个传感器节点的剩余能量作为优化问题的约束条件。仿真结果表明，该方案具有比常规压缩传感方案更好的性能。

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