Decentralized Random-Field Estimation for Sensor Networks Using Quantized Spatially Correlated Data and Fusion-Center Feedback
Decentralized Random-Field Estimation for Sensor Networks Using Quantized Spatially Correlated Data and Fusion-Center Feedback
复制标题
使用量化空间相关数据和融合中心反馈的传感器网络分散随机场估计
DOI：
10.1109/tsp.2008.2005753
复制
发表时间：
2008
影响因子：
5.4
通讯作者：
Dogandzic, A.
中科院分区：
文献类型：
--
作者：
Dogandzic, A.
在大规模无线传感器网络中，传感器处理器元件（节点）密集部署以监测环境；因此，他们的观察形成了空间上高度相关的随机场。我们考虑一种融合传感器网络架构，其中，由于带宽和能量限制，节点将量化数据传输到融合中心。融合中心通过向节点广播摘要信息来提供反馈。除了节省能源之外，这种反馈还确保了节点和融合中心故障的可靠性和鲁棒性。我们假设传感器观测结果遵循线性回归模型，且感兴趣区域内任意两个位置之间的空间协方差已知。我们提出了一个用于自适应量化、融合中心反馈以及随机场及其参数估计的贝叶斯框架。我们还推导了一个简单的次优方案来估计未知参数，将我们的估计方法应用于无反馈场景，讨论感兴趣区域内任意位置的现场预测，并提供数值示例来证明所提出方法的性能。
In large-scale wireless sensor networks, sensor-processor elements (nodes) are densely deployed to monitor the environment; consequently, their observations form arandom fieldthat is highly correlated in space. We consider a fusion sensor-network architecture where, due to the bandwidth and energy constraints, the nodes transmit quantized data to a fusion center. The fusion center provides feedback by broadcasting summary information to the nodes. In addition to saving energy, this feedback ensures reliability and robustness to node and fusion-center failures. We assume that the sensor observations follow a linear-regression model with known spatial covariances between any two locations within a region of interest. We propose a Bayesian framework for adaptive quantization, fusion-center feedback, and estimation of the random field and its parameters. We also derive a simple suboptimal scheme for estimating the unknown parameters, apply our estimation approach to the no-feedback scenario, discuss field prediction at arbitrary locations within the region of interest, and present numerical examples demonstrating the performance of the proposed methods.