In wireless sensor networks (WSNs), distributed sensors are often constrained by their limited battery energy and radio spectrum for transmission. This paper investigates an on-line parameter estimation problem of linear regression in a WSN, where each sensor is restricted to send a one-bit message $+1/-1$ to a fusion center in order to satisfy the spectrum and power constraints. Moreover, sensor nodes communicate with the fusion center over noisy links, which can randomly flip the binary message sent from each sensor to the fusion center. With the flipped bit stream, robust and sparse-robust learning algorithms respectively are proposed. In the proposed algorithms, the parameter estimation over a WSN with the imperfect binary communication is formulated hierarchically as Bayesian learning, and is equivalent to an expectation maximization realized by using the recursive least-squares methods. Theoretical and empirical research is carried out to assess the performance of the proposed algorithms, and a practical application of the proposed algorithms in estimation and tracking of frequencies of multiple sinusoids is also presented. These theoretical analysis and experimental results demonstrate the effectiveness of the proposed algorithms.

中文翻译：

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从翻转位中进行稀疏鲁棒学习

在无线传感器网络 (WSN) 中，分布式传感器通常受到其有限的电池能量和用于传输的无线电频谱的限制。本文研究了 WSN 中线性回归的在线参数估计问题，其中每个传感器被限制为向融合中心发送一位消息 $+1/-1$，以满足频谱和功率约束。此外，传感器节点通过嘈杂的链接与融合中心通信，这可以随机翻转从每个传感器发送到融合中心的二进制消息。针对翻转的比特流，分别提出了鲁棒和稀疏鲁棒的学习算法。在所提出的算法中，具有不完美二进制通信的 WSN 上的参数估计被分层表示为贝叶斯学习，等价于使用递归最小二乘法实现的期望最大化。进行了理论和实证研究以评估所提出算法的性能，并且还介绍了所提出算法在估计和跟踪多个正弦曲线的频率中的实际应用。这些理论分析和实验结果证明了所提出算法的有效性。