In the Internet of Things (IoT), asynchronous networks with varying topology are quite common. Meanwhile, Gaussian noise and impulsive noise widely exist in asynchronous networks. Existing works on distributed estimation problems in networks primarily consider fixed topologies and Gaussian noise. Thus, these algorithms are not suitable for distributed parameter estimation in asynchronous networks. To overcome this issue, we propose a distributed diffusion kernel risk-sensitive loss (d-KRSL) algorithm, which can achieve a good performance in asynchronous networks with varying topology, and maintains the robustness to both Gaussian and impulsive noise. The mean and mean square performances of the proposed algorithm are analyzed theoretically and verified by numerical simulation results.

中文翻译：

---

物联网异步网络的鲁棒扩散估计算法


在物联网 (IoT) 中，具有不同拓扑的异步网络非常常见。同时，异步网络中广泛存在高斯噪声和脉冲噪声。关于网络中分布式估计问题的现有工作主要考虑固定拓扑和高斯噪声。因此，这些算法不适合异步网络中的分布式参数估计。为了克服这个问题，我们提出了一种分布式扩散核风险敏感损失（d-KRSL）算法，该算法可以在具有不同拓扑的异步网络中实现良好的性能，并保持对高斯和脉冲噪声的鲁棒性。对所提算法的均值和均方性能进行了理论分析，并通过数值仿真结果进行了验证。