A Robust Diffusion Estimation Algorithm for Asynchronous Networks in IoT

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.