Topology Design for Robust IoT Data Gathering via Bayesian Networks

Internet of Things (IoT) systems have become the critical platform to enable a wide variety of smart applications. During IoT data gathering over wireless network, data may be missing due to the constraints of sensors as well as the reliability of communications. From a graph signal processing perspective, recovery of missing data may be strongly affected by the IoT system topology, which can be characterized by a directed adjacency matrix. To guarantee a robust data gathering, we propose a novel method in this paper to design the optimal topology for IoT networks via Bayesian networks, where the designed directed adjacency matrix is with orthogonal graph frequency components. Moreover, the gathering of IoT data becomes sparser in the graph frequency domain using the designed adjacency matrix and may hence improve the recovery performance of missing data. Experimental results show that our proposed methods outperform several existing algorithms.