mixFOCuS: A Communication‐Efficient Online Changepoint Detection Method in Distributed System for Mixed‐Type Data

With the advent of the Internet of Things, it is increasingly common to have large networks of sensors, where each sensor may collect different types of data, has limited local computing resources and the ability to transmit data to a central cloud. Detecting events that trigger changes in sensor data properties is a key concern. However, minimizing sensor‐to‐cloud communication might be necessary due either to privacy constraints or limited battery resources. To detect changes within such a network, we introduce a new method, mixFOCuS, which can detect changes in mixed‐type data, where data from different sensors follow different, possibly non‐Gaussian, distributions. This methods builds on the FOCuS algorithms, which are recent improvements of the classic approach of Page (1954), extending the approach to streaming data setting for distributed sensor networks. Our method does not require assuming known pre‐ and post‐change parameters, yet is still efficient in both computation and communication, and suitable for detecting changes in real‐time. We show the trade‐off between reduced transmission frequency and detection power. Simulation results indicate improved power for mixed‐type data and better performance than the existing works on Gaussian data.