The design of fast and effective coordination among sensors and actuators in Cyber-Physical Systems (CPS) is a fundamental, but challenging issue, especially when the system model is a priori unknown and multiple random events can simultaneously occur. We propose a novel collaborative state estimation and actuator scheduling algorithm with two phases. In the first phase, we propose a Gaussian Mixture Model (GMM)-based method using the random event physical field distribution to estimate the locations and the states of events. In the second phase, based on the number of identified events and the number of available ac-tuators, we study two actuator scheduling scenarios and formulate them as Integer Linear Programming (ILP) problems with the objective to minimize the actuation delay. We validate and demonstrate the performance of the proposed scheme through both simulations and physical experiments for a home temperature control application.