An Autonomous Model Construction Mechanism in Dynamic Sensor Networks

Internet-of-Things (IoT) plays a critical role in many intelligent scenarios, such as home automation, health care, connected vehicles and so on. Taking into account of several practical concerns like privacy, latency and battery issues, embedding machine learning algorithms in IoT devices is a potentially effective solution. Due to the fact that IoT devices are deployed in highly dynamic environment, reconstruction capability for model which can make the devices adapt to changes of the environment is imperative. In this paper, we design a novel autonomous model training mechanism based on curriculum learning which can deduce a set of labels to rebuild a new model adapting to dynamic environment. In particular, the label learning adopts a three-step strategy, including (1) curriculum generation, (2) curriculum refinement, and (3) curriculum teaching. In the first step, we design a reliability evaluation mechanism to pick out a high-quality set with higher reliability over the original curriculum. In the second step, based on confusion matrix and label propagation, we adjust the labels of the curriculum to accomplish the curriculum refinement. In the third step, the labels of the low-quality set will be adjusted based on the refined curriculum. Then an accurate training set can be obtained, based on which a new model can be built. Extensive experiments on three datasets demonstrate the efficiency of our approach compared to the state-of-the-art methods.