An estimation maximization based approach for finding reliable sensors in environmental sensing

Emerging Internet of Things (IoT)-based environmental sensing projects provide large-scale sensing data from individual sensors with high reading frequencies. These readings are usually produced by commodity sensors with varied reliabilities, and inevitably contain noises and errors. Most existing data cleaning techniques focus on issues such as communication overhead reduction and energy preservation, and do not take advantage of the unaggregated data from individual sensors that IoT environmental sensing projects offer. In this paper, we propose an Expectation Maximization algorithm for finding reliable sensors in environmental sensing data that assumes the preservation of individual sensor readings and high reading frequencies. Our approach simultaneously finds the environmental feature model and the faulty state of the sensors. Our extensive experiments show that the proposed approach is significantly more effective than existing approaches. Particularly, in a case where reliable sensors and faulty sensors differ significantly in their readings, the maximum squared error for other approaches exceeds 200, but for our approach is only 1.23.