The analysis of sensors' behavior becomes one of the essential challenges due to the growing use of these sensors for making a decision in IoT systems. The paper proposes an approach for a formal specification and analysis of such behavior starting from existing sensor traces. A model that embodies the sensor measurements over the time in the form of stochastic automata is built, then temporal properties are feed to Statistical Model Checker to simulate the learned model and to perform analysis. LTL properties are employed to predict sensors' readings in time and to check the conformity of sensed data with the sensor traces in order to detect any abnormal behavior.