Abstract
Data discovery for sensor data in an M2M network uses probabilistic models, such as Gaussian Mixing Models (GMMs) to represent attributes of the sensor data. The parameters of the probabilistic models can be provided to a discovery server (DS) that respond to queries concerning the sensor data. Since the parameters are compressed compared to the attributes of the sensor data itself, this can simplify the distribution of discovery data. A hierarchical arrangement of discovery servers can also be used with multiple levels of discovery servers where higher level discovery servers using more generic probabilistic models.