Learning sensor models for wireless sensor networks

Sensor data generation is a key component of high fidelity design and testing of applications at scale. In addition to its utility in validation of applications and network services, it provides a theoretical basis for the design of algorithms for efficient sampling, compression and exfiltration of the sensor readings. Modeling of the environmental processes that gives rise to sensor readings is the core problem in physical sciences. Sensor modeling for wireless sensor networks combine the physics of signal generation and propagation with models of transducer saturation and fault models for hardware. In this paper we introduce a novel modeling technique for constructing probabilistic models for censored sensor readings. The model is an extension of the Gaussian process regression and applies to continuous valued readings subject to censoring. We illustrate the performance of the proposed technique in modeling wireless propagation between nodes of a wireless sensor network. The model can capture the non-isotropic nature of the propagation characteristics and utilizes the information from the packet reception failures. We use measured data set from the Kansei sensor network testbed using 802.15.4 radios.