Improving Measurement Accuracy in Sensor Networks by an Object Model Generation and Application

The paper describes a novel method of calculating measurement results in sensor networks, which includes modifying the conventional measurement estimates based on the object under measurement model mined from the data collected by the sensor network itself previously and other information made available by domain experts. It is shown that the model application might produce a significant gain in measurement accuracy if the model is correct. The gain value is estimated and its dependence on various factors is studied by computer simulation and experimentation with real sensor networks built from Crossbow Telos ver. B motes. The conditions of achieving the gain versus suffering the loss are derived and the recommendations of how to shape the object model in order to achieve and maximize the gain value are provided.