SANY Sensors Anywhere

Within the SensorSA, the uncertainty of data sets is described using the UncertML. UncertML allows the information modeller to describe the uncertainty of a specific data set in an interchangeable way using an XML document conforming to the UncertML schema. This XML document can be embedded in a SensorML document to express information about the uncertainty of some process. In addition, UncertML can also be embedded in an O&M document to express the uncertainty of a specific sensor observation.

All data in SensorSA has an associated uncertainty depending on the available meta-information on how the data was observed (measured) or derived from other data sources.

Uncertainity of Measurements

In the case of data measured by sensors, the uncertainty can be classified into two categories:

• Type A: uncertainty arising from a random effect; evaluated by statistical methods, e.g. by computing the standard deviation of the mean of a series of independent observations or by analysing the variance and random effects in data in dependence of experimental parameters.

• Type B: uncertainty arising from a systematic effect. A typical cause of Type B uncertainty is is measurement bias due to the calibration of the measurement instrument or its behaviour in given environmental conditions (e.g. temperature, air pressure), or over time (deterioration of instrument, measurement drift). It is evaluated based on information about the instrument and environment. The measurement values may be corrected to compensate for known systematic effects.

Uncertainity of Processing

In addition to data arising from sensor measurements and observations, SensorSA also handles the results of various processing algorithms, such as spatial and temporal interpolations, or predictions arising from plume modeling. The results of such data processing steps are themselves uncertain, on the one hand due to the uncertainty of the
input data, on the other hand due to the probabilistic or approximate nature of the processing itself. The uncertainty of data arising from processing is typically expressed with one of the following:

• Probability density function, e.g. a normal distribution with known mean and variance. The data value would then lie within one standard deviation of the mean with probability 68% and within two standard deviations with probability 95%.

• Intervals (the data value lies in [a,b]). This does not a-priori assume a uniform distribution on this interval; this would however be the case if the distribution of maximum entropy were chosen. An important special case is when then the measurement instrument can assert that the data value is below or above a given threshold, but can provide no further information.
  
• Statistics such as standard deviation and moments, or quantiles (the data value lies in [a,b] with probability 95%)

Quality Assurance

In many environmental domains, the results of the measurements, observations or processing are subjected to one or more automatic, semi-automatized and manual "quality assurance" procedures. In the course of data quality assurance, the original data is usually annotated, and parts of the data may be declared invalid, or even corrected. The resulting "quality assured" data typically has lower uncertainty than the raw data,

SensorSA and OGC SWE foresee mechanisms for presenting the annotated data, e.g. by mean of the O&M "composite phenomena", but the quality assurance process itself and the type of annotation is domain dependent and therefore can not be fully specified on a generic level. In SANY, the feasibility of SensorSA-compliant quality assurance has been demonstrated within the Air Quality (SP4) pilot application.