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the geolocation and processing of any sensor system, rather than requiring the developer to locate and implement proprietary software for each sensor system.
In addition, this concept allows for the development of tools that can provide on-demand geolocation and mapping. As SensorML-enabled application software becomes more
common, there will be less need to store and distribute volumetrically costly latitude, longitude, altitude, and incident angles values per pixel. Furthermore, correction of
sensor geolocation requires only the redistribution of much smaller description files, rather than the redistribution of large collections of reprocessed sensor data. In fact,
correction or refinement of geolocation can be conducted by the end user as necessary, rather than relying strictly on the instrument team. Finally, the ability to provide spatial-
temporal knowledge of the sensor’s coverage, independent of the on-line presence of sensor data, allows much needed search and query capabilities for determining sensor
coverage for given a location or time, or for determining coincident sampling between two or more sensors.
In addition to the significant benefits discussed above, on-demand processing of geolocation has in many cases been shown to be as fast or faster than reading in and
processing pre-calculated location values. With CPU power increasing faster than IO rates, the improvement in on-demand calculation of geolocation will increase even
further in the future, with the added benefit of not wasting valuable capacity with storage of blocks of latitude and longitude values.
6.4 Importance to Sensor Web Enablement
The SWE architecture and design provides an important contribution for the enablement of future sensor webs. However, it is important to note that while SensorML is a key
component to the SWE initiative, SensorML is not dependent on the SWE framework and can be used on its own or in conjunction with other sensor system architectures.
In much the same way that HTML and HTTP standards enabled the exchange of any type of information on the World Wide Web, the OGC Sensor Web Enablement SWE
initiative is focused on developing standards to enable the discovery and exchange of sensor observations, as well as the tasking of sensor systems. The functionality that has
been targeted within a sensor web includes:
• Discovery of sensors and sensor observations that meet our needs • Determination of a sensor’s capabilities and quality of measurements
• Access to sensor parameters and processes that automatically allow software to
process and geolocate observations • Retrieval of real-time or time-series observations and coverages in standard
encodings • Tasking of sensors to acquire observations of interest
• Subscription to and publishing of alerts to be issued by sensors or sensor services based upon certain criteria
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SensorML is a key component for enabling autonomous and intelligent sensor webs. SensorML provides the information needed for discovery of sensors, including the
sensor’s capabilities, location, and taskability. It also provides the means by which real- time observations can be geolocated and processed “on-the-fly” by SensorML-aware
software. SensorML describes the interface and taskable parameters by which sensor tasking services can be enabled, and allows information about the sensor to accompany
alerts that are published by sensor systems. Finally, intelligent sensors can utilize SensorML descriptions during on-board processing to process and determine the location
of its observations.
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7 Design Criteria and Assumptions for
SensorML
7.1 Basic definition of a sensor
