As many have probably witnessed in recent years, there is explosive growth and demand for integration of sensor-based location intelligence within tiered geospatial applications. Whether applications are mapping RFID tags, digesting objects detected through video analytics, correlating events from within Mesh network clouds, tracking movement from GPS-enabled Smart phones, distributed, ad hoc networks are supporting an increasing number of sensor systems gathering location intelligence.
Historically, when dealing with satellite-based and areal-based remote sensing platforms, GIS and remote sensing analysts have discussed techniques for sensor fusion. Now, sensor fusion encompasses a larger universe where a multiplicty of sensors may be deployed, each gathering data that has a geospatial value associated with the data being collected. Real-world challenges involve moving these data through complex networks and maintaining connectivity with the associate metadata - metadata often describing not only the nature of the objects being sensed, but the information about the sensor location. This is especially true if this sensor is moving through space and time, harvesting information in voluminous amounts. Video is a good example and real challenge.
The significance of this evolution is that geospatial IT professionals are faced with understanding how to communicate, connect, and harvest the spatial components of the messaging coming from the increasingly complex sensor web deployed across global grids. Communication protocols and architectures for global sensor networks have been and will continue to be developed, and finding a way to integrate the spatial component into distributed geospatial analytical frameworks across a range of devices represents both opportunity and sophisticated system integration challenges. Geospatial architectures must now account for managing millions of messages a second, applying sophisticated algorithms for logic functions, visualizing these data in coherent and intelligeble user interfaces, and finding ways to push only relevant messages to the end user in a near realtime capacity. Dynamic spatial data with complex spatio-temporal components is rather new to the vast majority of the geospatial community.
While the Defense-INTEL community continues to support the most advanced GeoCOPs (geospatial common operational pictures), commercial applications are increasingly desired, and sophisticated Homeland Security applications demand a sensor-rich environment for monitoring a variety of complex dynamic objects in multi-scaled geospatial frameworks. As is the case in many geographical problems, learning to harvest the location intelligence from a distributed sensor network requires next-generation interdisciplinary teams to support interoperability and a solutions-based approach. It is one thing to know the where of sensor-based solution; it is another to develop the geospatial analytical tools to rapidly place the sensor data into a meaningful context for downstream decision-support. As millions of cell phones deploy across the planet, imagine the data gathering potential as well as the data management challenge to effectively communicate the spatial information in a coherent manner. One emerging approach is to embed geospatial functions at the firmware level in order to parse and analyze the sensor message volume at both the data collection point as well as the sensor node. GIS functions are seemingly becoming componetized into the very fabric of the sensor devices. -- Posted by Alex Philp, GCS Research