In a very interesting article, Location Intelligence describes how the capture of geospatial information traditionally done by using cameras, laser scanners and GPS sensors, is being revolutionized by recent advances in advances in nanotechnology. In fact, "nanotechnology has made it both feasible and economically viable to develop and deploy low-cost, low-power semi-autonomous sensor devices that are general-purpose computing platforms." These geosensors are leading to wireless networks which start to be used for real applications such as monitoring traffic patterns or tracking cars in cities.
Here is how this article describes sensor networks.
In sensor networks the objective is to get many such devices to collaborate and monitor specific phenomena. Each device then becomes a node of the network. The challenge is to aggregate sensor nodes into computational infrastructures that are able to produce globally meaningful information from raw local data obtained by individual sensor nodes: understanding for example that spikes in local measurements may correspond to a moving pollution front, and tracking its evolution.
These sensor networks usually contain sensors with different degrees of capturing, processing, and communication capacities and are typically powered by Linux or TinyOS.
But what will be the use for these geosensor networks? Here are some current applications.
For example, environmental applications include the use of sensor feeds to monitor drinking water quality and wildlife habitat monitoring in the ZebraNet Wildlife Tracking project.
The geographic space covered by the sensor network, or analyzed through its measurements, may range in scale from the confined environment of a room to the highly complex dynamics of a wide ecosystem region. Consider for example the deployment of distributed cameras to monitor traffic in a metropolitan area (see e.g. the Web accessible Montgomery County, MD traffic camera collections), or the use of sensors in a subway system to detect potential threats (e.g. the spread of chemical agents).
Other geosensor networks can track a specific car across multiple camera feeds as you can see on the two pictures below (Credit: Milcord LLC).
But one of the main characteristics of these new geosensor networks is that "information becomes increasingly spatiotemporal instead of just spatial, as sensor feeds capture the evolution over time of the properties they monitor."
In other words, "Geosensors also time-stamp their data, enabling a quantifiable assessment of change over time," as wrote ACM TechNews on March 1, 2006.
Sources: Anthony Stefanidis, for Location Intelligence, February 27, 2006; and various web sites
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