However, such an architecture would require something like the "Google Grid" and therefore your "network" profile, based on your data, is also available.
Through smart associations (difficult to achieve), the goal is to provide an "environmental" context to your data and this will lead the way to something called location based services (look this up in google).
The challange is to fully integrate the "environmental" sensor into the network. "Sensor" networksare the next big thing in networking and they will significantly change the world, if they can actually be made to work.
Example output:
One important characteristic of sensor networks is the need to reduce
energy consumption per device. The
industry goal is to power an individual WSN (wireless sensor node) for
one year on a pair of AA batteries.
The net effect of this will be huge.
Consumers will simply part with their money (or actually increase their credit card
debt) much more easily as the "association" network suddenly stocks their current
location with their preferences.
This basically means that handheld devices can be used to activate any particular
service or good that your interested in acquiring. The commercial world will love this
because consumers already shop by association.
Evolution over time looks something like this:
We are clearly now in Stage 3 and halfway toward stage 4.
That is, most of you already have a computer in addition to some mobile device that is almost a computer (e.g. PDA/Cell Phone). At the very least its a network device.
The Location Free TV is just one example of what exists now
Stage 5 is not that far off:
The behavior of pervasive computing depends critically on the degree to which individual
devices can become smart, networked devices that are reliable .
Toaster down problem.
To reach that goal, several challanges need to be overcome.
The core challenge:
Automatic identification of physical objects is key to coupling atoms (physical entities) to bits (information). RFID and sensor networks are the enablers and foundation layer of this vision. However, the raw data must be "understood" so that salient "events" can be extracted from the data stream