Friday, July 07, 2006

Project Iteration III: Fill in the gaps

Remember colour-by-numbers? Well, that's similar to this. Sort of. Ok, maybe not, but it's a fond childhood memory. Anyway...

So in the previous two posts, we've seen distance, and situation-based approaches. Now it's time to look at something a little more abstract. Imagine we were to take a large amount of readings around the university- it's not hard to imagine a map looking something like this:
Right. Now, due to the sheer number of readings presented above, let's not bother to try and figure out patterns or distances. Let's just consider it from a simple statistical standpoint. It would be easy enough to say, "if a point has a minimum of 3 surrounding points with readings already known, the current point will have a signal strength of Point1+Point2+...+Pointn divided by n". Applying this approach:
Well well well, we have full coverage all of a sudden. That's the first big advantage we see here. However, how much of that is due to the method, and how much is due to the sheer number of readings initially collected? This of course won't be known until we look at this method and the other two with the same data points. In addition, how sure can we be of the accuracy of estimating points from other estimated points? There certainly are some odd looking readings on the above chart.

  • (8,7) has a signal strength of 4, yet one square later has dropped to 1?
  • (6,8) to (7,8) actually increases in signal strength?
  • What's the deal with the top right access point? It's surrounded by 9s, and yet others have 10 all around.
And so forth. Still, the idea is there, and it shall be interesting to see if I can combine it with the other two in any way to increase my accuracy.


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