Indoor navigation with machine learning
GPS lets you determine the position of an object down to single-centimeter accuracy – as long as the object is outside. If the object is inside, the task is a bit more complicated. Satellite navigation doesn't work well through doors and rooftops, and even if you could replace the satellite signal with equivalent transmissions from locally placed beacons or WLAN access points, the presence of interior walls and furniture muddles up the results of classical analytical techniques such as those used with GPS. What is more, when someone is inside a building, the question is not so much about "What are his coordinates." What you really want to know is "What room is he in?" Such a problem is better addressed through the tools of machine learning.
Of course, creating a complete machine learning solution to find someone in a small house might seem like overkill, but this article is intended as an exercise to show these machine learning tools and techniques in a simple situation – a kind of machine learning "Hello, World" application. One could imagine scenarios where these techniques could find broader utility, such as tracking down an executive in a large office complex or even finding a lost set of car keys.
In this example, Tom, the protagonist, has lost his way. Fortunately, his smartphone shows the signal strength of seven hotspots in his vicinity (Figure 1). Because Tom often gets lost, I have mapped the four rooms as a precaution (the blue crosses in Figure 2), and I have a machine learning dataset I can use to train a program to find Tom.
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