RightSPOT: A Novel Sense of Location for a Smart Personal Object

Abstract

One of the main prerequisites for location-based services is knowledge of location. We present a simple algorithm for computing the location of a device based on signal strengths from FM radio stations. The motivation for this method comes from a new class of smart personal objects that will receive digital data encoded in regular FM radio broadcasts. Given their built-in ability to receive FM and to measure signal strengths, we show how to exploit this ability to measure the device’s location. Our algorithm, called RightSPOT, is designed to be robust to manufacturing variations among devices that affect how they measure signal strength. Toward this end, we present a location classification algorithm based not on absolute signal strengths, but on a ranking of signal strengths from multiple FM radio stations. In tests with three devices in six suburban areas, we show that we can correctly infer the device’s location about 80% of the time.