I want to go home: Empowering the lost mobile device

It is estimated that over 8 million cell phones are lost or stolen each year [7]; often the loss of a cell phone means the loss of personal data, time and enormous aggravation. In this paper we present machine-learning based algorithms by which a cell phone can discern that it may be lost, and take steps to enhance its chances of being successfully recovered. We use data collected from the Reality Mining project [10] to create a suite of realistic test cases that model lost cell phone behavior. On these data sets our best algorithms can identify cases of a lost mobile device, based on its behavior over the previous 3 hours, with close to 100% accuracy. In addition, the algorithm generates false positive identifications with probability less than 3%; for individuals with relatively predictable lifestyles the False Positive Rate is substantially less. We also use the Reality Mining data to construct a set of test cases that model the behavior of a stolen phone, and show that similar algorithmic techniques give reasonable results in this setting as well.