This document discusses using context such as mobility to optimize wireless network scanning. It proposes that scanning frequency could be adapted based on factors like location, movement, and usage. Measuring mobility directly with accelerometers is challenging due to noise, but categorizing mobility into levels like none, moderate, or high movement could help determine appropriate scanning intervals. Experiments show integrating accelerometer data to calculate velocity results in large errors, so alternative approaches may work better, such as using step counts as a proxy for mobility. Overall, the document explores how contextual information could help wireless networks scan more efficiently.