BreadCrumbs: Forecasting Mobile Connectivity
Abstract
Mobile devices cannot rely on a single managed network, but must exploit a wide variety of connectivity options as they travel. We argue that such systems must consider the derivative of connectivity--the changes inherent in movement between separately managed networks, with widely varying capabilities. With predictive knowledge of such changes, devices can more intelligently schedule network usage.
To exploit the derivative of connectivity, we observe that people are creatures of habit; they take similar paths every day. Our system, BreadCrumbs, tracks the movement of the device's owner, and customizes a predictive mobility model for that specific user. Combined with past observations of wireless network capabilities, BreadCrumbs generates connectivity forecasts. We have built a BreadCrumbs prototype, and demonstrated its potential with several weeks of real-world usage. Our results show that these forecasts are sufficiently accurate, even with as little as one week of training, to provide improved performance with reduced power consumption for several applications.
To exploit the derivative of connectivity, we observe that people are creatures of habit; they take similar paths every day. Our system, BreadCrumbs, tracks the movement of the device's owner, and customizes a predictive mobility model for that specific user. Combined with past observations of wireless network capabilities, BreadCrumbs generates connectivity forecasts. We have built a BreadCrumbs prototype, and demonstrated its potential with several weeks of real-world usage. Our results show that these forecasts are sufficiently accurate, even with as little as one week of training, to provide improved performance with reduced power consumption for several applications.
