MOEME: Real-time Mobility and Orientation Estimation for Mobile Environments

Knowledge of user movement in mobile environments paves the way for intelligent resource allocation and event scheduling for a variety of applications. Existing schemes for estimating user mobility are limited in their scope as they rely on repetitive patterns of user movement. Such patterns neither exist not easy to recognize in soft-real time, in open environments such as parks, malls, or streets. We propose a novel scheme for Real-time Mobility and Orientation Estimation for Mobile Environments (MOEME). MOEME employs the concept of temporal distances and uses logistic regression to make real time estimations about user movement. MOEME is also used to make predictions about the absolute orientation of users. MOEME relies only on opportunistic message exchange and is fully distributed, scalable, and requires neither a central infrastructure nor Global Positioning System. MOEME has been tested on real world and synthetic mobility traces—makes predictions about direction and count of users with up to 90% accuracy, enhances successful video downloads in shared environments.