Movement prediction of mobile users in emergencies using M2M networks

The ever-growing popularity of handheld mobile devices has been accelerated by their ubiquitous presence to support user mobility. In mobile environments, a prior prediction of user movements can improve both network and application-level performances. In the existing literature, a major research on mobility has focused on user behavior to predict their movements. Although emergency-affected users are more likely to deviate from their usual behavior, the influence of an emergency on user behavior and thus user movements has not been investigated until now. However, an accurate user movement prediction in emergencies is often more important due to post-emergency resource constrains in the network. Being closely carried by their users, mobile devices can be conveniently employed to monitor user behavior in emergencies and consequently predict user movements. Therefore, this work proposes a movement prediction framework for mobile users in emergencies using a machine-to-machine (M2M) network of mobile devices. Simulation results exhibit that the prediction accuracy reaches up to 100%, i.e, predicted movements by the proposed framework are exact to the real movements.