A Modified M2M-Based Movement Prediction for Realistic Emergency Environments

The number of emergencies has been observed to increase exponentially in recent years. Mobile devices, being ubiquitously present with users, often found to be a preferred way of communication in these emergencies. Since realistic prediction of user movements in emergencies is still an open issue, recently a machine to machine (M2M)-based movement prediction framework has been proposed. The proposed framework considers user's behavioral changes as well as simple geographical constraints initiated by emergencies. Nevertheless, these considerations are not sufficient to present a practical emergency environment and thus realistically predict emergency-affected user movements. Consequently, this paper proposes a modified M2M-based framework for movement prediction. The modified framework considers not only user's behavioral changes but also following factors: a realistic pre-emergency mobility model, arbitrarily-shaped obstacles, a signal propagation model and a practical personal belief computation. Simulation results have been used to verify effectiveness of the proposed framework.