An Application of Markov Jump Process Model for Activity-Based Indoor Mobility Prediction in Wireless Networks
One of the most important objectives of a wireless network is to facilitate a prediction of users' mobility regardless of their point of attachment to the network. In indoor environments the effective users' motion prediction system and wireless localization technology play an important role in all aspects of people's daily lives. In this paper, the authors propose an activity-based continuous-time Markov model to define and predict the human movement patterns. This model is a simple extension of an Activity based Mobility Prediction algorithm using Markov Modeling (AMPuMM) technique. Both models are experimentally evaluated in realistic small university campus scenario.