{'fraction_non_alphanumeric': 0.02610587382160986, 'fraction_numerical': 0.0, 'mean_word_length': 5.301369863013699, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Destination and Mobility Path Prediction Scheme for Mobile Networks

Mobile multimedia services are gaining great momentum among subscribers of mobile networks (MNs). An understanding of the network traffic behavior is essential in the evolution of today's MNs and, thus, leads to more efficient planning and management of the network's scarce bandwidth resources. The communication efficiency can be largely improved (i.e, optimizing the allocation of the network's limited resources and sustaining a desirable quality of service) if the network anticipates the needs of its users on the move and, thus, performs reservation of radio resources at cells along the path to the destination. In this vein, we propose a mobility prediction scheme for MNs; more specifically, we first apply probability and Dempster-Shafer processes for predicting the likelihood of the next destination, for an arbitrary user in an MN, based on the user's habits (e.g, frequently visited locations). Then, at each road junction, we apply the second-order Markov chain process for predicting the likelihood of the next road segment transition, given the path from the trip origin to that specific road junction and the direction to the destination. We evaluate our proposed scheme using real-life mobility traces; the simulation results show that the proposed scheme outperforms traditional schemes.