## 2 Related Work
### Reservation Schemes and Prediction in Literature  
In this sub-section we will introduce some contribution of scientific research about mobility prediction and reservation scheme topics.  
In [12], a scheme for the service patterns estimation by tracking mobile users is proposed: it is based on historical records and permits the estimation of the future cells which will be probably visited by a mobile user.  
The model presented in [25], instead, does not exploit the knowledge of the users' mobility history. In this way, the model is limited to make a prediction for the part of the network (cells, geographical area, etc.) where mobile users are likely to move (i.e., user's final destination), instead of the complete path to reach the final destination.  
In [26] the authors propose a mobility prediction scheme, based on Dempster-Shafer processes and Markov theory. In particular they consider a typical transportation topology, composed by roads and intersections, having the possibility of dividing the considered region into cells. They assume that mobility history can be analyzed periodically, mining information about user habits. The proposed DAMP scheme stores time and geolocation coordinates, and it is shown that the whole process can be modeled as a semi-Markovian one, obtaining high accuracy values (in terms of roads similarities). DAMP is able to predict mobility positions within a predefined time period.  
In [13] a mobility prediction scheme for cellular networks is proposed, in order to optimize hand-over procedures: the main idea is to a-priori know hand-off times, for speeding up hand-over switching. Mobility analysis is performed by a Kalman filter (for tracking GPS signals) and Hidden Markov Models (HMMs--for probability analysis). Both results are used for the estimation of the next probable cell.  
In [27], the location prediction using mobile phone traces has been considered. The authors made a deep analysis on mobility patterns, concluding that they are strongly correlated with co-located patterns and they affect user short-time mobility. They proposed a new prediction scheme, based also on the social interplay revealed in the cellular calls.  
In [28] the authors show how the knowledge about future mobility locations can enhance system performance in terms of hand-over droppings and blocking probabilities. The proposed MPRR is able to evaluate hand-off times (with a detailed traffic analysis) and future available bandwidth, integrating them with a CAC scheme. The obtained results show that service continuity can be guaranteed, while maintaining a good level of system utilization.  
The work in [29], argues about the management of Mobility Independent Predictive (MIP) and Mobility Dependent Predictive (MDP) services in adaptive networking. Adaptive QoS (also indicated as soft QoS) is the main concept of the paper, able to increase the overall wireless system utilization, on the base of a utility-based BRS.  
The works in [30; 31] propose some prediction techniques based on the CST distribution under the Random Way Point (RMM) model, arriving at a closed form for binding average speed, cell coverage radius and variation of the average speed. Moreover, in [32; 33] an unconventional-based prediction scheme (DPBMA) has been proposed, in order to manage the concept of passive reservations. A multiplexing algorithm has been integrated, in order to avoid wasting a lot of passive reservations. A distributed set of Markov chains has been adopted, evaluating its performance in terms of prediction accuracy, through many deep campaigns of simulations.