## 8 Conclusions  
This work addresses some important problems that play a key role in wireless environments with cellular coverage. There has been a lot of research and development in wireless networking, and the main aim has always been the mitigation of wireless effects, improving the quality of the offered services. This work makes a contribution in this field by taking into account wireless propagation phenomena and mobility management.  
A utility-oriented algorithm has been proposed for the CAC and bandwidth allocation; it considers wireless link conditions, giving more effectiveness to the obtained results. For the mobility management purpose, two prediction schemes have been proposed: the first is based on a static scheme, while the second has a different behavior, because it applies a dynamic technique that chooses the predicted cells. The NSIS with some extensions for the dynamic bandwidth re-assignment has been used for the flows management in a 2D wireless system, while user mobility has been modeled through CityMob and C4R. Two service classes have been considered: NSIS-MIP services request more guarantees than NSIS-MDP ones, because they have to ensure no degradations or droppings during hand-off events. Many simulations campaigns were carried out in order to verify the correctness and efficiency of the proposed idea. The obtained results have shown that the pre-reservation phase is suitable in wireless environments if a certain level of QoS and service continuity must be ensured (the NSIS-MDP dropping percentage is too much higher if compared with the one of the NSIS-MIP traffic, which is lower negligible).  
So, the pre-reservation phase is mandatory and two prediction schemes (static and dynamic) have been proposed, in order to realize the \"passive reservations\" policy. Both prediction algorithms have shown satisfactory results, but the dynamic one led to a more accurate \"long-term\" prediction for some specific values of input parameters. In particular, in terms of system utilization, our static/dynamic proposals cannot outperform a single-step predictor, given its very low number of passive reservations (the maximum gap is about 26.3% between static and ALEZi), but in terms of prediction error it ranges from 4.5% to 15.7%, completely outperforming the other considered algorithms (which range from 15.1% to 50.5%). Additionally, in terms of CDP, the static predictor maintains below 0.1, while the dynamic one reaches some results comparable with the other multi-step scheme, while ALEZi is completely outperformed. By the obtained results, we confirmed that the proposed predictive schemes (the dynamic in particular), are able to adapt to specific mobile scenario by setting a proper threshold and the input parameters, guaranteeing an optimized management of the available resources.  
As future works, we will consider naturalistic GIS applications, able to fix the errors in mobility samples collection and to obtain more realistic traces. Moreover, it would be interesting taking into consideration studies on human mobility traces for constructing mobility models and evaluating mobile opportunistic communication throughout the use of social networks such as [56].  
Conceptualization, P.F. and M.T.; methodology, M.T.; software, P.F. and M.T.; validation, P.F. and M.T.; formal analysis, M.T.; investigation, P.F.; writing--original draft preparation, M.T.; writing--review and editing, P.F.; visualization, M.T.; supervision, P.F. Both authors have read and agreed to the published version of the manuscript.  
This research received no external funding.