# Mobile Networks and Internet of Things Infrastructures to Characterize Smart Human Mobility
## 2 Fundamental Requirements of Smart Human Mobility
### Human Mobility Modeling and Prediction
#### 2.3.1 Challenges and Opportunities  
Based on literature we discuss some challenges such as next-location prediction (NLP), crowd flow prediction (CFP), and trajectory generation (TG). In NLP, individual's mobility patterns, geomarketing and recommendation of friends in social network platforms or location-aware advertisements are some areas that benefit with predicting people's movements [46, 47, 48]. However, it also presents many challenges because human mobility is mainly encoded in the temporal order of the visitation patterns and characterized by a high degree of predictability [49]. Furthermore, it can be based on other aspects, such as weather conditions and social contacts. Thus, NLP poses the main challenge that is the representation of spatio-temporal characteristics of human movements. CFP is another important task. Its challenge is to predict in a geographic region the incoming and outgoing flows [50]. It may help mitigate or prevent massive crowds of people streaming into a strip area and other dangerous situations [51, 52]. However, CFP presents a problem because it deals with spatio-temporal dependencies. In spatial dependencies, a model should consider that an area's out-flow may affect the in-flows of other areas. Regarding the temporal dependencies, human movements are interpreted by three properties: temporal trends, periodicity, and closeness. Trends are patterns that repeat over time (e.g., working days, months, or years); periodicity consider the repetitive relevant events (e.g., rush hours in the evening); temporal closeness highlights events that are close in time. Moreover, these properties can be affected by external factors such as weather conditions. The goal of TG models is to generate synthetic trajectories using realistic mobility patterns [53]. The synthetic trajectory model should reproduce a set of spatio-temporal mobility patterns. This model is relevant in many applications. For example, they perform analysis of networking protocols (e.g., mobile ad hoc networks), where network users are exploited to route and deliver the messages [54]. However, TG models capture, simultaneously, the spatio-temporal of human mobility patterns. In its turn, realistic mobility patterns should reproduce the number and sequence of visited locations together with the time, duration of the visits, including the temporal statistics observed empirically. It is challenging to characterize the distance heterogeneity of individuals [55], the tendency to move within short distances [56], daily trajectories of people [57], and the number of locations are constantly visited by individuals in time [58]. Based on previous models, advances in DL models are the most exciting in the AI area. They present evolution in classification and prediction accuracy without the need to use feature engineering (selection, from the raw data, of characteristics that can be used in predictive models) associated with traditional learning supervised. Instead of being programmed, these systems can be trained [59]. However, there are limitations in sight. Processes require large volumes of labeled data to accurately perform complex tasks and obtaining these large datasets can be difficult. Even when they are available, labeling efforts require massive human resources. Furthermore, it can be difficult to know how a mathematical model trained with deep learning arrives at a prediction, recommendation, or decision [60]. Therefore, we present other two emerging solutions for scholars of human mobility prediction: gravity model (GM) and spatial equilibrium model (SEM). In its turn, GM has demonstrated a great potential for human mobility research. It can be used as a representative model for trip generation and distribution, which are both stages of the several steps model for human flow prediction, where it traditionally used to predict trade flows across countries. Another gravity's advantage lies in its ability to use real data to assess the sensitivity of human mobility flows with respect to policy factors we are interested in.