# Mobile Networks and Internet of Things Infrastructures to Characterize Smart Human Mobility
## 2 Fundamental Requirements of Smart Human Mobility
### Ubiquitous Networking for the Internet of Things
#### 2.1.1 Urban Scenario  
Smart human mobility projects have been among the most actively discussed realizations, which are made possible by a purposeful and integrated IoT, data, and connectivity approach, leveraging a mix of challenges and solutions in urban scenarios. Data collection in smart cities is a challenge for researchers and authorities. Since the transformation of data into information raises privacy issues, there are several barriers that should be overcome in human mobility surveys. The data generated in these environments is often large, and special methods to handle such large streams are required. Big data is a term coined to gather information technology that enables this. It provides means to handle data streams, data collection, data transformation, and information generation from data streams. Categorizing different use cases for big data and smart cities may present technological advantages in the planning of new R&D projects targeting high volume data streams and smart city applications [15]. In Figure 2, the category of local preventive management depends on individual customer data, including transport cards, call location records, and individual messages from your service provider. The objective is to understand the root causes of citizens' mobility problems based on data and to anticipate solutions, which increases the efficiency of administration and avoids resource waste. For example, the dynamics of human activity recorded in [16] was used to predict crimes. The category for local operational management is based on data from local service providers, such as urban infrastructure and company resource data. The objective is to improve the operational processes of local governments and companies based on improving communication between resources, infrastructure, and citizens or visitors using data. Understanding human mobility and urban dynamics is vital for public transport operators, urban planners, and location-based service providers [17]. The category of local network development depends on user data to help them. This category improves the accessibility of user resource local networks based on a better understanding of these users. For example, while most existing empirical human mobility studies are based on location data from cell phones, [18] uses user's call history to record users' locations when they receive or initiate a text message or call. Finally, the 'local information diffusion' category depends on data from local service providers to help users. It has the goal to increase efficiency and identify useful information regarding cities, citizens and its visitors. This effort is often governed by data policies such as open data, where data are freely available to everyone. For instance, mobility data in [19] from Descartes Labs is available free of charge at the United States county level. Figure 2: Categorization of use cases for big data and smart cities. Another challenge comes into view regarding data handling, data fusion, and information creation in the context of smart cities. From the collection of data to the delivery of added value information in smart cities, challenges can be identified, as illustrated in Figure 3.  
For starters, data quality management is a prerequisite for trustworthy smart city information. This concerns aspects such as data isolation, data unavailability, data quality, and data costs [20]. Processes of data and information fusion with the objective to integrate different streams or sources of data inside smart cities or IoT applications is another challenge that can be identified. Figure 3 demonstrated different categories of data that can be collected from different multiple sources to create higher quality information for the betterment of citizens' lives. Nevertheless, linking data from different sources often comes with its own challenges due to organizational nature, data structures or interpretation.