# Mobile Networks and Internet of Things Infrastructures to Characterize Smart Human Mobility
## 2 Fundamental Requirements of Smart Human Mobility
### Metrics in Human Mobility Data Network
#### 2.2.1 Human Mobility Data Sources  
Thanks to new network technologies, finding a dataset to analyze people's mobility is not a big concern any more. However, an effort should be made to overcome the challenges to lead with different types of datasets, including phone call detail records (CDR), wi-fi or radio-frequency identification (RFID), location-based social network (LBSN) data, and global positioning systems (GPS), in order to obtain useful information from geographical movements. CDRs are collected by mobile network operators in the course of providing their service and do not require the use of smartphones. Therefore, their use in research does not preclude those from low socio-economic groups in the way that GPS data would, for instance. This is also valuable for countries where there are no integrated infrastructures for population census [33]. However, there are limitations that need to be taken into account in evaluating the use of CDRs in human mobility research. For studies that require CDRs to be collected by multiple operators, data availability, formats, and levels of aggregation vary, and this will influence the type of analysis that can be done [34]. There is an unknown level of discrepancy between phone owner and main user when phones are shared or bought for another person. This can call into question the validity of study findings focusing on particular demographics, since the extent of group representation is not known. In this work, CDR dataset is associated with a phone that made a voice call with the locations (latitude and longitude) of the cellular towers and reconstructs the spatio-temporal data of a mobile phone. Our research use 311 calls or service requests in New York City from January 2011 to December 2018 [35]. Wi-fi or RFID raises privacy issues since it collects involuntary information about devices, that can be used to infer information about their users using the MAC addresses and the service set identifiers (SSID) broadcasted. In addition, data collected can often reveal the last wi-fi spots where the device was connected. Therefore, new challenges should be dealt with, regarding the methods for analyzing the captured information which affects the methods used for unique device counts. Nevertheless, these measures aim to prevent the passive tracking and involuntary information capturing. To solve this problem, the current operating systems tend to broadcast less SSIDs, and that tactic will be more popular in the future where the only SSIDs broadcasted will be ones from hidden networks. This is done because the access points require this information to be broadcasted, in order to respond to the probe requests, thus still leaving an opportunity to analyze SSIDinformation [36]. In the case of wi-fi, we use the historical listing of LinkNYC Kiosks that save their location, and the status of the Link's wi-fi, tablet, latitude, longitude, geometry point, type of kiosk, registry date, and phone [37]. These devices are connected daily for thousands of users within New York City. Although, the available data were collected between January 2012 and December 2013, we consider only the user interactions from January 2013 to October 2013. LBSNs are location-based services that utilize location information with GPS enabled mobile phones or devices and facilitates social networking. They not only provide location information for social networks, additionally provide users a social structure derived from locations that they can share ideas especially for the places, review recommendations, and also creates knowledge of common interests, behavior, and activities from individuals' location-tagged data [38]. However, this data type presents several important limitations. First, the representativeness of social network data may not reflect the characteristics of the population as a whole in terms of socio-economic status, age, gender, or race, and the representativeness may vary geographically.