# Mobile Networks and Internet of Things Infrastructures to Characterize Smart Human Mobility
###### Abstract  
The evolution of Mobile Networks and Internet of Things (IoT) architectures allows one to rethink the way smart cities infrastructures are designed and managed, and solve a number of problems in terms of human mobility. The territories that adopt the sensoring era can take advantage of this disruptive technology to improve the quality of mobility of their citizens and the rationalization of their resources. However, with this rapid development of smart terminals and infrastructures, as well as the proliferation of diversified applications, even current networks may not be able to completely meet quickly rising human mobility demands. Thus, they are facing many challenges and to cope with these challenges, different standards and projects have been proposed so far. Accordingly, Artificial Intelligence (AI) has been utilized as a new paradigm for the design and optimization of mobile networks with a high level of intelligence. The objective of this work is to identify and discuss the challenges of mobile networks, alongside IoT and AI, to characterize smart human mobility and to discuss some workable solutions to these challenges. Finally, based on this discussion, we propose paths for future smart human mobility researches. a 2021 20 The growth mobile generation technologies, including wireless communications, has increased over recent decades [3]. In the past 50 years, each one of them has had an impact on \"mobile society\" or IoT device users. For example, 1G came when salespersons had a phone in their car so they could call orders while they were travelling. In 2G, we were able to start sending text messages, via the short message service (SMS), to each other and enabled phones into people's pockets. In its turn, 3G phones were not limited to a certain region and began working everywhere. Inter-operability between various networks was offered using 4G. Lastly, 5G comes to increase coverage and to suit users [4]. With the development of ubiquitous wireless mobile networks and systems, different performance requirements of smart city services, such as human mobility, can be met due to flexibility of network. No matter where we are or what we are trying to achieve, a high speed, reliable, and robust wireless network is the first step to build a human mobility service. This is the backbone of all potential smart human mobility applications. For example, these applications include geo-tagged posts from social networks [5]; tracks from mobile phone GPS [6]; and communication records between cellular network and phones [7]. This amount of digital data fostered several scientific productions on human mobility behavior, such as trajectory data mining [8], and the uncovering of statistical patterns [9]. Additionally, the data of these applications provided relevant metrics about population mobility, i.e., when there are peaks in the number of people in the same place or moving less (or more) than usual. These metrics open up a new dimension for understanding human mobility behaviors. Although ubiquitous, mobile phone data have their biases and limitations. Ordinarily, data stream of a semi-urban or urban population might be more representative than those living in rural areas. Popular analytical reports from large data providers show physical distancing (mobility) metrics, but the underlying data they represent and the aggregation methods used are typically not readily available. This scant transparency makes it hard to know the representativeness and limitations of these data before using it for modeling. A common framework is needed to analyze the characteristics of this disparate data and their outputs, to allow for better comparison across human mobility metrics and easier interpretation. On the other hand, data availability also enables human mobility forecasting tasks, i.e, next-location prediction, crowd flow prediction, and trajectory generation [10]. Currently, the literature is promoting towards the design of deep learning (DL) solutions [11, 12].