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.

Energy efficiency is regarded as an important objective in a world of limited resources. The sustainable use of energy is necessary for the continuity of life styles that do not jeopardize the future. Nevertheless, due to poor information about the impact of human actions on the environment, it is hard to promote and warn for sustainability. This work focuses on the use of ambient intelligence as a mean to constantly revise sustainability indicators in a way they may be used for user awareness and recommendation systems within communities. The approach in this research makes use of sustainable indicators monitored through ambient sensors which enable user accountability concerning their actions inside each environment. Also, it is possible to compare the effect of user actions in the environment, enabling decision making based on such comparison factors.

Intelligent environments and ambient intelligence enabled systems provide means to gather rich information from both environments and its users. With the help of such systems, it is possible to foster communities of ambient intelligence systems with community driven knowledge, which is created by individual actions and setups in each of the environments. Such arrangements provides the potential to build systems that promote better practices and more efficient and sustainable environments by promoting the community best examples and engaging users to adopt and develop proactive behaviors to improve their standings in the community. This work aims to use knowledge from communities of intelligent environments to their own benefit. The approach presented in this work uses information from different environments, ranking them according to their sustainability assessment. Recommendations are then computed using similarity and clustering functions ranking users and environments, updating their previous records and launching new recommendations in the process. Gamification concepts are used in order to keep users motivation and engage them actively to produce better results in terms of sustainability

Intelligent environments and ambient intelligence provide means to monitor physical environments and to learn from users, generating data to be used to promote sustainability. Communities of intelligent environments offer different answers and behaviors which can be computed and ranked. Such rankings are bound to be dynamic as users and environments exchange interactions on a daily basis. This work aims to use knowledge from communities of intelligent environments to their own benefit. The approach presented in this work uses information from each environment, ranking them according to their sustainability assessment. Recommendations are then computed using similarity and clustering functions ranking users and environments, updating their previous records and launching new recommendations in the process.

A design for a novel mobile sensing system, called Temperature Measurement System Architecture (TMSA), that uses people as mobile sensing nodes in a network to capture spatiotemporal properties of pedestrians in urban environments is presented in this paper. In this dynamic, microservices approach, real-time data and an open-source IoT platform are combined to provide weather conditions based on information generated by a fleet of mobile sensing platforms. TMSA also offers several advantages over traditional methods using participatory sensing or more recently crowd-sourced data from mobile devices, as it provides a framework in which citizens can bring to light data relevant to urban planning services or learn human behaviour patterns, aiming to change users' attitudes or behaviors through social influence. In this paper, we motivate the need for and demonstrate the potential of such a sensing paradigm, which supports a host of new research and application developments, and illustrate this with a practical urban sensing example.