Syncing a Smart City within an Evolutionary Dynamical Cooperative Environment

The cities are evolving in complex systems that compete for space as much as sustainability. In a constantly changing system of networked things and people, the understanding of the way to support the challenges of a smart city is linked to the enabling technologies such as 5G and beyond, automotive Internet of Things (IoT) and Multi Access Edge Computing (MEC). These enable innovative approach to develop sophisticated context and cognitive urban applications, crucial to increase the awareness of the surrounding environment in automotive fields. To this aim, we propose a theoretical approach to investigate a richer structure of the city in terms of depth of knowledge, to unveil hidden urban patterns. This approach is based on two weighted multiplex networks and allows defining and representing cognitive and dynamical interdependence between urban environment and automotive IoT devices (MEC nodes). The evolutionary game theory is applied to study the problem of cooperation of MEC nodes in a multi-service environment, shedding light on the joint impact of its dynamics and the multiplex structure on decreasing the blocking probability of the MEC nodes.