Multi-access edge computing (MEC) comes with the promise of enabling low-latency applications and of reducing core network load by offloading traffic to edge service instances. Recent standardization efforts, among which the ETSI MEC, have brought about detailed architectures for the MEC. Leveraging the ETSI model, in this paper we first present a flexible, yet full-fledged, MEC architecture that is compliant with the standard specifications. We then use such architecture, along with the popular OpenAir Interface (OAI), for the support of automotive services with very tight latency requirements. We focus in particular on the Extended Virtual Sensing (EVS) services, which aim at enhancing the sensor measurements aboard vehicles with the data collected by the network infrastructure, and exploit this information to achieve better safety and improved passengers/driver comfort. For the sake of concreteness, we select the intersection control as an EVS service and present its design and implementation within the MEC platform. Experimental measurements obtained through our testbed show the excellent performance of the MEC EVS service against its equivalent cloud-based implementation, proving the need for MEC to support critical automotive services, as well as the benefits of the solution we designed.

Dense heterogeneous networks constitute the paradigm for the future networks. In fact, recent studies demonstrate that the data traffic demand increases exponentially and the traditional cellular networks are not able to provide enough capacity. For this reason operators and standardisation bodies are particularly eager to solve the problem, hence there is a lot of ongoing research on this direction. In this paper we focus on extremely dense networks, that could be found, e.g., in crowded public places or in offices. In such deployments, energy consumption must be kept proportional to the traffic dispatched, otherwise operational costs will render them unsustainable from an economic perspective. In this paper, we propose a network model for the estimation of the power consumption of an LTE dense network of small cells, which takes into account the backhaul network. Furthermore, we introduce a new mechanism for the association of the users to base stations, aiming at minimizing the energy consumption of the LTE access network. The achieved trade-off among capacity and power consumption is then evaluated by means of a classical association policy that connects each user to the base station which received signal is the strongest.

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