LTE-based satellite systems in LEO constellations are a promising solution for extending broadband coverage to areas not connected to a terrestrial infrastructure. However, the large delays and Doppler shifts over the satellite channel pose severe technical challenges to a traditional LTE system. In this paper, two architectures are proposed for a LEO megaconstellation realizing a satellite-enabled LTE system, in which the on-ground LTE entity is either an eNB (Sat-eNB) or a Relay Node (Sat-RN). Focusing on the latter, the impact of large delays and Doppler shifts on LTE PHY/MAC procedures is discussed and assessed. It will be shown that, while carrier spacings, Random Access, and RN attach procedures do not pose specific issues, HARQ requires substantial modifications. Moreover, advanced handover procedures will be also required due to the satellites' movement.

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In a mobile edge computing (MEC) network, mobile devices, also called edge clients, offload their computations to multiple edge servers that provide additional computing resources. Since the edge servers are placed at the network edge, e.g., cell-phone towers, transmission delays between edge servers and edge clients are shorter compared to those of cloud computing. In addition, edge clients can offload their tasks to other nearby edge clients with available computing resources by exploiting the Fog Computing (FC) paradigm. A major challenge in MEC and FC networks is to assign the tasks from edge clients to edge servers, as well as to other edge clients, in such a way that their tasks are completed with minimum energy consumption and minimum processing delay. In this paper, we model task offloading in MEC as a constrained multi-objective optimization problem (CMOP) that minimizes both the energy consumption and task processing delay of the mobile devices. To solve the CMOP, we design an evolutionary algorithm that can efficiently find a representative sample of the best trade-offs between energy consumption and task processing delay, i.e., the Pareto-optimal front. Compared to existing approaches for task offloading in MEC, we see that our approach finds offloading decisions with lower energy consumption and task processing delay.

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In Smart Grids (SG) scenarios, the different nodes composing the system have to communicate to the Control Stations several type of information with different requirements. There are many communication technologies (CTs), with different Quality of Service characteristics, able to support the SG communication requirements. By focusing on wireless communications, it is possible to notice that spectrum is becoming a rare source due to its exponential increasing demand. Thus, resource allocation to support different types of SG nodes should be performed in order to maximize the resource efficiency and respect the SG requirements. Defining a cost function (CF) helps to accomplish this goal. To this aim, it is also needed to prioritize the different SG nodes based on their goals. By using the SG nodes prioritization and the CF, a priority table is defined in which the nodes and the CTs are put in order, based on their weights. The numerical results show that the proposed method allows selecting the best CT for each type of SG nodes.