Accurate path tracking is one of the main challenges autonomous vehicles have to deal with. It is known that, when dealing with real hardware systems, the presence of parametric uncertainty and unmodelled aspects of the system dynamics affects all model-based control approaches, hindering their nominal performance guarantees. To compensate for this issue, data-driven schemes have drawn significant attention from the scientific community thanks to their inherent ability to learn from experience, thus automatically compensate for system uncertainties and time-varying behaviours. This work aims to develop a reinforcement learning-based longitudinal and lateral dynamics control introducing mismatch and motion penalization metrics, validating the resulting controller in a simulated Formula SAE skid-test scenario employing the Sapienza Fast Charge Formula SAE Electric Racing Team dynamical model.

Model Predictive Control (MPC) is a popular control strategy that relies on the availability of a prediction model to estimate future system trajectories over a finite time horizon. Recently, researchers have introduced Neural Networks (NNs) into the MPC framework for the development of data-driven prediction models. In MPC, the control actions are computed by solving iteratively, at each time-step, an optimization problem subject to state and input constraints. Finding the optimal solution to such a problem is a crucial challenge in the data-driven setting, due to the complexity and black-box nature of data-driven models such as NNs. This paper addresses this challenge by proposing a hierarchical deep NN formed by a set of cascading one-step NN predictors whose combination constitutes an interpretable prediction model over the entire prediction horizon. Thanks to the proposed NN architecture, it is shown that the resulting optimal control problem is tractable, as it can be solved by employing efficient iterative algorithms, and interpretable, so that input and state constraints can be enforced seamlessly. The effectiveness of the proposed method is validated through numerical simulations. Note to Practitioners—Model Predictive Control (MPC) is a widely used methodology in the industry which typically relies on the availability of a model in the form of step response, transfer function or state-space models. In some cases, the explicit model might not be available or its accuracy may be not sufficient for the required closed-loop performance. This paper aims to develop a simple and practical framework for deploying a model-free data-driven MPC solution based on deep learning. This objective is pursued by suggesting a novel approach using simple neural networks in a cascading interpretable structure. Such networks are used to predict the one-step evolution of the system, and their cascade represents the MPC prediction model over an arbitrary long prediction horizon. We characterize such a neural model focusing on its interpretability and tractability, deriving the resulting optimal control problem to be solved in a receding horizon strategy. We then show that the MPC optimization can be solved efficiently using highly efficient iterative algorithms that can be implemented in practice. Numerical simulations involving the use of the Alternating Direction Method of Multipliers (ADMM) algorithm show its effectiveness for both linear and nonlinear systems.

Topology optimization in decentralised federated learning settings enables the design of policies aimed at minimizing the number of communication rounds needed to reach algorithmic convergence. Given a federation of autonomous agents, finding the optimal topology which guarantees that the underlying graph is connected is still an open issue. This paper proposes a novel energy-aware topology optimization algorithm with the goal to derive an optimal topology which maximizes the algebraic connectivity of the corresponding graph in presence of energy and communication constraints. The effectiveness of the proposed approach is validated in the context of a consensus-based federated learning algorithm over an e-Health scenario.

When dealing with distributed applications in Edge or Fog computing environments, the service latency that the user experiences at a given node can be considered an indicator of how much the node itself is loaded with respect to the others. Indeed, only considering the average CPU time or the RAM utilisation, for example, does not give a clear depiction of the load situation because these parameters are application- and hardware-agnostic. They do not give any information about how the application is performing from the user's perspective, and they cannot be used for a QoS-oriented load balancing. In this article, we propose a load balancing algorithm that is focused on the service latency with the objective of levelling it across all the nodes in a fully decentralised manner. In this way, no user will experience a worse QoS than the other. By providing a differential model of the system and an adaptive heuristic to find the solution to the problem in real settings, we show both in simulation and in a real-world deployment, based on a cluster of Raspberry Pi boards, that our approach is able to level the service latency among a set of heterogeneous nodes organised in different topologies.

One of the primary challenges associated with network functions virtualization (NFV) is the automated management of the service lifecycle. In this paper, we present a full software-based management and orchestration (MANO) stack which operates with OpenStack and OpenDaylight controllers and has the in-built functionality to automate the key phases of the NFV service lifecycle, namely resource discovery and matching, service mapping, service deployment, and monitoring. The MANO stack is being implemented by the EU FP7 project T-NOVA, with the components being released as open-source software. Service mapping and service deployment solutions developed in the scope of T-NOVA are presented in detail. As a proof-of-concept, we evaluate the performance of a virtualized traffic classifier network function, demonstrating the gains of virtualized hardware acceleration.

Cloud technologies can nowadays be considered as commodities. The possibility of getting access to storage, computing and networking virtual resources empowers any business that needs dynamic IT capabilities. The Cloud Management Broker (CMB) plays a crucial role to handle heterogeneous virtualized cloud resources in order to offer a unique set of interfaces to the cloud users. Moreover, the CMB is in charge of optimizing the usage of the cloud resources, satisfying the requirements declared by the users. This paper proposes a novel multi-cloud resource allocation algorithm, based on a Markov Decision Process (MDP), capable of dynamically assigning the resources requests to a set of IT resources (storage or computing resources), with the aim of maximizing the expected CMB revenue. Simulation results show the feasibility and the higher performances obtained by the proposed algorithm, compared to a greedy approach.

Convergence of one-way broadcast and bidirectional unicast networks can be leveraged for efficient delivery of on-demand broadband contents in future telecommunication systems. In this work we are modelling an integrated broadcast and unicast bidirectional network with transcoding capabilities and addressing optimization of on demand content delivery under alternative approaches. Two alternatives are considered which maximize the efficiency of the integrated network providing a compromise between the efficiency and QoS respectively. The approaches are validated through simulations and assessed again a broadcast network without transcoding facilities.

Full integration of satellite technology in future terrestrial infrastructures requires support for high-quality broadband bi-directional communications. Research efforts in the field of satellite communications are currently oriented in the study of QoS-aware solutions for DVB-S and DVB-RCS, which allowed seamless deployment in the Internet. In this paper the QoS architecture designed in the framework of the SATIP6 project, sponsored within the 5th EU Research Programme Framework, is presented. This is organized into two main modules, the traffic control and bandwidth on demand (BoD) module, whose aim are to provide for differentiated service of conveyed IP flows and efficient utilization of uplink bandwidth respectively. Experimental results obtained through Opnet simulations are reported and discussed to assess the effectiveness of the designed solution in terms of both service differentiation and efficient utilization of satellite resources.

In this paper, a novel radio modem architecture for a wireless local area network is proposed. The main focus is to reduce the power consumption, which is a crucial requirement for portable devices. In particular, the paper describes a new control architecture which is in charge of properly adapting the transmission power to the link's condition by controlling the transmission bit rate and the capacity request scheme. By using fuzzy logic and internal model control, the scheme manages to adapt the bit rate, granting at the same time the reduction of the power consumption and the satisfaction of the quality of service requirements.