Human driver behavior strongly influences traffic flow by increasing the spread of shock waves in a downstream direction. This paper addresses the problem of traffic congestion mitigation in a mixed scenario composed of connected human-driven and autonomous vehicles. The control protocol, driving the longitudinal motion of the autonomous vehicles, is designed for damping down traffic waves. The effectiveness of the strategy, and ability to cope with multiple and time-varying delays originated by the non-ideal wireless communication among connected vehicles, is both analytically and numerically analyzed. The asymptotic stability of the algorithm is mathematically proved by leveraging a Lyapunov-Krasovskii functional, while the head-to-tail stability tool is exploited for the tuning of the control gains. The performance of the control strategy is disclosed by using hardware-in-the-loop real-time simulation for an exemplary pattern of three vehicles. The effectiveness of the proposed cooperative strategy in longer queues of vehicles is, instead, investigated through PLEXE, an inter-vehicular communication and mobility simulator that includes features for autonomous vehicles as well as for the realistic emulation of the IEEE 802.11p standard. The results confirm the robustness of the approach and its ability in damping disturbances and mitigating stop-and-go effects according to the theoretical derivation.

Thanks to resource elasticity, cloud systems allow to build high performance applications by dynamically adapting resources to workload dynamics. In this paper, we present a novel approach for horizontally scaling cloud resources. The approach is based on an optimized feedback control scheme that leverages fuzzy logic to self-adjust its parameters in order to cope with unpredictable and highly time-varying public-cloud operating conditions. The proposed approach takes as input heterogeneous monitoring metrics related to distinct aspects of interest (i.e., CPU and network load) merged through a fitness function. Therefore, it is able to accomplish the application needs from different viewpoints. The extensive experimental evaluation performed in the Amazon EC2 environment showed how the proposed approach is robust against a number of realistic workloads-also when VM failures happen- and that it is flexible, as being suitable for applications with different needs. Finally, it also achieves better performance when compared to previously proposed solutions.

Nowadays, more and more the industry and market depend on cloud-based infrastructures for delivering IT services. To this aim cloud-based infrastructures are changing continuously, increasing their complexity especially for the management of cloud resources. Control and management of resources (e.g., virtual machines, VMs) are of paramount importance to adjust resources automatically allocated to an application and for delivering quality-assured services to final users. In this paper, we propose a feedback-based control approach for the management of VMs in the AWS EC2 public cloud. First, we evaluate the proposed Gain Scheduling policy against different workloads. Second, we provide results on the robustness of the proposed Gain Scheduling policy in presence of failures. Finally, we compare our approach to state-of-the-art control approaches for cloud resources. Our results indicate that the proposed control strategy guarantees, without the need of a priori information on system dynamics or complex estimations of the operating conditions, high performance with respect to both constant and time- varying workloads as well as in spite of sudden VM failures.

Automated and coordinated vehicles' driving (platooning) is gaining more and more attention today and it represents a challenging scenario heavily relying on wireless Inter-Vehicular Communication (IVC). In this paper, we propose a novel controller for vehicle platooning based on consensus. Opposed to current approaches where the logical control topology is fixed a priori and the control law designed consequently, we design a system whose control topology can be reconfigured depending on the actual network status. Moreover, the controller does not require the vehicles to be radar equipped and automatically compensates outdated information caused by network delays. We define the control law and analyze it in both analytical and simulative way, showing its robustness in different network scenarios. We consider three different wireless network settings: uncorrelated Bernoullian losses, correlated losses using a Gilbert-Elliott channel, and a realistic traffic scenario with interferences caused by other vehicles. Finally, we compare our strategy with another state of the art controller. The results show the ability of the proposed approach to maintain a stable string of vehicles even in the presence of strong interference, delays, and fading conditions, providing higher comfort and safety for platoon drivers.

Nowadays smart mobility, a new vision of urban mobility, is a reality. To implement smart mobility scenarios a deep integration among citizens, private and public transportation systems and ICT is required. With the S2-Move project we propose an architecture able to collect, update, and process real-time and heterogeneous information from various sources (tablets, smart-phones, probe vehicles) and actors of the urban scenario (public/private vehicles, pedestrians, infrastructures) in order to provide innovative mobility services. In this paper, we present a core component of the S2-Move project: the map-based web platform designed and implemented for providing smart mobility services. We present use cases and detail the design and the implementation of the platform. Finally, we evaluate the accuracy and efficiency of the Map Matching and Traffic Monitoring algorithms we implemented in our platform by using realistic urban traffic data generated through simulations in SUMO.

We analyze and solve the platooning problem by treating it as the problem of achieving consensus in a network of dynamical systems affected by time-varying heterogeneous delays due to wireless communication among vehicles. Specifically, a platoon is modeled as a dynamical network where: 1) each vehicle, with its own dynamics, is a node; 2) the presence of communication links between neighboring vehicles is represented by edges; and 3) the structure of the intervehicle communication is encoded in the network topology. A distributed control protocol, which acts on every vehicle in the platoon, is derived. It is composed of two terms: a local action depending on the state variables of the vehicle itself (measured onboard) and an action depending on the information received from neighboring vehicles through the communication network. The stability of the platoon is proven by using Lyapunov-Razumikhin theorem. Numerical results are included to confirm and illustrate the theoretical derivation.

In this paper the Cooperative Adaptive Cruise Control strategy for vehicles platooning is extended to the case when each vehicle can communicate with a subset of vehicles in the fleet. The control objective is to guarantee that the fleet moves forward with a given spacing policy at the leader velocity. To this aim each vehicle decides its control action using information from all neighboring vehicles through wireless communication. In so doing, a network of dynamical systems is formed, and it is shown that achieving platooning is equivalent to find a control algorithm so that the resulting network is asymptotically stable. A network protocol able to deal with heterogeneous time-varying communication delays is then proposed to solve the problem. A consistent proof of stability of the closed-loop system is provided and numerical results confirm the effectiveness of the approach and its robustness with respect to variations of the leader velocity, as well as to generic topologies of the underlying network emerging from the communication features.

In this paper we propose an architecture to supervise and operate platoons of vehicles. The resulting system is mainly composed by a decentralized architecture where each vehicle, equipped with an On Board Unit, collects the fundamental data and forwards them to an operation center that stores and reorganizes them. We focus here on the automatons implemented in each system and we show a first prototype for its experimental validation.