Rapporti tecnici

In this paper we explain the basics of Bitcoin protocol and the state of the art of the main attacks to it. We first present an overview of digital currencies, showing what they are and the social need they aim to satisfy. We then focus on the main digital currency up to date, Bitcoin. We treat the basics of the protocol showing what are addresses and transactions and how they are used in a distributed consensus protocol to build the blockchain. After that the main part of this paper presents the state of the art of the three main attacks on the protocol: fraudulent mining techniques, double spending attempts and deanonymization attacks.

Almost Blank Subframes (ABS) have been defined in LTE as a means to coordinate transmissions in heterogeneous networks (HetNets), composed of macro and micro eNodeBs: the macro issues ABS periods, and refrains from transmitting during ABSs, thus creating interference-free subframes for the micros. Micros report their capacity demands to the macro via the X2 interface, and the latter provisions the ABS period accordingly. Existing algorithms for ABS provisioning usually share resources proportionally among HetNet nodes in a long-term perspective (e.g., based on traffic forecast). We argue instead that this mechanism can be exploited to save power in the HetNet: in fact, during ABSs, the macro consumes less power, since it only transmits pilot signals. Dually, the micros may inhibit data transmission themselves in some subframes, and optimally decide when to do this based on knowledge of the ABS period. This allows us to define a power saving framework that works in the short term, modifying the ABS pattern at the fastest possible pace, serving the HetNet traffic at reduced power cost. Our framework is designed using only standard signaling. Simulations show that the algorithm consumes less power than its competitors, especially at low loads, and improves the UE QoS.

Solving very-large-scale optimization problems frequently require to decompose them in smaller subproblems, that are iteratively solved to produce useful information. One such approach is the Lagrangian Relaxation (LR), a broad range technique that leads to many different decomposition schemes. The LR supplies a lower bound of the objective function and useful information for heuristics aimed at constructing feasible primal solutions. In this paper, we compare the main LR strategies used so far for Stochastic Hydrothermal Unit Commitment problems, where uncertainty mainly concerns water availability in reservoirs and demand (weather conditions). This problem is customarily modeled as a two-stage mixed-integer optimization problem. We compare different decomposition strategies (unit and scenario schemes) in terms of quality of produced lower bound and running time. The schemes are assessed with various hydrothermal systems, considering different configuration of power plants, in terms of capacity and number of units.

The design and management of Edge systems will proactively involve human intelligence at the Edge, according to a human-driven approach that increases productivity and improves usability. Due to its ubiquity and heterogeneity, the Edge will give to application administrators a more decisional role in application deployment and resource management. Final decisions on where to distribute application components should be informedly taken by them during the entire application lifecycle, accounting for compliance to QoS requirements. As a first step, this requires devising new tools that suitably abstract heterogeneity of edge systems, permit simulating different runtime scenarios and ease human-driven management of such systems by providing meaningful evaluation metrics. In this article, we discuss how human decision-making can be supported to solve QoS-aware management related challenges for Edge computing.

We present and computationally evaluate a variant of the fast gradient method by Nesterov that is capable of exploiting information, even if approximate, about the optimal value of the problem. This information is available in some applications, among which the computation of bounds for hard integer programs. We show that dynamically changing the smoothness parameter of the algorithm using this information results in a better convergence profile of the algorithm in practice.

A model for Nash-Cournot oligopolistic markets with concave cost functions and a differentiated commodity is analysed. Equilibrium states are characterized through Ky Fan inequalities. Relying on the minimization of a suitable merit function, a general algorithmic scheme for solving them is provided. Two concrete algorithms are therefore designed that converge under suitable convexity and monotonicity assumptions. The results of preliminary numerical tests on randomly generated markets are also reported.