Institution profile

Consiglio Nazionale delle Ricerche

Academic institutioneurope · it
Official website
Research library201linked papers
Opportunities0open roles
Selected work

Representative Papers

Optimisation of cyber insurance coverage with selection of cost effective security controls

Feb 01, 2021Computers & security

This study addresses enterprise cybersecurity risk management by jointly optimizing cybersecurity investments (i.e., security control configurations) and cyber insurance decisions (coverage amount and premium) to minimize total risk cost. We propose the first unified optimization framework that simultaneously incorporates insurance strategies and technical security investments, thereby balancing risk transfer and risk reduction. Our methodology integrates integer nonlinear programming, attack graph modeling, Monte Carlo risk simulation, and cost–benefit sensitivity analysis. Evaluated across multiple industry case studies, the model reduces aggregate risk cost by 18–32% and significantly improves the risk-mitigation efficiency per unit security investment. The framework delivers a computationally tractable, empirically verifiable, and quantitatively grounded decision-support tool for strategic cybersecurity resource allocation.

23 citations1 influentialRead paper

Resource Allocation and Sharing in URLLC for IoT Applications Using Shareability Graphs

Oct 01, 2020IEEE Internet of Things Journal

To address the resource allocation challenge for ultra-reliable low-latency communication (URLLC) in smart factories—characterized by fragmented spectrum, absence of instantaneous channel state information (CSI), and strong external interference—this paper proposes a robust resource allocation method based on a *shareability graph*. We pioneer the adaptation of a graph-theoretic framework originally designed for shared mobility to wireless URLLC scenarios. Relying solely on network topology and statistical channel knowledge, we construct the shareability graph and solve for its maximum-weight matching to enable periodic, reliable, low-latency transmissions from devices to sink nodes. The approach jointly optimizes spectral efficiency and fairness: compared to an optimal benchmark, it achieves a 50% gain in spectral efficiency while simultaneously improving fairness metrics—demonstrating that high efficiency and fairness are mutually attainable.

12 citationsRead paper

Artificial intelligence in materials science and engineering: Current landscape, key challenges, and future trajectories

Jul 01, 2025Composite structures

Materials development faces significant challenges including data complexity, lengthy timelines, and low efficiency, necessitating intelligent approaches to accelerate discovery. This work provides a systematic review of artificial intelligence applications in materials science, integrating a spectrum of techniques from traditional machine learning to deep learning and generative AI. It focuses on representation methods and model construction for multimodal data—such as composition, structure, images, and text—and covers core algorithms including convolutional neural networks (CNNs), graph neural networks (GNNs), Transformers, and Gaussian processes. The review particularly highlights emerging directions such as uncertainty quantification, multi-source data fusion, and language-inspired representations, proposing a research pathway toward intelligent materials design. By offering a comprehensive AI framework, this study identifies critical challenges in data quality, standardization, and algorithmic adaptability, thereby significantly enhancing the efficiency and reliability of materials discovery and optimization.

10 citationsRead paper

Energy consumption of smartphones and IoT devices when using different versions of the HTTP protocol

Dec 01, 2023Pervasive and Mobile Computing

Quantifying the end-to-end energy consumption differences among HTTP/1.1, HTTP/2, and HTTP/3 on resource-constrained mobile and IoT devices remains an open challenge, particularly across diverse network conditions and usage patterns. Method: We conduct a systematic, controlled experimental study on real smartphones and IoT devices, covering machine-to-machine (M2M) communication and browser-like interaction scenarios under varied network conditions—including high packet loss—request sizes, and device models. Energy measurements leverage PowerMonitor hardware alongside low-level Android/iOS power APIs, complemented by cross-protocol traffic modeling. Contribution/Results: This is the first empirical, end-to-end energy comparison of all three HTTP versions on actual mobile and IoT endpoints. Results show HTTP/3 reduces energy consumption by 12–19% over HTTP/2 under high packet loss; conversely, HTTP/1.1 outperforms both in small-request scenarios due to lower handshake overhead. These findings have directly informed protocol stack optimizations at two IoT vendors and provide evidence-based guidance for HTTP protocol selection in mobile and embedded systems.

6 citations1 influentialRead paper

Channel, Mode and Power Optimization for Non-Orthogonal D2D Communications: A Hybrid Approach

Jun 01, 2020IEEE Transactions on Cognitive Communications and Networking

To address the challenge of spectrum- and energy-efficient coexistence between device-to-device (D2D) communications and cellular users in cellular networks, this paper proposes a hierarchical cooperative optimization framework. At the long-term epoch scale, the base station centrally determines channel allocation and communication mode selection; at the short-term slot scale, users distributively execute cognitive power control. The key contribution lies in the first-ever “centralized–distributed” two-timescale architecture, integrating cognitive radio principles, NOMA-compatible design, and game-theoretic power optimization—where the existence and closed-form solution of the optimal power strategy are rigorously proven. Simulation results demonstrate that, compared with state-of-the-art distributed schemes, the proposed framework achieves a 23% gain in system throughput, a 31% improvement in Jain’s fairness index, and significant reductions in inter-user interference and signaling overhead.

5 citationsRead paper
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