Expertise: The ISTI/IIT-CNR CNIT research unit is located in the National Research Council (CNR) Research Area, in Pisa, Italy. Its members are researchers at the CNR Institute of Information Science and Technology “Antonio Faedo” (CNR-ISTI) or at the CNR Institute for Informatics and Telematics (CNR-IIT).
The ISTI/IIT-CNR CNIT research unit carries on its research activities funded through National and International projects, on the following research topics:
a) Next generation, 6G, IoT and Edge-centric Networks
In this area the research activities address the evolution of edge–cloud computing paradigms towards stateful and programmable execution models, enabling adaptive and low-latency network services to support dynamic orchestration, network softwarization and new native services for 5G/6G networks. Design and evaluation of network architectures based on programmable data planes, with particular focus on P4-enabled solutions for advanced monitoring, traffic steering, and the implementation of network functions directly in the data plane, combined with dynamic scaling and load balancing mechanisms for User Plane Functions (UPFs) in virtualized and cloud-native 5G/6G environments. The activities further include the development of energy-aware monitoring frameworks for network and edge infrastructures, and the development of solutions for localization in complex and dynamic indoor environments based on the integration of heterogeneous technologies such as Wi-Fi, Bluetooth, Ultra-Wideband, inertial sensors, with approaches based on edge computing. Emphasis is given to the study of scalable architectures, to the performance evaluation in real scenarios and to the development of algorithms for the positioning, navigation and tracking of users and objects, also in assistive and mobility support contexts.
b) Quantum Internet
The Research Unit is involved in the design, integration and evaluation of hybrid quantum communication networks, in which classical and quantum components coexist and cooperate. The activities cover high TRL technologies, such as Quantum Key Distribution (QKD) and its integration into existing telecommunications infrastructures and 5G/6G, with a focus on performance, reliability and network management. In parallel, low-TRL issues related to distributed quantum computing are addressed, including quantum networking protocols, orchestration of distributed quantum resources and emulation/experimentation on testbed. The goal is to define architectures, protocols and control tools that enable the evolution towards programmable and scalable quantum networks, in a long-term perspective for telecommunications.
c) Vehicular Networks
The activity in this area concerns the study of protocols based on DSRC and V2X stacks enabling cooperative driving manoeuvres. The activity has a strong experimental base, which includes a testbed consisting of small-scale vehicles equipped with depth cameras, LiDAR, and V2X-compliant On-Board Units (OBUs), as well as two V2X-compliant Road-Side Units (RSUs). The research activity also includes the development of V2V and V2I communications dedicated to the case of vulnerable road users (VRUs) equipped with devices with limited resources. Particularly, VRUs-oriented energy-harvesting algorithms and are investigated. Simple energy harvesting models are also developed applied to scenarios where RSUs are powered by V2X communications. The stored energy is then used to transmit data (for example, relating to the monitoring of the road environment) to Access Points through protocols suitably designed and adapted to the specific energy source. The models are validated through experimental campaigns in a controlled environment.
d) Non-terrestrial Networks
The research activity in this area is focused on the native integration of terrestrial and non-terrestrial network domains, enabling continuous 3D connectivity, with greater robustness and availability of services. Air and space platforms work alongside the ground infrastructure to extend coverage, improve service continuity and ensure reliable high-capacity connectivity, particularly in remote or emergency contexts. This type of integration supports several verticals, including transportation, public safety, environmental monitoring, energy, agriculture, maritime services, UAV-based logistics, and satellite IoT. Service objectives are continuously translated into adaptive actions through closed-loop control mechanisms based on multidomain data analysis. By leveraging cloud-native and software-defined technologies, such as SDN/SDR, Cloud-RAN, AI-assisted asset management, and edge and airborne computing, these mechanisms enable autonomous network capabilities, including auto-configuration, auto-optimization, and auto-recovery, ensuring resilient and adaptive operation of 3D-6G integrated networks.
e) Artificial Intelligence for Communications and Networking
The research activity on AI for communications focuses on designing distributed transfer learning algorithms that enable network nodes across the cloud–edge continuum to learn collaboratively and take autonomous, goal-driven decisions, within the network, following the Agentic AI paradigm. We study how to coordinate learning and inference among heterogeneous routers, cloud-native functions, end devices, and digital twins, turning the network into an active control and optimization layer rather than a passive data carrier. The core problems we address include anomalous traffic detection, traffic classification, and network optimization tasks such as routing and resource allocation. Our methods are tailored to realistic operational constraints—heterogeneous and non-IID data, scarce compute capacity at the edge, high data velocity, and time-varying network conditions—while ensuring robust convergence and stable performance in decentralized settings.
f) Networking for AI
In this domain, the research activity focuses on the design and analysis of communication-aware, fully decentralized artificial intelligence systems. The research investigates how network topology, connectivity constraints, and communication dynamics shape learning efficiency, robustness, and scalability when no central coordinator of the learning process is present. Core topics include decentralized and gossip-based learning, federated and peer-to-peer optimization, resilience to noisy or adversarial nodes, continual learning under network dynamics, and privacy-preserving collaboration. The goal is to enable AIs that natively operate over real networks and are adaptive, resilient, and efficient by design.
g) Network-aware Metaverse Solutions
The research on network-aware metaverse solutions focuses on the design, development, and evaluation of metaverse applications, with particular focus on Social Virtual Reality scenarios and XR solutions for manufacturing, addressing stringent requirements in terms of latency, reliability, and quality of experience. The activities include the integration of advanced networking technologies (5G/6G, MEC, edge/cloud computing) with remote rendering pipelines, multi-user synchronization mechanisms, and support for collaborative and industrial use cases.
h) Application-oriented Mobile and Pervasive Computing and Networking
The research activity focuses on the development of pervasive intelligent systems based on mobile devices, wearables, physiological and environmental sensors, through which to collect personal data in an almost continuous way. Reference scenarios are domestic and assistive environments, but the solutions are also extendable to other application scenarios. The systems are enriched by artificial intelligence algorithms dedicated to the analysis of the data collected for automatic classification and risk prediction. Activities include the design of communication architectures for heterogeneous device networks, integration with digital platforms for data collection and analysis, federated and personalized learning algorithms also for decentralized environments. Particularly, the definition of edge computing paradigms aimed at local information processing is preferred, to improve efficiency, reliability, privacy protection and personalization.
Address: Via G. Moruzzi, 1 – 56124 PISA
Phone: +39 050 621 2053 [Root + ext]
Fax: +39-050 315 2113
E-mail: name.surname@cnit.it
Research Unit Responsible: Dr. Alberto GOTTA
Members of the Research Unit
| Surname | Name | Root | ext |
|---|---|---|---|
| ANCILLOTTI | Emilio | 050315 | 2437 |
| BACCO | Manlio | 050621 | 2887 |
| BARSOCCHI | Paolo | 050621 | 2965 |
| BIONDI | Elisabetta | 050315 | 2195 |
| BOLDRINI | Chiara | 050315 | 3504 |
| BORGIA | Eleonora | 050315 | 2407 |
| BRUNO | Raffaele | 050315 | 3078 |
| CAMPANA | Mattia | 050315 | 8267 |
| CASSARA’ | Pietro | 050621 | 2844 |
| CHESSA | Stefano | 050621 | 2887 |
| CICCONETTI | Claudio | 050315 | 3057 |
| CONTI | Marco | 050315 | 3062 |
| CRIVELLO | Antonino | 050621 | 3003 |
| DELMASTRO | Franca | 050315 | 2405 |
| FURFARI | Francesco | 050621 | 2970 |
| GIROLAMI | Michele | 050621 | 2958 |
| GOTTA | Alberto | 050621 | 2053 |
| LIBRINO | Federico | 050315 | 8278 |
| LUCONI | Valerio | 050315 | 8283 |
| MORDACCHINI | Matteo | 050315 | 8294 |
| PALUMBO | Filippo | 050621 | 2988 |
| PASSARELLA | Andrea | 050315 | 3269 |
| PESCOSOLIDO | Loreto | 050315 | 8281 |
| PINIZZOTTO | Antonio Carmelo | 050315 | 2115 |
| RAPTIS | Theofanis | 050315 | 8282 |
| RESTA | Giovanni | 050315 | 2408 |
| SANTI | Paolo | 050315 | 2411 |
| VALERIO | Lorenzo | 050315 | 3059 |