Periodic Reporting for period 1 - NANCY (An Artificial Intelligent Aided Unified Network for Secure Beyond 5G Long Term Evolution)
Periodo di rendicontazione: 2023-01-01 al 2024-06-30
Blockchain has been recognized as a disruptive technology that can be leveraged by the next generations of mobile networks. Specifically, the Blockchain-Radio Access Network (B-RAN) can significantly enhance network throughput via cross-network sharing and offloading. Likewise, B-RAN can attract stakeholders of different backgrounds, including network operators, spectrum owners, infrastructure manufacturers, and clients. Currently, the majority of individual access points have the potential to be organized into B-RAN and commodified to form a sizable and ubiquitous wireless network, which can significantly improve spectrum utilization and infrastructure exploitation. In practice, the rights, responsibilities and obligations of each participant in B-RAN need to and can be flexibly codified as smart contracts executed by blockchain.
NANCY aims to introduce a secure and intelligent architecture for B5G wireless networks. In this direction, three main objectives have been defined: a) to design a novel RAN that supports dynamic scalability, as well as high security and privacy; b) to transform networks beyond 5G to intelligent platforms integrating ultra-reliable connectivity and high-energy efficiency; c) to provide almost-zero latency and high-computational capabilities at the edge.
By leveraging AI and Blockchain, NANCY will enable secure and intelligent resource management, flexible networking, and orchestration. In this direction, novel architectures, namely point-to-point connectivity for device-to-device connectivity, mesh networking, and relay-based communications, as well as protocols for medium access, mobility management, and resource allocation will be designed. Finally, in order to accommodate the particularities of the new B-RAN that are generated due to the use of novel building blocks, such as blockchain, multi-access edge computing, and AI, a new experimentally verified information-theoretic framework will be proposed.
NANCY aims to introduce a secure and intelligent architecture for B5G wireless networks. In this direction, three main objectives have been defined: a) to design a novel RAN that supports dynamic scalability, as well as high security and privacy; b) to transform networks beyond 5G to intelligent platforms integrating ultra-reliable connectivity and high-energy efficiency; c) to provide almost-zero latency and high-computational capabilities at the edge.
By leveraging AI and Blockchain, NANCY will enable secure and intelligent resource management, flexible networking, and orchestration. In this direction, novel architectures, namely point-to-point connectivity for device-to-device connectivity, mesh networking, and relay-based communications, as well as protocols for medium access, mobility management, and resource allocation will be designed. Finally, in order to accommodate the particularities of the new B-RAN that are generated due to the use of novel building blocks, such as blockchain, multi-access edge computing, and AI, a new experimentally verified information-theoretic framework will be proposed.
Objective I-To design a novel RAN that supports dynamic scalability, as well as high security and privacy:
The NANCY consortium performed a thorough analysis based on O-RAN standards to pinpoint areas where the project could introduce innovations. The overall architecture of NANCY was outlined, including the placement of each component, the requirements for in-lab testbed and demonstrators, as well as their relevance to the usage scenarios. Furthermore, a theoretical framework based on Markov-chain modelling for B-RAN was created, while various potential security risks faced by B-RAN were identified. Also, the dynamic resources of B5G networks and the capabilities of the end nodes were theoretically modelled with an emphasis on a high-mobility use case. Additionally, significant advancements have been made in the creation of quantum safety mechanisms. These developments encompass simulations, experiments, and demonstrations for Quantum Key Distribution (QKD) with a specific focus on generating and exchanging quantum keys. Moreover, Post-Quantum Cryptography (PQC) has been utilized to ensure secure communication and enable digital signatures. Finally, the theoretical foundation for smart pricing has been set up, based on reinforcement learning and reverse auction theory. The initial version of the reinforcement learning environment has been built and experiments have been carried out to design agents that align with the NANCY objectives.
Objective II-To transform networks beyond 5G to intelligent platforms integrating ultra-reliable connectivity and high-energy efficiency:
The AI-based B-RAN orchestration techniques were developed for deploying services and adjusting resources, as well as effectively overseeing the NANCY service and resource orchestrations. Moreover, a new method for selecting ML models was introduced, utilizing a a DRL-based approach to choose models based on the user's location and environment. To realize the NANCY AI virtualizer, a new method for communication between different slices was shown to greatly reduce inefficiencies and conflicts. In this direction, a novel approach to monitoring unused CPU time in Linux was developed, allowing to identify over provisioned virtual environments. Furthermore, a DRL agent was designed, which efficiently allocates flows for integrated access backhaul (IAB) networks, while particular tools were designed to analyze workloads in real-time. Additionally, an extensive exploration into state-of-the-art semantic communications and networking architectures paved the way for proposing an approach to integrating semantic communications into 6G and future networks. To this end, knowledge extraction techniques were investigated for image-based communications and the establishment of a semantic communications framework enhanced by convolutional neural networks. Also, a goal-oriented approach was developed focusing on vehicle-to-everything (V2X) applications. Finally, cutting-edge explainability algorithms for B5G/6G networks were investigated. In more detail, the SHAP and LIME algorithms were developed, modified, and tested using publicly accessible data sets.
Objective III-To provide almost-zero latency and high-computational capabilities at the edge:
A detailed analysis of cutting-edge self-healing and self-recovery techniques in the domain of B5G/6G networks was conducted. The consortium thoroughly evaluated big data platforms to determine their suitability within the NANCY architecture. Diverse algorithmic solutions were investigated aimed at addressing challenges related to self-healing and self-recovery, taking into account their practicality and integration within the NANCY architecture and test environments. In addition, a detailed workflow for offloading and data caching was created specifically for the NANCY architecture. Also, the NANCY service level agreement (SLA) was defined and a method for overseeing the SLA from start to finish was specified. Finally, the consortium developed methods for handling communication and processing resources at the edge and developed decision-making tools to select the best approach for offloading
The NANCY consortium performed a thorough analysis based on O-RAN standards to pinpoint areas where the project could introduce innovations. The overall architecture of NANCY was outlined, including the placement of each component, the requirements for in-lab testbed and demonstrators, as well as their relevance to the usage scenarios. Furthermore, a theoretical framework based on Markov-chain modelling for B-RAN was created, while various potential security risks faced by B-RAN were identified. Also, the dynamic resources of B5G networks and the capabilities of the end nodes were theoretically modelled with an emphasis on a high-mobility use case. Additionally, significant advancements have been made in the creation of quantum safety mechanisms. These developments encompass simulations, experiments, and demonstrations for Quantum Key Distribution (QKD) with a specific focus on generating and exchanging quantum keys. Moreover, Post-Quantum Cryptography (PQC) has been utilized to ensure secure communication and enable digital signatures. Finally, the theoretical foundation for smart pricing has been set up, based on reinforcement learning and reverse auction theory. The initial version of the reinforcement learning environment has been built and experiments have been carried out to design agents that align with the NANCY objectives.
Objective II-To transform networks beyond 5G to intelligent platforms integrating ultra-reliable connectivity and high-energy efficiency:
The AI-based B-RAN orchestration techniques were developed for deploying services and adjusting resources, as well as effectively overseeing the NANCY service and resource orchestrations. Moreover, a new method for selecting ML models was introduced, utilizing a a DRL-based approach to choose models based on the user's location and environment. To realize the NANCY AI virtualizer, a new method for communication between different slices was shown to greatly reduce inefficiencies and conflicts. In this direction, a novel approach to monitoring unused CPU time in Linux was developed, allowing to identify over provisioned virtual environments. Furthermore, a DRL agent was designed, which efficiently allocates flows for integrated access backhaul (IAB) networks, while particular tools were designed to analyze workloads in real-time. Additionally, an extensive exploration into state-of-the-art semantic communications and networking architectures paved the way for proposing an approach to integrating semantic communications into 6G and future networks. To this end, knowledge extraction techniques were investigated for image-based communications and the establishment of a semantic communications framework enhanced by convolutional neural networks. Also, a goal-oriented approach was developed focusing on vehicle-to-everything (V2X) applications. Finally, cutting-edge explainability algorithms for B5G/6G networks were investigated. In more detail, the SHAP and LIME algorithms were developed, modified, and tested using publicly accessible data sets.
Objective III-To provide almost-zero latency and high-computational capabilities at the edge:
A detailed analysis of cutting-edge self-healing and self-recovery techniques in the domain of B5G/6G networks was conducted. The consortium thoroughly evaluated big data platforms to determine their suitability within the NANCY architecture. Diverse algorithmic solutions were investigated aimed at addressing challenges related to self-healing and self-recovery, taking into account their practicality and integration within the NANCY architecture and test environments. In addition, a detailed workflow for offloading and data caching was created specifically for the NANCY architecture. Also, the NANCY service level agreement (SLA) was defined and a method for overseeing the SLA from start to finish was specified. Finally, the consortium developed methods for handling communication and processing resources at the edge and developed decision-making tools to select the best approach for offloading
To achieve the aforementioned objectives, as well as monitor the progress towards their achievement, fifteen key results have been identified as follows:
R1-B-RAN architecture
R2-Novel trustworthy grant/cell-free cooperative access mechanisms
R3-A novel security and privacy toolbox that contains lightweight consensus mechanisms, and decentralized blockchain components
R4-Realistic blockchain and attacks models and an experimental validated B-RAN theoretical framework
R5-Quantum safety mechanisms to boost end-user privacy
R6-Smart pricing policies
R7-AI-based B-RAN orchestration with slicer instantiator
R8-A novel AI virtualiser for underutilized computational & communication resource exploitation
R9-Novel self-evolving AI model repository
R10-Experimentally driven reinforcement learning optimization of B-RAN
R11-Semantic & goal-oriented communication schemes for beyond Shannon excellence
R12-An explainable AI framework
R13-Next-generation SDN-enabled MEC for autonomous anomaly detection, self-healing and self-recovery
R14-A computational offloading mechanism with novel resource-aware/provision scaling mechanisms and novel battery as well as computational capabilities aware offloading policies
R15-User-centric caching mechanisms
R1-B-RAN architecture
R2-Novel trustworthy grant/cell-free cooperative access mechanisms
R3-A novel security and privacy toolbox that contains lightweight consensus mechanisms, and decentralized blockchain components
R4-Realistic blockchain and attacks models and an experimental validated B-RAN theoretical framework
R5-Quantum safety mechanisms to boost end-user privacy
R6-Smart pricing policies
R7-AI-based B-RAN orchestration with slicer instantiator
R8-A novel AI virtualiser for underutilized computational & communication resource exploitation
R9-Novel self-evolving AI model repository
R10-Experimentally driven reinforcement learning optimization of B-RAN
R11-Semantic & goal-oriented communication schemes for beyond Shannon excellence
R12-An explainable AI framework
R13-Next-generation SDN-enabled MEC for autonomous anomaly detection, self-healing and self-recovery
R14-A computational offloading mechanism with novel resource-aware/provision scaling mechanisms and novel battery as well as computational capabilities aware offloading policies
R15-User-centric caching mechanisms