Blockchain, NFT, Federated Learning and Model Cards enabled UAV Surveillance System for 5G/6G Network Sliced Environment
Conference
Bandara, E, Shetty, S, Foytik, P et al. (2023). Blockchain, NFT, Federated Learning and Model Cards enabled UAV Surveillance System for 5G/6G Network Sliced Environment
. 10.1109/ISNCC58260.2023.10323641
Bandara, E, Shetty, S, Foytik, P et al. (2023). Blockchain, NFT, Federated Learning and Model Cards enabled UAV Surveillance System for 5G/6G Network Sliced Environment
. 10.1109/ISNCC58260.2023.10323641
In recent years, the use of UAVs has expanded to various applications such as surveillance, disaster response, agriculture, and delivery. However, traditional UAV monitoring systems rely on direct communication between the UAV and the ground pilot, which has several limitations such as limited range, poor reliability, and susceptibility to interference. To overcome these limitations, there has been significant interest in integrating UAVs into cellular networks such as 5G/6G network slicing. The flexibility of network slicing allows UAVs to operate on different slices based on their communication needs, which can improve their performance and efficiency. However, integrating UAVs into network slicing also poses several challenges, such as managing communication and permissions of UAVs and base stations, access control of UAVs, and identity management of UAVs. To address these challenges, we propose a blockchain, Non-Fungible Token(NFT), Federated Learning(FL), and Zero-Trust(ZT) security-enabled UAV monitoring platform for 5G/6G network sliced environments. We propose a novel approach in which UAVs are represented as NFT tokens within the platform. This innovative representation allows for enhanced security and trust in the system, aligning with the principles of the Zero-Trust security model, which assumes no implicit trust in any network component or user. Furthermore, we propose a FL system that operates on top of the blockchain, which can analyze data from multiple UAVs across different network slices. Our proposed FL system uses coordinator-less models, which eliminates the attacks of a centralized coordinator. As a use case, we consider a scenario where our proposed system detects anomaly communications of UAVs and identifies attack surfaces via analyzing network traffic data of UAVs using FL. The 5G system testbed implemented with FreedomFi 5G gateway and Indoor Radio Cell.