Shard-DAG: A scalable and secure block-DAG sharding scheme for AI-driven 6G networks

The ultra-high speed, ultra-low latency, and massive connectivity of the 6th Generation Mobile Network (6G) present unprecedented challenges to network security. In addition, the deep integration of Artificial Intelligence (AI) into 6G networks introduces AI-native features that further complicate the design and implementation of secure network architectures. To meet the security demands posed by the massive number of devices and edge nodes in 6G networks, a decentralized security architecture is essential, as it effectively mitigates the performance bottlenecks typically associated with centralized systems. Blockchain technology offers a promising trust mechanism among devices in 6G networks. However, conventional blockchain systems suffer from limited scalability under high-load conditions, making them inadequate for supporting a large volume of nodes and frequent data exchanges. To overcome these limitations, We propose Shard-DAG, a scalable architecture that structurally integrates Directed Acyclic Graphs (DAG) and sharding. Each shard adopts a Block-DAG structure for parallel block processing, effectively overcoming the performance bottlenecks of traditional chain-based blockchains. Furthermore, we introduce a DAG-based transaction ordering mechanism within each shard to defend against double-spending attacks. To ensure inter-shard security, Block-DAG adopts a black-box interaction approach to prevent cross-shard double-spending. Theoretical analysis and experimental evaluations demonstrate that Shard-DAG achieves near-linear scalability. In a network of 1 , 200 nodes with 8 shards, Shard-DAG achieves peak throughput improvements of 14.64 times over traditional blockchains, 8.61 times over standalone Block-DAG, and 2.05 times over conventional sharded blockchains. The results validate Shard-DAG's ability to scale efficiently while maintaining robust security properties.