SMCCA: A sharded multi-task collaborative consensus algorithm for unmanned vehicle networks

Consensus mechanisms are a critical technology in modern networked collaborative systems and play a pivotal role in emerging scenarios such as Unmanned Vehicle Networks (UVNs). However, the escalating complexity and scale of unmanned systems have exposed two principal bottlenecks in existing consensus algorithms: (1) high communication overhead resulting from global consensus mechanisms, and (2) inadequate handling of intricate inter-task constraints, which severely limits the practical deployment of UVNs. To address these challenges, we propose a Sharded Multi-task Collaborative Consensus Algorithm (SMCCA), which employs a divide-and-conquer approach to decouple traditional global consensus problems into parallelizable local consensus sub-problems, thereby optimizing communication costs and enhancing task coordination. Specifically, SMCCA ensures efficient collaboration within UVNs through three core mechanisms. First, the algorithm partitions unmanned devices into multiple autonomous yet interoperable clusters using a sharding architecture, where leader shards act as global coordinators to enable fine-grained cross-shard task cooperation. Second, a Task Relationship Graph (TRG) is constructed to precisely quantify the dependencies and conflicts among tasks. Based on this, a hierarchical topological sorting algorithm is applied to generate an optimal execution sequence without constrained interactions. Third, a hierarchical global task view based on a Directed Acyclic Graph (DAG) is established to support efficient fault recovery and consistent state maintenance. Experimental results demonstrate that in a network with 256 nodes distributed across four shards, SMCCA achieves a throughput of 11,835 tasks per second and an average latency of approximately 0.5 s, significantly outperforming existing consensus algorithms.