Blockchain has been forming the central piece of various types of vehicle-to-everything (V2X) network for trusted data exchange. Recently, permissioned blockchains garner particular attention thanks to their improved scalability and diverse needs from different organizations. One representative example of permissioned blockchain is Hyperledger Fabric ("Fabric"). Due to its unique execute-order procedure, there is a critical need for a client to select an optimal number of peers. The interesting problem that this paper targets to address is the tradeoff in the number of peers: a too large number will degrade scalability while a too small number will make the network vulnerable to faulty nodes. This optimization issue gets especially challenging in V2X networks due to mobility of nodes: a transaction must be executed and the associated block must be committed before the vehicle leaves a network. To this end, this paper proposes an optimal peers selection mechanism based on reinforcement learning (RL) to keep a Fabric-empowered V2X network impervious to dynamicity due to mobility. We model the RL as a contextual multi-armed bandit (MAB) problem. The results demonstrate the outperformance of the proposed scheme.

翻译：屏障链一直是各种车辆到东西网络( V2X) 中值得信任的数据交换的核心部分。 最近, 被允许的区块链因其可缩缩和不同组织的不同需要而引起特别关注。 被允许的区块链的一个有代表性的例子是超Ledger Fabric (“ Fabric ”) 。 由于其独特的执行命令程序, 客户非常需要选择最优的同龄人数量。 本文要解决的有趣的问题是对等点数的权衡: 太多的区块链会降低可缩放性, 而数量太小的区块会使网络容易受到错误的节点。 由于节点的移动, 这个优化问题在V2X 网络中特别具有挑战性: 在车辆离开网络之前, 必须执行交易, 相关的区块必须承诺。 为此, 本文提出一个基于强化学习( RL) 的最佳同龄人选择机制, 以保持Fabric- edelf2X 网络因流动性而变得不易动性。 我们把 RL 模拟成一个背景多臂带(MAB) 方案的结果。