Recently, the emergence of blockchain has stirred great interests in the field of Internet of Things (IoT). However, numerous non-trivial problems in the current blockchain system prevent it from being used as a generic platform for large-scale services and applications in IoT. One notable drawback is the scalability problem. Lots of projects and researches have been done to solve this problem. Nevertheless, they do not consider different users’ conditions, only using a single consensus protocol as the best fit one, as well as the IoT system is heavily constrained by computing and networking resources. In this article, we study a permissioned blockchain-based IoT architecture. In order to improve the scalability of the blockchain system and meet the needs of different users, we propose a service-oriented permissioned blockchain, where different consensus protocols are launched according to users’ quality of service (QoS) requirements. Specially, we quantify a few popular consensus protocols. Additionally, we select block producers, which need a great number of computation resources, as well as dynamically allocate network bandwidth to the blockchain system. We formulate consensus protocols selection, block producers selection, and network bandwidth allocation as a joint optimization problem. We then use a dueling deep reinforcement learning approach to solve the problem. Simulation results demonstrate the effectiveness of our proposed scheme.

中文翻译：

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面向服务的物联网许可区块链

最近，区块链的出现激起了物联网（IoT）领域的极大兴趣。然而，当前区块链系统中的许多重要问题阻止它被用作物联网中大规模服务和应用程序的通用平台。一个显着的缺点是可扩展性问题。许多项目和研究已经完成来解决这个问题。然而，他们没有考虑不同用户的情况，只使用单一的共识协议作为最合适的协议，并且物联网系统受到计算和网络资源的严重限制。在本文中，我们研究了一种基于区块链的许可物联网架构。为了提高区块链系统的可扩展性，满足不同用户的需求，我们提出了面向服务的许可区块链，其中根据用户的服务质量（QoS）要求启动不同的共识协议。特别地，我们量化了一些流行的共识协议。此外，我们选择需要大量计算资源的区块生产者，并为区块链系统动态分配网络带宽。我们将共识协议选择、区块生产者选择和网络带宽分配制定为一个联合优化问题。然后我们使用决斗深度强化学习方法来解决问题。仿真结果证明了我们提出的方案的有效性。需要大量的计算资源，以及为区块链系统动态分配网络带宽。我们将共识协议选择、区块生产者选择和网络带宽分配制定为一个联合优化问题。然后我们使用决斗深度强化学习方法来解决问题。仿真结果证明了我们提出的方案的有效性。需要大量的计算资源，以及为区块链系统动态分配网络带宽。我们将共识协议选择、区块生产者选择和网络带宽分配制定为一个联合优化问题。然后我们使用决斗深度强化学习方法来解决问题。仿真结果证明了我们提出的方案的有效性。