Blockchain-Enabled Software-Defined Industrial Internet of Things With Deep Reinforcement Learning

Abstract

Recently, software-defined Industrial Internet of Things (SDIIoT), the integration of software-defined networking (SDN) and Industrial Internet of Things (IIoT), has emerged. It is perceived as an effective way to manage IIoT dynamically. Aiming to improve the scalability and flexibility of SDIIoT, multi-SDN has been applied to form a physically distributed control plane to handle a large amount of data generated by industrial devices. However, as the core of multi-SDN, reaching consensus among multiple SDN controllers is a thorny issue. To meet the required design principle, this article proposes a blockchain-enabled distributed SDIIoT to synchronize local views between distinct SDN controllers and finally reach the consensus of the global view. On the other hand, both the cryptographic operations of blockchain and the noncryptographic tasks have access to the same computational resource pool of mobile edge cloud (MEC). In order to optimize the system energy efficiency, we adaptively allocate computational resources and the batch size of the block by jointly considering the trust features of SDN controllers and the resource requirements of noncryptographic operations. To implement the truly distributed manner of blockchain, we describe our problem as a partially observable Markov decision process (POMDP) and propose a novel deep reinforcement learning (DRL) approach to solve it. In the simulation results, we compare three different protocols of blockchain and show the effectiveness of our scheme in each of them.