## 2 State-of-the-Art: A Review
[25] created a framework that uses the consensus mechanism, which is automatically implemented by every node and has the representative characteristics. Additionally, the authors in [28] proposed a hybrid technique based on BC and ML to detect attacks for energy trading applications. In addition, the authors suggested in [28] to store datasets used by ML models into a BC network in order to reduce data errors such as duplication, missing data value and noise. The authors in [29] suggested a reinforcement learning (RL) technique to address the block time minimization and transaction throughput of BC-based IIoT networks. The authors discussed how to obtain a low bias training of the agent in the RL and proposed some alternative solutions such as Q-learning, multi-armed bandit learning, actor-critic learning and deep policy optimization techniques. Applications of RL techniques in BC-based IIoT networks were discussed. They concluded that the Q-learning technique is appropriate for improving transaction throughput and minimizing forking events. Actor-critic learning and deep Q-learning techniques were discussed as a possible means to improve the energy efficiency of IoT devises. Q-learning and multi-armed bandit learning were also intended to minimize the time to finality and reduce the block time. Finally, deep Q-learning could be applied by adding an artificial noise in the network to protect broadcast/acknowledgement messages exchanged among IIoT-BC devices. [PERSON] et al. [15] proposed a new architecture based on smart contract, BC and ML for quality control in smart manufacturing. In addition, the authors suggested a BC distributed ledger to store an information-related automation contract among the supplier, manufacturer and retailer. Likewise, an architectural diagram of the predictive analysis based on XGBoost (a flexible and highly efficient algorithm that can avoid overfitting issues) was proposed by the authors in [15] covering data pre-processing, feature selection and dividing the dataset into training and testing sets. [PERSON] et al. [30] presented a review on BCT applications for Industry 4.0. Likewise, the authors exhibit the capabilities of BCT implementation in several industrial fields involving the healthcare domain, education services, logistics and transportation and government sectors. The authors in [31] propose an IIoT framework based on BC for sensors authentication. Therefore, a performance analysis is performed by measuring the probability of attack success, a created hazard by the intruder and authentication accuracy metrics versus the number of IoT devices via an NS2 simulator. [PERSON] et al. [33] present the various metrics for the usage of BC in manufacturing including data-temper resisting, data-provenance, decentralized decision, collaborative optimization, system flexibility, cost saving, system sustainability, system resilience, network transparency and reputation improvement. Finally, [PERSON] et al. [35] discuss the BC applications in Industry 4.0 such as authentication, asset tracking and smart contract exchange. In addition, BC concerns are exhibitedby the authors in terms of higher energy consumption, large storage capacity and the vulnerability of the last block against attacks. We provide hereinafter a more in-depth analysis of three of these contributions that focuses specifically on BCT applied to IIoT. Their potential benefits and limits to counteract cyber-attacks in the context of smart manufacturing are studied. The choice for these three contributions was motivated by the will to establish a fair comparison between our work and similar ones in terms of architectural design (i.e., number of layers and the functionality of each layer), smart contract data structure and application of smart contract in the manufacturing field. The first one is the GridMonitoring system [12], which is based on four layers such as the user layer, data processing and monitoring layer, registration and authentication layer and energy and data center layer.