## 2 State-of-the-Art: A Review
The system integrates an implementation of a smart meter for transparency purposes between the customer and electricity company provider. The benefit of the proposed solution, besides the usage of BCT, is the employment of authentication mechanisms and data centers to save all information to report any violations. Nonetheless, the solution applies a classical smart contract database to report any violations that can be the object of an injection or/and XSS (cross-site scripting) attack. The solution can be enhanced with an ML-based solution to prevent and detect injection and XSS attacks. The second one is the architecture proposed by [PERSON] et al. [13], which is presented in Figure 1. As depicted, the architecture is composed of perception, communication, BC-composite and industrial applications layers. The BC composite layer covers five sub-layers including data, network, consensus, incentive and services sub-layers. The data sub-layer includes a data block, chain structure, Merkle tree, Hash function, digital signature and cryptographic algorithms. The consensus sub-layer contains the architecture-supported consensus algorithms such as PoW, PoS, and practical Byzantine fault tolerance (PBFT). This architecture presents the advantage of a well-structured composite BC sub-layer including all components of BC node architecture. However, merging data and network in one layer can lead to a serious privacy violations in case of man-in-the-middle (MitM) attacks. The potential benefits of this approach in the context of smart manufacturing can be applied in supply chain, food industry, smart city, healthcare and Internet-of-Vehicles (IoV) applications. Meanwhile, some limitations can be found concerning the fact that the data block, chain structure and network tools are merged in one BC composite layer. The third studied architecture was proposed by [PERSON] et al. [14] and is presented in Figure 2, considering an application to the supply chain. As shown in Figure 2, the proposed architecture is based on three layers incorporating BC, data and governance layers. In the proposed model, all transactions including custom payment, producer registration, supplier registration, factor registration, contract term condition and refund are recorded  
Figure 1: The architecture proposed by [PERSON] et al. [13]. inside the BC data block structure. In addition, a Merkle tree is employed to save the hash function for every transaction. The latter architecture presents a reduced number of layers (i.e., three), but a high number of registrations is observed in the BC data structure for the producer, supplier, factor, delivery, shopping and customer, respectively. Our review led us to point out some limitations of the existing introduced state-of-the-art in the context of the smart manufacturing. These limitations concern mainly the lack of security mechanisms ensuring a sufficient protection level for sensing and control functions facing for example DoS (Denial of Service), DDoS (Distributed Denial of Service), injection, XSS and brute force attacks. For instance, the availability security metric is not preserved for [12] and the privacy security metric is not ensured for [12; 13; 14] as well. Our aim is to propose a complete architecture based on a decentralized BC data structure along with ML for securing smart manufacturing sensing and control functions. To the best of our knowledge, this is the first time ML and BCT are suggested to reach this objective for an IoT sensor access control system in the context of smart manufacturing applications.