## 2 Related Work  
In the smart industry [25; 26; 27], MIoT nodes perform a vital role in collecting and distributing data to end-users. The MIoT nodes are sensors that are used to observe the reading of various physical objects in the industry and contribute to productivity. The MIoT network is connected using wireless technology, and most of the proposed solutions are prone to failure in the case of dynamic topologies. Optimizing the data routing in the multimedia-based network (without disrupting the connected users or decreasing delay time) presents significant research challenges. Moreover, security is necessary toward protecting the privacy of multimedia data, maintaining integrity against malicious attacks. The authors proposed a lightweight blockchain architecture to decentralize the authentication mechanism and claimed the effectiveness of the proposed framework for smart industrial environments. The authors of [28] proposed a novel security-by-design method for the security of the industrial internet of things (IIoT) and demonstrated its applicability by applying it to a real case study of an IIoT scenario from the maritime sector. Their security method involves analyzing the IIoT environment at two different levels--the modeling level and the simulation level. At the modeling level, the method ensures modeling and analysis of connections between IIoT components, and at the simulation levels, it provides a set of algorithms for the automatic identification of potential attack paths and categorization of the importance of such paths. The authors claimed that the proposed method helped in the identification of security mechanisms to cope with attacks on critical assets. Due to the emergence of IIoT [29], process industries have adopted wireless sensor-actuator networks (WSANs) for the accomplishment of control applications. An end-to-end communication delay in such networks can be minimized by using efficient real-time routing. The authors proposed a conflict-aware real-time routing scheme for industrial WSANs. The proposed routing scheme is evaluated on a physical WSAN test bed based on simulations and experiments that show a three-fold improvement in the real-time capacity of WSANs. Reliability and high requirements for real-time communications are very important in IIoT. The authors of [30] proposed a many-objective optimization algorithm based on the dynamic reward and penalty mechanism (MaOEA-DRP). It optimizes the shared validation validity model. Moreover, it achieves an optimized blockchain sharding method. The simulation-based experimental results are proven to have significant improvements over other solutions. The authors in [31] proposed a novel clustering method based on power demand, which assures the security of data information in IIoT-based applications using machine learning. In a first step, from mutual information of the primary channel and eavesdropping channel, the security capacity of the system is calculated. After security capacity calculation, and then keeping the constraint of the maximum transmit power, an optimal transmit power is found using the deep learning technique, which maximizes the security capacity of the system. In the final stage, the network is clustered according to the calculated power demand. In [32], the authors proposed a routing algorithm that integrates various phases, such as dynamic cluster formation and cluster head selection with multi-path routing formation. It reduces the energy consumption and routing overheads among the nodes. The proposed algorithm utilizes a genetic algorithm (GA)-based meta-heuristic optimization and dynamically chooses the best path by using the cost function. The set of experiments were conducted and analyzed, showing improved performances compared to other solutions. The authors in [33] proposed a scheme and the wireless multimedia sensor network in collecting data. Firstly, mobile sensor nodes were grouped in the cluster and a single cluster head was selected for each cluster.