## 2 Related Work  
In this section, the salient features of the state-of-the-art methods are discussed. The intelligent architecture for cyber-physical systems management (IA-CPS) was introduced in Reference [26]. The IA-CPS is supported with a service-oriented architecture (SOA) that provides the functionalities of an event-driven system. This model permits the connection of different types of devices with the system; however, there is a possibility of transmission failure. A CPS based on a blockchain-enabled smart modular integrated construction (BSMIC) model was introduced in Reference [27]. A practical roadmap was used for the design, development, placement, and application of BSMIC with new guidelines and opportunities. However, an increased number of nodes in a system caused a rise in power use, which led to untimely defeat.  
The accepted requirements for the physical components and the unified invariant approach for cyber components are the reason for the improvement of such functions as the stability and security of CPSs [28]. Ensuring the autonomy of the system is a fundamental idea of the procedure, while invariants are used to determine the sequences of stable and unstable switching systems. As the authors assume, switched systems are controlled by a complex and distributed cyber process. However, in cases when the system does not respond or an analytical requirement arises, the system is not able to detect errors, and this method is restricted.  
The probability of the cluster-based method uses a large number of wireless sensors installed in one area to provide a solution to the problem of removing sensors from CPSs [29]. It is used when checking intruders, and, when detected, it is based on unreliable sensor data and draws a graph of the relationship between sensors and intruders, thereby analyzing the unstable attributes of the attacker. However, this system is not effective in areas, such as electrical conditions, vehicle monitoring, and public observation systems.  
An intelligent transportation system based on CPSs was introduced in Reference [30]. This system provided safety features for a transportation system and used the verbal warning utility scale for support. It was limited in safety and failed to provide warnings to vehicles in the case of an emergency. An industrial automation system introduced in Reference [31] used the concept of cyber-physical system mobility. It was composed of physical plants that perform physical processes and a network of embedded systems. This embedded system used programmable logic controllers (PLCs). However, it did not address the complexity of today's systems. The StreamLAB framework was introduced for computations with less memory consumption and guaranteed run-time. The applicability of StreamLAB employed typical monitoring tasks for a CPS. However, the authors failed to define and implement the idea properly. The cyber-physical system mobility concept was also used in vehicle applications introduced in Reference [32]. In this approach, the information provision service was provided to drivers to utilize the data communication network between vehicles and infrastructure. However, the combination of physical data analysis and cyber data analysis in real time was a problem.  
In this paper, we implement the concept of a human-in-the-loop cyber-physical system, in which brain signals can be converted into robot signals by a signal processing unit, which will then provide a physical component and work accordingly with mind signals.