Smart Factory concept is considered to play one of the crucial roles in the Industry 4.0 paradigm development and evolution. The informational space model provides a wide spectrum of opportunities for developers to implement new informational interaction mechanisms within the Smart Factory system. In this paper, we propose the informational message and the basic information space models for Smart Factory networks. The informational messages model allows to ensure confidentiality of data transmitted between Smart Factory nodes. In addition, we implement Smart Factory network in a simulation environment, apply described informational interaction models, and analyze further viability of the developed approach.
the proposed specification. The functional structure and abilities of the simulator are presented in Section 4.
Nowadays the smart factory is represented as a fully connected and flexible system [
Systems that combine the informational level (the level of computing and communication)
and the physical level are related to cyber-physical systems (CPS). CPS are engineering
systems whose operations are controlled, coordinated, and integrated by the computing core [
The Smart Factory’s functional structure and the sequencing production process mechanism
are well-studied topics [
We consider Smart Factory as a structure < , , , >. The elements of this structure are
the sets of Smart Factory objects: agents-robots set , informational space set , resources
set , products set . It is assumed that the Informational Space is a result of a function
= (). This assumption means that the set of informational messages (informational space)
is formed during the robots’ communication process. Due to the specific features described in
[
The set of agents is represented as = {(1|1), (2|2), . . . , (|)}, where is a particular agent’s access level to the Informational Space messages. The resource set can be described as = {1, 2, . . . , }, the set of Smart Factory products as = {1, 2, . . . , , }. Production process can be described as a function = (, , , , ) + where , , are some subsets of the agents’ set, functions and resources (, , , respectively) are involved in this product assembling; is the informational messages set; is the time spent on the production process, presents other features that have an impact on production process.
We introduce informational space as a structure < , > where is a set of elementary informational messages = {1, 2, . . . , } and is a set of the access parameters for the corresponding messages of the set , = {1, 2, . . . , }, 0 ≤ ≤ 1. The parameter can be calculated in the following ways.
• The message was sent by the agent to itself. In this case, the access parameter is calculated as = , where is the access level of the sender agent. • The message was sent to another agent. The sender can specify the parameter value and, in this case, it cannot be greater than the access level of the sender. In another case, the parameter value may be calculated automatically as = (, ), where is the access level of the sender, is the access level of the receiver. • In case the informational message receiver is represented as a set of all agents in the system (e.g. broadcasting mode), the access parameter value is taken as the minimum possible value of the access level of all agents: = (1, 2, . . . , ).
For analysis purposes, the described informational space can be represented as a two- or three-dimensional space.
A three-dimensional representation of the informational space is given by the coordinates , and , and is illustrated in Fig. 1. The axis illustrates the agents sending informational messages, the axis represents the agents receiving informational messages, the axis portrays the time. Considering this informational space representation, the following assumptions are introduced: • on the axis and the agents are displayed discretely; • the axes of the sender and receiver agents are limited by -th agent, where is the ID number of the last agent in the system, and the identifier has the highest value; • the time is a discrete value; • the time value = 0 is the initial system operation time moment; • the transmission time tends to zero, →0, therefore the time of sending and the time of informational message receiving are considered as equal: = .
These assumptions allow finding any transmitted message in case of the known time of its transmission and the IDs of the sender or receiver agent. Types of informational messages are introduced below.
bi time
timei
• = . In this case, the agent sent the message to itself. The type of the mes-sage is “the agent’s own message”, it can be a report of the accomplished work. The set of these messages is represented as ; • ∈ [1, − 1] ∪ [ + 1, ]. The messages of this type indicate interactions between agents and . The set of the messages passed between agents and is described as ; • = . These messages are broadcasted to all agents since this identifier value is an instruction. All the agents with ≥ have access to such messages. These messages are defined by the set .
At the same time, only the sender and the receiver agents, whose identifiers are specified when sending the message, have access to this informational message. Agents should have the minimum required access level of . This requirement was described earlier.
Informational space is considered as a set of informational messages’ subsets, grouped by messages current position following the specified identifier of the receiver agent, and can be described by equation (1).
= ∪ ∪ (1) The visualization of the informational space in this form is presented in Fig. 2.
Receiver ID n+1 n
0 field 2 b
n Sender ID
We assume that informational message has a packet structure. The structure of the message is presented in Figure 3. The message frame is divided into fields, each of them carries a certain type of data about agents and the message itself. The fields are described as follows: 1. "" is the ID of the sender agent; 2. "" is the ID of the receiver agent; 3. "" is the access parameter of the message; 4. "" is the message sending time. According to the introduced assumption, the time of sending and the time of receiving a message are considered equal; 5. "" is the informational message type. The field contains the information on a subset to which the message belongs (according to equation (1)); 6. " " is the informational message content; 7. "" is the sender agent digital signature, used as a basic measure to ensure data dissemination process security.
To model the informational interaction between agents in a Smart Factory we developed a custom software simulator using Python 3 programming language and the IDE PyCharm Edu 2019.3.2 environment. Python was chosen as the main tool by the reason that it provides free public libraries for developing function-oriented programs, working with diferent file formats, and allows to obtain and analyze statistics. As the basis, csv – File Reading and writing and matplotlib libraries were used. The first one allows to parse *.csv format files and modify it, the second was used to generate statistical plots. All basic operations conducted in the informational space were described by particular functions.
All agents introduced in Section 3 are described by csv-format strings. The first field contains the agent’s ID, the second - agent’s access level. The informational space is also presented as a csv-format file, where each string is a particular informational message that consists of the field described in Subsection 3.4. The realization of an agents’ list and the informational space is conditioned by the need to have the access to the concrete fields and the easy use of the *.csv format.
As the Python language uses Global Interpreter Lock (GIL) and allows the only thread to manage the Python interpreter, it is impossible to implement a multi-thread paradigm. To overcome this limitation, we developed a simulator of information interaction as the console program providing the interface to perform informational space operations on behalf of the Smart Factory agents during an infinite main cycle.
The developed simulator provides the following functions: • To transmit the informational message from agent to agent . Sender and receiver IDs and the message access parameter have to be entered manually. Otherwise, auto mode can be chosen. The program writes the message to the related csv-format file. • Message generation. A particular number of messages can be generated manually (with randomly chosen agents’ IDs and the content) and written to the informational space. • Message search. The message search with the use of known interacted agents’ IDs and the time when the message was sent can be performed manually. In this case, the condition when the access level must be higher than the access parameter is not considered, as we assume that this action is performed manually.
It is also possible to perform operations from the side of the agent directly. The operator needs to call the corresponding function and choose an ID of the desired agent. The following functions are accessible in this mode: • reading chosen messages. This option is similar to “Message search” function described earlier, but the access condition is mandatory. In this case, the agent does not have access rights to the message, and cannot read it;
• send a message. This function is similar to the function described earlier.
The scalability of the system is provided by a function that adds new agents with the chosen access level. The added agent and its access level are written to the csv-file. Statistics and plots generating functions include the possibility of building two- and three-dimensional spaces. An example of plots generated by these functions is illustrated in Fig. 4.
In the current moment, the mechanism of a digital signature is not yet implemented in the simulator. The main dificulty of the implementation is to develop the mechanism for storing private and public keys and their use by the agents.
The developed simulator demo-test showed that the model described in Section 3 is viable, and it is possible to implement it in production systems or digital twins of Smart Factory. The proposed interaction model provides basic measures to protect data and increase the information security level. The accuracy and eficiency of the model have not verified yet, as in this paper we were focused on the models’ formalization and demonstration of their implementation in a software simulation environment possibility. In the next step, we will outline eficiency and security metrics, provide experiments design, and conduct an empirical study to assess the proposed mechanisms via a developed software simulator.
The Smart Factory is considered to be a vital part of Industry 4.0 development and evolution. At the current moment, it is treated as a fully autonomous and self-organized manufacturing system that aimed to reduce the human factor influence on the production process. It brings a wide list of topics to be discussed. The rapid development of the Smart Factory concept arises the need to provide safe and secure interaction among system elements. To address this issue, in the present paper we proposed an informational space concept that allows implementing our developed model of the informational messages for communication among Smart Factory elements. The informational interaction custom software simulator was developed, the results showed that the presented interaction concept is viable and can be implemented in practice.
In further work, we plan to analyze simulation results on communication speed and security and assess the approach information security aspects.
This paper is supported by the Government of Russian Federation (grant 08-08).