=Paper=
{{Paper
|id=Vol-2893/short_3
|storemode=property
|title=Informational Messages and Space Models Application in Smart Factory Concept
|pdfUrl=https://ceur-ws.org/Vol-2893/short_3.pdf
|volume=Vol-2893
|authors=Maria Usova,Ilya Viksnin,Sergey Chuprov
|dblpUrl=https://dblp.org/rec/conf/micsecs/UsovaVC20
}}
==Informational Messages and Space Models Application in Smart Factory Concept==
https://ceur-ws.org/Vol-2893/short_3.pdf
Informational Messages and Space Models
Application in Smart Factory Concept
Maria Usovaa , Ilia Viksnina and Sergey Chuprova
a
ITMO University, Kronverksky Pr. 49, 197101, St. Petersburg, Russian Federation
Abstract
Smart Factory concept is considered to play one of the crucial roles in the Industry 4.0 paradigm de-
velopment and evolution. The informational space model provides a wide spectrum of opportunities
for developers to implement new informational interaction mechanisms within the Smart Factory sys-
tem. 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 sim-
ulation environment, apply described informational interaction models, and analyze further viability of
the developed approach.
Keywords
Smart Factory, Information Interaction, Informational Space
1. Introduction
Nowadays, increasing the manufactories’ autonomy level becomes one of the crucial factors for
successful competition on the market. Scientific communities are focused on the technologies
development that allows to accelerate the production process and to decrease a human-related
aspects’ influence on it. Automated factories became a reality, but we still experiencing dif-
ficulties associated with planning processes. An individualized product and its adaptation to
the market require an optimized time and resource consumption. The Smart Factory concept
and factory as a cyber-physical system allows solving the majority of challenges that we are
facing now. Smart Factory can be considered as a network of smart devices presented by
robots, conveyors, transportation, and management system. However, another obstacle is the
representation of smart devices and their communication in a mathematical way.
In this paper, we introduce a mathematically described Smart Factory system and its agents
via a multi-agent approach. In Subsections 3.2-3.3 we introduce the concept of the Informational
Space in Smart Factory and describe the approaches to represent it as a two-dimensional and
three-dimensional spaces. Subsection 3.4 focuses on the packet structure of the messages
transmitted in Smart Factory. As a main result, we introduce the developed software simulation
environment that allows modeling the informational interaction in Smart Factory according to
Proceedings of the 12th Majorov International Conference on Software Engineering and Computer Systems, December
10–11, 2020, Online Saint Petersburg, Russia
" gipurer@gmail.com (M. Usova); wixnin@mail.ru (I. Viksnin); chuprov@itmo.ru (S. Chuprov)
0000-0001-6981-035X (M. Usova); 0000-0002-3071-6937 (I. Viksnin); 0000-0001-7081-8797 (S. Chuprov)
© 2020 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
Workshop
Proceedings
http://ceur-ws.org
ISSN 1613-0073
CEUR Workshop Proceedings (CEUR-WS.org)
the proposed specification. The functional structure and abilities of the simulator are presented
in Section 4.
2. Related Work
Nowadays the smart factory is represented as a fully connected and flexible system [1] that
uses constant information and adapts it for new technological requests. Supply manufacturing
chains transform from a static sequence to a dynamic one that uses many sources of information
to drive a production process. According to this paper, the five key characteristics of a smart
factory are connected, optimized, transparent, proactivity, agile.
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 sys-
tems whose operations are controlled, coordinated, and integrated by the computing core [2].
Since the level of physical devices and the network level (set of informational elements) are
integrated, the Smart Factory system can be considered as a CPS. Cyber-components of the
system include components responsible for performing calculations, implementing algorithms,
and transmitting data over a network. The physical component of such a system is determined
by "analog" elements, other physical systems, and the environment itself. The use of CPS in
the manufacturing sector allows increasing the production process efficiency due to the full
integration of computing devices with enterprise mechanisms [3].
The Smart Factory’s functional structure and the sequencing production process mechanism
are well-studied topics [4]. The autonomous manufacturing physical side is researched, partially
implemented and simulated by various scientific communities [5]. The concept of the Industrial
Internet of Things (IIoT) is widely used as the basis to organize the factory elements’ interaction
with the use of IIoT routing protocols [6, 7]. However, the Smart Factory mathematical concept
formalization has not been proposed yet, as well as the interaction and communication models
for the Smart Factory elements.
3. Information Interaction in the Framework of Smart Factory
3.1. Mathematical Description of the Smart Factory
We consider Smart Factory as a structure < 𝐴, 𝐼, 𝑅, 𝑃 𝑟 >. The elements of this structure are
the sets of Smart Factory objects: agents-robots set 𝐴, informational space set 𝐼, resources
set 𝑅, products set 𝑃 𝑟. It is assumed that the Informational Space is a result of a function
𝐼 = 𝑓 (𝐴). 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
[3], Smart Factory can be described as a multi-agent system. The agents can be represented as
agents-robots (further - agents) of the set 𝐴. These agents are autonomous and perform specific
tasks to achieve the common system goal. In this case, agents assemble the products using the
information space I as a communication channel.
The set of agents is represented as 𝐴 = {(𝑎1 |𝑞1 ), (𝑎2 |𝑞2 ), . . . , (𝑎𝑛 |𝑞𝑛 )}, where 𝑞 is a particular
agent’s access level to the Informational Space messages. The resource set can be described
as 𝑅 = {𝑟1 , 𝑟2 , . . . , 𝑟𝑠 }, the set of Smart Factory products as 𝑃 𝑟 = {𝑝𝑟1 , 𝑝𝑟2 , . . . , 𝑝𝑟𝑣 , }.
Production process can be described as a function 𝑓 = (𝐴𝑖 , 𝐹𝑖 , 𝑅𝑖 , 𝐼, 𝑡) + 𝜑𝑖 where 𝐴𝑖 , 𝐹𝑖 ,
𝑅𝑖 are some subsets of the agents’ set, functions and resources (𝐴, 𝐹 , 𝑅, respectively) are
involved in this product assembling; 𝐼 is the informational messages set; 𝑡 is the time spent on
the production process, 𝜑𝑖 presents other features that have an impact on production process.
3.2. The Structure of the Informational Space
We introduce informational space as a structure < 𝐼, 𝐷 > where 𝐼 is a set of elementary
informational messages 𝐼 = {𝑖1 , 𝑖2 , . . . , 𝑖𝑙 } and 𝐷 is a set of the access parameters for the
corresponding messages of the set 𝐼, 𝐷 = {𝑑1 , 𝑑2 , . . . , 𝑑𝑙 }, 0 ≤ 𝑑𝑘 ≤ 1. The parameter 𝑑𝑘 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 𝑑𝑘 = 𝑞𝑠𝑒𝑛𝑑𝑒𝑟 , where 𝑞𝑠𝑒𝑛𝑑𝑒𝑟 is the access level of the sender agent.
• The message was sent to another agent. The sender can specify the 𝑑𝑘 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 𝑑𝑘 = 𝑚𝑖𝑛(𝑞𝑠𝑒𝑛𝑑𝑒𝑟 , 𝑞𝑟𝑒𝑐𝑒𝑖𝑣𝑒𝑟 ),
where 𝑞𝑠𝑒𝑛𝑑𝑒𝑟 is the access level of the sender, 𝑞𝑟𝑒𝑐𝑒𝑖𝑣𝑒𝑟 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: 𝑑𝑘 = 𝑚𝑖𝑛(𝑞1 , 𝑞2 , . . . , 𝑞𝑛 ).
3.3. Informational Space Representation Approaches
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
𝑎, 𝑏 and 𝑡, and is illustrated in Fig. 1. The axis 𝑎 illustrates the agents sending informational
messages, the axis 𝑏 represents the agents receiving informational messages, the 𝑡 axis portrays
the time. Considering this informational space representation, the following assumptions are
introduced:
• on the axis 𝑎 and 𝑏 the agents are displayed discretely;
• the axes of the sender and receiver agents are limited by 𝑎𝑛 -th agent, where 𝑛 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 𝑡 = 0 is the initial system operation time moment;
• the transmission time tends to zero, 𝑡𝑡𝑟𝑎𝑛𝑠𝑚𝑖𝑠𝑠𝑖𝑜𝑛→0 , therefore the time of sending and
the time of informational message receiving are considered as equal: 𝑡𝑠𝑒𝑛𝑑 = 𝑡𝑟𝑒𝑐𝑒𝑖𝑣𝑒 .
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.
b
bi
ai a
i
timei
time
Figure 1: Three-dimensional representation of the Informational Space.
• 𝑏 = 𝑎𝑠𝑒𝑛𝑑𝑒𝑟 . 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 𝐼𝑜𝑤𝑛 ;
• 𝑏 ∈ [1, 𝑎𝑠𝑒𝑛𝑑𝑒𝑟 − 1] ∪ [𝑎𝑠𝑒𝑛𝑑𝑒𝑟 + 1, 𝑛]. The messages of this type indicate interactions
between agents 𝑎 and 𝑏. The set of the messages passed between agents 𝑎 and 𝑏 is
described as 𝐼𝑖𝑛𝑡𝑒𝑟𝑎𝑐𝑡𝑖𝑜𝑛 ;
• 𝑏 = 𝑎𝑛 . These messages are broadcasted to all agents since this identifier value is an 𝑎𝑙𝑙
instruction. All the agents with 𝑞𝑘 ≥ 𝑑 have access to such messages. These messages
are defined by the set 𝐼𝑎𝑙𝑙 .
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 𝑞. 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).
𝐼 = 𝐼𝑎𝑙𝑙 ∪ 𝐼𝑖𝑛𝑡𝑒𝑟𝑎𝑐𝑡𝑖𝑜𝑛𝑠 ∪ 𝐼𝑜𝑤𝑛 (1)
The visualization of the informational space in this form is presented in Fig. 2.
Receiver ID
n+1
Iall
n
IInteraction Iown
IInteraction
0 n Sender ID
Figure 2: Two-dimensional representation of the Informational Space.
field 1 field 2 field 3 field 4 field 5 field 6 field 7
a b d time type info DS
Figure 3: The structure of the informational message.
3.4. The Informational Message Structure
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. "𝑎" is the ID of the sender agent;
2. "𝑏" is the ID of the receiver agent;
3. "𝑑" is the access parameter of the message;
4. "𝑡𝑖𝑚𝑒" 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. "𝑡𝑦𝑝𝑒" is the informational message type. The field contains the information on a subset
to which the message belongs (according to equation (1));
6. "𝑖𝑛𝑓 𝑜" is the informational message content;
7. "𝐷𝑆" is the sender agent digital signature, used as a basic measure to ensure data dissem-
ination process security.
4. Simulation
4.1. Simulator Description
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 different 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.
4.2. Available actions in the simulator
The developed simulator provides the following functions:
• To transmit the informational message from agent 𝑎 to agent 𝑏. 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;
Figure 4: The two- and three-dimensional representations of the informational spaces generated by
the simulator.
• 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 difficulty of the implementation is to develop the mechanism for storing
private and public keys and their use by the agents.
4.3. Results
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 efficiency 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 efficiency and security
metrics, provide experiments design, and conduct an empirical study to assess the proposed
mechanisms via a developed software simulator.
5. Conclusion
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.
Acknowledgments
This paper is supported by the Government of Russian Federation (grant 08-08).
References
[1] R. Burke, A. Mussomeli, S. Laaper, M. Hartigan, B. Sniderman, The smart factory-responsive,
adaptive, connected manufacturing. deloitte insights, 2017.
[2] R. Rajkumar, I. Lee, L. Sha, J. Stankovic, Cyber-physical systems: the next computing
revolution, in: Design automation conference, IEEE, 2010, pp. 731–736.
[3] B. Pozdneev, M. Sutyagin, I. Kupriyanenko, V. Tikhomirova, A. Levchenko, New horizons
of standardization in the age of digital education and manufacturing, Vestn. Mosk. Gos.
Tekhnol. Univ., Stankin (2015) 35.
[4] S. Wang, J. Wan, D. Zhang, D. Li, C. Zhang, Towards smart factory for industry 4.0: a
self-organized multi-agent system with big data based feedback and coordination, Computer
Networks 101 (2016) 158–168.
[5] R. Harrison, D. Vera, B. Ahmad, Engineering the smart factory, Chinese Journal of
Mechanical Engineering 29 (2016) 1046–1051.
[6] A. Chehri, G. Jeon, Routing protocol in the industrial internet of things for smart factory
monitoring, in: Innovation in Medicine and Healthcare Systems, and Multimedia, Springer,
2019, pp. 505–515.
[7] J. Jang, E.-J. Kim, Survey on industrial wireless network technologies for smart factory,
Journal of Platform Technology 4 (2016) 3–10.