=Paper=
{{Paper
|id=Vol-3909/Paper_24.pdf
|storemode=property
|title=Software Modeling and Implementation of Information Network for Smart Home Technology
|pdfUrl=https://ceur-ws.org/Vol-3909/Paper_24.pdf
|volume=Vol-3909
|authors=Juliy Boiko,Volodymir Druzhynin,Ilya Pyatin,Lesya Karpova
|dblpUrl=https://dblp.org/rec/conf/iti2/BoikoDPK24
}}
==Software Modeling and Implementation of Information Network for Smart Home Technology==
https://ceur-ws.org/Vol-3909/Paper_24.pdf
Software Modeling and Implementation of Information
Network for Smart Home Technology
Juliy Boiko1, , Volodymir Druzhynin2, , Ilya Pyatin3, and Lesya Karpova1
1
Khmelnytskyi National University, 11, str., Khmelnytskyi, 29016, Ukraine
2
Taras Shevchenko National University of Kyiv, 60 Volodymyrska str., Kyiv, 01033, Ukraine
3
Khmelnytskyi Polytechnic Professional College by Lviv Polytechnic National University, 10, Zarichanska str., Khmelnytskyi,
29019, Ukraine
Abstract
The article discusses the design and control of the Internet of Things networks. The requirements for
effective network management are defined. A model of the information network based on Smart Home
technology in the Cisco Packet Tracer environment is built, user authentication is established, the address
space is partitioned, smart devices are selected, and the requirements for the bandwidth of the information
network are analyzed. A popular approach to implementing a Smart Home is to use sensors and cameras
to monitor the home environment and detect motion and control the home environment, which alerts
homeowners in the event of a security breach. Indoor temperature, humidity levels, motion detection data,
and water level readings collected by sensors can be stored in a database on the server for further analysis.
The system also uses the generated logs to monitor performance and identify potential threats and signal
in the event of security breaches. The proposed Smart Home strategy, in comparison to traditional
approaches, is characterized by enhanced system integration, improved scalability, optimized resource
utilization, and heightened security.
Keywords
internet of things, smart home, information network, Wi-Fi, smart devices 1
1. Introduction
At present, the specifics of the deployment of the Internet of Things (IoT) environment are
characterized by the presence of a wide range of diverse and generally resource-limited applications.
There are a number of developed and standardized IoT protocols [1, 2]. Among such technologies, it
is worth highlighting Zigbee, BLE, LoraWAN and Sigfox solutions, as well as individual solutions
for network management in the LWM2M, CoMI format [3]. Communication protocols are relevant
when using means with limited resources, and solutions have been developed for routing such
devices, such as 6LowPAN and RPL, respectively.
However, as the analysis of works [4, 5, 6] shows, due to heterogeneity and certain resource
limitations, the implementation of IoT networks is associated with a number of problematic issues
that ultimately affect their performance. Mainly, as discussed in [7], such problems are caused by the
quality of reliable communication, the consequences of network overload, and failure of IoT devices.
In this context, an important problem arises, directly related to the implementation of the flexible IoT
network management format to maintain performance indicators. Here, as noted in [8], it is
important to ensure a low level of end-to-end latency or satisfactory energy efficiency. Analysis of
Information Technology and Implementation (IT&I-2024), November 20-21, 2024, Kyiv, Ukraine
Corresponding author.
These authors contributed equally.
boiko_julius@ukr.net (J. Boiko); v_druzhinin@ukr.net (V. Druzhynin); ilkhmel@ukr.net (I. Pyatin);
rtlesya@gmail.com (L. Karpova)
0000-0003-0603-7827 (J. Boiko); 0000-0002-5340-6237 (V. Druzhynin); 0000-0003-1898-6755 (I. Pyatin); 0000-0001-5015-
2107 (L. Karpova)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
296
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
current works on IoT [9, 10] allows us to formulate a list of requirements for the implementation of
flexible control of IoT networks, among which it is necessary to highlight the functions of resource
provision, authentication, routing and monitoring [11]. In addition, it is important to ensure timely
software updates for the relevant devices, in particular their firmware, error correction [12,
13, 14], etc. Thus, the implementation of the above functional slots allows us to form a network
service environment in order to maintain the performance indicators of IoT, in the form presented
in Figure 1.
Figure 1: The concept of forming the IoT architecture.
The model in Figure 1 includes IoT devices, IoT technologies, and IoT applications and services.
Devices usually have a wireless module that corresponds to a specific IoT technology: LoRa, ZigBee,
Wi-Fi. Information exchange is provided by a specific protocol. In this case, in the presented article,
when organizing the smart home (SH) network, we use Wi-Fi technology [15, 16]. At the top level
of the model, there are applications that allow you to configure the interaction of intelligent devices.
These are some applications and network services.
In the works [17, 18] it was emphasized that the implementation of the SH project allows for an
unprecedented level of control and volume in order to gain access and control over home devices
from the subscriber's current location at the required time. In general, as noted in the works [19], the
main purpose of smart devices is to create an interconnected ecosystem inside the home, where IoT
technology is used as the main one for device control. As discussed in the works [20, 21], in this
context we can talk about the formation of a network of physical devices containing appropriate
sensors and allowing for data exchange in the IoT format [22].
The content of the presented work is aimed at supplementing and implementing through
modeling the proposed concept of building an information network of the SH type. We used the
Cisco Packet Tracer (CPT) environment to design such a network. The implementation of the
proposed network architecture is realized through a user authentication mechanism, partitioning the
address space, choosing reasonable devices and analyzing the bandwidth requirements of the
proposed network. The proposed SH system uses generated logs to monitor performance and
identifies potential threats and signals in case of security breaches.
297
2. Control methods in IoT networks
This section of the article analyses possible approaches to control methods in IoT networks. A
classification of control solutions in the network is presented. A low-power IoT control architecture
is described. Low-power IoT network management protocols have been developed to ensure and
optimize network performance while using small resources for network control operations.
2.1. Conceptual foundations in IoT control solutions
The concept of network control is based on a number of operations, among which it is advisable to
highlight: monitoring in relation to devices; providing the control process with routing and security.
The main direction of such operations is associated with increasing network performance, in
particular, in the context of minimizing delays, reducing energy consumption, localizing packet loss,
etc. Consequently, it is possible to highlight a typical control structure based on the formation of
logical subsystems based on a network manager, managed devices and agents. Thus, Figure 2
presents the concept of engaging functional elements activating the network control process [23].
Figure 2: Devices participating in the control network.
According to Figure 2, the "Network Administrator" provides overall control of a group of nodes.
The "Controlled device" refers to a network device that provides a set of parameters (e.g. IP address,
CPU load, remaining battery charge, etc.) that are control (via read/write operations) by the network
manager. The "Agent" refers to the software running on the managed device. It collects raw data
from the control device and transmits it in a usable format to the network manager. The control
database contains information about the parameters of the control device. Messaging protocols can
be used to exchange information between the network manager and the control devices. This allows
the network manager to receive parameters from the control devices and make appropriate decisions
on reconfiguring the network devices.
There are several key requirements for managing IoT networks. Accordingly, the key
requirements for effective IoT performance can be formulated as follows: scalability, fault tolerance,
energy efficiency, quality of service (QoS) [24], and security. Consequently, IoT must provide low
power consumption with the ability to expand by adding new devices (Figure 3).
It is equally important to satisfy the fault tolerance requirement. The point is that such a
requirement must guarantee that the network will perform as expected in the presence of a fault (e.g.
node fault, network fault, receiver fault, software fault) in the network. QoS characterizes the degree
of consumer satisfaction. This requirement includes mechanisms for localizing packet loss,
minimizing delays, etc. The energy efficiency requirement imposes obligations to perform the main
functions of IoT with minimal power consumption, which is especially relevant in the context of
using battery power.
Next, we discuss such a requirement as security. Having a secure network is the key to preventing
potential risks of data forgery. Self-configuration refers to the ability of IoT devices to adapt their
behavior in accordance with the network state.
298
Figure 3: Low power IoT Control Solutions.
By analyzing the listed requirements for IoT, approaches to implementing network control solutions
for devices with limited resources can be formed. These solutions can be used to create certain
categories (Figure 3) of control, in particular, as network control protocols for low-power IoT
networks, SDN-based platforms, cloud platforms, semantic frameworks, and machine learning
frameworks.
2.2. Low-power IoT network control approaches
There are various network management protocols for remote control of devices with limited
resources. These protocols include: LWM2M [25], CoMI, NETCONF Light and 6LowPAN-SNMP
(Figure 4).
Figure 4: LWM2M Architecture.
LWM2M is a client-server protocol designed for low-power IoT device control. The LWM2M
server resides on the network manager device, and the LWM2M client is typically hosted on the
control devices. IoT device resources are organized into objects (e.g., a location object that contains
all the resources that provide location information for IoT devices). CoMI is a control interface
designed for low-power IoT devices and networks. This network control protocol enables resource
management operations of IoT devices. 6LowPAN-SNMP is an adaptation of SNMP for IPv6 low-
power wireless personal area network (6LowPAN). An example of such an architecture is shown in
Figure 5.
299
Figure 5: Architecture of control of smart devices based on a cloud platform for working with
sensors.
3. Building a smart home model
In this chapter, we introduce the proposed SH network model. The process of setting up a wireless
SH network will be discussed. The format for setting up the connection between smart devices and
the server will be described.
3.1. Setting up the SH network model
SH systems are now being deployed in private companies, government agencies, and residential
buildings to automate operations that make life and work more convenient: they control lighting,
household appliances, monitor the home, etc. To build an SH model, we will use CPT. In Figure 6,
we present a structural diagram of the designed information network using SH technology. The
network has sections with wired and wireless connections. The global network provider connects
two offices using routers Router 1 and Router 2.
Figure 6: Structural diagram of the proposed information network using SH technology: IoT0 is the
window control; IoT1 is the door control; IoT2 is the siren control; IoT3 motion detector; IoT4 is the
light control; IoT5 is the wall fan control; IoT6 is the lawn sprinkler; IoT7 is the garage door; IoT7 is
the webcam control.
300
Figure 7: First office network.
SH network and monitoring of connected devices in the laptop web browser. Figure 8 shows the
settings window for connecting smart devices to the network - siren (IoT2 - Siren).
Figure 8: Settings for connecting smart devices to the network.
The physical level
all smart devices are connected to the network, it is necessary to configure the actions performed
when certain events occur: when a window is opened, a siren is triggered, which can only be
excluded by the user; when a motion detector is triggered, the lighting is turned on. To do this, go
to the Conditions tab in the laptop web browser (Laptop 0) from the Home tab. The settings window
in the laptop browser is shown in Figure 10. The alarm setup - turning on the siren when a window
is opened is shown in Figure 11.
301
Figure 9: Settings window for connecting a device named IoT4.
Figure 10: Settings window in laptop browser.
Figure 11: Display the ability to turn on the siren when opening a window.
If the alarm is not needed, we turn off the siren. Setting up smart devices in the laptop web
browser (Laptop 0) is shown in Figure 12.
Figure 12: Setting up smart devices in your laptop's web browser.
302
Routers and switches of the local network are connected using twisted pair category 5e (see Figure
6). For four pairs the transmission rate is up to 1000 Mbit/sec, for a two-pair twisted pair, respectively,
up to 100 Mbit/sec. The frequency band is 100 MHz [26].
Global connections with the provider are made using single-mode optic fiber [27].
To transmit packets to the global network, the first office has a gateway 192.168.1.250 with a
white IP address of 1.1.1.1. Another office has a gateway 192.168.2.1 with a white IP address of 2.2.2.2.
To work with smart devices in the network, there is a server 192.168.1.1 and workstations with a
wired and wireless connection.
3.2. Setting up and control of the global SH network
When setting up a global network, we must be able to connect from the second office via the global
network to the first office. This is done using a gateway. In the network of the first office, the gateway
has the address 192.168.1.250. The Home/Office1 node setup window looks like (Figure 13).
For the second office, the gateway has the address 192.168.2.1. For each network device, we must
write down the IP address of this device, the subnet mask, and the gateway. The Office2 node
configuration window looks like this (Figure 14).
For office 2, we also need to specify a gateway to access the global network. In order for us to
have access from the first office to the second and from the second to the first, we need to set up
global connections. We have two routers. They have global IP addresses: for the first office 1.1.1.1
and for the second office 2.2.2.2. We need to record the default gateway in each office node - this is
the router at 192.168.1.250. We need to do the same with the second office.
Figure 13: Home/Office1 Node Configuration Window.
Figure 14: Office2 Node Configuration Window.
303
The window for configuring Gigabit Ethernet addresses of the provider node looks like (Figure
15).
Figure 15: Gigabit Ethernet address configuration window for the provider node.
Before setting up routing tables on the routers, let's check for a connection with the first router
in the second office. On the Laptop 1 node, open the command line, as shown in Figure 16.
Figure 16: Command line on node Laptop 1.
To ensure communication between the second office and the first, requests from nodes at the first
address 192.168.1.0 with a mask of 255.255.255.0 must be redirected to the router port with the
address 2.2.2.3.
Open the static address settings window for the Home/Office1 node and write down that all
requests from nodes 192.168.2.0 should be forwarded to router 1.1.1.3.
Only after this we will configure the routing table on the provider router. All requests coming to
the 192.168.1.0 network should be transmitted to the router with the address 1.1.1.1, and requests to
the address 192.168.2.0 should be sent to the router with the address 2.2.2.2.
304
Open the command line in the node (Laptop 0) of office 2 and ping node 11, located in the first
office. In this case, it is possible that the first three sent packets were lost, which is related to the
compilation of the routing table. When the routing table is configured with the server 192.168.1.1,
the first ping remains unanswered. Then three packets arrive successfully.
Let's add a new device to the network - a tablet, from which we can control the operation of smart
devices in the house and configure them according to Figure 17.
Figure 17: Wireless network settings window for tablet
In Figure 17 we have shown the configuration in case of configured Wi-Fi parameters.
After pinging the presence of Tablet PC 0 on the network, we connected to the web server
and checked the ability to control smart devices at home.
After entering the login and password, a window opens in which all connected smart devices are
displayed, as shown in Figure 18.
Figure 18: Smart Devices Settings Window of the laptop1 node
305
3.3. Modeling the scaling process of the SH network
By scaling the SH network, we added entrance doors to the building and garage. There is a device at
the door that records the state of closing and opening, which is connected to the network using a radio
module. We configure the connection of this device according to Figure 19.
We set up the garage door in a similar way. We also included a sensor that works via Wi-Fi [16].
Figure 19: Connecting a Smart IoT7 Device
Next, we established a connection between the smart devices and the server that controls them.
Everything is shown in Figure 20.
Figure 20: Communication between smart devices and server
Now the user needs to remotely check whether the door lock is closed he can do this using the
IoT Server Device Conditions window, as shown in Figure 21.
Below we present the calculation of the required SH network bandwidth [28, 29]. We take into
account that for each 0.1-megapixel resolution of the webcam, two Mbit/s of Internet rate are
required to ensure reliable connection (Table 1).
306
Figure 21: IoT Server Settings Window Device Conditions
Table 1
Determining the bandwidth of a camera with different resolutions
Frame size Number of pixels Frame frequency Rate (Mbit/s)
(frames/sec)
320·340 76800 25 1.6
640·480 307200 25 6.0
1296·972 1259712 25 26.0
1640·1232 2020480 25 40.0
To calculate the bandwidth of the SH network at home using an IP video camera, the following
expression was used:
𝐹𝑓𝑠 · 1024 · 8 · 𝑅 · 𝑁 (1)
𝐵= ,
106
where Ffs is the frame frequency; R is the video resolution; N is the total number of cameras
involved in the network.
Below we present the calculation of the bandwidths of cameras with different codecs and
summarize it in Table 2.
Table 2
Key camera bandwidth indicators depending on codec type
Frame size Video resolution Codec type Rate (Mbit/s)
(MP)
1280·720 1 2.0
1280·720 1 mjpeg 6.0
1920·1080 2 4.0
1920·1080 2 mjpeg 12.0
2560·1440 4 8.0
2560·1440 4 mjpeg 24.0
It is important to note that increasing the frame size affects the quality of recognition of small
details. If we set the camera to turn on when the motion detector is triggered, we can reduce the
required Internet rate. It should be emphasized that depending on the location of the video camera,
different bandwidth can be obtained. Here it is important to determine whether the cameras are
located outdoors or indoors. It should also be taken into account that some cameras have high
resolution or purity of recording of frame sequences, due to the built-in image processing algorithm.
In this case, the bandwidth increases.
We also provide a flow chart for sending messages to an IoT network user (Figure 22).
307
Figure 22: IoT Server Settings Window Device Conditions
So, in accordance with Figure 22, we record the following sequence of actions. Using the built-in
Wi-Fi [12, 16], the IoT device sends information from the sensors to the cloud service. The IoT
platform forms a certain set of rules that have the ability to activate according to certain signs -
opening the garage, opening the window or using the motion detector, that is, what is described
above in our network [30]. The resulting set of rules includes a message service and information is
sent to e-mail. Thus, the rate of IoT response in the cloud service [31, 32] database increases.
However, network control protocols are not able to meet all the requirements of low-power IoT
networks mentioned earlier. Let us consider cloud platforms for control low-power Internet
networks. Cloud computing is a model that provides ubiquitous, convenient, on-demand network
access to a shared pool of computing resources [33]. The architecture of low-power IoT network
control on a cloud platform consists of three layers: 1) the first layer consists of resource-constrained
devices; 2) the second level consists of cloud infrastructure; 3) the third level consists of IoT
applications.
The IoT network deployment solutions described in Section 2 formed the basis for the SH network
design process presented in Section 3.
4. Conclusion
This paper describes the process of building a model of the SH network in the environment.
According to the simulation results, user authentication is established, the address space is
partitioned, smart devices are selected, and the bandwidth requirements of the IoT network are
analyzed. The owner can access the SH from anywhere in the world using a smartphone. The use of
sensors and cameras is configured to monitor the home environment and detect motion and control
the home environment, which alerts homeowners in the event of a security breach. Different
programming languages of sensor boards and smart devices are used to build the network. The
indoor temperature, humidity level, motion detection data and water level readings collected by the
sensors can be stored in the database on the server for further analysis. The system also uses the
generated logs to monitor performance and identify potential threats and alarms in case of security
breaches. The calculation of the required network bandwidth to support video data from multiple IP
cameras in SH settings is provided.
Declaration on Generative AI
The authors have not employed any Generative AI tools.
References
[1] J. Lin, W. Yu, N. Zhang, X. Yang, H. Zhang, W. Zhao, "A Survey on Internet of Things:
Architecture, Enabling Technologies, Security and Privacy, and Applications," IEEE Internet of
Things Journal, 4.5 (2017): 1125-1142, doi:10.1109/JIOT.2017.2683200.
[2] Kamaldeep, M. Malik, M. Dutta, J. Granjal, "IoT-Sentry: A Cross-Layer-Based Intrusion
Detection System in Standardized Internet of Things," IEEE Sensors Journal, 21.24 (2021): 28066-
28076, doi:10.1109/JSEN.2021.3124886.
308
[3] A. Garcés-Jiménez, A. Rodrigues, J. M. Gómez-Pulido, D. Raposo, J. A. Gómez-Pulido, J. Sá Silva,
F. Boavida, "Industrial Internet of Things embedded devices fault detection and classification. A
case study," Internet of Things, 25 (2024): 101042, doi:10.1016/j.iot.2023.101042.
[4] X. Deng, J. Zhu, X. Pei, L. Zhang, Z. Ling, K. Xue, "Flow Topology-Based Graph Convolutional
Network for Intrusion Detection in Label-Limited IoT Networks," IEEE Transactions on
Network and Service Management, 20.1 (2023): 684-696, doi:10.1109/TNSM.2022.3213807.
[5] A. Campbell, M. E. Hariri, M. Parvania, "Asynchronous Distributed IoT-Enabled Customer
Characterization in Distribution Networks: Theory and Hardware Implementation," IEEE
Transactions on Smart Grid, 13.6 (2022): 4392-4404, doi: 10.1109/TSG.2022.3182210.
[6] M. Rostami, S. Goli-Bidgoli, "An overview of QoS-aware load balancing techniques in SDN-
based IoT networks," Journal of Cloud Computing, 13.89 (2024), doi:10.1186/s13677-024-00651-
7.
[7] K. Erzun, R. Avoub, P. Mercati, T. Rosing, Improving Mean Time to Failure of IoT Networks
with Reliability-Aware Routing, in: Proceedings of the 2021 10th Mediterranean Conference on
Embedded Computing, MECO, IEEE Press, Budva, Montenegro, 2021, pp. 1-4,
doi:10.1109/MECO52532.2021.9460211.
[8] G. Zhang, F. Shen, Z. Liu, Y. Yang, K. Wang, M. -T. Zhou, "FEMTO: Fair and Energy- Minimized
Task Offloading for Fog-Enabled IoT Networks," IEEE Internet of Things Journal, 6.3 (2019):
4388-4400, doi:10.1109/JIOT.2018.2887229.
[9] P. Anitha, H. S. Vimala, J. Shreyas, "Comprehensive review on congestion detection, alleviation,
and control for IoT networks," Journal of Network and Computer Applications, 221 (2024):
103749, doi:10.1016/j.jnca.2023.103749.
[10] X. Li, S. Wang, J. Cao, "An IoT-Enabled Control Paradigm for Building Process Control: An
Experimental Study," IEEE Internet of Things Journal, 11.9 (2024): 15465-15474,
doi:10.1109/JIOT.2023.3348125.
[11] C. Li, T. Yashiro, AFWA: Flexible IoT Access Control Framework with Web API Integration, in:
Proceedings of the 2022 IEEE 4th Global Conference on Life Sciences and Technologies, IEEE
Press, LifeTech, Osaka, Japan, 2022, pp. 354-356, doi:10.1109/LifeTech53646.2022.9754921.
[12] J. Boiko, I. Pyatin, O. Eromenko, L. Karpova, Evaluation of the Capabilities of LDPC Codes for
Network Applications in the 802.11ax Standard, in: Joby, P.P., Alencar, M.S., Falkowski-Gilski,
P. (Eds.), IoT Based Control Networks and Intelligent Systems. Lecture Notes in Networks and
Systems, volume 789, Springer, Singapore, 2024, pp. 369 383, doi:10.1007/978-981-99-6586- 1_25.
[13] J. Boiko, V. Druzhynin, S. Buchyk, I. Pyatin, A. Kulko, "Methodology of FPGA Implementation
and Performance Evaluation of Polar Coding for 5G Communications", CEUR Workshop
Proceedings, 3654 (2024): 15-24, urn:nbn:de:0074-3654-7.
[14] B. Zhurakovskiy, J. Boiko, V. Druzhynin, I. Pyatin, "Performance Analysis of Concatenated
Coding for OFDM Under Selective Fading Conditions", CEUR Workshop Proceedings, 3624
(2023): 403-413, https://ceur-ws.org/Vol-3624/Paper_33.pdf.
[15] A. Boni, V. Bianchi, A. Ricci, I. De Munari, "NB-IoT and Wi-Fi Technologies: An Integrated
Approach to Enhance Portability of Smart Sensors," IEEE Access, 9 (2021): 74589-74599, 2021,
doi:10.1109/ACCESS.2021.3082006.
[16] J. Boiko, I. Pyatin, V. Druzhynin, Possibilities of the MUSIC Algorithm for WI-FI Positioning
According to the IEEE 802.11az Standard, in: Proceedings of the 2023 IEEE International
Conference on Information and Telecommunication Technologies and Radio Electronics,
UkrMiCo, IEEE Press, Kyiv, Ukraine, 2023, pp. 1-6, doi:10.1109/UKRMICO61577.2023.10380354.
[17] B. Zhurakovskyi, O. Nedashkivskiy, M. Klymash, O. Pliushch, M. Moshenchenko, Smart House
Management System, in: Klymash, M., Luntovskyy, A., Beshley, M., Melnyk, I., Schill, A. (Eds.),
Emerging Networking in the Digital Transformation Age. Lecture Notes in Electrical
Engineering, volume 965, Springer, Cham, 2023, pp. 268 283, doi:10.1007/978-3-031-24963-1_15.
[18] M. Khan, B. N. Silva, K. Han, "Internet of Things Based Energy Aware Smart Home Control
System," IEEE Access, 4 (2016): 7556-7566, doi:10.1109/ACCESS.2016.2621752.
309
[19] D. Pal, S. Funilkul, N. Charoenkitkarn, P. Kanthamanon, "Internet-of-Things and Smart Homes
for Elderly Healthcare: An End User Perspective," IEEE Access, 6 (2018): 10483-10496,
doi:10.1109/ACCESS.2018.2808472.
[20] P. Malini, Dr. K.R. Kavitha, "An efficient deep learning mechanisms for IoT/Non-IoT devices
classification and attack detection in SDN-enabled smart environment," Computers & Security,
141 (2024): 103818, doi:10.1016/j.cose.2024.103818.
[21] S. Wan, Q. Li, H. Wang, H. Li, L. Sun, "DevTag: A Benchmark for Fingerprinting IoT Devices,"
IEEE Internet of Things Journal, 10.7(2023): 6388-6399, doi:10.1109/JIOT.2022.3225580.
[22] D. Wajgi, J.V. Tembhurne, R. Wajgi, T. Jain, Communication in IoT Devices, in: Gunjan, V.K.,
Ansari, M.D., Usman, M., Nguyen, T. (Eds.), Modern Approaches in IoT and Machine Learning
for Cyber Security. Internet of Things. Springer, Cham, 2024, pp 21 44, doi:10.1007/978-3-031-
09955-7_2.
[23] A. Jamali, B. Shahgholi Ghahfarokhi, M. Abedini, "Improving Performance of Association
Control in IEEE 802.11ah-Based Massive IoT Networks," IEEE Internet of Things Journal, 9.11
(2022): 8572-8583, doi:10.1109/JIOT.2021.3114192.
[24] M. Singh, G. Baranwal, Quality of Service (QoS) in Internet of Things, in: Proceedings of the
2018 3rd International Conference On Internet of Things: Smart Innovation and Usages, IoT-
SIU, IEEE Press, Bhimtal, India, 2018, pp. 1-6, doi:10.1109/IoT-SIU.2018.8519862.
[25] A.J. Simla, C. Rekha, L.M. Leo, "Agricultural intrusion detection (AID) based on the internet of
things and deep learning with the enhanced lightweight M2M protocol," Soft Computing (2023).
doi: 10.1007/s00500-023-07935-1.
[26] B. Zhurakovskyi, J. Boiko, V. Druzhynin, I. Zeniv, O. Eromenko. "Increasing the efficiency of
information transmission in communication channels," Indonesian Journal of Electrical
Engineering and Computer Science (IJEECS), 19.3 (2020): 1306-1315.
doi:10.11591/ijeecs.v19.i3.pp1306-1315.
[27] P.S. Macheso, F.G.D. Thulu, Roles of Optical Fiber Sensors in the Internet of Things: Applications
and Challenges, in: Ranganathan, G., EL Allioui, Y., Piramuthu, S. (Eds.), Soft Computing for
Security Applications. Advances in Intelligent Systems and Computing, volume 1449, Springer,
Singapore, 2023, pp. 923 933, doi:10.1007/978-981-99-3608-3_64.
[28] E. Manziuk, O. Barmak, I. Krak, O. Mazurets, O. Pylypiak, Method of features analysis on
transition data, in: Proceedings of the 2021 IEEE 3rd International Conference on Advanced
Trends in Information Theory, ATIT, IEEE Press, Kyiv, Ukraine, 2021, pp. 272-277,
doi:10.1109/ATIT54053.2021.9678787.
[29] M. Kushnir, H. Kosovan, P. Kroialo, "Method of encrypting images based on two
multidimensional chaotic systems using fuzzy logic," Radioelectronic and Computer Systems, 4
(2022): 117-128, doi:10.32620/reks.2022.4.09.
[30] J. Boiko, I. Pyatin, O. Eromenko, Analysis of Signal Synchronization Conditions in 5G Mobile
Information Technologies, in: Proceedings of the 2022 IEEE 16th International Conference on
Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering, TCSET,
IEEE Press, Lviv-Slavske, Ukraine, 2022, pp. 01-06, doi: 10.1109/tcset55632.2022.9766899.
[31] X. Li, Q. Wang, X. Lan, X. Chen, N. Zhang, D. Chen, "Enhancing Cloud-Based IoT Security
Through Trustworthy Cloud Service: An Integration of Security and Reputation Approach,"
IEEE Access, 7(2019): 9368-9383, doi: 10.1109/ACCESS.2018.2890432.
[32] , -based services in network management solutions," in:
Proceedings of the 2020 43rd International Convention on Information, Communication and
Electronic Technology, MIPRO, IEEE Press, Opatija, Croatia, 2020, pp. 419-424, doi:
10.23919/MIPRO48935.2020.9245117.
[33] I. Pyatin, J. Boiko, O. Eromenko, "Algorithmization and Hardware Implementation of Polar
Coding for 5G Telecommunications," Transport and Telecommunication Journal, 25.3 (2024):
300-310, doi: 10.2478/ttj-2024-0022.
310