Large cities in the modern world are high-tech innovative socio-economic centers that generate complex population migration processes and increase their demand for resources. Those are complex systems where various entities interact and within which residents and visitors of the city interact with these systems every day. This interaction is accompanied by the city systems and processes continuous measurement state through a large number of sensors and devices integrated into extensive network technology platforms. The following document describes the features of cyber-physical systems and communication networks used in practical smart city projects. Based on the analysis of cyber-physical systems used in smart cities, their classification has been developed and presented in graphic form. A smart cities functional model has also been developed. It is based on cyber-physical systems that can measure and control the air environment parameters of smart buildings. The communication networks classification and features used in smart cities cyber-physical systems are studied. A developed method allows the information and technical set general structure creation for the services and applications formation based on smart cities cyber-physical systems. On the DaaS model basis, the NDN network structure has been developed which allows the network measurement and the air quality regulation in the "TNTU Smart Campus". For this is used a IoT devices mesh network operating under the LoRa 433 MHz protocol. Two ways of choosing paths between the "Producer" and "Consumer" of data are proposed: reactive and proactive.
Modern large cities are high-tech, innovative socio-economic centers that generate population migration processes and increase the need for resources. They are complex systems in which heterogeneous and diverse entities interact, with which residents and visitors interact on a daily basis. This interaction is accompanied by continuous measurement of the urban systems and processes state using a sensors and devices large number that are integrated into extensive networked information technology platforms. In general, any object in the city can perform the collecting functions, transmitting, and consuming information. For example, this may relate to energy consumption, resource supply, production processes, city residents and visitors mobility, as well as measurement of living and environmental characteristics.
"A smart city is a city that uses information and communication technologies to improve its
resident’s quality of life and the urban systems functioning" [
At the present stage, municipalities, institutions and organizations are actively implementing the smart city model to improve
2023 Copyright for this paper by its authors. CEUR
ceur-ws.org the citizens quality of life and optimize the urban resources use. The smart city projects implementation is often based on the information and communication technologies (ICT) wide range aimed at improving the modern cities functioning, ensuring their sustainable development, expanding the range and improving the quality of municipal services. At the same time, there is a growing interest in information technologies aimed at improving healthcare, education, and the overall quality of life of citizens.
Smart city services are being implemented using a advanced information and communication technologies variety, including cyber-physical systems (CPS), the Internet of Things (IoT), wireless sensor networks (WSNs), edge computing, fog computing, 5G, cloud computing, data lakes, and many others. These smart services and solutions use helps to optimize resource and financial resources.
The innovative technologies use, including the IoT, is creating a basic information infrastructure for the smart cities development. This infrastructure includes cyber-physical systems that allow the smart things integration into the urban environment physical objects. This creates the opportunity to develop innovative smart city services and applications that urban activity various aspects support at any time and from any urban location.
These smart things and devices are controlled by special software applications that promptly monitor decision-making processes and increase the city operation and management efficiency. At the same time, smart devices and things provide information about their status in real time, including information about the air conditioning filters operation and possible malfunctions, which helps to predict the need for maintenance and repair.
Wireless sensor networks are used for urban infrastructure and resource supply systems real-time
measurement. Sensor devices powered by the Internet of Things collect information about the smart
building’s physical environment, including air temperature parameters and warehouse conditions.
Cyber-physical systems combine network and physical processes with computing paradigms of cloud,
fog, and edge computing to observe and control the smart city physical environment [
Smart city services and applications actively use the cloud computing paradigm. This provides an
accessible and scalable information technology platform for storing data from IoT devices and
cyberphysical systems, analytical processing, and supporting decision-making processes [
CPS are steadily spreading in industrial and scientific circles. CPS are complex, heterogeneous,
distributed systems that interact between cybernetic components, such as sensors, actuators, control
centers, and physical processes, such as controlling the residential premises parameters, managing
traffic, detecting emergencies or fires, etc. Reference [
The authors [
The cyber-physical systems proliferation is associated with the Internet of Things (IoT) emergence, a vast devices set with limited computing capabilities that transmit information and provide services via the Internet and the TCP/IP protocol stack. At the same time, cyber-physical systems are physical computing devices networks that interact with each other. The active cyber-physical systems development has covered various domains and led to the software and algorithmic applications creation in various economic sectors. At present, a large number of smart cities cyber-physical systems different types have been formed (see Fig. 1).
In smart cities, CPS are used for implement real-time data collection and exchange processes. This involves the complex integration of a devices different types large number, including sensors, actuators, microcontrollers, mobile communications, server hardware, network and cloud infrastructure.
In smart cities, CPS are used to: • creating an interactive and adaptive smart urban environment; • expanding the list and improving the quality of municipal services; • improving the decision-making processes efficiency; • creating a smart urban infrastructure; • improving the digital, cyber and physical resource management processes characteristics; • complex urban tasks and problems integrated solution; • transportation management; • improving energy efficiency; • improving public health; • reducing environmental pollution; • introducing innovative tactile applications to improve the urban resident’s quality of life; • optimization of the communication innovative means implementation and use processes, in particular, 5G, etc.
Cyber-physical systems transmit data to central systems for processing, correlation, and
decisionmaking to activate actuators or inform citizens, municipal institutions, and organizations. They typically
operate alongside many other cyber-physical systems, such as smart HVAC systems, smart vehicles,
smart production lines, smart buildings, smart infrastructure, etc. This complicates or even makes it
impossible to create and implement unique information technology structures for managing
cyberphysical systems on a large scale for a reasons number [
If necessary, cyber-physical systems can be used to quickly create and deploy new municipal
services, set up procedures and algorithms for assessing the state of urban systems, and use software
and algorithmic tools to support decision-making processes. The cyber-physical systems integration is
a new area of modern scientific research that allows managing, coordinating, and organizing integrated
cyber-physical systems, sensors, actuators, and resources to create high-quality smart city services [
The cyber-physical systems complex (see Fig. 2) is a smart cities new stage in the development, which can be expanded to larger or smaller territorial and sectoral formations. For example, it can represent a "smart community", "smart city", "smart sociopolis", "smart region" or "smart country", etc.
Complex smart cities cyber-physical systems are formed from smaller systems set belonging to
different administrative or territorial domains, for example, different private owners or a private and
public owners conglomeration based on a federated cooperative approach [
Based on the one published in [
Payment and
Financial Services Weather Forecast
Other Services
Smart Energy Smart Waste Management
Smart Transport
Smart Community
Smart Buildings
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Smart Environment Smart Infrastructures
Smart Hospitals Smart Security
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The cooperation between smart cities cyber-physical systems benefits creates innovative opportunities for the networked information technology platforms and applications implementation that improve the citizens living conditions. This has created the need to design the network platform information technology architecture for supporting objects in complex cyber-physical systems of smart cities based on a variety of modern ICTs
A smart city formed by cyber-physical systems means contains a different objects wide range that
are connected through networks and communication systems various types. At the same time, a
powerful network infrastructure is used to identify, authorize, track the location and transmit data of
IoT devices integrated into the urban environment. Communication networks of smart cities are
characterized by [
Currently, a wired and wireless network protocols wide range are commonly used in smart cities cyber-physical systems based on IoT devices. They are used for city networks and smart applications different classes (see Fig. 4).
BAN
100kb/s Range: 1cm-10cm
PAN
HAN
100kb/s Range: 10-100m
LAN
100kb/s-10Mb/s Range: 10m-10km
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10Mb/s-1GB/s Range: 10-200km
10Mb/s-11GB/s Range: 10-200km
Body Area Networks, or BANs, use cyber-physical systems to integrate wearable RFID tags, sensors, and IoT devices, and connect to smart services and urban surveillance and control systems through communication technologies such as NFC, Bluetooth/BLE, and others. BANs are also connected to personal area networks, or PANs, which are used to connect the human body to information platforms and smart systems using technologies
ZigBee,
Z-wave, etc. [
A Home Area Network (HAN) includes RFID sensors, actuators, actuators, video cameras, scanners, etc. that are connected to smart devices such as fans, air conditioners, heaters, meters, etc. HANs are connected to higher-level networks and the Internet and use short-range communication protocols ZigBee, Z-Wave, WiFi, MQTT, HTTP, XMPP, CoAP, etc.
The commonly known local area network (LAN) is mostly a short- or medium-range network within a small area. LANs use communication technologies similar to global networks and are the user part of various IoT interactions in smart cities cyber-physical systems.
The Neighborhood Area Network (NAN) is formed for smart buildings and premises, network stations, smart campuses, city institutions and organizations. Medium-range communication protocols are used to connect cyber-physical systems objects to the Internet. Power-Line Communication (PLC), Digital Subscriber Line (DSL), LoRaWAN, Sigfox, LTE and 5G mobile communications, Nuel, WiFi, etc. are used for smart NAN communication.
A Metropolitan Area Network (MAN) is a network type that operates on the large smart metropolis territory. MANs have appeared relatively recently. They are characterized by fairly large distances between nodes and high-quality communication lines with high data transmission speeds. When building a MAN, existing communication lines are not used, but are laid anew. MANs occupy an intermediate position between LANs and WANs and combine them.
Smart cities connected via Wide Area Networks (WAN) are long-range networks used to connect cyber-physical systems, objects, devices over long distances and with high data transfer speed, low latency, high security and efficiency
An information technology set is a set of tools, methods, technologies and processes used to create,
store, process and disseminate information [
).
The
used to collect data from sensors ( ) by IoT devices. The primary information and resource filling process of the smart city information system functional architecture is being implemented. The direct impact on the physical environment is carried out using actuators ( ). Special drivers ( and interfaces (
) are used to interact with devices integrated into the physical environment, recuperators, air conditioners, humidifiers. Network technologies LoRa, Bluetooth, Zigbee, RFID, etc. are usually used to transmit the selected data.
Data from sensors ( ) is transmitted using the local ℎ and processed by communication nodes that communicate with local data processing devices. Smart building integration tools are responsible for collecting information that is used to generate smart services. Local data processing ), ) devices use concentrating it in centralized cloud facilities.
, elements, as this shifts part of the computing load to the edge nodes instead of supports a network access technologies variety. This functional layer provides the wireless technologies implementation. Smart building integration tools define communication technologies, use appropriate access points and interfaces to receive and transmit data. At this level, implemented, which allows you to transfer part of the computing load to network nodes and equipment, is in turn unloading centralized cloud facilities.
On
the functions execution is provided: • storing information in
; creating queries for searching, filtering, and selecting data; formation data hypercubes (
); analytical data processing tools use; decision support systems implementation.
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Tools DecisionMaking Support Systems Internet MPLS LTE/4G 5G
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Boards, Indicators and Panels formation based on the smart cities cyber-physical systems
In the services set formation context in smart cities, each of them can be integrated in different applications. A smart city significant part applications requires to function properly.
However, some services can be realized using lower-level computing and communication tools. In particular, this is inherent in the implementation of smart ventilation or smart air conditioning systems.
contains a vast array of various smart services, including smart building management systems, smart building environments, smart ventilation and smart air conditioning systems, etc. The such services classification may be based on a wide range of criteria and parameters. In services each class provided, applications different types can be developed and implemented, focused on solving single individual or systemic complex tasks. 5. Network support for measurement and regulation of air quality indicators in "TNTU Smart Campus"
The DaaS service delivery model allows users to access data as a service. NDN (Named Data Networking) is an
architecture that provides an efficient way to exchange data between network devices. Thus, NDN is a technology that is effectively used to deliver smart services based on the DaaS model. A
, in which devices are connected to a other devices set. This enables
data transmission processes between devices in different directions, improving network reliability and
performance. Currently, some researchers [
Making Decisions
Clustering of Collected Network Status Monitoring Data
Control Level
be established, the
Data Level
"Data consumer"
Control
Messages IoT Node 2 "Type A"
Control
Messages
The NDN network for measurement and regulating air quality indicators at "TNTU Smart Campus" is formed in the form of a software-configurable network (Software-Defined Networking, SDN). Its software and algorithmic and network tools are aimed at flexible and adaptive NDN operation in the conditions of IoT devices unstable interaction of "TNTU Smart Campus" CPS via the LoRa protocol. The NDN network main functions are to support the air quality indicators measurement and regulating processes at "TNTU Smart Campus": • the wireless ℎ centralized monitoring; • dynamic decision-making to select the best path and configuration of the NDN according to the selected routes. • A network information system consists of functional components: • SDN controller - the central network management unit; • network nodes that provide NDN communication via wireless ℎ.
The SDN controller is a key component of the NDN network for measurement and regulating air
quality indicators at "TNTU Smart Campus", which provides NDN integration with dynamic software
and algorithmic tools of the wireless routing protocol [
SettainFgIBNurpDeNaconCrNdonDfofNirgrueorauacttheior(nocuretea)ting NThDNeNocnodonedt1Nreo,oslIldo(efeTro4rcNo)eonxdfaeimgu3p,rlees, Net Mesh ISoeNTrvoeSdreRenos4oomr Sensor Server 1
Measurement The controller BME280 5V I2C Collecting information about each periodically monitors the Teshteabplaisthhebde:tNwoedeen 1th→e"IDoaTt-aNoCodnes3um→er" and the "Data Producer" is node LoRa network Node 4
In the process of monitoring the state of ℎ data is collected centrally from IoT nodes
distributed across the "TNTU Smart Campus". At the same time, the each IoT node neighbors and the
routes between them are detected. The routing between channels cost is based on the BATMAN routing
protocol quality metrics for IoT networks [
IoT Gateway
Node 1
W5100 Ethernet
Shield
Data Data
"Data Producer"
Data Data
Control Messages IoT Node 3 "Type B" Control Messages
The information system stores metadata (MTD) on the processes of the NDN network functioning and the measurement and regulating processes the "TNTU Smart Campus" air quality indicators. In particular, the SDN controller associates the data prefix with the corresponding for the NDN routes used and the data freshness information to determine whether the data is cached. The controller is located in a wireless mesh IoT network and communicates with IoT nodes. The ℎ consists of interconnected wireless nodes "Type A" and "Type B" (see Table 1) that communicate via wireless channels using the LoRa protocol. The NDN and independent and integrated with the SDN controller.
MicroSD card module and real- 7-segment LED indicator 0.56", time module DS1302, W5100 ethernet shield 4 digits, common anode and/or RS-485 to USB converter (CH340)
Both nodes types (see Fig. 7) are implemented on the Arduino UNO R3 (CH340). Sensors "DHT22" or "DHT11" were connected to individual IoT nodes to monitor the classrooms air quality in accordance with the "TNTU Smart Campus" concept. If local storage of the collected data was required, these nodes ("Type A") were equipped with a microSD card module and a "DS1302" real-time module. Some "Type B" nodes were equipped with a 7-segment LED indicator to directly display the air quality indicators in a particular room where they are installed. The "Type A" and "Type B" nodes are equipped with a 433 MHz "LoRa" modem on an "SX1278" chip, as well as a "MAX485" and "UART-RS485" interface module.
(А) (B) (A) - the IoT node "Type A" components set. (B) - the IoT node "Type B" components set The
ℎ was formed using LoRa modems on the "SX1278" chip. LoRa modems with a transmission frequency of 433 MHz were chosen because they provide a longer signal transmission range and better penetration through obstacles, including the server rooms walls. However, additional difficulties may arise in high-loaded areas due to the limited bandwidth at this frequency.
At the same time, the controller monitors the ℎ operation to set up the NDN. In an NDN network, consumers send an interest packet to the network to receive the corresponding data packet containing the requested content and .
For the interaction of the formed ℎ for monitoring air quality indicators with the higher levels of the network information technology platform multilayer architecture for supporting the smart cities cyber-physical systems objects, IoT gateways and the server room IoT sensors are used (Table 2). Additional components
W5100 ethernet shield
IoT nodes "Type B"
Arduino UNO R3 (CH340) LoRa 433 MHz on SX1278 chip
BME280 5В I2C
MAX485 UART-RS485 Relay module 12V 10A, or 4-channel relay module 12V
10A W5100 ethernet shield;
MicroSD card module; Real-time module DS1302
The IoT sensor shown in Fig. 8 was developed on the Arduino Leonardo (ATmega32u4) with an expansion board "Wiznet W5100" Ethernet controller.
Each IoT node uses three information components: a content repository, a Pending Interest Table (PIT), and a FIB. To ensure the NDN network effective operation for "TNTU Smart Campus" air quality indicators measurement and regulating, reactive and proactive tactics for choosing paths between the "Producer" and "Consumer" of data are proposed.
With the reactive tactic of selecting an NDN path, it is coordinated with the stored ℎ routing data. In this case, each IoT node selects the best route for each destination from its neighbors set, which is sent by the SDN controller during the ℎ dynamic configuration. For example (see Fig. 6), the "Data Consumer" (IoT Gateway, Node 1) intends to receive data with the - prefix "Sensor Server 1". It sends a request to the SDN controller asking it to inform it about the specific data. If the data is not cached in the ℎ, then the SDN controller finds the best path to the "Data Producer" (IoT sensor, Node 4). For example, "Node 1 → IoT-Node 3 → Node 4" and establishes NDN routes. At the next stage, the SDN controller initiates the "Data Consumer" to transmit the packet of interest along the formed path.
Data consumer 1 2 3
5 4 6 7 8 9 10
Data Producer 11
The proactive tactic of choosing NDN paths is based on the information collected by the nodes. In
this case, each IoT node in the network periodically collects information about its neighbours, including
the received signal strength (RSSI) and delay time. The collected LoRa network monitoring data is
transmitted to the SDN controller via the control channel. The SDN controller clusters the collected
data (see Fig. 9) and selects the best paths, setting up the NDN network ℎ accordingly.
Compared to the reactive tactic, the SDN controller redirects all data requests to the selected path until
the next best path is evaluated. Therefore, we consider this tactic to be proactive, as it is based on partial
clustering and distance measurement based on the data collected similarity by IoT nodes. Fig. 9
illustrates the process of selecting an NDN path based on the clustering results. Consider four IoT nodes
clusters sorted from best to worst based on the average value within the cluster. The thickness and
length of the line indicates the cluster to which each connection belongs, i.e., the thinnest lines indicate
the clusters with the worst performance, and the solid lines indicate the cluster with the best
performance, respectively. Each path is characterized by the worst link, i.e., the worst path contains at
least one link belonging to the worst cluster. For example, in Fig. 9, the best path runs through the nodes
1-4-7-6-11, since its links are clustered as the best, i.e., marked with solid lines. The clustering process
is based on the similarity between time series according assessment to the Dynamic Time Warping
(DTW) algorithm [
This paper presents the cyber-physical systems and communication networks characteristics used for the innovative smart city projects practical implementation. Based on the smart cities using cyberphysical systems peculiarities analysis, their classification is presented in graphical form. The smart cities functional model based on cyber-physical systems, expanded by the ability to observe and control the smart buildings air environment parameters, is formed in graphical form. The communication networks classes features of smart cities cyber-physical systems are analysed. A method is proposed that allows developing a generalized structure of an information technology set for the services and applications formation based on smart cities cyber-physical systems. On the DaaS model basis, the NDN network structure has been developed, which makes it possible to provide network support for the air quality indicators measurement and regulating processes in the "TNTU Smart Campus" based on a IoT devices mesh network using the LoRa 433 MHz protocol. Reactive and proactive tactics for choosing paths between the "Producer" and "Consumer" of data are proposed.