IoT is one of the most developing ICT technology during the last 15 years. There are many cases of IoT implementation in various spheres including critical infrastructures (transport, banks, ICT, etc.). In the paper IoT concepts and requirements were analyzed, advantages and disadvantages were defined as well as benefits for companies were declared. Main standards and best practices in different aspects of IoT implementation were analyzed in this study. Based on the developed mathematical models of WSN, model studies were conducted to verify the theoretical dependences of the collision probability basis of the collision probability modeling, which allowed to verification of the proposed models. Cloud-based monitoring information technology was further developed, which allowed to development of software and hardware monitoring of real-time environmental parameters in the real-time IoT concept. It can be effectively implemented in various critical infrastructures for both cybersecurity and physical security parameters monitoring. The next steps will be related to software realization of the proposed models for cloud IoTbased monitoring system realization in critical infrastructures.
The Internet of Things is a global infrastructure for the information society that connects (physical and virtual) objects using emerging, interoperable information and communication technologies to enable improved services. The Internet of Things (IoT) fully utilizes objects to provide services to a variety of applications while meeting security and privacy needs by utilizing identification, data collecting, processing, and communication capabilities. The IoT may be seen as a vision having both technological and societal ramifications when seen from a wider angle [1].
Devices can interact with each other in one of three ways (see Fig. 1): directly (case c), over a communication network without a gateway (case a), or through a communication network with a gateway (case b). Additionally, combinations of cases are possible; for instance, devices can communicate with one another directly through a local network (case c), which is a network that provides local connectivity between devices and between devices and a gateway, such as an ad-hoc network, and then indirectly through a local network gateway (case a). For IoT systems, the security considerations [2–3] are pertinent and crucial.
2022 Copyright for this paper by its authors.
Information security (IoT Security), scaling up the expanding amount of technological devices and data (IoT Scalability), and solving IoT Technical Solutions and Low-Power Consumption have been highlighted as the three connected fundamental concerns for the IoT idea. Protocols for completing IoT activities were also examined.
MQTT is a protocol for data collection from devices and transmission to their servers (D2S); XMPP is a protocol for establishing connections between devices and people, which is a subset of the D2Sscheme; DDS is a quick bus for the fusion of intelligent devices (D2D); and AMQP is a queuing system for establishing connections between servers (S2S).
IoT combines devices, getaway, communication, physical and virtual things, etc (Fig. 1).
The following are the IoT's core characteristics: Things-related services: Within the limitations of things, such as privacy protection and semantic coherence between physical things and their associated virtual things, the IoT is capable of offering thing-related services. Both the technology in the physical world and the information world will alter in order to deliver thing-related services within the limitations of things. Enormous scale: At least a factor of ten more devices than those currently linked to the Internet will need to be controlled and be able to communicate with one another. There will be a discernible change in favor of device-triggered communication in the ratio of communication prompted by devices to communication caused by people. The management of the produced data and its interpretation for application purposes will be even more crucial. This has to do with both the semantics of data and effective data processing. Heterogeneity: As they are based on many hardware platforms and networks, IoT devices are heterogeneous in nature. Through multiple networks, they may communicate with other gadgets or service platforms. Interconnectivity: Anything may be connected to the global information and communication infrastructure in terms of the Internet of Things. Dynamic changes: Device context, such as location and speed, as well as the states of devices, such as sleeping and waking up, connected and/or disconnected, also alter dynamically. Additionally, the quantity of devices may fluctuate.
Analysis of up-to-date papers [4-8] gives a possibility to define both the main advantages and disadvantages of the IoT in the context of simulation and practical implementation of this technology for critical infrastructures (with high-security requirements).
Today Critical Infrastructure (Fig. 2) contains the following sectors (in the USA for example): Chemical, Commercial Facilities, Communications, Critical Manufacturing, Dams, Defense Industrial Base, Emergency Services, Energy, Transportation Systems, and other sectors are among them.
One of the most effective way of IoT use in critical infrastructure is monitoring – it can include cybersecurity parameters monitoring as well as physical parameters monitoring.
In Fig. 3 shows the ecosystem of a typical IoT architecture according to the mentioned international standard [1]. These shortcomings of IoT negatively affect its basic functions, in particular, its application for monitoring, in addition to security problems, faces the problem of collisions during scaling, as well as the high energy needs of known solutions, most of which are deterministic.
Today it is necessary to create a new class of wireless networks (WSN) that allow to fill certain gaps in the development of WSN networks related to solving problems such as: obtaining low financial costs in terms of network nodes equipped with sensors for general and simple applications, ease of operation, in particular, sensor algorithms and ease of connecting and disconnecting new components, significant limitation of the occupied band of radio frequencies in the context of the growing deficit of the radio frequency spectrum, significant energy savings at the nodes (reduction of nodes for data processing, no receiver signal, autonomous operation of nodes in the intervals of very short activity and short-term radio radiation), especially due to lack of energy replenishment directly related to node operation time, complete independence of the nodes from each other.
Thus, the first section identifies the shortcomings of the known approaches and proves the need for mathematical models, methods, and communication protocols of WSN networks with random access and appropriate monitoring information technology to ensure high performance, quality and survivability of their operation. Fig. 4 presents basic security requirements that must be implemented for risk identification and mitigation in context of cyber incident realization.
WSN stochastic models were developed to assess the probability of signal collision in the system.
Let’s mark ′ as event, that means collision absence in the interval [0, ] ( > 0). Also let’s mark ( ′) as the probability of collision absence in the interval [0, ]. Let’s consider [0, ], where > . Suppose that ( ) = , that is quantity of transmissions in the interval [0, ] equals ( ≥ 1). Random vector ( 1, … , ) of the time between transmissions is even distributed in the set ∗ = {( 1, … , ): 1 + ⋯ + ≤ } with conditional density for ( 1, … , ) ∈ ∗, and also 0 beyond that. In this way conditional density of collision absence in the ( 1, … , | ( ) = ) = !/ interval [0, ], supposing ( ) = , is equal: ( ′/ ( ) = ) = ( 1 > , … , > ) = (1 − ) , + where expression + determines as + = for ≥ 0 and + = 0 for < 0.
Conditional probability of collision in the length interval s, where > , by condition ( ) = , forms by the following expression:
The probability of collision in the length interval s, where > , determines by the following expression: transmission. where is number of nodes, is the average time between node transmissions, is the time of protocol have expression:
The question of the number of nodes that remain in collision in the length interval is also analyzed for > . The probability of collision in the length interval is investigated for > . Below are models that characterize the lower and upper estimates of the conditional probability of the number of gears that remain in conflict, in the length interval , assuming that the number of gears in the transmission interval is in the length interval ( > ) equals .
Let’s mark as number of transmissions in collision in the length interval . In this case we will ( / ( ) = ) = 1 − (1 − ( ) = ∑ ∞ =2 − ( ) !
)
+ [ 1 − (1 − )+], (1) (2) ∞ =2 ≤
) ! ( ( ) − ≤ ( = ⁄ ( ) = ) ≤ ( ) [ +21](1 − ) −[ +21], ≤ ( = )
Models that characterize the lower and upper estimates of the expected number of gears in conflict and the variance of the number of gears in conflict in the length interval s ( , 2( )). Let’s suppose s t .
p Then ∞ =2 ≤ ∑
∞ ∑
) ! ( (1−− [ ∑
∞ =2 ∞ ∑ − ⋅ =2 ≤ 2( ) ≤
) ! ( ) ! ( (1−− [ ∑
∞ =2 ∞ ∑ − ⋅ =2 (
) ! ( properties of the Poisson process with respect to the probability of collision over a sufficiently long transmission time.
The graphs illustrate the probability of collision depending on the number of nodes (sensors) for the set average time between messages (Fig. 5), and also shows the dependence on the average time of protocol transmission, if the number of nodes is set (Fig. 6). For the average time between transmissions of a node equal to 10 s, the maximum number of nodes, which ensures the quality of transmission at a probability level not exceeding 10-2, is 10, and for the average time between transmissions of a node equal to 30 s, the maximum number of nodes is 50. Further increase in the average time between node transmissions allows you to increase the maximum number of nodes. For a given number of nodes, increasing the average time between collisions causes a decrease in the probability of collision. 2 180 s. and average time between transmissions of a node for = 5, 20, 50
Using graphs, you can find the optimal values of the parameters that affect the correctness of the transfer (n, T, tp). Graphs make it possible to determine in which range the transmission quality is provided at a given level or for which values (n, T, tp) the probability of collision increases sharply. You can determine the order of collision probability values for arbitrarily selected parameters: for example, for tp = 3.2 × 10-5, the number of transducer sensors equal to 10, and providing each sensor with an average transmission time every T = 60 s the collision probability is 1.65 × 10-4. 180 s. and nodes number where = 10 s., 30 s., 60 s. a set of end points of the system = { 1, . . . , } (for begin = 1).
The three-dimensional coordinate system (Fig. 7) shows a set of end devices = { 1, . . . , } and = { 1, . . . , } from the begin of a set of coordinators of network
Distance between points ( , , ) and ( , , ) equals:
2 = √( − ) + ( − ) + ( − ) .
2 2 There are a set of such distances С = {с11, . . . , 1, . . . , 1 , . . . , }. The distance between the nodes should not exceed the maximum data transmission range. We will assume that the maximum transmission range between any WSN nodes is the same and equal to max.
The monitoring system based on this approach and developed software technical complex can be switched in following four operation
modes in critical infrastructures (based on cybersecurity requirements [10-12] from international standards and best practices):
Normal (standard operation, normal operation). The tasks of normal operation consist of emergency planning, the main purpose of which is to gather information to predict the possible occurrence and development of the crisis regime and control its consequences, determine the resources of telecommunications networks and tools needed to resolve crises, develop special forecasts to respond effectively in anticipation of the problem, taking into account all the forces and means to implement the objectives. In this mode, regulatory, legislative and other mechanisms aimed at minimizing the risk and damage from the crisis are identified and created.
Increased preparedness (non-standard operation, active preparation, and practical implementation of several preventive/precautionary measures). To do this, collect and use in the monitoring system data on the state of internal and external structure, data for current and retrospective analysis with the possibility of preventive planning of trends in the current situation, as well as planning resources, forces, and means necessary to neutralize, stabilize and reduce the severity of the consequences of the crisis. Lack of necessary information often becomes a major obstacle to the functioning of the monitoring system to prevent possible consequences. In many cases, this is due to untimely provision of data, detection, and use of the necessary resources of interconnected, sensory means and telecommunications networks of different operators.
Crisis (actions in a crisis situation). In a crisis mode, the monitoring system should provide a real-time operational mode. Tasks must be implemented on a limited time interval quickly and continuously. In the event of crisis situations in the monitoring system, there may be problems of peak load on all elements, in connection with which they may significantly exceed the functional limitations for their use.
Post-crisis (elimination of long-term consequences of the crisis regime). The post-crisis regime is transitional to the usual and includes analysis of the crisis situation, features for its elimination, modification of the content of databases and knowledge bases, and restoration of normal modes of operation of the components of the monitoring system [13].
Utilizing IoT performance monitoring solutions, businesses may get observable outcomes [14], like: Gain a thorough grasp of all IoT ecosystem components and real-time data. You will be able to enhance the standard of customer service, address issues, and other things. Recognize the data transmission rate, the locations of the delays, and the locations of these bottlenecks. Data gathering and analysis. Analyze a wide range of Internet-based IoT data from linked devices, customers, and applications. Filling up performance gaps. Enhance the functionality of several apps, APIs, networks, and protocols. A real-time warning system. Receive warnings of issues before they have a substantial impact on your business. Increased resource efficiency and longer equipment service life. The sensors must detect equipment issues since limited awareness of equipment deterioration might result in expensive replacement [15].
The following requirements must be met by an efficient IoT performance management and monitoring system [16-18]: ability to manage all new devices, independent of the communication standard or performance metrics; the capacity to handle the rapid development of traffic and data quantities; ability to operate simultaneously with IPv4 and IPv6 protocols; visibility of traffic in each segment with an accuracy of seconds; one-screen administration of a hybrid cloud system includes physical and virtual KPIs for all monitoring levels.
Following the recommendations of E.430, E.800, X.134, and other documents of the International Telecommunication Union, the Quality of Service (QoS) is understood as a generalized (integral) beneficial effect of the service, which is determined by the degree of satisfaction the user both from the received service and from the service system itself. The QoS criterion in the telecommunications business is usually determined by a set of indicators of the properties of both the provided telecommunications service and the network resources used. Service quality indicators are called service QoS parameters, and network resource quality indicators are called network performance parameters (NP). To quantify most of the properties of the quality of telecommunications services defined in the recommendations of TL 9000 and E.800, the corresponding indicators are introduced, which are determined based on the performance characteristics (parameters) of the network. The analysis of recommendations І.350 showed that the quality of the provided telecommunication services is ensured at three stages [18-20]: 1. access to information transfer (connection establishment); 2. transfer of user information; 3. termination of the information transfer session (disconnection).
In the paper IoT concepts and requirements were analyzed, advantages and disadvantages were defined as well as benefits for companies were declared. Main standards and best practices in different aspects of IoT implementation were analyzed in this study.
Based on the developed mathematical models of WSN, model studies were conducted to verify the theoretical dependences of the collision probability basis of the collision probability modeling, which allowed to verification of the proposed models. Cloud-based monitoring information technology was further developed, which through the use of stochastic models of wireless sensor networks and advanced monitoring methods, allowed to development of software and hardware monitoring of real-time environmental parameters in real real-time IoT concepts. This complex of real-time environmental parameters monitoring can be used as a prototype for the organization of monitoring in dynamically changing environments and the event of various critical situations. It can be effectively implemented in various critical infrastructures for both cybersecurity and physical security parameters monitoring.
The next steps will be related to software realization of the proposed models for cloud IoT-based monitoring system realization in the critical infrastructures.
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