An emergence of intelligent devices, a large number of sensors, the issue of their identification

and interaction becomes relevant in the era of information technologies development. A control

object, a structural diagram of an identification system, an algorithm for the correlation method

of identification have been considered in the article, and a general idea of managing the process

of recognizing an information network object has been given

Bondarchuk, Andrii

Korshun, Natalia

Polonevych, Olha

Shevchenko, Olha

Zhurakovskyi, Bohdan

Borys Grinchenko Kyiv University Institutional repository

 277 Methods of the Objects Identification and Recognition Research in the Networks with the IoT Concept Support  Olha Shevchenkoa, Andrii Bondarchuka, Olha Polonevycha, Bohdan Zhurakovskyib, and Nataliia Korshunc  a State University of Telecommunications, 7 Solomianska str., Kyiv, 03110, Ukraine b National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute,” 37 Peremohy ave., Kyiv, 03056, Ukraine c Bo Borys Grinchenko Kyiv University, 18/2 Bulvarno-Kudriavska str., Kyiv, 04053, Ukraine  Abstract An emergence of intelligent devices, a large number of sensors, the issue of their identification and interaction becomes relevant in the era of information technologies development. A control object, a structural diagram of an identification system, an algorithm for the correlation method of identification have been considered in the article, and a general idea of managing the process of recognizing an information network object has been given.  Keywords 1 Identification, recognition, Internet of things, correlation method of identification, networks. 1. Introduction One of the fundamental concepts in real life is the concept of identifiers, the variety of which is infinite, the scope of which is limitless [1]. The development of electronic communications is constantly increasing the threshold of permissible entropy in identification systems. Digital objects are becoming more complex, they have many previously missing functions and properties. One and the same digital object can be described in different identification systems, but at the same time, the task is to reduce the description of these properties into a single base and manage these properties from the point of view of such a single base [2–4]. The growing number of telecommunications network facilities leads to problems in recognition and identification, management to ensure the quality of networks. This problem is one of the most important in wireless LANs operating in the unlicensed frequency band [5]. There is a need to study the methods of forming an object recognition system, to solve the problem of object selection and determine its type and functionality. The recognition system is a dynamic system consisting of technical means of obtaining and processing information for solving based on the algorithm of object recognition problems. There is a need to analyze the possibilities of implementing intelligent recognition systems to optimize the identification process at the access level, without the involvement of central management and identification systems [6].                                                        Cybersecurity Providing in Information and Telecommunication Systems, January 28, 2021, Kyiv, Ukraine EMAIL: olia.shevchenko@ukr.net (A.1); dekan.it@ukr.net (A.2); nevdachinaolya@i.ua (A.3); zhurakovskiybyu@tk.kpi.ua (B.4); n.korshun@kubg.edu.ua (C.5) ORCID: 0000-0002-2554-2004 (A.1); 0000-0001-5124-5102 (A.2); 0000-0002-4906-2785 (A.3); 0000-0003-3990-5205 (B.4); 0000-0003-2908-970X (C.5)  ©  2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).   CEUR Workshop Proceedings (CEUR-WS.org)   278 2. Problem Identification Consider a control object described by the equations 𝑥 = 𝐴𝑥 + 𝑏𝑢 + 𝜓𝑓;     (1) 𝑦 = 𝑑𝑥 + 𝑥,      (2) where the matrix A and the n-dimensional vectors 𝑏, 𝜓, 𝑑′ are unknown; f(t) and x(t) are the external disturbance and noise, which are immeasurable random functions with zero mathematical expectation. Depending on the identification method, additional restrictions will be imposed on external disturbances and interferences (their white noise, limited dispersion, etc.). Since the object (1), (2) is excited by a random external action, the estimate of the vector of its unknown parameters will be a random variable. This value should have the properties of unbiasedness, consistency, efficiency, and sufficiency. The purpose of object identification (1), (2) is to determine estimates of its parameters that have the listed properties. The essence of the correlation method. Consider an object described by the equations 𝑥 = 𝐴𝑥 + 𝜓𝑓; 𝑦 = 𝑑𝑥 + 𝑥.     (3) The solution of these equations for zero initial conditions has the form 𝑥 = 𝐴𝑥 + 𝜓𝑓;      (4) where ℎ(𝑡 − 𝜏) is an impulse transient function, which is determined by the correlation method. Equation (4) can be written at 𝑡0 = −∞ as a convolution integral 𝑦(𝑡) = ∫ ℎ(𝜃)𝑓(𝑡 − 𝜃)𝑑𝜃 + 𝑥(𝑡).∞0     (5) We multiply (5) by 𝑓(𝑡 − 𝜏), then we obtain 𝑧(𝑡) = 𝑦(𝑡)𝑓(𝑡 − 𝜏) = ∫ ℎ(𝜃)𝑓(𝑡 − 𝜃)𝑓(𝑡 − 𝜏)𝑑𝜃 + 𝑥(𝑡)𝑓(𝑡 − 𝜏).∞0 Assuming further 𝑀{𝑓(𝑡)} = 𝑀{𝑥(𝑡)} = 0 and applying the mathematical expectation operation, we write 𝑀{𝑦(𝑡)𝑓(𝑡 − 𝜏)} = ∫ ℎ(𝜃)𝑀{𝑓(𝑡 − 𝜃)𝑓(𝑡 − 𝜏)}𝑑𝜃 + 𝑀{𝑥(𝑡)𝑓(𝑡 − 𝜏)}.∞0            (6) If external influence 𝑓(𝑡) and hindrance 𝑥(𝑡) are independent, then 𝑀{𝑥(𝑡)𝑓(𝑡 − 𝜏)} = 0. In addition, denoting the correlation function 𝑀{𝑓(𝑡 − 𝜃)𝑓(𝑡 − 𝜏)} = 𝑟𝑓𝑓(𝜏 − 𝜃), and the cross-correlation function 𝑀{𝑦(𝑡)𝑓(𝑡 − 𝜏)} = 𝑟𝑓𝑦(𝜏), we write (6) in the form of the Wiener-Hopf equation 𝑟𝑓𝑦(𝜏) = ∫ ℎ(𝜃)𝑟𝑓𝑓(𝜏 − 𝜃)𝑑𝜃.∞0    (7) Let the input signal 𝑓(𝑡) is the white noise. It means that 𝑟𝑓𝑓(𝜏) = 𝑟𝑓𝑓(1)𝛿(𝑡 − 𝜏),    (8) where r1ff is a known number characterizing the intensity of the “white noise.”    279 Substituting (8) into (7), we obtain 𝑟𝑓𝑦(𝜏) = 𝑟𝑓𝑓(1)ℎ(𝜏).    (9) Thus, if the external influence is a random process of the white noise type, uncorrelated with the measurement noise, then the cross-correlation function of the input and output signals is directly proportional to the impulse transient function. The block diagram of the identification system is shown in Ошибка! Источник ссылки не найден..  Figure 1: Block diagram of the identification system  Integrator serves to calculate the cross-correlation function, which, in the case of a stationary random process with the ergodic property, is defined as 𝑟𝑓𝑦(𝜏) = lim𝑡1→∞1𝑡1∫ 𝑓(𝑡 − 𝜏)𝑦(𝑡)𝑑𝑡.𝑡10    (10) Returning to the general case, we note that equation (7) is an integral equation for the unknown function ℎ(𝜃). The numerical solution of this equation forms the basis of the correlation identification method. 3. Algorithm of the Correlation Identification Method Passing to the solution of equation (7), we replace the upper limit in the integral by a finite number 𝑡1. This means that the impulse transient function will be determined on the interval [0, 𝑡1], and for 𝑡 > 𝑡1ℎ(𝑡) = 0. This assumption is quite acceptable for asymptotically stable objects. In addition, we will determine the value of the function 𝑓(𝑡) for discrete instants of time that differ from one another by the value T, therefore, we divide the interval [0, 𝑡1) into 𝑁 = 𝑡1/𝑇 intervals. Thus, we will assume that }ℎ(𝑡)=ℎ(𝑖𝑇)𝑟𝑓𝑓(𝑡)=𝑟𝑓𝑓(𝑖𝑇) 𝑖𝑇 ≤ 𝑡 ≤ (𝑖 + 1)𝑇  (𝑖 = 0, 𝑁̅̅ ̅̅ ̅).     (11) Then equation (7) takes the form 𝑟𝑓𝑦(𝑖𝑇) = 𝑇 ∑ 𝑟𝑓𝑓[(𝑖 − 𝑙)𝑇ℎ(𝑙𝑇)𝑁𝑙=0  (𝑖 = 0, 𝑁̅̅ ̅̅ ̅).   (12) For 𝑖 = 0, equation (12) is written as 𝑟𝑓𝑦(0) = [𝑟𝑓𝑓(0)ℎ(0) + 𝑟𝑓𝑓(−𝑇)ℎ(𝑇) + ⋯ + 𝑟𝑓𝑓(−𝑁𝑇)ℎ(𝑁𝑇)]𝑇; For 𝑖 = 1 𝑟𝑓𝑦(𝑇) = [𝑟𝑓𝑓(𝑇)ℎ(0) + 𝑟𝑓𝑓(0)ℎ(𝑇) + ⋯ + 𝑟𝑓𝑓(−(𝑁 − 1)𝑇)ℎ(𝑁𝑇)]𝑇;  280 For 𝑖 = 2 𝑟𝑓𝑦(2𝑇) = [𝑟𝑓𝑓(2𝑇)ℎ(0) + 𝑟𝑓𝑓(𝑇)ℎ(𝑇) + ⋯ + 𝑟𝑓𝑓(−(𝑁 − 2)𝑇)ℎ(𝑁𝑇)]𝑇 etc. Let us introduce vectors 𝑟𝑓𝑦′ = ‖𝑟𝑓𝑦(0)𝑟𝑓𝑦(𝑇) … 𝑟𝑓𝑦(𝑁𝑇)‖; ℎ′ = ‖ℎ(0), ℎ(𝑇) … ℎ(𝑁𝑇)‖ and matrix 𝑅 = ‖‖𝑟𝑓𝑓(0) 𝑟𝑓𝑓(−𝑇), … , 𝑟𝑓𝑓(−𝑁𝑇)𝑟𝑓𝑓(𝑇) 𝑟𝑓𝑓(0), … , 𝑟𝑓𝑓[−(𝑁 − 1)𝑇]⋮𝑟𝑓𝑓(𝑁𝑇)⋮𝑟𝑓𝑓[(𝑁 − 1)𝑇], … , 𝑟𝑓𝑓(0)‖‖.  Note that the matrix R is symmetric, since the correlation function is even, therefore 𝑟𝑓𝑓(𝑖𝑇) = 𝑟𝑓𝑓(−𝑖𝑇) (𝑖 = 0, 𝑁̅̅ ̅̅ ̅). Taking into account the adopted designations, equation (12) takes the form 𝑟𝑓𝑦 = 𝑇𝑅ℎ.      (13) Whence the required vector is ℎ = 𝑅−1𝑟𝑓𝑦𝑇.     (14) Let us now determine the vector 𝑟𝑓𝑦 and the matrix 𝑅 from the experimental data. In this connection, we write on the basis of (10) an approximate expression 𝑟𝑓𝑦(𝑖𝑇) =1𝑁∑ 𝑦(𝑠𝑇)𝑓[(𝑠 + 𝑖)𝑇]𝑁−1𝑠=0  (𝑖 = 0, 𝑁̅̅ ̅̅ ̅),   (15) Similarly, 𝑟𝑓𝑓(𝑖𝑇) =1𝑁∑ 𝑓(𝑠𝑇)𝑓[(𝑠 + 𝑖)𝑇]𝑁−1𝑠=0  (𝑖 = 0, 𝑁̅̅ ̅̅ ̅).  (16) Thus, the algorithm for identifying the impulse transient function is reduced to calculating the correlation and cross-correlation functions by formulas (16), (15), and then solving equation (14). 4. General Idea of Recognition Process Control Algorithm The considered concepts and definitions make it possible to construct an algorithm for the recognition process in the form of a rule of the sequential search for solutions, which ensures the development of an optimal plan for conducting experiments. The meaning of such an algorithm is that, based on the prehistory of experimentation, as well as on the basis of information obtained as a result of previous experiments, it determines the optimal plan for further experiments, all subsequent stages of experiments, i.e., determines at each step what next technical means should be used and what features of the object with the help of these means should be identified.    281 The general record of the algorithm providing sequential planning of experiments can be represented as  𝑅 = {𝑧𝑖0;  𝑎1, 𝑧𝑖1(𝑥𝑎1); 𝑎2(𝑥𝑎1), 𝑧𝑖2(𝑥𝑎1 , 𝑥𝑎2); … ; 𝑎𝑘(𝑥𝑎1 , … , 𝑥𝑎𝑘−1), 𝑧𝑖𝑘(𝑥𝑎1 , … , 𝑥𝑎𝑘), … }, (7.10) In the algorithm 𝑧𝑖0 means that the final decision that an object belongs to the 𝛺𝑖 class is made without experiments. In this case, all operations indicated in the algorithm 𝑎1, … , 𝑎𝑘;  𝑧𝑖1, . . , 𝑧𝑖𝑘  are absent. If the algorithm 𝑧𝑖0 is absent, then the first stage experiments are assigned 𝑎1. If, on the basis of the characteristics of the object, determined from the information of the experiments of the first stage, a final decision is made about its belonging to any class 𝑧𝑖1(𝑥𝑎1), then all operations indicated in the algorithm 𝑎2, … , 𝑎𝑘;  𝑧𝑖0, 𝑧𝑖2, . . , 𝑧𝑖𝑘   absent. The presence of a member in the algorithm 𝑎𝑘(𝑥𝑎1 , … , 𝑥𝑎𝑘−1) means that, based on the study of the features of the recognized object, obtained as a result outcome experiments 𝑥𝑎1 , … , 𝑥𝑎𝑘−1. If the algorithm contains a term 𝑧𝑖𝑘(𝑥𝑎1 , … , 𝑥𝑎𝑘), then this means that after receiving the outcomes 𝑥𝑎1 , … , 𝑥𝑎𝑘  experiments 𝑎1, … , 𝑎𝑘, conducted according to the rule 𝑅, the final decision is made about the belonging of the recognized object to the 𝛺𝑖 class and no further experiments are carried out. The procedure for planning experiments in accordance with the algorithm (17) is schematically shown in Ошибка! Источник ссылки не найден..   Figure 2: Procedure for planning experiments  It follows from the consideration of the scheme that the algorithm works as follows. Let the object 𝜔 enter the recognition system. It was found that making a final decision without conducting experiments 𝑧𝑖0 inappropriate and to determine its sign it was decided to conduct the experiment 𝑎1. Let us assume that the possible outcomes of the experiment are 𝑎1 − 𝑥𝑎1′  and 𝑥𝑎1′′ . These outcomes are  282 analyzed in the experimental results analysis unit, ERAU. Moreover, if the outcome of the experiment is 𝑎1 − 𝑥𝑎1′′ , then, for example, the final decision 𝑧𝑙1(𝑥𝑎1′′ ) is made, and if the outcome of the experiment is 𝑥𝑎1′ , then the decision is made to conduct a new experiment 𝑎2(𝑥𝑎1′ ),. Let, its possible outcomes 𝑥𝑎2′ , 𝑥𝑎2′′  and 𝑥𝑎2′′′. Then their analysis can lead, for example, to such decisions: if the outcomes of the experiment 𝑎2(𝑥𝑎1′ ) will be  𝑥𝑎2′  or  𝑥𝑎2′′′, then the final decisions should be made 𝑧𝑔2(𝑥𝑎1′ , 𝑥𝑎2′ ) or 𝑧𝑔2(𝑥𝑎1′ , 𝑥𝑎2′′ ), 𝑙, 𝑞, 𝑔 = 1, … , 𝑚, and if the outcome is 𝑥𝑎2′′ , then it is necessary to conduct an experiment 𝑎3(𝑥𝑎1′ , 𝑥𝑎2′′ ). Outcomes of this experiment 𝑥𝑎3′  and 𝑥𝑎3′′  are analyzed again, and a plan for the further development of experiments is developed. The algorithm works like a feedback system. Indeed, every time. the experimental results are used to adjust the plan for subsequent experiments. 5. References [1] A. G. Alexandrov, Optimal and adaptive systems: Textbook for universities, 1989. [2] M. A. Medrish, et al., Digital identification of objects, 2016. [3] A. L. Gorelik, V. A. Skripkin, Recognition methods: A textbook for universities, 1977. [4] V. S. Mikhalevich, Sequential optimization algorithms and their application. [5] I. Kuzminykh, et al., Investigation of the IoT Device Lifetime with Secure Data Transmission, Internet of Things, Smart Spaces, and Next Generation Networks and Systems (2019) 16–27. doi: 10.1007/978-3-030-30859-9_2. [6] V. N. Fomin, Recurrent Estimation and Adaptive Filtering, 1984.  

Methods of the Objects Identification and Recognition Research in the Networks with the IoT Concept Support

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Methods of the Objects Identification and Recognition Research in the Networks with the IoT Concept Support

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