Volume 1(66)

        CONTENTS

  1. Rodionov A. S, Matkurbanov T. A., Khairullaev U. B. Construction of a UAV Flight Trajectory for Monitoring Agricultural Land
  2. Akhatov A. R., Eshtemirov B. Sh., Nazarov F. M. Methods of Determining and Analyzing the Traffic Index on City Roads
  3. Aleeva V. N. Approach to Effective Implementation of Numerical Algorithms
  4. Bredikhin S. V. Scherbakova N. G. Weighted Multiplex Network of Scientific Journal Authors
  5. Vishnevsky V.M., Avramenko Y.A., Nguyen V.H., Kalmykov N. S. Assessment of the Performance Characteristics of a Wireless Network Based on Tethered UAVs

A.S. Rodionov*,**. T.A. Matkurbanov**. U. B. Kliairullaov**

*Institute of Computational Mathematics and Mathematical Geophysics SB RAS, 630090, Novosibirsk, Russia,
**Novosibirsk State University, 630090, Novosibirsk, Russia

CONSTRUCTION OF A UAV FLIGHT TRAJECTORY FOR MONITORING AGRICULTURAL LAND

DOI: 10.24412/2073-0667-2025-1-5-17
EDN: XFUXBA

When using agricultural lands, one of the tasks is to monitor their condition, in particular soil moisture and the quality of crops. For monitoring, various types of sensors are used, data from which must be periodically transmitted to the processing center. It is not economically feasible to create a wired network of sensors; wireless technologies are used. The lifetime of the sensor network is determined by the battery discharge rate. The least energy consuming is data transmission on demand. In conditions of extensive farmland, such collection can be organized using unmanned aerial vehicles (UAVs). In turn, the cost of collecting data using a UAV is determined by the length of the chosen route. The problem of constructing an optimal flight path for data collection is considered. There may be a shortage of fuel (battery charge in the case of an electric motor) of the UAV and it is necessary to lay out several routes of limited length, the total length of which is minimal. This paper solves the problem of optimizing the trajectory of an individual UAV. An essential feature of the problem is the ability to simultaneously collect data from several sources within the radio visibility radius, which eliminates the need to fly up to each of the sensors. The paper experimentally compares two approaches to solving the problem: solving the traveling salesman problem using previously found points and removing from the list of unvisited points those points that are within the visibility of the next visited one. It is shown that in the latter case, the algorithm that solves the classical traveling salesman problem better may be inferior to a faster and less accurate algorithm for solving the classical problem. Keywords: sensor, UAV, traveling salesman problem, algorithm, optimal route, programming.

Key words: sensor, UAV, traveling salesman problem, algorithm, optimal route.

References

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  5. Khoufi I., Laouiti A., Adjih C. A Survey of Recent Extended Variants of the Traveling Salesman and Vehicle Routing Problems for Unmanned Aerial Vehicles // Drones. 2019. 08. V. 3. P. 66.
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  14. Alekhin R. A. i dr. Obzor metaevristicheskih metodov optimizacii, primenyaemyh pri reshenii elektroenergeticheskih zadach // Vestnik Samarskogo gosudarstvennogo tekhnicheskogo universiteta. Seriya: Tekhnicheskie nauki. 2019. N 3 (63). S. 6-19.
  15. Lyu V. Metody planirovaniya puti v srede s prepyatstviyami (obzor) // Matematika i matematicheskoe modelirovanie. 2018. N 1. S. 15-58.
  16. Semenov S. S. i dr. Analiz trudoemkosti razlichnyh algoritmicheskih podhodov dlya resheniya zadachi kommivoyazhera // Sistemy upravleniya, svyazi i bezopasnosti. 2017. N 1. S. 116-131.
  17. Sahin M. Solving TSP by using combinatorial Bees algorithm with nearest neighbor method // Neural Computing and Applications. 2022. 10. V. 35.
  18. Sargolzaei A., Abbaspour A., Crane C.D. Control of Cooperative Unmanned Aerial Vehicles: Review of Applications, Challenges, and Algorithms // Optimization, Learning, and Control for Interdependent Complex Networks / ed. by Amini M.H. Cham: Springer International Publishing, 2020. P. 229-255. [Electron, res.]: https://doi.org/10.1007/978-3-030-34094-0_10.
  19. Zhuk A. A., Bulojchik V.M., Akulich S.V. Planirovanie optimal’nogo marshruta dvizheniya bespilotnogo letatel’nogo apparata po kriteriyu minimuma obshchego raskhoda topliva // Sistemnyj analiz i prikladnaya informatika. 2022. N 3. S. 43-49.

 

Bibliographic reference: Rodionov A. S, Matkurbanov T. A., Khairullaev U. B. Construction of a UAV Flight Trajectory for Monitoring Agricultural Land //journal “Problems of informatics”. 2025, № 1. P.5-17. DOI: 10.24412/2073-0667-2024-5-17

A. R. Akhatov, B. Sh. Eshtemirov, F.M. Nazarov

Samarkand State University named after Sharof Rashidov, Samarkand, Uzbekistan

METHODS OF DETERMINING AND ANALYZING THE TRAFFIC INDEX ON CITY ROADS

DOI: 10.24412/2073-0667-2025-1-18-28
EDN: XXXLMB

Traffic congestion has become a major issue impacting the economy, the ecology, and the general well-being of urban dwellers in the rapidly urbanizing 21st-century landscape. In order to effectively address and reduce the consequences of congestion, it is becoming more and more vital to develop and employ Road Congestion Index (RCI) calculations. The significance of RCI and its influence on the future of the city were examined in this article. For the purpose of analyzing traffic flow, the index of road congestion is crucial. Road congestion assessment, traffic planning and organization for road management, and the ability of drivers and passengers to make educated judgments on traffic are all dependent on the calculation and analysis of the congestion index. A number of assessment index approaches were examined. Traffic congestion affects the economy, the environment, public health, and general quality of life, hence it is imperative to address it for a number of reasons. Additionally, it decreases overall efficiency and wastes fuel and time. Because they make it easier for people and cars to move around, efficient triband systems are essential for economic expansion. Significant financial costs are also associated with traffic, such as higher fuel consumption, higher auto maintenance expenses, and longer freight delays, all of which can raise the price of goods and services.

Key words: Urban traffic, index of traffic jam, saturation degree, average velocity, speed interval, map show color.

References

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  15. Akhatov A., Rashidov A., Renavikar A. Optimization of the database structure based on Machine Learning algorithms in case of increased data flow // Artificial Intelligence. Blockchain. Computing and Security Volume 2, CRC Press, 2023. P. 675-680.
  16. Guo W., Xiong N., Vasilakos, A. V., Chen G., Cheng H. Multi-source temporal data aggregation in wireless sensor networks // Wirel. Pers. Commun. 2011. N 56. P. 359-370.
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  18. Rashidov A., Akhatov A., Aminov L, Mardonov D. Distribution of data flows in distributed systems using hierarchical clustering // International conference on Artificial Intelligence and Information Technologies (ICAIIT 2023), Uzbekistan, Samarkand, 2023.
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  21. Akhatov A., Nazarov F. M., Eshtemirov B. Detection and analysis of traffic jams using computer vision technologies // International conference on Artificial Intelligence and Information Technologies (ICAIIT 2023). Uzbekistan, Samarkand, 2023. N 2. P. 761-766.
  22. Nazarov F.M., У. S. S. o’g’li, Е. В. S. o’g’li. Algorithms То Increase Data Reliability In Video Transcription // 2022 IEEE 16th International Conference on Application of Information and Communication Technologies (AICT), Washington DC, DC, USA, 2022, P. 1-6, DOI: 10.1109/AICT55583.2022.10013558.
  23. Ghosh B., Basu B., O’Mahony M. Bayesian Time-Series Model for Short-Term Traffic Flow Forecasting //J. Transp. Eng. 2007. N 133. P. 180-189.
  24. Chow A.H., Santacreu A., Tsapakis I., Tanasaranond G., Cheng T. Empirical assessment of urban traffic congestion //J. Adv. Transp. 2014. N 48. P. 1000-1016.
  25. Guo J., Huang W., Williams B.M. Adaptive Kalman filter approach for stochastic short-term traffic flow rate prediction and uncertainty quantification // Transp. Res. Part C 2014. N 43. P. 50-64.
  26. Yang Q., Zhang B., Gao P. Based on improved dynamic recurrent neural network for short time prediction of traffic volume //J. Jilin Univ. Eng. Edit. 2012. N 4. P. 887-891.
  27. Shankar H., Raju P. L.N., Rao K.R.M. Multi model criteria for the estimation of road traffic congestion from traffic flow information based on fuzzy logic //J. Transp. Technol. 2012. N 2. P. 50.
  28. Li S., Da Xu L., Zhao S. 5G Internet of Things: A survey. J. Ind. Inf. // Integr. 2018. N 10. P. 1-9.
  29. Duan W., Gu J., Wen M., Zhang G., Ji Y., Mumtaz S. Emerging Technologies for 5G-IoV Networks: Applications, Trends and Opportunities // IEEE Netw. 2020. N 34. P. 283-289.
  30. Wang Z., Li T., Xiong N., Pan Y. A novel dynamic network data replication scheme based on historical access record and proactive deletion //J. Supercomput. 2012. N 62. P. 227-250.
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  32. Makhmadiyarovich N.F., Sherzodjon Y. Methods of increasing data reliability based on distributed and parallel technologies based on blockchain // Artificial Intelligence, Blockchain, Computing and Security Volume 2. eBook ISBN: 9781032684994, P. 637-642, January 2023.
  33. Akhatov A., Rashidov A., Renavikar A. Optimization of the database structure based on Machine Learning algorithms in case of increased data flow // Artificial Intelligence. Blockchain. Computing and Security Volume 2, CRC Press, 2023. P. 675-680.
  34. Guo W., Xiong N., Vasilakos, A. V., Chen G., Cheng H. Multi-source temporal data aggregation in wireless sensor networks // Wirel. Pers. Commun. 2011. N 56. P. 359-370.
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  37. Sabharwal M., Nazarov F.M., Eshtemirov B. Effectiveness Analysis Of Blockchain Mechanisms Using Consensus Algorithms // 2022 4th International Conference on Advances in Computing, Communication Control and Networking (ICAC3N), ISBN: 978-1-6654-7436-8/22/, DOI: 10.1109/ICAC3N56670.2022.10074408, 16-17 December, 2022.
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Bibliographic reference: Akhatov A. R., Eshtemirov B. Sh., Nazarov F. M. Methods of Determining and Analyzing the Traffic Index on City Roads //journal “Problems of informatics”. 2025, № 1. P.18-28. DOI: 10.24412/2073-0667-2024-18-28

V.N. Aleeva

South Ural State University (National Research University), 454080, Chelyabinsk, Russia

APPROACH TO EFFECTIVE IMPLEMENTATION OF NUMERICAL ALGORITHMS

DOI: 10.24412/2073-0667-2025-1-29-44
EDN: QALLET

The problem of effective implementation of numerical algorithms is actual. The paper proposes a solution to this problem. The solution uses the author’s concept of a Q-determinant. In the paper, we describe the notions of the concept of a Q-determinant used for research. These are the following notions.

An algorithmic problem has the form у = F(N,B), where N = {ni,... ,nk} is the set of parameters of the problem dimension or N is the empty set, B is the set of input data, у = {у1,... ,ут} is the set of output data. Here ni (i G {1,...,k}) is any positive integer. If N = {n1,...,nk}, then by N = {n1,... ,nk} we denote a set of k positive integers, where ni is some given value of the parameter ni for each i G {1,... ,k}. We denote the set of all possible k-tuples N bу {N}. An algorithm for solving an algorithmic problem is denoted by A, and the set of operations used by algorithm 4 is denoted by Q. An expression over B and Q is a term in the standard sense of mathematical logic. A chain of length n is the result of applying some associative operation from Q to n expressions. If N = 0. then any expression w over B and Q is an unconditional Q-term. If N = 0 and V is the set of all expressions over B and Q. then any mapping w : {N} ^ VU 0 is also called an unconditional Q-term. w(N) = 0 means that the value of w(N) is undefined. If N = 0 and the expression w over B and Q has a mine of logical type under any interpretation of the variables B, then we call the unconditional Q-term w an unconditional logical Q-term. Let N = 0 and w be an unconditional Q-term. Suppose also that the expression w(N) for each N G {N} has a value of logical type under any interpretation of the variables B. In this ease, we call the unconditional Q-term w an unconditional logical Q-term. Suppose that u1,... ,ui are unconditional logical Q-tcrms. w1,...,wi are unconditional Q-tcrms. Then the set I of pairs (u,w) = {(ui,wi)}ie{1,...,/} is a conditional Q-term of length I. Let (u,w) = {(ui,wi)}i=1,2,... be a countable set of pairs of unconditional Q-terms. Sup pose that {(ui,wi)}ie{1,...,/} is a conditional Q-term of length I for any I < от. Then we call (u,w) a. conditional infinite Q-term. Q-terms can be calculated. Suppose that algorithm A consists in fin ding for each i G {1,... ,m} the value yi by computing the value of the Q-term fi. Then the set of Q-terms {fi | i G {1,... ,m}} we call the Q-determinant of the algorithm A. Moreover, we call the system of equations {yi = fi | i G {1,... ,m}} a. representation of the algorithm A in the form of a Q-determinant.

The process of computing Q-terms {fi | i G {1,... ,m}} of algorithm A is called an implementation of algorithm A. An implementation of algorithm A is called parallel if there are operations of the algorithm that are executed simultaneously. An implementation of algorithm A is called Q-effective if Q-terms {fi | i G {1,...,m}} are computed simultaneously, operations are executed as they are ready, and chain operations are computed using the doubling scheme. A Q-effective implementation uses the entire parallelism resource of the algorithm. The concepts of height and width of an algorithm characterize the parallelism resource of the algorithm. An implementation of the algorithm A is called realizable if it is such that a finite number of operations must be executed simultaneously. There are algorithms such that the Q-effective implementation is not realizable.

We can use the Q-determinant of a numerical algorithm for design parallel programs implementing the algorithm, including effective programs. On the base of this idea, the author has developed a method for designing effective programs. In this paper, we describe the proposed method. The method involves the following steps: construction of the Q-determinant of the algorithm, description of the Q-effective implementation of the algorithm, development of a program for an realizable Q-effective implementation of the algorithm. We call Q-effective programs developed using the effective program design method. Therefore, we can also call this method the method of designing Q-effective programs. Note that Q-effective programs use the parallelism resource of algorithms completely. In the paper, we provide an overview of the numerical algorithms for which Q-effective programs have been developed. We also describe the features of applying the design method of Q-effective programs to algorithms. Note that we are considering algorithms that solve various algorithmic problems and have a different structure of Q-determinants.

In addition, we present the following results of a study of Q-effective programs. 1) We introduce the concept of the computing infrastructure of the program. This is a set of conditions for the development and execution of a program. We prove that each of the Q-effective programs for a given numerical algorithm is the most efficient for its computing infrastructure among programs implementing the same algorithm.

We show that any numerical algorithm can be implemented using a potentially infinite set of Q-effective programs for various computing infrastructures. From this, we can conclude that of all the Q-effective programs for this algorithm, there is probably not the most efficient one. However, each of the Q-effective programs is effective for its computing infrastructure.

So, we can say that the method of designing Q-effective programs has theoretical and practical importance.

Key words: improving parallel computing efficiency, Q-determinant of algorithm, representation of algorithm in form of Q-determinant, Q-effective implementation of algorithm, parallelism resource of algorithm, Q-effective program.

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Bibliographic reference: Aleeva V. N. Approach to Effective Implementation of Numerical Algorithms //journal “Problems of informatics”. 2025, № 1. P.29-44. DOI: 10.24412/2073-0667-2024-29-44

S.V. Bredikhin, N.G. Scherbakova

Institute of Computational Mathematics and Mathematical Geophysics SB RAS, 630090, Novosibirsk, Russia

WEIGHTED MULTIPLEX NETWORK OF SCIENTIFIC JOURNAL AUTHORS

DOI: 10.24412/2073-0667-2025-1-4.5-59
EDN: BHCALJ

The organizational structure and topology of complex systems (CS) represented as a set of interacting objects are traditionally studied based on their representation in the form of network structures. The standard approach consists of studying graphs whose vertices correspond to objects and edges correspond to one of the possible types of connections [1-3]. Such a model as applied to the system of scientific co-authorship was used in the fundamental works [4, 5]. This representation allows to study such properties of the system as evolution [6, 7], prediction of new co-authorship links [8, 9], identifying communities [10]. An important limitation of this approach is that it captures only binary interactions. A possible way to overcome this limitation is to generalize binary interaction to the interaction of an arbitrary set of actors, for example, by using the formalism of bipartite graphs, hypergraphs, and simplicial complexes. A modern methodology for modeling group relations in CS is described in [19-21]. It is based on the definition of multilayer networks and is suitable for representing most CS. Multilayer networks are defined by a set of nodes interacting with each other in several ways simultaneously. Each type of connection corresponds to a layer, a copy of any “physical” node can be present in several layers.

The simplest classification of multilayer networks distinguishes two categories based on the absence or presence of inter-layer connectivity, which is a significant topological discriminator between two classes of models: non-interconnected networks of networks and interconnected ones. In the case where the connections cannot be explicitly determined based on the data, an important tool for studying the topology is the concept of an inter-layer edge (multilink) [22]. It defines the structure of connections between nodes in all layers. If relationships can be identified from the data and layers are explicitly connected to each other, tensor-like structures are needed [19]. One class of interconnected networks are multiplex networks, in which the nodes of each layer represent copies of the same object and only inter-layer connections among copies of the same physical node are allowed. The mathematical apparatus used to describe and analyze multiplex networks is proposed in [22, 27, 29].

This paper presents the results of the analysis of the weighted multiplex network Mcc constructed on the basis of real data extracted from the articles of the scientific journal “Sakharnyi Diabet”. The Mcc structure consists of two weighted layers, the nodes of which are the authors of the articles. The edges between the nodes of the first layer are established based on the binary co-authorship relation, and the edges of the second layer are based on the citation relation. The basic properties of nodes and links that determine the network structure are analyzed. The parameters of nodes of each layer that affect the topology of weighted networks, such as degree, weighted degree, and the inverse participation coefficient, are calculated. Their distributions and correlations including interlayer ones are presented.

 

The high level of inter-layer correlation of node degrees indicates that nodes with a significant number of links in one layer are also highly connected in another. This is also true for the weighted degree.

An important characteristic of multiplexity is the concept of the overlap between two layers reflecting the presence of links between the same nodes in both layers. The obtained values of the overlap coefficients indicate active interaction between the authors. The parameters of the multiplex nodes based on the concept of a multilink provide additional information about the relationship between nodes in different layers, in contrast to the sum of links in a single-layer aggregated network. Multidegree, multistrength, the inverse coefficient of multiparticipation, their distributions and correlations were calculated. The analysis of the results allows us to conclude that although it is impossible to conclude from the limited data that co-authors cite mainly co-authors, it is possible to conclude that mutual citations prevails between co-authors. The presented formalism allows us to significantly expand the understanding of author relationships in the scientific field under consideration.

Key words: complex systems, data analysis, multilayer networks, scientific c.o-authorship, bibliometrics.

This work was carried out under state contract with ICMMG SB RAS (0251-2021-0005)

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Bibliographic reference: Bredikhin S. V. Scherbakova N. G. Weighted Multiplex Network of Scientific Journal Authors //journal “Problems of informatics”. 2025, № 1. P.45-59. DOI: 10.24412/2073-0667-2024-45-59

V.M. Vishnevsky*,**. Y. A. Avramenko*. V. H. Nguyen**. N.S. Kalmykov*

institute of Control Sciences of Russian Academy of Sciences, 117997, Moscow, Russia,
**Moscow Institute of Physics and Technology (National Research University), 141700, Dolgprudny, Russia

ASSESSMENT OF THE PERFORMANCE CHARACTERISTICS OF A WIRELESS NETWORK BASED ON TETHERED UAVS

DOI: 10.24412/2073-0667-2025-1-60-77
EDN: DISNNV

This paper describes the advantages of implementing a broadband wireless network based on a tethered drone and evaluates its performance characteristics. It includes calculations for the expansion of the telecommunications coverage area (line-of-sight area) and the channel parameters between the base station (BS) on the drone and the ground station (GS) within line-of-sight. A stochastic polling model with batch packet servicing is proposed, which adequately describes the operation of the broadband wireless network with a centralized management mechanism. The network architecture and the protocol for interaction between the BS and GS are outlined to provide the necessary data for numerical calculations. A new approach for performance evaluation and numerical analysis is developed, combining machine learning methods with simulation modeling.

Key words: tethered drone, wireless network, line-of-sight, stochastic polling.

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Bibliographic reference: Vishnevsky V.M., Avramenko Y.A., Nguyen V.H., Kalmykov N. S. Assessment of the Performance Characteristics of a Wireless Network Based on Tethered UAVs //journal “Problems of informatics”. 2025, № 1. P.60-77. DOI: 10.24412/2073-0667-2024-60-77