Information and Telecommunication Sciences

EFFICIENCY ANALYSIS OF DATA PREPROCESSING ALGORITHM VECTORISATION FOR INTERNET OF THINGS EDGE DEVICES

Dmytro Trofimtsov — 2026-06-18

Background. The growth of data volumes in Internet of Things (IoT) networks requires a transition to the Edge Computing paradigm to offload radio channels. However, modern end nodes face severe constraints in computing power and energy consumption. Furthermore, classic scalar algorithms for processing noisy sensor data suffer critical performance losses due to CPU pipeline degradation (branch prediction errors) and limited memory bus bandwidth.

Objective. Investigation of the impact of architecture-dependent programming methods (SIMD) and compiler optimisations on the speed, determinism, and overall energy efficiency of preliminary sensor data aggregation and filtering processes in edge computing nodes.

Methods. Experimental modeling of basic digital signal processing algorithms on the ARM Cortex-A53 microprocessor platform (Raspberry Pi Zero 2 W). A comparative analysis of scalar C++ code execution, automatic compiler vectorisation (GCC), and manual control of the NEON vector coprocessor using intrinsic functions was performed.

Results. It was established that the use of 128-bit SIMD instructions provides a 2.7–2.9 times speedup of basic computations, reaching the physical limit of memory subsystem bandwidth (up to 2 GB/s). It was proven that during stochastic noise processing, manual vectorisation using bitwise masks (vector predication) eliminates branch prediction penalties, reducing threshold filtering execution time from 143.1 ms to 52.5 ms. The implementation of parallel reduction decreased the floating-point accumulation error to 2.4%.

Conclusions. Architecture-dependent optimisation allows reducing the active microprocessor time by 60–65%, facilitating a quicker shift of the device into deep sleep mode and correspondingly lowering its energy usage. This capability permits the execution of intricate filtering algorithms on economical autonomous platforms, thereby considerably minimising the payload size prior to transmission through a wireless telecommunication channel.

DATA RATE AND MODULATION ADAPTATION IN 5G IoT NETWORKS WITH VARIABLE NODE DENSITY

Anton Paradovskyi — 2026-06-18

Background. The rapid development of Internet of Things (IoT) technologies has led to a significant increase in the number of connected devices and the expansion of IoT applications in industrial automation, logistics, smart buildings, transport systems, and other areas with intensive radio resource usage. Under such conditions, the task of ensuring efficient wireless communication with a large and dynamically changing number of connected devices becomes increasingly relevant. At the same time, the performance of a wireless IoT system depends not only on the availability of network resources but also on the ability of the physical layer to adapt to variable channel conditions caused by dynamic node density. Although fifth-generation communication systems (5G) provide support for massive Machine-Type Communications (mMTC), the existing channel adaptation mechanisms were developed mainly for broadband user equipment (modems, routers) and require further refinement with due regard for the specific features of dense IoT environments. In particular, IoT networks are characterised by limited energy resources, irregular traffic generation, and dynamic fluctuations in the number of simultaneously active nodes. Consequently, static or poorly adaptive configurations of physical-layer parameters may result in inefficient spectrum utilisation, an increased error probability, and a deterioration in communication quality.

Objective. The paper aims to analyse the influence of variable node density on the physical-layer parameters of 5G IoT networks and to present a conceptual approach to the adaptation of data transmission rate and modulation scheme, taking into account the current state of the radio channel.

Methods. The paper proposes an analytical approach that combines a review of recent scientific publications in the fields of 5G, mMTC, and physical-layer parameter adaptation with a formalised description of the influence of active node density on the received signal power and the signal-to-interference-plus-noise ratio (SINR - the ratio of useful signal power to the sum of interference and noise powers). On this basis, the set of admissible transmission modes is considered, and a conceptual density-aware approach to data rate and modulation adaptation without changing the transmission power is proposed.

Results. The paper considers variable active node density as a physically significant parameter affecting the operation of the wireless channel through changes in interference and the spatial characteristics of the network. Data transmission rate and modulation scheme are proposed as the main physical-layer adaptation parameters for the class of tasks under consideration. An adaptation concept is proposed within which the choice of the transmission mode is associated with the current SINR level, the density of active nodes, and the distance between the transmitter and the receiver.

Conclusions. The proposed approach enables the correlation of channel parameter adaptation at the physical layer with the actual dynamics of the radio environment in 5G IoT networks. The obtained results may be used as a theoretical basis for further modelling and for the development of applied adaptation algorithms in systems with massive device connectivity.

LATENCY-AWARE ACCURACY EVALUATION OF NETWORK TRAFFIC PREDICTION MODELS FOR PROACTIVE QoS CONTROL IN 5G/IoT NETWORKS

Yevhen Karashevych — 2026-06-18

Background. The growth of 5G/IoT traffic and strict QoS targets (delay, jitter, loss) motivate proactive control based on short-horizon traffic prediction. Therefore, models must be evaluated not only by accuracy but also by inference timeliness on target hardware.

Objective. The paper aims to develop a deployment-oriented evaluation methodology that jointly assesses forecasting accuracy and inference-time feasibility for Software-Defined Networking (SDN) and Network Function Virtualisation (NFV) based control loops in 5G/IoT networks.

Methods. We analyse representative forecasting families (statistical baselines, LSTM/GRU, hybrid CNN–RNN, and spatio-temporal graph neural networks) and define a latency-aware protocol measuring error metrics together with online single-sample latency, throughput, memory footprint, and model size under realistic execution settings.

Results. The methodology introduces an explicit feasibility criterion from end-to-end control-loop delay decomposition, enabling feasibility filtering and Pareto ranking across error–latency–memory objectives. For topology-aware data, STGNNs provide the accuracy ceiling, while teacher–student spatio-temporal distillation is positioned as a practical compression step to obtain an edge-feasible student.

Conclusions. Accuracy-only comparisons are insufficient for proactive QoS without verifying timeliness on target hardware. The proposed methodology supports reproducible, deployment-relevant comparisons and motivates distillation under strict latency budgets.

EXPERT-BASED ANP MODEL FOR CYBERSECURITY RISK MITIGATION IN IoT COMMUNICATION NETWORKS FOR CRITICAL INFRASTRUCTURE

Vadim Romanuke — 2026-06-18

Background. The increasing integration of Internet of Things (IoT) communication networks into critical infrastructure has significantly expanded the attack surface of energy systems, including small-scale renewable power plants supplying businesses and local communities. In such cyber-physical systems, cybersecurity effectiveness depends not only on individual technical controls, but also on complex interdependencies among system architecture, incident response effectiveness, threat detection capability (network reliability), vulnerability management, and human and organisational factors. Many of these characteristics cannot be assessed through direct measurement and therefore rely on expert judgment. Traditional hierarchical evaluation methods are insufficient for capturing mutual influences and feedback effects inherent in IoT-based critical infrastructure, motivating the application of network-based decision models.

Objective. The objective of this study is to prioritise cybersecurity-related components of an IoT communication network supporting a small-sized solar power plant, accounting explicitly for interdependencies among components. From a practical perspective, the objective is to identify which system components should be strengthened first under limited investment resources, in order to mitigate cybersecurity risks and maximise the overall cybersecurity resilience of the facility.

Methods. An expert-based Analytic Network Process (ANP) model is developed, in which five interdependent system components are evaluated through pairwise influence comparison matrices constructed using Saaty’s ratio scale. Expert judgments are aggregated by solving an optimisation problem that identifies the closest aggregate matrix preserving the judgment scale, rather than applying conventional geometric averaging. The resulting supermatrix is normalised to obtain the global priority vector, reflecting the relative importance of each component within the interdependent IoT communication network.

Results. The obtained global priorities reveal that system architecture robustness (weight 27.76 %) and human and organisational factors (weight 25.55 %) have the highest importance, followed by incident response effectiveness (weight 23.13 %), while vulnerability management and threat detection capability receive lower priorities (9.69 % and 13.87 %, respectively). Notably, the human and organisational component attains a high priority despite being influenced by only two other components, indicating that strong dependencies on architecturally and operationally central elements can outweigh a larger number of weaker interactions. These results demonstrate that components enabling or constraining other protective measures dominate the overall cybersecurity posture of the solar plant’s IoT network.

Conclusions. The case study shows that effective cybersecurity investment in IoT-based critical infrastructure should prioritise architectural robustness and organisational preparedness alongside incident response effectiveness. The ANP-based prioritisation reveals that improving technical controls in isolation is insufficient when systemic dependencies are ignored. For small-scale solar power plants, the global priorities provide actionable guidance for allocating limited resources to measures that yield the greatest systemic impact, confirming the practical value of expert-based ANP modelling for cybersecurity decision support in interdependent IoT environments.

METHOD OF CRYPTOGRAPHICALLY RESILIENT EVENT CORRELATION IN SIEM BASED ON RISK-ORIENTED ASSESSMENT AND CONTEXT-HASHED SIMILARITY

Yuliia Kostiuk — 2026-06-18

Background. The rapid growth of telemetry and event logs in corporate and public-sector information and communication systems complicates the formation of reliable incidents in SIEM platforms. Traditional correlation methods that rely on fixed rules, temporal windows, and heuristic similarity standards fall short in the face of evolving threats and fail to consider the risk context associated with assets. In addition, correlation is vulnerable to log manipulation (log injection, partial context modification, timestamp distortion), which can generate false incidents or conceal multi-stage attacks and overload SOC operations.

Objective. This paper aims to develop a cryptographically resilient SIEM event-correlation method based on risk-oriented assessment and context-hashed similarity, improving the trustworthiness of incident formation and ensuring robustness against injection, substitution, and partial compromise of event logs.

Methods. We formalise a security event as a structured object comprising context, temporal parameters, and risk, and propose normalisation and canonicalisation procedures for context features with a fixed ordering. To protect context, we employ a context-hashed representation with multi-level fingerprints . Event similarity is defined via a cryptographically resilient fingerprint-matching metric, while correlation is computed using an integral indicator Corr that combines contextual similarity with the risk significance of events. A time-window correlation algorithm with a threshold-based decision rule is constructed.

Results. The proposed method combines cryptographically protected context similarity with risk-oriented assessment, providing resilience to context and log manipulation. We develop a threat model for SIEM alerts as correlation objects and a mapping table “attack class → impact → mitigation mechanism.” Simulation experiments in a corporate environment confirm a reduced false-incident rate and improved correlation precision while maintaining comparable incident-detection recall; robustness is demonstrated under log injection, partial tampering, and timestamp shift scenarios. In practice, the method can be integrated as a correlation module into existing SIEM platforms without substantial architectural redesign.

Conclusions. Integrating context-hashed representations with risk-oriented correlation yields a cryptographically resilient mechanism for automated incident formation in SIEM, mitigates alert flooding, and increases SOC trust in correlation outcomes under dynamic threats. Future work should focus on adaptive tuning of α and θ using SOC feedback and ML-based models, as well as expanding experimental validation on real industrial data.

EVALUATION OF CRYPTANALYTIC METHODS TARGETING THE RSA CRYPTOSYSTEM

Oksana Onyshchuk — 2026-06-18

Background. In modern digital environments, cryptographic mechanisms are essential for ensuring data confidentiality, integrity, and authenticity. One of the most widely used public-key cryptosystems is RSA, which underpins digital signatures, secure communication protocols (TLS/HTTPS), electronic document management systems, and financial transactions. Despite its robust mathematical foundation rooted in the challenges associated with factoring large integers, the practical security of RSA largely depends on accurate implementation and proper usage. Vulnerabilities typically arise from improper parameter selection, flawed protocol design, or insecure key management practices.

Objective. The study aims to identify the conditions under which cryptanalytic attacks on RSA become feasible and to develop practical recommendations for improving system security. From an applied perspective, the research focuses on three types of attacks – keyless reading, digital signature attacks in notary-based schemes, and chosen-ciphertext attacks – in order to evaluate their feasibility, limitations, and impact on information system security.

Methods. A sequential research methodology was applied. First, the RSA algorithm was analysed, including key generation, encryption, decryption, and digital signature mechanisms. Next, three attack models were examined: the keyless reading cycle attack, the notary-based digital signature attack, and the chosen-ciphertext attack. These methods were implemented in Python to experimentally evaluate their effectiveness under different parameter configurations. Finally, a comparative analysis was conducted considering both theoretical assumptions and practical implementation constraints.

Results. The results indicate that the keyless reading attack is effective only when RSA parameters are weak and short cycles exist; with key lengths of 1024 bits or more, it becomes practically infeasible. In contrast, digital signature attacks demonstrate significant effectiveness in the presence of protocol-level flaws, such as blind signing, lack of input validation, or key reuse. The chosen-ciphertext attack demonstrates moderate effectiveness and depends on violations of secure key usage practices. Overall, the findings confirm that real-world vulnerabilities stem primarily from implementation weaknesses rather than inherent mathematical flaws of RSA.

Conclusions. The study confirms that RSA remains secure when modern cryptographic best practices are properly applied. Most vulnerabilities arise from improper implementation, insecure configurations, or protocol design flaws rather than theoretical weaknesses of the RSA algorithm itself. To enhance security, it is recommended to use key lengths of at least 2048 bits, apply cryptographic hashing before signing, avoid key reuse across different cryptographic functions, and incorporate random parameters such as salts or timestamps. These measures significantly strengthen the robustness of RSA-based systems against cryptanalytic attacks.

JOINT MINIMISATION METHOD OF BOOLEAN FUNCTION SYSTEMS FOR COMBINATIONAL LOGIC SYNTHESIS

Vadym Minziuk — 2026-06-18

Background. Combinational circuits offer higher performance compared to digital automata, including software-based ones, and therefore remain relevant for the corresponding tasks. During the functional-logical design phase, one of the key tasks is the minimisation of Boolean functions, which allows for a reduction in circuit complexity (the number of logic elements and connections between them). Modern combinational devices have not only many inputs but also many outputs. The mathematical model of such a circuit is a system of Boolean functions of a common set of variables, requiring joint minimisation of the functions. The increasing complexity of digital systems and the growing number of input signals lead to an exponential increase in computational costs, causing classical minimisation methods to lose their effectiveness. Consequently, there is a need to develop new approaches for high-dimensional problems. One such approach is the bitwise partitioning method of a set of conjunctive terms with a chain covering of a set of prime implicants. However, this method was developed to minimise a single function.

Objective. The study aims to adapt and develop a method for the bitwise partitioning of a set of conjunctive terms with a chain covering of a set of prime implicants to ensure efficient joint minimisation of systems of Boolean functions.

Methods. To solve the problems at hand, we applied the tools of mathematical logic and Boolean algebra, the theory of digital automata, set theory, and combinatorics, which made it possible to extend the scope of the minimisation method to high-dimensional Boolean function systems.

Results. The first stage of the two-stage minimisation in the method under consideration has remained virtually unchanged. Given sufficient computational resources, we launch the process of generating prime implicants for each function of the system in parallel processes using the bitwise sorting method. For joint minimisation of the functions, we place the obtained prime implicants of all system functions in a common implicant table, indicating their affiliation with specific functions. If a prime implicant common to several functions is encountered during the chain-covering process, we compare its weight with the total weight of all prime implicants of those functions with which it intersects.

Conclusions. The proposed changes make it possible to apply the method under consideration to the joint minimisation of a system of Boolean functions.

COUPLED OSCILLATIONS OF DIELECTRIC RESONATORS WITH DEGENERATE MODES (ANALYTICAL APPROACH)

Alexander Trubin — 2026-06-18

Background. Unique elements of modern optical communication systems are dielectric resonators with whispering gallery modes. The exceptionally high inherent quality factor of this class of resonators enables their application in filters, delay lines, multiplexers, and many other elements of optical systems. Unfortunately, resonators of this type are characterised by a very high degree of degeneracy of their eigen oscillations, which makes it impossible to calculate complex structures built on their basis, with a large number of elements. In some cases, an effective solution to this problem can be the use of analytical methods for solving Maxwell's equations, which are based on electrodynamic modelling of the eigen oscillations of complex resonator structures.

Objective. The study aims to obtain general systems of linear equations describing complex systems of dielectric resonators with a large number of degenerate types of natural oscillations, the solution of which allows obtaining analytical expressions for amplitudes and complex frequencies, suitable for calculations.

Methods. To find analytical expressions, analytical methods of perturbation theory and index theory for large systems of linear equations are used. The final result is the derivation of new general systems of equations, convenient for obtaining analytical formulas for frequencies and amplitudes of resonators.

Results. The theory concerning the natural oscillations of one-dimensional, two-dimensional, and three-dimensional lattices, including ring lattices of dielectric resonators exhibiting degenerate modes, has been developed. New analytical expressions for complex frequencies and amplitude distributions of resonators have been obtained. It has been shown that the obtained solutions in special cases exactly coincide with the analytical expressions for resonators with non-degenerate oscillations, and in the case of degenerate oscillations with the results of numerical calculations.

Conclusions. The developed theory is the basis for building new mathematical models of various types of optical delay lines (CROW), filters, and many other devices of the optical wavelength range, which are built on the basis of the use of a large number of dielectric resonators with degenerate types of natural oscillations. The developed theory facilitates the construction of novel and more efficient mathematical models for a range of optical communication devices, which are unattainable through numerical methods.

OVERVIEW OF BALANCED MICROWAVE FILTER STRUCTURES AND THEIR KEY FEATURES

Volodymyr Ilchenko — 2026-06-18

Background. Balanced microwave filters are critical components in modern communication systems, offering superior common-mode noise rejection and reduced electromagnetic interference compared to single-ended counterparts. The growing demand for high-performance differential signal processing in contemporary wireless and wired communication channels necessitates a systematic understanding of available balanced filter topologies and their design trade-offs.

Objective. The paper aims to provide a comprehensive overview of balanced microwave filter architectures, covering microstrip and substrate integrated waveguide (SIW) implementations, and to establish quantitative performance benchmarks across published designs.

Methods. The study systematically examines differential-mode signal processing principles and analyses various structural configurations, including coupled resonator designs and metamaterial-inspired topologies. Key parameters – common-mode rejection ratio (CMRR), differential-mode insertion loss, fractional bandwidth, and stopband characteristics – are evaluated across a representative set of published implementations. The evolution from traditional filters to reflectionless is traced.

Results. The analysis reveals significant advances in miniaturisation techniques, wideband common-mode suppression, and absorptive filtering approaches. The integration of metamaterial concepts enables enhanced CMRR and compact form factors. Quantitative comparisons demonstrate the trade-offs between filter size, CMRR, insertion loss, and operational bandwidth.

Conclusions. This overview establishes a structured foundation for understanding the diverse landscape of balanced filter architectures and their applications in differential communication systems.

DESIGN OF A HAIRPIN MICROSTRIP BANDPASS FILTER FOR THE 2.4 GHz BAND

Igor Trubarov — 2026-06-18

Background. Modern wireless communication systems, particularly in the 2.4 GHz band, impose stringent requirements on the selectivity, size, and manufacturability of band-pass filters. Microstrip filters based on U-shaped (hairpin) resonators are attractive for their compactness; however, they are highly sensitive to geometric parameters, especially the spacing between resonators. This complicates their practical implementation and leads to deviations of the actual characteristics from the designed ones.

Objective. The paper aims to design a microstrip band-pass filter based on U-shaped resonators for the 2.4 GHz band with specified frequency characteristics, and to analyse the influence of geometric parameters on its electrodynamic properties.

Methods. The filter design is carried out using classical synthesis methods for band-pass filters followed by their transformation into a microstrip implementation. The geometrical parameters are initially determined using analytical relations and then refined through numerical electromagnetic simulation based on the finite element method. To improve synthesis accuracy, an approach based on the coupling coefficient evaluation between resonators, while considering their dependence on the structure geometry, is applied.

Results. A microstrip filter structure based on U-shaped resonators for the 2.4 GHz band has been developed, and its frequency response has been obtained using electromagnetic simulation. The influence of key geometrical parameters, particularly the spacing between resonators, on the transmission and reflection characteristics of the filter has been analysed.

Conclusions. It is shown that microstrip filters based on U-shaped resonators enable the realisation of compact band-pass structures for the 2.4 GHz band; however, their performance strongly depends on fabrication accuracy. The use of a combined approach that integrates analytical synthesis and electromagnetic simulation improves the accuracy of determining geometrical parameters and enhances the agreement between simulated and actual filter characteristics.