About the Journal
The Journal of Telecommunications and Information Technology is published quarterly. It comprises original contributions, dealing with a wide range of topics related to telecommunications and information technology. All papers are peer-reviewed. The articles presented in JTIT focus primarily on experimental research results advancing scientific and technological knowledge about telecommunications and information technology.
Current Issue
Vol. 104 No. 2 (2026)
Explore the current issue of the JTIT
The current issue of the Journal of Telecommunication and Information Technology (JTIT) offers high-quality original articles and showcases the results of key research projects conducted by recognized scientists and dealing with a variety of topics involving telecommunications and information technology, with a particular emphasis placed on the current literature, theory, research and practice.
The articles published in this issue are available under the open access (OA), “publish-as-you-go” scheme. Four issues of JTIT are published each year.
The Journal of Telecommunications and Information Technology is the official publication of the National Institute of Telecommunications - the leading government organization focusing on advances in telecommunications technologies.
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Published:
2026-06-30
ARTICLES FROM THIS ISSUE
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Effects of Deformation of Main Reflector of Double Reflector Spherical Antenna on Its Aperture Field - ROT-54/2.6 Antenna Case
Abstract
The aim of this study is to model the impact of main reflector deformations in a double-reflector spherical antenna system on the phase distribution of the electromagnetic field across the aperture and the associated gain loss. The study focuses on the antenna of the ROT-54/2.6 radio-optical telescope (Herouni radio telescope) - a spherical double-reflector system with a fixed primary reflector with a 54 m diameter, composed of 3738 panels. An analytical model is developed to evaluate phase distortions induced by deviations from the spherical geometry. The model computes local phase shifts across the aperture and predicts gain degradation using Ruze's formula which relates the RMS surface error to efficiency losses. This approach is important for pre-alignment procedures and functional restoration of the antenna, enabling geometry corrections prior to full-scale observations. Based on terrestrial laser scanning (TLS) data, the methodology allows for a quantitative assessment of structural phase errors and corresponding gain degradation, confirming its suitability for practical diagnostics of large reflector systems.
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Optimized Fuzzy Secure Scheme for Trust Assessment in IoMT
Abstract
Rapid development of technologies associated with the Internet of Medical Things (IoMT) has enabled continuous patient monitoring, diagnosis, and integration of medical devices with various healthcare infrastructures. However, the increasing heterogeneity of IoMT systems and their connectivity-related features introduce also security risks, such as data tampering, unauthorized access, and unsafe behavior of the devices themselves. Traditional trust assessment techniques often fail to handle the uncertainty inherent in medical data and devices. This paper presents a fuzzy logic-based secure trust assessment scheme designed for IoMT, which integrates behavioral and communication indicators to compute trust scores for a device. The scheme employs a fuzzy logic-based approach and provides a trust level evaluation procedure suitable for resource-limited IoMT devices. A fuzzy inference system was developed specifically for this scheme and further optimized by applying evolutionary algorithms. The experimental results demonstrate an improved accuracy of the optimized model in evaluating the trust level of devices and show its enhanced accuracy compared to a classical trust mechanism.
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Improving Performance of GNSS Acquisition Systems by Optimizing TM-CFAR Thresholds Using Metaheuristics
Abstract
Signal acquisition is one of the key signal processing tasks performed by global navigation satellite system (GNSS) receivers. It involves detecting the presence or absence of a signal by comparing it with a predefined threshold, which can be either fixed or adaptive. This study focuses on optimizing the threshold of the trimmed mean constant false alarm rate (TM-CFAR) detector under Rayleigh fading conditions, employing metaheuristic optimization techniques, due to their proven efficacy in solving complex optimization problems. Furthermore, two TM-CFAR detectors are applied to the data and pilot channels of the GNSS system. Their outputs are then combined using two logical fusion strategies: AND and OR rules. Simulation results demonstrate that the optimized thresholds improve the performance of the GNSS signal acquisition system.
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Fairness-aware Joint Pattern and Power Design for Downlink PDMA Systems
Abstract
Pattern division multiple access (PDMA) is recognized as a promising non-orthogonal multiple access technique for overloaded wireless systems, capable of being used for multiplexing multiple users over a limited set of resources. However, the real performance of PDMA is determined not only by the access principle itself, but also by the joint interaction between pattern design, transmit power allocation, and receiver interference cancellation. This paper proposes a fairness PDMA scheme for overloaded downlink systems based on joint pattern assignment, power allocation, and adaptive successive interference cancellation (SIC). The design aims to improve spectral efficiency and user fairness under real residual-interference conditions. Simulation results show that the proposed PDMA consistently outperforms orthogonal multiple access (OMA) and fixed-pattern PDMA techniques. At 30 dB, the proposed scheme achieves an average sum rate of approximately 14.5 bit/s/Hz under ideal SIC, compared with nearly 12 bit/s/Hz for OMA and approx. 8.5 bit/s/Hz for fixed-pattern PDMA. In terms of fairness, at an overload factor of \textlambda = 1.5, the proposed method attains a Jain's fairness index of approx. 0.84, whereas OMA and fixed-pattern PDMA achieve nearly 0.58 and 0.44, respectively. These results confirm that an adaptive joint design allows to obtain both high throughput and balanced user performance in overloaded PDMA systems.
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Evaluating AES Payload Encryption for Securing MQTT-based Smart Home Networks with Machine Learning-based Intrusion Detection
Abstract
The message queuing telemetry transport (MQTT) protocol is widely adopted in smart home IoT ecosystems despite its default configuration failing to offer adequate protection against eavesdropping or payload manipulation. This study addresses an important research gap and attempts to determine whether AES-128 payload encryption is capable of securing MQTT transmissions without degrading the effectiveness of machine learning-based intrusion detection systems (IDS). Three security configurations, namely TLS, payload encryption, and token-based authentication, deployed on the ESP32 microcontroller family, are compared and their impact on message latency is measured. Experimental results show that the AES-128 encryption overhead remains at below 25% of the message publication time on ESP32-S3. To evaluate the robustness of IDS under encryption, we apply a reproducible modification to the MQTTset benchmark dataset that replaces variable-length plaintext payloads with fixed-length ciphertext representations while simultaneously preserving feature semantics and labeling consistency. 5 out of 6 evaluated classifiers maintained their accuracy level at above 99% on the modified dataset, with tree-based and neural models showing no meaningful degradation. Only Naive Bayes proved unsuitable, with its accuracy dropping from 98.79% to 62.15% due to its independence assumptions being violated by cryptographic uniformity. These results confirm that AES-based MQTT payload encryption is a practical and efficient security measure for resource-constrained IoT environments, provided that appropriate classifiers are employed.
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Hybrid Feature Selection Framework for Machine Learning-based Bot Detection on Social Media
Abstract
Nowadays, social media impact all aspects of our lives, making us vulnerable to fraud and scams. Bots are believed to be the most prevalent form of malware that may be found in social media environments. New detection methods are required to keep up with the pace of their continuous advancement. This paper offers an overview of machine learning-based bot detection methods. The study revealed that the effectiveness of machine learning (ML) models can be significantly hindered by redundant and irrelevant features present in the datasets, which can lead to performance degradation. A hybrid feature selection (FS) combining characteristics of the genetic algorithm (GA) and the mutual information (MI) approach is proposed to overcome this challenge. The proposed method is evaluated using the following approaches: random forest (RF), decision tree (DT), support vector machine (SVM), and logistic regression (LR). Compared to the state-of-the-art models, the proposed method is capable of efficiently identifying bots using only a small number of features. For the dataset used, we achieved a classification accuracy of 0.99 using 4 features only.
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Anonymous Stateless Communication Architecture: Design, Network Performance Analysis, and Integration of Tor Hidden Services for Privileged Communications
Abstract
This paper presents the network architecture and empirical performance analysis of the Proof of Concept (POC) for a stateless Tor-based communication system designed for privileged communication. Unlike existing secure messaging platforms relying on centralized server infrastructures, persistent session states, or identifiable network endpoints, the proposed solution achieves server-side and client anonymity simultaneously through the integration of Tor hidden services v3, stateless application design, and containerized microservice decomposition. We formally describe the system's model and its constituent components: an application server, an ephemeral identity registry, and a browser-based client operating over WebCrypto. Next, we analyze performance of the network layer across 100 measurement cycles. Empirical results confirm that cryptographic operations contribute less than 2 ms of overhead relative to dominant Tor circuit latency (mean value of 8100 ms per circuit). Immunity to traffic, session linkability, and server deanonymization are examined against a realistic network adversary model. POC is compared to SecureDrop, Ricochet, and Signal in terms of five architectural properties and is shown to be the only system under evaluation satisfying all five requirements simultaneously. Deployment considerations for production-grade privileged communication environments, including operational security procedures for public key registration, are discussed as well.
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A Lightweight Adaptive Holding-time Policy for Clustered Wireless Sensor Networks
Abstract
In clustered wireless sensor networks (WSNs), re-shaping the topology can redistribute cluster head load, but each such task consumes energy. This paper studies the refresh timing problem in static clustered WSNs, where the controller decides not only whether to rebuild the topology but also determines the time over which the selected topology remains active. The proposed method formulates topology maintenance as a semi-Markov adaptive holding-time control problem. At each control epoch, the controller selects a refresh indicator, a target cluster count, and a holding time. The topology builder uses explicit cluster head election, nearest head member association, and intra-cluster chain forwarding with one-hop cluster head transmission to the base station. Under nominal deployment, the proposed controller reaches a half-node death (HND) point of 1969.1 ±8.4 rounds with 0.104 J of control energy, while periodic refresh with T = 10 reaches 1819.7 ±32.6 rounds and consumes 1.133 J. Across seven tested deployment scenarios, the proposed method gives a higher HND point with lower control energy than the tested refresh-enabled baselines. Therefore, the method is positioned as a lifetime overhead control mechanism, favoring lower control energy and longer mid-life operation, whereas periodic refresh remains preferable when delivery performance is the primary objective.
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Analyzing Performance of Eigenvalue-based Spectrum Sensing within LoRaCog Framework
Abstract
The CR technology enhances spectrum utilization by allowing access to unused licensed channels, while spectrum sensing allows secondary users to verify channel availability before the transmission. This study relies on the LoRaCog framework, a solution integrating the CR technology with LoRa LPWAN networks, to evaluate the performance of eigenvalue-based detection algorithms, such as maximum eigenvalue detection (MED), maximum to minimum eigenvalue (MME), energy-to-minimum eigenvalue (EME) and maximum-to-mean eigenvalue detection (MMED), with the comparisons based on energy detection (ED). The said algorithms were evaluated under three scenarios characterized by an increasing degree of complexity. These included the following: an ideal additive white Gaussian noise (AWGN) channel, followed by a multipath fading channel with noise uncertainty using a SISO receiver and, finally, a SIMO multiantenna receiver system. The simulation results for the AWGN channel showed that the ED algorithm achieved the best detection probability and the lowest sensing time. When multipath fading and noise uncertainty were introduced, eigenvalue-based algorithms achieved higher detection probabilities while maintaining comparable detection times. The MME algorithm achieved the highest detection probability when used with the SIMO multi-antenna reception system.
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Federated Learning for Low-rate DDoS Detection in Multi-controller Software Defined Networks: A Meta Analysis
Abstract
Multi-controller SDN environments suffer from a blind spot when it comes to detecting low-rate DDoS attacks. Each controller sees only its own traffic slice, meaning that an LDDoS campaign looking, at every controller, like background noise is still capable of draining the network. Federated learning (FL) is a reasonable answer to this challenge, due to such controllers sharing model updates rather than raw logs. However, the published literature on FL-based detection is fragmented enough that the results have not been systematically compared up to date.
We analyze 39 papers published between 2020 and 2026. 35 of those reported quantitative results, with the pooled mean detection precision equaling 98.25% (SD ±0.91) and the mean F1 score amounting to 97.98% (SD ±1.10). Federated models averaged an accuracy score of 98.33%, compared to 98.06% for centralized approaches - a 0.27 pp gap that is practically negligible. LSTM and hybrid CNN + RNN architectures ranked the highest in terms of the most metrics. Four aggregation strategies were mentioned repeatedly: weighted aggregation, asynchronous FL, personalized FL, and standard FedAvg.
The widest gap we identified was in the datasets. No available benchmark simultaneously models multi-controller SDN topology, low-rate attack patterns, and heterogeneous traffic distributions across various controllers. Until that changes, high-accuracy scores on CICIDS2017 or CICDDoS2019 should be interpreted with some caution.
