Journal of Communications is a scholarly peer-reviewed international scientific journal published bimonthly, focusing on theories, systems, methods, algorithms and applications in communications.
AI-Based Fault Detection in Communication Transmission Lines Using Electromagnetic Parameter Analysis
Reliable fault detection in communication transmission lines is crucial for modern infrastructure. This paper presents a simulation-driven method for classifying transmission line faults — normal, open, short, and mismatch — using physically grounded electromagnetic features. By leveraging key parameters such as the reflection coefficient, voltage standing wave ratio (VSWR), and material properties (complex permittivity, conductivity), we build a dataset that mimics real-world fault scenarios. A support vector machine (SVM) classifier, trained on these engineered features, achieves near-perfect classification accuracy and transparent decision boundaries. Our approach balances theoretical analysis and practical machine learning, offering a robust, interpretable pipeline for early diagnosis. Future directions include multi-frequency feature augmentation, uncertainty calibration, and deployment in hardware monitoring systems.
Copyright (c) 2026 Journal of Communications
Review of Energy-Efficient, Cyber-Secure, and Sustainable Internet of Things for Smart Environments
With IoT systems proliferating rapidly across smart cities, healthcare, energy, transport and industry the need for sustainable cybersecurity by design is critical. But prior studies usually consider energy efficiency, environmental sustainability and security as separate aspects, without unified cross-layer analytical models. Following the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) methodology, this paper provides a systematic literature review (2018–2025) and is substantiated by bibliometric analysis to assess validation maturity, research trends and distribution across domains. The contributions are threefold. First, a Sustainability Interaction Model (SIM) is proposed that embeds interdependencies of energy efficiency, carbon footprint, stringency of cybersecurity, and level of AI intelligence including edge–fog–cloud orchestration effects across the system lifecycle. Second, a repeatable evaluation framework is presented with measurable sustainability Key Performance Indicators (KPIs) such as Energy-per-bit (Ebit), Energy-per-inference (Einf), Lifecycle Assessment (LCA) and carbon intensity metrics. Third, a sustainability-aware threat prioritization matrix and energy–security trade-off model is devised considering Internet of Things (IoT) design as a multi-objective optimization problem that minimizes energy consumption, carbon emission, and cyber risk. The analysis pinpoints some key gaps in lifecycle quantification and real-world validation. Finally, we propose phased sustainability–security roadmaps according to feasibility and readiness for deployment, thereby bringing IoT research closer towards composite systems by design with analytical foundations.
Topic: Emerging Technologies in the Internet of Things (IoT)
Autonomous Communication Networks Powered by Quantum Variational Circuits for Real-Time Traffic Prediction and Resource Optimization
The wide development of the next-generation communication networks needs intelligent systems capable of processing dynamism in the traffic pattern, rigorous requirement of latency and adaptation to the changing security threats. In this paper, the autonomous communication architecture that is suggested is the application of quantum variational circuits combined with classical learning to generate real time traffic forecasting as well as adaptable compilation of the resources. The proposed solution will be an application of hybrid quantum-classical optimization, where variational circuit simulations to calculate complex network states will be more efficient to do so, enabling quicker attraction of growing congestion and optimal pathfinding. It is built as a multistage pipeline that is a mix of quantum enhanced feature encoding, probabilistic prediction and reinforcement-based allocation policies. Hardware noise, qubit availability, and scaling issues bring suffering to the implementation process since the scaling of large network graphs to quantum structures is challenging. To solve them, a variational architecture is optimized by sharing the parameters alongside quantum-inspired reduction protocol is developed in a way that circuit depth and computation overhead are lessened. The merits of this research lie in the fact that it incorporates an autonomous decision and quantum optimization which allows the network to become self-monitoring under the conditions of random traffic. It can be attributed to the fact that they demonstrate that variational quantum circuits can outperform significantly in capturing nonlinear dependence in traffic, as compared to classical predictors, thereby giving more accurate prediction and energy-aware routing of complex communication systems.
Tri-Band Patch Array Antenna Design for 5G Medical Data Communication in Sub-6 GHz and mmWave Bands
This paper presents the design and validation of a tri-band patch array antenna intended for 5G-based medical data communication. The proposed antenna operates efficiently across 0.7 GHz, 2.6 GHz (sub-6 GHz), and 26 GHz (mmWave), aligning with the 5G spectrum allocations used in Thailand. The antenna’s T–F shaped single-patch structure is optimized to achieve impedance matching, high gain, and radiation efficiency across all three bands. Simulations in CST Microwave Studio were performed to evaluate |S11|, gain, and efficiency across different array configurations (2×1, 2×2, 4×1, and 4×2). Based on these results, the 2×2 array was selected for prototype fabrication and experimental testing. The fabricated prototype was characterized using a Vector Network Analyzer and radiation pattern measurements in a controlled indoor setup. Furthermore, an over-the-air communication test using USRP software-defined radios was conducted to measure Packet Error Rate (PER) and Signal-to-Interference-plus-Noise Ratio (SINR) at different distances. Results demonstrated that the 2×2 array significantly outperformed the single-element antenna, with lower PER and higher SINR at both 0.7 and 2.6 GHz. The proposed antenna, designed for non-implantable, device-level integration, is well suited for applications such as remote patient monitoring and medical IoT. Its tri-band capability ensures reliable connectivity across multiple 5G layers, while its compact design enables integration into portable medical devices. Future work will extend to hybrid beamforming and full 5G link validation including BER and latency analysis.
Topic: Emerging Applications and Challenges of mmWave Communications
Antenna array, 5G, Microstrip antenna, medical communication, Tri-band
Integration of Named Data Networking (NDN) in 5G Architecture for Handover Optimization and QoS Enhancement
The rapid increase in mobile data traffic within fifth-generation (5G) networks has heightened the demand for efficient handover (HO) management and data delivery mechanisms. An integrated 5G–Named Data Networking (NDN) architecture is proposed to enhance network reliability and user experience during mobility. This design features an NDN overlay on the 5G user plane, deploying caching functions at the Multi-access Edge Computing (MEC) layer and core routers to facilitate localized content retrieval. A Python-based simulation evaluated three network configurations: baseline 5G, edge caching, and full NDN deployment. Performance metrics analyzed include Handover Success Rate (HSR), Fallback Efficiency, Cache Hit Ratio (CHR), and Quality of Service (QoS) indicators such as latency, throughput, and packet loss. Results indicate that the NDN-integrated 5G architecture substantially improves HO performance and content delivery efficiency. The full NDN deployment scenario yielded up to a 35% reduction in latency, a 40% increase in throughput, and 36.8% fallback efficiency compared to the baseline. Additionally, caching at both edge and core layers increased the average HSR by 25–40%, resulting in a higher combined Quality of Experience (QoE) score. In summary, integrating NDN within the 5G framework offers a scalable and resilient solution to support seamless mobility and enhanced service continuity in future mobile networks.
Joint Trajectory and Power Optimization for UAV-Relay: A Constraint Handling Approach with the Bat Algorithm
Unmanned Aerial Vehicles (UAVs) have emerged as flexible relay platforms capable of enhancing wireless connectivity in beyond-5G and 6G networks. This paper investigates the joint optimization of UAV trajectory and power allocation to maximize end-to-end throughput under practical mobility and power constraints. The problem is highly non-convex due to the strong coupling between trajectory variables and transmission power. To address this challenge, we develop a penalty-based metaheuristic framework that incorporates a constraint-handling mechanism into the Bat Algorithm (BAT). Simulation results show that the proposed BAT-based approach achieves significant throughput improvement, efficient power allocation, and fast convergence compared with baseline convex optimization and heuristic schemes. These findings highlight the potential of BAT for reliable and energy-efficient UAV-assisted communication in future wireless networks.
Topic: Communication and Networking Technologies for UAVs
UAV communications, Bat Algorithm, trajectory optimization, power allocation, metaheuristics.
Dynamic Threat Prevention in Cloud Firewalls Using Hybrid Machine Learning for IP Reputation Intelligence
The current cloud security solutions face serious issues related to the control of the changed cyber threats, thus the need to increase Internet Protocol Reputation (IPR) validation and enhanced threat detection systems. Traditional IPR validation strategies prospectively look at the concept of fixed blacklists and rule-based detection techniques, which produces a huge number of false positives and cannot distinguish between legitimate and malicious traffic. The suggested paper will present a new hybrid machine-learning system in inference of IPR in Amazon Web Services (AWS) cloud infrastructure, which will help to enhance automated defence and threat intelligence. The framework suggested consumes both of the following types of logs: GuardDuty logs along with Security Operations Centre (SOC) and Web Application Firewall (WAF) security logs to make it possible to detect threats in real time. A machine learning model that uses a combination of Random Forest (RF), Support Vector Machine (SVM), and Logistic Regression (LR) to classify IP threat information is used to generate blocking rules automatically in AWS WAF. The architecture combines both adaptive management of IP-lists and real-time blacklisting of IP-lists to support constant threat learning during the evolution of threats. F1 -scores of greater than 0.97 in benign traffic classification and greater than 0.99 in attacker detection in various AWS deployment areas are experimentally verified.
Topic: Next Generation Network: Challenge and Future
Hybrid Machine Learning; IP Reputation Validation; Cloud Web Application Firewall; Security Operations Centre Integration; Dynamic Threat Detection; Automated Defence Systems
Securing IoT Networks from DDoS Attacks using Redundant IP Pool Strategy
The security of Machine-to-Machine (M2M) communication in the Internet of Things (IoT) is the primary focus of this research article. Despite the increasing reliance on IoT as a cornerstone for future technological advancements, M2M devices face significant challenges, such as processing and storage limitations, which make them vulnerable to security breaches and various attacks, including Distributed Denial of Service (DDoS) attacks. This paper introduces an innovative security measure against DDoS, called the Redundant IP Pool Strategy (RIPPS). RIPPS utilizes temporary IP pools during an attack to thwart unauthorized access by limiting the time available for infiltration. We conducted a simulation experiment using the OMNeT++ simulator to examine the behavior of IoT networks under DDoS attacks. Our study focuses on traffic management in typical scenarios, including Human-to-Human traffic (e.g., Voice over IP and File Transfer Protocol traffic) and M2M traffic. The simulation results demonstrate that implementing RIPPS significantly enhances network stability by reducing packet loss and latency across all traffic scenarios. The RIPPS effectively bolsters the security and resilience of IoT environments against DDoS attacks.
Enhancing QoS in Dense IEEE 802.11ax Networks using a Dynamic Airtime-Based Soft Admission Control Mechanism
Even with the advanced capabilities of IEEE 802.11ax (Wi-Fi 6), dense Wireless Local Area Networks (WLANs) often struggle to meet the strict latency demands of modern applications. High-efficiency features like OFDMA and Target Wake Time (TWT) improve spectral efficiency, yet they cannot prevent network saturation when traffic demand simply outstrips the available airtime. To address this, we introduce an Airtime-Based Soft Call Admission Control (AS-CAC) framework designed specifically for saturated, multi-access point environments. Instead of relying on hard connection limits, our approach regulates access based on the actual airtime required by each flow. A key strategy is the “Soft-CAC” mechanism, which moves beyond static thresholds. It strictly prioritizes real-time streams like VoIP and Video but remains flexible enough to admit best-effort bursty traffic whenever spare capacity exists, maximizing channel utilization without breaking QoS guarantees. We further enhance this with AS-CAC+, an adaptive variant that dynamically adjusts thresholds based on network health. We validated this framework using extensive ns-3 simulations, backed by a theoretical multi-rate Erlang loss model. Results from dense deployment scenarios including those with Co-Channel and Adjacent Channel Interference show that AS-CAC stabilizes throughput and keeps end-to-end VoIP latency under 2 ms, a dramatic improvement over the 45+ ms delays seen in uncontrolled networks. AS-CAC+ achieves 19.2% throughput improvement and 97.4% channel utilization while maintaining strict QoS. These findings offer a practical, scalable strategy for next-generation enterprise WLANs.
Neural Adaptive Bitrate Streaming for Video Delivery over Named Data Networking: A Comparative Evaluation
The surge of online video traffic, accounting for more than 80% of global internet usage, has underscored the importance of efficient adaptive bitrate (ABR) strategies. While state-of-the-art ABR methods such as Pensieve and BOLA have been extensively studied in IP-based environments, their applicability to future internet architectures like Named Data Networking (NDN) remains largely unexplored. This paper presents one of the first comparative evaluations of neural-based ABR in an NDN video streaming framework. Five ABR schemes—Buffer-Based, Rate-Based, Simple, Hybrid, and Neural—were implemented in a customized Shaka Player integrated with the NDNts library and tested on an NDN-enabled testbed. The evaluation considered key Quality of Experience (QoE) metrics, including startup latency, buffering duration, dropped frames, and effective bitrate. Results demonstrate that the proposed Neural ABR achieves the lowest startup latency (24 ms at 2–3 Mbps and 107 ms at 5 Mbps), with zero buffering and no frame drops, outperforming all baseline methods. These findings highlight the potential of lightweight neural models to improve playback stability and QoE in NDN environments, offering a practical pathway for next-generation content-centric video delivery. To the best of our knowledge, this work represents one of the first implementations of neural ABR in NDN-based streaming, bridging the gap between IP-centric adaptation models and future content-centric networking.
Design and Implementation of a Programmable High-Capacity Multi-User Network Testbed for SOI Asia Research and Education Partners
Research and Education Networks (RENs) increasingly require shared, high-performance platforms to support collaborative experimentation in areas such as 5G/6G, Internet of Things (IoT), and vehicular communications. This paper presents the design and implementation of a programmable 100 Gbps multi-user testbed that enables concurrent experiments at near line-rate performance. The testbed integrates P4-programmable switches, a software-defined networking (SDN) controller, and an orchestration module that automates deployment, ensures repeatability, and provides secure remote access through VPN tunnels. Multi-user functionality is provided by a reservation and scheduling framework that guarantees resource isolation and fairness. To support the SOI Asia initiative, the testbed is accessible through existing REN infrastructure, enabling SOI Asia university partners to conduct experiments without incurring the cost of local high-capacity hardware. Experiment results demonstrate that the platform achieves throughputs of 95–100 Gbps, microsecond-scale latency, and stable operation under concurrent workloads. Compared to global and regional testbeds, the proposed system offers a unique combination of carrier-grade performance, programmability, and slice-based access tailored to the Asia-Pacific research needs.<span lang="EN-US" style="font-size: 10.0pt; mso-bidi-font-size: 9.0pt; font-family: Symbol; mso-ascii-font-family: 'Times New Roman'; mso-fareast-font-family: 'MS Mincho'; mso-hansi-font-family: 'Times New Roman'; mso-bidi-font-family: 'Times New Roman'; mso-ansi-language: EN-US; mso-fareast-language: EN-US; mso-bidi-language: AR-SA; mso-char-type: symbol; mso-symbol-font-family: Symbol; mso-bidi-font-weight: bold;">
Long-Term Production-Scale Empirical Characterization of SRT Transport Metrics for Reinforcement Learning-Based Adaptive Streaming Control
To the best of our knowledge, this study presents the first long-term, production-scale empirical characterization of Secure Reliable Transport (SRT) protocol metrics designed to inform reinforcement learning (RL)-based adaptive controller design for low-latency live video streaming. Production environment data spanning 722 hours (approximately 30 days) with 64,340 aligned multivariate samples were collected and analyzed using statistical methods and machine learning-based anomaly detection. Key findings include: (1) strong positive correlation between average round-trip time (RTT) and jitter (r = 0.912, 95% CI: 0.909–0.915), indicating co-varying latency dynamics; (2) Isolation Forest detected 4.8 times more anomalies than the Interquartile Range (IQR) method (6,425 vs. 1,339), demonstrating superior multivariate pattern recognition; (3) three distinct anomaly regimes were identified (congestion-driven, timing-driven, and bandwidth-limited); (4) a data-driven three-level operational monitoring system (Green/Yellow/Red) was derived for five SRT metrics; and (5) proof-of-concept RL validation demonstrated that derived thresholds translate into effective reward signals, with rule-based (+82%) and Q-learning (+54%) controllers outperforming static baselines. These findings establish empirical foundations for future RL-based SRT controllers, bridging the gap between network measurement research and adaptive streaming system development.
SRT protocol, reinforcement learning, anomaly detection, low-latency video streaming, multivariate analysis, Isolation Forest, adaptive bitrate control, operational thresholds
Design and optimization of Array Patch Antenna for 6G and Biomedical Applications
To improve the performance of conventional patch antennas, this paper presents the use of array strategy instead of single element. The proposed design is a 1×4 array antenna with four T-shaped radiating elements. The array resonates at 3.77 THz and designed on a polyimide substrate with a thickness of 8µm and dimensions of 100×90 µm². The comparison between the single-patch and array configurations results, demonstrates the advantages of the array structure, in terms of radiation characteristics and impedance matching. The suggested array achieved excellent performance, with an S₁₁ of –44.14 dB, a VSWR of 1.02, a gain of 5.65 dB, and a directivity of 6.74 dB, making it suitable for both biomedical and 6G communication applications. All results were obtained using HFSS software.
Improved Intrusion Detection System Using a Modified African Vultures Algorithm
The evolution of cyber threats has imposed the need for robust Intrusion Detection Systems (IDSs) that are capable of detecting intrusions with high accuracy. This paper proposes an IDS based on Machine Learning (ML), which uses a modified African Vultures Optimization Algorithm (AVOA) for feature selection (FS). The AVOA was enhanced using a crossover function. The crossover was implemented to enhance the algorithm's ability to explore the FS solution space. The proposed model was further evaluated on the UNSW-NB15 dataset with binary classification. Binary classification showed an excellent performance using the XGBoost and HistGradientBoosting classifiers, with an accuracy and a precision of 99.77%. These results further indicate the efficiency of using AVOA for intrusion detection. This study depicts the potential of ML in the development of accurate and robust IDS solutions, thus setting a benchmark for future research in cybersecurity. Manuscript received xx xx 2023; revised xx xx 2023, accepted xx xx 2023.
Machine Learning (ML), Feature Selection (FS), Intrusion Detection System (IDS), African Vultures Algorithm (AVOA).
IoT-Enabled Wireless Communication Monitoring and Fault Diagnosis for Transformer Oil Condition Using Machine Learning
Distribution transformers play a critical role in ensuring uninterrupted power supply, yet their performance is often compromised by undetected internal faults and oil degradation. This paper presents the development and validation of an intelligent, low-cost, and real-time monitoring and fault diagnosis system for distribution transformers by integrating Internet of Things (IoT) technologies with Artificial Intelligence (AI)-based classification algorithms. The system employs wireless sensors to continuously monitor key operational parameters, including temperature, light intensity, and oil level, with data transmitted via ZigBee modules to a central LabVIEW interface and secure cloud platform for real-time visualization, control, and remote access. To ensure reliable communication, the proposed framework incorporates robust data transmission protocols and minimal latency for continuous monitoring. For intelligent fault classification, Decision Tree (DT) and Multi-Layer Perceptron (MLP) classifiers were implemented. Experimental results demonstrate that the DT model achieved a training accuracy of 95.9% and testing accuracy of 94.5%, outperforming MLP with 94.9% and 90.9%, respectively. The DT classifier also yielded superior F1-score (0.931) and specificity (0.954) across multiple transformer oil condition classes. These results confirm the effectiveness of the proposed hybrid IoT-AI solution in reliably detecting and classifying oil condition anomalies, enabling timely maintenance actions and improving transformer lifespan and operational safety. The system offers a scalable and cost-effective alternative to conventional SCADA and PLC-based monitoring, with potential for integration into predictive maintenance frameworks for smart grid applications.
Privacy-Preserving Software Engineering Protocol for V2V Charging Using Lattice-Based Linkable Ring Signature
Driven by the proliferation of electric vehicles (EVs), decentralized vehicle-to-vehicle (V2V) energy trading is proposed as a flexible and scalable approach to energy sharing. However, the openness of V2V networks and the transparent property of blockchain records have posed great threats to user privacy, transaction unlinkability, as well as quantum-resistance. In this paper, we design a privacy-preserving V2V charging scheme by using the lattice-based linkable ring signature to provide vehicle-user identities anonymity and protect user’s privacy as well as that of data managers and platform services providers based on the complexity of the Ring-SIS problem for potential threats in post-quantum era. Our scheme uses a Certificateless key infrastructure that consists of Key Generation Center (KGC) along with the existence of Certification Authority (CA) to avoid the problem of key escrow and scaleable identity management. Moreover, to enhance recipient anonymity, we include a lattice-based stealth address construction protocol that allows one-time unlinkable payment addresses to be used for each transaction. The protocol works in the setting of a consortium blockchain, relying on Practical Byzantine Fault Tolerance (PBFT) to achieve a high throughput and the ability to verify transactions. We provide a thorough security analysis under forgery, replay and quantum attacks showing that they are secure and can be used in regulated settings to trace the transactions. Performance evaluations demonstrate that our proposed scheme has low computation overhead, slim communication consumption, and high efficiency over the existing elliptic curve and pairing-based schemes; therefore, it is applicable for EV constrained environments as well as future V2V energy trading systems
A Post-Quantum Certificateless Aggregate Signature Scheme for VANETs Resilient to Rogue-Key Attacks
Vehicular Ad Hoc Networks (VANETs) must have lightweight, scalable and secure authentication mechanisms for trust between vehicles that are constantly in motion. In (CLAS) schemes, pseud a Certificateless Aggregate Signature efficient message authentication without management of certification but residue although the technology has many advantages However, for most current CLAS schemes, the user still needs to conduct is absorbed into cryptographic keys. This makes them vulnerable to quantum attacks even at low velocities, and are subject to ephemeral forgery by rogue keys. In this paper, we present a new certificateless aggregate signature scheme designed to withstand quantum attacks and greatly enhance security against rogue key attacks. The scheme is based on the Ring Learning With Errors (RLWE) assumption. Apart form being computationally efficient, our scheme introduces a dual-verification mechanism by adding an aggregator signature. This enables the identification of forgeries while the aggregates can still be computed with minimum computational and communication costs. We have performed a detailed security analysis showing that our scheme resists impersonation, replay, man-in-the-middle, and malicious KGC attacks. Experimental results show that when compared with existing pairing-free schemes, our design has lower computational overhead (requires fewer calculations of pairing operations per signature) and communication costs are competitive. The scheme envisages an optimistic future for secure and high-effective message authentication within VANET networks, which are being designed specifically to handle the coming onslaught of quantum technology
Mohanad Ahmed Abdulrazzaq Diwan Alzamili
RouteNet-TGNN- A Temporal Graph Neural Network for Delay and Loss Forecasting in Dynamic Communication Networks
The continuous evolution of modern communication networks—driven by technologies such as 5G, vehicular systems, and software-defined infrastructures—demands predictive intelligence capable of adapting to rapidly changing topologies and traffic conditions. Conventional graph-based approaches, including RouteNet, provide accurate predictions in static scenarios but lack the ability to model temporal dependencies inherent in dynamic environments. This work presents RouteNet-TGNN, a Temporal Graph Neural Network that augments the RouteNet framework with Gated Recurrent Units (GRUs) to capture both spatial correlations and temporal dynamics across sequential network states. To ensure comprehensive evaluation, synthetic datasets were generated to emulate realistic operating conditions, incorporating diverse topologies, fluctuating traffic demands, routing reconfigurations, and link disruptions. Experimental analysis demonstrates that RouteNet-TGNN significantly outperforms static GNN baselines, yielding reductions of 27.5% in delay prediction error, 21.3% in jitter estimation error, and 18.9% in packet loss forecasting error. The framework is effective for short-term forecasting horizons (t+1 to t+3), enabling proactive Quality of Service (QoS) management and early anomaly detection. The findings validate the potential of temporal GNN architectures for real-time predictive analytics in software-defined, edge-enabled, and mobility-centric networks, providing a foundation for intelligent, adaptive, and self-optimizing communication infrastructures.
Metamaterial-Inspired Decoupling of MIMO Antennas for Wireless Systems
In this paper, we delve into an innovative technique aimed at diminishing the mutual interference technically termed 'mutual coupling' between two Microstrip Patch Antenna (MPA) elements. These elements, operating in tandem at the significant frequency of 5.8 GHz, are positioned in close proximity, merely 7 mm apart. The core of our investigation revolves around the strategic placement of various metamaterial structures between these two adjacent MIMO (Multiple Input Multiple Output) elements. The study reveals a striking observation: without the incorporation of metamaterial, the mutual coupling coefficient, denoted as |S21|, stands at -21.7744 dB. However, a transformative shift occurs with the introduction of a specific metamaterial composed of five Circular Complementary Split Ring Resonator (CSRR) unit cells. This particular arrangement results in a dramatic enhancement, plummeting the |S21| value from its initial -21.7744 dB to an astounding -43.3581 dB. This significant reduction showcases the effectiveness of our approach. Further examination of the proposed MIMO antenna, integrated with the Circular CSRR metamaterial, unveils a suite of impressive characteristics. Notably, it boasts a reflection coefficient (|S11|) maintained below -10 dB, a noteworthy feat in antenna design. Moreover, the antenna demonstrates a superbly high value of diversity gain, quantified at 9.99999, alongside an exceptionally low envelope correlation coefficient (ECC), recorded at a minuscule 0.000001. These attributes collectively underscore the antenna's exceptional performance capabilities. It becomes evident that the proposed antenna, enhanced with the Circular CSRR metamaterial, emerges as a highly promising candidate for application within MIMO antenna systems. Its ability to significantly reduce mutual coupling, coupled with its remarkable performance metrics, positions it as a pivotal development in the realm of antenna technology, particularly for applications demanding high efficiency and minimal interference.
Intelligent Multi-Service Caching-based Load Balancing Scheme in Multi-RAT Vehicle-to-Infrastructure Communication
Vehicular-to-Everything (V2X) communication which allows vehicles to exchange data with each other and with Base Stations (BSs) is critical for autonomous driving. However, increasing vehicle density causes congestion in the Radio Access Network (RAN). To address this, a Multi-Radio Access Technology (Multi-RAT) network is proposed, offering different services and data rates to improve network throughput, Packet Delivery Ratio (PDR), and reduce latency. This introduces a new challenge: Load Balancing (LB) across RATs, as some may become overloaded while others remain underutilized. Existing LB methods, based on Received Signal Strength Indicator (RSSI) and Time-To-Leave (TTL), fall short in dynamic environments with varying vehicle speeds and service demands. This paper proposes two LB models: Multi-Service Caching-based Load Balancing (MSCLB) and Intelligent Multi-Service Caching-based Load Balancing (IMSCLB). The IMSCLB combines service-aware caching with Long Short-Term Memory (LSTM) deep learning to predict RAT load and optimize distribution. Simulations involving three service types (Safety, Traffic, and Information) and three RATs (IEEE 802.11n, 802.11ac, and 802.11ad) demonstrate that MSCLB significantly outperforms RSSI- and TTL-based methods in throughput, PDR, and latency. With 802.11ac, MSCLB achieves a 10 Mbps increase in throughput, a 10% improvement in PDR, and up to 6% lower latency. IMSCLB further improves performance, achieving satisfaction rates of 99.8%, 99.9%, and 99.9% for IEEE 802.11n, 802.11ac, and 802.11ad, respectively.
Caching, Load Balancing, multi-RAT, V2X, multi-service
EWPO-SA-Net: A Hybrid Optimized Attention Network for Intrusion Detection in Smart City IoT Applications
The Rapid evolution of Internet of Things (IoT) devices in advanced urban environments has transformed traditional cities into intelligent ecosystems, which is typically named as smart cities. These interlinked structures improve urban services as well as citizens' lifetime through smart transportation, public safety solutions, environmental monitoring, as well as energy management. Nevertheless, the incorporation of several IoT devices also increases important security concerns, since they enable potential attack vectors for malicious entities. Therefore, robust Intrusion Detection Systems (IDS) are important to protect sensitive data and ensure the integrity and availability of smart city applications.To mitigate these shortcomings, intrusion detection in IoT-based smart city applications develops Exponentially Weighted Parrot Optimizer_Shuffle Attention Network (EWPO_SA-Net). An input data is subjected for data normalization that is done by employing linear normalization. Furthermore, the features are selected by utilizing Mahalanobis distance.The intrusions are finally detected through Shuffle Attention Network (SA-Net) that is trained by EWPO. EWPO_SA-Net obtains superior efficiency by analyzing analytics measures like accuracy and sensitivity of 86.922% and 87.392%.
Hybrid Deep Reinforcement Learning and Grey Wolf Optimizer for Interference-Aware Beamforming in Wireless Networks
Millimeter-wave and terahertz systems with large antenna arrays require adaptive interference-aware beamforming under severe hardware and channel constraints. Conventional digital multiple-input multiple-output or codebook-based analog schemes either rely on accurate channel state information or remain interference-unaware, leading to performance degradation in dense deployments. To overcome this limitation, a hybrid framework combining deep reinforcement learning and the grey wolf optimizer is introduced. Deep reinforcement learning agents interact with the wireless environment using only power measurements, progressively improving beamforming actions under constant-modulus and quantized-phase constraints without channel estimation or coordination. The pretrained deep reinforcement learning policy provides high-quality initialization, while the grey wolf optimizer performs metaheuristic refinement with a tailored fitness function explicitly defined in terms of signal-to-interference-plus-noise ratio maximization. Numerical results demonstrate clear advantages: fast convergence from negative signal-to-interference-plus-noise ratio to stable values around 18 dB during deep reinforcement learning training, 2–3 dB superiority of the hybrid deep reinforcement learning and grey wolf optimizer approach over the grey wolf optimizer alone, and significant nullshaping gains in the presence of multiple interferers. Further evaluation highlights robustness across phase-shifter resolutions, achieving near-optimal 30 dB signal-to-interferenceplus-noise ratio at 4–5 bits, and real-time feasibility with convergence below 500 ms. These outcomes confirm the hybrid deep reinforcement learning and grey wolf optimizer approach as a scalable and channel-state-informationfree solution for interference suppression in next-generation millimeter-wave and terahertz networks.
Optimizing the Energy Detection based spectrum sensing Through Bernstein Polynomial Approximation and deep learning
In the context of energy-based spectrum sensing, effective noise reduction is paramount to enhance the signal-to-noise ratio (SNR) and improve the probability of detection, particularly in cognitive radio networks. This paper presents a novel approach that synergistically combines Fractional Bernstein Approximation, Fast Fourier Transform (FFT), and Convolutional Neural Networks (CNN) to robustly detect signals under noisy conditions. The Fractional Bernstein Approximation is employed as a pre-processing step to smooth the signal and mitigate noise effects. FFT is then utilized to transform the signal into the frequency domain, where CNN is applied to extract features that differentiate between signal and noise. Performance metrics such as SNR improvement, probability of detection, and computational efficiency are analysed under different noise scenarios. Our results demonstrate that the proposed method significantly outperforms other approaches, particularly in low SNR environments, offering a robust and scalable solution for spectrum sensing. The suggested approach is compared in this work against a number of other methods, including CNN with windowing segmentation for QPSK and 8PSK modulations and cyclostationary features. The combination of CNN features and QPSK signals with the Bernstein polynomial approximation maintains a higher Pd (~96%) at lower SNRs (-10dB) than cyclostationary features.
Resilient UAV Swarm-to-Ground Communication via Relay-Driven DSR Protocol
The operational effectiveness of autonomous drone swarms is critically dependent on resilient communication, particularly for the transmission of high-bandwidth, quality-sensitive data from a leader drone to a ground station in infrastructure-less environments. This study addresses the limitations of standard routing protocols in such scenarios by proposing and evaluating a novel, relay-driven communication strategy for Flying Ad-Hoc Networks (FANETs). The purpose of this research is to enhance the reliability of Swarm-to-Ground (S2G) data links through a quality-driven optimization approach. The methodology involves augmenting the Dynamic Source Routing (DSR) protocol with an intelligent, multi-objective relay selection algorithm. Our methodology employs a quality-aware A* search algorithm, guided by a novel cost function that integrates predicted data fidelity with path distance. This allows the system to proactively discover a communication route that offers the optimal balance between network efficiency and the integrity of the mission-critical data payload. The system's performance was validated in the Webots simulation platform using a swarm of Crazyflie 2.0 (CF2) nano-quadcopter models within a challenging 80m x 80m operational area featuring obstacles. The principal results of the simulation demonstrate that the framework successfully leverages follower drones as a dynamic relay infrastructure, maintaining a high-fidelity data link even when the leader is beyond direct communication range. The major conclusion is that this quality-aware routing strategy significantly enhances communication resilience and data integrity compared to standard, reactive protocols, enabling the robust transmission of diverse sensor data and facilitating more complex, long-range swarm missions.
HHybridization of Deep Learning Models for Multiclass Attack Detection in Wireless Sensor Networksybridization of Deep Learning Models for Multiclass Attack Detection in Wireless Sensor Networks
— Flooding, blackhole, and forwarding are Wireless Sensor Networks (WSNs) attack types that have a severe impact on the reliability of the network and the integrity of data. This paper reports on a detailed benchmarking and hybridization analysis of seven deep learning models regarding multiclass intrusion detection on the WSNBFSF dataset. Comparison is made of the Artificial Neural Network (ANN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and their mixtures CNN-RNN, CNN-LSTM, and RNN-LSTM. The same experimental conditions were used in training all the models to make the evaluation fair. The experimental findings are indicative of the fact that the ANN has the highest performance with accuracy, precision, recall, and F1-score of 99.81 percent, offering close to perfect discrimination of all attack types. The LSTM (98.27%) and RNN-LSTM (97.78%) memory augmented models also demonstrate a high ability to capture traffic time dependencies in WSNs, whereas the CNN-based models are efficient in spatial pattern extraction but slightly worse on fine-grained attack detection. Conversely, a standalone RNN has problems of vanishing gradients and thus gives an accuracy of 84.77 percent. In summary, the results provided serve as a pointer to architecture-specific strengths, and they provide important information in designing a scalable, real-time, and resource-efficient deep learning-based intrusion detection system with the peculiarities of WSN environments.
Wireless Sensor Networks; Deep Learning; Flooding Attack; Blackhole Attack; Forwarding Attack; WSNBFSF dataset
Design and Evaluation of Self-Adaptive 6G Quantum-Enhanced 3D Networks for Surface and Subsurface Connectivity on Mars
As humanity moves toward establishing a sustained presence on Mars, the demand for reliable, high-capacity communication networks to support both surface and subsurface operations becomes increasingly critical. This paper presents a self-adaptive communication architecture based on a 6G-enabled network that integrates quantum-enhanced links to facilitate seamless connectivity across Martian environments. The proposed system incorporates subsurface nodes, including rovers and sensors, aerial relays via UAVs, and orbital CubeSats, interconnected through a hybrid combination of quantum entanglement-based channels and classical 6G protocols. To mitigate the challenges posed by Martian environmental conditions, such as dust storms, terrain variability, and power constraints, the network employs machine learning (ML)-driven adaptation for real-time network topology optimization and resource allocation. By leveraging the low-latency and high-throughput capabilities of 6Galongside quantum-secured links, the proposed architecture ensures stable and resilient data exchange for scientific exploration, rover control, and human operations. The system's performance is evaluated using a hybrid simulation approach, combining Python-based theoretical modeling with ns-3 network evaluation. The ns-3 simulations analyze end-to-end latency, throughput, packet loss, and adaptive network responses under varying environmental conditions. Results indicate that latency remains below 50 ms, with packet loss reduced to less than 1% through ML-based optimizations. Furthermore, quantum entanglement fidelity exceeds 85%, ensuring secure communication even under adverse conditions. The system also demonstrates a 42% reduction in energy consumption, enhancing overall communication efficiency and stability. This study provides a scalable and adaptable framework for extraterrestrial communication, bridging terrestrial networking advancements with the unique constraints of deep-space exploration. The findings contribute to the development of robust, high-performance Martian networks, offering essential infrastructure for future robotic and human missions.
6G, quantum communication, 3D networks, Martian subsurface, self-adaptive networks, space exploration.
Energy-Efficient and Fair Computation Offloading for Multi-User MEC with EH Devices
The increasing demand for low-latency and energy-efficient mobile applications has propelled the development of Mobile Edge Computing (MEC), enabling the offloading of computational activities from resource-constrained mobile devices (MDs) to nearby edge servers. This study examines a joint problem of computation offloading and resource allocation issue in a wireless multi-user, multi-server MEC system with energy harvesting (EH) capabilities. Our objective is to reduce long-term energy consumption while adhering to limitations related to latency, energy causality, server capacity, and signal-to-interference-plus-noise ratio (SINR). To address the complexities of system dynamics and uncertainty in energy arrivals, we propose a low-complexity online approach utilizing Lyapunov optimization. The proposed method dynamically modifies offloading ratios, transmission power, CPU frequencies, and server allocations without requiring future data. The simulation results show that our method achieves significant energy savings, has low delays, and ensures fairness among users, even in highly congested scenarios. A comparative analysis with benchmark algorithms validates the efficacy and resilience of the proposed framework in real MEC situations.
Copyright (c) 2025 Journal of Communications
Analysis of Communication Protocols in Electrical Substations: Trends, Standards, and Simulation Tools with an emphasis on IEC 61850
Technological evolution in the field of electrical substation automation has driven the development of communication protocols aimed at the monitoring, control, and efficient operation of devices in critical environments. In these contexts, the need to ensure interoperability between equipment from different manufacturers and to reduce dependence on proprietary technologies has encouraged the adoption of open standards that promote greater system reliability, operational safety, and implementation cost efficiency. This article presents an analysis of the main research trends around communication protocols applied to electrical substation automation. Through an approach that combines bibliometric tools and a scoping review, the following protocols are examined: DNP3, Modbus, IEC 60870, and, notably, the IEC 61850 standard proposed by the International Electrotechnical Commission (IEC). The latter, with its object-oriented architecture and support for protocols such as Generic Object-Oriented Substation Events (GOOSE), Sampled Values (SV), and Manufacturing Message Specification (MMS), has significantly transformed the way Intelligent Electronic Devices (IEDs) communicate in modern electrical systems. The results provide an updated overview of technological developments in this area, highlighting the benefits of standardization as well as its current challenges. Finally, the advantages and disadvantages of digitalization in substations, research trends in communication protocols, and the relevance of simulators as key tools for the training, design, and validation of solutions based on the IEC 61850 standard are discussed.
High-performance multiband THz MIMO antenna for future 6G wireless Communications with Machine Learning validation
This study presents a comprehensive industrial and innovation design along with an in-depth analysis of a THz Multiple Input Multiple Output (MIMO) antenna intended for future 6G communication systems. The antenna utilizes polyimide as the substrate and graphene as the patch material, with copper serving as the ground plane. This design enables the antenna to operate across six distinct frequency bands, making it a multiband antenna. The resonance frequencies of the antenna are 3.512 THz, 4.4448 THz, 6.704 THz, 7.672 THz, 8.664 THz, and 9.672 THz, with corresponding gains of 11.72 dB, 12.59 dB, 13.077 dB, 13.945 dB, 14.77 dB, and 16.028 dB, respectively. To further understand the electrical behavior of the antenna, an RLC equivalent circuit was developed using Advanced Design System (ADS) software. Subsequently, we employed supervised regression machine learning techniques following extensive data sampling using CST MWS (Microwave Studio) simulations. The results, highlighted by robust R-squared and variance scores, demonstrate that Extra Trees Regression delivers exceptional accuracy, approaching 98%. Additionally, it achieves the lowest error in predicting resonance frequency.
Quantum Resistant Digital Signatures Constructed from A Nonstandard Discrete Logarithm Problem Over Finite Fields
The rise of quantum computing is putting tremendous pressure on existing cryptographic systems, particularly digital signature schemes based on RSA and Elliptic Curve Cryptography. Quantum algorithms such as Shor and Grover have demonstrated the ability to severely weaken traditional security assumptions, creating an urgent need to develop new quantum-resistant digital signature schemes. Rather than relying on standard approaches such as lattice-based or multivariate-based cryptography, this paper explores an alternative direction: leveraging a new nonlinear exponentiation problem defined over finite fields, whose mathematical structure is designed to render Shor's algorithm inapplicable and to reduce the effectiveness of Grover's algorithm. Based on this newly proposed hard problem, we introduce multiple digital signature schemes, each with a distinct structure in its signing and verification algorithms. Although these schemes differ in operational mechanisms, they all maintain correctness, remain secure against classical attacks, offer strong quantum resistance, and are fully compatible with existing Public Key Infrastructure systems. Through both theoretical analysis and performance evaluation, we demonstrate that diversifying digital signature constructions from a single underlying hard problem is not only feasible but also offers practical advantages: it allows selecting a design best suited for specific application environments while maintaining post-quantum security. This result opens a promising new path toward the development of flexible, efficient, and long-term secure digital signature schemes for the post-quantum era.