Journal of Communications is a scholarly peer-reviewed international scientific journal published bimonthly, focusing on theories, systems, methods, algorithms and applications in communications.
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
Tracing of Cyber Intrusion Detection and Identification Approach Using Fine-Tuned SVM-RF Model in Wireless IoT-23 Networks
Many crucial domains have already begun integrating the Internet of Things (IoT) to better serve people and make their lives easier. The Internet of Things revolution has reshaped digital services across industries by increasing efficacy, productivity, and cost-effectiveness. The security of IoT devices is still a matter of concern despite many service providers having incorporated or adapted them into core portions of their systems' operation. Machine learning-based anomaly detection has the potential to design an efficient protection system against various IoT cyberattacks, hence reducing the risk to IoT networks. Although many detection methods have been developed, current methods only deal with a subset of cyberattacks and evaluate their efficacy using increasingly antiquated datasets. This paper proposes a smart, efficient, and lightweight model for detecting multiple IoT threats in this study. It is called fine-tuned Support Vector Machine-Random Forest (SVM-RF). To construct a powerful and efficient detection model, our proposed framework uses a shared approach for selecting features to choose the most informative and relevant features. Moreover, we suggested a fine-tuned hybrid learning algorithm to enhance classification for predicting multiple types of IoT assaults during the detection phase. Our testing results demonstrate that our proposed method has a good level of accuracy (97%), precision (97%), recall (96%), and F1-score (97%) in predicting a variety of IoT threats.
Topic: Emerging Technologies in the Internet of Things (IoT)
Algorithm for Combining Acoustic and Electromagnetic Channels to Perform an Attack on RSA in the GnuPG Program
In this paper, we investigate the acceleration of an acoustic channel attack on the RSA algorithm in GnuPG software by removing electromagnetic leaks. The technical justification of the feasibility of the study is formulated. The main attention is paid to the analysis of the interaction of acoustic and electromagnetic signals generated by the device during decryption using RSA-4096. Specific patterns corresponding to decryption operations have been identified from acoustic and electromagnetic leaks. Models are presented that demonstrate the interaction and possibilities of correcting the acoustic signal using data obtained from electromagnetic leaks. As a result, an attack model was developed to reduce the time for extracting private keys, a stand was developed, and separate stages of the attack were carried out.
Evaluating RSA PKCS#1 v1.5 Performance in WebAssembly and JavaScript: Insights into Cryptographic Efficiency
This paper presents a performance comparison of RSA PKCS#1 v1.5 encryption and decryption implemented in WebAssembly (Wasm) and JavaScript. With the increasing reliance on cryptographic algorithms in secure communications, understanding the trade-offs between these technologies is crucial. While RSA is commonly used for digital signature generation and verification in web applications, this study focuses on encryption and decryption to evaluate their performance characteristics in scenarios requiring secure data transmission. The study evaluates metrics such as encryption time, decryption time, and consistency across scenarios involving single operations, iterative workloads, and large data handling. Results indicate that Wasm consistently outperforms JavaScript in raw computational tasks, particularly in decryption and large data scenarios, due to its precompiled nature and low memory overhead. However, JavaScript leverages Just-In-Time (JIT) optimizations to achieve competitive encryption times during iterative tasks. Related studies highlight Wasm's broader efficiency in computation-intensive tasks. These findings confirm Wasm’s suitability for cryptographic operations and provide a foundation for future exploration of its applications in security tools like antivirus solutions and real-time threat analysis with also having applications in which energy efficiency is needed.
A Privacy-Preserving Authentication Protocol for Secure Self-Driving Vehicles
As self-driving vehicles (SDV’s) become increasingly popular, ensuring their security and safeguarding user privacy become critical challenges. This paper introduces a novel privacy-preserving authentication protocol designed to address these issues. The protocol leverages 5G communication, elliptic curve cryptography (ECC), and Zero-Knowledge Proofs (ZKPs) to provide robust security while preserving user privacy by preventing unauthorized access of vehicle live locations. A key feature of the protocol is its dynamic monitoring of geofencing compliance, which triggers multi-factor authentication (MFA) and vehicle lockdown in case of suspicious activity or theft. We evaluated the protocol's performance in a MATLAB simulation environment, focusing on metrics such as validation latency, communication overhead, cryptographic efficiency, and detection rates. The results demonstrate that the protocol achieves low latency, scalable communication overhead, efficient cryptographic operations, and reliable detection capabilities across varying vehicle densities. These findings highlight the protocol’s potential to provide secure, scalable, and privacy-preserving solutions for self-driving vehicle networks. This work lays the groundwork for future advancements in the security of autonomous systems and privacy-preserving technologies.
AI-Integrated Quantum Computing for Optimized UAV Systems
This paper examines how quantum computing can enhance Unmanned Aerial Vehicle (UAV) operations by addressing environmental challenges in real-time applications like surveillance and disaster response. UAVs are well-suited for mobile access networks, but factors like obstacles and energy limits pose challenges. Quantum computing, leveraging principles like superposition and entanglement, provides a promising solution for these challenges through rapid, efficient problem-solving in optimization and machine learning tasks. By leveraging quantum optimization and AI-driven error correction, our approach enables adaptive UAV path planning and improved fault tolerance. This interdisciplinary framework offers a scalable solution for autonomous UAV deployment, advancing UAV applications in complex environments and next-generation networks.
Topic: Communication and Networking Technologies for UAVs
A Hierarchical Network Study and Comparison of the Health Ratio Improvement Mechanism in Wireless Sensor Network
Few methods which are within the literature like Depth Based Ratio (DBR), DEAD along with RBCMIC are studied along with performance measures of various parameters. DBR will find the multiple routes between Data Sensing Point (DSP) in the DSPN, for each of the routes depth ratio formulae is computed, and from the set of routes the maximum value of depth ratio is chosen to pick the good route. The link count is high for DBR based method which needs to be worked upon in order to improve the performance of the DSPN. The DEAD- based method is built on top of DBR by having an additional group head DSP in each region. Once the routes are found the route which is chosen to deliver data chunks will be based on the maximum value of the routes remaining energy. This will decrease the link count but still suffers from the consumption of more energy. To overcome such disadvantage RBCMIC method is developed which will compute a fitness factor for each DSP by taking the distance from the DSP to Base Station (BS), remaining energy, and mobility information. The head DSP is chosen in each Part based on the maximum value of the fitness factor. In order to have better paths formed from the destination Part, multiple DSPs known as co-operative DSPs are chosen which are based on distance computation with respect to destination DSP along with the remaining energy of DSPs. If there are N DSPs then a ratio N/2 is selected as cooperative nodes which have better performance. Several existing techniques to improve health ratio have been discussed and specifically, RBCMIC along with DEAD and DBR methods are compared with respect to several parameters.
Priority Signal Transmission for Emergency Vehicles through Optimized Control using Wireless Sensor Network
In order to distinguish malevolent nodes from the system, a one-way hashing sequence with a fresh key representing the node’s performance is used. Elliptical Curve Cryptography (ECC), in which couple keys are utilized for encrypt and decrypt since it is required for Public Key Cryptography (PKC). PKC uses a variety of techniques to provide security and verification. Moreover, a secret session key is created to increase WSN energy efficiency and ensure the transfer of data is protected. For secure and energy-efficient data transmissions in WSN, ECC based Data Encryption with Energy Management (ECC_DEEM) scheme is proposed. Both public and private keys are produced using scaled primitives (prime keys) and encryption operations are carried out as security measures. As a result of this technique, security measures are enhanced while node energy consumption is decreased. The path must be hidden from malevolent nodes, and even if an adversary node learns the public key, it must not be able to launder data. Malevolent entities find it challenging to identify the private key component [B, n], making it computationally impossible to determine the receiver side decryption method. The software simulation tool analyses the network configurations and proves the efficiency of proposed protocol.
5G Open RAN Network Planning for Indonesia’s New Capital City: A Capacity and Coverage Analysis
The development of Ibu Kota Nusantara (Nusantara Capital City, IKN), Indonesia’s new capital, necessitates robust telecommunications infrastructure to support smart city initiatives and government operations. This study presents a 5G network planning framework utilizing Open Radio Access Network (Open RAN) architecture for the Kawasan Inti Pusat Pemerintahan (Core Government Administrative Area, KIPP-1A) region of IKN. Through systematic capacity and coverage dimensioning, optimal infrastructure requirements were determined to support both immediate government needs and future smart city applications. The implementation utilized 2300 MHz frequency with 30 MHz bandwidth, with analysis forecasting user growth from 76,695 in 2024 to 94,644 by 2028. Capacity planning calculations indicated a requirement for 16 sites to accommodate the projected traffic demand of 83,667.97 Mbps, while coverage planning determined 12 sites would suffice from a signal propagation perspective. Network simulation results demonstrated excellent performance metrics: mean Reference Signal Received Power (SS-RSRP) of -65.8 dBm with 96% optimal coverage; mean Signal-to-Interference-plus-Noise Ratio (SS-SINR) of 15.25 dB within satisfactory range; and average throughput of 1,067.54 Mbps, substantially exceeding fifth-generation mobile network requirements. The proposed architecture incorporates 1 Centralized Unit (CU) and 6 Distributed Units (DUs) supporting 16 radio sites. This research provides a strategic and technical plan for efficient telecommunications deployment in greenfield smart city environments, balancing coverage, capacity, and implementation complexity.
Detection of Denial-of-Service Attacks in Wireless Sensor Networks Using Machine Learning
Widespread use of Wireless Sensor Networks (WSNs) is due to their characteristics and performance and are the main reasons for their rapid expansion in various fields. However, these networks are extremely susceptible to multiple security threats, including denial-of-service (DoS) attacks, which are among the most prevalent in these networks. To ensure the security and dependability of WSN services, an intrusion detection system capable of detecting the largest possible number of security threats is needed. The Orange toolbox was used with the 10-fold cross-validation method to help better detect and accurately classify different Denial of Service (DoS) attacks. To train several machine learning (ML) models, a specialized dataset for WSN-DS acquired from Kaggle is employed to detect and classify four types of Denial of Service (DoS) attacks: Blackhole, Grayhole, Flooding, and Scheduling attacks, in addition to the normal behavior. The investigated classifiers, including Adaboost, CN2 Rule Inducer, and RF, achieved an accuracy rate of over 99.33%. The Adaboost classifier performs better than the other models, reaching an accuracy rating of 100%. In addition, the performance metrics that included accuracy, F-measure, precision, and sensitivity showed that Adaboost classifier also outperformed the other models, reaching an accuracy rating of 100%, whereas both the CN2 Rule Inducer and RF classifiers achieved an accuracy rate of 99%. Additional performance measures have been used, including confusion matrix, to test the various ML classifiers mentioned above to determine their rates of accurately detecting hazardous behavior. Following that, the three classifiers again achieved a high accuracy rate
Distance-based User Scheduling for Fully Connected Hybrid Precoding
Benefiting from the ultra-wide bandwidth, terahertz (THz) communication is a promising solution for future 6G wireless communications. For THz communication, precoding is an essential technique to overcome the key challenges of THz signals such as the distance-dependent path loss, the beam split effect and the severe path loss. Moreover, the sharp increase in the number of antennas results in high complexity for the channel information estimation and user scheduling. In this paper, we proposed a novel scheduling, termed as Distance-based User Scheduling (DBUS), which firstly selects the users in different distance to exploit the characteristic distance-dependent path loss in order to increase the spatial multiplexing gain. Secondly, the proposed scheduling chooses users who are semi orthogonal to each other in the beamspace to reduce the complexity of exhausted search. Through the numerical analysis we confirmed that DBUS scheduling can approximate the achievable rate of Maximum Rate (MR) method and outperform the Round Robin (RR) scheduling.
Accurate Hybrid HF Radio Direction Finder Approximation for Territorial Border Monitoring
High Frequecy (HF) Radio Direction Finding is essential for signal intelligence, maritime communication, search and rescue operations. Achieving high approximation accuracy in HF radio direction finding is challenging due to ionospheric fluctuations,multipath propagation and environmetal noise. This work presents a hybrid approxmation technique that integrates Angle of Arrival (AoA) and Time Difference of Arrival (TDOA) methodologies to improve the High Frequency Radio Direction Finder (RDF) system for enhanced security awareness. The system architecture comprises three sensor stations and a transmitter target equipped with a location finder. This technique ascertains the bearing of a Radio Emitter (RE) throughout the HF spectrum of 3 MHz to 30 MHz from each station. Data exchange facilitates triangulations to identify target locations and evaluate the precision of the HF RDF system. The proposed hybrid system enhanced accuracy, optimisation techniques, precision, stability and reduced bearing errors. The findings indicate that the hybrid method combining Angle of Arrival (AOA) and Time Difference of Arrival (TDOA) hold potential for enhancing accuracy in practical positioning applications.
A Proposal on Passive Optical Network for Local IoT Services
Passive Optical Network (PON) technologies have been deployed widely for Fiber-To-The-Home (FTTH) services. These technologies configure the star topology network via passive optical splitters. They provide an economical solution by cost reduction of cabling, footprint in the central office, and energy consumption. Recently, various IoT services have been attracted. PON is a promising candidate for aggregation from a number of end devices in these IoT services. This study proposes the architecture for the simplified and economical PON, named as Subcarrier Digital Modulation PON (SDM-PON). This architecture provides dedicated bandwidth for upstream traffic, which seems to be majority in IoT services on the star topology. This paper describes the mechanism of SDM-PON and use cases for local IoT services, e.g., services for industrial fields and social infrastructure.
Energy Efficient Hybrid Precoding in Terahertz MIMO Systems
Terahertz (THz) spectrum communication is currently widely used to satisfy the need for ultra-high bandwidth in the future. The multi-input multi-output (MIMO) technique enhances network capacity by accommodating more users and enabling multiplexing. This study addresses the challenge of optimizing energy efficiency (EE) in THz-MIMO systems. Leveraging channel correlation properties, we introduce a THz-MIMO framework that considers both power consumption and implementation complexity. A hybrid precoding strategy based on a sub-connection architecture is adopted to improve system performance. Additionally, this paper thoroughly evaluates key THz precoding algorithms for next-generation 6G networks, highlighting critical challenges and future opportunities. Differences between millimeter-wave and THz channels are explored, alongside challenges unique to THz precoding, such as distance-dependent direction loss, beam split effects, and high-power consumption. To address these issues, three distinct THz precoding systems, including hybrid precoding, are proposed, and their performance is compared to existing methods. Simulation results reveal that the proposed approach enhances energy efficiency, reduces power consumption, and achieves an improved sum rate in THz cache-enabled networks.
Topic: Emerging Applications and Challenges of mmWave Communications
Performance analysis of underwater communication using Li-Fi across different water qualities
Underwater wireless communication is a fast-growing area with growing needs for high-speed, reliable data transmission, particularly in scientific, military, and environmental monitoring applications. Due to high attenuation and limited bandwidth, conventional methods such as radio frequency (RF) and acoustic communication are severely limited underwater. In this study, the viability and effectiveness of Light Fidelity (Li-Fi), a novel optical wireless communication (OWC) technique for underwater settings, are examined. This work offers a comprehensive analysis of Li-Fi communication performance in a variety of water conditions, including clear, turbid, and saltwater. Through experiments and simulations, metrics such as bit error rate (BER), data rate, transmission range, and signal attenuation are examined. The results show that Li-Fi performance is significantly impacted by water quality, with clearer water enabling higher data rates and greater communication ranges. The impact of particle concentration, absorption, and scattering on signal transmission is also covered in detail in the paper.
Topic: Advanced Technologies and Applications in Optical Communication and Networking
Analyzing Ionosphere Delay Error in GBAS Approach Service Types (GASTs) Comprehensive Review
the Ionospheric delay error, besides the user multipath error, contribute the greatest effect on the availability of the Ground-Based Augmentation System (GBAS) due to their unpredictability. In spite the fact that GBAS system has supported aviation with a reliable solution for improving integrity, accuracy, and availability for landing operations in aviation domain, the Ionospheric delay error is still a limiting factor in its total error budget. However, GBAS has been certified since 2014 as Category CAT-I, and since 2018 as CAT-I. the unpredicted Ionospheric anomalies deviate GBAS systems from achieving aviation requirement needed for ICAO standards in the higher CAT-II and III performance, or recently called (GAST –D and E) performances, respectively. This comprehensive review paper aims to identify the latest ionosphere error predictions using either time of year or location of installation, in both using real-time observation and dual-frequency methods. It also aims to assess the ionosphere error impact on the higher performances using the common ICAO error model. Consequently, this research would propose a step ahead in the certification process for achieving better performance of GBAS-based navigational aids, especially for GAST –D in low altitudes. Results of this review show that in order to both ensure and enhance GBAS airworthiness for landing operation, there is a necessity to continuously monitor the ionosphere error threats, either by time steps or by site allocations wherever the GBAS systems are intended to be installed, particularly in low latitudes airports.
A High Directional Antenna at All Three Operating Bands for Wide-band IoT Applications Based on IEEE 802.11ax/be Standard
IEEE 802.11ax/be standard is the latest Wireless Local Area Network technology generation, usually called WiFi 6/7. It is a well-known communication for the Internet of Things and Next Generation Internet of Things devices. Operating at three bands of 2.4GHz, 5GHz, and 6GHz with wide bandwidths of over 300MHz that can achieve faster speeds and increased capacity, it is not easy to design a multiband antenna that can resonate at three admired frequencies as well as ensure the required wide bands and high gain. In this paper, we propose a complex structure based on a double circular-shaped one that can optimize for different operating frequencies. They are 2.47 GHz, 5.08 GHz, and 6.07 GHz, with a high directionality of over 6.1dB and without changing the maximum radiation direction, which was a big challenge for multiband antenna designs. Besides, the antenna achieves wide bandwidths of over 460MHz, covering three bands: 802.11ax/be and 802.11bd. The results are analyzed not only by using CST software and VNA equipment but also by using a reality WiFi system.
Enhanced Network Communication Security Through Hybrid Dragonfly-Bat Feature Selection for Intrusion Detection
Network Intrusion Detection and Prevention Systems (NIDPS) play a critical role in securing network communications by detecting and mitigating cyber threats. Machine learning (ML)-based NIDPS have proven to be highly effective in identifying network intrusions; however, their performance deteriorates when dealing with high-dimensional data. To address this, an efficient feature selection technique is essential to eliminate redundant or less relevant features, enhancing both accuracy and computational efficiency. A novel hybrid feature selection approach combining the Dragonfly Algorithm (DA) and Bat Algorithm (BA) is proposed to reduce dimensionality. Using the optimized feature subset, classification is performed with Extreme Gradient Boosting (XGBoost), Random Forest (RF), Logistic Regression (LR), and Support Vector Machine (SVM). This study employs the UNSW-NB15 dataset to train and evaluate an NIDPS framework. Experimental results demonstrate that the DA-BA feature selection method significantly improves classification performance, with XGBoost and Decision Tree (DT) achieving 100% accuracy, highlighting the effectiveness of the suggested approach in intrusion detection.
Dynamic Slot Allocation in Wireless Body Area Networks: Exploring Q-learning Approaches
Real-time monitoring through wearable and implanted devices is made possible by Wireless Body Area Networks (WBANs), which have emerged as a key component of modern healthcare. These networks provide substantial advantages for patient treatment by enabling ongoing health data collection. The requirement for fast throughput, low packet delay, packet delivery ratio (PDR), and energy economy under dynamic network conditions makes creating a Medium Access Control (MAC) protocol crucial. A Q-learning-based MAC protocol (QL-MAC) designed for slot allocation in WBANs is proposed in this paper. QL-MAC improves network performance across important metrics by dynamically optimizing slot allocation using reinforcement learning (RL). By adjusting to different network densities and traffic patterns, the protocol guarantees steady gains in communication. QL-MAC outperforms ADT-MAC, DMTM-MAC, TA-MAC, McMAC, and IEEE 802.15.6 MAC protocols. Experimental results show that QL-MAC achieves higher throughput, reduces latency, maintains a better PDR, and has lower energy consumption, even as network density increases. The benefits of QL-MAC make it especially appropriate for applications where reliable communication and energy efficiency are critical, like chronic disease management and remote patient monitoring. This study also reaffirms the role of machine learning in optimizing communication protocols for next-generation healthcare systems. QL-MAC represents a significant step forward in improving the reliability, efficiency, and sustainability of WBANs. The results highlight the potential of RL-based approaches to address the unique challenges of WBANs, such as dynamic channel conditions and resource contention. QL-MAC ensures dependable and energy-efficient communication by intelligently managing slot allocation, opening the way for advanced healthcare applications.
Analyzing the Doppler Effect on Carrier Aggregation and Millimeter-Wave Performance in 5G Systems
The rapid growth in the number of connected users and the increasing demand for advanced applications have placed significant pressure on modern communication networks. This has necessitated the development of innovative technologies to enhance signal-to-interference-plus-noise ratio (SINR) and improve network efficiency. Among these technologies, carrier aggregation and millimeter-wave play a crucial role in supporting high SINR and boosting overall network performance, despite some limitations, such as restricted coverage below 200 meters. This research employs the SIMU5G framework within the OMNeT++ simulator to model and analyze the performance of fifth-generation (5G) networks. It specifically investigates the combined impact of carrier aggregation and millimeter-wave technologies. The findings demonstrate a substantial enhancement in both SINR and spectral efficiency when these technologies are utilized together. Furthermore, the study reveals that carrier selection in carrier aggregation is not arbitrary but depends on network conditions and the availability of frequencies. It also highlights the importance of choosing the appropriate numerology value, as it directly influences the bandwidth of each carrier, thus affecting SINR and spectral efficiency. The study also examines the adverse effects of Doppler shift on mobile users, particularly as user mobility increases. It is observed that a higher number of users contributes to greater interference and a decline in SINR due to the compounded effects of mobility and inter-user interference. This research provides valuable insights into optimal carrier selection strategies under varying network conditions and emphasizes the effectiveness of integrating carrier aggregation with millimeter-wave technology to mitigate performance degradation caused by the Doppler effect.
Resource Allocation Enablers and Allocation Methods for Beyond 5G Vehicle-to-Everything Systems: Review Paper
Abstract— In the modern time era of widespread use of wireless devices and their underlying connectivity requirements in the form of the internet of things have made efficient use of resources across networks inevitable. The 5G emerging techniques aim to equip each application with advanced features and to make connectivity between the devices ubiquitous. Therefore, the Resource Allocation (RA) in the 5G and 6G networks has gained more research traction to make effective use of the sparse resources in the wireless channels. In Vehicular communication encompassing autonomous driving, there is a need to embed the multi-Radio Access Technology (multi-RAT) to form heterogeneous networks (HetNet) and meet the diverse network requirements. Thus, this paper provides an overview of the resource allocation enablers and methods for Beyond 5G (B5G) Vehicle-to-Everything (V2X) systems. It begins by discussing the key features of B5G V2X systems such as the slicing RA technique. The paper then proposes and describes a taxonomy that represents the enablers of RA in B5G V2X systems in the network layer and physical layer. The paper also discusses various allocation methods used as keys of 6G networks such as machine learning algorithms, deep learning algorithms, and management modelling such as graph and game theories. At the end of this paper, some open research challenges are discussed such as the efficient use of machine learning algorithms in RA and the use of virtual RA backhauling.
Topic: Next Generation Network: Challenge and Future
Enhanced Wearable Antenna Design for Medical and 5G Applications with Fabric–Metal Barrier for SAR Reduction
This study focuses on designing an enhanced wearable antenna for medical and 5G applications, addressing the dual challenges of high-performance operation and Specific Absorption Rate (SAR) reduction to ensure user safety. To achieve this, the proposed antenna incorporates a compact 3×3 cross-slot metamaterial (MTM) structure and a fabric-metal barrier. The antenna, with dimensions of 55 × 34 × 1 mm, features a copper square-slot patch and partial ground on a felt substrate. Furthermore, its performance was evaluated through simulations and laboratory measurements, considering free-space and human-body models, using a Vector Network Analyzer and liquid phantoms to mimic human tissues. As a result, the proposed design operates at 2.4 GHz with a wide bandwidth of 91.6% and a gain of 8.3 dBi. SAR values were significantly reduced from 0.25 W/kg for 1 g and 0.112 W/kg for 10 g to 0.0122 W/kg and 0.035 W/kg, respectively, on head liquid phantoms. Moreover, the fabric-metal barrier further enhanced SAR reduction, achieving values close to zero. Measurement results aligned closely with simulations, confirming the antenna’s reliability and safety for wearable use. In conclusion, the integration of a 3×3 MTM structure and fabric-metal barrier not only enhances antenna performance but also ensures user safety through significant SAR reduction, demonstrating the potential for widespread adoption in medical and 5G wearable applications and offering a robust and practical solution for real-world challenges.
An Effective MIMO Antenna Design for 5G and Beyond
The development of sustainable and resilient networking and communication technologies, artificial intelligence, dispersed computing, and beyond 5G are expected to play a crucial role in the realization of Industry 5.0. More specifically, antennas are recognized as a vital technology to support the continuously growing demand for 5G/6G connectivity and data rates. In the development of wireless communication systems, an effective antenna design is critical. In this work, we present the design and modeling of a hexagonal microstrip antenna for using in ultra-wide band applications such as 5G mobile networks. The suggested antenna has a frequency range of 3.3 GHz to 4.5 GHz with percentage bandwidth of 36.36%, dimensions of 20 mm × 14 mm, and is built on an FR-4 substrate, with a relative permittivity of 4.3, and of 1.6 mm in height. The printed antenna size is of 18.9mm × 2.3mm which represents the dipole and hexagon shape heights times the dipole width. It is expected that this antenna will operate in two notched bands with resonance frequencies of 3.3 GHz and 4.5 GHz and a voltage standing wave ratio (VSWR) of less than 2. The proposed antenna, which has been expanded to be a multiple-input multiple-output (MIMO) antenna, was created and modeled using CST Microwave Studio. The comparison against the recent works shows the efficiency of the proposed design.
Dense Convolutional Bidirectional Gated Network with Twin Attention Mechanism for Black Hole Attacks Detection in MANETs
Mobile Ad Hoc Networks (MANETs) are potent technologies frequently used to collect data from their environments and disseminate it to other nodes or servers. Since message exchanges occur over the open public networks, these networks are exposed to numerous cyber threats such as Denial of Service (DoS) and black hole attacks. The dynamic and decentralized nature of these networks also renders them vulnerable to attacks. In black hole attacks, adversaries conceal their presence by redirecting traffic to incorrect points and dropping data packets. In this paper, a deep learning-based SA-Dense Convolutional Bidirectional Gated Network (DCBiGNet) with a twin attention mechanism is presented for effective detection of black hole attacks in dynamic networks such as MANETs. The performance of the model is evaluated in a python environment using the WSN-DS IoT dataset. The results indicate that this model achieves accuracy, precision, recall and F1-Score of 99.70%, 99.00%, 98.3%, and 99.02% respectively Manuscript received xx xx 2023; revised xx xx 2023, accepted xx xx 2023.
Innovative Health: How 6G and Wearable Devices are Shaping the Future
The advent of the semiconductor industry and the advancements in communication technologies in the Internet of Things (IoT) era have led to the development of intelligent wearable devices. Additionally, the increased data rates in 6th-generation mobile networks (6G) have spurred the explosive growth of smart systems, particularly in smart healthcare applications. Due to their capability to collect information flexibly and conveniently via sensors, wearable devices have become an integral component of smart healthcare systems. However, their compact structure and reliance on battery power present significant challenges, notably in computing capacity and energy consumption. This paper provides a comprehensive overview of wearable devices currently employed in healthcare. We propose an efficient three-layer offloading strategy for wearables-based 6G healthcare applications. The offloading problem and its related constraints are modeled through mathematical formulations and convex optimization problems. Finally, it discusses the challenges and suggests feasible research directions.
Bus Signal Priority: Enhancing Urban Mobility and Communication Efficiency in Smart Cities in Semarang, Indonesia
Congestion, environmental impact, and public transit inefficiency are all key concerns for urban mobility. This study investigates the deployment of a Bus Signal Priority (BSP) system in Semarang, Indonesia, as part of a larger smart city project is aimed at improving urban mobility and encouraging the transition from private vehicles to public transportation. The major goal of this project is to create and implement BSP technology that enhances bus travel efficiency by reducing delays at traffic signals. The project combines a combination of traffic data gathering, simulation-based analysis with VISSIM software, and real-world implementation of RFID-based BSP Systems at major crossings across Semarang. The process comprises field surveys that analyze traffic volume, queue lengths, and vehicle speeds, followed by the deployment of RFID sensors and IoT-enabled devices for real-time traffic signal modifications. The BSP system can reduce bus travel time by up to 30% during peak hours, resulting in lower CO2 emissions and increased public transit reliability. The implementation also encourages more public transportation use, which aligns with the city's sustainable transportation goals. System calibration, public awareness, and regulatory alignment are examined, as well as proposals for incorporating Artificial Intelligence (AI) to improve signal timing and traffic flow. This work advances intelligent transportation systems and serves as an example for other cities seeking to implement BSP for sustainable urban mobility.
Classification of Spectral Occupancy in Cognitive Radio Networks based in Machine Learning
Cognitive Radio (CR) is a technology that seeks to maximize the frequency resources application by allowing unlicensed users to access spectrum bands opportunistically. Spectrum decision is a fundamental function in CR, which depends on the network spectral occupancy. This paper analyzes spectral occupancy classification for decision-making by implementing feature extraction and classification rules. Feature extraction is performed through Deep Learning and classification rules by implementing four Machine Learning classifiers. The objective is to analyze the decision-making process through the three traffic levels classification. In order to evaluate the decision-making process, three cost metrics and one benefit metric were used; for the metrics, the analysis of results showed that the classifier using Support Vector Machines presents the best performance.
A Comparative Study of Deterministic and Uncertain Area Coverage Strategies in WSNs
The introduction and the emergence of the Internet of Things (IoT), Internet of Vehicles (IoV), and 5G networks present unparalleled challenges for securing data and connecting IoT devices more securely. Cryptography, in its two main forms - classical and modern - is a very useful tool for securing information communication. The Asymmetric encryption algorithm is widely used to ensure confidentiality and authentication in the 5G and Zero Trust parameters, and secure data transmission which involves a pair of keys; a public key and a private key. Securing the transmission and authentication of trust parameters in the context of 5G networks is crucial to maintaining the integrity and privacy of communications. Cryptosystems play a fundamental role in achieving these goals. This paper is a survey that studies the application of cryptography in blockchain, Zero-Trust, and authentication in both IoT and 5G and beyond networks in the literature and shows the trends of cryptography in other networks as; Nanonetworks, 5G-6G Enabled IoT, and IoV, WSN, MANET and VANET.
A Compact Wide-Band Array Antenna for Dual-Band Applications
Nowadays, K-band and Ka-band frequencies are used in high-resolution, close-range targeting radars; military aircraft; space telescopes; commercial, wireless point-to-point microwave communication systems; vehicle speed detection systems; and satellite communications. In this work, a new design concept of a compact, wide-band, 10-element array antenna has been proposed for dual bands such as K-band (18 GHz-26 GHz) and Ka-band (26 GHz-40 GHz) based on the log-periodic structure. The proposed antenna is implemented on a 31.3 x 10.37 x 0.787 mm³ RT duroid 5880 substrates with a relative dielectric constant of r = 2.2 and a loss tangent of 0.0009. The proposed antenna is simulated for essential parameters like reflection coefficient, VSWR, and gain. Following the simulation, fabricate and measure the antenna for the same essential parameters. At the end, the simulated and measured parameters are compared for the validation of an antenna to work in the K-Band and Ka-Band frequencies. Based on the comparison, the proposed antenna yields better results, like return loss less than -10 dB, VSWR < 2, and peak gain of 13.2dBi. In addition, for the proposed antenna, a wide bandwidth of 16.75 GHz is achieved in the frequency range of 22GHz to 38.75 GHz.
Legal and Communication Challenges in Smart Grid Cybersecurity: Classification of Network Resilience Under Cyber Attacks Using Machine Learning
The transition to renewable energy sources, facilitated by smart grid (SG) communication networks, has introduced significant cybersecurity vulnerabilities that threaten the reliability of power generation, transmission, and distribution. Denial of Service (DoS) and Distributed Denial of Service (DDoS) attacks disrupt the stability of blockchain-based energy networks, raising serious legal, regulatory, and security concerns. This study investigates the legal obligations and communication challenges associated with securing smart grid networks against cyber threats. Specifically, we analyze how existing data protection laws (GDPR, NIS Directive) and cybersecurity policies (NIST Cybersecurity Framework, EU Energy Policy) apply to energy communication infrastructures and assess the legal accountability of energy providers in ensuring grid resilience. To evaluate network resilience under cyberattacks, we employ Machine Learning (ML) models, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Random Forest (RF), to classify three operational states in the network: normal operation, single attack scenario, and multiple attacks scenario. The classification models are trained and tested on big data obtained from blockchain-powered renewable energy networks, assessing their performance based on accuracy, precision, recall, and F1-score. The RF model achieved the highest classification accuracy of 98%, effectively distinguishing between normal and compromised states. Our findings underscore the urgent need for enhanced regulatory measures and legal frameworks that address AI-driven security risks in energy communication networks. We recommend policy updates that incorporate automated cybersecurity solutions, enforce data protection compliance, and establish liability protocols for smart grid operators. This research bridges the gap between communication security, legal governance, and energy resilience, providing a policy-driven perspective on the future of cybersecurity regulation in sustainable energy networks.