A new paradigm for satisfying the ever-growing demands of real-time Sixth Generation (6G) applications is Mobile Edge Computing (MEC). Additionally, base stations and Internet of Things devices that incorporate renewable energy harvesting capabilities have the potential to lower grid energy use. To maximize system potential and lower carbon emissions, it is crucial to make effective decisions about job offloading and resource allocation. A carbon-aware MEC architecture that uses both grid and renewable energy sources is proposed in this paper. Our goal is to jointly manage resource allocation and task offloading while monitoring carbon emissions and task queue delays to optimize system behavior under uncertainty, specifically for stochastic workloads and variable renewable generation. To balance these two cost components (emissions and queue length), we create a combined optimization problem. We develop a deep deterministic policy gradient (DDPG)-based joint optimization technique to address this issue in a constantly changing environment. In the optimization, we consider greedy policy (GP) and full offloading (FO), as well as time-average carbon emission (TACE) and time-average queue length (TAQL) as performance metrics, and time-average queue length (TAQL) and full execution (FE) as baseline strategies; we also evaluate normalized time-average cumulative reward (NTACR). This method uses continuous-action reinforcement learning to generate efficient, real-time control policies. For the proposed MEC network, numerical statistics show that our approach can lead to effective offloading and lower carbon emissions.

Internet of Things (IoT) devices are vulnerable to zero-day attacks because most of them have weak or no inherent security due to the resource constraints of the devices. This weakness underscores the growing need for anomaly-based intrusion detection systems tailored to IoT networks. Nevertheless, general anomaly detection traditionally has a high number of false positives that drain analysts' time. Also, a semantic difference exists between the system's results and the operators' interpretations. We introduce a machine learning-based framework to tackle these issues in traditional systems in this paper by combining large language models (LLMs). Our model is effective in identifying possible threats as well as filling the semantic gap. The framework uses isolation forests to detect anomalies and random forests to measure device integrity. To further improve the assessment of anomalies and increase interpretability, system insights are further refined using GPT-4o mini, an LLM. The model gives statistical summaries of the IoT traffic, a risk score, and an explanation in easy language, which is easy to understand and therefore makes the process of decision-making easier. Such a novel system reduces the reliance on dedicated network operators and allows non-technical users to better understand and act on the results of the system.

Recent developments of the smart grid integrate cyber-physical systems (SG-CPS), information and communication technology, and the Internet of Things (IoT). Specifically, SG energy trading throughout cyber-physical systems (CPS) incorporates IoT-based devices, sensors, and actuators that are operated via both wired and wireless communication technologies. These combinations are essential for monitoring and controlling the operations of supervisory control and data acquisition (SCADA), wide area measurement systems (WAMS), and advanced metering infrastructure (AMI). SG-CPS data communication, processing, and aggregation using both secure and insecure network systems. Thus, the increasing reliance of SG-CPS on communication systems has led to unauthorized access and vulnerabilities, resulting in cyberattacks. Recent literature has reviewed advancements in SG-CPS cybersecurity, focusing on individual components of SG architectures, communication systems, standards, protocols, and security solutions, which include lightweight and authenticated key agreement protocols, machine learning, deep learning, and blockchain. Although notable progress has been made, SG, cyber-physical components, applications, and a cybersecurity roadmap are unavailable. The significance of this study lies in its in-depth understanding of the SG-CPS progression, including its domains, principles, energy trading systems, communication technology, and categorization of general, technical, and security-related standards and protocols. Then, critically discussed cybersecurity perspectives and status, including objectives, requirements, vulnerability zones, advanced solutions, constraints, and research opportunities. Subsequently, the review identified a lack of data aggregation, challenges in data privacy and security, and the need for robust security mechanisms to predict, detect, and mitigate cyber-attacks. Furthermore, based on the findings, it proposes a conceptual framework and process that leverages hash-graph, a lightweight and privacy-preserving quantum-resistant protocol, along with a machine learning approach. Accordingly, it provides a system model, design goals, a three-phase methodology, performance and security assessment metrics, simulation items, environments, and requirements that will guide new and experienced researchers in advancing SG-CPS cybersecurity.

Federated Learning (FL) is an attractive method that allows the devices to train local models on the basis of private data and share only the model parameters with a central server, which guarantees privacy of data in terms of collaborative model training. Nevertheless, synchronous model updates and aggregation of all involved devices are impractical in mobile devices because of the limited wireless bandwidth and energy capabilities. In such a way, the choice of devices to fit in each training round is a serious issue, which influences the use of resources, energy efficiency, and communication delays. The issues are compounded by the device disparities and evolving network environments in 5G-enabled Mobile Edge Computing (MEC) environments that are capable of supporting Machine Type Communication (MTC). To sustain the performance and scalability, a well-coordinated resource management is required. To solve this, an adaptive resource management framework of MEC-assisted FL has been suggested and integrates smart device choice and dynamic resource allocation based on a Double Deep Q-Network (DDQN). The model used to model the device selection process as a Markov Decision Process (MDP) is that the DDQN can make adaptive decisions based on experience, considering energy consumption, computational load, and bandwidth consumption under different network and device conditions.

This paper addresses the dual challenge of energy efficiency (EE) and user fairness in downlink Cognitive Radio Non-Orthogonal Multiple Access (CR-NOMA) small-cell networks, where conventional resource allocation schemes often Favor strong-channel users at the expense of cell-edge performance and system-wide sustainability. Building upon the Fairness Index Average Rate (FIAR) framework, we propose EE-FIAR, an advanced power allocation algorithm (EEFIARA) that jointly optimizes Energy Efficient modeled with realistic circuit power consumption and a dynamic, variance-sensitive fairness metric under strict interference temperature and minimum rate constraints. Unlike prior approaches that treat fairness as a static or secondary objective, EE-FIARA adapts its fairness threshold in real time based on channel heterogeneity and user load, ensuring robust equity without sacrificing efficiency. The simulation results with the same parameters as the recent benchmarks prove that EE-FIARA is more efficient in energy consumption by up to 9.2 % when compared to the state-of-the-art methods, including the original EE-FIAR and with superior fairness stability, especially in dense and cell-edge cases. These profits are converted into physical sustainability gains to energy constrained areas where equitable low-power connectivity is critical in digital inclusion. The suggested framework is therefore a viable, socially responsible measure towards the realization of green and equitable 5G/6G small-cell implementations.

Physical-layer Network Coding (PNC) was investigated to achieve high performance of wireless communication systems. This paper introduces a new relay de-noising algorithm called RDA for the two-way relaying channel of PNC (TWRN-PNC) over a Rician fading channel. In addition, the error probability of the proposed scenario is analyzed and derived for the multiple access (MA) phase and the end-to-end communication link. The scenario considers square M-ary Quadrature Amplitude Modulation (square M-QAM) at all nodes, and RDA generates a custom Latin square (CLS) based on clustering rules for constellation points to remove the singular fade state (SFS). Furthermore, theoretical and simulation results of average error probability are obtained to verify the mathematical model for both MA and end-to-end links. The results indicate that the RDA improves the performance of PNC systems with high-order modulation and reduces the average Symbol Error Rate (SER) by 20% for 4-QAM and 16-QAM schemes.

Low-altitude remote sensing networks are increasingly important for applications, such as environmental monitoring, disaster response, infrastructure inspection, and real-time sensing services. However, when many sensing nodes share limited spectrum resources, severe cochannel interference can degrade communication reliability and delay sensing-data delivery. This challenge becomes more critical in edge-enabled deployments, where control decisions must be made under strict latency, memory, and computational constraints. To address this issue, this article proposes a large language model (LLM)-enhanced edge-aware lightweight reconfigurable intelligent surface (RIS)-assisted dynamic channel allocation (EL-RIS-DCA) framework for interference mitigation in dense low-altitude remote sensing networks. The novelty of the proposed framework lies in a two-stage edge-control design: a lightweight large language model first generates a fast candidate decision for channel allocation and RIS phase adjustment from summarized network observations and retrieved historical patterns, and the EL-RIS-DCA module then performs feasibility verification, interference-aware refinement, and safe execution under edge constraints. Simulations are conducted using distance-based path loss with Rayleigh fading for direct and RIS-assisted cascaded links in a dense low-altitude sensing scenario with edge-constrained operation. Simulation results show that the proposed method achieves higher average signal-to-interference-plus-noise ratio, lower outage probability, faster convergence, and near-optimal performance under limited computation budgets compared with the raw LLM proposal, the EL-RIS-DCA without LLM, and the considered non-RIS or centralized optimization baselines. The results also indicate that moderate RIS sizes can provide strong performance gains while keeping the computational complexity suitable for real-time edge deployment. Overall, the proposed framework offers an effective and practical solution for interference mitigation in dense and resource-constrained low-altitude remote sensing networks.

The Internet of Medical Things (IoMT) plays a crucial role in enabling precision diagnosis and optimal recommendations for patients monitored remotely. However, conventional encryption–signature mechanisms introduce significant computational and communication overhead, making them unsuitable for resource-constrained IoMT devices that require secure and real-time transmission of health vitals. To address this gap, this article proposes a lightweight signcryption scheme tailored explicitly for IoMT data security. The novelty of the scheme lies in employing a triple-truncated DES-based signcryption with non-linear shared integrity verification, which simultaneously ensures confidentiality, authenticity, and resistance against replay and forgery attacks while reducing complexity. The operation increasingly adheres to critical preparation, signing, and distribution protocols even for fine-grained time periods, with truncation reaching succinct key pairs with low overhead. A lightweight CNN-based verification module is used to authenticate the sequence and handle truncation without introducing extra complexity, as required for IoMT efficiency. Experimental results demonstrate that the proposed approach improves detection accuracy by 14.12%, reduces service failures by 13.67%, and achieves computational time savings compared to baseline schemes. In all, the scheme provides an IoMT-optimized, low-complexity, and secure solution for protecting sensitive medical information against attacks in adversarial environments.

The joint optimization of hybrid beamforming and reconfigurable intelligent surface (RIS) phase shifts in multi-user millimeter-wave (mmWave) MIMO systems is a challenging problem, mainly due to high computational complexity and the lack of adaptive interference management. Existing approaches typically rely on fixed Zero-Forcing (ZF) or Maximum-Ratio Transmission (MRT) designs or require iterative optimization with high overhead, limiting their practical use in dense 6G environments. To overcome these challenges, this research proposes a RIS-Aided Adaptive Zero-Forcing and Maximum-Ratio Transmission Hybrid Precoding (RA-ZMHP) framework for 6G mmWave multi-user MIMO systems. The main novelty of the method lies in an adaptive ZF–MRT mixing strategy that dynamically balances interference suppression and beamforming gain based on real-time channel conditions. Unlike conventional hybrid beamforming schemes, the proposed framework jointly optimizes the digital baseband precoder, the analog RF precoder, the RIS phase shifts, and the power allocation using a weighted minimum mean-square error (WMMSE) formulation. To keep computational complexity low, closed-form updates based on Karush–Kuhn–Tucker (KKT) conditions are derived for power allocation, MMSE receiver design, and the adaptive ZF–MRT mixing factor. This ensures stable and fast convergence while avoiding complex iterative procedures. Simulation results show that the proposed RA-ZMHP scheme achieves lower MMSE, higher SINR, and faster convergence compared to conventional ZF, MRT, and fixed hybrid beamforming methods. Furthermore, performance improves with increasing transmit power and with a larger number of RIS elements, confirming the effectiveness and practicality of the proposed framework for interference mitigation and performance enhancement in future 6G wireless networks.

The smart grid integrates cyber physical system by information and communication technology facilitate data transmission between smart meters and data aggregator points. These advancements also introduce risks of cyber-attacks and unauthorized access, which can compromise the privacy and security of both customer information and system operational data. Thus, cryptographic key agreement protocols have emerged to ensure data confidentiality, integrity, forward secrecy, and authentication. However, existing protocols often suffer from excessive computational overhead which making them unsuitable for resource-constrained devices such as smart meters. Additionally, inadequate authentication mechanisms in these protocols have led to increased vulnerability to cyber-attacks. Therefore, this proposed lightweight-secured key agreement protocol leveraging elliptic curve cryptography for efficient encryption and decryption for smart meter and aggregator point. Ultimately, comparative analysis shows that the combination of ECC, hash functions, timestamps, and pseudo-random number proposed protocols enhanced security features with significantly reducing computation time and bit overheads. The formal and informal analyses based on the Dolev- Y ao threat model and A VISP A tool confirm that the proposed protocol preserves mutual authentication with the intended security features. The proposed protocol is suitable for resource-constrained devices as this protocol ensures secure communication without compromising performance.

Fifth-generation (5G) networks are essential for meeting the increasing demands of emerging wireless applications that require high-quality service, ultra-fast data rates, and efficient management of rapidly growing data traffic. This paper presents a compact slotted patch antenna integrated with parasitic elements, designed for superior return loss performance in the W-band to support 5G wireless systems. The antenna is fabricated on a Rogers RT5880 substrate, characterized by a dielectric constant of 2.2 and a loss tangent of 0.0009, and features a miniaturized footprint of 3.2 x 4.57 x 0.1518 mm(3). By incorporating strategically placed slots and parasitic coupling elements, the design achieves enhanced return loss performance. Comprehensive full-wave electromagnetic simulations using CST Studio Suite validate the antenna's performance across both frequency and time domains. At an operational frequency of 87 GHz, the antenna exhibits a bandwidth of 4.1 GHz (84.9 - 89 GHz), a peak gain of 8.13 dBi, a radiation efficiency of 83%, a return loss of 73.7 dB, and a voltage standing wave ratio (VSWR) of 1.0004, indicating exceptional impedance matching and minimal signal reflection. These findings demonstrate the antenna's suitability for highdensity 5G environments and its potential to reduce interference while improving radiation performance in next-generation communication systems.

Future 6th Generation (6G) networks will rely on Terahertz (THz) wireless communication as their main enabler for delivering both ultra-high data speed and minimal delay. THz wireless systems become crucial for upcoming communications by using Unmanned Aerial Vehicles (UAVs) together with Intelligent Reflecting Surfaces (IRS) while improving reliability and efficiency. In UAV-IRS-assisted networks, minimizing mission completion time and energy consumption is critical. However, achieving rapid mission execution often requires UAVs to operate at higher speeds, increasing energy usage and creating a trade-off that demands optimization. This paper addresses the challenge of optimizing UAV-IRS trajectories in THz networks to reduce mission time while adhering to energy constraints. Given the non-convex and NP-hard nature of the problem, traditional optimization methods are insufficient. To tackle this, we propose a Multi-Agent Deep Reinforcement Learning (MADRL) algorithm, which provides an efficient, low-complexity solution for trajectory optimization. MADRL dynamically adapts UAV-IRS paths, balancing mission efficiency and energy savings. Simulation results demonstrate that the proposed MADRL-based approach outperforms existing benchmarks, achieving shorter mission times and near-optimal energy consumption across varying scenarios. By leveraging cooperative learning, the algorithm effectively handles complex environments with multiple users and IRS elements. This work highlights the potential of MADRL for UAV-IRS trajectory optimization, offering a scalable solution for energy-efficient and high-performance THz communication systems.

The Internet of Underwater Things is a rapidly emerging technology that enhances a diverse range of underwater applications. In IoUT, UNs have batteries that are difficult to remove and recharge, which requires a method to maximise energy efficiency. Thus, to provide energy-efficient communication. Energy-aware optimisation algorithms ensure resilient and efficient network operations. The purpose of this study is to model an efficient energy-aware approach using meta-heuristics and reinforcement learning approaches driven by quality of service for IoUT networks. The proposed framework addresses various energy optimisation problems in hierarchical solutions by optimising clustering management based on stable election protocol computations to develop an energy-aware stable election protocol optimised by integrated artificial bee colony and lightweight Q-learning approaches known as (ABCQL-EASE). Furthermore, the proposed approach is improved by a multi-criteria decision-making scheme to optimise inter-cluster routing decisions by ensuring that the CHs successfully relay data to the surface base station. The primary novelty of this study lies in three key contributions. First, a hybrid ABC-QL approach for dynamic cluster head management. Second, integration of an MCDM-based inter-cluster routing scheme that optimises data forwarding and QoS. Third, a fully integrated and self-adaptive architecture that balances exploration and exploitation while minimising computational overhead. The proposed approach is evaluated and compared to other previously developed methods, providing better performance in reducing energy consumption by 45

In the modern era, the usefulness of sixth-generation (6G) based devices and other devices of the Internet of Things will greatly increase. In distant and sensitive places, aerial surveying is the main application for these instruments. It is concerning that, as technology has advanced, problems with information control and stalking have gotten worse. The approach to enhance the security and privacy of device data based on a 6G network is presented in this study using Blockchain Technology (BCT). An Internet of Things (IoT) application is implemented in a virtual 6G device monitoring system to assess the suggested design. Penta-tope-based Elliptic curve cryptography and SHA are used to secure data storage privacy for the technical information regarding device instructions, authentication, integrity, and 6G device reactions. This information is saved in a cloud platform. Afterward, the information is kept on a public blockchain powered by Ethereum to facilitate smooth BCT transactions. The Ganache platform for BCT, which guarantees data security and privacy, is used by this system. Furthermore, an Ethaline Meta mask wallet is needed to conduct BCT transactions. Thus, the suggested methodology aids in safeguarding data against ciphertext attacks, plaintext attacks, and stalkers. The outcomes validate the proposed approach's security and efficiency compared to the state-of-the-art.

The VHF Band III spectrum (174-230 MHz), recognized for its low utilization, was opened by the FCC in 2002 for opportunistic use by communication technologies. This paper presents a quantitative analysis of the availability of VHF Band III for cognitive radio CR access in Malaysia. Measurements were conducted over a week in 18 Malaysian cities to analyze spectrum utilization and received signal strength, alongside the impact of broadcasts from neighboring countries. The findings highlight actual utilization patterns and reveal the spectrum’s potential for opportunistic access, especially post-digital switchover (DSO).