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Fairness-Aware Comparison of PD-NOMA and OMA Under Max-Min, Proportional, and Round-Robin Scheduling 最大最小、比例和轮循调度下PD-NOMA和OMA的公平性感知比较
IF 0.5 Q4 TELECOMMUNICATIONS Pub Date : 2026-02-04 DOI: 10.1002/itl2.70219
Yakov Kryukov, Dmitriy Pokamestov, Artem Shinkevich, Georgy Shalin, Sergey Eremeev, Dmitriy Ilinskiy

This letter presents a comparative analysis of power-domain non-orthogonal multiple access (PD-NOMA) and orthogonal multiple access (OMA) under max–min fairness (MMF), proportional fair (PF), and Round Robin (RR) scheduling, with an emphasis on preserving fairness across both systems. Motivated by the lack of consistent evaluation methods for multiuser scenarios, we develop a unified fairness-aware framework that enforces identical user-rate distributions and resource constraints in PD-NOMA and OMA. Fairness preservation is guaranteed by the proposed rate-matching methodology applied to both schemes under identical transmit-power budgets. A stochastic single-carrier downlink model over Rayleigh fading is used to evaluate systems with up to 20 users. The results demonstrate that PD-NOMA consistently outperforms OMA in sum-rate across all scheduling policies, with the gain increasing with the number of users and with median SNR and approaching an asymptotic limit at high SNR and large user counts. Beyond three users, the incremental gain becomes negligible relative to the added system complexity, indicating that multiplexing two or three users provides the most practical performance–complexity trade-off, while larger group sizes may still be relevant in connectivity-centric scenarios.

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引用次数: 0
Intelligent Green Resource Management for Blockchain-Powered IoT Networks Through Deep Reinforcement Learning 基于深度强化学习的区块链驱动物联网智能绿色资源管理
IF 0.5 Q4 TELECOMMUNICATIONS Pub Date : 2026-02-04 DOI: 10.1002/itl2.70232
Jiaojiao Qin, Yanlong Yang

Integrating blockchain technology with Internet of Things (IoT) networks presents opportunities and challenges for sustainable computing. While blockchain ensures secure and transparent data management, its energy-intensive nature poses significant environmental concerns, particularly in resource-constrained IoT environments. This paper proposes SERO-DRL, a novel deep reinforcement learning approach for energy-efficient resource optimization in blockchain-enabled sustainable IoT networks. We develop a comprehensive framework that jointly optimizes computational offloading and resource allocation while considering renewable energy availability and environmental impact. The framework includes an innovative reward mechanism that incentivizes energy-efficient behavior while ensuring fair resource allocation among IoT devices. Experimental results demonstrate SERO-DRL's superior performance, achieving an 18.5% reduction in total system costs and a 40% decrease in environmental impact compared to baseline approaches.

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引用次数: 0
Improving Security and Energy Optimization Using a Hybrid Secure Deep Learning Approach for IoMT Systems 使用混合安全深度学习方法提高IoMT系统的安全性和能源优化
IF 0.5 Q4 TELECOMMUNICATIONS Pub Date : 2026-02-03 DOI: 10.1002/itl2.70231
Yasser Alharbi, Vikas Wasson, Narmadha Thangarasu, Albert Mayan John, Manoranjan Parhi, Anita Gehlot

In healthcare 5.0, the Internet of Medical Things (IoMT) is driving rapid developments that require energy-efficient and secure solutions. This paper proposes a novel hybrid approach that integrates Public Key Infrastructure (PKI) with Spiking Neural Networks (SNNs) to enhance secure communication and reduce energy consumption in IoMT-based healthcare systems. The PKI framework provides secure device authentication and encrypted data transmission, while the biologically inspired SNNs enable low-power, real-time anomaly detection. Furthermore, the Zebra Optimization Algorithm (ZOA) further enhances system performance. A simulation study shows that the proposed model outperforms existing protocols from a delay perspective, throughput perspective, packet delivery ratio perspective, and residual energy perspective, showing that it is suitable for real-time, resource-constrained healthcare.

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引用次数: 0
Collaborative Control of Overcharging-Swapping-Wireless Charging Systems via Industrial IoT for Extreme Cold Regions 基于工业物联网的极寒地区过充-交换-无线充电系统协同控制
IF 0.5 Q4 TELECOMMUNICATIONS Pub Date : 2026-02-03 DOI: 10.1002/itl2.70210
Xiaoming Lu, Pengfei Li, Wenqiang Li, Xiao Jia, Meihan Liu, Jingjie Zhao

In this study, a cooperative control algorithm for overcharging-exchange-wireless charging three-stage system is designed based on industrial Internet of Things (IoT) technology to address the problems of low charging efficiency and battery performance degradation of electric vehicles in extremely cold regions. This study proposes a collaborative control method based on the improved NSGA-III multi-objective optimization algorithm and fuzzy PID temperature compensation strategy. Through industrial IoT, it achieves dynamic collaborative optimization of the three-phase system (overcharging, battery swapping, and wireless charging) under extreme cold conditions. Innovations include: (1) The first integration of multi-objective optimization with distributed reinforcement learning to enable system-level energy scheduling; (2) Proposing a dynamic mutual inductance model and bilateral LCC compensation topology to address wireless charging parameter drift; (3) Employing a two-dimensional fuzzy controller with fuzzy PID temperature compensation, which significantly enhances system stability and efficiency in extreme low-temperature environments.

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引用次数: 0
Quantum-Enhanced Federated Learning Architecture for Privacy-Preserving Smart Grid IoT Security 用于保护隐私的智能电网物联网安全的量子增强联邦学习架构
IF 0.5 Q4 TELECOMMUNICATIONS Pub Date : 2026-02-03 DOI: 10.1002/itl2.70229
Rami Baazeem

The increasing complexity of smart grid IoT ecosystems demands security architectures capable of resisting quantum-era threats, protecting data privacy, and scaling across large distributed infrastructures. This study introduces a novel hybrid security framework that integrates quantum cryptography, deep learning–based intrusion detection, and federated learning into a unified, high-assurance design tailored for next-generation smart grid environments. The architecture employs quantum key distribution (QKD) for secure key generation, an adaptive deep feature obfuscation layer to mitigate adversarial manipulation, and a privacy-preserving federated learning pipeline that eliminates centralized data exposure. Simulation-based evaluations demonstrate substantial performance gains, achieving a low quantum bit error rate (1.8%), high key-generation throughput (4900 keys/s), low latency (18 ms), and intrusion detection accuracy of 98.7%, consistently outperforming conventional cryptographic and machine learning baselines. The framework further exhibits enhanced resilience against quantum-based and adversarial attacks, with efficient performance maintained even under increasing network density. While real-world deployment will require hardware-in-the-loop validation and optimization for heterogeneous traffic conditions, the results indicate strong potential for securing future smart grid IoT infrastructures and supporting sustainable smart city applications.

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引用次数: 0
Machine Learning-Driven Security for Malware Detection in Wireless Android Devices 无线Android设备中恶意软件检测的机器学习驱动安全性
IF 0.5 Q4 TELECOMMUNICATIONS Pub Date : 2026-02-03 DOI: 10.1002/itl2.70221
Anuradha Dahiya, Sukhdip Singh, Gulshan Shrivastava

The persistent growth of Android malware has necessitated the development of advanced detection techniques to protect users and devices. This work proposes a machine learning method based on API calls and permissions to discriminate between malicious and harmless applications. The primary intent is to advance Android security by offering a comprehensible approach for detecting potentially dangerous apps before end users download them. A dataset of 11 000 applications (benign and malicious) is collected from the Androzoo Android apps collection. The binary dataset constructed from the extracted API calls and permissions has high dimensionality and sparsity, so a hybrid feature selection pipeline has been followed to filter out redundant, irrelevant, and low-impact features while preserving the differential signs. The proposed method provides a scalable solution for risk assessment by differentiating between safe and risky apps with adequate outcomes. Experimental results show that combining permissions and API calls achieved an accuracy of 98.36% for classification, which is significantly higher than using either permissions or API calls alone.

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引用次数: 0
A Particle Swarm Optimization-Based Approach for Accurate Localization in Wireless Sensor Networks 基于粒子群优化的无线传感器网络精确定位方法
IF 0.5 Q4 TELECOMMUNICATIONS Pub Date : 2026-01-30 DOI: 10.1002/itl2.70226
Abdullah J. Alzahrani

This paper presents a particle swarm optimization (PSO)-based approach to enhance node localization accuracy in wireless sensor networks (WSNs). Traditional range-based methods such as RSSI suffer from high positioning errors, while conventional DV-Hop algorithms rely on assumptions that often fail under real-world conditions. To overcome these limitations, the proposed method integrates a bounding box technique to constrain the initial search space, along with anticipatory and refinement strategies to address flip ambiguity. Additionally, inter-node distance information, including distances between unknown nodes, is leveraged to further improve localization accuracy. Simulation results demonstrate that the proposed PSO-based approach significantly reduces localization error, enhances accuracy and coverage, and lowers variance compared to standard DV-Hop and Improved DV-Hop (IDV-Hop) algorithms. These improvements contribute to more accurate, reliable, and computationally efficient localization in WSNs.

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引用次数: 0
An AI Embedded AI Framework for Real-Time Collision Detection and Navigation in Smart Warehouses Using 2D LiDAR and 6G Communication 基于二维激光雷达和6G通信的智能仓库实时碰撞检测与导航嵌入式AI框架
IF 0.5 Q4 TELECOMMUNICATIONS Pub Date : 2026-01-19 DOI: 10.1002/itl2.70169
Mohammed I. Habelalmateen, Lukman Audah

Considering the rapid move to Industry 5.0 and the convergence of 6G communication technology in AI, there is a burgeoning interest on AGV's role for smart warehousing use cases. We propose a novel AI-enhanced simulation framework with integrating 2D LiDAR based sensing and 6G-enabled wireless sensor networks to better implement real-time navigation and collision avoidance. The dynamic environment of a warehouse is modelled, and through a combination of spatial filtering and ray-casting techniques, along with intelligent communications protocols acquired situation awareness is maximized. Computational load is greatly reduced by AI based signal processing and decision making, with high accuracy of obstacle detection. The platform supports ultra-low-latency data transmission as well, and will enable real-time AGV coordination by simulating edge-to-edge communication through future-ready (6G) wireless technology. This tool not only facilitates safe operations, but also reduces reliance on physical experiments at the early design phases making this a scalable capability for researchers to develop next generation, communication rich, autonomous systems.

考虑到工业5.0的快速发展以及人工智能中6G通信技术的融合,人们对AGV在智能仓储用例中的作用越来越感兴趣。我们提出了一种新的人工智能增强仿真框架,该框架集成了基于2D激光雷达的传感和支持6g的无线传感器网络,以更好地实现实时导航和避碰。仓库的动态环境被建模,并通过空间过滤和光线投射技术的组合,以及智能通信协议,最大限度地获得态势感知。基于人工智能的信号处理和决策大大减少了计算量,障碍物检测精度高。该平台还支持超低延迟数据传输,并将通过未来就绪(6G)无线技术模拟边缘到边缘通信,实现实时AGV协调。该工具不仅有助于安全操作,而且还减少了在早期设计阶段对物理实验的依赖,使其成为研究人员开发下一代通信丰富的自主系统的可扩展能力。
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引用次数: 0
Joint Link Selection and Power Allocation in Space-Terrestrial Integrated Industrial IoT Networks 天地融合工业物联网网络中的联合链路选择与功率分配
IF 0.5 Q4 TELECOMMUNICATIONS Pub Date : 2026-01-19 DOI: 10.1002/itl2.70211
N. N. Song, Y. Z. Li, Y. K. Zhao, J. Li, W. Li

Single backhaul networks face significant limitations in simultaneously satisfying the user coverage and stringent service requirements of future Industrial Internet of Things (IIoT) systems envisioned for Industry 5.0. To address this challenge, this study proposes a joint optimization method for dual-mode backhaul link selection and power allocation based on satellite-terrestrial base station cooperation, tailored for resilient IIoT connectivity. The approach constructs a Space-Terrestrial Integrated Network (STIN) backhaul architecture to ensure ubiquitous coverage and reliable communication for distributed industrial assets. It formulates an optimization problem with the objective of maximizing delay tolerance resilience for critical IIoT applications, such as real-time monitoring and autonomous control. A decomposition optimization strategy is employed for solution—applying the Hungarian algorithm for the link allocation subproblem and the Lagrangian dual method for the power allocation subproblem. Simulation results demonstrate that compared to existing algorithms, the proposed method reduces average latency by 18% for uRLLC packets and 13% for eMBB packets, significantly enhancing system robustness while effectively reducing user service latency. This advancement supports the ultra-reliable, low-latency communication essential for human-centric, intelligent, and sustainable operations in Industry 5.0 environments.

单回程网络在同时满足工业5.0所设想的未来工业物联网(IIoT)系统的用户覆盖和严格的服务要求方面面临重大限制。为了应对这一挑战,本研究提出了一种基于星地基站合作的双模回程链路选择和功率分配的联合优化方法,为弹性工业物联网连接量身定制。该方法构建了一种空间-地面综合网络(STIN)回程体系结构,以保证分布式工业资产的无所不在覆盖和可靠通信。它制定了一个优化问题,目标是最大限度地提高关键工业物联网应用的延迟容忍弹性,如实时监控和自主控制。采用分解优化策略,对链路分配子问题采用匈牙利算法,对功率分配子问题采用拉格朗日对偶方法。仿真结果表明,与现有算法相比,所提方法可将uRLLC数据包和eMBB数据包的平均延迟分别降低18%和13%,在有效降低用户业务延迟的同时,显著增强了系统的鲁棒性。这一进步支持在工业5.0环境中以人为本、智能和可持续运营所必需的超可靠、低延迟通信。
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引用次数: 0
Real-Time Motion Capture and AI-Assisted Training Strategy Using Edge Computing and Smartphone Sensors 基于边缘计算和智能手机传感器的实时运动捕捉和人工智能辅助训练策略
IF 0.5 Q4 TELECOMMUNICATIONS Pub Date : 2026-01-19 DOI: 10.1002/itl2.70220
Yashan Zhang, Wenwen Pan

Traditional cloud-centric architectures face significant challenges when processing high-frequency multimodal data from massive smart devices and smartphone sensors used during motion capture and training. Current approaches also struggle with the low-latency computational demands of various sensors. Edge computing has appeared as a novel way to reduce the processing latency. Hence, to address these challenges, a real-time motion capture method is proposed under the edge computing framework for an AI-assisted sports training strategy. First, an edge computing-based framework is designed to leverage distributed edge resources and lightweight AI models for sensor data capture and training guidance on smartphones. Besides, based on the lightweight Long Short-Term Memory (LSTM) model, we propose a real-time motion capture and AI-assisted sport training strategy. The LSTM is integrated to analyze temporal dependencies in sequential data, which is ideal for capturing motion patterns, while the feedback mechanism is applied to optimize the sports training process iteratively. By comparing the proposed method with recent state-of-the-art approaches, the experimental results demonstrate that our method shows better performance in latency, accuracy, and F1-score.

传统的以云为中心的架构在处理来自运动捕捉和训练期间使用的大量智能设备和智能手机传感器的高频多模态数据时面临重大挑战。目前的方法也与各种传感器的低延迟计算需求作斗争。边缘计算作为一种减少处理延迟的新方法而出现。因此,为了解决这些挑战,在边缘计算框架下提出了一种用于人工智能辅助运动训练策略的实时动作捕捉方法。首先,设计了一个基于边缘计算的框架,利用分布式边缘资源和轻量级AI模型在智能手机上进行传感器数据捕获和训练指导。此外,基于轻量级长短期记忆(LSTM)模型,提出了一种实时动作捕捉和人工智能辅助运动训练策略。将LSTM集成到序列数据的时间依赖性分析中,这是捕获运动模式的理想方法,而将反馈机制应用于迭代优化运动训练过程。通过与最新的方法进行比较,实验结果表明,我们的方法在延迟、准确性和f1分数方面表现出更好的性能。
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引用次数: 0
期刊
Internet Technology Letters
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