Internet Technology Letters最新文献

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.