Internet Technology Letters最新文献

CSI and RSSI fingerprints are applied in this paper to produce a robust indoor localization framework within IEEE 802.11n wireless networks. This system employs a two-phase fingerprinting approach consisting of calibration and positioning to operate effectively in complex indoor environments. A detailed radio map is created using advanced CSI processing and aggregation followed by a probabilistic location estimation based on correlations. Through the use of multiple testbeds, such as the IT-1 and IT-2 buildings, the proposed model is evaluated against traditional methods including RSSI, FIFS, and CSI-MIMO. As compared to existing approaches, the proposed model consistently outperforms them in indoor settings. In light of these findings, the location-based indoor services provided by the system are of high precision.

Unprecedented security challenges were offered by the rapid evolution of 6G networks. These unprecedented security challenges, especially segmented attacks (SA), exploit network susceptibilities. Here, advanced detection and migration methods are needed to ensure robust security. Here, the limited potential of the limited feature extraction (FE) and feature classification of the intrusion detection (ID) systems (IDS) may result in the lack of real-time (RT) adaptability. This IDS also fails to detect advanced segmentation-based threats accurately. For 6G networks, an AI-driven intrusion detection system (IDS) with deep packet inspection (AI-IDS-DSP) is suggested in this paper. This suggested method will assist in overcoming those limitations. Then, the digital signal processing (DSP) techniques are also integrated into this suggested method, and this integration will help in analyzing signal anomalies. Those DSP methods include wavelet transforms (WT) and Fourier transforms (FT). Then, the hybrid AI model (CNN + Transformer) is utilized by the suggested method for the purpose of anomaly detection (AD). The application of reinforcement learning (RL) may enhance the adaptive security measures in the RT. Finally, the sensitive financial transactions are secured by the suggested robust network security (NS) method. This suggested NS application will help in preventing single account (SA) issues and offers proactive detection. The data integrity (DI) in university financial management systems were also implemented by this suggested NS method.

Reliable crowd counting for IoT video analytics requires strong generalization across heterogeneous edge cameras. However, models trained on a labeled source domain often degrade on unseen cameras due to shifts in appearance, viewpoint, and density statistics. We propose MDANet, a deployment-oriented framework for cross-domain crowd counting that performs complementary alignment at three levels while keeping test-time inference identical to a lightweight backbone. At the data level, Fourier Amplitude Mix reduces camera-dependent style gaps by mixing low-frequency amplitudes. At the feature level, global–local High-Entropy Adversarial Regularization suppresses domain-discriminative cues under spatial heterogeneity. At the domain level, Density-Conditional Alignment modulates alignment strength according to predicted density to mitigate congestion-dependent errors. Extensive experiments show that MDANet achieves competitive or state-of-the-art accuracy with a favorable accuracy-efficiency trade-off, and additional evaluations under common stream degradations confirm its stability for edge deployment.

Reliable crowd counting for IoT video analytics requires strong generalization across heterogeneous edge cameras. However, models trained on a labeled source domain often degrade on unseen cameras due to shifts in appearance, viewpoint, and density statistics. We propose MDANet, a deployment-oriented framework for cross-domain crowd counting that performs complementary alignment at three levels while keeping test-time inference identical to a lightweight backbone. At the data level, Fourier Amplitude Mix reduces camera-dependent style gaps by mixing low-frequency amplitudes. At the feature level, global–local High-Entropy Adversarial Regularization suppresses domain-discriminative cues under spatial heterogeneity. At the domain level, Density-Conditional Alignment modulates alignment strength according to predicted density to mitigate congestion-dependent errors. Extensive experiments show that MDANet achieves competitive or state-of-the-art accuracy with a favorable accuracy-efficiency trade-off, and additional evaluations under common stream degradations confirm its stability for edge deployment.

With the evolution of 6G wireless networks, wireless sensor networks are facing new challenges, particularly when it comes to energy efficiency and reliable data transmission. This paper proposes an energy-aware, intelligent routing framework that uses deep reinforcement learning to extend the lifetime of networks and increase data throughput in 6G networks. Through the implementation of a deep recurrent Q-learning mechanism, the framework enables dynamic routing with residual energy and node proximity as criteria for selecting the next hop, either in single-hop or multi-hop scenarios. As demonstrated by experimental results, the proposed model delivers higher packets, consumes less energy, and has a lower latency while achieving greater throughput than conventional PSO or clustering-based methods. WSNs of the future can take advantage of its robust routing capabilities.

Recent grammar error correction (GEC) systems have scaled rapidly in model size and architectural depth, creating a growing mismatch between algorithmic improvements and the latency and energy constraints of edge devices. The method reformulates English GEC as a task-constrained latent editing problem, where grammatical corrections are represented as low-rank perturbations in a compact linear subspace. A Tiny-LM-style weight re-parameterization aligns the latent editing vectors with a minimal set of re-parameterized weights, ensuring that English grammatical reasoning is concentrated in a hardware-friendly linear manifold. To improve correction fidelity under tight computational budgets, a two-stage progressive refinement strategy is employed: a fixed-window lookahead performs coarse structural edits, followed by a sparse consistency filter that selectively verifies candidate token corrections under INT8/INT4 quantization. The entire pipeline is static-shape and operator-regular, relying solely on linear, NPU-native operations for predictable latency and bounded memory footprint. Experiments on public datasets show that the proposed model outperforms large Transformer baselines in F0.5 score on typical edge NPUs while reducing latency by 3–7×, demonstrating that accurate, low-latency, on-device English GEC is achievable using generic NPU operators without heavyweight language models.

Recent grammar error correction (GEC) systems have scaled rapidly in model size and architectural depth, creating a growing mismatch between algorithmic improvements and the latency and energy constraints of edge devices. The method reformulates English GEC as a task-constrained latent editing problem, where grammatical corrections are represented as low-rank perturbations in a compact linear subspace. A Tiny-LM-style weight re-parameterization aligns the latent editing vectors with a minimal set of re-parameterized weights, ensuring that English grammatical reasoning is concentrated in a hardware-friendly linear manifold. To improve correction fidelity under tight computational budgets, a two-stage progressive refinement strategy is employed: a fixed-window lookahead performs coarse structural edits, followed by a sparse consistency filter that selectively verifies candidate token corrections under INT8/INT4 quantization. The entire pipeline is static-shape and operator-regular, relying solely on linear, NPU-native operations for predictable latency and bounded memory footprint. Experiments on public datasets show that the proposed model outperforms large Transformer baselines in F0.5 score on typical edge NPUs while reducing latency by 3–7×, demonstrating that accurate, low-latency, on-device English GEC is achievable using generic NPU operators without heavyweight language models.

This paper introduces a novel federated learning framework that integrates dynamic blockchain sharding and Byzantine fault tolerance mechanisms (FL-Sharding-BFT). The proposed framework dynamically adjusts shard configurations based on node capacity and network conditions, reducing communication overhead and enhancing model synchronization. The Byzantine fault tolerance mechanism further ensures robustness by identifying and isolating malicious nodes during model aggregation. It also ensures stronger robustness and faster convergence, highlighting its scalability and security for federated learning in 5G edge networks.

This paper introduces a novel federated learning framework that integrates dynamic blockchain sharding and Byzantine fault tolerance mechanisms (FL-Sharding-BFT). The proposed framework dynamically adjusts shard configurations based on node capacity and network conditions, reducing communication overhead and enhancing model synchronization. The Byzantine fault tolerance mechanism further ensures robustness by identifying and isolating malicious nodes during model aggregation. It also ensures stronger robustness and faster convergence, highlighting its scalability and security for federated learning in 5G edge networks.

In this work, a quad modal mode division multiplexing system utilizing linearly polarized (LP) modes, namely, LP[0,1], LP[2,2], LP[0,3], and LP[1,3], is designed and analyzed under the impact of free space optics (FSO) and multimode fiber integrated link impairments. Results depict that reliable transmission over 100 m fiber and 100 m FSO links can be achieved at 160 Gbps data rate considering 1–7.5 dB insertion loss, free-space weather, and turbulence. Also, the spatial shift of 12 μm and spatial tilt of 10° can be supported with low modal crosstalk. Compared to existing works, this work provides long-reach and high-speed communication for 5G based networks.