Search Results (67)

The convergence of Large Language Models (LLMs) and Internet of Things (IoT) systems has created a new class of intelligent applications across healthcare, industrial automation, smart cities, and connected homes. However, this integration introduces a complex and largely underexplored security landscape. LLMs deployed in IoT contexts face threats spanning both the AI and embedded systems domains, including prompt injection through sensor-driven inputs, model extraction from edge devices, data poisoning of IoT data streams, and privacy leakage through LLM-generated responses grounded in personal data. Simultaneously, LLMs are proving to be powerful tools for IoT security, with LLM-based intrusion detection systems achieving 95–99% accuracy on standard IoT datasets and LLM-driven threat intelligence outperforming traditional machine learning by significant margins. We systematically review 88 papers from IEEE, ACM, MDPI, and arXiv (2020–2025), providing: (1) a structured taxonomy of security threats targeting LLM-IoT systems, (2) a review of LLMs as security enablers for IoT, (3) an evaluation of privacy-preserving architectures including federated learning, differential privacy, homomorphic encryption, and trusted execution environments, (4) domain-specific security analysis across healthcare, industrial, smart home, smart grid, and vehicular IoT, and (5) a literature-based comparative analysis of LLM-based security systems. A central finding is the accuracy–efficiency–privacy trilemma: the model compression techniques needed to deploy LLMs on resource-constrained IoT devices can degrade security and even introduce new vulnerabilities. Our analysis provides researchers and practitioners with a structured understanding of both the risks and opportunities at the frontier of LLM-IoT security. Full article

With the advancement of IoT technology, a vast amount of floating-point time series data has emerged, posing significant challenges for data storage and transmission. To address this issue, the efficient compression of floating-point time series data has become increasingly important. In the field of lossless compression where precision loss is not allowed, compression and decompression are a symmetrical and reversible transformation. The optimization of its encoding and decoding strategies remains the current optimal path for lossless compression. Based on the existing lossless compression algorithms for time series, this paper proposes Rabbit, which is a new floating-point time series data stream lossless compression algorithm. This method can perceive the data characteristics and, by leveraging the temporal locality of the time series, predict the branch distribution of the data stream during compression, thereby dynamically encoding the flag bits. This algorithm designs a TOE encoding method specifically for the significant bits to reduce the number of compressed bits. Compared with traditional floating-point compression schemes, its performance has been significantly improved. Experimental evaluations on 28 datasets show that this algorithm consistently outperforms existing methods with an average improvement of 4.15% over the baseline ACTF algorithm. Notably, on datasets such as server31, server34, and server41, the compression ratio can be reduced by up to 43.04%. Additionally, the compression and decompression time metrics have improved by 4.27% and 3.74%, respectively. Overall, Rabbit offers an effective lossless compression approach for floating-point time-series data, improving the compression ratio without compromising encoding/decoding throughput. Full article

Accurate and non-destructive evaluation of grape quality is crucial for intelligent viticulture, yet most existing approaches address cultivar classification and soluble solid content (SSC) prediction as independent tasks based on single-modality data, limiting robustness and practical applicability. This study proposes DualStream-RTNet, a unified multimodal deep learning framework that simultaneously performs grape cultivar classification and SSC prediction by integrating RGB-HSV fused images and PCA-compressed hyperspectral spectra. The dual-stream architecture enables the complementary learning of external chromatic–textural cues and internal physicochemical information, while a Transformer-enhanced fusion module strengthens global representation and cross-modal correlation. A dataset of 864 berries from five grape cultivars was used to validate the model. DualStream-RTNet achieved 93.64% classification accuracy, outperforming ResNet18 and other CNN baselines, and produced more compact and consistent confusion-matrix patterns. For SSC prediction, it consistently yielded the highest performance across cultivars, with R2p values up to 0.9693 and RMSE as low as 0.2567, surpassing the PLSR, SVR, LSTM, and Transformer regression models. These results demonstrate the superiority of the proposed framework in capturing both visual and spectral characteristics. DualStream-RTNet provides an efficient and scalable solution for comprehensive grape quality assessment, offering strong potential for real-time sorting, precision grading, and smart agricultural applications. Full article

In the field of intelligent inspection, high-definition video data collected by quadruped robot dogs face severe transmission and storage constraints. Although existing advanced lossy video coding standards can significantly improve compression efficiency, they inevitably introduce severe compression artifacts in low-bit-rate scenarios. To address this issue, this paper proposes a video decoding quality enhancement network named Video Quality Restoration Network (VQRNet), based on a dual-stream architecture. Specifically, the Local Feature Extraction component incorporates a Progressive Feature Fusion Module (PFFM) with a four-stage progressive structure. By integrating reparameterized convolution and attention mechanisms, PFFM focuses on capturing high-frequency texture details to repair small-scale distortions. Simultaneously, the Multi-Scale Lightweight Spatial Attention Module (MLSA) performs spatial feature recalibration, leveraging multi-scale convolution to adaptively identify and enhance key spatial regions, specifically addressing multi-scale distortion. In the Global Feature Extraction component, the State-Space Attention Module (SSAM) combines State-Space Models (SSMs) with attention mechanisms to capture long-range dependencies and contextual information, for large-scale distortions caused by high-intensity compression. To verify the performance of the proposed algorithm, a dedicated dataset comprising 20 real-world video sequences captured by quadruped robot dogs (partitioned into 15 training and 5 testing sequences) was constructed, and the VTM 23.4 reference software was employed to simulate compression degradation using four quantization parameters (QP 30, 35, 40, and 45). Experimental results demonstrate that VQRNet outperforms state-of-the-art quality enhancement methods in terms of core metrics, including PSNR and SSIM, specifically including MIRNet, NAFNet, TRRHA, and CTNet. In the QP = 30 scenario, VQRNet achieves an average PSNR of 40.33 dB, a significant improvement of 3.32 dB over the VTM 23.4 baseline (37.01 dB), while demonstrating significant advantages in computational complexity and parameter efficiency—requiring only 5.27 G FLOPs and 1.40 M parameters, with an average inference latency of only 11.82 ms per 128 × 128 patch. This work provides robust technical support for the efficient video perception of quadruped robot dogs. Full article

Occupant presence is a primary driver of Heating, Ventilation, and Air Conditioning (HVAC) and lighting energy consumption in office environments. Existing occupancy-sensing solutions often rely on privacy-sensitive modalities or require costly infrastructure, limiting their applicability in Small and Medium Offices (SMOs). To address these limitations, this study proposes a lightweight CSI-based occupancy-sensing framework based on a dual-core ESP32-S3 architecture, enabling concurrent CSI processing, environmental sensing, and cloud communication. A multi-stage signal preprocessing pipeline compresses raw CSI streams into a compact $56\times 8$ statistical feature matrix, achieving 98.86% classification accuracy for multi-level occupancy estimation. Compared with image-based baselines such as DenseNet121, the proposed approach reduces input data size to 24 kB and model parameters to 138 K, yielding over 129× reduction in transmission volume without sacrificing performance. These results demonstrate that the proposed framework provides a practical, privacy-preserving, and edge-deployable solution for occupancy-aware energy management in SMOs. Full article

The construction industry faces intensifying pressure to mitigate its environmental impact, particularly concerning the reliance on non-biodegradable materials and hazardous formaldehyde-based adhesives. Although bio-based alternatives are emerging, many still depend on virgin feedstocks, and the valorization rates for abundant waste streams like used cooking oil remain critically low. To bridge this gap, this study developed a sustainable, formaldehyde-free Modified Reused Palm Oil-Polyurethane (MRPO-PU) adhesive specifically for binding sugarcane bagasse particleboards. The synthesis process involved filtering used palm oil and subjecting it to epoxidation and hydroxylation reactions to yield a functional bio-polyol, the chemical structure of which was validated via Fourier Transform Infrared Spectroscopy (FTIR). This bio-polyol was subsequently mixed with polymeric diphenylmethane diisocyanate (pMDI) and combined with alkali-treated bagasse at varying adhesive ratios ranging from 15 to 85 wt%. Physical and mechanical evaluations demonstrated a robust positive correlation between adhesive content and composite integrity. Specifically, increasing the adhesive loading enhanced density up to 444 kg/m³ and minimized thickness swelling to 5.1%, while flexural and compressive strengths significantly improved. The data suggests an optimal efficiency range between 45 and 55 wt%. Ultimately, this research validates a dual-waste valorization strategy, offering a scalable circular economy model that transforms agricultural residues and spent oils into high-performance, eco-friendly construction materials. Full article

Scalable video coding plays an essential role in supporting heterogeneous devices, network conditions, and application requirements in modern video streaming systems. However, most existing scalable coding approaches primarily optimize human perceptual quality and provide limited support for data privacy, as well as for machine analyses and the integration of heterogeneous sensor data. This limitation motivated the development of adaptive scalable video coding frameworks. The proposed approach is designed to serve both human viewers and automated analysis systems while ensuring high security and compression efficiency. The method adaptively encrypts selected layers during transmission to protect sensitive content without degrading decoding or analysis performance. Experimental evaluations on benchmark datasets demonstrate that the proposed framework achieves superior rate distortion efficiency and reconstruction quality, while also improving machine analysis accuracy compared to existing traditional and learning-based codes. In video surveillance scenarios, where the base layer is preserved for analysis, the proposed scalable human machine coding (SHMC) method outperforms scalable extensions of H.265/High Efficiency Video Coding (HEVC), Scalable High Efficiency Video Coding (SHVC), reducing the average bit-per-pixel (bpp) by 26.38%, 30.76%, and 60.29% at equivalent mean Average Precision (mAP), Peak Signal-to-Noise Ratio (PSNR), and Multi-Scale Structural Similarity (MS-SSIM) levels. These results confirm the effectiveness of integrating scalable video coding with intelligent encryption for secure and efficient video transmission. Full article

Event-based sensors provide sparse, motion-centric measurements that can reduce data bandwidth and enable always-on perception on resource-constrained edge devices. This paper presents an event-based machine vision framework for smart-home AIoT that couples a Dynamic Vision Sensor (DVS) with compute-efficient algorithms for (i) human/object detection, (ii) 2D human pose estimation, (iii) hand posture recognition for human–machine interfaces. The main methodological contributions are timestamp-based, polarity-agnostic recency encoding that preserves moving-edge structure while suppressing static background, and task-specific network optimizations (architectural reduction and mixed-bit quantization) tailored to sparse event images. With a fixed downstream network, the recency encoding improves action recognition accuracy over temporal accumulation (0.908 vs. 0.896). In a 24 h indoor monitoring experiment (640 × 480), the raw DVS stream is about 30× smaller than conventional CMOS video and remains about 5× smaller after standard compression. For human detection, the optimized event processing reduces computation from 5.8 G to 81 M FLOPs and runtime from 172 ms to 15 ms (more than 11× speed-up). For pose estimation, a pruned HRNet reduces model size from 127 MB to 19 MB and inference time from 70 ms to 6 ms on an NVIDIA Titan X while maintaining a comparable accuracy (mAP from 0.95 to 0.94) on MS COCO 2017 using synthetic event streams generated by an event simulator. For hand posture recognition, a compact CNN achieves 99.19% recall and 0.0926% FAR with 14.31 ms latency on a single i5-4590 CPU core using 10-frame sequence voting. These results indicate that event-based sensing combined with lightweight inference is a practical approach to privacy-friendly, real-time perception under strict edge constraints. Full article

Devices equipped with the Global Positioning System (GPS) generate massive volumes of trajectory data on a daily basis, imposing substantial computational, network, and storage burdens. Online trajectory simplification reduces redundant points in a streaming manner while preserving essential spatial and temporal characteristics. A representative method in this line of research is Directed acyclic graph-based Online Trajectory Simplification (DOTS). However, DOTS does not preserve stay-related information and can incur high computational cost. To address these limitations, we propose Directed acyclic graph-based Online Trajectory Simplification with Stay Areas (DOTSSA), a fast online simplification method that integrates DOTS with an online stay area detection algorithm (SA). In DOTSSA, SA continuously monitors movement patterns to detect stay areas and segments the incoming trajectory accordingly, after which DOTS is applied to the extracted segments. This approach ensures the preservation of stay areas while reducing computational overhead through localized DAG construction. Experimental evaluations on a real-world dataset show that, compared with DOTS, DOTSSA can reduce compression time, while achieving comparable compression ratios and preserving key trajectory features. Full article

Whey and its permeates constitute highly organic, low-alkalinity dairy streams whose management remains suboptimal in many processing facilities. This narrative review integrates recent evidence on the anaerobic digestion (AD) of whey, linking substrate composition and biodegradability with microbial pathways, inhibition mechanisms, biogas quality, and techno-economic and environmental feasibility in industrial settings. Data for sweet whey, acid whey, and their permeates are synthesized, with emphasis on operational windows, micronutrient requirements, and co-digestion or C/N/P/S balancing strategies that sustain resilient methanogenic communities. Options for biogas conditioning and upgrading towards combined heat and power, boiler applications, and compressed or liquefied biomethane are examined, and selection criteria are proposed based on impurity profiles, thermal integration, and methane-recovery performance. Finally, critical R&D gaps are identified, including mechanistic monitoring, bioavailable micronutrition, modular upgrading architectures, and the valorization of digestate as a recovered fertilizer. This review provides an integrated framework to guide the design and operation of technically stable, environmentally verifiable, and economically viable whey-to-biomethane schemes for the dairy industry. Full article

The fusion of Artificial Intelligence and the Internet of Things (AI-IoT, also widely referred to as AIoT) offers transformative potential for smart cities, yet presents a critical challenge: how to process heterogeneous data streams from intelligent sensing—particularly crowd sensing data derived from citizen interactions like text, voice, and system logs—into reliable intelligence for sustainable urban governance. To address this challenge, we introduce the Intelligent Multimodal Ticket Processing System (IMTPS), a novel AI-IoT smart system. Unlike ad hoc solutions, the novelty of IMTPS resides in its theoretically grounded architecture, which orchestrates Information Theory and Game Theory for efficient, verifiable extraction, and employs Causal Inference and Meta-Learning for robust reasoning, thereby synergistically converting noisy, heterogeneous data streams into reliable governance intelligence. This principled design endows IMTPS with four foundational capabilities essential for modern smart city applications: Sustainable and Efficient AI-IoT Operations: Guided by Information Theory, the IMTPS compression module achieves provably efficient semantic-preserving compression, drastically reducing data storage and energy costs. Trustworthy Data Extraction: A Game Theory-based adversarial verification network ensures high reliability in extracting critical information, mitigating the risk of model hallucination in high-stakes citizen services. Robust Multimodal Fusion: The fusion engine leverages Causal Inference to distinguish true causality from spurious correlations, enabling trustworthy integration of complex, multi-source urban data. Adaptive Intelligent System: A Meta-Learning-based retrieval mechanism allows the system to rapidly adapt to new and evolving query patterns, ensuring long-term effectiveness in dynamic urban environments. We validate IMTPS on a large-scale, publicly released benchmark dataset of 14,230 multimodal records. IMTPS demonstrates state-of-the-art performance, achieving a 96.9% reduction in storage footprint and a 47% decrease in critical data extraction errors. By open-sourcing our implementation, we aim to provide a replicable blueprint for building the next generation of trustworthy and sustainable AI-IoT systems for citizen-centric smart cities. Full article

Compressing a mixture of soil, water, and stabilizer forms compressed stabilized earth blocks (CSEBs), a modernized earthen construction material capable of performance similar to that of engineered masonry with added sustainability achieved by usage of raw materials on-site, reduction in transportation costs of bulk materials to the build site, and improved thermal performance of built CSEB structures. CSEBs have a wide range of potential physical properties due to variations in base soil, mix composition, stabilizer, admixtures, and initial compression achieved in CSEB creation. While CSEB construction offers several opportunities to improve the sustainability of construction practices, assuring codifiable, standardized mix design for a target strength or durability remains a challenge as the mechanical character of the primary base soil varies from site to site. Quality control may be achieved through creation and testing of CSEB samples, but this adds time to a construction schedule. Such delays may be reduced through development of predictive CSEB compressive strength estimation models. This study experimentally determined CSEB compressive strength for six different mix compositions. Compressive strength predictive models were developed for 7-day and 28-day CSEB samples through multiple numerical models (i.e., linear regression, back-propagation neural network) designed and implemented to relate design inputs to 7-day and 28-day compressive strength. Model results provide insight into the predictive performance of linear regression and back-propagation neural networks operating on designed data streams. Performance, robustness, and significance of changes to the model dataset and feature set are characterized, revealing that linear regression outperformed neural networks on 28-day data and that inclusion of downstream data (i.e., cylinder compressive strength) did not significantly impact model performance. Full article

Intelligent personnel safety management is a critical component of smart manufacturing infrastructure. This paper presents an integrated framework combining a structurally optimized neural network (enhanced with spatial and channel feature fusion mechanisms for multi-scale detection) with an agent-based large language model (LLM) enhanced with retrieval-augmented generation (RAG) capabilities for factory safety monitoring. The visual detection component employs the Similarity-Aware Channel Pruning (SACP) method for automated, performance-preserving compression by identifying and suppressing redundant channels based on similarity and norm regularization, while the agent-based LLM with RAG capabilities dynamically integrates real-time violation data with established safety management protocols to generate precise diagnostic reports and operational recommendations. The optimized network achieves real-time violation detection in parallel video streams, and the LLM-powered assistant facilitates intelligent decision-making through natural language querying. Extensive evaluations on multiple benchmark datasets and a real-world safety helmet detection dataset demonstrate the scheme’s superior performance in both accuracy and practical applicability for industrial deployment. Full article

Image steganography is often employed in information security and confidential communications, yet it typically faces challenges of imperceptibility and robustness during transmission. Meanwhile, insufficient attention has been paid to preserving the quality of the secret image after JPEG compression at the receiver, which limits the effectiveness of steganography. In this study, we propose an anti-compression attention-based diffusion pattern steganography model using GAN (ADPGAN). ADPGAN leverages dense connectivity to fuse shallow and deep image features with secret data, achieving high robustness against JPEG compression. Meanwhile, an enhanced attention module and a discriminator are employed to minimize image distortion caused by data embedding, thereby significantly improving the imperceptibility of the host image. Based on ADPGAN, we propose a novel JPEG-compression-resistant image framework that improves the quality of the recovered image by ensuring that the degradation of the reconstructed image primarily stems from sampling rather than JPEG compression. Unlike direct embedding of full-size secret images, we downsample the secret image into a secret data stream and embed it into the cover image via ADPGAN, demonstrating high distortion resistance and high-fidelity recovery of the secret image. Ablation studies validate the effectiveness of ADPGAN, achieving a 0-bit error rate (BER) under JPEG compression at a quality factor of 20, yielding an average Peak Signal-to-Noise Ratio (PSNR) of 39.70 dB for the recovered images. Full article

This paper introduces a new analytical model of a multi-service system to which a heterogeneous mixture of stream, elastic, and adaptive traffic is offered, corresponding to data streams transmitted in real-world networks. The heterogeneity results from the coexistence of compressed and non-compressed traffic classes, where the compressed traffic is subject to a finite compression mechanism and includes both elastic and adaptive traffic. The model accounts for access constraints on resources for stream traffic, a strategy frequently used in modern ICT networks to manage the impact of high-rate data streams on overall system performance. The proposed model is developed based on an analysis of the service process within the system using multi-dimensional Markov processes. To assess the accuracy of the analytical model, a digital simulation is conducted. A comparative analysis of the results obtained from the analytical model and the simulation experiments confirms the high accuracy and robustness of the proposed approach, making it suitable for the design and optimization of modern ICT networks, including 5G systems. Full article