Search Results (395)

We analyze a model of the packet buffer in which a new packet can be discarded with a probability connected to the buffer occupancy through an arbitrary dropping function. Crucially, it is assumed that packet lengths can be correlated in any way and that the interarrival time has a general distribution. From an engineering perspective, such a model constitutes a generalization of many active buffer management algorithms proposed for Internet routers. From a theoretical perspective, it generalizes a class of finite-buffer models with the tail-drop discarding policy. The contributions include formulae for the distribution of buffer occupancy and the average buffer occupancy, at arbitrary times and also in steady state. The formulae are illustrated with numerical calculations performed for various dropping functions. The formulae are also validated via discrete-event simulations. Full article

With rapid advancements in transformers, the reconstruction of hyperspectral images from RGB images, also known as spectral super-resolution (SSR), has made significant breakthroughs. However, existing transformer-based methods often struggle to balance computational efficiency with long-range receptive fields. Recently, Mamba has demonstrated linear complexity in modeling long-range dependencies and shown broad applicability in vision tasks. This paper proposes a multi-scale spectral–spatial sequence learning method, named MSS-Mamba, for reconstructing hyperspectral images from RGB images. First, we introduce a continuous spectral–spatial scan (CS3) mechanism to improve cross-dimensional feature extraction of the foundational Mamba model. Second, we propose a sequence tokenization strategy that generates multi-scale-aware sequences to overcome Mamba’s limitations in hierarchically learning multi-scale information. Specifically, we design the multi-scale information fusion (MIF) module, which tokenizes input sequences before feeding them into Mamba. The MIF employs a dual-branch architecture to process global and local information separately, dynamically fusing features through an adaptive router that generates weighting coefficients. This produces feature maps that contain both global contextual information and local details, ultimately reconstructing a high-fidelity hyperspectral image. Experimental results on the ARAD_1k, CAVE and grss_dfc_2018 dataset demonstrate the performance of MSS-Mamba. Full article

The automotive industry is undergoing a transition toward Software-Defined Vehicles (SDVs), necessitating the integration of AUTOSAR Adaptive, a standard for vehicle control, with ROS2, a platform for autonomous driving research. However, current static bridge approaches present notable limitations, chiefly regarding unnecessary resource consumption and compatibility issues with Quality of Service (QoS). To tackle these challenges, in this paper, we put forward a dynamic bridge architecture consisting of three components: a Discovery Manager, a Bridge Manager, and a Message Router. The proposed dynamic SOME/IP-DDS bridge dynamically detects service discovery events from the SOME/IP and DDS domains in real time, allowing for the creation and destruction of communication entities as needed. Additionally, it automatically manages QoS settings to ensure that they remain compatible. The experimental results indicate that this architecture maintains a stable latency even with a growing number of connections, demonstrating high scalability while also reducing memory usage during idle periods compared to static methods. Moreover, real-world assessments using an autonomous driving robot confirm its real-time applicability by reliably relaying sensor data to Autoware with minimal end-to-end latency. This research contributes to expediting the integration of autonomous driving exploration and production vehicle platforms by offering a more efficient and robust interoperability solution. Full article

This research introduces a novel de-anonymization technique targeting the Tor network, addressing limitations in prior attack models, particularly concerning router positioning following the introduction of bridge relays. Our method exploits two specific, inherent protocol-level vulnerabilities: the absence of a continuity check for circuit-level cells and anomalous residual values in RELAY_EARLY cell counters, working by manipulating cell headers to embed a covert signal. This signal is composed of reserved fields, start and end delimiters, and a payload that encodes target identifiers. Using this signal, malicious routers can effectively mark data flows for later identification. These routers employ a finite state machine (FSM) to adaptively switch between signal injection and detection. Experimental evaluations, conducted within a controlled environment using attacker-controlled onion routers, demonstrated that the embedded signals are undetectable by standard Tor routers, cause no noticeable performance degradation, and allow reliable correlation of Tor users with public services and deanonymization of hidden service IP addresses. This work reveals a fundamental design trade-off in Tor: the decision to conceal circuit length inadvertently exposes cell transmission characteristics. This creates a bidirectional vector for stealthy, protocol-level de-anonymization attacks, even though Tor payloads remain encrypted. Full article

Semantic segmentation of ultra-high-resolution remote sensing (UHR-RS) imagery plays a critical role in land use and land cover analysis, yet it remains computationally intensive due to the enormous input size and high spatial complexity. Existing studies have commonly employed strategies such as patch-wise processing, multi-scale model architectures, lightweight networks, and representation sparsification to reduce resource demands, but they have often struggled to maintain long-range contextual awareness and scalability for inputs of arbitrary size. To address this, we propose RingFormer-Seg, a scalable Vision Transformer framework that enables long-range context learning through multi-device parallelism in UHR-RS image segmentation. RingFormer-Seg decomposes the input into spatial subregions and processes them through a distributed three-stage pipeline. First, the Saliency-Aware Token Filter (STF) selects informative tokens to reduce redundancy. Next, the Efficient Local Context Module (ELCM) enhances intra-region features via memory-efficient attention. Finally, the Cross-Device Context Router (CDCR) exchanges token-level information across devices to capture global dependencies. Fine-grained detail is preserved through the residual integration of unselected tokens, and a hierarchical decoder generates high-resolution segmentation outputs. We conducted extensive experiments on three benchmarks covering UHR-RS images from 2048 × 2048 to 8192 × 8192 pixels. Results show that our framework achieves top segmentation accuracy while significantly improving computational efficiency across the DeepGlobe, Wuhan, and Guangdong datasets. RingFormer-Seg offers a versatile solution for UHR-RS image segmentation and demonstrates potential for practical deployment in nationwide land cover mapping, supporting informed decision-making in land resource management, environmental policy planning, and sustainable development. Full article

Metal additive manufacturing techniques have seen technological advancements in recent years, fueled by their ability to provide industrial use parts with excellent mechanical properties. Wire Arc Additive Manufacturing is a technology that is being widely used in critical industries, and much research is conducted in this field due to the multiple factors involved in the overall process. Within WAAM, gas metal arc welding stands out for its low cost, high production volume, high quality and capability for automation. In this study, a CNC router was retrofitted with a gas metal arc welding setup to facilitate precise metal printing. The flexibility in this process allows for rapid repairs on site without the need to replace the entire part. The literature predominantly focuses on the macro-mechanical properties of GMAW parts, and very few studies try to study the interaction and influence of different process parameters on the mechanical properties. Thus, this study focused on the GMAW WAAM of stainless-steel parts by studying the influence of the wire feed rate, arc voltage and strain rate on the UTS, yield strength, toughness and percentage elongation. ANOVA and interaction plots were analyzed to study the interaction between the input parameters on each output parameter. Results showed that printing stainless steel through the gas metal arc welding process with an arc voltage of 18.7 V and a wire feed rate of 6 m/min resulted in poor mechanical properties. The input parameter that influenced the mechanical properties the highest was the wire feed rate, followed by the arc voltage and strain rate. Printing with an arc voltage of 18.7 V and a wire feed rate of 5 m/min, tested at a crosshead speed of 1 mm/min, gave the best mechanical properties. Full article

Reusing cached data is a widely adopted technique for improving network and system performance. Future Internet architectures such as Named Data Networking (NDN) leverage intermediate nodes—such as proxy servers and routers—to cache and deliver data, reducing latency and alleviating load on original data sources. However, a fundamental challenge of this approach is the lack of trust in intermediate nodes, as users cannot reliably identify and verify them. To address this issue, many systems adopt data-oriented verification rather than sender authentication, using Merkle Hash Trees (MHTs) to enable users to verify both the integrity and authenticity of received data. Despite its advantages, MHT-based authentication incurs significant redundancy: identical hash values are often recomputed, and witness data are repeatedly transmitted for each segment. These redundancies lead to increased computational and communication overhead, particularly in large-scale data publishing scenarios. This paper proposes a novel scheme to reduce such inefficiencies by enabling the reuse of previously verified node values, especially transmitted witnesses. The proposed scheme improves both computational and transmission efficiency by eliminating redundant computation arising from repeated calculation of identical node values. To achieve this, it stores and reuses received witness values. As a result, when verifying 2ⁿ segments (n > 8), the proposed method achieves more than an 80% reduction in total hash operations compared to the standard MHT. Moreover, our method preserves the security guarantees of the MHT while significantly optimizing its performance in terms of both computation and transmission costs. Full article

Restricting and filtering harmful content on the Internet is a serious problem that is often addressed even at the state and legislative levels. Existing solutions for restricting and filtering online content are usually installed on end-user devices and are easily circumvented and difficult to adapt to larger groups of users with different filtering needs. To mitigate this problem, this study proposed a model of a web page classification and filtering solution suitable for use on home routers or other low-resource web page filtering devices. The proposed system combines the constantly updated web page category list approach with machine learning-based text classification methods. Unlike existing web page filtering solutions, such an approach does not require additional software on the client-side, is more difficult to circumvent for ordinary users and can be implemented using common low-resource routers intended for home and organizations usage. This study evaluated the feasibility of the proposed solution by creating the less resource-demanding implementations of machine learning-based web page classification methods adapted for low-resource home routers that could be used to classify and filter unwanted Internet pages in real-time based on the text of the page. The experimental evaluation of softmax regression, decision tree, random forest, and linear SVM (support vector machine) machine learning methods implemented in the C/C++ programming language was performed using a commercial home router Asus RT-AC85P with 256 MB RAM (random access memory) and MediaTek MT7621AT 880 MHz CPU (central processing unit). The implementation of the linear SVM classifier demonstrated the best accuracy of 0.9198 and required 1.86 s to process a web page. The random forest model was only slightly faster (1.56 s to process a web page), while its accuracy reached only 0.7879. Full article

Microwave energy is ideal for wearable devices due to its stable wireless power transfer capabilities. However, rigid receiving antennas in conventional RF energy harvesters compromise wearability. This study presents a wearable system using a flexible dual-band antenna (915 MHz/2.45 GHz) fabricated via conformal 3D printing on arm-mimicking curvatures, minimizing bending-induced performance loss. A hybrid microstrip–lumped element rectifier circuit enhances energy conversion efficiency. Tested with commercial 915 MHz transmitters and Wi-Fi routers, the system consistently delivers 3.27–3.31 V within an operational range, enabling continuous power supply for real-time physiological monitoring (e.g., pulse detection) and data transmission. This work demonstrates a practical solution for sustainable energy harvesting in flexible wearables. Full article

The rapid growth of high-quality telecommunications demands enhanced queueing system performance. Traditional bandwidth distribution often struggles to adapt to dynamic changes, network conditions, and erratic traffic patterns. Internet traffic fluctuates over time, causing resource underutilization. To address these challenges, this paper proposes a new adaptive algorithm called Weighted Fair Queues continual Deep Reinforcement Learning (WFQ continual-DRL), which integrates the advanced deep reinforcement learning Soft Actor-Critic (SAC) algorithm with the Elastic Weight Consolidation (EWC) approach. This technique is designed to overcome neural networks’ catastrophic forgetting, thereby enhancing network routers’ dynamic bandwidth allocation. The agent is trained to allocate bandwidth weights for multiple queues dynamically by interacting with the environment to observe queue lengths. The performance of the proposed adaptive algorithm was evaluated for eight queues until it expanded to twelve-queue systems. The model achieved higher cumulative rewards as compared to previous studies, indicating improved overall performance. The values of the Mean Squared Error (MSE) and Mean Absolute Error (MAE) decreased, suggesting effectively optimized bandwidth allocation. Reducing Root Mean Square Error (RMSE) indicated improved prediction accuracy and enhanced fairness computed by Jain’s index. The proposed algorithm was validated by employing real-world network traffic data, ensuring a robust model under dynamic queuing requirements. Full article

Composite materials have the advantages of high strength and low weight, and are therefore used in many areas. However, in humid and marine environments, mechanical properties may deteriorate due to moisture diffusion, especially in glass fiber reinforced polymers (GFRP) and carbon fiber reinforced polymers (CFRP). This study investigated the damage formation and changes in mechanical properties of single-layer adhesive-bonded GFRP and CFRP connections under the effect of sea water. In the experiment, 0/90 orientation, twill-woven GFRP (7 ply) and CFRP (8 ply) plates were produced as prepreg using the hand lay-up method in accordance with ASTM D5868-01 standard. CNC Router was used to cut 36 samples were cut from the plates produced for the experiments. The samples were kept in sea water taken from the Aegean Sea, at 3.3–3.7% salinity and 23.5 °C temperature, for 1, 2, 3, 6, and 15 months. Moisture absorption was monitored by periodic weighings; then, the connections were subjected to three-point bending tests according to the ASTM D790 standard. The damages were analyzed microscopically with SEM (ZEISS GEMINI SEM 560). As a result of 15 months of seawater storage, moisture absorption reached 4.83% in GFRP and 0.96% in CFRP. According to the three-point bending tests, the Young modulus of GFRP connections decreased by 25.23% compared to dry samples; this decrease was 11.13% in CFRP. Moisture diffusion and retention behavior were analyzed according to Fick’s laws, and the moisture transfer mechanism of single-lap adhesively bonded composites under the effect of seawater was evaluated. Full article

Wireless Local Area Networks (WLANs), particularly Wi-Fi, serve as the backbone of modern connectivity, supporting billions of devices globally and forming a critical component in Internet of Things (IoT) ecosystems. However, the increasing ubiquity of WLANs also presents an expanding attack surface for adversaries—especially Advanced Persistent Threats (APTs), which operate with high levels of sophistication, resources, and long-term strategic objectives. This paper provides a holistic security analysis of WLANs under the lens of APT threat models, categorizing APT actors by capability tiers and examining their ability to compromise WLANs through logical attack surfaces. The study identifies and explores three primary attack surfaces: Radio Access Control interfaces, compromised insider nodes, and ISP gateway-level exposures. A series of empirical experiments—ranging from traffic analysis of ISP-controlled routers to offline password attack modeling—evaluate the current resilience of WLANs and highlight specific vulnerabilities such as credential reuse, firmware-based leakage, and protocol downgrade attacks. Furthermore, the paper demonstrates how APT resources significantly accelerate attacks through formal models of computational scaling. It also incorporates threat modeling frameworks, including STRIDE and MITRE ATT&CK, to contextualize risks and map adversary tactics. Based on these insights, this paper offers practical recommendations for enhancing WLAN resilience through improved authentication mechanisms, network segmentation, AI-based anomaly detection, and open firmware adoption. The findings underscore that while current WLAN implementations offer basic protections, they remain highly susceptible to well-resourced adversaries, necessitating a shift toward more robust, context-aware security architectures. Full article

This paper proposes a feature-driven topology reconfiguration framework to enhance the resilience of Industrial Internet of Things (IIoT) systems against heterogeneous attacks. By dynamically partitioning IIoT into subnetworks based on localized attack features and reconstructing each subnetwork with tailored topologies, our framework significantly improves connectivity and communication efficiency. Evaluations on a real-world dataset (Tech-Routers-RF) characterizing IIoT topologies with 2113 nodes show that under diverse attack scenarios, connectivity and communication efficiency improve by more than 70% and 50%, respectively. Leveraging information entropy to quantify the trade-off between structural diversity and connection predictability, our work bridges adaptive network design with real-world attack dynamics, offering a scalable solution for securing large-scale IIoT deployments. Full article

Networked microgrids (NMG) are gaining popularity as an example of smart grids (SG), where power networks are integrated with communication technologies. Communication technologies enable NMGs to be monitored and controlled via communication networks. However, ensuring that communication networks in NMGs satisfy quality of delivery (QoD) metrics such as the round trip time (RTT) of NMG control data is necessary. This paper addresses the communication network types and communication technologies used in NMGs. We present various NMG deployments to demonstrate real-life applicability in different contexts. We develop a real-time NMG testbed using real hardware, such as Cisco 4331 Integrated Services Routers (ISR). We evaluate QoD in NMG control data by measuring RTT under varying relative network congestion levels. The results reveal that high-variance background traffic leads to greater RTTs, surpassing the industrial communication response time requirement specified by the European Telecommunications Standards Institute (ETSI) by over 25 times. Full article

As a data-centric next-generation network architecture, Named Data Networking (NDN) exhibits inherent compatibility with the distributed nature of the Internet of Things (IoT) through its name-based routing mechanism. However, existing signature schemes for NDN-IoT face dual challenges: resource-constrained IoT terminals struggle with certificate management and computationally intensive bilinear pairings under traditional Public Key Infrastructure (PKI), while NDN routers require low-latency batch verification for high-speed data forwarding. To address these issues, this study proposes ECAE, an efficient certificateless aggregate signature scheme based on elliptic curve cryptography (ECC). ECAE introduces a partial private key distribution mechanism in key generation, enabling the authentication of identity by a Key Generation Center (KGC) for terminal devices. It leverages ECC and universal hash functions to construct an aggregate verification model that eliminates bilinear pairing operations and reduces communication overhead. Security analysis formally proves that ECAE resists forgery, replay, and man-in-the-middle attacks under the random oracle model. Experimental results demonstrate substantial efficiency gains: total computation overhead is reduced by up to 46.18%, and communication overhead is reduced by 55.56% compared to state-of-the-art schemes. This lightweight yet robust framework offers a trusted and scalable verification solution for NDN-IoT environments. Full article