Search Results (28)

The proliferation of Internet of Things (IoT) devices has created unprecedented challenges in cybersecurity, as billions of interconnected devices generate, process, and transmit sensitive data across diverse networks. This study addresses critical security vulnerabilities in IoT ecosystems, focusing on the development of a comprehensive security framework that encompasses device authentication, an attribute access control mechanism, and privacy preservation. This work introduces HADA, a proposed hybrid authentication method that combines the validation of unique credentials and trust value. For the authentication of the data owner and user, the following credentials are validated: identity, certificate, reconfigurable physical unclonable function (PUF), and trust. Differential privacy is used to secure the credentials during information exchange. Then, the newly developed dynamic attribute access control method selects the number of attributes and matches the attributes; these two processes are performed using the Bi-Fuzzy logic and graph neural network (GNN) algorithms, respectively. After matching the data, the user is allowed to access them from the cloud server. For data encryption, the lightweight SKINNY algorithm is implemented in Hyperledger Fabric blockchain. The proposed system performs better than existing methods in terms of throughput, latency, and resource utilization. Full article

On 12 July 2016, the ruling on the South China Sea Arbitration was announced and rapidly drew worldwide attention, turning the event into a major international hotspot. Quantifying the dynamics of such hotspot events and understanding the evolution of media-based inter-actor networks during major shocks are of substantial research interest. Viewing these interactions as dynamic networks, we analyze the time-varying actor interaction structure surrounding the arbitration using the Global Database of Events, Location and Tone (GDELT), a large-scale media-based event database with global coverage since 1979. We extract nearly 30,000 events related to the arbitration from 5 July to 25 July 2016, constructing daily cooperation and conflict networks to quantify structural changes via network size and degree-entropy dynamics. To further reveal actor-level structural roles, we learn node embeddings on each daily network via an entropy-driven graph representation learning scheme and perform embedding-based clustering with automatically selected cluster numbers, visualized via t-SNE. The results show that key dates in the event window are associated with pronounced structural shifts in the networks, including changes in participation breadth, degree-distribution heterogeneity, and clearer differentiation and reconfiguration of actor roles, with distinct patterns between cooperation and conflict networks. These findings demonstrate the potential of massive media event data for characterizing structural responses and actor-role evolution in event-driven inter-actor networks. Full article

With the evolution of Reconfigurable Intelligent Surface (RIS) technology, its potential for dynamically optimizing wireless channels has garnered significant attention. However, existing methods still face challenges in real-time control in complex environments due to high computational complexity. To address this, this paper proposes a reconfigurable wireless channel optimization framework based on Intelligent Metasurfaces 2.0 and designs a low-complexity control strategy. The strategy integrates an adaptive adjustment mechanism and multi-dimensional feedback, aiming to reduce system computational load. Experimental results show that compared to traditional methods (such as MRC and MMSE), the proposed method improves signal transmission quality (SNR improvement of 3.8 dB) and system stability (exponential increase to 0.92). When compared to advanced deep reinforcement learning (DRL) and graph neural network (GNN) methods, it achieves similar signal quality while reducing computational overhead by 20.0% and energy consumption by approximately 32.4%. Ablation experiments further verify the effectiveness and synergistic role of the proposed core modules. This study provides a feasible approach toward high-efficiency, low-complexity dynamic channel optimization in 5G and future communication networks. Full article

Rapid urbanization is fragmenting ecological spaces in megacities, threatening biodiversity and ecosystem services. Yet, it remains unclear whether, and under what conditions, urban ecological networks (ENs) can recover robustness once heavily disrupted. This study aims to (i) develop a dynamic assessment framework that couples network robustness and connectivity, and (ii) apply it to examine how ENs evolve under sustained urbanization and shifting policy regimes. Using multi-period data for Shenzhen, China (2000–2025), we simulate deliberate and random attacks on patches and corridors to derive data-driven thresholds that grade the importance of ecological elements, and integrate these with graph-based connectivity metrics to track changes in network structure and node centrality over time. Shenzhen’s EN exhibits a typical “fragmentation–reconfiguration–optimization” pathway, with a “rapid decline–deceleration–recovery” trajectory in robustness that closely aligns with the introduction of strict ecological control lines and subsequent restoration initiatives. The results show that targeted protection of residual core habitats, combined with strategic reconnection and infill greening in the urban interior, can reverse earlier losses in network robustness. The proposed robustness-informed framework provides operational guidance for prioritizing protection, restoration, and optimization of ecological space, and offers a transferable approach for adaptive EN planning in high-density tropical and subtropical megacities. Full article

The increasing complexity of integrating modern convolutional neural networks into software systems imposes significant computational demands on machine learning infrastructures. Existing MLOps systems lack mechanisms for dynamic model selection based on dataset complexity, leading to inefficient resource utilization and limited scalability under high-load conditions. This study employs convolutional neural network-based machine learning algorithms for image classification and ensemble methods for quantitative feature classification. The paper presents a self-optimizing machine learning pipeline that integrates a microservices-based architecture with a formal process for estimating image complexity and an optimization-based model selection strategy. The proposed methodology is based on designing an adaptive microservice-based ML pipeline that dynamically reconfigures its computation graph at runtime. The results confirm the effectiveness of the proposed approach for building resilient and high-performance distributed systems. The mechanism proposed in this work enables the adaptive use of modern deep learning algorithms, leading to improved result quality. A comparative analysis with existing approaches demonstrates superiority in model selection complexity, pipeline overhead, and scalability. The outcome of the proposed mechanism is an adaptive algorithm selection process based on bias-related parameters, enabling the selection of the most suitable module for data processing. Full article

The deparser stage in the Protocol-Independent Switch Architecture (PISA) is often overshadowed by parser and match-action optimizations. Yet, it remains a critical performance bottleneck in P4-programmable FPGA data planes. Challenges associated with the deparser stem from dynamic header layouts, variable emission orders, and alignment constraints, which often necessitate resource-intensive designs, such as wide, dynamic crossbar routing. While compile-time specialization techniques can reduce logic usage, they sacrifice runtime adaptability: any change to the protocol graph, including adding, removing, or reordering headers, requires full hardware resynthesis and re-implementation, limiting their practicality for evolving or multi-tenant workloads. This work presents a unified FPGA-targeted deparser architecture that merges templated and overlay concepts within a hardware–software co-design framework. At design time, template parameters define upper bounds on protocol complexity, enabling resource-efficient synthesis tailored to specific workloads. Within these bounds, runtime reconfiguration is supported through overlay control tables derived from static deparser DAG analysis, which capture the per-path emission order, header alignments, and offsets. These tables drive protocol-agnostic, chunk-based emission blocks that eliminate the overhead of crossbar interconnects, thereby significantly reducing complexity and resource usage. The proposed design sustains high throughput while preserving the flexibility needed for in-field updates and long-term protocol evolution. Full article

A special class of complex adaptive systems—biological and social—thrive not by passively accumulating patterns, but by engineering coherence, i.e., the deliberate alignment of prior knowledge, real-time updates, and teleonomic purposes. By contrast, today’s AI stacks—Large Language Models (LLMs) wrapped in agentic toolchains—remain rooted in a Turing-paradigm architecture: statistical world models (opaque weights) bolted onto brittle, imperative workflows. They excel at pattern completion, but they externalize governance, memory, and purpose, thereby accumulating coherence debt—a structural fragility manifested as hallucinations, shallow and siloed memory, ad hoc guardrails, and costly human oversight. The shortcoming of current AI relative to human-like intelligence is therefore less about raw performance or scaling, and more about an architectural limitation: knowledge is treated as an after-the-fact annotation on computation, rather than as an organizing substrate that shapes computation. This paper introduces Mindful Machines, a computational paradigm that operationalizes coherence as an architectural property rather than an emergent afterthought. A Mindful Machine is specified by a Digital Genome (encoding purposes, constraints, and knowledge structures) and orchestrated by an Autopoietic and Meta-Cognitive Operating System (AMOS) that runs a continuous Discover–Reflect–Apply–Share (D-R-A-S) loop. Instead of a static model embedded in a one-shot ML pipeline or deep learning neural network, the architecture separates (1) a structural knowledge layer (Digital Genome and knowledge graphs), (2) an autopoietic control plane (health checks, rollback, and self-repair), and (3) meta-cognitive governance (critique-then-commit gates, audit trails, and policy enforcement). We validate this approach on the classic Credit Default Prediction problem by comparing a traditional, static Logistic Regression pipeline (monolithic training, fixed features, external scripting for deployment) with a distributed Mindful Machine implementation whose components can reconfigure logic, update rules, and migrate workloads at runtime. The Mindful Machine not only matches the predictive task, but also achieves autopoiesis (self-healing services and live schema evolution), explainability (causal, event-driven audit trails), and dynamic adaptation (real-time logic and threshold switching driven by knowledge constraints), thereby reducing the coherence debt that characterizes contemporary ML- and LLM-centric AI architectures. The case study demonstrates “a hybrid, runtime-switchable combination of machine learning and rule-based simulation, orchestrated by AMOS under knowledge and policy constraints”. Full article

Modern power systems face growing challenges in stability assessment due to large-scale renewable energy integration and rapidly changing operating conditions. Data-driven approaches have emerged as promising solutions for real-time stability assessment, yet their performance often degrades under network topology reconfigurations. To address this limitation, the Spatiotemporal Contrastive Graph Convolutional Network (STCGCN) is proposed for the joint task prediction of voltage and transient stability across known and unknown topologies. The framework integrates a graph convolutional network (GCN) encoder to capture spatial dependencies and a temporal convolutional network to model electromechanical dynamics. It also employs supervised contrastive learning to extract discriminative features due to the grid topology variation, enhance stability class separability, and mitigate class imbalance under varying operating conditions, such as fluctuating loads and renewable integration. Case studies on the IEEE 39-bus system demonstrate that STCGCN achieves 89.66% accuracy on in-sample datasets from known topologies and 87.73% on out-of-sample datasets from unknown topologies, outperforming single-task learning approaches. These results highlight the method’s robustness to topology variations and its strong generalization across configurations, providing a topology-aware and resilient solution for real-time joint voltage and transient stability assessment in power systems. Full article

This paper addresses the challenge of cooperatively escorting a moving underwater target, such as a human-occupied vehicle (HOV), using a multi-AUV formation in complex ocean environments. We propose a comprehensive framework, RG-SAPF scheme, that integrates a rigidity graph (RG)-based reconfigurable formation control strategy with a safe artificial potential field (SAPF) motion planning method. The RG-based controller enables the AUVs to form and dynamically reconfigure a 3D escort formation around the target using only relative position information, ensuring the target remains within the formation’s convex hull. Meanwhile, the SAPF algorithm, enhanced with an adaptive Widrow–Hoff rule, enables real-time and collision-free path planning in obstacle-rich environments. Simulation and experimental results demonstrate that the proposed method effectively maintains formation integrity, supports flexible obstacle avoidance, and provides continuous target escort under dynamic conditions, validating its potential for practical underwater escort applications. Full article

The proliferation of algorithmically generated malicious URLs necessitates a shift from syntactic detection to semantic analysis. This paper introduces PhishGraph, a disk-aware Approximate Nearest Neighbor (ANN) search system designed to perform billion-scale semantic similarity searches on URL embeddings for threat intelligence applications. Traditional in-memory ANN indexes are prohibitively expensive at this scale, while existing disk-based solutions fail to address the unique challenges of the cybersecurity domain: the high velocity of streaming data, the complexity of hybrid queries involving rich metadata, and the highly skewed, adversarial nature of query workloads. PhishGraph addresses these challenges through a synergistic architecture built upon the foundational principles of DiskANN. Its core is a Vamana proximity graph optimized for SSD residency, but it extends this with three key innovations: a Hybrid Fusion Distance metric that natively integrates structured attributes into the graph’s topology for efficient constrained search; a dual-mode update mechanism that combines high-throughput batch consolidation with low-latency in-place updates for streaming data; and an adaptive maintenance policy that monitors query patterns and dynamically reconfigures graph hotspots to mitigate performance degradation from skewed workloads. Our comprehensive experimental evaluation on a billion-point dataset demonstrates that PhishGraph’s adaptive, hybrid design significantly outperforms strong baselines, offering a robust, scalable, and efficient solution for modern threat intelligence. Full article

With the rapid advance of digital technologies, the service industry has become a key driver of sustainable economic growth and the restructuring of international trade. Drawing on value-added trade flows for five pivotal service industries—construction, air transportation, postal telecommunications, financial intermediation, and education—over 2013–2021, this study examines the spatial evolution of the global service value chain (GSVC). Using social network analysis combined with a Temporal Exponential Random Graph Model (TERGM), we assess the dynamics of the GSVC’ core–periphery structure and identify heterogeneous determinants shaping their spatial networks. The findings are as follows: (1) Exports across the five industries display an “East rising, West declining” pattern, with markedly heterogeneous magnitudes of change. (2) The construction industry is Europe-centered; air transportation exhibits a U.S.–China bipolar structure; postal telecommunications show the most pronounced “East rising, West declining” shift, forming four poles (United States, United Kingdom, Germany, China); financial intermediation contracts to a five-pole core (China, United States, United Kingdom, Switzerland, Germany); and education becomes increasingly multipolar. (3) The GSVC core–periphery system undergoes substantial reconfiguration, with some peripheral economies moving toward the core; the core expands in air transportation, while postal telecommunications exhibit strong regionalization. (4) Digital technology, foreign direct investment, and manufacturing structure promote network evolution, whereas income similarity may dampen it; the effects of economic freedom and labor-force size on spatial network restructuring differ significantly by industry. These results underscore the complex interplay of structural, institutional, and geographic drivers in reshaping GSVC networks and carry implications for fostering sustainable services trade, enhancing interregional connectivity, narrowing global development gaps, and advancing an inclusive digital transformation. Full article

Wireless sensor networks (WSNs) are a key enabling technology for modern e-Health applications. However, their deployment in clinical environments faces critical challenges due to dynamic network topologies, signal interference, and stringent energy constraints. Static resource allocation schemes often prove inadequate in these mission-critical settings, leading to communication failures that can compromise data integrity and patient safety. This paper proposes a novel hybrid framework for intelligent, dynamic resource allocation that addresses these challenges. The framework combines classical graph-coloring heuristics—Greedy and Recursive Largest First (RLF) for efficient initial channel assignment with the adaptive power of metaheuristics, specifically Simulated Annealing and Genetic Algorithms, for localized refinement. Unlike conventional approaches that require costly, network-wide reconfigurations, our method performs targeted adaptations only in interference-affected regions, thereby optimizing the trade-off between network reliability and energy efficiency. Comprehensive simulations modeled on dynamic, hospital-scale WSNs demonstrate the effectiveness of various hybrid strategies. Notably, our results demonstrate that a hybrid strategy using a Genetic Algorithm can most effectively minimize interference and ensure high data reliability, validating the framework as a scalable and resilient solution. These results validate the proposed framework as a scalable, energy-aware solution for resilient, real-time healthcare telecommunication infrastructures. Full article

Continual reinforcement learning (CRL) agents face significant challenges when encountering distributional shifts. This paper formalizes these shifts into two key scenarios, namely virtual drift (domain switches), where object semantics change (e.g., walls becoming lava), and concept drift (task switches), where the environment’s structure is reconfigured (e.g., moving from object navigation to a door key puzzle). This paper demonstrates that while conventional convolutional neural networks (CNNs) struggle to preserve relational knowledge during these transitions, graph convolutional networks (GCNs) can inherently mitigate catastrophic forgetting by encoding object interactions through explicit topological reasoning. A unified framework is proposed that integrates GCN-based state representation learning with a proximal policy optimization (PPO) agent. The GCN’s message-passing mechanism preserves invariant relational structures, which diminishes performance degradation during abrupt domain switches. Experiments conducted in procedurally generated MiniGrid environments show that the method significantly reduces catastrophic forgetting in domain switch scenarios. While showing comparable mean performance in task switch scenarios, our method demonstrates substantially lower performance variance (Levene’s test, $p<1.0\times {10}^{-10}$), indicating superior learning stability compared to CNN-based methods. By bridging graph representation learning with robust policy optimization in CRL, this research advances the stability of decision-making in dynamic environments and establishes GCNs as a principled alternative to CNNs for applications requiring stable, continual learning. Full article

This article studies the cooperative underwater payload transportation problem for multiple unmanned underwater vehicles (UUVs) operating in a constrained workspace with both static and dynamic obstacles. A novel cooperative formation control algorithm has been presented in this paper for the transportation of a large payload in underwater scenarios. More precisely, by using the advantages of multi-UUV formation cooperation, based on rigidity graph theory and backstepping technology, the distance between each UUV, as well as the UUV and the transport payload, is controlled to form a three-dimensional rigid structure so that the load remains balanced and stable, to coordinate the transport of objects within the feasible area of the workspace. Moreover, a neural network (NN) is utilized to maintain system stability despite unknown nonlinearities and disturbances in the system dynamics. In addition, based on the interfered fluid flow algorithm, a collision-free motion trajectory was planned for formation systems. The control scheme also performs real-time formation reconfiguration according to the size and position of obstacles in space, thereby enhancing the flexibility of cooperative handling. The uniform ultimate boundedness of the formation distance errors is comprehensively demonstrated by utilizing the Lyapunov stability theory. Finally, the simulation results show that the UUVs can quickly form and maintain the desired formation, transport the payload along the planned trajectory to shuttle in multi-obstacle environments, verify the feasibility of the method proposed in this paper, and achieve the purpose of the collaborative transportation of large underwater payload by multiple UUVs and their targeted delivery. Full article

The quantity of wind and photovoltaic power-based distributed generators (DGs) is continually rising within the distribution network, presenting obstacles to its safe, steady, and cost-effective functioning. Active distribution network dynamic reconfiguration (ADNDR) improves the consumption rate of renewable energy, reduces line losses, and optimizes voltage quality by optimizing the distribution network structure. Despite being formulated as a highly dimensional and combinatorial nonconvex stochastic programming task, conventional model-based solvers often suffer from computational inefficiency and approximation errors, whereas population-based search methods frequently exhibit premature convergence to suboptimal solutions. Moreover, when dealing with high-dimensional ADNDR problems, these algorithms often face modeling difficulties due to their large scale. Deep reinforcement learning algorithms can effectively solve the problems above. Therefore, by combining the graph attention network (GAT) with the deep deterministic policy gradient (DDPG) algorithm, a method based on the graph attention network deep deterministic policy gradient (GATDDPG) algorithm is proposed to online solve the ADNDR problem with the uncertain outputs of DGs and loads. Firstly, considering the uncertainty in distributed power generation outputs and loads, a nonlinear stochastic optimization mathematical model for ADNDR is constructed. Secondly, to mitigate the dimensionality of the decision space in ADNDR, a cyclic topology encoding mechanism is implemented, which leverages graph-theoretic principles to reformulate the grid infrastructure as an adaptive structural mapping characterized by time-varying node–edge interactions Furthermore, the GATDDPG method proposed in this paper is used to solve the ADNDR problem. The GAT is employed to extract characteristics pertaining to the distribution network state, while the DDPG serves the purpose of enhancing the process of reconfiguration decision-making. This collaboration aims to ensure the safe, stable, and cost-effective operation of the distribution network. Finally, we verified the effectiveness of our method using an enhanced IEEE 33-bus power system model. The outcomes of the simulations demonstrate its capacity to significantly enhance the economic performance and stability of the distribution network, thereby affirming the proposed method’s effectiveness in this study. Full article