Search Results (446)

Global low-carbon innovation networks face increasing vulnerabilities amid growing geopolitical tensions and technological competition. The interdependent structure of low-carbon innovation networks and the risk propagation dynamics within them remain poorly understood. This study investigates vulnerability patterns by constructing a two-layer interdependent network model based on Chinese low-carbon patent data, comprising a low-carbon collaboration network of innovation entities and a low-carbon knowledge network of technological components. Applying dynamic shock propagation modeling, we analyze how risks spread within and between network layers under various shocks. Our findings reveal significant differences in vulnerability distribution: the knowledge network consistently demonstrates greater susceptibility to cascading failures than the collaboration network, reaching complete system failure, while the latter maintains partial resilience, with resilience levels stabilizing at approximately 0.64. Critical node analysis identifies State Grid Corporation as a vulnerability point in the collaboration network, while multiple critical knowledge elements can independently trigger system-wide failures. Cross-network propagation follows distinct patterns, with knowledge-network failures consistently preceding collaboration network disruptions. In addition, propagation from the collaboration network to the knowledge network showed sharp transitions at specific threshold values, while propagation in the reverse direction displayed more gradual responses. These insights suggest tailored resilience strategies, including policy decentralization approaches, ensuring technological redundancy across critical knowledge domains and strengthening cross-network coordination mechanisms to enhance low-carbon innovation system stability. Full article

Urbanization has profoundly transformed land surface morphology and amplified thermal environmental modifications, culminating in intensified urban heat island (UHI) phenomena. Local climate zones (LCZs) provide a robust methodological framework for quantifying thermal heterogeneity and dynamics at local scales. Our study investigated the Changsha–Zhuzhou–Xiangtan urban agglomeration (CZXA) as a case study and systematically examined spatiotemporal patterns of LCZs and land surface temperature (LST) from 2002 to 2019, while elucidating mechanisms influencing urban thermal environments and proposing optimized cooling strategies. Key findings demonstrated that through multi-source remote sensing data integration, long-term LCZ classification was achieved with 1,592 training samples, maintaining an overall accuracy exceeding 70%. Landscape pattern analysis revealed that increased fragmentation, configurational complexity, and diversity indices coupled with diminished spatial connectivity significantly elevate LST. Rapid development of the city in the vertical direction also led to an increase in LST. Among seven urban morphological parameters, impervious surface fraction (ISF) and pervious surface fraction (PSF) demonstrated the strongest correlations with LST, showing Pearson coefficients of 0.82 and −0.82, respectively. Pearson coefficients of mean building height (BH), building surface fraction (BSF), and mean street width (SW) also reached 0.50, 0.55, and 0.66. Redundancy analysis (RDA) results revealed that the connectivity and fragmentation degree of LCZ_8 (COHESION8) was the most critical parameter affecting urban thermal environment, explaining 58.5% of LST. Based on these findings and materiality assessment, the regional cooling model of “cooling resistance surface–cooling source–cooling corridor–cooling node” of CZXA was constructed. In the future, particular attention should be paid to the shape and distribution of buildings, especially large, openly arranged buildings with one to three stories, as well as to controlling building height and density. Moreover, tailored protection strategies should be formulated and implemented for cooling sources, corridors, and nodes based on their hierarchical significance within urban thermal regulation systems. These research outcomes offer a robust scientific foundation for evidence-based decision-making in mitigating UHI effects and promoting sustainable urban ecosystem development across urban agglomerations. Full article

Routing in flying ad hoc networks (FANETs) is hindered by high mobility, trajectory-induced topology dynamics, and energy constraints. Conventional topology-based or position-based protocols often fail due to stale link information and limited neighbor awareness. This paper proposes a trajectory-informed routing protocol enhanced by Q-learning: Traj-Q-GPSR, tailored for mission-oriented UAV swarm networks. By leveraging mission-planned flight trajectories, the protocol builds time-aware two-hop neighbor tables, enabling routing decisions based on both current connectivity and predicted link availability. This spatiotemporal information is integrated into a reinforcement learning framework that dynamically optimizes next-hop selection based on link stability, queue length, and node mobility patterns. To further enhance adaptability, the learning parameters are adjusted in real time according to network dynamics. Additionally, a delay-aware queuing model is introduced to forecast optimal transmission timing, thereby reducing buffering overhead and mitigating redundant retransmissions. Extensive ns-3 simulations across diverse mobility, density, and CBR connections demonstrate that the proposed protocol consistently outperforms GPSR, achieving up to 23% lower packet loss, over 80% reduction in average end-to-end delay, and improvements of up to 37% and 52% in throughput and routing efficiency, respectively. Full article

Kubernetes clusters are deployed across data centers for geo-redundancy and low-latency access, resulting in new challenges in scheduling workloads optimally. This paper presents a practical evaluation of network-aware scheduling in a distributed Kubernetes cluster that spans multiple network zones. A custom scheduling plugin is implemented within the scheduling framework to incorporate real-time network telemetry (inter-node ping latency) into pod placement decisions. The assessment methodology combines a custom scheduler plugin, realistic network latency measurements, and representative distributed benchmarks to assess the impact of scheduling on traffic patterns. The results provide strong empirical confirmation of the findings previously established through simulation, offering a validated path forward to integrate not only network metrics, but also other performance-critical metrics such as energy efficiency, hardware utilization, and fault tolerance. Full article

With the growing deployment of latency-sensitive applications, the Age of Information (AoI) has emerged as a key performance metric for the evaluation of data freshness in networked systems. While prior studies have extensively explored the AoI under centralized scheduling or random-access protocols such as carrier sense multiple access (CSMA) and ALOHA, most assume that sources generate independent information. However, in practical scenarios such as environmental monitoring and visual sensing, information correlation frequently exists among correlated sources, providing new opportunities to enhance network timeliness. In this paper, we propose a novel analytical framework that captures the interplay between CSMA channel contention and spatial information correlation among sources. By leveraging the stochastic hybrid systems (SHS) methodology, we jointly model random backoff behavior, medium access collisions, and correlated updates in a scalable and mathematically tractable manner. We derive closed-form expressions for the average AoI under general correlation structures and further propose a lightweight estimation approach for scenarios where the correlation matrix is partially known or unknown. To our knowledge, this is the first work that integrates correlation-aware modeling into AoI analysis under distributed CSMA protocols. Extensive simulations confirm the accuracy of the theoretical results and demonstrate that exploiting information redundancy can significantly reduce the AoI, particularly under high node densities and constrained sampling budgets. These findings offer practical guidance for the design of efficient and timely data acquisition strategies in dense or energy-constrained Internet of Things (IoT) networks. Full article

The rapid growth of Internet of Things (IoT) enabled devices in industrial environments and the associated increase in data generation are paving the way for the development of localized, distributed datacenters. In this paper, we have proposed a novel mini-datacenter in the form of wireless sensor networks to efficiently handle query-based data collection from Industrial IoT (IIoT) devices. The mini-datacenter comprises a command center, gateways, and IoT sensors, designed to manage stochastic query-response traffic flow. We have developed a duplication/aggregation query flow model, tailored to emphasize reliable transmission. We have developed a dataflow management framework that employs a multi-modal query forwarding approach to forward queries from the command center to gateways under varying environments. The query forwarding includes coarse-grain and fine-grain strategies, where the coarse-grain strategy uses a direct data flow using a single gateway at the expense of reliability, while the fine-grain approach uses redundant gateways to enhance reliability. A fuzzy-logic-based intelligence system is integrated into the framework to dynamically select the appropriate granularity of the forwarding strategy based on the resource availability and network conditions, aided by a buffer watching algorithm that tracks real-time buffer status. We carried out several experiments with gateway nodes varying from 10 to 100 to evaluate the framework’s scalability and robustness in handling the query flow under complex environments. The experimental results demonstrate that the framework provides a flexible and adaptive solution that balances buffer usage while maintaining over 95% reliability in most queries. Full article

In space, high-energy particle radiation poses a serious threat to the data stability and reliability of SRAM. Existing radiation-tolerant techniques, such as Triple Modular Redundancy (TMR) and Error Correction Code (ECC), have disadvantages such as large area, high power consumption, and additional delay, making them unsuitable for small satellite systems. To overcome these limitations, this paper proposes a 16-transistor-based radiation-tolerant SRAM cell, WRTU-16T, which applies a read-decoupled structure and a charge-sharing suppression mechanism. The proposed structure effectively isolates the storage node from external disturbances and improves the recovery capability for single-event inversion (SEU) and multiple-node inversion (SEMNU) by reducing charge loss. WRTU-16T shows superior performance in terms of write delay, charge recovery capability (Qc), hold power, and word line write threshold voltage (WWTV) compared to existing radiation-tolerant SRAM designs. The integrated circuit is implemented using a 90 nm CMOS process and has an operating voltage of 1V. Full article

Situation awareness (SA) involves analyzing sensory data, such as audio signals, to identify anomalies. While acoustic features are widely used in audio analysis, existing methods face critical limitations; they often overlook the relevance of SA audio segments, failing to capture the complex relational patterns in audio data that are essential for SA. In this study, we first propose a graph neural network (GNN) with an attention mechanism that models SA audio features through graph structures, capturing both node attributes and their relationships for richer representations than traditional methods. Our analysis identifies suitable audio feature combinations and graph constructions for SA tasks. Building on this, we introduce a situation awareness gated-attention GNN (SAGA-GNN), which dynamically filters irrelevant nodes through max-relevance neighbor sampling to reduce redundant connections, and a learnable edge gated-attention mechanism that suppresses noise while amplifying critical events. The proposed method employs sigmoid-activated attention weights conditioned on both node features and temporal relationships, enabling adaptive node emphasizing for different acoustic environments. Experiments reveal that the proposed graph-based audio features demonstrate superior representation capacity compared to traditional methods. Additionally, both proposed graph-based methods outperform existing approaches. Specifically, owing to the combination of graph-based audio features and dynamic selection of audio nodes based on gated-attention, SAGA-GNN achieved superior results on two real datasets. This work underscores the importance and potential value of graph-based audio features and attention mechanism-based GNNs, particularly in situational awareness applications. Full article

As urban traffic systems become increasingly complex, traditional traffic data collection methods based on fixed detectors face challenges such as poor data quality and acquisition difficulties. Traditional methods also lack the ability to capture complete vehicle path information essential for signal optimization. While vehicle trajectory data can provide rich spatiotemporal information, its sampling characteristics present new technical challenges for traffic parameter extraction. This study addresses the key issue of extracting traffic parameters suitable for signal timing optimization from sampled trajectory data by proposing a comprehensive method for traffic parameter extraction and analysis based on vehicle trajectory data. The method comprises five modules: data preprocessing, basic feature processing, exploratory data analysis, key feature extraction, and data visualization. An innovative algorithm is proposed to identify which intersections vehicles pass through, effectively solving the challenge of mapping GPS points to road network nodes. A dual calculation method based on instantaneous speed and time difference is adopted, improving parameter estimation accuracy through multi-source data fusion. A highly automated processing toolchain based on Python and MATLAB is developed. The method advances the state of the art through a novel polygon-based trajectory mapping algorithm and a systematic multi-source parameter extraction framework specifically designed for signal control optimization. Validation using actual trajectory data containing 2.48 million records successfully eliminated 30.80% redundant data and accurately identified complete paths for 7252 vehicles. The extracted multi-dimensional parameters, including link flow, average speed, travel time, and OD matrices, accurately reflect network operational status, identifying congestion hotspots, tidal traffic characteristics, and unstable road segments. The research outcomes provide a feasible technical solution for areas lacking traditional detection equipment. The extracted parameters can directly support signal optimization applications such as traffic signal coordination, timing optimization, and congestion management, providing crucial support for implementing data-driven intelligent traffic control. This research presents a theoretical framework validated with real-world data, providing a foundation for future implementation in operational signal control systems. Full article

Deep learning greatly improves the detection efficiency of abnormal traffic through autonomous learning and effective extraction of data feature information. Among them, Graph Neural Networks (GNN) effectively fit the data features of abnormal traffic by aggregating the features and structural information of network nodes. However, the performance of GNN in the field of industrial Internet of Things (IIoT) is still insufficient. Since the asymmetry of GNN traffic data is greater than that of the traditional Internet, it is necessary to propose a detection method with high detection rate. At present, many algorithms overly emphasize the optimization of graph neural network models, while ignoring the heterogeneity of resources caused by the diversity of devices in IIoT networks, and the different traffic characteristics caused by multi type protocols. Therefore, universal GNN may not be fully applicable. Therefore, a novel intrusion detection framework incorporating graph neural networks is developed for Industrial Internet of Things systems. Design mini-batch sampling to support data parallelism and accelerate the training process in response to the distributed characteristics of the IIoT. Due to the strong real-time characteristics of the industrial IIoT, data packets in concentrated time periods contain a large number of feature attributes, and the high redundancy of features due to the correlation between features. This paper establishes a model temporal correlation and designs a new model. The performance of the proposed GIDS model is evaluated on several benchmark datasets such as BoT-IoT, ACI-IoT-2023 and OPCUA. The results marked that the method performs well on both binary classification task and multiclass classification task. The accuracy on binary classification task is 93.63%, 97.34% and 100% with F1 values of 94.34%, 97.68% and 100.00% respectively. The accuracy on multiclass classification task is 92.34%, 93.68% and 99.99% with F1 values of 94.55%, 94.12% and 99.99% respectively. Through experimental measurements, the model effectively utilizes the natural distribution characteristics of network traffic in both temporal and spatial dimensions, achieving better detection results. Full article

Complex networks of stochastic processes are crucial for modeling the dynamics of interacting systems, particularly those involving high-order interactions (HOIs) among three or more components. Traditional measures—such as mutual information (MI), interaction information (II), the redundancy-synergy index (RSI), and O-information (OI)—are typically limited to static analyses not accounting for temporal correlations and become computationally unfeasible in large networks due to the exponential growth of the number of interactions to be analyzed. To address these challenges, first a framework is introduced to extend these information-theoretic measures to dynamic processes. This includes the II rate (IIR), RSI rate (RSIR), and the OI rate gradient ($\Delta \mathrm{OIR}$), enabling the dynamic analysis of HOIs. Moreover, a stepwise strategy identifying groups of nodes (multiplets) that maximize either redundant or synergistic HOIs is devised, offering deeper insights into complex interdependencies. The framework is validated through simulations of networks composed of cascade, common drive, and common target mechanisms, modelled using vector autoregressive (VAR) processes. The feasibility of the proposed approach is demonstrated through its application in climatology, specifically by analyzing the relationships between climate variables that govern El Niño and the Southern Oscillation (ENSO) using historical climate data. Full article

In the present research, a methodology was developed for the deployment and optimal location of PMUs with redundancy and system observability constraints at 100%, considering the topological changes generated by transmission expansion planning (TEP). To determine the expansion scenarios and topology changes of the power system, the original IEEE test systems of 14, 30, and 118 buses were used as a starting point, in which a 20-year horizon was considered and an increase in load nodes was proposed, with which the new lines needed were determined by applying TEP. Under these temporal and expansion scenarios, the optimal location of PMUs was determined based on mixed-integer linear optimization, with which the order of location of PMUs was determined. The results of the methodology allow defining the order of PMU implementation in power systems that expand over time. Full article

This study proposes a multi-dimensional urban transportation network optimization framework (MTNO-RQDC) to address structural failure risks from aging infrastructure and regional connectivity bottlenecks. Through dual-dataset validation using both the Baltimore road network and PeMS07 traffic flow data, we first develop a traffic simulation model integrating Dijkstra’s algorithm with capacity-constrained allocation strategies for guiding reconstruction planning for the collapsed Francis Scott Key Bridge. Next, we create a dynamic adaptive public transit optimization model using an entropy weight-TOPSIS decision framework coupled with an improved simulated annealing algorithm (ISA-TS), achieving coordinated suburban–urban network optimization while maintaining 92.3% solution stability under simulated node failure conditions. The framework introduces three key innovations: (1) a dual-layer regional division model combining K-means geographical partitioning with spectral clustering functional zoning; (2) fault-tolerant network topology optimization demonstrated through 1000-epoch Monte Carlo failure simulations; (3) cross-dataset transferability validation showing 15.7% performance variance between Baltimore and PeMS07 environments. Experimental results demonstrate a 28.7% reduction in road network traffic variance (from 42,760 to 32,100), 22.4% improvement in public transit path redundancy, and 30.4–44.6% decrease in regional traffic load variance with minimal costs. Hyperparameter analysis reveals two optimal operational modes: rapid cooling (rate = 0.90) achieves 85% improvement within 50 epochs for emergency response, while slow cooling (rate = 0.99) yields 12.7% superior solutions for long-term planning. The framework establishes a new multi-objective paradigm balancing structural resilience, functional connectivity, and computational robustness for sustainable smart city transportation systems. Full article

The Internet of Things (IoT) aims to wirelessly connect billions of physical things to the IT infrastructure. Although there are several radio access technologies available, few of them meet the needs of Internet of Things applications, such as long range, low cost, and low energy consumption. The low data rate of low-power wide-area network (LPWAN) technologies, particularly SigFox, makes them appropriate for Internet of Things applications since the longer the radio link’s useable distance, the lower the data rate. Network reliability is the primary goal of SigFox technology, which aims to deliver data messages successfully through redundancy. This raises concerns about SigFox’s scalability and leads to one of its flaws, namely the high collision rate. In this paper, the goal is to prevent collisions by switching to time division multiple access (TDMA) from SigFox’s Aloha-based medium access protocol, utilizing only orthogonal channels, and eliminating redundancy. Consequently, during a designated time slot, each node transmits a single copy of the data message over a particular orthogonal channel. To achieve this, a multi-agent, off-policy reinforcement learning (RL) Q-Learning technique will be used on top of SigFox. In other words, the objective is to increase SigFox’s scalability through the use of Reinforcement Learning based time slot allocation (RL-SCAP). The findings show that, especially in situations with high node densities or constrained communication slots, the proposed protocol performs better than the basic SCAP (Slot and Channel Allocation Protocol) by obtaining a higher Packet Delivery Ratio (PDR) in average of 60.58%, greater throughput in average of 60.90%, and a notable decrease in collisions up to 79.37%. Full article

Unmanned aerial vehicles (UAVs), owing to their flexible coverage expansion and dynamic adjustment capabilities, hold significant application potential across various fields. With the emergence of urban low-altitude air traffic dominated by UAVs, the integrated aviation information network combining UAVs and manned aircraft has evolved into a complex space–air–ground integrated Internet of Things (IoT) system. The application of 5G/6G network technologies, such as cloud computing, network function virtualization (NFV), and edge computing, has enhanced the flexibility of air traffic services based on service function chains (SFCs), while simultaneously expanding the network attack surface. Compared to traditional networks, the aviation information network integrating UAVs exhibits greater heterogeneity and demands higher service reliability. To address the failure issues of SFCs under attack, this study proposes an efficient SFC recovery method for recovery rate optimization (ERRRO) based on virtual network functions (VNFs) migration technology. The method first determines the recovery order of failed SFCs according to their recovery costs, prioritizing the restoration of SFCs with the lowest costs. Next, the migration priorities of the failed VNFs are ranked based on their neighborhood certainty, with the VNFs exhibiting the highest neighborhood certainty being migrated first. Finally, the destination nodes for migrating the failed VNFs are determined by comprehensively considering attributes such as the instantiated SFC paths, delay of physical platforms, and residual resources. Experiments demonstrate that the ERRRO performs well under networks with varying resource redundancy and different types of attacks. Compared to methods reported in the literature, the ERRRO achieves superior performance in terms of the SFC recovery rate and delay. Full article