Search Results (173)

End–edge–cloud collaboration enables significant improvements in system resource utilization by integrating heterogeneous resources while ensuring application-level quality of service (QoS). However, achieving efficient collaborative decision-making in such architectures poses critical challenges within dynamic and complex computing network environments, including dynamic resource allocation, incentive alignment between cloud and edge entities, and multi-objective optimization. To address these issues, this paper proposes a dynamic resource optimization framework for complex cloud–edge collaborative networks, decomposing the problem into two hierarchical decision schemes: cloud-level coordination and edge-side coordination, thereby achieving adaptive resource orchestration across the End–edge–cloud continuum. Furthermore, leveraging differential game theory, we model the dynamic resource allocation and cooperation incentives between cloud and edge nodes, and derive a feedback Nash equilibrium to maximize the overall system utility, effectively resolving the inherent conflicts of interest in cloud–edge collaboration. Additionally, we formulate a joint optimization model for energy consumption and latency, and propose an Improved Discrete Artificial Hummingbird Algorithm (IDAHA) to achieve an optimal trade-off between these competing objectives, addressing the challenge of multi-objective coordination from the user perspective. Extensive simulation results demonstrate that the proposed methods exhibit superior performance in multi-objective optimization, incentive alignment, and dynamic resource decision-making, significantly enhancing the adaptability and collaborative efficiency of complex cloud–edge networks. Full article

In the context of the rapid growth of the Internet of Things and mobile health services, sensors and smart wearable devices are continuously collecting and uploading dynamic health data. Together with the long-term accumulated electronic medical records and multi-source heterogeneous clinical data from healthcare institutions, these data form the cornerstone of intelligent healthcare. In the context of medical data sharing, previous studies have mainly focused on privacy protection and secure data transmission, while relatively few have addressed the issue of incentive mechanisms. However, relying solely on technical means is insufficient to solve the problem of individuals’ willingness to share their data. To address this challenge, this paper proposes a three-party Stackelberg-game-based incentive mechanism for medical data sharing. The mechanism captures the hierarchical interactions among the intermediator, electronic device users, and data consumers. In this framework, the intermediator acts as the leader, setting the transaction fee; electronic device users serve as the first-level followers, determining the data price; and data consumers function as the second-level followers, deciding on the purchase volume. A social network externality is incorporated into the model to reflect the diffusion effect of data demand, and the optimal strategies and system equilibrium are derived through backward induction. Theoretical analysis and numerical experiments demonstrate that the proposed mechanism effectively enhances users’ willingness to share data and improves the overall system utility, achieving a balanced benefit among the cloud platform, electronic device users, and data consumers. This study not only enriches the game-theoretic modeling approaches to medical data sharing but also provides practical insights for designing incentive mechanisms in IoT-based healthcare systems. Full article

In modern high-demand applications, such as real-time video communication, cloud gaming, and high-definition live streaming, achieving both superior transmission speed and high visual fidelity is paramount. However, unstable networks and packet loss remain major bottlenecks, making accurate and low-latency video error concealment a critical challenge. Traditional error control strategies, such as Forward Error Correction (FEC) and Automatic Repeat Request (ARQ), often introduce excessive latency or bandwidth overhead. Meanwhile, receiver-side concealment methods struggle under high motion or significant packet loss, motivating the exploration of predictive models. SimVP-v2, with its efficient convolutional architecture and Gated Spatiotemporal Attention (GSTA) mechanism, provides a strong baseline by reducing complexity and achieving competitive prediction performance. Despite its merits, SimVP-v2’s reliance on 2D convolutions for implicit temporal aggregation limits its capacity to capture complex motion trajectories and long-term dependencies. This often results in artifacts such as motion blur, detail loss, and accumulated errors. Furthermore, its single-modality design ignores the complementary contextual cues embedded in the audio stream. To overcome these issues, we propose A3DSimVP (Audio- and 3D-Enhanced SimVP-v2), which integrates explicit spatio-temporal modeling with multimodal feature fusion. Architecturally, we replace the 2D depthwise separable convolutions within the GSTA module with their 3D counterparts, introducing a redesigned GSTA-3D module that significantly improves motion coherence across frames. Additionally, an efficient audio–visual fusion strategy supplements visual features with contextual audio guidance, thereby enhancing the model’s robustness and perceptual realism. We validate the effectiveness of A3DSimVP’s improvements through extensive experiments on the KTH dataset. Our model achieves a PSNR of 27.35 dB, surpassing the 27.04 of the SimVP-v2 baseline. Concurrently, our improved A3DSimVP model reduces the loss metrics on the KTH dataset, achieving an MSE of 43.82 and an MAE of 385.73, both lower than the baseline. Crucially, our LPIPS metric is substantially lowered to 0.22. These data tangibly confirm that A3DSimVP significantly enhances both structural fidelity and perceptual quality while maintaining high predictive accuracy. Notably, A3DSimVP attains faster inference speeds than the baseline with only a marginal increase in computational overhead. These results establish A3DSimVP as an efficient and robust solution for latency-critical video applications. Full article

Efficient resource allocation is a central challenge in multi-tenant cloud, fog, and edge environments, where heterogeneous tenants compete for shared resources under dynamic and uncertain workloads. Static or purely heuristic methods often fail to capture strategic tenant behavior, whereas many existing game-theoretic approaches overlook stochastic demand variability, fairness, or scalability. This paper proposes a Dynamic and Stochastic Game-Theoretic Allocation (DSGTA) model that jointly models non-cooperative tenant interactions, repeated strategy adaptation, and random workload fluctuations. The framework combines a Nash-like dynamic equilibrium, achieved via a lightweight best-response update rule, with an approximate Shapley-value-based fairness mechanism that remains tractable for large tenant populations. The model is evaluated on synthetic scenarios, with a trace-driven setup built from the Google 2019 Cluster dataset, and a scalability study is conducted with up to $K=500$ heterogeneous tenants. Using a consistent set of core metrics (tenant utility, resource cost, fairness index, and SLA satisfaction rate), DSGTA is compared against a static game-theoretic allocation (SGTA) and a dynamic pricing-based allocation (DPBA). The results, supported by statistical significance tests, show that DSGTA achieves higher utility, lower average cost, improved fairness and competitive utilization across diverse strategy profiles and stochastic conditions, thereby demonstrating its practical relevance for scalable, fair, and economically efficient resource allocation in realistic multi-tenant cloud environments. Full article

In order to reduce the risk of collapse disasters during tunnel construction in mountainous areas and to make full use of the available data, a collapse risk assessment model for highway tunnel construction was established based on the WOA–XGBOOST algorithm. Three major categories of tunnel construction risk, namely engineering geological factors, survey and design factors, and construction management factors, were selected as the first-level indicators, and 14 secondary indicators were further specified as the input variables of the collapse risk assessment model for tunnel construction. The confusion matrix and accuracy metrics were employed to evaluate the training and prediction performance of the risk assessment model on both the training set and the test set. The results show that subjective weights derived from the G1 method were integrated with objective weights generated by the WOA–XGBOOST algorithm. A game-theory-based weight integration strategy was then applied to optimize the combined weights, effectively mitigating the biases inherent in single-method weighting approaches. Risk quantification was systematically conducted using a cloud model, while spatial risk distribution patterns were visualized through graphical cloud-mapping techniques. After completion of model training, the proposed model achieved a high accuracy of over 99% on the training set and around 95% on the held-out test set based on an available dataset of 100 collapse-prone tunnel construction sections. Case-based verification further suggests that, in the studied collapse scenarios, the predicted risk levels are generally consistent with the actual engineering risks, indicating that the model is a promising tool for assisting tunnel construction risk assessment under similar conditions. The research outcomes provide an efficient and reliable approach for assessing risks in tunnel construction, thereby offering a scientific basis for engineering decision-making processes. Full article

The integration of digital technologies in mathematics is becoming increasingly significant, particularly in promoting conceptual understanding and student engagement. This study systematically reviews the literature on applications of Computer Algebra Systems, Artificial Intelligence, Visualisation Tools, augmented-reality technologies, Statistical Software, game-based learning and cloud-based learning in higher education mathematics. This meta-analysis synthesises findings from 88 empirical studies conducted between 1990 and 2025 to evaluate the impact of these technologies. The included studies encompass diverse geographical regions, providing a comprehensive global perspective on the integration of digital technologies in higher mathematics education. Using the PRISMA framework and quantitative effect size calculations, the results indicate that all interventions had a statistically significant impact on student performance. Among them, Visualisation Tools demonstrated the highest average percentage improvement in academic performance (39%), whereas cloud-based learning and game-based approaches, while beneficial, showed comparatively modest gains. The findings highlight the effectiveness of an interactive environment in fostering a deeper understanding of mathematical concepts. This study provides insights for educators and policymakers seeking to improve the quality and equity of mathematics education in the digital era. Full article

A game-changing strategy for increasing crop productivity while preserving vital resources is precision agriculture. The development of cloud computing and the Internet of Things (IoT) has made it possible and efficient to monitor soil and environmental data in real time. In order to monitor temperature, soil moisture, humidity, and light intensity, this work proposes an inexpensive, IoT-enabled smart agriculture system that uses low-cost sensors. The real-time data is wirelessly transmitted by an ESP32 edge computing device and stored and analyzed on cloud platforms like Firebase or ThingSpeak. A rule-based algorithm generates alerts when sensor values surpass predefined thresholds, enabling prompt and informed decision-making. Field experiments reveal that the proposed system is accurate, economical, and energy-efficient, making it ideal for automation and remote monitoring in precision agriculture. A user-friendly dashboard allows farmers to easily visualize data trends and receive timely notifications. The system supports scalability and can be adapted to different crop types and soil conditions with minimal effort. Moreover, by optimizing water and resource usage, the system contributes to sustainable farming practices and environmental conservation. This deployable solution offers a practical and affordable pathway for small- and medium-sized farmers to adopt smart agriculture technologies and improve crop yield outcomes efficiently. Full article

As the core component of pumped storage stations (PSS), pumped storage units (PSU) require a scientific and comprehensive evaluation method to guide the selection of optimal units and support the development of the new-type power system (NPS). This paper aims to address the symmetry issues in PSU evaluation methods by proposing an innovative approach based on evolutionary combination weighting and cloud model theory, thereby adapting to the long-term asymmetric evolution of the power system. First, the subjective and objective weights of indicators at all levels for PSU are obtained using the analytic hierarchy process (AHP) and the entropy weight method (EWM). Then, the optimal combination coefficients for subjective and objective weights are determined through game theory, achieving symmetry and balance between the subjective and objective weights. Subsequently, dynamic correction of the indicator weights is realized using a designed evolutionary response function, enabling the weights to evolve dynamically in response to the asymmetric development of the power system. Finally, the cloud model is employed to characterize the randomness and fuzziness of evaluation boundaries, which enhances the adaptability of the evaluation process and the interpretability of results. The simulation results show that, when considering the long-term asymmetric evolution of the power system, the expected score deviations of secondary indicators are approximately 4.7%, 1.3%, 3.5%, and 7.7%, respectively, with an overall score deviation of about 6.4%. The proposed method not only achieves symmetry and balance between subjective and objective factors in traditional evaluation but also accommodates the asymmetric evolution requirements of the power system. Full article

The ecological protection and high-quality development of the Yellow River Basin is a major national strategy in China. The Yellow River “Ji” Bay is an important part of the basin. This study evaluates the comprehensive risk of the water–energy–food–ecology–carbon (WEFEC) complex system within the “Ji” Bay. Using 2004–2023 panel data from nineteen regional cities, this study develops a 24-indicator WEFEC index system that assesses reliability, synergy, and resilience. A comprehensive evaluation method based on the game theory–cloud model is employed to determine the risk levels. The study results show the following: (1) the multi-year average comprehensive risk of the WEFEC complex system in the “Ji” Bay from 2004 to 2023 was at a high alert level; (2) the overall synergy of the “Ji” Bay was moderate; (3) spatially, the number of cities in extreme and high alert states decreased, whereas the number of cities in no alert and light alert states increased; and (4) indicators such as per capita water resources, water production modulus, and water area ratio are the main factors restricting the comprehensive risk of the WEFEC complex system. Based on these findings, this paper proposes policy recommendations using the following three aspects: criterion layers, risk factors, and different regions. Full article

Secure authentication in vehicular ad hoc networks (VANETs) remains a fundamental challenge due to their dynamic topology, susceptibility to attacks, and scalability constraints in multi-hop communication. Existing approaches based on elliptic curve cryptography (ECC), blockchain, and fog computing have achieved partial success but suffer from latency, resource overhead, and limited adaptability, leaving a gap for lightweight and hardware-rooted trust models. To address this, we propose a multi-hop mutual authentication protocol leveraging Physical Unclonable Functions (PUFs), which provide tamper-evident, device-specific responses for cryptographic key generation. Our design introduces a structured sequence of phases, including pre-deployment, registration, login, authentication, key establishment, and session maintenance, with optional multi-hop extension through relay vehicles. Unlike prior schemes, our protocol integrates fuzzy extractors for error tolerance, employs both inductive and game-based proofs for security guarantees, and maps BAN-logic reasoning to specific attack resistances, ensuring robustness against replay, impersonation, and man-in-the-middle attacks. The protocol achieves mutual trust between vehicles and RSUs while preserving anonymity via temporary identifiers and achieving forward secrecy through non-reused CRPs. Conceptual comparison with state-of-the-art PUF-based and non-PUF schemes highlights the potential for reduced latency, lower communication overhead, and improved scalability via cloud-assisted CRP lifecycle management, while pointing to the need for future empirical validation through simulation and prototyping. This work not only provides a secure and efficient solution for VANET authentication but also advances the field by offering the first integrated taxonomy-driven evaluation of PUF-enabled V2X protocols in multi-hop Wi-Fi environments. Full article

Frequent water and mud inrush accidents during karst tunnel construction severely impact tunnel construction safety, environmental sustainability, and the long-term use of infrastructure. Therefore, conducting practical risk assessment for karst tunnel water and mud inrush is crucial for promoting sustainable practices in tunnel engineering, as it can mitigate catastrophic events that lead to resource waste, ecological damage, and economic loss. This paper establishes an improved weighted cloud model for karst tunnel water and mud inrush risk to evaluate the associated risk factors. The calculation of subjective weight for risk metrics adopts the ordinal relationship method (G1 method), which is a subjective weighting method improved from the analytic hierarchy process. The calculation of objective weight employs the improved entropy weight method, which is superior to the traditional entropy weight method by effectively preventing calculation distortion. Game theory is applied to calculate the optimal weight combination coefficient for two computational methods, and cloud model theory is finally introduced to reduce the fuzziness of the membership interval during the assessment process. This study applied the established risk assessment model to five sections of the Furong Tunnel and Cushishan Tunnel in Southwest China. The final risk ratings for these sections were determined as “High Risk,” “High Risk,” “Medium Risk,” “High Risk,” and “Moderate Risk”, respectively. These results align with the findings from field investigations, validating the effectiveness and reliability of the cloud model-based mud and water outburst risk assessment using combined weighting. Compared to traditional methods such as fuzzy comprehensive evaluation and entropy weighting, the evaluation results from this study’s model demonstrate higher similarity and reliability. This provides a foundation for assessing mud and water outburst hazards and other tunnel disasters. Full article

Slope instability may cause severe casualties, property losses, and ecological damage. To accurately evaluate slope stability grades and mitigate geological hazards, a dynamic stability assessment method based on variable weight theory and trapezoidal cloud model is proposed. First, an evaluation index system for slope stability is established following the principles of uniqueness, purposefulness, and scientific validity. Then, to improve the accuracy of subjective constant weights, the intuitionistic fuzzy analytic hierarchy process (IFAHP) is employed to calculate subjective constant weights. Considering the contrast intensity and conflict among indicators, an improved CRITIC method is applied to determine objective constant weights. To balance subjective and objective factors and avoid constant weight imbalance, the optimal comprehensive constant weights are computed based on game theory, effectively reducing bias caused by single weighting methods. Furthermore, to fully account for the influence of indicator state values on their weights, variable weight theory is introduced to dynamically adjust the comprehensive constant weights. Finally, based on the variable weights of evaluation indicators, a trapezoidal cloud model is utilized to construct the slope stability evaluation model, which is validated through an engineering case study. The results indicate that the stability grade of Stage 1 is assessed as basically stable, while Stages 2 and 3 are evaluated as stable. Numerical simulations show the safety factors of the three stages are 1.36, 1.83, and 2.36, respectively, verifying the correctness of the proposed model. The proposed model demonstrates practical engineering value in slope stability assessment and can be referenced for slope reinforcement and hazard prevention in later stages. Full article

Indoor sports facilities present unique challenges for air quality management due to high crowd densities and limited ventilation. This study investigated air quality in a municipal athletic facility in Komotini, Greece, focusing on concentrations of airborne particulate matter (PM_1.0, PM_2.5, PM₁₀), humidity, and temperature across spectator zones, under varying mask scenarios. Sensing devices were installed in the stands to collect high-frequency environmental data. The system, based on optical particle counters and cloud-enabled analytics, enabled real-time data capture and retrospective analysis. The main experiment investigated the impact of spectators wearing medical masks during two basketball games. The results show consistently elevated PM levels during games, often exceeding recommended international thresholds in the spectator area. Notably, the use of masks by spectators led to measurable reductions in PM_1.0 and PM_2.5 concentrations, because they seem to have limited the release of human-generated aerosols as well as the amount of movement among spectators, supporting their effectiveness in limiting fine particulate exposure in inadequately ventilated environments. Humidity emerged as a reliable indicator of occupancy and potential high-risk periods, making it a valuable parameter for real-time monitoring. The findings underscore the urgent need for improved ventilation strategies in small to medium-sized indoor sports facilities and support the deployment of low-cost sensor networks for actionable environmental health management. Full article

The acceleration of global urbanization has caused severe damage to, and even the disappearance of, traditional villages, significantly reducing the diversity of cultural landscapes. To effectively preserve and transmit the cultural landscape characteristics of traditional villages, this study adopts the “landscape gene” theory and proposes a traditional village landscape gene perception evaluation method combining game theory-based weight assignment and the cloud model. Using Huangtutang Village in Wuxi, China, as a case study, the study follows the framework and paradigm of “identification-translation-perception evaluation-preservation inheritance” to identify, translate, map, and comprehensively evaluate its landscape genes. Finally, targeted strategies for the preservation and development of Huangtutang Village are proposed based on the evaluation results. The results indicate that residents and tourists generally perceive the landscape genes of Huangtutang Village as “Satisfied,” with perception levels ranking from high to low as follows: environmental pattern, cultural characteristics, architectural character, and spatial layout characteristics. Perceptions of traffic location, street texture, building form, roof form, facade features, folk tales, and historical and cultural context were relatively low, showing lower “expectation values.” The findings provide valuable references for the preservation and development of Huangtutang Village and other traditional villages. The proposed traditional village landscape gene perception evaluation model advances the development of landscape gene theory, effectively supplements existing methods for traditional village preservation and sustainable development, and demonstrates broad applicability. Full article

Urban historic districts play a vital role in shaping the cultural identity and heritage of cities. However, many of these areas face challenges such as aging buildings and deteriorating infrastructure. At the same time, the increasing frequency of extreme rainfall has led to a rise in flood events, placing these vulnerable districts at greater risk. Therefore, it is essential to carry out a comprehensive and objective assessment of their resilience to flood disasters. This study establishes a G-IC model for evaluating the resilience of urban historic districts to flood disasters based on the game combination empowerment-improved cloud model method. The proposed method has been demonstrated in the Soviet-style building complex of the Daye Steel Plant in Huangshi and reveals that the driving force layer exhibits weak resilience; the pressure and state layers show general resilience; the impact and response layers demonstrate weak resilience; and the overall resilience of the district is categorized as weak. The consistency of the results was verified by calculating the cloud similarity, which shows that the constructed new model has certain rationality and feasibility, and the evaluation results are relatively accurate. The findings offer valuable insights for policy-making and support for decision-makers in local government departments. Full article