Concurrency and Computation-Practice & Experience最新文献

Edge-cloud computing offers an efficient method to flexibly allocate various computing resources for periodic workflow applications commonly employed in industrial production, commercial operations, and scientific research. Rationalized allocation of computational resources for scheduling in the edge-cloud environment is the key to reducing energy consumption of the periodic workflows scheduling process. To this end, this paper proposes an optimization method for dynamic energy-efficient scheduling of periodic workflows based on the collaboration of edge-cloud computational resources while satisfying the constraints of workflow deadlines. In our method, periodic workflow scheduling is defined to be performed on a three-tier integrated scheduling architecture of user terminals, edge computing platform, and cloud computing platform. Task groups are generated based on workflow critical paths, and appropriate edge-cloud computing resources are selected for workflow tasks using corresponding scheduling policies at each scheduling stage. It reduces energy consumption during task scheduling while satisfying workflow deadline constraints. Comparative experiments in a simulated edge-cloud environment show that our method reduces energy consumption of the scheduling process by 19.38%, 22.7%, and 37.34% compared to GA, PSO, and cloud computing, respectively. That is, the method effectively reduces the scheduling energy consumption during periodic workflow processing and significantly improves computational resource utilization.

Routing protocols are pivotal in Vehicular Ad hoc Networks (VANETs), serving as the backbone for efficient routing discovery, particularly within the realm of Intelligent Transportation Systems (ITS). However, ensuring their seamless functionality within VANET environments necessitates rigorous verification and formal modeling. Colored Timed Petri Nets (CTPNs) stand out as a valuable mathematical and formal method for this purpose. This study shows a new way to describe the Ad hoc On-Demand Distance Vector (AODV) routing system in VANETs using CTPNs. There are nine pages of detailed analysis using this new modeling method, which allows you to examine success across many levels of a hierarchy. This study provides a strong foundation for building and testing the AODV routing system in VANETs, showing how well it functions in real-life situations. It is interesting to see how the results of the CTPN–based model and simulations compare. Notably, the model finds routes in an average of 32 s, while tests show that it takes 56 s. Additionally, the model's overall number of sent and received packets closely matches the results from the exercise. Furthermore, the suggested plan shows a yield of 41%. Strict T-tests indicate that the modeling results are highly reliable.

Traditional Oncomelania hupensis detection relies on human eye observation, which results in reduced efficiency due to easy fatigue of the human eye and limited individual cognition, an improved YOLOv8 O. hupensis detection algorithm, YOLOv8-ESW(expectation–maximization attention [EMA], Small Target Detection Layer, and Wise-IoU), is proposed. The original dataset is augmented using the OpenCV library. To imitate image blur caused by motion jitter, salt and pepper, and Gaussian noise were added to the dataset; to imitate images from different angles captured by the camera in an instant, affine, translation, flip, and other transformations were performed on the original data, resulting in a total of 6000 images after data enhancement. Considering the insufficient feature fusion problem caused by lightweight convolution, We present the expectation–EMA module (E), which innovatively incorporates a coordinate attention mechanism and convolutional layers to introduce a specialized layer for small target detection (S). This design significantly improves the network's ability to synergize information from both superficial and deeper layers, better focusing on small target O. hupensis and occluded O. hupensis. To tackle the challenge of quality imbalance among O. hupensis samples, we employ the Wise-IoU (WIoU) loss function (W). This approach uses a gradient gain distribution strategy and improves the model convergence speed and regression accuracy. The YOLOv8-ESW model, with 16.8 million parameters and requiring 98.4 GFLOPS for computations, achieved a mAP of 92.74% when tested on the O. hupensis dataset, marking a 4.09% improvement over the baseline model. Comprehensive testing confirms the enhanced network's efficacy, significantly elevating O. hupensis detection precision, minimizing both missed and false detections, and fulfilling real-time processing criteria. Compared with the current mainstream models, it has certain advantages in detection accuracy and has reference value for subsequent research in actual detection.

A three-party post-quantum key agreement protocol involves server with two communicating parties securely agreeing on a shared secret key in a way that is resistant to quantum attacks. Once the shared secret key is shared using authenticated key agreement protocol, then user (A), and user (B) can use it for securing communication channel using symmetric-key encryption AES-256 algorithm. Although there are few third-party post-quantum authenticated and key agreement schemes exist, but the recent studies in this paper illustrates that they are not satisfying properties like unlinkability, anonymity, perfect forward secrecy, and signal leakage attacks. Therefore, the proposed protocol ensures anonymity, unlinkablity, perfect forward secrecy, and resistant against signal leakage attacks. The proposed protocol uses different random numbers for each of sessions and ensures freshness of the session key to maintain forward secrecy. In this protocol, the user (A) only communicates with server, and establish an authenticated session key with user (B) which avoids server overheads. The use of ring learning with errors (RLWE) instead of the simpler learning with errors (LWE) is primarily motivated by the need for efficiency, compactness, and scalability in cryptographic applications. A comparative study, including both performance and security assessments, demonstrates that the proposed design is more secure and efficient.

The differential evolution algorithm, as a simple yet effective random search algorithm, often faces challenges in terms of rapid convergence and a sharp decline in population diversity during the evolutionary process. To address this issue, an improved differential evolution algorithm, namely the multi-population collaboration improved differential evolution (MPC-DE) algorithm, is introduced in this article. The algorithm proposes a multi-population collaboration mechanism and a two-stage mutation operator. Through the multi-population collaboration mechanism, the diversity of individuals involved in mutation is effectively controlled, enhancing the algorithm's global search capability. The two-stage mutation operator efficiently balances the requirements of the exploration and exploitation stages. Additionally, a perturbation operator is introduced to enhance the algorithm's ability to escape local optima and improve stability. By conducting comprehensive comparisons with 15 well-known optimization algorithms on CEC2005 and CEC2017 test functions, MPC-DE is thoroughly evaluated in terms of solution accuracy, convergence, stability, and scalability. Furthermore, validation on 57 real-world engineering optimization problems in CEC2020 demonstrates the robustness of the MPC-DE. Experimental results reveal that, compared to other algorithms, MPC-DE exhibits superior convergence accuracy and robustness in both constrained and unconstrained optimization problems. These research findings provide strong support for the widespread applicability of multi-population collaboration in differential evolution algorithms for addressing practical engineering problems.

In the realm of natural language processing (NLP), anomaly detection plays a critical role in identifying irregularities and outliers within textual data. Traditional methods often struggle with the high-dimensional and sparse nature of text data, leading to inefficiencies in detecting meaningful anomalies, especially in the big data application context. To address these challenges, this paper proposes the integration of LSHiForest (Locality-Sensitive Hashing Isolation Forest) into the process of English text anomaly detection. LSHiForest, which synergistically combines the dimensionality reduction capabilities of locality-sensitive hashing (LSH) with the robust outlier detection of Isolation Forest, offers a novel approach to handling the complexities of textual data. The proposed approach involves transforming English text into feature vectors, followed by the application of LSHiForest to detect anomalies across various text datasets. The effectiveness of this approach is evaluated through comparative experiments with traditional anomaly detection methods, with various performance metrics. The experimental results demonstrate that LSHiForest significantly improves the efficiency and accuracy of outlier identification in English text, particularly in scenarios involving large-scale and high-dimensional datasets.

The reliability of the catenary system is crucial for the safety and efficiency of heavy-haul railways. This study presents a probabilistic risk analysis model for the catenary system, employing causal inference methods to capture the complex relationships among risk factors. Using historical operational data, we identify key risk contributors such as environmental conditions, vehicular loads, and equipment failures. By combining fault tree analysis (FTA) and failure mode and effects analysis (FMEA), we establish risk propagation pathways. The proposed method utilizes Bayesian networks to quantify conditional probabilities and trace the causal chains leading to potential failures. Through reverse inference, we identify critical risk nodes and their impact on system performance. This approach enhances the accuracy of risk assessment and provides an effective tool for proactive risk management in heavy-haul railways, aiding in the optimization of maintenance strategies and strengthening the resilience of the catenary system under varying operational conditions.

Access control is a critical security measure to ensure that sensitive information and resources are accessed only by authorized users. However, attribute-based access control in the big data environment faces challenges such as a large number of entity attributes, poor availability, and difficulty in manual labeling. In this paper, we focus on the problem of mining and optimizing security attributes of unstructured data resources and propose a method for mining security attributes of unstructured textual data based on multi-model collaboration. First, we utilize unsupervised methods to extract candidate attributes from textual resources, and then weight the results of multiple methods using rough set theory to obtain the optimal result. Second, considering various factors including the text itself and the candidate attributes, we construct a feature vector consisting of 45 categories to represent the candidate attributes. Third, we employ a multi-model voting method to collaboratively train the attribute mining model and obtain the security attributes of textual resources. Finally, based on HowNet, we optimize the security attributes to achieve automated and intelligent mining of access control data resource security attributes, providing an attribute foundation for precise access control. The experiments indicate that the attribute mining precision rate of the method proposed in this paper can reach up to 92.36%, F1-score can reach up to 82.51%. The attribute scale can be compressed to 69.59% of its original size after optimization. This method has a greater advantage over other methods and can provide attribute support for access control of large data resources.

Over the past decade, as demand for cloud services has surged, the strategic selection of these services has become increasingly crucial. The growing complexity within the cloud industry underscores the urgent need for a robust model for choosing cloud services effectively. Users often struggle to make informed decisions due to the dynamic nature and varying quality of available cloud services. In response, this paper introduces a novel decision-making approach aimed at optimizing the selection process by identifying the most suitable combination of cloud services. The focus is on integrating these services into a cohesive ensemble to better fulfill user requirements. In contrast to existing methodologies, our approach evaluates cloud services on a continuous scale, taking into account critical tasks such as workload balancing, storage management, and network resource handling. We propose a model for selecting optimal composite cloud services, which includes real-time optimization and addresses the consideration of null values for Quality of Service (QoS)-based attributes (e.g., response time, cost, availability, and reliability) in the dataset—a factor overlooked by current literature. The proposed algorithm is inspired by computational intelligence and driven by an evolutionary algorithm-based approach that undergoes evaluation across multiple datasets. The results illustrate its superiority, showcasing its ability to outperform existing optimization-based methods in terms of execution time.

Optimizing routes in road networks is crucial for smooth transportation and economic progress. Different methods exist for finding the best routes, including genetic algorithms, particle swarm optimization, and simulated annealing. Ant Colony Optimization (ACO) stands out for its efficiency. In this study, we introduce a modified version called MACO, which considers accidents when determining optimal routes. Evaluating different ACO versions reveals differences in solution quality, runtime, and number of iterations. Performance metrics including maximum obtained solution, runtime, and iteration number were evaluated for each method. In Case 1, TACO, and AACO both achieved a maximum of 21 solutions from the available possible solution of 24, exhibiting run-times of 0.4359 and 0.4575 s, respectively. Meanwhile, MACO attained a maximum of 22 solutions from available possible solution 24, in a runtime of 0.5345 s and 10 iterations. In the second scenario, TACO, AACO, and MACO achieved maximum solutions of 20 with obtained solutions of 15, 16, and 17, respectively. TACO demonstrated a runtime of 0.1853 s with 26 iterations, AACO ran in 0.1749 s with 22 iterations, and MACO completed in 0.5799 s with 15 iterations. These findings highlight the varying performance of the optimization methods and suggest MACO as a promising approach for balancing solution quality and computational efficiency in road network path optimization.