Concurrency and Computation-Practice & Experience最新文献

The ability of mobile ad hoc networks (MANET) to be used as communication tools in a variety of industries, including healthcare, the military, smart traffic, and smart cities, has drawn special consideration. Traditional Manet's multicast routing methods seem to be inappropriate to massive with Adaptive systems because the problem is NP-complete, resulting in an enchanting QoS restrictions. In order to conquer that the paper proficiently introduces the Conglomerate Crumb Horde Formicary Meta-Heuristic (CCHFMO) with Asymmetrical Meander Diffie-Hellman (AMDH) to tackle the major obstacles are multicast routing problems and lack of data protection. Initially, the fusion of crumb horde optimization (CHO) and formicary optimization (FO) is exploited to strengthen QoS limitations and reduce QoS data loss. However, the massive and dynamic nature of the network with the combination of more QoS restrictions, deficient security has become extremely difficult. Therefore, the research work establishes the asymmetrical meander Diffie-Hellman (AMDH) to significantly improve performance and concealment while ensuring channel security during data transfer. Finally, the results demonstrated that by employing the novel optimization approaches, the MANET can increase data protection while still achieving high transmission rates and sophistication of communication. As a consequence, it adequately explicates the article to improve QoS performances.

SSL (semi-supervised learning) is widely used in machine learning, which leverages labeled and unlabeled data to improve model performance. SSL aims to optimize class mutual information, but noisy pseudo-labels introduce false class information due to the scarcity of labels. Therefore, these algorithms often need significant training time to refine pseudo-labels for performance improvement iteratively. To tackle this challenge, we propose a novel plug-and-play method named Accelerating semi-supervised learning via contrastive learning (ASCL). This method combines contrastive learning with uncertainty-based selection for performance improvement and accelerates the convergence of SSL algorithms. Contrastive learning initially emphasizes the mutual information between samples as a means to decrease dependence on pseudo-labels. Subsequently, it gradually turns to maximizing the mutual information between classes, aligning with the objective of semi-supervised learning. Uncertainty-based selection provides a robust mechanism for acquiring pseudo-labels. The combination of the contrastive learning module and the uncertainty-based selection module forms a virtuous cycle to improve the performance of the proposed model. Extensive experiments demonstrate that ASCL outperforms state-of-the-art methods in terms of both convergence efficiency and performance. In the experimental scenario where only one label is assigned per class in the CIFAR-10 dataset, the application of ASCL to Pseudo-label, UDA (unsupervised data augmentation for consistency training), and Fixmatch benefits substantial improvements in classification accuracy. Specifically, the results demonstrate notable improvements in respect of 16.32%, 6.9%, and 24.43% when compared to the original outcomes. Moreover, the required training time is reduced by almost 50%.

Complex temporal networks have become instrumental in modeling dynamic systems across various disciplines, presenting unique challenges and opportunities in understanding and influencing their behavior. Controllability, a fundamental aspect of network dynamics, plays a pivotal role in manipulating these systems towards desired states. Temporal motifs are important patterns in temporal complex networks that have many applications in solving problems related to this type of networks. In this paper, a novel method for controlling temporal complex networks using temporal motifs is proposed. First, the most important effective temporal motifs in the controllability processes of complex networks have been identified and it has been shown that the network can be fully controlled using these temporal motifs. Then, an algorithm for extracting temporal motifs is proposed. This algorithm has been proposed to identify effective temporal motifs in network controllability to optimally identify control nodes. To increase the efficiency of extracting temporal motifs, a method for predicting the temporal motif-based link has been proposed, which predicts temporal motifs. The results of the simulation of the proposed method based on temporal motifs and its implementation on real-world temporal complex networks demonstrates that its performance was better than the conventional controllability methods.

The proposed work introduces two schemes for secure device authentication and key agreement (SDA & KA) mechanisms. Initially, an efficient implicit certificate approach based on the Elliptic curve Qu–Vanstone (EIC-EcQuV) scheme is developed in the first stage to instantly concur on the session key. The proposed scheme implicitly performs quick authentication of the public key. Also, this scheme prevents the attacker from creating fake key combinations. Through EIC-EcQuV, the implicit certificate (IC) is distributed which helps to implicitly authenticate the user. This work also proposes ithe developed Public Key Certificateless Cryptosystem (PKCIC) scheme in the second stage, whch was also for the SDA & KA mechanism. In the EIC-EcQuV scheme, efficient authentication is enabled, but public key theft is possible. However, in the PKCIC scheme, authentication is performed through partial keys, and the public key is secured via the Schnorr signature. The efficiency of the proposed schemes is proved by comparing the attained results with previous schemes. The proposed method obtains the computational cost of 0.0583 s for end-to-end devices, 0.06111 for network servers, and 0.00071 s for the gateway, with an execution time of 78.624 for 1000 devices. The attained key agreement of the proposed EIC-EcQuV is 0.953 s, and PKCIC is 0.9988 s.

Superposition coding (SC) is a non-orthogonal multiple access (NOMA) scheme for the downlink communication system, which allows signals of different users to be stacked and forwarded simultaneously in the same frequency band. The codeword for each user is assigned a different weight at the transmitter to avoid signal conflicts and interference after stacking, and successive interference cancellation (SIC) is adopted to decode. A novel SC scheme based on polar code, suitable for the downlink multi-user underwater acoustic (UWA) communication system using orthogonal frequency division multiplexing (OFDM), is proposed in this paper. Polar code is utilized to create a nested code structure that is divided into several subsets. We exploit this feature of the polar codes and assign each subset to the corresponding user in the channel coding process. The information bits are placed on the independent subset for each user, and the random bits are placed on the subset of other users while all the users share the same set of frozen bits. Then, the codewords are stacked with different weights. The selection range of power factors can be expanded through the double superposition of the coding and the power domains, and higher system throughput and fairness can be achieved. The simulation and tank experiment results significantly verify the effectiveness of the proposed scheme, making it most suitable for the downlink UWA multi-user communication systems.

Optimizing energy consumption and maximizing throughput in multi-core real-time architectures through dynamic task scheduling is a critical design challenge. While significant attention has been devoted to addressing this challenge in the domain of real-time multi-core scheduling, the focus has primarily centered on considering periodic tasks as independent. However, the existing literature notably lacks comprehensive study of scheduling methodologies on multi-core systems that consider dependent tasks, though typical real-time systems execute tasks that share resources. Earlier studies have predominantly examined scenarios involving random new tasks and task instances (jobs), which are executed in different power levels. Each task (and job) has distinct execution time corresponding to each power level. By considering these parameters (power levels and execution times of jobs), various combinations of energy signatures have been found to attain an optimum system state. Building upon this prior research, our paper extends the scope to encompass task scheduling in multi-core systems with task dependencies. We introduce a novel approach that categorizes dependent tasks into ASAP (as soon as possible) and ALAP (as late as possible) groups, prioritizing task execution based on task mobility—defined as the disparity between the last cycle the task can be scheduled in and the current cycle. Furthermore, our model demonstrates an approach for efficient scheduling of sporadic and aperiodic tasks within this framework. Through experimental validation using randomized task sets, our results indicate that the proposed model achieves a minimum of 5% reduction in normalized total energy consumption compared to existing methodologies.

Cloud data centers (CDCs) have revolutionized global computing by offering extensive storage and processing capabilities. Nevertheless, the environmental impact of these processes, including their substantial energy consumption and carbon emissions, calls for implementing more efficient techniques. Efficient virtual machine (VM) consolidation is crucial in optimizing resource utilization and reducing energy consumption. Current methods for enhancing energy efficiency often lead to issues such as service level agreements (SLAs) violations and quality of services (QoS) degradation. This study presents a novel approach to host selection using a grey-extreme (GE) machine learning model, which accurately predicts over and underutilized hosts. In addition, a VM placement technique called enhanced black widow optimization (EBWO) utilizes black widow optimization heuristic techniques and a differential evolutionary approach to optimize VM placement. The proposed dynamic VM consolidation technique optimizes energy utilization while meeting strict SLA requirements and enhancing QoS metrics in CDCs. Extensive analyses were conducted using the Cloudsim toolkit to validate the approach's effectiveness. These analyses encompassed conditions such as random workloads in heterogeneous environments. The simulation results showed that GE-EBWO outperforms other techniques and improves energy efficiency by 12%–15%. In addition, it significantly decreases VM migrations by 11%–14% compared to other advanced methods. The study validates the practicality of the proposed technique in moving towards environmentally friendly CDCs.

Cloud storage provides a convenient way of collaboration and sharing. An increasing number of users are becoming more open to sharing their data with others. Consequently, the integrity of shared data has started to draw significant attention due to the loss control of it. Remote data integrity techniques can solve this problem. To resist the collusion between the third party auditor and the cloud server, existing integrity auditing schemes utilize blockchain to replace the third party auditor to do some work. However, these schemes still involve collusion between the third party auditor and the miners and cannot guarantee the third party auditor to execute the auditing process honestly. Considering this, we propose a blockchain-based transparent and certificateless data integrity auditing scheme. Specifically, we design the smart contract combining the commitment technology to generate the challenge information and record the auditing information. Besides, we combine certificateless cryptography and the blind technology to achieve the privacy-preserving. The proposed scheme can resist collusion, improve the transparency of the auditing process, protect the data privacy and reduce the overhead of certificate management and key management. The security analysis and performance evaluation demonstrate the security and efficiency of the scheme.