Concurrency and Computation-Practice & Experience最新文献

Quality of Service (QoS) regulates and controls network resources by setting priorities for specific data types. Many clustering algorithms are used to cluster cloud workloads, most of which are static. However, the lack of dynamic algorithms is seen in the face of huge databases that are real-time and according to the existing clustering conditions. Additionally, fair allocation of tasks on servers and efficient resource utilization pose challenges. In this research, two solutions are proposed to improve the quality of service: the first solution uses the chameleon dynamic algorithm, a method to improve service quality. The chameleon algorithm has been able to show significant performance due to its high accuracy in detecting the smallest distance between clusters. This dynamic algorithm outperforms static algorithms with classification accuracy and response speed, which are the most important parameters of service quality. The second part of the proposed solution is to use the Scheduling Model using Cloud Data Centers (SM-CDC) system to select the best service provider based on the clustering done in the previous step. A SM-CDC technique is developed to handle cloud storage center tasks that are stored in electronic devices. According to the comparison with existing scheduling policies, SM-CDC offered 36% decrease on response time, 50% reduction on cost of resources, and 40% improvement on QoS Satisfaction.

Camellia oleifera typically thrives in unstructured environments, making the identification of its trunks crucial for advancing agricultural robots towards modernization and sustainability. Traditional target detection algorithms, however, fall short in accurately identifying Camellia oleifera trunks, especially in scenarios characterized by small targets and poor lighting. This article introduces an enhanced trunk detection algorithm for Camellia oleifera based on an improved YOLOv7 model. This model incorporates dynamic snake convolution instead of standard convolutions to bolster its feature extraction capabilities. It integrates more contextual information, thus enhancing the model's generalization ability across various scenes. Additionally, coordinate attention is introduced to refine the model's spatial feature representation, amplifying the network's focus on essential target region features, which in turn boosts detection accuracy and robustness. This feature selectively strengthens response levels across different channels, prioritizing key attributes for classification and localization. Moreover, the original coordinate loss function of YOLOv7 is replaced with EIoU loss, further enhancing the model's robustness and convergence speed. Experimental results demonstrate a recall rate of 96%, a mean average precision (mAP) of 87.9%, an F1 score of 0.87, and a detection speed of 18 milliseconds per frame. When compared with other models like Faster-RCNN, YOLOv3, ScaledYOLOv4, YOLOv5, and the original YOLOv7, our improved model shows mAP increases of 8.1%, 7.0%, 7.5%, and 6.6% respectively. Occupying only 70.8 MB, our model requires 9.8 MB less memory than the original YOLOv7. This model not only achieves high accuracy and detection efficiency but is also easily deployable on mobile devices, providing a robust foundation for future intelligent harvesting technologies.

Cloud Computing (CC) and big data are growing technology in the business. Big data is demonstrated in terms of volume, variety, and velocity. CC is employed for storing, processing, and accessing data. Many cryptographic techniques have been developed to enhance big data security in cloud computing. However, security and privacy are the primary concerns in protecting data, as it is highly sensitive. Yet, it faces the major problems of inefficient performance, increased time consumption, and lack of data confidentiality and integrity. To address this issue, proposed Extreme Learning with Projection Relational Algebraic Secured Data Transmission (ELPRA-SDT) is introduced to secure data transactions from cloud users to cloud servers with enhanced data confidentiality and reduced time consumption for big cloud data. The proposed ELPRA-SDT consists of two major processes namely registration and key generation. At first, the user's IP address is registered employing a transitive advanced set relation theory graph model in a cloud server (CS) for retrieving the numerous services. The CS generates private and public keys for each registered user's IP address using the Transitive Operational and Time Synchronized Random Winternitz Key generation model. After, the user sends a request to the CS for acquiring data. The CS validates the requested user based on security policy attributes. Second, the Projection Relational Algebraic Signcryption and Unsigncryption algorithm performs signature verification to ensure secure data access for protecting the data. Results of experiments carried out by using Coburg Intrusion Detection Data Sets-001 dataset in Java. ELPRA-SDT method is more efficient and more suitable for providing security and privacy to network traces in the Cloud. The result shows maximum performance with data confidentiality by 10% and data integrity by 13%. In addition, delay is reduced by 32%, and data delivery time and communication complexity is decreased by 28% and 24% to other existing methods.

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.

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.

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 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.