Concurrency and Computation-Practice & Experience最新文献

The Social Internet of Things (SIoT) allows for more efficient and intelligent communication between entities (humans and objects). Managing the digital identity of objects, protecting the owner's privacy, and resisting impersonation attacks are crucial challenges in SIoT. On the other hand, the classic centralized identity provider servers contain valuable information for hackers and malicious parties. Effective solutions can be presented using decentralized technologies such as blockchains. This paper proposes a decentralized identity management system for SIoT based on smart contract technology. This system drives object identity control; conversely, a zero-knowledge-based protocol and double-spend prevention ideas are proposed for privacy preservation and Sybil attack resistance, respectively. The central idea is to implement self-sovereign identity (SSI) so that even certifiers are not privy to object-sensitive information. Additionally, it attempts to prevent the owner from creating Sybil objects using the web of trust method. The method's scalability and convergence can be proven by considering all acknowledgment nodes and different paths. This method was compared with the most novel available methods; the results from this comparison depict the scalability and effectiveness of the proposed method for large networks.

The convergence of reliable and self-organizing characteristics of Wireless Sensor Networks (WSNs) and the IoT has increased the utilization of WSN in different scenarios such as healthcare, industrial units, battlefield monitoring and so forth, yet has also led to significant security risks in their deployment. So, several researchers are developing efficient authentication frameworks with various security and privacy characteristics for WSNs. Subsequently, we review and examine a recently proposed robust key management protocol for an industrial sensor network system. However, their work is incompetent to proffer expedient security and is susceptible to several security attacks. We demonstrate their vulnerabilities against man-in-the-middle attacks, privileged insider attacks, secret key leakage attacks, user, gateway, and sensor node impersonation attacks, and offline password-guessing attacks. We further highlight the design flaw of no session key agreement in Itoo et al. Therefore to alleviate the existing security issues, we devise an improved key agreement and mutual authentication framework. Our protocol outperforms Itoo et al.'s drawbacks, as demonstrated by the comprehensive security proof performed using the real-or-random (ROR) model and the formal verification accomplished using the Automated Validation of Internet Security Protocols (AVISPA) tool.

Smartphone devices have occupied an indispensable place in human life. These devices have some restrictions, like short lifetime of battery, imperfect computation power, less memory size and unpredictable network connectivity. Hence, a number of methods previously presented to decrease these restrictions as well as increase the battery lifespan with the help of offloading strategy. This manuscript proposes a new enhancing mobile data security using red panda optimized approach with chaotic fuzzy encryption in mobile cloud computing (RPO-CFE-SMC) to offload intensive computation tasks from mobile device to the cloud. The proposed model utilizes a red panda optimization algorithm (RPOA) to scale dynamically the offloading decision under energy consumption, CPU utilization, execution time, memory usage parameters. Before the work is transferred to the cloud, an innovative security layer is applied for encrypting the data using AES chaotic fuzzy encryption (CFE) technology. The proposed RPO-CFE-SMC method provides 20.63%, 25.25%, 25.28%, and 32.47% lower encryption time and 23.66%, 24.25%, and 26.47% lower energy consumption compared with existing EFFORT-SMC, EESH-SMC, and CP-ABE-SMC models respectively. In conclusion, the simulation results prove that the improved efficiency of proposed model in offloading computation to the cloud with enhanced data protection using chaotic fuzzy encryption.

Applications of intelligent systems installed in vehicles require substantial computational processing for various tasks. These intensive computations result in high energy consumption and power demands within vehicles. Computational offloading based on Vehicle-to-Vehicle (V2V) communication in vehicular fog computing (VFC) has been proposed as a promising solution to enhance energy efficiency in transportation applications. In this paper, the primary objective is addressing this concern by identifying the optimal nearby vehicle that minimizes energy consumption for the offloading and execution of computational tasks. Therefore, a decision-making and intelligent task offloading mechanism based on queueing theory is proposed. By modeling the problem environment based on queueing theory and modeling the behavior of distributed tasks with discrete-time Markov chain, the proposed solution can predict the future behavior of vehicles in selecting the most energy-efficient processing node. Therefore, this paper investigates three energy decision parameters based on queueing theory extracted from the Markov model to enhance the performance of the proposed algorithm. Experimental results demonstrate that the computational energy parameter achieves the most significant improvement. The proposed algorithm outperforms previous methods, improving energy-efficient system performance by 6.25% and 2.67%, and reducing delivery failure rate by 6.52% and 2.72%. It also decreases overall transportation system processing energy consumption by 0.05% for 100–500 vehicle arrival rates, resulting in an average total processing energy consumption of 0.48%.

In the present era, blockchain technology has an extensive variety of uses in the financial, marketing, and so forth. The performance and security of the blockchain system are directly affected by the consensus algorithm. Since there are 30 consensus techniques like proof of stake, delegated proof of stake, and proof of work, and so forth in the previous years, the operating efficiency, safety, and stability still lag behind the basic requirements. To this concern, this research manuscript introduces a novel consensus mechanism-based game model using upgraded delegated proof of stake (UDPoS) and Nash equilibrium (NE) that is UDPoS-NE. In a blockchain network powered by UDPoS, blockchain miners provide computing power to publish blocks. Due to a single miner's limited Computing Power (CP), miners frequently join mining pools and divide the pool's profits in accordance with their respective contributions. However, certain miners launch block-withholding attacks that lead to loss the computer power and endanger the blockchain network's effectiveness. Therefore, NE is used to calculate cooperative game solutions and optimize policy decisions of the miners to frustrate the block withholding attack by the influence of stakes and computing power on producing blocks. Using this mechanism, efficiency, and stability in the consensus process is achieved through the combined influence of computing resources and stakes on block generation. Finally, the suggested model's applicability and validity are confirmed with a throughput (600 TPS), delay (3 s), and energy consumption (<100 KWh) for 10 nodes and processing time (0.9 s) for 200 nodes.

This paper proposes a supply chain network design method suitable for multi-product and multi-inventory models, and uses the improved BP neural network to evaluate and provide feedback on the collaborative performance of the supply chain, adjusting the supply chain network design scheme on time. In the context of the Internet of Things (IoT) in manufacturing, it has been found that supply chain operations are difficult to meet personalized customer needs with high precision and quality. Therefore, we adopted a dynamic library strategy, supply chain network optimization model, hybrid algorithm, and the improved BP neural network to solve the above problems. First, this paper designs a corresponding inventory strategy selection mechanism for the various ordering methods of retailers in the manufacturing IoT environment. Based on this, we have constructed a dual objective model for a sustainable supply chain network to minimize total cost and maximize customer satisfaction. Second, we have developed a hybrid improved Grey Wolf and Whale Algorithm (OLDGWOA) that can accurately solve the above model. The hybrid algorithm divides the population into two parts through opposition-based learning, and then we use the improved grey wolf algorithm and whale algorithm to solve the two populations, and seek the optimal solution in the results, resulting in a hybrid algorithm. Finally, we constructed a supply chain performance evaluation model and feedback mechanism based on the improved BP neural network to adjust inventory strategies and network design at any time. We also validated the developed model and algorithm through numerical examples, and the results showed that: (1) the hybrid algorithm has certain advantages in search and solution speed, (2) the advantages of supply chain network design based on supply chain performance evaluation and feedback mechanisms, and (3) the trade-off between ordering methods and inventory strategies, as well as the trade-off between location and inventory strategies.

Recently, lightweight convolutional neural networks (CNNs) on single image super-resolution (SISR) tasks have received impressive improvement with delicate structures. However, numerous lightweight methods may reduce the representation capacity of the network due to decreasing the model size and computational complexities, leading to unsatisfactory performance. In this paper, we propose an efficient multi-scale large asymmetric-kernel network (MLAN) for lightweight SISR. Specifically, MLAN is built with a succession of feature cross extraction blocks (FCEBs), which better models local and long-range interactive information of features for SR. Each of the FCEB contains a multi-scale asymmetric large-kernel attention block (MACAB) by using multiple convolutional kernels to extract features in different receptive fields and a gated mechanism to preserve the useful information for SR. Extensive experimental results on five public benchmark datasets demonstrate the superiority of MLAN over the other advanced lightweight SISR competitors. The average PSNR values are about 0.12, 0.17 and 0.11 dB greater than the second-best competitors under scaling factors of ×2, ×3 and ×4, respectively. The proposed efficient blocks enable our MLAN to make a better balance between model size and performance and achieve comparable performance with Transformer-based methods at a similar level of parameters.

With the continuous expansion scale of data center networks (DCNs), the probability of network failures becomes high. Trustworthy fault-tolerant routing is extremely significant for reliable communication in data centers. In this article, we tackle the challenge by proposing a novel fault-tolerant routing scheme for a torus-based DCN. First, we present a multipath information transmission model based on the trust degree of reachable paths and propose a novel Hamiltonian odd–even turning model without deadlock. Second, we design an efficient deadlock-free fault-routing algorithm by constructing the longest fault-free path between any two fault-free nodes in DCN. Extensive simulation results show that the proposed fault-tolerant routing outperforms the previous algorithms. Compared with the most advanced fault-tolerant routing algorithms, the proposed algorithm has a 21.5% to 25.3% increase in throughput and packet arrival rate. Moreover, it can reduce the average delay of 18.6% and the maximum delay of 23.7% in the network respectively.

Heterogeneous systems deliver high computing performance when effectively utilized. It is crucial to execute each application on the most suitable device while maintaining system balance. However, achieving equal distribution of the computing load is challenging due to variations in computing power and device architectures within the system. Moreover, scheduling applications at real-time further complicates this task, as prior information about the submitted applications is absent. In this context, we introduce “Equalizer,” a real-time load balancer for heterogeneous systems. “Equalizer” leverages machine learning to continuously monitor the system's state, predicting optimal devices for application execution at runtime. It assigns applications to devices that minimize system imbalance. To quantify system imbalance, we propose a novel metric that reflects the disparity in computing loads across the system's devices. This metric is calculated using predicted execution times of applications. To validate the performance of “Equalizer,” we conducted a comparative study against widely adopted approaches, namely Round Robin and Device Suitability. The experiments were performed on a heterogeneous cluster comprising a master host and three slave servers, equipped with a total of 4 central processing units (CPUs) and 4 graphics processing units (GPUs). All approaches were deployed on the cluster and evaluated using three distinct workloads categorized by their computing intensity: medium intensity, heavy intensity, and a combination of heavy and medium intensity, simulating real-world scenarios. Each workload consisted of a set of 80 OpenCL applications with varying input data sizes. The experimental results demonstrate that “Equalizer” effectively minimized the system's imbalance, reduced the idle time of devices, and eliminated overloads. Moreover, “Equalizer” exhibited significant improvements in workload execution time, resource utilization, throughput, and energy consumption. Across all tested scenarios, “Equalizer” consistently outperformed alternative approaches, showcasing its robustness, adaptability to dynamic environments, and applicability in real-world practice.

NERSC's newest system, Perlmutter, features a 35 PB all-flash Lustre file system built on HPE Cray ClusterStor E1000. We present its architecture, early performance figures, and performance considerations unique to this architecture. We demonstrate the performance of E1000 OSSes through low-level Lustre tests that achieve over 90% of the theoretical bandwidth of the SSDs at the OST and LNet levels. We also show end-to-end performance for both traditional dimensions of I/O performance (peak bulk-synchronous bandwidth) and nonoptimal workloads endemic to production computing (small, incoherent I/Os at random offsets) and compare them to NERSC's previous system, Cori, to illustrate that Perlmutter achieves the performance of a burst buffer and the resilience of a scratch file system. Finally, we discuss performance considerations unique to all-flash Lustre and present ways in which users and HPC facilities can adjust their I/O patterns and operations to make optimal use of such architectures.