Subjects -> MATHEMATICS (Total: 1013 journals)
    - APPLIED MATHEMATICS (92 journals)
    - GEOMETRY AND TOPOLOGY (23 journals)
    - MATHEMATICS (714 journals)
    - MATHEMATICS (GENERAL) (45 journals)
    - NUMERICAL ANALYSIS (26 journals)
    - PROBABILITIES AND MATH STATISTICS (113 journals)

MATHEMATICS (714 journals)            First | 1 2 3 4     

Showing 601 - 538 of 538 Journals sorted alphabetically
Results in Mathematics     Hybrid Journal  
Results in Nonlinear Analysis     Open Access  
Review of Symbolic Logic     Full-text available via subscription   (Followers: 2)
Reviews in Mathematical Physics     Hybrid Journal   (Followers: 1)
Revista Baiana de Educação Matemática     Open Access  
Revista Bases de la Ciencia     Open Access  
Revista BoEM - Boletim online de Educação Matemática     Open Access  
Revista Colombiana de Matemáticas     Open Access   (Followers: 1)
Revista de Ciencias     Open Access  
Revista de Educación Matemática     Open Access  
Revista de la Escuela de Perfeccionamiento en Investigación Operativa     Open Access  
Revista de la Real Academia de Ciencias Exactas, Fisicas y Naturales. Serie A. Matematicas     Partially Free  
Revista de Matemática : Teoría y Aplicaciones     Open Access   (Followers: 1)
Revista Digital: Matemática, Educación e Internet     Open Access  
Revista Electrónica de Conocimientos, Saberes y Prácticas     Open Access  
Revista Integración : Temas de Matemáticas     Open Access  
Revista Internacional de Sistemas     Open Access  
Revista Latinoamericana de Etnomatemática     Open Access  
Revista Latinoamericana de Investigación en Matemática Educativa     Open Access  
Revista Matemática Complutense     Hybrid Journal  
Revista REAMEC : Rede Amazônica de Educação em Ciências e Matemática     Open Access  
Revista SIGMA     Open Access  
Ricerche di Matematica     Hybrid Journal  
RMS : Research in Mathematics & Statistics     Open Access  
Royal Society Open Science     Open Access   (Followers: 7)
Russian Journal of Mathematical Physics     Full-text available via subscription  
Russian Mathematics     Hybrid Journal  
Sahand Communications in Mathematical Analysis     Open Access  
Sampling Theory, Signal Processing, and Data Analysis     Hybrid Journal  
São Paulo Journal of Mathematical Sciences     Hybrid Journal  
Science China Mathematics     Hybrid Journal   (Followers: 1)
Science Progress     Full-text available via subscription   (Followers: 1)
Sciences & Technologie A : sciences exactes     Open Access  
Selecta Mathematica     Hybrid Journal   (Followers: 1)
SeMA Journal     Hybrid Journal  
Semigroup Forum     Hybrid Journal   (Followers: 1)
Set-Valued and Variational Analysis     Hybrid Journal  
SIAM Journal on Applied Mathematics     Hybrid Journal   (Followers: 11)
SIAM Journal on Computing     Hybrid Journal   (Followers: 11)
SIAM Journal on Control and Optimization     Hybrid Journal   (Followers: 18)
SIAM Journal on Discrete Mathematics     Hybrid Journal   (Followers: 8)
SIAM Journal on Financial Mathematics     Hybrid Journal   (Followers: 3)
SIAM Journal on Mathematics of Data Science     Hybrid Journal   (Followers: 1)
SIAM Journal on Matrix Analysis and Applications     Hybrid Journal   (Followers: 3)
SIAM Journal on Optimization     Hybrid Journal   (Followers: 12)
Siberian Advances in Mathematics     Hybrid Journal  
Siberian Mathematical Journal     Hybrid Journal  
Sigmae     Open Access  
SILICON     Hybrid Journal  
SN Partial Differential Equations and Applications     Hybrid Journal  
Soft Computing     Hybrid Journal   (Followers: 7)
Statistics and Computing     Hybrid Journal   (Followers: 13)
Stochastic Analysis and Applications     Hybrid Journal   (Followers: 2)
Stochastic Partial Differential Equations : Analysis and Computations     Hybrid Journal   (Followers: 1)
Stochastic Processes and their Applications     Hybrid Journal   (Followers: 5)
Stochastics and Dynamics     Hybrid Journal  
Studia Scientiarum Mathematicarum Hungarica     Full-text available via subscription   (Followers: 1)
Studia Universitatis Babeș-Bolyai Informatica     Open Access  
Studies In Applied Mathematics     Hybrid Journal   (Followers: 1)
Studies in Mathematical Sciences     Open Access   (Followers: 1)
Superficies y vacio     Open Access  
Suska Journal of Mathematics Education     Open Access   (Followers: 1)
Swiss Journal of Geosciences     Hybrid Journal   (Followers: 1)
Synthesis Lectures on Algorithms and Software in Engineering     Full-text available via subscription   (Followers: 2)
Synthesis Lectures on Mathematics and Statistics     Full-text available via subscription   (Followers: 1)
Tamkang Journal of Mathematics     Open Access  
Tatra Mountains Mathematical Publications     Open Access  
Teaching Mathematics     Full-text available via subscription   (Followers: 10)
Teaching Mathematics and its Applications: An International Journal of the IMA     Hybrid Journal   (Followers: 4)
Teaching Statistics     Hybrid Journal   (Followers: 8)
Technometrics     Full-text available via subscription   (Followers: 8)
The Journal of Supercomputing     Hybrid Journal   (Followers: 1)
The Mathematica journal     Open Access  
The Mathematical Gazette     Full-text available via subscription   (Followers: 1)
The Mathematical Intelligencer     Hybrid Journal  
The Ramanujan Journal     Hybrid Journal  
The VLDB Journal     Hybrid Journal   (Followers: 2)
Theoretical and Mathematical Physics     Hybrid Journal   (Followers: 7)
Theory and Applications of Graphs     Open Access  
Topological Methods in Nonlinear Analysis     Full-text available via subscription  
Transactions of the London Mathematical Society     Open Access   (Followers: 1)
Transformation Groups     Hybrid Journal  
Turkish Journal of Mathematics     Open Access  
Ukrainian Mathematical Journal     Hybrid Journal  
Uniciencia     Open Access  
Uniform Distribution Theory     Open Access  
Unisda Journal of Mathematics and Computer Science     Open Access  
Unnes Journal of Mathematics     Open Access   (Followers: 2)
Unnes Journal of Mathematics Education     Open Access   (Followers: 2)
Unnes Journal of Mathematics Education Research     Open Access   (Followers: 1)
Ural Mathematical Journal     Open Access  
Vestnik Samarskogo Gosudarstvennogo Tekhnicheskogo Universiteta. Seriya Fiziko-Matematicheskie Nauki     Open Access  
Vestnik St. Petersburg University: Mathematics     Hybrid Journal  
VFAST Transactions on Mathematics     Open Access   (Followers: 1)
Vietnam Journal of Mathematics     Hybrid Journal  
Vinculum     Full-text available via subscription  
Visnyk of V. N. Karazin Kharkiv National University. Ser. Mathematics, Applied Mathematics and Mechanics     Open Access   (Followers: 1)
Water SA     Open Access   (Followers: 2)
Water Waves     Hybrid Journal  
Zamm-Zeitschrift Fuer Angewandte Mathematik Und Mechanik     Hybrid Journal   (Followers: 1)
ZDM     Hybrid Journal   (Followers: 2)
Zeitschrift für angewandte Mathematik und Physik     Hybrid Journal   (Followers: 2)
Zeitschrift fur Energiewirtschaft     Hybrid Journal  
Zetetike     Open Access  

  First | 1 2 3 4     

Similar Journals
Journal Cover
The Journal of Supercomputing
Journal Prestige (SJR): 0.407
Citation Impact (citeScore): 2
Number of Followers: 1  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1573-0484 - ISSN (Online) 0920-8542
Published by Springer-Verlag Homepage  [2469 journals]
  • Runtime and energy constrained work scheduling for heterogeneous systems

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      Abstract: Abstract Heterogeneous hardware systems consisting of CPUs and different types of accelerators are wide-spread nowadays for large supercomputers as well as smaller cluster systems in the field of high-performance computing (HPC). A fundamental problem for such systems is the distribution of the workload of data-parallel HPC applications onto heterogeneous compute devices. The distribution of the workload tries to achieve (1) a well-balanced and runtime efficient program execution and (2) energy efficiency. However, typically both goals are contradicting objectives resulting in a challenging bi-criteria optimization problem. In this paper, we present an efficient scheduling algorithm that assigns work bundles to heterogeneous compute devices and determines an optimal solution for minimizing the makespan of a task under a given energy constraint. Work bundles are equal-sized, medium-grained data chunks that are obtained by partitioning the workload of data-parallel applications. Energy consumption and execution time for processing a single work bundle varies depending on the respective compute device and is essential for beneficial scheduling strategies. We formulate our optimization problem as an Integer Linear Program and devise an efficient bisection algorithm, which computes optimal solutions with logarithmic-time complexity. Experiments emphasize the efficiency of our algorithm. Further we investigate the two-dimensional optimization space and sketch an algorithm for Strong Pareto Optimal solutions.
      PubDate: 2022-05-16
       
  • A secret sharing-based scheme for secure and energy efficient data
           transfer in sensor-based IoT

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      Abstract: Abstract The sensed data from Internet of Things (IoT) devices are important for accurate decision making. Thus, the data integrity, non-repudiation, data confidentiality, data freshness, etc., are necessary requirements in sensor-based IoT networks. Further, the IoT devices are resource constrained in terms of computation and communication capabilities. Hence, striking a balance between network lifetime and data security is of utmost importance. The present work explores the sensor-based IoT-specific security threats like, data modification, selective forwarding and replay attacks. Further, a scheme is proposed based on secret sharing and cryptographic hash functions which detects these attacks by a malicious entity and protects the data from passive listeners too. Extensive simulations were performed to evaluate the efficacy of the scheme, and results show that the proposed scheme outperforms previously explored schemes like SIGN-share, SHAM-share, and PIP algorithm, in terms of sensor processing time, energy consumption during in-node processing and aggregation time. Network lifetime has been further analyzed to show the efficacy of the scheme.
      PubDate: 2022-05-16
       
  • Resource discovery approaches in cloudIoT: a systematic review

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      Abstract: Abstract The cloud of things (CloudIoT) represents a general system of supporting infrastructure for storing and processing information gathered from smart objects and their communications.There are many resources used to respond to requests in the CloudIoT environment. Therefore, a primary challenge in these systems is resource discovery based on the requests. Discovering and accessing resources, overcoming user constraints, and focusing on dynamic requirements, such as failure nodes, are the most critical issues to be addressed. This paper focuses on several resource discovery mechanisms using the systematic literature review method in the CloudIoT environment. The research aims at analyzing and reviewing studies published from 2016 to 2021 (June) on resource discovery in CloudIoT. The technical classification of resource discovery is based on selected studies by considering architecture, algorithm, middleware, and protocol approaches. These studies are discussed in terms of their main ideas, advantages, and weaknesses. Finally, future research opportunities related to resource discovery in the cloud of things are identified.
      PubDate: 2022-05-16
       
  • Multimedia content delivery services in the cloud with partial sleep and
           abandonment

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      Abstract: Abstract The recent past has witnessed tremendous advances in the fields of storage computing and communication technology, enabling the realization of multimedia service networks. These networks lead to rapid consumption of network bandwidth due to the voluminous transfer of real-time data. An example of a multimedia service network is the Video-On-Demand service which enables the streaming of video on demand. In this paper, we consider the impatient behavior of viewers while waiting for the video and allow a fixed number of servers to take sleep when idle. We observe that idle servers consume a significant amount of energy. Dynamically sending servers into hibernation or sleep shall increase energy efficiency, leading to lower costs incurred by content providers. We enable a fixed number of servers to take a partial synchronous sleep when idle. We model it by a GI/M/c/N queue with abandonment and partial synchronous sleep mode. We propose a cost function for the model and examine the dependence upon the various system parameters. The number of servers that should be commissioned such that the system meets prescribed standards for performance, while minimizing associated cost is determined. Also, the performance measures of the model are compared on a variety of fronts, including cost and energy expended.
      PubDate: 2022-05-16
       
  • PARCSIM: a parallel computing simulator for scalable software optimization

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      Abstract: Abstract PARCSIM is a parallel software simulator that allows a user to capture, through a graphical interface, matrix algorithm schemes that solve scientific problems. With this tool, the user can analyse the execution times that would be obtained by using different spatio-temporal mapping of computational tasks on available computational units, parallelism parameters and computational libraries. Furthermore, for complex problem models, the self-optimization engine incorporated in this tool analyses the huge tree of possible calculations grouping and mapping strategies in search of the choice that makes the best use of the available hardware resources. This tool also offers polyalgorithmic resolution by making automatically the best decision between different software approaches to solve a given problem on the hardware system available. This work shows the usefulness of this simulator to efficiently solve hierarchical problems constructed from previously modelled subproblems. This task is performed by reusing, in a scalable way, the optimization information of these subproblems to establish the best execution configuration for the composite problem.
      PubDate: 2022-05-16
       
  • CNN supported framework for automatic extraction and evaluation of
           dermoscopy images

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      Abstract: Abstract Skin Cancer is one of the acute diseases listed under top 5 groups in 2020 report of World Health Organisation. This research aims to propose a Convolutional Neural Network framework to extract and evaluate the suspicious skin region. This framework consists following phases; (i) Image collection and resizing, (ii) Suspicious skin section extraction using VGG-UNet, (iii) Deep-feature extraction, (iv) Handcrafted features mining from the suspicious skin section, (v) serial feature integration, and (vi) Classifier training and validation. This research considered dermoscopy images of International Skin Imaging Collaboration benchmark dataset for the experimental assessment and the result of the proposed framework is separately analysed for segmentation and classification tasks. In this work, benign and malignant class images are considered for the examination and during the classification task, integration of the deep and handcrafted features are considered. The experimental results of this study present a segmentation accuracy of > 98% with UNet and a classification accuracy of > 98% with VGG16 combined with Random Forest classifier.
      PubDate: 2022-05-14
       
  • CDA: a novel multicore scheduling for cost-aware deadline-constrained
           scientific workflows on the IaaS cloud

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      Abstract: Abstract The variety of pricing models offered by cloud service providers and the availability of a wide diversity of computing resources has increased the popularity of this paradigm for scientific applications. Such a scalable platform can be an ideal option for the execution of loosely coupled parallel applications, such as scientific workflows. Scientific workflows are regarded as one of the most important elements in different scientific fields in which a complex application may be divided into several dependent tasks. Given that the cost of leasing multicore VMs on the cloud will rise with an increase in the number of processing cores, an efficient scheduling algorithm focusing on utilizing multicore resources can significantly reduce execution costs. As an extension of its authors’ previous research, the current paper proposes a heuristic scheduling algorithm, the Cluster Dividing Algorithm that concentrates on expanding the utilization of multicore resources to reduce execution costs while also meeting the user-defined deadline. To increase resource utilization, the proposed scheduling employs different techniques, such as task clustering, directed graph leveling, and task duplicating. The experimental results reveal that the presented algorithm leads to lower execution costs while complying with the deadline.
      PubDate: 2022-05-13
       
  • Hybrid trust and weight evaluation-based trust assessment using ECK-ANFIS
           and AOMDV-REPO-based optimal routing in MANET environment

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      Abstract: Abstract Many researchers have been inspired to work on diverse challenges by a particularly favourable platform, namely mobile ad hoc networks (MANET) routing optimization. However, the lack of trust assessment is one of MANET’s main flaws. As a result, trust-based routing has received increasing attention in MANET over the last few years. Hence, the majority of recent work has focused on the development of routing protocols for security enhancement in a hostile environment. However, on the MANET environment, these protocols have many weaknesses and are also not that much secure. Hence, the primary goal of this study is to design a framework for balancing multiple performance measures in order to find the optimal multipath routing solution. In this scheme, we have employed the exponential cauchy kernelized adaptive neuro-fuzzy inference system (ECK-ANFIS) focused trust assessment with hybrid trust (HT) evaluation and optimal MANET routing. The ECK-ANFIS evaluates the trust after the nodes are initialised where, HT and the weight value, which are estimated for each node throughout the evaluation. The performance of the proposed mechanism has been measured using the various metrics defined in the existing protocols and also proved the superiority of the scheme by comparing it with other related ones.
      PubDate: 2022-05-13
       
  • A denoising method of mine microseismic signal based on NAEEMD and
           frequency-constrained SVD

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      Abstract: Abstract The microseismic signals collected in the process of mine microseismic monitoring are mixed with interference signals. It will seriously affect microseismic signal recognition, first arrival picking and source location. To accurately obtain effective microseismic signals from the original collected signals, a denoising method based on novel adaptive ensemble empirical mode decomposition (NAEEMD) and frequency constrained singular value decomposition (SVD) is proposed. Firstly, the original mixed signal is decomposed into intrinsic modal components (IMF) with the order from high to low and residual components by NAEEMD. Then, the transition component is determined according to the correlation coefficient, and the IMF components are adaptively divided into the signal-dominated IMF components and the noise-dominated IMF components. Taking the sum of the signal-dominated IMF components as the frequency constraint condition, the noise-dominated IMF component and transition component are denoised by SVD. Finally, the denoised components and residual components are reconstructed to obtain the denoised microseismic signal. The simulation analysis is carried out with the simulated signal. The results indicate that the proposed method is easier to obtain useful signals and has good frequency convergence and signal-to-noise ratio (SNR). In addition, the denoising experiment is carried out with the measured signal. Compared with empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), SVD, and NAEEMD, the three evaluation indexes of SNR, energy percentage (E), and standard deviation (RMSE) are calculated quantitatively. The results show that the SNR of the proposed method is 15.6258 dB higher than that of other methods, the E is as high as 94.8625%, and the RMSE is 0.0216. The proposed method is effective in denoising mine microseismic signals and has better denoising effect than EMD, EEMD, SVD, and NAEEMD.
      PubDate: 2022-05-13
       
  • An effective 3-D fast fourier transform framework for multi-GPU
           accelerated distributed-memory systems

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      Abstract: Abstract This paper introduces an efficient and flexible 3D FFT framework for state-of-the-art multi-GPU distributed-memory systems. In contrast to the traditional pure MPI implementation, the multi-GPU distributed-memory systems can be exploited by employing a hybrid multi-GPU programming model that combines MPI with OpenMP to achieve effective communication. An asynchronous strategy that creates multiple streams and threads to reduce blocking time is adopted to accelerate intra-node communication. Furthermore, we combine our scheme with the GPU-Aware MPI implementation to perform GPU-GPU data transfers without CPU involvement. We also optimize the local FFT and transpose by creating fast parallel kernels to accelerate the total transform. Results show that our framework outperforms the state-of-the-art distributed 3D FFT library, being up to achieve 2× faster in a single node and 1.65× faster using two nodes.
      PubDate: 2022-05-13
       
  • A hybrid algorithm for scheduling scientific workflows in IaaS cloud with
           deadline constraint

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      Abstract: Abstract Scientific workflows are used to process large amounts of data and perform complex analyses; thus, they require powerful computing resources to produce the desired results in an acceptable time and at reasonable costs. For this purpose, distributed resources such as cloud computing, with access to virtualized, infinite, and elastic resources are used to execute the workflows. For mapping tasks to computational resources, the problem must be modeled as a scheduling problem. The algorithm presented in this research is a hybrid algorithm based on a mathematical model called MHPSLP that performs the scheduling problem by breaking the problem into smaller subsets including scheduling bags of tasks, providing resources using an mixed integer linear mathematical (MILP) model. The benefit of this method against compared scheduling algorithms is reduction of executed task’s cost in a deadline constraint.
      PubDate: 2022-05-12
       
  • A self-learning approach for proactive resource and service provisioning
           in fog environment

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      Abstract: Abstract With increasing growth in IoT, the number of devices connected to the Internet is constantly growing. Moreover, the increase in the volume of data and their transmission through the Internet of Things, as well as the existence of inadequate bandwidth, limits cloud-based storage and data processing. Both fog and cloud computing provide the storage space, application, and data for users; however, fog is more proximate to the end user with wider geographical distribution. When bringing the computing resources closer to the required location in the fog environment, the efficiency of the system increases, and the distance at which data must be transmitted decreases. On the other hand, implementing IoT applications and satisfying the requests of end users in fog computing will create new challenges in resource allocation and dynamic resource provisioning. The flexible and usually automatic mechanisms require the determination of required virtual resources to minimize the resource consumption and service level agreement (SLA). In this paper, we introduce a framework for increasing resource management efficiency in the IoT ecosystem based on deep reinforcement learning (DRL). The proposed deep neural network (DNN) method for estimating value functions improves adaptability to different oscillating conditions, learns past sensible strategies, and as a self-learning adaptive system by replicating interactions with the fog environment. The DRL algorithm finds the best destination for implementing IoT services to compromise between minimizing average power consumption, minimizing average service latency, reducing costs, and balancing resource allocation. Finally, through simulations, we show that under different loading rates, the policy used compared to other comparable solutions is to increase utilization and reduce the rate of delay, while ensuring an acceptable level of service quality.
      PubDate: 2022-05-12
       
  • Trusted consensus protocol for blockchain networks based on fuzzy
           inference system

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      Abstract: Abstract Blockchain technology with its inherent security features revolutionizes the field of distributed networks and has become one of the significant areas of research. To preserve the security features and to maintain its global state, consensus mechanisms are very essential and are performed by a set of peers in the underlying network called miners. Therefore, the miners need to be a trusted entity and their trustworthiness plays a vital role in preserving the security of the asset ledger. To ensure trusted nodes perform the consensus process, fuzzy-based trust models are robust and effective. Therefore, fuzzy inference system-based trusted consensus mechanism (FISTCON) is proposed as an effective security solution resulting in a fast and secure consensus process. The proposed scheme works in two phases. In phase 1, a fuzzy-based trust model that includes transaction history and trust feedback (F-THTF trust model) to identify trusted miners for consensus is proposed. In phase 2, a fuzzy-based effective practical byzantine fault tolerance (F-EpBFT) consensus protocol with an optimized broadcasting mechanism to decrease the communication overhead is proposed. The proposed work is implemented in the Hyperledger fabric framework, and the outcomes are thoroughly analyzed to prove the efficiency of the proposed scheme in a variety of scenarios.
      PubDate: 2022-05-12
       
  • Advanced data modeling for industrial drying machine energy optimization

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      Abstract: Abstract In this article, a digital twin approach is proposed for modeling a pharmaceutical drying process using machine learning techniques, driven by data from different sensors captured in-line. The current difficulty with the drying process is mainly due to the manual operator control for choosing the end-point for terminating the drying step. This results in significant variability, depending on which human operator is supervising, and due to the multi-tasking nature of the job, generally allows a longer processing time to be sure the material is completely dry. The objective is to automate the end-point identification, thus optimizing the processing duration and therefore the overall energy consumption of the process. The point at which the drying is complete is indicated by the temperature difference between the ingoing and outgoing air flow. However, the stochastic nature of the process makes the data modeling a challenge. Firstly, a wide selection of supervised statistical and machine learning algorithms was benchmarked to find the one (CatBoost) which gave the best performance with the data. Next, the set of hyper-parameters was found for CatBoost which gave the optimum performance. This gave a best performance of 0.788 (R2) fitting of the drying end-point prediction with the real values, for a large number of batches (over 700 K records). This is considered a good result taking into account the high residual of data models for these data and the stochastic nature of the process. The approach has been deployed in a real setting digital twin to control the drying process cutoff in the production plant. The results show the viability of the approach for modeling the process and automatically identifying the optimum end-point for the drying process, thus achieving significant energy savings which have been quantified as approximately 3.7 MWh per year for the pharmaceutical company.
      PubDate: 2022-05-11
       
  • Network attack detection scheme based on variational quantum neural
           network

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      Abstract: Abstract Network attack may have a serious impact on network security. With the rapid development of quantum machine learning, variational quantum neural network (VQNN) has demonstrated quantum advantages in classification problems. The intrusion detection system (IDS) based on quantum machine learning has higher accuracy and efficiency than the IDS based on traditional machine learning. In this work, we propose a intrusion detection scheme based on VQNN, which is composed of variational quantum circuit (VQC) and classical machine learning (ML) strategy. In order to verify the effectiveness of the scheme, we used the VQNN model and some classic ML models (Such as artificial neural network, support vector machines, K-Nearest Neighbors, Naive Bayes, decision tree) to conduct comparative experiments. The results indicate that the proposed IDS model based on VQNN has a 97.21% precision, which is higher than other classic IDS models. Furthermore, our VQC can be deployed on the overwhelming majority of recent noisy intermediate-scale quantum machines (such as IBM). This research will contribute to the construction of general variational quantum framework and experimental design and highlight the potential hopes and challenges of hybrid quantum classical learning schemes.
      PubDate: 2022-05-11
       
  • POPS: an off-peak precomputing scheme for privacy-preserving computing

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      Abstract: Abstract Emerging privacy-preserving technologies help protect sensitive data during application executions. Recently, the secure two-party computing (TPC) scheme has demonstrated its potential, especially for the secure model inference of a deep learning application by protecting both the user input data and the model parameters. Nevertheless, existing TPC protocols incur excessive communications during the program execution, which lengthens the execution time. In this work, we propose the precomputing scheme, POPS, to address the problem, which is done by shifting the required communications from during the execution to the time prior to the execution. Particular, the multiplication triple generation is computed beforehand with POPS to remove the overhead at runtime. We have analyzed the TPC protocols to ensure that the precomputing scheme conforms the existing secure protocols. Our results show that POPS takes a step forward in the secure inference by delivering up to \(20\times \) and \(5\times \) speedups against the prior work for the microbenchmark and the convolutional neural network experiments, respectively.
      PubDate: 2022-05-11
       
  • A data distribution scheme for VANET based on fountain code

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      Abstract: Abstract Data dissemination is one of the applications used to provide infotainment to the end-users in vehicular ad hoc networks (VANET). During this process, the vehicles receive the data broadcast by the RoadSide Unit (RSU). However, it is difficult for vehicles to collect the complete content within the communication range of one RSU when the vehicle moves at a high speed and the amount of broadcast data is large. To solve this problem, a multi-RSU cooperative data distribution scheme based on fountain code (MRFC) is proposed in this paper. The source data are encoded by fountain code and poured into the VANET by multiple cooperative RSUs, then the vehicles in the area share data packets through the V2V resource compensation method, so that all vehicles can obtain enough encoded packets to reconstruct the source data. To improve channel resource utilization and reduce delivery delays, the RSUs use fuzzy logic to determine the number of fountain code packets according to their locations, the speed and density of surrounding vehicles. The experimental results show that on the premise of ensuring the delivery rate, the proposed scheme can reduce the delivery delay by 30–50%, and achieve a significant improvement in performance.
      PubDate: 2022-05-11
       
  • Leveraging data aggregation algorithm in LoRa networks

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      Abstract: Abstract Long Range (LoRa) is an interference-free, single-hop, low-power wide area network (LPWAN) technology. LoRa offers customization of its physical layer parameters like spreading factor, coding rate, bandwidth, and transmission power to achieve high network coverage. Requirements for high coverage precipice the problem of high energy consumption. As a solution, our article presents an energy-efficient data packet aggregation scheme for LoRa communication to reduce high energy consumption. We propose a load balancing algorithm that yields better results in network communication. On comparing data aggregation strategy in LoRa with conventional star connected LoRa communication, and with other existing protocols our approach shows significant improvement in performance and energy saving. By adopting our strategy, conventional LoRa networks can achieve a longer lifetime and network stability.
      PubDate: 2022-05-11
       
  • RAM: resource allocation in MIMO–MISO cognitive IoT for 5G wireless
           networks using two-level weighted majority cooperative game

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      Abstract: Abstract Cognitive Internet of things (CIoT) is the solution for resource allocation problem in an exponentially increasing number of Internet of things in fifth generation wireless networks. Two-tier massive multiple-input-multiple-output (MIMO) and multiple-input-single-output small cell-based CIoT using weighted majority cooperative game (WMCG) is deployed in RAM. We have proposed a two-level WMCG theoretic model and two utility functions, to detect the channel is free or not and for allocating the spectrum to a high majority CIoT device by the spectrum manager based on their applied maximum weight or price values. In RAM, we have calculated power consumption, signal-to-interference-plus-noise ratio (SINR), spectral efficiency, and energy efficiency of the proposed network. We have simulated the RAM using QualNet 7.1 simulator. The power consumption of the RAM has been shown to decrease by approximately 15–30%, and SINR has been shown to increase by approximately 6–7% as compared to the existing approaches.
      PubDate: 2022-05-11
       
  • A lightweight authentication and key agreement protocol for heterogeneous
           IoT with special attention to sensing devices and gateway

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      Abstract: Abstract Focusing specifically on sensing devices with restricted resources, heterogeneous internet of things (HIoT) is an attractive scenario for IoT networks. Nonetheless, the very nature of wireless channels in these networks has given rise to a series of security challenges, which need to be considered while developing authentication protocols. Here, we scrutinized Yu and Park’s, Kumari et al.’s, and Ostad-sharif et al.'s protocols and illustrated their weaknesses against key compromise attacks, insider attacks, and violation of anonymity. Furthermore, for heterogeneous IoT contexts, a lightweight and secure authentication and key agreement protocol for heterogeneous IoT environments is presented. Concerning the restricted resources of sensing devices, an attempt is made to provide an efficient HIoT-based authentication protocol to enhance network security and performance. The gateway as a trusted authority with the maximum workload and sensing devices with the highest restrictions on resources are considered in the suggested protocol. As a result, the user bears the brunt of the workload in the individual session. The Burrows–Abadi–Needham (BAN) logic is used to validate the proposed protocol, and the Automated Validation of Internet Security Protocols and Applications (AVISPA) tool is utilized to demonstrate resilience to existing active attacks. Simulation findings and performance assessment revealed that our protocol improved communication overheads by up to 110%, computation overheads by up to 83%, and sensing device maximum storage capacity by up to 51%.
      PubDate: 2022-05-10
       
 
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