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Internet of Things (IoT) powered by high level synthesis (HLS) provides huge opportunity of progress in the area of hardware design. However, the present era of hardware design involves globalization which poses security threat to the design integrators that rely on third party intellectual property (IP) cores for increasing design productivity at reduced design time. This paper presents for the first time in the literature, a low cost optimized hardware Trojan secured HLS approach for hardware (or application specific core) designs that is based on single or nested loop control data flow graphs (CDFG) applications. The paper presents multiple novel vendor allocation schemes (each with its own attribute of delay and area) as security constraint that yield Trojan secured schedules at behavioral level. Demonstration of the proposed approach on a nested loop case study asserts our proposed theory. Results on standard benchmarks indicate significant reduction in final solution cost compared to a similar approach. [ABSTRACT FROM AUTHOR]

Under the envisioned smart grid paradigm, there is an increasing demand for a fast, accurate, and efficient power flow solution for distribution system operation and control. Various solution techniques have been proposed, each with its own unique formulation, solution methodology, advantages, and drawbacks. Motivated by challenges associated with the integration of renewable distributed energy resources and electric vehicles into distribution systems and further by the speed and convergence limitations of existing tools, this paper presents a novel graph‐based power flow solution for smart grid's real‐time operation and control, named Flow‐AugmentationPF algorithm. The proposed method formulates a power flow problem as a network‐flow problem and solves it by using a maximum‐flow algorithm, inspired by the push‐relabel max‐flow technique. The performance of the proposed algorithm is tested and validated using several benchmark networks of different sizes, topologies, and parameters and compared against the most commonly used solution techniques and commercial software packages, namely PSS/E and PSCAD. The proposed formulation is simple, accurate, fast, yet computationally efficient, as it is based on matrix‐vector multiplication, and is also scalable, considering the formulation works as a graph‐based method, which, inherently, allows for parallel computation for added computational speed. [ABSTRACT FROM AUTHOR]

It is crucial to evaluate and anticipate stability under various conditions, as the ability to stabilize a smart grid (SG) is one of its key features for assessing the effectiveness of its design. Intelligent approaches to stability forecasting are necessary to mitigate inadvertent instability in SG design. This is particularly crucial with the expansion of residential and commercial infrastructures, along with the growing integration of renewable energies into these grids. Predicting the stability of SGs is currently a major challenge. The concept of an SG encompasses a broad range of emerging technologies in which artificial intelligence (AI) plays a crucial role and is increasingly being utilized in light of the limitations of conventional methods. It empowers informed decision-making and adaptable responses to fluctuations in customer energy needs, unexpected power outages, rapid changes in renewable energy generation, or any unforeseen crises within an SG system. In this paper, we propose a symmetric approach to enhance SG stability by integrating various machine learning (ML) and deep learning (DL) algorithms, where symmetry is observed in the balanced application of these diverse computational techniques to predict and ensure the grid's stability. These algorithms utilized a dataset containing the simulation results of the SG stability. The learning phase of these algorithms is based on imprecise and unreliable data. To overcome this limitation, the fusion of classifiers can be a powerful approach to modeling inaccurate and uncertain data, providing more robust and reliable predictions than individual classifiers. Voting and Dempster–Shafer (DS) methods, two commonly used techniques in ensemble learning, were employed and compared. The results show that the use of the fusion of distinct classifiers with voting theory achieves an accuracy of 99.8% and outperforms several other methods including the DS method. [ABSTRACT FROM AUTHOR]

Owing to the enormous advancement in miniature hardware, modern smart mobile devices (SMDs) have become computationally powerful. Mobile crowd computing (MCC) is the computing paradigm that uses public-owned SMDs to garner affordable high-performance computing (HPC). Though several empirical works have established the feasibility of mobile-based computing for various applications, there is a lack of comprehensive coverage of MCC. This paper aims to explore the fundamentals and other nitty–gritty of the idea of MCC in a comprehensive manner. Starting with an explicit definition of MCC, the enabling backdrops and the detailed architectural layouts of different models of MCC are presented, along with categorising different types of MCC based on infrastructure and application demands. MCC is compared extensively with other HPC systems (e.g. desktop grid, cloud, clusters and supercomputers) and similar mobile computing systems (e.g. mobile grid, mobile cloud, ad hoc mobile cloud, and mobile crowdsourcing). MCC being a complex system, various design requirements and considerations are extensively analysed. The potential benefits of MCC are meticulously mentioned, with special discussions on the ubiquity and sustainability of MCC. The issues and challenges of MCC are critically presented in light of further research scopes. Several real-world applications of MCC are identified and propositioned. Finally, to carry forward the accomplishment of the MCC vision, the future prospects are briefly elucidated. [ABSTRACT FROM AUTHOR]

Generally, the computational grid consists of a large number of computing nodes, some of them are idle due to the uneven geographical distribution of computing requirements. This may cause workload unbalancing problems, which affect the performance of large-scale computational grids. In order to balance the computing requirements and computing nodes, we propose a job scheduling algorithm based on the workload prediction of computing nodes. We first analyze the causes of workload imbalance and the feasibility of reallocating computing resources. Secondly, we design an application and workload-aware scheduling algorithm (AWAS) by combining the previously designed workload prediction model. To reduce the complexity of the AWAS algorithm, we propose a parallel job scheduling method based on computing node workload prediction. The experiments show that the AWAS algorithm can balance the workload among different computing nodes on the real-world dataset. In addition, we propose the parallelism of workload prediction model from the perspective of internal structure and data set to make AWAS apply to more computing nodes of the large-scale computing grids. Experimental results show that the combination of the two can achieve satisfactory acceleration efficiency. • In this paper, we propose an application and grid computing nodes workload aware scheduling algorithm (AWAS). • AWAS optimizes grid loads using greedy and heuristic methods. • This paper parallelizes the scheduling process of AWAS to improve its efficiency. • The experiment results show that the AWAS can achieve workload balancing in China National Grid. [ABSTRACT FROM AUTHOR]

This paper is concerned with developing an efficient numerical algorithm for the fast implementation of the sparse grid method for computing the d-dimensional integral of a given function. The new algorithm, called the MDI-SG (multilevel dimension iteration sparse grid) method, implements the sparse grid method based on a dimension iteration/reduction procedure. It does not need to store the integration points, nor does it compute the function values independently at each integration point; instead, it reuses the computation for function evaluations as much as possible by performing the function evaluations at all integration points in a cluster and iteratively along coordinate directions. It is shown numerically that the computational complexity (in terms of CPU time) of the proposed MDI-SG method is of polynomial order O (d 3 N b) (b ≤ 2) or better, compared to the exponential order O (N (log N) d − 1) for the standard sparse grid method, where N denotes the maximum number of integration points in each coordinate direction. As a result, the proposed MDI-SG method effectively circumvents the curse of dimensionality suffered by the standard sparse grid method for high-dimensional numerical integration. [ABSTRACT FROM AUTHOR]

The electric vehicle (EV) industry is quickly growing in the present scenario, and will have more demand in the future. A sharp increase in the sales of EVs by 160% in 2021 represents 26% of new sales in the worldwide automotive market. EVs are deemed to be the transportation of the future, as they offer significant cost savings and reduce carbon emissions. However, their interactions with the power grid, charging stations, and households require new communication and control techniques. EVs show unprecedented behavior during vehicle battery charging, and sending the charge from the vehicle's battery back to the grid via a charging station during peak hours has an impact on the grid operation. Balancing the load during peak hours, i.e., managing the energy between the grid and vehicle, requires efficient communication protocols, standards, and computational technologies that are essential for improving the performance, efficiency, and security of vehicle-to-vehicle, vehicle-to-grid (V2G), and grid-to-vehicle (G2V) communication. Machine learning and deep learning technologies are being used to manage EV-charging station interactions, estimate the charging behavior, and to use EVs in the load balancing and stability control of smart grids. Internet of Things (IoT) technology can be used for managing EV charging stations and monitoring EV batteries. Recently, much work has been presented in the EV communication and control domain. In order to categorize these efforts in a meaningful manner and highlight their contributions to advancing EV migration, a thorough survey is required. This paper presents existing literature on emerging protocols, standards, communication technologies, and computational technologies for EVs. Frameworks, standards, architectures, and protocols proposed by various authors are discussed in the paper to serve the need of various researchers for implementing the applications in the EV domain. Security plays a vital role in EV authentication and billing activities. Hackers may exploit the hardware, such as sensors and other electronic systems and software of the EV, for various malicious activities. Various authors proposed standards and protocols for mitigating cyber-attacks on security aspects in the complex EV ecosystem. [ABSTRACT FROM AUTHOR]

Power distribution and battery thermal management are important technologies for improving the energy efficiency of plug-in hybrid electric vehicles (PHEVs). In response to the global optimization of integrated energy thermal management strategy (IETMS) for PHEVs, a dynamic programming algorithm based on adaptive grid optimization (AGO–DP) is proposed in this paper to improve optimization performance by reducing the optimization range of SOC and battery temperature, and adaptively adjusting the grid distribution of state variables according to the actual feasible region. The simulation results indicate that through AGO–DP optimization, the reduction ratio of the state feasible region is more than 30% under different driving conditions. Meanwhile, the algorithm can obtain better global optimal driving costs more rapidly and accurately than traditional dynamic programming algorithms (DP). The computation time is reduced by 33.29–84.67%, and the accuracy of the global optimal solution is improved by 0.94–16.85% compared to DP. The optimal control of the engine and air conditioning system is also more efficient and reasonable. Furthermore, AGO–DP is applied to explore IETMS energy-saving potential for PHEVs. It is found that the IETMS energy-saving potential range is 3.68–23.74% under various driving conditions, which increases the energy-saving potential by 0.55–3.26% compared to just doing the energy management. [ABSTRACT FROM AUTHOR]

Smart grid is an efficient and reliable technical framework for controlling computers and automation equipment, and how to ensure the communication security in smart grid is an important issue. Cryptographic authentication scheme is a feasible solution, and the existing authentication schemes for smart grid seek to ensure better performance. Some existing authentication schemes lack comprehensive security considerations and have security or privacy vulnerabilities, which makes them vulnerable to specific attacks. The paper reviews a recent scheme ISG-SLAS (Yu and Park, 2022) and analyzes its potential insecure aspects in detail, including unable to resist ESL attack, cannot provide un-traceability and etc. To this end, the paper designs an improved authentication scheme for smart grid based on symmetric cryptography. Through informal security analyses and formal security analyses with real-or-random (ROR) model and Scyther platform, the security of the proposed scheme is demonstrated. From the perspective of performance, the proposed scheme is compared with ten advanced authentication schemes for smart grid, and the results show that the proposed scheme excels other recent schemes in computational overhead, communication overhead and storage overhead, reduced by 10.1%, 30.8% and 36.1% of ISG-SLAS and 58.9%, 64.1% and 24.3% of the average value of all alternatives respectively. [ABSTRACT FROM AUTHOR]

To improve the quality of knowledge service selection in a cloud manufacturing environment, this paper proposes a cloud manufacturing knowledge service optimization decision method based on users' psychological behavior. Based on the characteristic analysis of cloud manufacturing knowledge service, establish the optimal evaluation index system of cloud manufacturing knowledge service, use the rough set theory to assign initial weights to each evaluation index, and adjust the initial weights according to the user's multiattribute preference to ensure that the consequences are allocated correctly. The system can help counselors acquire psychological knowledge in time and identify counselors with suicidal tendencies to prevent danger. This paper collected some psychological information data and built a knowledge graph by creating a dictionary and generating entities and relationships. The Han language processing word segmentation tool generates keywords, and CHI (Chi-square) feature selection is used to classify the problem. This feature selection is a statistical premise test that is acceptable when the chi-square test results are distributed with the null hypothesis. It includes the Pearson chi-square test and its variations. The Chi-square test has several benefits, including its distributed processing resilience, ease of computation, broad information gained from the test, usage in research when statistical assumptions are not satisfied, and adaptability in organizing information from multiple or many more group investigations. For improving question and answer efficiency, compared with other models, the BiLSTM (bidirectional long short-term memory) model is preferred to build suicidal tendencies. The Han language processing is a method that is used for word segmentation, and the advantage of this method is that it plays a key role in the word segmentation tool and generates keywords, and CHI (Chi-square) feature selection is used to classify the problem. Text classifier detects dangerous user utterances, question template matching, and answer generation by computing similarity scores. Finally, the system accuracy test is carried out, proving that the system can effectively answer the questions related to psychological counseling. The extensive experiments reveal that the method in this paper's accuracy rate, recall rate, and F1 value is much superior to other standard models in detecting psychological issues. [ABSTRACT FROM AUTHOR]

Global concerns regarding environmental preservation and energy sustainability have emerged due to the various impacts of constantly increasing energy demands and climate changes. With advancements in smart grid, edge computing, and Metaverse-based technologies, it has become apparent that conventional private power networks are insufficient to meet the demanding requirements of industrial applications. The unique capabilities of 5G, such as numerous connections, high reliability, low latency, and large bandwidth, make it an excellent choice for smart grid services. The 5G network industry will heavily rely on the Internet of Things (IoT) to progress, which will act as a catalyst for the development of the future smart grid. This comprehensive platform will not only include communication infrastructure for smart grid edge computing, but also Metaverse platforms. Therefore, optimizing the IoT is crucial to achieve a sustainable edge computing network. This paper presents the design, fabrication, and evaluation of a super-efficient GSM triplexer for 5G-enabled IoT in sustainable smart grid edge computing and the Metaverse. This component is intended to operate at 0.815/1.58/2.65 GHz for 5G applications. The physical layout of our triplexer is new, and it is presented for the first time in this work. The overall size of our triplexer is only 0.007 λg2, which is the smallest compared to the previous works. The proposed triplexer has very low insertion losses of 0.12 dB, 0.09 dB, and 0.42 dB at the first, second, and third channels, respectively. We achieved the minimum insertion losses compared to previous triplexers. Additionally, the common port return losses (RLs) were better than 26 dB at all channels. [ABSTRACT FROM AUTHOR]

Many stochastic systems face a time-dependent demand. Especially in stochastic service systems, for example, in call centers, customers may leave the queue if their waiting time exceeds their personal patience. As discussed in the extant literature, it can be useful to use general distributions to model such customer patience. This paper analyzes the time-dependent performance of a multiserver queue with a nonhomogeneous Poisson arrival process with a time-dependent arrival rate, exponentially distributed processing times, and generally distributed time to abandon. Fast and accurate performance approximations are essential for decision support in such queueing systems, but the extant literature lacks appropriate methods for the setting we consider. To approximate time-dependent performance measures for small- and medium-sized systems, we develop a new stationary backlog-carryover (SBC) approach that allows for the analysis of underloaded and overloaded systems. Abandonments are considered in two steps of the algorithm: (i) in the approximation of the utilization as a reduced arrival stream and (ii) in the approximation of waiting-based performance measures with a stationary model for general abandonments. To improve the approximation quality, we discuss an adjustment to the interval lengths. We present a limit result that indicates convergence of the method for stationary parameters. The numerical study compares the approximation quality of different adjustments to the interval length. The new SBC approach is effective for instances with small numbers of time-dependent servers and gamma-distributed abandonment times with different coefficients of variation and for an empirical distribution of the abandonment times from real-world data obtained from a call center. A discrete-event simulation benchmark confirms that the SBC algorithm approximates the performance of the queueing system with abandonments very well for different parameter configurations. Summary of Contribution: The paper presents a fast and accurate numerical method to approximate the performance measures of a time‐dependent queueing system with generally distributed abandonments. The presented stationary backlog carryover approach with abandonment combines algorithmic ideas with stationary queueing models for generally distributed abandonment times. The reliability of the method is analyzed for transient systems and numerically studied with real‐world data. [ABSTRACT FROM AUTHOR]

This paper studies the distributed linearly separable computation problem, which is a generalization of many existing distributed computing problems such as distributed gradient coding and distributed linear transform. A master asks ${\mathsf {N}}$ distributed workers to compute a linearly separable function of ${\mathsf {K}}$ datasets, which is a set of ${\mathsf {K}}_{\mathrm{ c}}$ linear combinations of ${\mathsf {K}}$ equal-length messages (each message is a function of one dataset). We assign some datasets to each worker in an uncoded manner, who then computes the corresponding messages and returns some function of these messages, such that from the answers of any ${\mathsf {N}}_{\mathrm{ r}}$ out of ${\mathsf {N}}$ workers the master can recover the task function with high probability. In the literature, the specific case where ${\mathsf {K}}_{\mathrm{ c}}=1$ or where the computation cost is minimum has been considered. In this paper, we focus on the general case (i.e., general ${\mathsf {K}}_{\mathrm{ c}} $ and general computation cost) and aim to find the minimum communication cost. We first propose a novel converse bound on the communication cost under the constraint of the popular cyclic assignment (widely considered in the literature), which assigns the datasets to the workers in a cyclic way. Motivated by the observation that existing strategies for distributed computing fall short of achieving the converse bound, we propose a novel distributed computing scheme for some system parameters. The proposed computing scheme is optimal for any assignment when ${\mathsf {K}}_{\mathrm{ c}}$ is large and is optimal under the cyclic assignment when the numbers of workers and datasets are equal or ${\mathsf {K}}_{\mathrm{ c}}$ is small. In addition, it is order optimal within a factor of 2 under the cyclic assignment for the remaining cases. [ABSTRACT FROM AUTHOR]

Smart grid planning is a standard method used to reduce the net loss of distribution networks and improve the economic efficiency of power systems. As the distribution networks expand, the scale is getting more complex, leading to low efficiency and a long planning time when using traditional methods to implement new routes. To solve this problem, this paper proposed a new approach for intelligent grid expansion planning with speculative particle swarm optimization. First, the expansion planning model for the smart grid is established based on particle swarm optimization from the classical control system. Secondly, the model is parallelized with the speculative parallelism technique to overcome the influence of the internal control dependency and get rid of the original processing order. Finally, the parallel model is implemented on a distributed computing platform to improve the planning efficiency for complex intelligent grids significantly. Experiments show that the proposed approach can improve the efficiency by about 40% in an Apache Spark cluster consisting of 20 nodes compared with the conventional ones. Moreover, the proposed method can also fully utilize the distributed cluster's computing resources. [ABSTRACT FROM AUTHOR]

A wireless sensor network (WSN) consists of a very large number of sensors which are deployed in the specific area of interest. A sensor is an electronic device equipped with a small processor and has a small-capacity memory. The WSN has the functions of low cost, easy deployment, and random reconfiguration. In this paper, an energy-efficient load balancing tree-based data aggregation scheme (LB-TBDAS) for grid-based WSNs is proposed. In this scheme, the sensing area is partitioned into many cells of a grid and then the sensor node with the maximum residual energy is elected to be the cell head in each cell. Then, the tree-like path is established by using the minimum spanning tree algorithm. In the tree construction, it must meet the three constraints, which are the minimum energy consumption spanning tree, the network depth, and the maximum number of child nodes. In the data transmission process, the cell head is responsible for collecting the sensing data in each cell, and the collected data are transmitted along the tree-like path to the base station (BS). Simulation results show that the total energy consumption of LB-TBDAS is significantly less than that of GB-PEDAP and PEDAP. Compared to GB-PEDAP and PEDAP, the proposed LB-TBDAS extends the network lifetime by more than 100%. The proposed LB-TBDAS can avoid excessive energy consumption of sensor nodes during multi-hop data transmission and can also avoid the hotspot problem of WSNs. [ABSTRACT FROM AUTHOR]

The growing integration of blockchain and fog computing with Smart Grid (SG) technologies highlights the need for strong security protocols, dependable communication channels, and sensitive user data protection. In order to meet the unique needs of SG, this research paper presents a novel and effective anonymous authentication scheme enhanced by fog computing and blockchain. The suggested scheme makes use of blockchain's decentralized structure to strengthen security protocols and ensure user privacy and anonymity within the SG ecosystem. Through the combination of fog computing's low latency benefits and blockchain's immutability, the suggested scheme creates a robust framework for secure authentication. The suggested approach gives considerable weight to reducing computational overhead and optimizing user identity privacy. Through comprehensive analysis, the study highlights the scheme's efficiency concerning authentication speed, resource utilization, and resilience against potential attacks. These results offer an important remedy for the ever-changing landscape of energy management systems and substantially advance the development of safe and privacy-preserving SG architectures. [ABSTRACT FROM AUTHOR]

Computational complexities in modern power systems are reportedly increasing daily, and it is anticipated that traditional computers might be inadequate to provide the computation prerequisite in future complex power grids. In that given context, quantum computing (QC) can be considered a next‐generation alternative solution to deal with upcoming computational challenges in smart grids. The QC is a relatively new yet promising technology that leverages the unique phenomena of quantum mechanics in processing information and computations. This emerging paradigm shows a significant potential to overcome the barrier of computational limitations with better and faster solutions in optimization, simulations, and machine learning problems. In recent years, substantial progress in developing advanced quantum hardware, software, and algorithms have made QC more feasible to apply in various research areas, including smart grids. It is evident that considerable research has already been carried out, and such efforts are remarkably continuing. As QC is a highly evolving field of study, a brief review of the existing literature will be vital to realize the state‐of‐art on QC for smart grid applications. Therefore, this article summarizes the research outcomes of the most recent papers, highlights their suggestions for utilizing QC techniques for various smart grid applications, and further identifies the potential smart grid applications. Several real‐world QC case studies in various research fields besides power and energy systems are demonstrated. Moreover, a brief overview of available quantum hardware specifications, software tools, and algorithms is described with a comparative analysis. [ABSTRACT FROM AUTHOR]

With the continuous progress of renewable energy technology and the large-scale construction of microgrids, the architecture of power systems is becoming increasingly complex and huge. In order to achieve efficient and low-delay data processing and meet the needs of smart grid users, emerging smart energy systems are often deployed at the edge of the power grid, and edge computing modules are integrated into the microgrids system, so as to realize the cost-optimal control decision of the microgrids under the condition of load balancing. Therefore, this paper presents a bilevel optimization control model, which is divided into an upper-level optimal control module and a lower-level optimal control module. The purpose of the two-layer optimization modules is to optimize the cost of the power distribution of microgrids. The function of the upper-level optimal control module is to set decision variables for the lower-level module, while the function of the lower-level module is to find the optimal solution by mathematical methods on the basis of the upper-level and then feed back the optimal solution to the upper-layer. The upper-level and lower-level modules affect system decisions together. Finally, the feasibility of the bilevel optimization model is demonstrated by experiments. [ABSTRACT FROM AUTHOR]

Task scheduling is an essential component of any distributed system because it routes tasks to appropriate resources for execution, such as grids, clouds, and peer-to-peer networks. Common scheduling algorithms include downsides, such as high temporal complexity, non-simultaneous processing of input tasks, and longer program execution times. Exploration-based scheduling algorithms prioritize tasks using a variety of methods, resulting in long execution times on heterogeneous distributed computing systems. As a result, task prioritization becomes a bottleneck in such systems. It is appropriate to prioritize tasks with the shortest execution time using faster algorithms. The genetic algorithm (GA) is one of the evolutionary approaches used to solve complex problems quickly. This paper proposes a parallel GA with a MapReduce architecture for scheduling jobs on cloud computing with various priority queues. The fundamental aim of this study is to employ a MapReduce architecture to minimize the total execution time of the task scheduling process in the cloud computing environment. The proposed method accomplishes task scheduling in two stages: first, the GA was used in conjunction with heuristic techniques to assign tasks to processors, and then the GA was used in conjunction with the MapReduce framework to assign jobs to processors. In our experiments, we consider heterogeneous resources that differ in their ability to execute various tasks, as well as running a job on different resources with varying execution durations. The results show that the proposed method outperforms other algorithms such as particle swarm optimization, whale optimization algorithm, moth-flame optimization, and intelligent water drops. [ABSTRACT FROM AUTHOR]

In order to efficiently perform the direct numerical simulation for shock wave boundary layer interference, a large grid computing scale is needed, and a single graphics processing unit cannot meet the urgent needs of computing. This paper proposes a parallel algorithm based on the architecture of multiple graphics processing units to effectively deal with the direct numerical simulation for shock wave boundary layer interference. The algorithm achieves a high overlap between graphics processing unit computation and boundary data exchange to improve the parallel efficiency when the grid computing scale is large. The working principle of the multi-graphics-processing-unit parallel algorithm is introduced, and the accuracy and feasibility of the algorithm are verified. Finally, through the application of the algorithm in the research of the compression-expansion slope boundary layer, fine flow structures such as large-scale vortex structure, shocklet, and attached shock wave can be captured. As a conclusion, the multi-graphics-processing-unit parallel algorithm proposed in this paper can accurately display the flow field of shock wave boundary layer interference. • A parallel algorithm based on multiple GPUs is proposed. • The accuracy, feasibility, and efficiency of the parallel algorithm are verified. • The DNS of the SWBLI based on the algorithm is tested and analyzed. [ABSTRACT FROM AUTHOR]

To solve the problem of the mismatch of the wideband underdetermined direction of arrival (DOA) estimation under the condition of the On‐grid model, this paper extends the narrowband Off‐Grid model to wideband and proposes a new DOA estimation algorithm for Off‐Grid sources based on group sparsity. The proposed algorithm first obtains the preliminary estimation result under the current predefined discrete grid through the group sparsity wideband DOA estimation algorithm. Then, the Off‐Grid optimisation problem is adopted to calculate the Off‐Grid deviation vector. It is also assumed that the off‐grid deviation vectors of each frequency subband are exactly the same, thereby reducing the number of parameters to be estimated. Therefore, the proposed algorithm can not only maintain similar or even better estimation accuracy but also greatly reduce the computational complexity. Finally, simulation is conducted and the results verify the effectiveness and performance of the proposed method. [ABSTRACT FROM AUTHOR]

Conventional direction-of-arrival (DOA) estimation algorithms are sensitive to array imperfections and outliers, making it challenging to realize accurate estimates in real applications. Facing the challenge, we propose a robust variational Bayesian inference based DOA estimation algorithm using the linear sparse array in this paper, where accurate DOA estimation with increased number of degrees of freedom (DOFs) is realized. With the mixture of von Mises model, a prior-grid scheme is further proposed to alleviate the computational burden introduced by the Bayesian variational inference framework. Since there is no restriction on the prior knowledge of the number of sources, the proposed algorithm is friendly to actual scenarios. Simulation results demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

This paper proposes a method for estimating the appropriate grid impedance for applications in which distributed generators are connected to the grid through three-phase inverters. The method is based on variations in active and reactive power injected by the inverters into the power system. These variations give rise to changes in the direct synchronous voltage v d and in the synchronous direct and quadrature currents i d and i q at the point of common coupling between the inverter and the power grid. These changes serve to compute the grid resistance and inductance seen by the inverter. This paper suggests the usage of a previously proposed technique for estimating the positive sequence from the grid voltage, so the method is robust against harmonic distortions. The technique is improved, employing a Fourier filter. To further enhance the method, a morphological filter is incorporated to filter out the synchronous currents. This filter is suitable to be applied in DC signals, like the ones related to synchronous reference frames. A complete evaluation of the proposed method is performed through simulations in MATLAB/Simulink environment. The results show that the positive sequence estimator and the morphological filter significantly increase the accuracy of the grid impedance estimation. [ABSTRACT FROM AUTHOR]

This paper presents a novel Graphics Processing Unit (GPU)-accelerated method for large-scale data processing of tomographic particle image velocimetry. The multiplicative algebraic reconstruction technique (MART) is utilized to reconstruct three-dimensional (3D) particle fields, and cross-correlation with fast Fourier transform is used to generate the displacement vectors. The Compute Unified Device Architecture (CUDA) C programming model is used to port the velocity field reconstruction from CPU code to GPU code to improve efficiency. For similar reconstruction tasks, a particular thread grid hierarchy is designed to construct the corresponding computational kernel functions, and each task is launched in a single thread. A modified strategy of pixel batch processing is then used to manage the GPU memory access. Subsequently, the asynchronous stream concurrency is used to generate the velocity field with the GPU cuFFT library. A synthetic 3D experiment with a ring vortex is carried out to verify the accuracy and efficiency of the developed method. The parallel results agree well with the generated data and other research conclusions reported in the literature. The speed-up ratio by multi-core CPU (Intel® Xeon® Platinum 8168) parallel implementation with OpenMP converges to 2.5 × in MFG-MART and 3.0 × in cross-correlation. In contrast to a 24-core CPU implementation, a GPU (NVIDIA Tesla V100S, 32 GB) under maximum memory usage achieves an impressive speed-up ratio of over 20 × in parallel MFG-MART and 4 × in concurrent cross-correlation. The measurement of turbulent flow in a circular jet flow at Reynolds 3,000 is used to examine the efficiency promotion of the parallelized framework in real experimental settings. For the synthetic volume reconstruction of 700 × 700 × 140 voxels and cross-correlation with 413 voxels window in a 75% overlap, and the experimental volume reconstruction of 550 × 1100 × 550 voxels and cross-correlation with 323 voxels window in a 50% overlap, a frame of velocity field can be completed within 2 min in each domain. [ABSTRACT FROM AUTHOR]

Coded computation techniques provide robustness against straggling workers in distributed computing. However, most of the existing schemes require exact provisioning of the straggling behavior and ignore the computations carried out by straggling workers. Moreover, these schemes are typically designed to recover the desired computation results accurately, while in many machine learning and iterative optimization algorithms, faster approximate solutions are known to result in an improvement in the overall convergence time. In this paper, we first introduce a novel coded matrix-vector multiplication scheme, called coded computation with partial recovery (CCPR), which benefits from the advantages of both coded and uncoded computation schemes, and reduces both the computation time and the decoding complexity by allowing a trade-off between the accuracy and the speed of computation. We then extend this approach to distributed implementation of more general computation tasks by proposing a coded communication scheme with partial recovery, where the results of subtasks computed by the workers are coded before being communicated. Numerical simulations on a large linear regression task confirm the benefits of the proposed scheme in terms of the trade-off between the computation accuracy and latency. [ABSTRACT FROM AUTHOR]

Grid scheduling is one of the most known NP-complete problems. Heterogeneity of machines causes mapping of tasks to be a challenging problem. Several meta-heuristic algorithms have been designed to reach optimality as possible. Sort-Mid is a recent efficient scheduler of excellent resource utilization. Its strategy was based on computing the mean of two consecutive middle values in the sorted list of completion time. Thereafter, it maps the task with the maximum mean to the machine having the minimum completion time. However, Range-Suffrage scheduler obtains a better makespan. It was built on searching for the maximum average value of completion times among certain tasks. These tasks were selected according to their suffrage values under specified constraint. This paper proposes RSSM as highly efficient scheduler. It combines Sort-Mid and Range-Suffrage algorithms to achieve the maximum resource utilization and the minimum makespan. RSSM methodology is based on sorting only the completion time corresponding to tasks satisfying Range-Suffrage constraint. Experimental tests manifest the superiority of proposed algorithm over the two original meta- heuristics and other promising algorithms such as Min-Min. [ABSTRACT FROM AUTHOR]

Distributed computing enables parallel execution of smaller tasks that make up a large computing job. Its purpose is to reduce the job completion time. However, random fluctuations in task service times lead to straggling tasks with long execution times. Redundancy provides diversity that allows job completion when only a subset of redundant tasks is executed, thus removing the dependency on the straggling tasks. Under constrained resources (here, a fixed number of parallel servers), increasing redundancy reduces the available resources for parallelism. In this paper, we characterize the diversity vs. parallelism trade-off and identify the optimal strategy among replication, coding, and splitting, which minimizes the expected job completion time. We consider three common service time distributions and establish three models that describe the scaling of these distributions with the task size. We find that different distributions with different scaling models operate optimally at different redundancy levels, thus requiring very different code rates. [ABSTRACT FROM AUTHOR]

Automatic reasoning is a very challenging research area in the field of artificial intelligence. Automatic inference based on lattice-valued logic is an important basic research content in the field of intelligent information processing. It is of great academic significance to deal with incomparability, linguistic valued information, and formal verification of the correctness of corresponding software and hardware systems in various applications. Based on the extensive work of α-resolution automated reasoning in linguistic truth-valued lattice-valued logic, this paper discusses the structure and its α-resolvability of the generalized literal. For further improving the efficiency, a topology graph deduction is proposed as well as its soundness, completeness, and universal algorithm. The use of distributed computing and resource sharing is the core idea of this deduction. In the decision process, as long as one satisfiability solution is found by subtask, the decision result can be given directly, shortening the decision time. The algorithm reduces the time complexity from factorial level to square. [ABSTRACT FROM AUTHOR]

Modern scientific research challenges require new technologies, integrated tools, reusable and complex experiments in distributed computing infrastructures. But above all, computing power for efficient data processing and analyzing. Containers technologies have emerged as a new paradigm to address such intensive scientific applications problems. Their easy deployment in a reasonable amount of time and the few required computational resource make them more suitable. Containers are considered light virtualization solutions. They enable performance isolation and flexible deployment of complex, parallel, and high-performance systems. Moreover, they gained popularity to modernize and migrate scientific applications in computing infrastructure management. Additionally, they reduce computational time processing. In this paper, we first give an overview of virtualization and containerization technologies. We discuss the taxonomies of containerization technologies of the literature, and then we provide a new one that covers and completes those proposed in the literature. We identify the most important application domains of containerization and their technological progress. Furthermore, we discuss the performance metrics used in most containerization techniques. Finally, we point out research gaps in the related aspects of containerization technology that require more research. [ABSTRACT FROM AUTHOR]

In any multi-device / party systems supporting GRID and cloud-based applications, an essential constraint of these systems is the need of all tools and participants to interconnect with each other as members of a group in a secure approach. Group key management method is an essential functional element for any protected distributed communication setting. Key distribution method is a crucial factor in securing communication in grid computing. After the secure key management is executed, messages will be able to be securely exchanged between the grid units. A number of protocols have been proposed to maintain secure group key management. In this paper we present a new password base protocol for secure group key management in Grid computing environment, which is organized in two dynamic servicing layers: the grid application that needs grid services, and the grid services that act on behalf of the user. [ABSTRACT FROM AUTHOR]

The article presents an overview of papers that appeared in the November 2003 issue of the journal "The International Journal of High Performance Computer Applications." The papers included are from the Third International Workshop on Grid Computing. The first paper by researcher Craig A. Lee investigates distributed query processing on the Grid using a prototype query language and a bioinformatics case study. The next paper by researcher Jim Smith et al looks at the throughput behavior for Monte Carlo simulations on the Grid. By contrast, the paper by researcher Marcio Faerman et al. looks at guided searches through parameter spaces. The paper by researcher William H. Bell et al. evaluates data replication algorithms by simulating the EU DataGrid TestBed 1 sites including their network topology. Coupling existing, parallel numerical codes on the Grid is an issue addressed by researcher Christian Péerez et al. The paper by researcher Gregor von Laszewski et al. investigates computational thermo-chemistry where enthalpy diagrams and thermochemical reaction tables are used to manage the associated computations through a Grid framework. Last, but not least, researcher Gabrielle Allen et al. report on GridLab and the Grid Application Toolkit that will define a set of generic, high-level APIs for building Grid applications.

Torus network topology offers many advantages such as higher speed, lower latency, better fairness, and lower energy consumption. For these kinds of benefits, nowadays, it is used to construct many parallel and distributed systems like IBM Blue Gene, IBM Sequoia, Mira, and Sugon TC8600. In parallel and distributed computing, multiple nodes act together to carry out large tasks fast. Hence, coordination is indispensable among these nodes to complete such tasks. A possible coordination method could be to elect a leader among the nodes. Along with coordination, the elected leader node also manages other activities such as task allocation, result aggregation, clock synchronization, proficient resource sharing management, and communication among the system's nodes. In this paper, we propose a lower bound Ω (N log 3 N) of message complexity on a comparison-based leader election for a 2D torus network (where N is the number of nodes in the network). Next, we sketch a new leader election algorithm (Lea-TN) considering both the node and link failures for a 2D torus network. This Lea-TN is a deterministic and robust algorithm that elects a leader for a synchronous distributed system. The algorithm chooses a leader, even when there are some link or node failures in the system. We consider the number of non-faulty links and the subsisting nodes' failure rate to elect a reliable leader. We introduce new patterns for sending messages that help reduce the number of exchanged messages and the execution time of the election process. The proposed algorithm (Lea-TN) enables a node to identify its link failures during the election also. Further, we simulate the Lea-TN algorithm and compare its performance with that of the well-known existing algorithms. [ABSTRACT FROM AUTHOR]

Temporal decomposition is a potential approach to relieve the computation cost of power system multi-interval scheduling problems, such as economic dispatch. In this form of decomposition, the considered scheduling horizon is partitioned into several subhorizons. A subproblem is formulated for each subhorizon, and a distributed optimization algorithm strategy is used to coordinate subproblems. The main existing challenge is decomposing the scheduling horizon to gain the most time saving from distributed computing. This paper serves as an extension to our previous work and presents a machine learning-aided temporal decomposition strategy to partition a scheduling horizon optimally. We have found that the load profile, known before solving economic dispatch, significantly affects the best number of subhorizons. We have used load profiles as inputs to a learner whose goal is to assign a temporal decomposition class to each load profile. Possible decomposition classes are divisors of the considered scheduling horizon. Thus, the proposed learning procedure is a multiclass classification. We have selected Extreme Gradient Boosting that is a tree-based classification learner. Simulation results using real-world load profiles show the promising performance of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Wireless computing has revolutionized our life with the technological advancement from the traditional networking into a new epoch for communication in ad hoc decorum. An energy efficient network of sensors based on wireless communication and networking principles can enhance the effectiveness of computing during unpredictable circumstances. If a computable required resource is readily available within the reachable region and it is identified to be idle and it is ready to share the corresponding information from its end without affecting the normal behavior of the device or the node, then there exists an opportunity to utilize those resources for computing. Opportunistic computing has a great potential of growth in the field of wireless ad hoc network computing. In traditional network computing technology, mobile computing, grid computing, distributed computing, ubiquitous computing and cloud computing, formerly ensues the communication with minimal resources currently available at the terminal point. This paper proposes a novel framework for an effective utilization of sharable resources, which are available within the reachable region, by creating an opportunity to frame an opportunistic computing while preserving the user's privacy. The proposed framework adopts a bio-inspired technique for identifying and collecting resources information, and thereby recognizes which resource is ready to participate in the opportunistic computing. Experimental results of a system that implements the bio-inspired technique along with the natural behavior of the bee colony approach was analyzed and found that the proposed system shows comparatively high performance in terms of computation resource searching, identifying, emergency data transfer, and participative node privacy preserving. [ABSTRACT FROM AUTHOR]

Training on Grid technologies has traditionally used existing Grid infrastructures to implement the hands-on education activities. However, these infrastructures are insufficient to develop all training skills as they can only be employed for the development of Grid applications, and they are limited for learning the management and configuration of Grid resources. The paper presents a set of educational activities grouped on a Project Based Learning (PBL) framework for training on Grid technologies. A Cloud-based tool has been implemented to provide Grid infrastructures as a Service on the cloud, with enhanced scalability and administration capabilities. The PBL has achieved a high impact in the teaching-learning process, addressing the training in all the necessary skills and efficiently providing Grid infrastructure resources on public clouds at a moderate cost. Finally, we evaluated the students' opinion on the activities achieving a very satisfactory result and a reasonable balance on the complexity of the PBL stages. • Cloud Computing improves teaching activities on Grid Computing. • Grid as a Service provides Virtual Grid Infrastructures to improve PBL activities. • Grid as a Service allows addressing the Grid training in all related skills. • Grid as a Service provides Grid infrastructures without scalability and administration limits. • Cloud computing provides Grid infrastructure resources on public clouds at a moderate cost. [ABSTRACT FROM AUTHOR]

The paper surveys the state of the art of task scheduling in Desktop Grid computing systems. We describe the general architecture of a Desktop Grid system and the computing model adopted by the BOINC middleware. We summarize research papers to bring together and examine the optimization criteria and methods proposed by researchers so far for improving Desktop Grid task scheduling (assigning tasks to computing nodes by a server). In addition, we review related papers, which address non-regular architectures, like hierarchical and peer-to-peer Desktop Grids, as well as Desktop Grids designed for solving interdependent tasks. [ABSTRACT FROM AUTHOR]

Efficient workflow scheduling plays a critical role in achieving high performance in heterogeneous distributed computing systems. Given its key importance, workflow scheduling has been extensively studied, and various algorithms have been proposed in the literature mainly for systems with homogeneous or heterogeneous processors. Most of the algorithms leverage the average computation cost to prioritize tasks, and few focus on the combination of the level and out-degree of tasks, which both have a considerable impact on scheduling. A new list scheduling algorithm called level and out-degree earliest finish time (LOEFT) is proposed in this paper to address the problem of static workflow scheduling in a heterogeneous computing environment to reduce the workflow execution time. This algorithm has three major phases: task leveling, task prioritization and processor selection. In the task leveling phase, tasks are categorized into different groups based on depth value to ensure data transmission completeness. In the task prioritizing phase, the upward rank value is combined with the out-degree of every task to calculate the heterogeneous priority rank value on different processors and leverage the value to sort all tasks. In the processor selection phase, the selected task is assigned to the processor, which minimizes the former's earliest finish time. The experimental simulation of randomly generated DAG and real-world application workflows proves that the LOEFT algorithm can significantly reduce the workflow execution time. [ABSTRACT FROM AUTHOR]

Collaborative filtering (CF) recommender systems are static in nature and does not adapt well with changing user preferences. User preferences may change after interaction with a system or after buying a product. Conventional CF clustering algorithms only identifies the distribution of patterns and hidden correlations globally. However, the impossibility of discovering local patterns by these algorithms, headed to the popularization of bi-clustering algorithms. Bi-clustering algorithms can analyze all dataset dimensions simultaneously and consequently, discover local patterns that deliver a better understanding of the underlying hidden correlations. In this paper, we modelled the recommendation problem as a sequential decision-making problem using Markov Decision Processes (MDP). To perform state representation for MDP, we first converted user-item votings matrix to a binary matrix. Then we performed bi-clustering on this binary matrix to determine a subset of similar rows and columns. A bi-cluster merging algorithm is designed to merge similar and overlapping bi-clusters. These bi-clusters are then mapped to a squared grid (SG). RL is applied on this SG to determine best policy to give recommendation to users. Start state is determined using Improved Triangle Similarity (ITR similarity measure. Reward function is computed as grid state overlapping in terms of users and items in current and prospective next state. A thorough comparative analysis was conducted, encompassing a diverse array of methodologies, including RL-based, pure Collaborative Filtering (CF), and clustering methods. The results demonstrate that our proposed method outperforms its competitors in terms of precision, recall, and optimal policy learning. [ABSTRACT FROM AUTHOR]