This paper addresses the need for a secure data management framework for lightweight Internet of Things (IoT) networks. Existing approaches rely on capable nodes or external cloud technology to carry out key functions...
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ISBN:
(纸本)9781728174327
This paper addresses the need for a secure data management framework for lightweight Internet of Things (IoT) networks. Existing approaches rely on capable nodes or external cloud technology to carry out key functions for the lightweight IoT nodes, such as data processing, storage or routing. Instead, this paper considers a fully lightweight network or an existing network where the more capable nodes have failed. We propose a novel data management framework using a lightweight distributed ledger for IoT networks. This includes a trust-based data aggregation protocol in which data from untrustworthy nodes is not disregarded, but is instead utilised to strengthen the aggregation result. MATLAB simulations show that the proposed aggregation scheme infers data with high accuracy for both trustworthy and untrustworthy networks. Neutral networks exhibit a higher error rate but the maximum error rate decreases for larger networks.
With the continuous development of data-centric Internet applications, data management solutions for massive data have gradually become a new research direction in vector map rendering. The introduction of distributed...
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ISBN:
(纸本)9781665435741
With the continuous development of data-centric Internet applications, data management solutions for massive data have gradually become a new research direction in vector map rendering. The introduction of distributed "cloud computing" technology has continuously improved the storage and management of massive data. Supported by a large number of distributedsystems, it provides normal services to the outside world, as well as fast data location services. The system has good performance in data storage efficiency, node load balancing and system scalability, and has an efficient data positioning mechanism. The basic idea of the system design draws on the hash algorithm and the consistent hash algorithm, and improves the scalability of the basic algorithm. Through the three-tier layout: front-end server layer, consistent hash layer, data node layer, to maximize the consistency, availability, efficiency and scalability of the system.
The increasing availability of large open datasets and computational resources offers unprecedented opportunities for new insight and discoveries. However, researchers are often faced with the challenge of running ana...
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ISBN:
(数字)9798350360325
ISBN:
(纸本)9798350360332
The increasing availability of large open datasets and computational resources offers unprecedented opportunities for new insight and discoveries. However, researchers are often faced with the challenge of running analyses at scale. We consider here an example from the domain of plant phenology and show how a Python-based deployable analysis environment leveraging Dask for parallel and distributed computing as well as Jupyter for interactive data exploration could be employed to run regression-based phenological models at high spatial resolution and continental scale.
Stealth addresses protect recipient identity privacy in blockchain systems by allowing a sender to derive a stealth address using the recipient’s public key, with the receiver deriving a corresponding one-time privat...
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ISBN:
(数字)9798331509712
ISBN:
(纸本)9798331509729
Stealth addresses protect recipient identity privacy in blockchain systems by allowing a sender to derive a stealth address using the recipient’s public key, with the receiver deriving a corresponding one-time private key. In threshold signatures, where distributed recipient entities only hold private key shares and the full private key never appears, this becomes challenging. To address this, we propose a distributed stealth address generation (DSAG) protocol for threshold signatures. In our approach, distributed sender entities derive the stealth address, and distributed recipient entities derive corresponding one-time private key shares using Lagrange interpolation. The correctness of these shares is verified by ensuring that they satisfy the necessary Lagrange interpolation and discrete logarithm relationships. Our protocol avoids bilinear mappings and relies solely on elliptic curve exponentiations, leading to higher efficiency. Experimental tests demonstrate that our scheme instance takes just 4 milliseconds confirming its practicality.
In recent years, with the rapid development of quantum technology, quantum machine learning has attracted wide attention. In this paper, we propose a novel quantum machine learning model which introduces variational q...
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ISBN:
(数字)9798350352719
ISBN:
(纸本)9798350352726
In recent years, with the rapid development of quantum technology, quantum machine learning has attracted wide attention. In this paper, we propose a novel quantum machine learning model which introduces variational quantum gates in the quantum fuzzy inference circuits, and adjusts the variational gate parameters to automatically learn fuzzy rules for data classification through quantum machine learning training. Comparison results on the iris dataset show that our variational quantum fuzzy inference quantum convolutional neural network (VQFI-QCNN) outperforms classical multi layer perceptron (MLP) neural networks with a similar number of parameters, demonstrating the potential advantages of quantum. Due to the better results on the iris structured dataset, our VQFI-QCNN deserves further investigation, and as the number of quantum bits available for quantum computers increases, our method may also be applied in the future to efficiently classify structured data in mega data.
In this paper, we address the cooperative robust parallel operation problem of an electric drive shaft system. In contrast to prior research, this work explicitly incorporates system uncertainties and external disturb...
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ISBN:
(数字)9798350356830
ISBN:
(纸本)9798350356847
In this paper, we address the cooperative robust parallel operation problem of an electric drive shaft system. In contrast to prior research, this work explicitly incorporates system uncertainties and external disturbances affecting both the shaft and the motors, enhancing the robustness and practicality of the proposed approach. To address this challenge, we first establish a dynamic output feedback controller utilizing the internal model principle. Then, we demonstrate that the cooperative robust parallel operation of the electric drive shaft system can be achieved under directed communication networks, effectively overcoming the adverse effects of system uncertainties and external disturbances. Finally, the efficacy of our proposed distributed controller is rigorously validated through its application to an electric drive shaft system equipped with five actuator motors.
Designing and testing distributedsystems can be a daunting task, especially when the system is composed of heterogeneous devices, it is expected to be robust to disruptions, and is meant to scale to a large number of...
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ISBN:
(数字)9798331527211
ISBN:
(纸本)9798331527228
Designing and testing distributedsystems can be a daunting task, especially when the system is composed of heterogeneous devices, it is expected to be robust to disruptions, and is meant to scale to a large number of nodes. In practice, this mandates the use of simulations to verify the system during development. Additionally, disruptions and disturbance patterns are hardly predictable, and the system’s behavior must thus be subject to test under widely varying conditions, typically implying a stochastic process and multiple simulation runs to assess whether the system meets its functional requirements. Often, running multiple simulations while a system is being developed is time- and resource-consuming, as results are visible and aggregated at termination, especially in cases in which simulations are distributed across multiple devices to speed up the computation. In this paper, we discuss a potential solution to monitor multiple distributed simulations, querying data efficiently while they progress, in order to provide early feedback to the developers. We present a general architecture for the system, discuss the technological opportunities and challenges, and provide a functional open-source prototype implementation based on a simulator used for several scientific exemplaries in the last decade.
Nowcasting is vital for PV farms in order to match supply and demand on energy markets. The accuracy of existing forecasting methods relies on short term modelling of cloud movement and dynamics by combining satellite...
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ISBN:
(纸本)9781665432818
Nowcasting is vital for PV farms in order to match supply and demand on energy markets. The accuracy of existing forecasting methods relies on short term modelling of cloud movement and dynamics by combining satellite images with ground based allsky observations. Cloud movement is modelled based on complex nonlinear numerical models which on the short term are outperformed by image analysis techniques. In this research we propose a simplified nature inspired model for nowcasting which scales with the data. The model is based on forecasting wind direction extracted from motion vector fields by modelling wind using a modified Boids Flocking algorithm. We show that the model is accurate in predicting the cloud coverage up to several hours ahead and that it scales with the number of particles on shared memory systems suited for commodity machines available to PV farm operators.
Aggregated HPC resources have rigid allocation systems and programming models which struggle to adapt to diverse and changing workloads. Consequently, HPC systems fail to efficiently use the large pools of unused memo...
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ISBN:
(数字)9798350387117
ISBN:
(纸本)9798350387124
Aggregated HPC resources have rigid allocation systems and programming models which struggle to adapt to diverse and changing workloads. Consequently, HPC systems fail to efficiently use the large pools of unused memory and increase the utilization of idle computing resources. Prior work attempted to increase the throughput and efficiency of supercomputing systems through workload co-location and resource disaggregation. However, these methods fall short of providing a solution that can be applied to existing systems without major hardware modifications and performance losses. In this paper, we improve the utilization of supercomputers by employing the new cloud paradigm of serverless computing. We show how serverless functions provide fine-grained access to the resources of batchmanaged cluster nodes. We present an HPC-oriented Functionas-a-Service (FaaS) that satisfies the requirements of high-performance applications. We demonstrate a software resource disaggregation approach where placing functions on unallocated and underutilized nodes allows idle cores and accelerators to be utilized while retaining near-native *** Paper Version: https://***/abs/2401.10852HPC FaaS Implementation: https://***/spcl/rFaaS
Advancements in network technology, such as 5G and 6G systems, have increased the need for large-scale data communication. This has led to a higher demand for efficient FFT processors capable of handling big data. In ...
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ISBN:
(数字)9798350389210
ISBN:
(纸本)9798350389227
Advancements in network technology, such as 5G and 6G systems, have increased the need for large-scale data communication. This has led to a higher demand for efficient FFT processors capable of handling big data. In this paper, we propose a low-power variable-length FFT architecture with distributed memory to enhance data processing efficiency. The architecture employs an auto-correction rotator to efficiently and accurately generate the corresponding twiddle factors without excessive memory storage. To further reduce power consumption, distributed memory is used for storing computational data, with dynamic pointers facilitating data access. Simulations demonstrate improvements in processing speed and memory efficiency, addressing the demands for efficient data transmission. The proposed FFT chip design shows promise for application in next-generation network systems.
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