Quantum processing units (QPUs) are currently exclusively available from cloud vendors. However, with recent advancements, hosting QPUs will soon be possible everywhere. Existing work has yet to draw from research in ...
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ISBN:
(纸本)9798350304794
Quantum processing units (QPUs) are currently exclusively available from cloud vendors. However, with recent advancements, hosting QPUs will soon be possible everywhere. Existing work has yet to draw from research in edge computing to explore systems exploiting mobile QPUs, or how hybrid applications can benefit from distributed heterogeneous resources. Hence, this work presents an architecture for Quantum computing in the edge-cloud continuum. We discuss the necessity, challenges, and solution approaches for extending existing work on classical edge computing to integrate QPUs. We describe how warm-starting allows defining workflows that exploit the hierarchical resources spread across the continuum. Then, we introduce a distributed inference engine with hybrid classical-quantum neural networks (QNNs) to aid system designers in accommodating applications with complex requirements that incur the highest degree of heterogeneity. We propose solutions focusing on classical layer partitioning and quantum circuit cutting to demonstrate the potential of utilizing classical and quantum computation across the continuum. To evaluate the importance and feasibility of our vision, we provide a proof of concept that exemplifies how extending a classical partition method to integrate quantum circuits can improve the solution quality. Specifically, we implement a split neural network with optional hybrid QNN predictors. Our results show that extending classical methods with QNNs is viable and promising for future work.
With the advancements in technologies such as artificial intelligence, communication, and microelectronics, unmanned systems have rapidly developed and gradually replaced humans in certain challenging and adversarial ...
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The integration of the blockchain technology into electronic voting systems is an innovation that has gained popularity due to the issues prevailing with the conventional voting methods. The purpose of the work is to ...
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Robotic swarms or portable sensor networks are emerging technologies for sensing physical processes that are spatially distributed- and temporally dynamic, both on Earth and in future Moon/Mars exploration missions. W...
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ISBN:
(纸本)9798350371420;9781737749769
Robotic swarms or portable sensor networks are emerging technologies for sensing physical processes that are spatially distributed- and temporally dynamic, both on Earth and in future Moon/Mars exploration missions. We develop a portable network composed of a multitude of self-organized "sensor eggs". These eggs are equipped with ultra-wideband (UWB) transceivers, providing precise time and position information without additional infrastructures like Global Navigation Satellite systems (GNSSs). Each egg is additionally equipped with environmental sensors, for example, a Sulfur dioxide gas sensor to explore volcanic activity. We use a real time decentralized particle filter (DPF) to estimate the a-posteriori probability density functions (PDFs) of the egg positions. These PDFs are then used in a static state binary Bayes filter for estimating the gas sources with potentially complex structures such as cracks on the volcano surface. The proposed sensor network is verified with an in-field experiment at La Fossa volcano on the island of Vulcano, Italy, in 2023.
In relation to large industrially significant systems (using the example of nuclear power plants (NPP)), the ways of developing the architecture and functionality of cloud systems are considered. The development ways ...
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Graph clustering is an important technique to detect community clusters in complex networks. SCAN (Structural Clustering Algorithm for Networks) is a well-studied graph clustering algorithm that has been widely applie...
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ISBN:
(纸本)9781665473156
Graph clustering is an important technique to detect community clusters in complex networks. SCAN (Structural Clustering Algorithm for Networks) is a well-studied graph clustering algorithm that has been widely applied over the years. However, the processing time cost of sequential SCAN and its variants cannot be tolerable on large graphs. The existing parallel variants of SCAN are focusing on fully utilizing the computing capacity of multi-core computer architectures and inventing sophisticated optimization techniques on single computing node. As the objects and their relationships in cyberspace are varying over time, the scale of graph data is increasing with high rate. The graph clustering algorithms on single node are facing challenges from limited computing resources, such as computing performance, memory size and storage volume. The distributed processing algorithm is called for processing large graphs. This work presents a distributed structural graph clustering algorithm using Spark. Furthermore, the edge pruning technique and adaptive checking are optimized to improve clustering efficiency. And the label propagation clustering is simplified to reduce the communication cost in the distributed clustering iterations. It also conduct extensive experiments on real-world datasets to testify the efficiency and scalability of the distributed algorithm. Experimental results show that efficient clustering performance can be achieved and it scales well under different settings.
The tremendous increase in implementing IoT devices for critical infrastructures yells for higher performance and robust protection. Traditional cloud based IoT architecture is unsuitable for delay-sensitive or real-t...
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Delayed delivery of packets hinders the performance of time-sensitive Internet of Things (IoT) applications and incurs increased power consumption. Stochastic routing schemes solve the problem of saving all participat...
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ISBN:
(纸本)9798350333398
Delayed delivery of packets hinders the performance of time-sensitive Internet of Things (IoT) applications and incurs increased power consumption. Stochastic routing schemes solve the problem of saving all participating nodes from getting their power drained out quickly. However, stochastic routing incurs the problem of delivery delays and reliable end-to-end delivery. This paper proposes a novel routing scheme, called Qij routing, to solve these problems. The proposed Qij routing scheme is a combination of a classic routing scheme, called Distance Vector Algorithm, with a novel re-definition of the cost of a link to find the best path from source to destination. Qij takes into account the wireless link reliability of any connection between two nodes, and the transmission delay of IoT devices working together in a distributed network. With the presented mathematical model, a routing table is maintained that let an individual node in a network find the distinctly prominent reliable path among many routes from source to destination. The superior efficiency of Qij routing scheme over eminent stochastic routing schemes is proven through simulation results in terms of reduced end-to-end expected delivery delay and increased expected delivery ratio.
Real-time embedded systems filed tend to deploy functions of different critical levels on a unified platform for reasons related to SWaP (size, weight and power) and cost considerations. Highly critical tasks represen...
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Wireless sensor network has shown a tremendous multifold growth in terms of applications in multiple domains. These domains are with quite continuous execution in nature. The survival time of the sensor-node's (SN...
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