An analysis of drone detection methods was carried out in this paper. there is a reasoned choice of the optimal method for implementation based on the analysis of acoustic signals generated by drones. the set of metho...
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WSNs are currently seeing widespread adoption across a variety of settings, including the medical industry, manufacturing environments, and a number of other domains. WSNs are distinguished by a number of distinguishi...
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Locational detection of the false data injection attack (FDIA) is essential for smart grid cyber-security. However, the FDIA detection techniques often falter in scalability as power network complexity increases. To a...
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An event-triggered (ET) recursive distributed filtering approach is designed for a class of stochastic systems with correlated noises. the correlated noises are represented by known matrices and the Kronecker δ funct...
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For the problems of uneven clustering, unreasonable selection of cluster heads and excessive energy consumption in wireless sensor networks for smart grid, an energy efficient clustering algorithm for wireless sensor ...
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ISBN:
(纸本)9781665489577
For the problems of uneven clustering, unreasonable selection of cluster heads and excessive energy consumption in wireless sensor networks for smart grid, an energy efficient clustering algorithm for wireless sensor networks in smart grid (EECSG) is proposed. the algorithm first calculates the optimal number of cluster heads, and then uses k-means algorithm to evenly divide the whole network based on the optimal number of cluster heads. In the selection of cluster head, residual node energy and distance influence factor are introduced to comprehensively select, and finally the data is transmitted stably. the simulation results show that EECSG can dynamically determine the cluster head through the introduction of cluster head selection factor, which reduces the energy loss during data communication in the network and improves the overall life of the wireless sensor network.
the research outlined in this paper focuses on designing and implementing a secure data transmission system within a smart home environment, utilizing the RC4-streamcipher (RSC) algorithm. the emphasis lies on integra...
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the proceedings contain 85 papers. the topics discussed include: tuberculosis screening with cough sounds using the deep learning models;ship trajectory compression in fish net area based on improved sliding window al...
ISBN:
(纸本)9798350329490
the proceedings contain 85 papers. the topics discussed include: tuberculosis screening with cough sounds using the deep learning models;ship trajectory compression in fish net area based on improved sliding window algorithm;critical system design based on high availability cluster technology;monthly rainfall prediction based on VMD-GRA-Elman model;light information access when operating a small drone;a convolutional neural network based method for masked face detection;control over distributed topology of wire-less sensor network based on power optimization;deep CNN-RNN with self-attention model for electric IoT traffic classification;machine learning modeling on acoustic emission leak detection of metal-sealed pressure vessel;a restricted embedding transfer model for hyperspectral anomaly detection;and text labels classification model based on BERT algorithm.
Use of the serverless paradigm in cloud application development is growing rapidly, primarily driven by its promise to free developers from the responsibility of provisioning, operating, and scaling the underlying inf...
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ISBN:
(纸本)9798350304831
Use of the serverless paradigm in cloud application development is growing rapidly, primarily driven by its promise to free developers from the responsibility of provisioning, operating, and scaling the underlying infrastructure. However, modern cloud-edge infrastructures are characterized by large numbers of disparate providers, constrained resource devices, platform heterogeneity, infrastructural dynamicity, and the need to orchestrate geographically distributed nodes and devices over public networks. this presents significant management complexity that must be addressed if serverless technologies are to be used in production systems. this position paper introduces COGNIT, a major new European initiative aiming to integrate AI technology into cloud-edge management systems to create a Cognitive Cloud reference framework and associated tools for serverless computing at the edge. COGNIT aims to: 1) support an innovative new serverless paradigm for edge application management and enhanced digital sovereignty for users and developers;2) enable on-demand deployment of large-scale, highly distributed and self-adaptive serverless environments using existing cloud resources;3) optimize data placement according to changes in energy efficiency heuristics and application demands and behavior;4) enable secure and trusted execution of serverless runtimes. We identify and discuss seven research challenges related to the integration of serverless technologies with multi-provider Edge infrastructures and present our vision for how these challenges can be solved. We introduce a high-level view of our reference architecture for serverless cloud-edge continuum systems, and detail four motivating real-world use cases that will be used for validation, drawing from domains within Smart Cities, Agriculture and Environment, Energy, and Cybersecurity.
Respiratory health is a crucial aspect of human well-being. According to a survey conducted in 2023, India accounts for 32% of the global burden of respiratory diseases. It is essential to regularly monitor respirator...
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Collaborative learning is an emerging field of machine learning. In this framework, multiple learning algorithms try to learn from a distributed database. the main idea is to improve the performance of each algorithm ...
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