In recent years, whether we are talking about industrial implementations or whether we are talking about products offered to the masses, there is an accelerated trend of the appearance of IoT devices, devices that col...
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ISBN:
(纸本)9798350362442;9798350362435
In recent years, whether we are talking about industrial implementations or whether we are talking about products offered to the masses, there is an accelerated trend of the appearance of IoT devices, devices that collect information and, depending on their specifics, can make certain decisions in the process. This increase automatically leads to the generation of more generated data that must be sent for processing, analyzed, stored, a certain decision being made based on the result of the analysis. This process can be long, the duration strictly determined by the volume of data and the performance of the Cloud infrastructure. Fog and Edge computing has come to our aid with an innovative solution, acting as an additional layer between IoT and Cloud devices. This layer aims to reduce the response time by analyzing the data at the edge of the network, in this way it is no longer necessary to send all the information directly to the Cloud. Whether we are talking about smart cities, the health field or the industrial fields, the presence of IoT devices shows the usefulness of the need to implement Fog and Edge systems. Starting from this growth in the IoT field, the authors wish through this article to analyze the existing implementations that use Fog and Edge computing, the existing architectural levels, the analysis of the areas that present vulnerabilities, as well as the possible improvements that can be added to make the processes more efficient.
In order to overcome the selfishness of users and encourage more vehicles to participate in this process, this paper combined the computing and caching capabilities of mobile edge cloud, proposed a new service caching...
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Centralized Learning (CL) in Machine Learning (ML) raises privacy concerns due to data aggregation on a central server. Federated Learning (FL) addresses this by allowing clients to share model updates instead of raw ...
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ISBN:
(纸本)9798350330656;9798350330649
Centralized Learning (CL) in Machine Learning (ML) raises privacy concerns due to data aggregation on a central server. Federated Learning (FL) addresses this by allowing clients to share model updates instead of raw data. However, the varying capabilities of edge devices hinder FL's effectiveness in real-world scenarios. This work proposes a Hybrid Federated Centralized Learning (HFCL) model to overcome these limitations. HFCL optimizes task distribution based on individual client capabilities, enabling collaboration regardless of resource limitations. We implement CL, FL, and HFCL using MNIST and CIFAR-10 datasets with Convolutional Neural Networks (CNNs). We analyze the impact of varying active/passive client ratios within HFCL and different learning rates on accuracy. Results demonstrate that HFCL significantly improves learning performance compared to pure FL or CL. This highlights its potential to address limitations in edge computing environments with disparate computational resources. Future research directions include further exploration of HFCL within edge-based FL.
The aim of educational innovation is to foster students' creative and problem-solving skills via the integration of several disciplines, including science, technology, engineering, art, and mathematics. Efficientl...
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The development of sustainability of any country depends not only on a strong government. Academic institutions also have critical role in this. A theoretical analysis approach to investigate the potential role of uni...
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Smart farming involves leveraging modern technology to optimize agricultural practices, aiming to enhance both the quantity and quality of agricultural yields. One such approach is edge-based smart farming, which focu...
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Smart farming involves leveraging modern technology to optimize agricultural practices, aiming to enhance both the quantity and quality of agricultural yields. One such approach is edge-based smart farming, which focuses on field crop monitoring using sensors and automating irrigation systems to meet agricultural demands efficiently. Unlike traditional cloud-based systems reliant on IoT models, edge-based systems offer reduced latency, extended battery life for IoT devices, cost-effective information management, and improved access to knowledge management and AI. In this paper, we propose the Ensembled Enabled Edge computing algorithm for developing a smarter farming system, particularly for crop recommendation, aiming to achieve superior results. Comparative analysis with established classifiers like SVM and Naive-Bayes reveals that our algorithm attains the highest accuracy, reaching 95%.
The traditional von Neumann computing architecture has relatively-low information processing speed and high power consumption,making it difficult to meet the computing needs of artificial intelligence(AI).Neuromorphic...
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The traditional von Neumann computing architecture has relatively-low information processing speed and high power consumption,making it difficult to meet the computing needs of artificial intelligence(AI).Neuromorphic computing systems,with massively parallel computing capability and low power consumption,have been considered as an ideal option for data storage and AI computing in the ***,as the fourth basic electronic component besides resistance,capacitance and inductance,is one of the most competitive candidates for neuromorphic computing systems benefiting from the simple structure,continuously adjustable conductivity state,ultra-low power consumption,high switching speed and compatibility with existing CMOS *** memristors with applying MXene-based hybrids have attracted significant attention in recent ***,we introduce the latest progress in the synthesis of MXene-based hybrids and summarize their potential applications in memristor devices and neuromorphological *** explore the development trend of memristors constructed by combining MXenes with other functional materials and emphatically discuss the potential mechanism of MXenes-based memristor ***,the future prospects and directions of MXene-based memristors are briefly described.
Emotion recognition using EEG signals has gained growing attention in recent years due to its promising applications in human-computer interaction, affective computing, and mental health assessment. This study investi...
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this paper gives a singular method for analyzing actual time-implemented applications in silicon-on-chip (SOC) architectures. The proposed technique uses a mixture of analytical answers and hardware degree simulations...
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In recent years, the technology of quantum computing has been continuously improving, with organizations such as IBM, Google, and NASA establishing quantum computers and applying them in fields like artificial intelli...
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