This paper analyzes the advantages of using edge computingtechnology to realize video monitoring function and gives the video monitoring system architecture based on edge computing, as well as the composition, functi...
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In this paper, we infer remote measurements such as remote voltages and currents online with change in AC power flows using spiking neural network (SNN) as grid-edge technology for efficient coordination of power elec...
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ISBN:
(纸本)9798350318562;9798350318555
In this paper, we infer remote measurements such as remote voltages and currents online with change in AC power flows using spiking neural network (SNN) as grid-edge technology for efficient coordination of power electronic converters. This work unifies power and information as a means of data normalization using a multi-modal regime in the form of spikes using energy-efficient neuromorphic learning and event-driven asynchronous data collection. Firstly, we organize the synchronous real-valued measurements at each edge and translate them into asynchronous spike-based events to collect sparse data for training of SNN at each edge. Instead of relying on error-dependent supervised data-driven learning theory, we exploit the latency-driven unsupervised Hebbian learning rule to obtain modulation pulses for switching of power electronic converters that can now comprehend grid disturbances locally and adapt their operation without requiring explicit infrastructure for global coordination. Not only does this philosophy block exogenous path arrival for cyber attackers by dismissing the cyber layer, it also entails converter adaptation to system reconfiguration and parameter mismatch issues. We conclude this work by validating its energy-efficient and effective online learning performance under various scenarios in different system sizes, including modified IEEE 14-bus system and under experimental conditions.
Edge and cloud computing offer cost-effective computational options, including free services, for IoT devices with limited resources. Offloading refers to the practice of transferring tasks to the edge and/or cloud sy...
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The resurgence of ransomware has emerged as a pressing security threat in computer networks and Internet connected machines and IoT devices. To address this challenge, accurate ransomware detectors are required to aut...
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ISBN:
(纸本)9798350371000;9798350370997
The resurgence of ransomware has emerged as a pressing security threat in computer networks and Internet connected machines and IoT devices. To address this challenge, accurate ransomware detectors are required to automatically detect and block the malicious traffic. Most ransomware detectors only detect whether the traffic is benign or ransomware. However, detecting the family of ransomware would be greatly useful to promptly eliminate or mitigate its destructive effects. To tackle this issue, we propose machine learning models that accurately detect each ransomware family. Our models aim to detect the ransomware network traffic and thwart it at the network edge before it enters the network. Considering that ransomwares directly work with the memory dump and file system, the information extracted from the operating system's functions on the memory dump is very useful to detect a ransomware attack. However, that information could be collected only when the ransomware has already infected the device and is actively disrupting the file system. In our research, we propose a framework that blocks the ransomware at the network edge. This restricts our research to using a dataset that extracts network traffic features with no access to the device's operating system's functionalities. An edge computing intrusion detection system is also beneficial for resource contained network devices, such as IoT, which have limited computational resources and cannot dynamically analyze the network traffic and run a strong intrusion detection system. We worked on CICAndMal2017 dataset and proposed a feature selection-based framework along with different machine learning models. We also applied a data augmentation technique to the training set to strengthen the data used to build our models. We extensively studied our proposed framework. Our experimental results demonstrated that chi-square feature selection with Random Forest and XGBoost models surpass other models and the state of the a
With the rapid development of smart highways, effective visualization and intelligent analysis of data information are important means to improve management efficiency and gain insight into traffic trends. This paper ...
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The Internet and computer commercialization have transformed the computing systems area over the past sixty years, affecting society. Computer systems have evolved to meet diverse social needs thanks to technological ...
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With the development of computer technology, the number of connected devices and data on the network is growing. In this regard, the question arises of implementing technologies that provide not only high-speed data p...
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The utilization of virtual reality (VR) has been increasingly applied in various fields, including medicine and education, to address the challenges faced by children with attention deficit hyperactivity disorder (ADH...
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Mobile edge computing (MEC) can enhance application performance effectively by offloading computation tasks to the edge server hosting corresponding service via multi-access wireless networks. However, existing servic...
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ISBN:
(纸本)9798400717048
Mobile edge computing (MEC) can enhance application performance effectively by offloading computation tasks to the edge server hosting corresponding service via multi-access wireless networks. However, existing service placement policies are more based on the assumption that application services can be used without restrictions, while ignoring the constraints of software usage by license, which always limits user number, usage periods, etc. To address these challenges, in this paper, we introduce an architecture of digital twin-empowered MEC with good scalability and reliability, formulate an optimal problem by jointly optimizing task offloading and service placement, which takes into account the usage number limitation for one service at the same time. To tackle this mix integer non-linear programming problem (MINLP), we propose a DRL-based algorithm JTOSP to deal with high-dimensional input data from MEC system, and to cope with the stochastic nature in dynamic underlying network efficiently. Numerical experiments are conducted and the simulation results show that our algorithm outperform other algorithms and reduce energy consumption efficiently.
Quantum computing has emerged as a transformative tool for future data management. Classical problems in database domains, including query optimization, data integration, and transaction management, have recently been...
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