Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 137 for “"Edge computing"”.
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Edge computing para IoT
… un nuevo paradigma de computación llamado Edge Computing que acerca parte de las capacidades de análisis y procesamiento que actualmente ofrecen los sistemas en la nube al lugar en el que se generan los datos, disminuyendo de esta forma el volumen de información que debe enviarse a la red y …
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MULTIPARTY COLLABORATION IN EDGE COMPUTING SYSTEMS
… supporting multiple services, particularly edge computing systems, invariably consist of multiple subsystems designed, installed, or managed by a different vendor, organization, or party. In particular, each physical cluster of devices or the services offered on top of these physical …
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Resource management for Edge computing systems
Current computing techniques using the Cloud as a centralised server will become untenable as billions of devices get connected to the Internet. This will lead to the degradation of the Quality-of-Service (QoS) of Cloud-hosted applications. Recently Edge computing is proposed as a potential …
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Network-Aware Task Scheduling for Edge Computing
Edge computing promises low-latency computation by moving data processing closer to the source. Tasks executed at the edge of the network have seen a significant increase in their complexity. The demand for low-latency computation for delay-sensitive applications at the edge is also increasing. To …
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Multi-server federated learning in vehicular edge computing
… and roadside units (RSUs) host FL servers at the edge. However, practical deployments face multiple challenges: highly non-IID and noisy data across vehicles, heterogeneous computation and communication resources, private training costs, intermittent connectivity, and the need to coordinate …
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Orchestrating Edge Computing Services with Efficient Data Planes
L'abstract è presente nell'allegato / the abstract is in the attachment
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AI-based Edge Computing System for Event Based Analytics
… we have witnessed advanced research for edge computing and its potential benefits of reducing latency, desirable availability, and privacy protection. However, cloud-based AI solutions are not readily deployable to the edge in IoT's data-driven world because of the difficulties of dealing …
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Towards Energy-Efficient Edge Computing for tiny AI Applications
… (AI) applications become more common on the edge of networks, like Raspberry Pi servers, it is crucial to optimize their energy use. This research project investigates how AI algorithms affect energy efficiency and resource usage on Raspberry Pi servers. Two models were created: one predicts …
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Unmanned Aerial Vehicles and Edge Computing in Wireless Networks
… the enabling technologies in wireless networks, edge computing is proposed to offload users' computation tasks to edge servers to reduce users' latency and energy consumption. However, this requires efficient utilization of both communication resources and computation resources. Furthermore, …
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Distributed AI-defense for Cyber Threats on Edge Computing Systems
… increasingly sophisticated strategies, cutting-edge cyber security has become a necessity for industry organizations and government agencies. A deluge of novel threat strategies has overwhelmed many state-of-the-art cyber security models. Mutating hashes, complex obfuscation mechanisms, …
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Deep learning for DDoS attack detection in mobile edge computing
Mobile edge computing (MEC) has become a disruptive technology that brings computation closer to end users, reducing latency and allowing faster response times. However, MEC like other networks is facing cyber security issues, particularly the Distributed Denial of Service attack (DDoS) which has …
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Machine Learning Approach for Resource Allocation in Mobile Edge Computing
<p>Mobile Edge Computing (MEC) is recognized as a pivotal technology supporting cloud computing and innovative services at the network edge, offering significant reductions in system delay and mitigating network traffic congestion. It supports latency-sensitive applications like Augmented Reality …
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Applying Kuiper Connectivity with Edge Computing to New Industry Verticals
Large “megaconstellations” comprising hundreds or even thousands of satellites in Low Earth Orbit (LEO) to provide global connectivity coverage have been a goal of satellite operators for decades. Within the last 5 years, these projects have become technically feasible due to advances in …
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Flexible Energy-Aware Image and Transformer Processors for Edge Computing
Machine learning inference on edge devices for image and language processing has become increasingly common in recent years, but faces challenges associated with high memory and computation requirements, coupled with limited energy resources. This work applies different quantization schemes and …
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Online Optimization for Edge Computing under Uncertainty in Wireless Networks
Edge computing is an emerging technology that can overcome the limitations of centralized cloud computing by enabling distributed, low-latency computation at a network edge. Particularly, in edge computing, some of the cloud's functionalities such as storage, processing, and computing are migrated …
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EdgeFn: A Lightweight Customizable Data Store for Serverless Edge Computing
Serverless Edge Computing is an extension of the serverless computing paradigm that enables the deployment and execution of modular software functions on resource-constrained edge devices. However, it poses several challenges due to the edge network's dynamic nature and serverless applications' …
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Multi-criteria decision support for energy-efficient IoT edge computing offloading
… IoT applications have led to the adoption of the edge computing paradigm, where the data is processed at the edge of the network, closer to the IoT devices. The decision as to whether cloud or edge resources will be utilised is typically taken at the design stage, based on the type of the IoT …
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Simulating an Optical Neural Network for Deep Learning in Edge Computing
… rise has been driven by improvements in parallel computing from graphics processing units (GPUs) as well as large data sets. Applying deep learning to edge computing is challenging because deep neural network (DNN) hardware must not only possess the needed computational power but must also satisfy …
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Empirical Evaluation of Edge Computing for Smart Building Streaming IoT Applications
… ubiquitous connectivity have given rise to a new computing paradigm, referred to as "Edge computing", which argues for data analysis to be performed at the "edge" of the IoT infrastructure, near the data source. The development of efficient Edge computing systems must be based on advanced …
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Low-Power Wireless Sensor Node with Edge Computing for Pig Behavior Classifications
… The proposed work solves this issue through WSN edge computing solution, in which a Random Forest Classifier (RFC) is trained and implemented into WSNs. The implementation of RFC on WSNs does not save power, but the RFC predicts animal behavior such that WSNs can adaptively adjust the data …
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