Searched for: subject%3A%22edge%255C%2Bcomputing%22
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den Blanken, Douwe (author)
The growing interest in edge computing is driving the demand for more efficient deep learning models that fit into resource-constrained edge devices like Internet-of-Things (IoT) sensors. The challenging limitations of these devices in terms of size and power has given rise to the field of tinyML, focusing on enabling low-cost machine learning...
master thesis 2023
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Zheng, Jingjing (author), Li, Kai (author), Mhaisen, N. (author), Ni, Wei (author), Tovar, Eduardo (author), Guizani, Mohsen (author)
Federated learning (FL) is increasingly considered to circumvent the disclosure of private data in mobile edge computing (MEC) systems. Training with large data can enhance FL learning accuracy, which is associated with non-negligible energy use. Scheduled edge devices with small data save energy but decrease FL learning accuracy due to a...
conference paper 2023
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Amanatidis, Petros (author), Karampatzakis, Dimitris (author), Iosifidis, G. (author), Lagkas, Thomas (author), Nikitas, Alexandros (author)
The development of computer hardware and communications has brought with it many exciting applications in the Internet of Things. More and more Single Board Computers (SBC) with high performance and low power consumption are used to infer deep learning models at the edge of the network. In this article, we investigate a cooperative task...
journal article 2023
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Zhang, Qinglong (author), Han, Rui (author), Liu, Chi Harold (author), Wang, Guoren (author), Chen, Lydia Y. (author)
Vision applications powered by deep neural networks (DNNs) are widely deployed on edge devices and solve the learning tasks of incoming data streams whose class label and input feature continuously evolve, known as domain shift. Despite its prominent presence in real-world edge scenarios, existing benchmarks used by domain adaptation methods...
conference paper 2023
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Zhao, Zheyu (author), Cheng, H. (author), Xu, Xiaohua (author)
Massive terminal users have brought explosive need of data residing at edge of overall network. Multiple Mobile Edge Computing (MEC) servers are built in/near base station to meet this need. However, optimal distribution of these servers to multiple users in real time is still a problem. Reinforcement Learning (RL) as a framework to solve...
conference paper 2023
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Gupta, Akshit (author)
Urban forests and vegetation are fundamental for developing resilient cities. Thus, the effective management and protection of urban trees and greenery are essential. Nowadays, urban trees are experiencing atypical amount of natural and human-induced stresses which affects their functionality, productivity and survival. The current methods for...
master thesis 2022
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Mukhopadhyay, Atri (author), Iosifidis, G. (author), Ruffini, Marco (author)
The development of Multi-access edge computing (MEC) has resulted from the requirement for supporting next generation mobile services, which need high capacity, high reliability and low latency. The key issue in such MEC architectures is to decide which edge nodes will be employed for serving the needs of the different end users. Here, we...
journal article 2022
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Peltonen, Ella (author), Ahmad, Ijaz (author), Aral, Atakan (author), Capobianco, Michele (author), Ding, Aaron Yi (author), Gil-Castineira, Felipe (author), Gilman, Ekaterina (author), Harjula, Erkki (author), Jurmu, Marko (author)
Edge Intelligence (EI) is an emerging computing and communication paradigm that enables Artificial Intelligence (AI) functionality at the network edge. In this article, we highlight EI as an emerging and important field of research, discuss the state of research, analyze research gaps and highlight important research challenges with the...
journal article 2022
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Cozzolino, Vittorio (author), Tonetto, Leonardo (author), Mohan, Nitinder (author), Ding, Aaron Yi (author), Ott, Jorg (author)
Widespread adoption of mobile augmented reality (AR) and virtual reality (VR) applications depends on their smoothness and immersiveness. Modern AR applications applying computationally intensive computer vision algorithms can burden today's mobile devices, and cause high energy consumption and/or poor performance. To tackle this challenge, it...
journal article 2022
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Ding, Aaron Yi (author), Peltonen, Ella (author), Meuser, Tobias (author), Aral, Atakan (author), Becker, Christian (author), Dustdar, Schahram (author), Hiessl, Thomas (author), Kranzlmüller, Dieter (author), Liyanage, Madhusanka (author), Maghsudi, Setareh (author), Mohan, Nitinder (author), Ott, Jörg (author), Rellermeyer, Jan S. (author), Schulte, Stefan (author), Schulzrinne, Henning (author), Solmaz, Gürkan (author), Tarkoma, Sasu (author), Varghese, Blesson (author), Wolf, Lars (author)
Based on the collective input of Dagstuhl Seminar (21342), this paper presents a comprehensive discussion on AI methods and capabilities in the context of edge computing, referred as Edge AI. In a nutshell, we envision Edge AI to provide adaptation for data-driven applications, enhance network and radio access, and allow the creation,...
journal article 2022
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Galanopoulos, Apostolos (author), Iosifidis, G. (author), Salonidis, Theodoros (author), Leith, Douglas J. (author)
An increasing number of mobile applications rely on Machine Learning (ML) routines for analyzing data. Executing such tasks at the user devices saves the energy spent on transmitting and processing large data volumes at distant cloud-deployed servers. However, due to memory and computing limitations, the devices often cannot support the...
journal article 2022
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Scargill, Tim (author), Lan, G. (author), Gorlatova, Maria (author)
The personalization of augmented reality (AR) experiences based on environmental and user context is key to unlocking their full potential. The recent addition of eye tracking to AR headsets provides a convenient method for detecting user context, but complex analysis of raw gaze data is required to detect where a user's attention and thoughts...
conference paper 2022
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Li, Meng (author), Zhu, Liehuang (author), Zhang, Zijian (author), Lal, C. (author), Conti, M. (author), Alazab, Mamoun (author)
Traffic monitoring services collect traffic reports and respond to users' traffic queries. However, the reports and queries may reveal the user's identity and location. Although different anonymization techniques have been applied to protect user privacy, a new security threat arises, namely, n-by-1 jamming attack, in which an anonymous...
journal article 2022
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Zheng, Jingjing (author), Li, Kai (author), Mhaisen, N. (author), Ni, Wei (author), Tovar, Eduardo (author), Guizani, Mohsen (author)
Federated learning (FL) has been increasingly considered to preserve data training privacy from eavesdropping attacks in mobile-edge computing-based Internet of Things (EdgeIoT). On the one hand, the learning accuracy of FL can be improved by selecting the IoT devices with large data sets for training, which gives rise to a higher energy...
journal article 2022
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Mhaisen, N. (author), Allahham, Mhd Saria (author), Mohamed, Amr (author), Erbad, Aiman (author), Guizani, Mohsen (author)
Service provisioning systems assign users to service providers according to allocation criteria that strike an optimal trade-off between users' Quality of Experience (QoE) and the operation cost endured by providers. These systems have been leveraging Smart Contracts (SCs) to add trust and transparency to their criteria. However, deploying...
journal article 2022
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Zhao, Jianxin (author), Han, Rui (author), Yang, Yongkai (author), Catterall, Benjamin (author), Liu, Chi Harold (author), Chen, Lydia Y. (author), Mortier, Richard (author), Crowcroft, Jon (author), Wang, Liang (author)
With the massive amount of data generated from mobile devices and the increase of computing power of edge devices, the paradigm of Federated Learning has attracted great momentum. In federated learning, distributed and heterogeneous nodes collaborate to learn model parameters. However, while providing benefits such as privacy by design and...
journal article 2022
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Belzer, Nick (author)
Mobile networks deal with an increasing portion of the IP Traffic due to the significant growth in the number of mobile devices and the accompanied lifestyle. A large fraction of this IP traffic is spent on duplicate transfers for the same resources. Previous work has shown that a Content Delivery Network (CDN) based on edge-nodes can reduce...
master thesis 2021
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van Veelen, Frank (author)
In the past years, small Earth Observation (EO) satellites have become increasingly capable of taking high-resolution images at high sample rates. These images contain valuable information for different sectors, such as the agricultural and military sector. Furthermore they can contain important information about the climate and climate change....
master thesis 2021
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Varghese, Blesson (author), De Lara, Eyal (author), Ding, Aaron Yi (author), Hong, Cheol Ho (author), Bonomi, Flavio (author), Dustdar, Schahram (author), Harvey, Paul (author), Hewkin, Peter (author), Shi, Weisong (author)
This article argues that low latency, high bandwidth, device proliferation, sustainable digital infrastructure, and data privacy and sovereignty continue to motivate the need for edge computing research even though its initial concepts were formulated more than a decade ago.
journal article 2021
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Galanopoulos, Apostolos (author), Ayala-Romero, Jose A. (author), Leith, Douglas J. (author), Iosifidis, G. (author)
Video analytics constitute a core component of many wireless services that require processing of voluminous data streams emanating from handheld devices. Multi-Access Edge Computing (MEC) is a promising solution for supporting such resource-hungry services, but there is a plethora of configuration parameters affecting their performance in an...
conference paper 2021
Searched for: subject%3A%22edge%255C%2Bcomputing%22
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