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Data-Driven Analytics Task Management Reasoning Mechanism in Edge Computing
2022
Smart Cities
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In this context, we contribute a task-management mechanism based on approximate fuzzy inference over the popularity of tasks and the percentage of overlapping between the data required by a data-driven ...
Consequently, computationally data-driven tasks at the network edge, such as machine learning models' training and inference, have become more prevalent. ...
As a consequence, data-driven analytic task execution times have been minimized. ...
doi:10.3390/smartcities5020030
doaj:3a6b7bf8c0e84ef7bdcc6ece373cbd70
fatcat:ehsucb6bqjczvotbidicggdxui
Edge computing-oriented smart agricultural supply chain mechanism with auction and fuzzy neural networks
2024
Journal of Cloud Computing: Advances, Systems and Applications
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Although the edge computing framework manages real-time crop monitoring and data collection, market-based mechanisms, such as auctions and fuzzy optimization models, support decision-making for smooth ...
A two-phase hybrid learning approach is formulated. Fuzzy optimization models were formulated using domain expertise for three key supply chain decision problems. ...
Our unified edge computing, auction, and fuzzy neural network approach is uniquely positioned to overcome these limitations through a context-aware, transparent, and data-driven smart agriculture automation ...
doi:10.1186/s13677-024-00626-8
fatcat:cu2gxmahs5d6bkhbkmbrn7hzri
Edge of Things: The Big Picture on the Integration of Edge, IoT and the Cloud in a Distributed Computing Environment
2018
IEEE Access
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With the integration of EC, the processing capabilities are pushed to the edge of network devices such as smart phones, sensor nodes, wearables, and on-board units, where data analytics and knowledge generation ...
Irrespective of the fact that EC shifts the workload from a centralized cloud to the edge, the analogy between EC and the cloud pertaining to factors such as resource management and computation optimization ...
A virtual machine migration approach is utilized to minimize the energy consumption at the data centers. ...
doi:10.1109/access.2017.2780087
fatcat:rbebqib7z5e3dmrt6q2wp3dvzq
Resource Management in Fog/Edge Computing: A Survey
[article]
2018
arXiv
pre-print
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Contrary to using distant and centralized cloud data center resources, employing decentralized resources at the edge of a network for processing data closer to user devices, such as smartphones and tablets ...
Fog/edge resources are typically resource-constrained, heterogeneous, and dynamic compared to the cloud, thereby making resource management an important challenge that needs to be addressed. ...
[140] developed a unified edge and cloud platform for real-time data analytics. ...
arXiv:1810.00305v1
fatcat:erskczbjtjh5jigiu2dy4jgmdu
Explainability and Interpretability Concepts for Edge AI Systems
[chapter]
2024
Advancing Edge Artificial Intelligence
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Edge AI, which combines AI, Internet of Things (IoT) and edge com puting to enable real-time collection, processing, analytics, and decisionmaking, introduces new challenges to acheiving explainable and ...
why and how a model generates its results. ...
Acknowledgements This research was conducted as part of the EdgeAI "Edge AI Technologies for Optimised Performance Embedded Processing" project, which has received funding from KDT JU under grant agreement ...
doi:10.1201/9781003478713-9
fatcat:bvsoobtctngkdiw4hhq4zjooua
AI Augmented Edge and Fog Computing: Trends and Challenges
[article]
2022
arXiv
pre-print
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This survey reviews the evolution of data-driven AI-augmented technologies and their impact on computing systems. ...
We demystify new techniques and draw key insights in Edge, Fog and Cloud resource management-related uses of AI methods and also look at how AI can innovate traditional applications for enhanced Quality ...
Acknowledgments This work is supported by the the President's Ph.D. Scholarship at Imperial College London and Australian Research Council Discovery Project. ...
arXiv:2208.00761v1
fatcat:tfrhvlenyvbg7kidoydjzqejai
Big data driven edge-cloud collaboration architecture for cloud manufacturing: a software defined perspective
2020
IEEE Access
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Hierarchical gateways connecting and managing shop-floor things at the "edge" side are introduced to support latency-sensitive applications for real-time responses. ...
Big data processed both at the gateways and in the cloud will be used to guide continuous improvement and evolution of edge-cloud systems for better performance. ...
Considering the goals, uncertainties and stakeholders' preferences to incorporate big data analytics in manufacturing systems, a goal-oriented modelling and fuzzy logic-based approach, was proposed to ...
doi:10.1109/access.2020.2977846
fatcat:zsrf52qhyvd7zi6zghstoiht7a
Explainable AI in Internet of Control System Distributed at Edge-Cloud Architecture
2021
International Journal of Engineering and Advanced Technology
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The IoCS attempts to unleash AI services using resources at the edge near the autonomous agents and make intelligent edge for dynamic, adaptive, and optimized AI control. ...
In this architecture the designed controller is distributed across the edge and cloud platform using explainable AI. ...
Providing distribution of AI control at edge requires the support of edge analytics. ...
doi:10.35940/ijeat.c2246.0210321
fatcat:yft4airnknefjoayljdf2rl3va
A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing
[article]
2018
arXiv
pre-print
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Edge computing is promoted to meet increasing performance needs of data-driven services using computational and storage resources close to the end devices, at the edge of the current network. ...
Our analysis shows that resource management at the edge requires a deeper understanding of how methods applied at different levels and geared towards different resource types interact. ...
ACKNOWLEDGMENTS This work was supported by the Swedish national graduate school in computer science (CUGS). ...
arXiv:1801.05610v3
fatcat:6qvk6orhszgrdg2bbpdxpi4m6q
Edge AI for Internet of Energy: Challenges and Perspectives
[article]
2023
arXiv
pre-print
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The myriad benefits, spanning from reduced latency and real-time analytics to the pivotal aspects of information security, scalability, and cost-efficiency, underscore the indispensability of edge AI in ...
The digital landscape of the Internet of Energy (IoE) is on the brink of a revolutionary transformation with the integration of edge Artificial Intelligence (AI). ...
NPRP14S-0401-210122 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. ...
arXiv:2311.16851v1
fatcat:jcpcw7yh6vfzra2mjwspg6l3tu
Intelligent Edge-Embedded Technologies for Digitising Industry
[chapter]
2022
Intelligent Edge-Embedded Technologies for Digitising Industry
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The "River Publishers Series in Communications and Networking" is a series of comprehensive academic and professional books which focus on communication and network systems. ...
The series includes research monographs, edited volumes, handbooks and textbooks, providing professionals, researchers, educators, and advanced students in the field with an invaluable insight into the ...
The project has received funding from the ECSEL Joint Undertaking (JU) under grant agreement No 826060. ...
doi:10.13052/rp-9788770226103
fatcat:mgz277pmkbetvbpoaomoktzgzi
Resource Management in Fog/Edge Computing
2019
ACM Computing Surveys
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[124] developed a unified edge and cloud platform for real-time data analytics. ...
The Edge Mesh approach aims at distributing decision-making across different edge nodes [142] . ...
ACKNOWLEDGMENTS The authors are grateful to the anonymous reviewers for their valuable comments and suggestions. ...
doi:10.1145/3326066
fatcat:4hkvzb2djfaezipbhqwoncg5wu
Towards Landslides Early Warning System With Fog - Edge Computing And Artificial Intelligence**
2021
Journal of Ubiquitous Systems and Pervasive Networks
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As matter of fact, the variety of landslides' types make their monitoring a sophisticated task to accomplish. ...
The novelty of the work proposed in this paper is the integration of Federated Learning based on Fog-Edge approaches to continuously improve prediction models. ...
On the other side, Fog nodes composing the fog cluster provide storage and computing resources to edge devices. Finally, they propose a task scheduler which organizes all tasks in the fog cluster. ...
doi:10.5383/juspn.15.02.002
fatcat:zlz4kn4o3vganbqf6to2przdxy
Live Data Analytics With Collaborative Edge and Cloud Processing in Wireless IoT Networks
2017
IEEE Access
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INDEX TERMS Big data, data analytics, internet of things (IoT), cloud computing, edge computing, fog computing. ...
Starting with the main features, key enablers and the challenges of big data analytics, we provide various synergies and distinctions between cloud and edge processing. ...
Moreover, this cloud-based approach will lead to lower-error, higher-precision, and more dynamic treatment of data than the conventional data analytic approaches [16] . ...
doi:10.1109/access.2017.2682640
fatcat:2el6vv5irrbotn3k4g6inw3zwa
Scheduling IoT Applications in Edge and Fog Computing Environments: A Taxonomy and Future Directions
[article]
2022
arXiv
pre-print
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Fog computing, as a distributed paradigm, offers cloud-like services at the edge of the network with low latency and high-access bandwidth to support a diverse range of IoT application scenarios. ...
This paper presents a taxonomy of recent literature on scheduling IoT applications in Fog computing. ...
To illustrate, applications requiring low latency and startup time can be managed at the low-level FSs (i.e., at the Edge), and then be scheduled based on the decision engine deployed at the Edge. ...
arXiv:2204.12580v1
fatcat:ctcbm6r3yjadbo6cf7i7imshsu
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