dc.contributor.author |
Afachao, F
|
|
dc.contributor.author |
Abu-Mahfouz, Adnan MI
|
|
dc.date.accessioned |
2024-03-19T07:38:00Z |
|
dc.date.available |
2024-03-19T07:38:00Z |
|
dc.date.issued |
2023-11 |
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dc.identifier.citation |
Afachao, F. & Abu-Mahfouz, A.M. 2023. Towards energy-efficient intelligent edge computing. http://hdl.handle.net/10204/13642 . |
en_ZA |
dc.identifier.isbn |
979-8-3503-2781-6 |
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dc.identifier.uri |
DOI: 10.1109/ICECET58911.2023.10389568
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|
dc.identifier.uri |
http://hdl.handle.net/10204/13642
|
|
dc.description.abstract |
This research focuses on energy-efficient edge computing in the context of intelligent edge computing. With the increasing demand for computation and data processing at edge network nodes, the study explores the use of artificial intelligence (AI) techniques, specifically reinforcement learning, to enhance energy management. By conducting a comparative analysis of algorithms, including the Markov Decision Process, Q-learning, Fair heuristic, and Random heuristic, on the basis of waiting time of servers, response time, and energy consumption, the research aims to identify the most effective approach for optimizing the intelligent edge system. Simulations using the iFogSim simulator kit provide empirical evidence for performance evaluation. The findings highlight the advantages of AI-based schemes and emphasize the need for further advancements in intelligent edge computing applications. |
en_US |
dc.format |
Abstract |
en_US |
dc.language.iso |
en |
en_US |
dc.relation.uri |
https://ieeexplore.ieee.org/document/10389568 |
en_US |
dc.source |
3rd International Conference on Electrical, Computer and Energy Technologies (ICECET 2023), Cape Town, South Africa, 1-17 November 2023 |
en_US |
dc.subject |
Internet of Things |
en_US |
dc.subject |
IoT |
en_US |
dc.subject |
Intelligent edge |
en_US |
dc.subject |
Resource management |
en_US |
dc.subject |
Reinforcement learning |
en_US |
dc.title |
Towards energy-efficient intelligent edge computing |
en_US |
dc.type |
Conference Presentation |
en_US |
dc.description.pages |
6 |
en_US |
dc.description.note |
© 2023 IEEE. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, please consult the publisher's website: https://ieeexplore.ieee.org/document/10389568 |
en_US |
dc.description.cluster |
Next Generation Enterprises & Institutions |
en_US |
dc.description.impactarea |
EDT4IR Management |
en_US |
dc.identifier.apacitation |
Afachao, F., & Abu-Mahfouz, A. M. (2023). Towards energy-efficient intelligent edge computing. http://hdl.handle.net/10204/13642 |
en_ZA |
dc.identifier.chicagocitation |
Afachao, F, and Adnan MI Abu-Mahfouz. "Towards energy-efficient intelligent edge computing." <i>3rd International Conference on Electrical, Computer and Energy Technologies (ICECET 2023), Cape Town, South Africa, 1-17 November 2023</i> (2023): http://hdl.handle.net/10204/13642 |
en_ZA |
dc.identifier.vancouvercitation |
Afachao F, Abu-Mahfouz AM, Towards energy-efficient intelligent edge computing; 2023. http://hdl.handle.net/10204/13642 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - Afachao, F
AU - Abu-Mahfouz, Adnan MI
AB - This research focuses on energy-efficient edge computing in the context of intelligent edge computing. With the increasing demand for computation and data processing at edge network nodes, the study explores the use of artificial intelligence (AI) techniques, specifically reinforcement learning, to enhance energy management. By conducting a comparative analysis of algorithms, including the Markov Decision Process, Q-learning, Fair heuristic, and Random heuristic, on the basis of waiting time of servers, response time, and energy consumption, the research aims to identify the most effective approach for optimizing the intelligent edge system. Simulations using the iFogSim simulator kit provide empirical evidence for performance evaluation. The findings highlight the advantages of AI-based schemes and emphasize the need for further advancements in intelligent edge computing applications.
DA - 2023-11
DB - ResearchSpace
DP - CSIR
J1 - 3rd International Conference on Electrical, Computer and Energy Technologies (ICECET 2023), Cape Town, South Africa, 1-17 November 2023
KW - Internet of Things
KW - IoT
KW - Intelligent edge
KW - Resource management
KW - Reinforcement learning
LK - https://researchspace.csir.co.za
PY - 2023
SM - 979-8-3503-2781-6
T1 - Towards energy-efficient intelligent edge computing
TI - Towards energy-efficient intelligent edge computing
UR - http://hdl.handle.net/10204/13642
ER -
|
en_ZA |
dc.identifier.worklist |
27613 |
en_US |