View in EDS

Ensembled combination of Q-Learning and Deep Extreme learning machine to achieve the high performance and less latency to handle the large IoT and Fog Nodes.

Saved in:
Bibliographic Details
Title: Ensembled combination of Q-Learning and Deep Extreme learning machine to achieve the high performance and less latency to handle the large IoT and Fog Nodes.
Authors: Kumar, Sharan, Kaneti, Venkata Ramana, Sharma, Vandana
Source: Journal of Smart Internet of Things (JSIoT); Dec2024, Vol. 2024 Issue 2, p106-119, 14p
Subject Terms: DECISION making, INTERNET of things, INDUSTRY 4.0, INTERNET security, AUTOMATION, ARTIFICIAL intelligence
Abstract: The proliferation of IoT devices and the adoption of Fog computing architectures have transformed data processing and real-time decision-making across various domains. These advancements enable seamless connectivity and distributed computational power, fostering the development of more intelligent systems. However, managing large-scale IoT and Fog networks presents critical challenges, including high latency, inefficient resource utilization, and scalability limitations, which can undermine system performance. To address these challenges, this research proposes an innovative framework combining Q-Learning and Deep Extreme Learning Machine (DELM). Q-Learning optimizes resource allocation by intelligently learning and adapting to dynamic network conditions, ensuring efficient utilization of resources. It enhances decision-making processes by identifying optimal strategies to manage complex IoT and Fog environments. Meanwhile, DELM provides high-speed and accurate data processing capabilities, enabling it to handle the intensive computational demands of large-scale networks. By leveraging the complementary strengths of these methods, the framework aims to enhance latency, resource utilization, and scalability in large-scale environments. Extensive experimental evaluations validate the framework's effectiveness, demonstrating significant reductions in latency, improved computational efficiency, and enhanced throughput. Furthermore, the framework efficiently handles complex data processing tasks with minimal overhead, making it suitable for diverse real-time applications across IoT and Fog systems. This study highlights the transformative potential of the proposed approach, offering high performance and real-time efficiency for complex, large-scale IoT and Fog computing environments. [ABSTRACT FROM AUTHOR]
Copyright of Journal of Smart Internet of Things (JSIoT) is the property of Sciendo and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Complementary Index
Description
Abstract:The proliferation of IoT devices and the adoption of Fog computing architectures have transformed data processing and real-time decision-making across various domains. These advancements enable seamless connectivity and distributed computational power, fostering the development of more intelligent systems. However, managing large-scale IoT and Fog networks presents critical challenges, including high latency, inefficient resource utilization, and scalability limitations, which can undermine system performance. To address these challenges, this research proposes an innovative framework combining Q-Learning and Deep Extreme Learning Machine (DELM). Q-Learning optimizes resource allocation by intelligently learning and adapting to dynamic network conditions, ensuring efficient utilization of resources. It enhances decision-making processes by identifying optimal strategies to manage complex IoT and Fog environments. Meanwhile, DELM provides high-speed and accurate data processing capabilities, enabling it to handle the intensive computational demands of large-scale networks. By leveraging the complementary strengths of these methods, the framework aims to enhance latency, resource utilization, and scalability in large-scale environments. Extensive experimental evaluations validate the framework's effectiveness, demonstrating significant reductions in latency, improved computational efficiency, and enhanced throughput. Furthermore, the framework efficiently handles complex data processing tasks with minimal overhead, making it suitable for diverse real-time applications across IoT and Fog systems. This study highlights the transformative potential of the proposed approach, offering high performance and real-time efficiency for complex, large-scale IoT and Fog computing environments. [ABSTRACT FROM AUTHOR]
ISSN:29568323
DOI:10.2478/jsiot-2024-0015