Abstract
This study examines the problem of allocating resources for edge computing in IoT networks. In this model, each end device acts independently in deciding whether or not to offload compute chores to itself. As network states, the received signal from end nodes and the point of entry, the computation job column, and the leftover information processing source of the end nodes are considered in order to lessen the long-term summation cost, which encompasses the power requirements and the total completion latency. Cloud computing is the foundation of edge computing, which simply relocates processing, archiving, and connecting nodes closer to the data itself. The IoT architecture is akin to cloud computing in every respect. Limiting latency while making optimal use of energy is a major challenge in edge computing when processing activities provided by IoT devices. Deep learning technology is used to the issue of a chain of choices having to be made at the end devices. Starting with the challenges of resource management in cellular IoT and low-power IoT networks, the usual resource management solutions for these systems justify DL approaches. In future, deep learning research will be recommended for IoT network resource organization.
Original language | English |
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Title of host publication | Proceedings of the 2023 2nd International Conference on Electronics and Renewable Systems, ICEARS 2023 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 516-522 |
Number of pages | 7 |
ISBN (Electronic) | 9798350346640 |
DOIs | |
State | Published - 2023 |
Externally published | Yes |
Event | 2nd International Conference on Electronics and Renewable Systems, ICEARS 2023 - Tuticorin, India Duration: 2 Mar 2023 → 4 Mar 2023 |
Publication series
Name | Proceedings of the 2023 2nd International Conference on Electronics and Renewable Systems, ICEARS 2023 |
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Conference
Conference | 2nd International Conference on Electronics and Renewable Systems, ICEARS 2023 |
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Country/Territory | India |
City | Tuticorin |
Period | 2/03/23 → 4/03/23 |
Bibliographical note
Publisher Copyright:© 2023 IEEE.
Keywords
- Edge computing
- Internet of Things (IoT)
- computational task
- deep learning
- resource allocation