High-Efficiency Resource Allocation Scheme Introducing the Concept of Resource Sharing Paths in Industrial IoTopen access
- Authors
- Seo, Dongyeong; Jung, Kwansoo; Roh, Hakgyun; Kim, Sangdae; Oh, Seungmin
- Issue Date
- 2023
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- Industrial Internet of Things (IIoT); Industry 40; reliability; resource allocation; resource-efficiency; scalability; scheduling; WirelessHART
- Citation
- IEEE Access, v.11, pp 63821 - 63833
- Pages
- 13
- Journal Title
- IEEE Access
- Volume
- 11
- Start Page
- 63821
- End Page
- 63833
- URI
- https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/25220
- DOI
- 10.1109/ACCESS.2023.3284754
- ISSN
- 2169-3536
- Abstract
- Industrial IoT is a key field of the 4th Industrial Revolution and wireless network technology is essential to support strict industrial requirements. Industrial wireless networks have reliable and real-time data transmission as the main requirement, and to achieve this, the network exploits two schemes: routing graph generation which calculates the data forwarding paths of each node, and scheduling, which allocates the communication time and frequency resources of each node. However, since conventional scheduling approaches allocate resources for all the calculated graphs, specific paths including routes close to the sink are used more redundantly. This phenomenon leads to unnecessary resource wastage and poor scalability in the industrial environment where the number of equipment used increases. To mitigate these problems, we propose a shared path-based scheduling scheme for efficient resource management and scalability improvement by setting the frequently overlapping sections of data flows as resource-sharing paths. The proposed scheme designates the resource sharing path and allocates resources for the shared path in consideration of network statuses, such as congestion and reliability. Simulation results show the proposed scheme is effective in terms of scalability and resource efficiency compared to traditional scheduling.
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Collections - College of Medical Sciences > Department of Medical IT Engineering > 1. Journal Articles
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