Multichannel S-ALOHA-Enabled Autonomous Self-Healing in Industrial IoT Networks
- Authors
- Liu, Jie; Lee, Howon; Jin, Hu
- Issue Date
- Dec-2022
- Publisher
- Institute of Electrical and Electronics Engineers
- Keywords
- Industrial Internet of Things; Throughput; Reliability; Logic gates; Optimization; Resource management; Performance evaluation; Autonomous self-healing; industrial Internet of Things (IIoT); multichannel random access; online estimation
- Citation
- IEEE Transactions on Industrial Informatics, v.18, no.12, pp 8576 - 8585
- Pages
- 10
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE Transactions on Industrial Informatics
- Volume
- 18
- Number
- 12
- Start Page
- 8576
- End Page
- 8585
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/112683
- DOI
- 10.1109/TII.2022.3149908
- ISSN
- 1551-3203
1941-0050
- Abstract
- For industrial Internet of Things network operators, undesired and abrupt network failure is a critical problem to be resolved quickly. In order to provide reliable communication services to devices in faulty cells, in this article, we propose a distributed autonomous self-healing mechanism that allows a random-access-based instantaneous communication to the neighbor cells. The design of the self-healing mechanism is challenged by the diverse device locations and the different available number of channels provided by the neighbor cells due to their intracell traffic load. By estimating the number of devices communicating with each neighbor cell in an online manner, our proposed mechanism can control the channel access probability of each cell to maximize throughput. In addition, the devices can reselect their serving cells in a distributed manner while realizing maximum but fair throughput among devices. Through extensive simulations, we show that our proposed mechanism can provide effective performance for autonomous self-healing.
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Collections - COLLEGE OF ENGINEERING SCIENCES > SCHOOL OF ELECTRICAL ENGINEERING > 1. Journal Articles

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