SDN-Enabled Energy-Efficient Routing Optimization Framework for Industrial Internet of Things
Abstract
The traditional Internet architecture relies on the best-effort principle, which is not suitable for critical industrial Internet of Things (IIoT) applications such as healthcare systems with stringent quality-of-service (QoS) requirements. In this article, a software-defined network (SDN) based on an analytical parallel routing framework is proposed by using the massive processing power of a graphics processing unit (GPU) for dynamically optimizing multiconstrained QoS parameters in the IIoT. The framework considers three types of QoS applications for smart healthcare traffic: loss-sensitive, delay-sensitive, and jitter-sensitive. A QoS-enabled routing optimization problem is formulated as a max-flow min-cost problem, while a greedy heuristic that dispatches the path calculation task concurrently to the GPU for calculating optimal forwarding paths considering the QoS requirement of each flow is proposed. The results show that the proposed scheme efficiently utilizes the limited bandwidth cost in terms of energy and bandwidth while satisfying the QoS requirement of each flow with maximizing the network resources for future IIoT traffic flows. Comparative analysis of simulation results with shortest path delay, Lagrangian relaxation-based aggregated cost, and Sway schemes indicate a reduced violation in the service-level agreement by 17%, 19%, and 4%, respectively, by using the AttMpls topology, while it is 48%, 44%, and 7% when the Goodnet topology is used. Moreover, SEQOS is seen to be energy efficient and eight times faster than the benchmark algorithms in large IIoT networks.
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Reliance on centralized servers for data learning makes them vulnerable to security threats and poses a significant threat to patient safety and privacy.
“Furthermore, the reliance on centralized servers for data learning makes them vulnerable and exposed to various types of security threats and thus, poses a significant threat to patient's safety and privacy [4].”
From Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey (2023) · cites this paper as [4]
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Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey
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- Authors
- Naeem, F., Tariq, M., & Poor, H. V.
- Affiliations
- National University of Computer and Emerging Sciences (PK)
- Princeton University (US)
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2021-08
- Volume
- 17
- Issue
- 8
- Pages
- 5660-5667
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W3039292752
- Cited by
- 83
- References
- 22
- Citations by year
- 2026: 5 · 2025: 15 · 2024: 16 · 2023: 16 · 2022: 18 · 2021: 11 · 2020: 2
- FWCI
- 5.52
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Industrial Informatics (publishedVersion)
- Funders
- National Science Foundation (CCF-0939370)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Software-Defined Networks and 5G
- Physical Sciences › Computer Science › Computer Networks and Communications › Network Traffic and Congestion Control
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Advanced Optical Network Technologies
- Keywords
- software-defined networking, Industrial Internet of Things, energy-efficient routing, quality of service, greedy algorithm, delay-sensitive traffic, service level agreements, network resource allocation
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