2022
DOI: 10.1109/access.2022.3203070
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SHE Networks: Security, Health, and Emergency Networks Traffic Priority Management Based on ML and SDN

Abstract: Recently, the increasing demand to transfer data through the Internet has pushed the Internet infrastructure to the final edge of the ability of these networks. This high demand causes a deficiency of rapid response to emergencies and disasters to control or reduce the devastating effects of these disasters. As one of the main cornerstones to address the data traffic forwarding issue, the Internet networks need to impose the highest priority on the special networks: Security, Health, and Emergency (SHE) data t… Show more

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Cited by 14 publications
(10 citation statements)
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References 24 publications
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“…Interesting novelty is the proposal of automatic selection of only selected bits of packets headers, which can reduce the number of features, and thus the processing time and energy consumption, at the same time giving satisfactory accuracy. In [26] Yaseen et al propose usage of a similar system to classify traffic and assign a DSCP field. The system has been implemented within an SDN controller and evaluated in the Mininet emulator in an emergency traffic prioritization scenario.…”
Section: Related Workmentioning
confidence: 99%
“…Interesting novelty is the proposal of automatic selection of only selected bits of packets headers, which can reduce the number of features, and thus the processing time and energy consumption, at the same time giving satisfactory accuracy. In [26] Yaseen et al propose usage of a similar system to classify traffic and assign a DSCP field. The system has been implemented within an SDN controller and evaluated in the Mininet emulator in an emergency traffic prioritization scenario.…”
Section: Related Workmentioning
confidence: 99%
“…To optimize the network design energy efficiency, they assigned computational resources and the block size together to be considered as the confidence components of SDN controllers and the resource needs of non-encrypted operations. According to [19], a novel priority management of a network flow had suggested depending on SDN and ML to optimize network traffic with high control by assigning a necessary traffic flow priority. The proposal realized flow prioritizing by selecting specific bits from the packet's header by utilizing the ML to prioritize data forwarding based on the priority levels by controlling the Differentiated Services Code Point (DSCP).…”
Section: Related Workmentioning
confidence: 99%
“…The DSCP is the eight bits of the traffic class field that includes six bits of DSCP to manage the priority packet category. The remaining two bits are Explicit Congestion Notification (ECN) priority weights that split into two scopes; non-congestion and congestion control traffic [19]. In our proposal, the semi-supervised machine learning technique based on a simple One Dimension Convolution Neural Network (1D-CNN) [37] receives the public key, link load, router status, and data traffic prioritization as input parameters.…”
Section: ML Functionsmentioning
confidence: 99%
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“…During the restoration phase, it is used in resource distribution for beneficiary identification and inventory monitoring. In combination with artificial intelligence [38], data analysis and virtual reality, IoT can contribute to disaster forecasting and predictive maintenance for infrastructure protection in the mitigation phase.…”
Section: Background and Related Workmentioning
confidence: 99%