Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment
1
College of Computer and Mathematic Sciences, Tikrit University, Tikrit 34001, Iraq
2
College of Computer Science and Information Technology, University of Basrah, Basra 61004, Iraq
3
Instituto de Investigación para la Gestión Integrada de zonas Costeras, Universitat Politècnica de València, Grao de Gandia, Gandia, 46370 Valencia, Spain
4
Instituto de Telecomunicaciones y Aplicaciones Multimedia, Universitat Politècnica de València, 46022 Valencia, Spain
*
Author to whom correspondence should be addressed.
Electronics 2019, 8(6), 607; https://doi.org/10.3390/electronics8060607
Received: 17 March 2019 / Revised: 18 May 2019 / Accepted: 24 May 2019 / Published: 30 May 2019
(This article belongs to the Special Issue Recent Machine Learning Applications to Internet of Things (IoT))
The 5G network is a next-generation wireless form of communication and the latest mobile technology. In practice, 5G utilizes the Internet of Things (IoT) to work in high-traffic networks with multiple nodes/sensors in an attempt to transmit their packets to a destination simultaneously, which is a characteristic of IoT applications. Due to this, 5G offers vast bandwidth, low delay, and extremely high data transfer speed. Thus, 5G presents opportunities and motivations for utilizing next-generation protocols, especially the stream control transmission protocol (SCTP). However, the congestion control mechanisms of the conventional SCTP negatively influence overall performance. Moreover, existing mechanisms contribute to reduce 5G and IoT performance. Thus, a new machine learning model based on a decision tree (DT) algorithm is proposed in this study to predict optimal enhancement of congestion control in the wireless sensors of 5G IoT networks. The model was implemented on a training dataset to determine the optimal parametric setting in a 5G environment. The dataset was used to train the machine learning model and enable the prediction of optimal alternatives that can enhance the performance of the congestion control approach. The DT approach can be used for other functions, especially prediction and classification. DT algorithms provide graphs that can be used by any user to understand the prediction approach. The DT C4.5 provided promising results, with more than 92% precision and recall.
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Keywords:
machine learning; decision tree algorithm; IoT; WSN; C4.5; congestion control; 5G network
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MDPI and ACS Style
Najm, I.A.; Hamoud, A.K.; Lloret, J.; Bosch, I. Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment. Electronics 2019, 8, 607. https://doi.org/10.3390/electronics8060607
AMA Style
Najm IA, Hamoud AK, Lloret J, Bosch I. Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment. Electronics. 2019; 8(6):607. https://doi.org/10.3390/electronics8060607
Chicago/Turabian StyleNajm, Ihab A.; Hamoud, Alaa K.; Lloret, Jaime; Bosch, Ignacio. 2019. "Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment" Electronics 8, no. 6: 607. https://doi.org/10.3390/electronics8060607
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