Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment
Abstract
:1. Introduction
2. Related Work
3. Proposed Congestion Control Prediction Approach
3.1. Modeling and Mathematical Formulation
3.1.1. Enhanced Slow-Start
3.1.2. Enhanced Congestion Avoidance
3.2. Development Phase
3.3. Preparing the Training Dataset
3.3.1. Applying the Machine Learning Approach
- Verify if all issues belong to the same class. Thus, a tree is a leaf that is labeled according to class.
- Compute the information gain and the information for a piece of the attribute.
- On the basis of the present selection criterion, locate the optimal splitting attribute.
3.3.2. Model Evaluation
4. Performance Evaluation
4.1. Simulation Setup
4.2. Machine Learning Implementation
4.3. Effect of DT Prediction
4.4. Effect of Delay
4.5. Effect of Jitter
5. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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N | CWND | Throughput | Queue Size | Packet Loss | Optimal |
---|---|---|---|---|---|
1 | 120 | 9,881,687 | 48424 | 209 | N |
. | . | . | . | . | . |
. | . | . | . | . | O |
. | . | . | . | . | . |
100 | cwnd | throu | que | pkt loss | N |
Sequence | Step |
---|---|
1 | K: = empty set of rules |
2 | while not P is empty |
3 | k: = best single rule for P |
4 | K: = add k to P |
5 | remove those instances from P that are covered by k |
6 | return K |
Machine Learning | Algorithm | TP Rate | FP Rate | Precision | Recall | ROC Area | PRC Area |
---|---|---|---|---|---|---|---|
C4.5 | 0.924 | 0.205 | 0.927 | 0.924 | 0.889 | 0.915 | |
DT | RepTree | 0.913 | 0.207 | 0.919 | 0.913 | 0.891 | 0.916 |
Random Tree | 0.913 | 0.207 | 0.919 | 0.913 | 0.891 | 0.916 | |
Clustering | Simple K Means | 0.891 | 0.018 | 0.939 | 0.891 | 0.937 | 0.923 |
Hierarchical Clustering | 0.857 | 0.870 | 0.752 | 0.857 | 0.527 | 0.771 | |
Stacking | Zero + Decision Table | 0.859 | 0.859 | 0.737 | 0.859 | 0.413 | 0.737 |
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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
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 Ahmed, Alaa Khalaf Hamoud, Jaime Lloret, and Ignacio Bosch. 2019. "Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment" Electronics 8, no. 6: 607. https://doi.org/10.3390/electronics8060607
APA StyleNajm, I. A., Hamoud, A. K., Lloret, J., & Bosch, I. (2019). Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment. Electronics, 8(6), 607. https://doi.org/10.3390/electronics8060607