machine learning for wireless networks @bestcom2016

Machine learning for improving wireless network performance

Merima Kulin, Eli De Poorter, Dirk Deschrijver, Tom Dhaene and Ingrid [email protected] Internet Based Communication Networks and Services research group (IBCN)- IDLabDepartment of Information Technology (INTEC)Ghent University - imec BESTCOM, 21.10.2016., Louvain

Machine learning for wireless networks• Introduction• Data-driven design: examples• Data science in wireless networks: a

tutorial• Conclusion

2

Why machine learning?• Gartner's Hype Cycle for Emerging Technologies

2015.

2016.

Computationalpower

Massive amounts of data

Unprecedented advances

in ML

Why machine learning?

Data is the new oil!

What kind of data are generating wireless networks?

IoTNetwork

monitoring

Cognitive radio

Wireless networksas data sources

Data Science

What is data science?

Machine learning

Data mining Data analysis

ML algorithm selection

Model Evaluation

Pre-processing

…

Kulin, Merima, Carolina Fortuna, Eli De Poorter, Dirk Deschrijver, and Ingrid Moerman. "Data-Driven Design of Intelligent Wireless Networks: An Overview and Tutorial." Sensors 16, no. 6 (2016): 790.

• Machine learning vs.

• Data mining vs.

• Data science

“Data science is the study of generalizable extraction of knowledge from data”.

Machine learning in wireless networks• Introduction• Data driven design: examples• Data science in wireless networks: a


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Data mining/Machine learning approaches8

Regression Classification

Clustering Anomaly detection

Regression9

Regression

RegressionX Y

Regression - example10

Application area: Localization

Vanheel, F.; Verhaevert, J.; Laermans, E.; Moerman, I.; Demeester, P. Automated linear regression tools improve rssi wsn localization in multipath indoor environment. EURASIP J. Wirel. Commun. Netw. 2011, 2011, 1–27.

RegressionRSSI distance

Classification11

ClassifierX Y

Classification

C1C2

C3

C1

C2

C3

Classification: example12

ClassifierRSSIZigbee

WiFi

Bluetooth

Application area: System recognition

Zheng, Xiaolong, et al. "ZiSense: towards interference resilient duty cycling in wireless sensor networks." Proceedings of the 12th ACM Conference on Embedded Network Sensor Systems. ACM, 2014.

Microwave

-

-

--

Clustering13

ClusteringX ?C1

C2

C3

Clustering

Clustering: example14

Application area: System identification

Shetty, N.; Pollin, S.; Pawełczak, P. Identifying spectrum usage by unknown systems using experiments in machine learning. In Proceedings of the 2009 IEEE Wireless Communications and Networking Conference, Budapest, Hungary, 5–8 April 2009

ClusteringX ?Zigbee

WiFi

Noise

Anomaly detection15

AnomalyX Y/?C1

C2

C3

Anomaly detection

Which DM/ML method can you use?16

X Y

Uluagac, A. Selcuk, et al. "A passive technique for fingerprinting wireless deviceswith wired-side observations." Communications and Network Security (CNS), 2013IEEE Conference on. IEEE, 2013

Application area: ?

Measurements from a device Device type

?Classification

Machine learning in wireless networks• Introduction• Data driven design: examples• Data science in wireless networks: a


17

The knowledge discovery process

The knowledge discovery processStep 1: Understanding the problem domain

Problem formulation Fingerprinting wireless devices Identify devices and device types classification problem

Goal A new solution for

Network Access Control to enhance network security

Assumptions Packet generation is

influenced by hardware architecture (CPU, DMA, L1/L2 cache, ..)

Hypothesis Identify devices and/or

device types based on statistical properties of their traffic flows

Data collection Analyze inter-arrival times

(IATs) from several devices

The knowledge discovery process

Collected data• IAT traces

Validate the data Is the selected data a

representative sample for solving the formulated problem?

Validate the hypothesis Is the stated hypothesis true

and the selected data mining task is likely to prove it?

Step 2: Understanding the data

Device fingerprinting• Data collection

• Data repositories (e.g. CRAWDAD)• Run experiments on testbed facilities• Collect data in situ

Overall 94 files Total ~137 mil. Mean ~1.46 mil. Std ~1.3 mil

Dell

iPad

iPhone

Nokia

Device fingerprintingStep 2: Understanding the data

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Visual techniques Computational techniques

Five number summary Standard deviation, variance skewness Coefficient of determination (R2) Coefficient of correlation …

boxplot

PDF and CDF

Scatter plots

Histograms


• Visual techniques• PDF, time-series, histograms…

23


• Computational techniques• 5-num summary

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• Computational• Coefficient of determination (R2)• Analysis for device identification

25

How much can data from one Dell Notebook tell about the data from other Dell Notebooks?

DN2

DN

3

The knowledge discovery processStep 3: Data pre-processing

Raw data• Traces of IAT

data points

Training data Features extraction Feature vectors Training examples

Device fingerprintingData pre-processing

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• Features extraction

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The knowledge discovery processStep 4: Data mining

Training data• Feature vectors

of histogram bins

Model Neural network

HL=6, α=0.1, learned weights

k-Nearest Neighbors K=1

Decision trees Logistic regression …

ML

31

Device fingerprintingStep 5: Performance evaluation

Test data• Test set of

feature vectorsPerformance indication RMSE, MAE, R2… Precision, Recall, Confusion matrix …

• Algorithm selection: k-fold cross validation

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• Performance evaluation• Confusion matrix• Accuracy, Precision, Recall, accuracy, F1-score

33


34


• Device type classification results

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• Model tuning: neural networks

Kulin, Merima, Carolina Fortuna, Eli De Poorter, Dirk Deschrijver, and Ingrid Moerman. "Data-Driven Design of Intelligent Wireless Networks: An Overview and Tutorial." Sensors 16, no. 6 (2016): 790.

More details about how to tune your algorithm can be found:

Conclusion36

• Data-driven network design can be used for• Failure detection• Systems recognition• Performance optimization…

• Data traces are valuable• Considering releasing data traces after use

• Need for increased collaboration• Network experts, testbed experts, data mining

experts, statisticians, wireless communication, etc.