Abstract. Short-time traffic flow prediction in particular systems will expedite discovering of an optimal path for packet transmitting in dynamic wireless networks. The main goal is to predict traffic overload while changing a network topology. Machine learning techniques and process mining can help analyze traffic produced by several moving nodes. Several related approaches are observed. Research framework structure is presented. The idea of process mining approach is proposed.

1. Process Mining Approach for Traffic
Analysis in Wireless Mesh Networks
E.Kalishenko, K.Krinkin, S.P.Shiva Prakash
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2. Research goals
Long-term
 Develop and implement effective traffic prediction
methods and topology suggesting algorithm for
WMN;
Short-term
 Create a framework for traffic analysis in WMS
based on process mining;
 Investigate and identify real-life traffic patterns;
 Implement topology suggesting scheme;
 Enable power awareness in routing.
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3. Problem on the picture
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4. Existing approaches
• Wavelet neural
networks
• Clustering Approach
• Graph Mining
• Time series analysis
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5. Time series analysis
 widely used for WMN analysis
 input sequences like
– bytes per time period
– drops per time period
 statistical processing and prediction
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6. Wavelet-neural networks
 network as a three-layer structure
– P inputs for P values of time series;
– N neurons on hidden layer;
– one output neuron which presents predisction for time
series;
 Scheme allows predict next parameter value by
fixed history (time series length).
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7. Clustering
 Clustering is based on threshold value for cells in
cellular networks;
 Euclid's distance is used as a metric;
 Network topology depends on geographical
positions leaders of clusters.
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8. Limitations
 Wavelet neural networks:
– not efficient if lack of representative traffic statistics;
 Clustering:
– works only for fixed topologies, doesn't work for WMN;
 Graph mining:
– Resource intensive, mainly dedicated for dynamic
topologies;
 Time series:
– takes into account only internal traffic, mostly with periodic
nature
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9. Process mining in two words
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10. Process mining for WMN
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11. ProM – process analysis tool
 Control-flow Discovery
 Organizational Mining
 Conformance Analysis / Process Model Evaluation
 Performance Analysis
 Simulation
 Process redesign
 Semantic Process Mining
 Analysis / Verification
 see: processmining.org
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12. Basic steps
 Network modeling (NS-3) – real life or synthetic
processes can be used;
 Trace transformation – converting NS-3 output to
ProM input format;
 Process extraction (ProM) – significant processes
identification and classification;
 Template search – checking in Db similar
processes or patterns;
 Recommendations;
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13. Implementation details
MXML plugin MXML
logs
NS-3 event callbacks
NS-3
Simulation ProM
analysis
XML «pcap» Wireshark
report traces analysis
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14. Applications
 Planning Wireless Mesh Backbone;
 Adaptive topology reconstruction;
 Traffic optimization;
 Enabling power save awareness in routing
protocols;
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15. Current work status
 Framework for modeling;
 Data extractor (MXML) plugin;
 Network configurator (preliminary version):
– topology;
– node moving;
– traffic generators;
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16. Under development
 Real-life traffic extractor (based on wireshark);
 Network visualizer;
 Topology advising algorithm;
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17. Long-term plans
 Connection with city maps:
– roads;
– access points;
 Power awareness implementation in routing;
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18. Contacts and references
 NS3 homepage http://www.nsnam.org/
 ProM homepage: http://www.processmining.org/
 MXML sources: https://github.com/OSLL/promin
 Project tracker: http://osll.spb.ru/projects/promin
 email: promin@osll.spb.ru
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19. Questions & Answers
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20. ===BACKUP SLIDES===
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21. Simple test network
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22. Petri Network and Events
Network
 Start send packet
 Packet dropped
 Packet transmitted...
Moving
 Distance changed...
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23. Possibilities
Analysis and Optimization
– Conformance checking
– Repairing models
– Extending the model with frequencies and temporal information
– Constructing predictive models
– Operational support (prediction, recommendation, etc.)
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24. Results & Further Work
Current Results
 Set of NS-3 simple dynamic mesh networks
 MXML plug-in for NS-3 as a library
 Network process is extracted by some algorithms in ProM framework
 Some algorithms are marked as improper
Further Work
 Elaborate an algorithm for routes optimization
 Implement routing metric in the mesh-network routing protocol in NS-3
 Integrate metric with QoS service
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25. Process mining in two words
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29.08.12
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