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Detecting Node encounters through WiFi

Detecting Node encounters through WiFi. By: Karim Keramat Jahromi. Supervisor: Prof Adriano Moreira. Co-Supervisor: Prof Filipe Meneses. Oct 2013. Motivation. Analysis of Wi-Fi data for understanding Encounter Pattern can provide significant knowledge about human mobility patterns.

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Detecting Node encounters through WiFi

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  1. Detecting Node encounters through WiFi By: KarimKeramatJahromi Supervisor: Prof Adriano Moreira Co-Supervisor: Prof Filipe Meneses Oct 2013

  2. Motivation • Analysis of Wi-Fi data for understanding Encounter Pattern can provide significant knowledge about human mobility patterns. • Human Mobility Modeling can be used in many application domain: Urban Planning, Social Science, Epidemiology, Network Communications. • In Network Communications, realistic human mobility models have an important role in simulations of wireless networks. • Understanding of nodal encounter patterns have important role in design of protocols and efficient deployment of mobile networks.

  3. Objectives • Detecting Pairs Node Encounters. • Solution for Detecting and Smoothing Ping-Pong Events. • Analyzing Statistics of Pair Encounters.

  4. Physical encounters • Detecting Node Encounters

  5. Observed Encounters • Encounters observed through the Wi-Fi network, using usage logs (RADIUS) • Definition of Encounter: two or more devices connected to the same AP simultaneously. • Direct and Indirect Encounters

  6. Detecting Node Encounters Challenges Nodes aren’t necessarily associated with the geographically nearest AP. Different devices have different aggressiveness for changing association with different APs. Ping-PongEvents. Overlap among coverage areas of different APs .

  7. Detecting Node Encounters Related Work “On Modeling of User Associations in Wireless LAN Traces on University Campuses”, April 2006, by Ahmad Helmy: Study Large scale data Trace Proposing metrics for describing Individual Mobile Node behavior “On Nodal Encounter Patterns in Wireless LAN Traces”, Nov 2010, by Ahmad Helmy: Analyzing multiple wireless LAN traces from university and corporate campuses Looking for understanding encounter patterns using graph analyzing approach

  8. Detecting Node Encounters Wi-Fi Data Set Anonymized part of Wi Fi Trace

  9. Detecting Node Encounters Workflow for finding Pairs Encounters

  10. Detecting Node Encounters Anonymzed Part of Wi-Fi Trace after filtering and adding start time

  11. Detecting and Smoothing Ping-Pong Events Detecting Ping-Pong Events Frequent Change in APs association. Transition Time should be short ( as threshold) . Short Access Session Time ( as threshold ).

  12. Detecting and Smoothing Ping-Pong Events Detecting Ping-Pong Events Access Session Time less than . Transition Time is less than . Occurrence of more than one AP change during +.

  13. Detecting and Smoothing Ping-Pong Events Smoothing Ping-Pong Events Non Ping-Pong APs are kept unchanged. APs which involves in Ping-Pong Intervals, will be replaced by one of nearest Non Ping-Pong APs based on max Access Session Time and/or summation Access Session Time in Ping-Pong Interval.

  14. Detecting Node Encounters Finding Pair Encounters Algorithm for finding pair encounters is based on common definition of Pairs Encounters but after Smoothing Ping-Pong. Usually the number of Pair Encounters is larger than the initial number of Wi-Fi observations (after filtering). Anonymized part of Pair Encounter List

  15. Detecting Node Encounters Merged Pairs Encounters Definition of Threshold Anonymizedpart of merged Pair Encounter List

  16. Detecting Node Encounters Choosing values for time Thresholds The Number of Pair Encounters is affected by the chosen values of and .

  17. Statistics of Pair Encounters Contact Time STA-a STA-a STA-a STA-a STA-b STA-b STA-b STA-b Inter- Contact Time a contact time an inter-contact time Time Comparison of Contact Time distributions for a specific pair of nodes. Comparison of Aggregate Contact Time distributions.

  18. Statistics of Pair Encounters Inter- Contact Time • Comparison of Aggregate Inter Contact Time distributions. Comparison of Inter Contact Time distributions for a specific pair of nodes.

  19. Statistics of Pair Encounters • Aggregate Encounter Distribution isn’t always representative of Pair Encounter Distribution. • Comparison of Aggregate Inter Contact Time distributions. • Inter Contact Time Distributions for a few pairs of nodes with different number of encounter events (3 months).

  20. Scale Free behavior Main properties of human mobility indicate Scale behavior on temporal dimension. • Aggregate Inter Contact Time Distributions on different observation periods. • Aggregate Contact Time Distributions on different observation periods.

  21. Next Step Analysis Periodicity in Pair Encounters Transform List of Pair Encounters into Time Series Analyze periodicity by applying power spectral analysis (autocorrelation (ACF)+ Fourier Analysis ) Calculating of Encounters by considering overlap coverage areas. Strategies for calculating number of encounters

  22. Conclusion and have direct impact on detecting Ping-Pong events and number and Concrete of Encounters. Power law trends illustrate heterogeneity in human movement characteristics. Aggregate Encounter Distribution isn’t always representative of Pair Encounter Distribution. Human Mobility Connectivity properties show scale free behavior on temporal dimension.

  23. Questions and Suggestions Thank You

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