1. Detecting Node encounters through WiFi By: Karim Keramat Jahromi 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-Pong Events.  Overlap among coverage areas of different APs.

7.  Detecting Node Encounters  Related Work  “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  “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

8.  Detecting Node Encounters  Wi-Fi Data Set idacessoTimestampAcct_Session_IdAPSTAAcct_Session_TimeAcct_Status_Type 251852703/1/2011 0:0010614Mac 1Mac 7NULLStart 251852713/1/2011 0:00000024B8Mac 2Mac 8NULLStart 251852723/1/2011 0:00000044BBMac 3Mac 894Stop 251852733/1/2011 0:009554Mac 4Mac 959Stop 251852743/1/2011 0:00000067CBMac 5Mac 10NULLStart 251852753/1/2011 0:000000BCD2Mac 6Mac 1061Stop Anonymized part of Wi Fi Trace

9.  Detecting Node Encounters  Workflow for finding Pairs Encounters

10.  Detecting Node Encounters idacessoTimestampAcct_Session_IdAPSTA Access Session Time[s] Acct_Status_Type Start TimeEnd Time 25185270 3/1/2011 0:00:1410614 Mac 1Mac 6 94stop 2011-02-28 23:58:40 2011-03-01 00:00:14 25185271 3/1/2011 0:00:17000024B8 Mac 2Mac 7 59stop 2011-02-28 23:59:18 2011-03-01 00:00:17 25185272 3/1/2011 0:00:17000044BB Mac 3Mac 8 61Stop 2011-02-28 23:59:16 2011-03-01 00:00:17 25185273 3/1/2011 0:00:239554 Mac 4Mac 9 15989Stop 2011-02-28 19:33:54 2011-03-01 00:00:23 25185274 3/1/2011 0:00:25000067CB Mac 5Mac 10 1024stop 2011-02-28 23:43:21 2011-03-01 00:00:25 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 Flag Ping-PongSTAAP_Primary Access Session Time[s] Start TimeEnd Time 0Mac 1Mac 212011-03-02 08:38:072011-03-02 08:38:08 0Mac 1Mac 322011-03-02 17:55:452011-03-02 17:55:47 1Mac 1Mac 212011-03-02 17:55:522011-03-02 17:55:53 1Mac 1Mac 492011-03-02 17:55:552011-03-02 17:56:04 1Mac 1Mac 212011-03-02 17:56:072011-03-02 17:56:08 1Mac 1Mac 412011-03-02 17:56:212011-03-02 17:56:22 0Mac 1Mac 228732011-03-02 17:56:352011-03-02 18:44:28

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 APSTA_ASTA_BEncounter Start TimeEncounter End Time Mac-1Mac-3Mac-72011-03-01 09:40:082011-03-01 10:02:17 Mac-1Mac-3Mac-72011-03-01 10:02:542011-03-01 10:39:34 Mac-2Mac-4Mac-82011-03-02 09:37:572011-03-02 09:38:41 Mac-2Mac-5Mac-82011-03-02 14:14:462011-03-02 15:09:00 Mac-2Mac-6Mac-82011-03-02 10:17:062011-03-02 10:17:19 Mac-2Mac-6Mac-82011-03-02 10:42:052011-03-02 10:42:32 Anonymized part of Pair Encounter List  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).

15.  Detecting Node Encounters  Merged Pairs Encounters APSTA_ASTA_BEncounter Start TimeEncounter End Time Merged Encounter Start Time Merged Encounter End Time MAC-1 MAC-2MAC-32011-03-01 10:50:222011-03-01 10:50:432011-03-01 10:50:222011-03-01 10:50:43 MAC-1 MAC-2MAC-32011-03-01 11:06:482011-03-01 11:07:372011-03-01 11:06:482011-03-01 11:09:02 MAC-1 MAC-2MAC-32011-03-01 11:07:382011-03-01 11:08:01NULL MAC-1 MAC-2MAC-32011-03-01 11:08:012011-03-01 11:08:27NULL MAC-1 MAC-2MAC-32011-03-01 11:08:272011-03-01 11:09:02NULL MAC-1 MAC-2MAC-32011-03-01 11:09:192011-03-01 11:10:352011-03-01 11:09:192011-03-01 11:46:19 MAC-1 MAC-2MAC-32011-03-01 11:10:362011-03-01 11:10:37NULL MAC-1 MAC-2MAC-32011-03-01 11:10:402011-03-01 11:11:28NULL MAC-1 MAC-2MAC-32011-03-01 11:11:312011-03-01 11:28:30NULL MAC-1 MAC-2MAC-32011-03-01 11:28:312011-03-01 11:36:04NULL MAC-1 MAC-2MAC-32011-03-01 11:36:052011-03-01 11:46:19NULL MAC-1 MAC-2MAC-32011-03-01 11:46:382011-03-01 11:52:502011-03-01 11:46:382011-03-01 11:53:15 Anonymized part of merged Pair Encounter List

16.  Detecting Node Encounters  Choosing values for time Thresholds Time Threshold[s]Number of Ping- Pong events Number of distinct APs in WiFi trace Number of distinct APs in encounters list Number of encounters / variation after smoothing (%) Without smoothing-1483685(7050952)-20.9 1148311481685+0.45-24,0 1993611480685+0.90-27.7 2584491480684+1.3-30.4 3532671478683+1.9-34.4 4301371477681+2.4-37.8 3887071477683+2.2-35.4 4410591474683+2.5-36.0 4837011473679+2.7-36.5 4976351473679+2.8-36.7 5065361472679+2.9-37.8

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

19.  Statistics of Pair Encounters 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).  Aggregate Encounter Distribution isn’t always representative of Pair Encounter Distribution.

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  Power law trends illustrate heterogeneity in human movement characteristics.  Human Mobility Connectivity properties show scale free behavior on temporal dimension.  Aggregate Encounter Distribution isn’t always representative of Pair Encounter Distribution.

23.  Questions and Suggestions Thank You