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Detecting Node encounters through WiFi By: Karim Keramat Jahromi Supervisor: Prof Adriano Moreira Co-Supervisor: Prof Filipe Meneses Oct 2013

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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.

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

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Physical encounters Detecting Node Encounters

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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

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Detecting Node Encounters Challenges Nodes arent 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.

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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

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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

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Detecting Node Encounters Workflow for finding Pairs Encounters

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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

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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 ).

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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

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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.

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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).

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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

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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

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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.

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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.

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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 isnt always representative of Pair Encounter Distribution.

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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.

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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

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Conclusion Power law trends illustrate heterogeneity in human movement characteristics. Human Mobility Connectivity properties show scale free behavior on temporal dimension. Aggregate Encounter Distribution isnt always representative of Pair Encounter Distribution.

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Questions and Suggestions Thank You