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

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Presentation transcript:

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.  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.

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

 Physical encounters  Detecting Node Encounters

 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

 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.

 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

 Detecting Node Encounters  Wi-Fi Data Set idacessoTimestampAcct_Session_IdAPSTAAcct_Session_TimeAcct_Status_Type /1/2011 0: Mac 1Mac 7NULLStart /1/2011 0: B8Mac 2Mac 8NULLStart /1/2011 0: BBMac 3Mac 894Stop /1/2011 0:009554Mac 4Mac 959Stop /1/2011 0: CBMac 5Mac 10NULLStart /1/2011 0:000000BCD2Mac 6Mac 1061Stop Anonymized part of Wi Fi Trace

 Detecting Node Encounters  Workflow for finding Pairs Encounters

 Detecting Node Encounters idacessoTimestampAcct_Session_IdAPSTA Access Session Time[s] Acct_Status_Type Start TimeEnd Time /1/2011 0:00: Mac 1Mac 6 94stop :58: :00: /1/2011 0:00: B8 Mac 2Mac 7 59stop :59: :00: /1/2011 0:00: BB Mac 3Mac 8 61Stop :59: :00: /1/2011 0:00: Mac 4Mac Stop :33: :00: /1/2011 0:00: CB Mac 5Mac stop :43: :00:25 Anonymzed Part of Wi-Fi Trace after filtering and adding start time

 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 ).

 Detecting and Smoothing Ping-Pong Events  Detecting Ping-Pong Events Flag Ping-PongSTAAP_Primary Access Session Time[s] Start TimeEnd Time 0Mac 1Mac :38: :38:08 0Mac 1Mac :55: :55:47 1Mac 1Mac :55: :55:53 1Mac 1Mac :55: :56:04 1Mac 1Mac :56: :56:08 1Mac 1Mac :56: :56:22 0Mac 1Mac :56: :44:28

 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.

 Detecting Node Encounters  Finding Pair Encounters APSTA_ASTA_BEncounter Start TimeEncounter End Time Mac-1Mac-3Mac :40: :02:17 Mac-1Mac-3Mac :02: :39:34 Mac-2Mac-4Mac :37: :38:41 Mac-2Mac-5Mac :14: :09:00 Mac-2Mac-6Mac :17: :17:19 Mac-2Mac-6Mac :42: :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).

 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 :50: :50: :50: :50:43 MAC-1 MAC-2MAC :06: :07: :06: :09:02 MAC-1 MAC-2MAC :07: :08:01NULL MAC-1 MAC-2MAC :08: :08:27NULL MAC-1 MAC-2MAC :08: :09:02NULL MAC-1 MAC-2MAC :09: :10: :09: :46:19 MAC-1 MAC-2MAC :10: :10:37NULL MAC-1 MAC-2MAC :10: :11:28NULL MAC-1 MAC-2MAC :11: :28:30NULL MAC-1 MAC-2MAC :28: :36:04NULL MAC-1 MAC-2MAC :36: :46:19NULL MAC-1 MAC-2MAC :46: :52: :46: :53:15 Anonymized part of merged Pair Encounter List

 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 ( ) ,

 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.

 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.

 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.

 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.

 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

 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.

 Questions and Suggestions Thank You