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Sungwook Moon, Ahmed Helmy Dept. of Computer and Information Science and Engineering

Understanding periodicity and regularity of nodal encounters in mobile networks: A spectral analysis. Sungwook Moon, Ahmed Helmy Dept. of Computer and Information Science and Engineering University of Florida. Contents. Introduction Data sets Methodology Time Series Representation

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Sungwook Moon, Ahmed Helmy Dept. of Computer and Information Science and Engineering

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  1. Understanding periodicity and regularity of nodal encounters in mobile networks: A spectral analysis Sungwook Moon, Ahmed Helmy Dept. of Computer and Information Science and Engineering University of Florida

  2. Contents • Introduction • Data sets • Methodology • Time Series Representation • Auto Correlation • Spectral Analysis • Periodicity in Nodal Encounter • Regular Encounters • Applications & Related Work • Conclusions & Future Work

  3. Introduction • Network Environment • Mobile networks • Communication via wireless signal between the mobile nodes • Basic Definitions • Mobile Nodes • An entity that can move around with wireless communication devices (e.g. PDA, smartphone) • Encounter (contact) [2][3] • Two mobile nodes present within the wireless communication range. (e.g. Bluetooth discovery) • Encounter and contact are used interchangeably in literatures

  4. Introduction • Assumption • Encounter in WLAN • Mobile users using the same access points at the same time. • Commonly used assumption in other literatures [1][3] • Bluetooth encounter • Detected by Bluetooth beacon signal. [1] AugustinChaintreau, Pan Hui, Jon Crowcroft, Christophe Diot, Richard Gass, and James Scott. Impact of human mobility on the design of opportunistic forwarding algorithms. In Proc. IEEE INFOCOM, Apr 2006. [3] W. Hsu, D. Dutta, and A. Helmy. Mining behavioral groups in large wireless lans. In Proc. ACM MobiCom, Sep 2007.

  5. Introduction • Motivations • Efficient and intelligent deployment of mobile networks requires deep knowledge on behavioral pattern of mobile nodes. • Yet, our understanding about the behavioral pattern of mobile nodes is mainly limited to mobility and aggregate information analysis of encounter. • Challenges • Identifying the important spaces to explore among multiple dimensions of variables to understand the behavior of mobile nodes. • Processing different forms of data sets to derive generic encounter behavior of mobile nodes.

  6. Overview • Problem Statement • Can we identify encounter pattern of mobile users? • What are the important dimensional spaces to explore? • How to analyze periodicity of mobile encounter? • Can we utilize the identified characteristics of encounter pattern?

  7. Introduction • Encounter Pattern • Critical information for mobile networking that directly transfers data in the event of encounter. (e.g. Bluetooth data transfer between two nodes). • No need of location information. • Type of analysis • Encountered pairs (i, j) • Encounter of two mobile nodes, i and j • Individual nodal encounter • Aggregate encounter information for each mobile node

  8. Data Sets

  9. Data Sets • Example trace format • Processed WLAN trace format • Encounter trace format for pair (i, j) * MAC address is anonymized before processing.

  10. Contents • Introduction • Data sets • Methodology • Time Series Representation • Auto Correlation • Spectral Analysis • Periodicity in Nodal Encounter • Regular Encounters • Applications & Related Work • Conclusions & Future Work

  11. Periodicity • Methodology • Transform a variety of network traces to encounter trace in a form of time series data. • Analyze periodicity by applying power spectral analysis (autocorrelation(ACF) + Fourier transform). • Practice of power spectral analysis • Analysis of stock market [7] • Analysis of Network traffic [4] [4] AlefiyaHussain, John Heidemann, and Christos Papadopoulos. Identification of repeated denial of service attacks. In Proc. IEEE INFOCOM, Apr 2006. [7] C. Chatfield. Analysis of Time Series, pages 18–24, 105–134. Chapman and Hall, 1989.

  12. Periodicity • Time Domain Representation of Encountered Pattern • Daily encounter • Binary process, = 1, for each encounter count on time d , where d = day (1,2,…T); otherwise = 0 • In our extended report, we analyze about the encounter frequency and encounter duration as well. • Example time series data of daily encounter for an encounter pair (i, j) 1 d (days) 0 5 21 54 72 128 * Sungwook Moon and Ahmed Helmy. Understanding periodicity and regularity of nodal encounters in mobile networks. Technical report, Aug 2010, arxXv:1004.4437.

  13. Periodicity • Example time series data of daily encounter for an encountered pair (i, j) 1 0 5 21 54 72 128 d (days)

  14. Periodicity • Daily encounter rate • Let be a daily encounter for a pair (i, j), such that where T is the observed period. • Analyzing by the encounter rate • Rarely encountering pattern takes up majority of encounter pairs; thus, may hinder other patterns in overall observation if analyzed together. • Therefore, we analyze the encounter pairs by the groups of different encounter rate. • Rarely encountering pairs: (0.1 ≤ Drate < 0.2) • Frequently encountering pairs:(0.5 ≤ Drate < 0.6)

  15. Periodicity • Auto Correlation Function (ACF) • Apply ACF to the time-domain representation of encounter data to find repetitive patterns. • ACF (Auto Correlation Function): a measure of how similar the stream of data is to itself shifted in time by lag k. • k: lag, d: day; T: overall time; λ: avg. encounter rate

  16. Periodicity (encounter pairs) • Various encounter pattern is showing but weekly encounter pattern (lag = 7) shows the strongest pattern. • Some of other lags (i.e. lag =14 and 21) are artifacts of a smaller lag (i.e. lag=7) Figure. Autocorrelation coefficient for each lag at USC encounter trace.

  17. Periodicity • Conversion to frequency domain representation • Converting from time domain to frequency domain shows dominant repetitive pattern more clearly while filtering out the artifacts. • Apply Fourier transform to convert time series data to the frequency domain. • c: frequency component

  18. Periodicity • Frequency domain graph • X axis: frequency component • number of replicas over the observed period of time • e.g. peak observed at 18 of the X-axis indicates that certain pattern has repeated for 18 times over the observed period of time (128 days). • Y axis: normalized frequency magnitude in probability density.

  19. Contents • Introduction • Data sets • Methodology • Time Series Representation • Auto Correlation • Spectral Analysis • Periodicity in Nodal Encounter • Regular Encounters • Applications & Related Work • Conclusions & Future Work

  20. Periodicity (encounter pairs) • Weekly encounter pattern is very strong. (see around 18 at frequency component) 18 Figure. Normalized frequency magnitude for the rarely encountering pairs (0.1 ≤ Drate < 0.2)

  21. Periodicity (encounter pairs) • Weekly encounter pattern is still strong but weaker than rarely encountering pairs. • This frequency of different periodicities can be used for profiling mobile nodes. Figure. Normalized frequency magnitude for the frequently encountering pairs (0.5 ≤ Drate < 0.6)

  22. Periodicity (individual node) • Weekly encounter pattern is stronger than encounter pairs. (see around 18 at frequency component) Figure. Normalized frequency magnitude for the rarely encountering nodes (0.1 ≤ Drate < 0.2)

  23. Periodicity (individual node) • Weekly encounter pattern is stronger than encounter pairs. (see around 18 at frequency component) Figure. Normalized frequency magnitude for the frequently encountering nodes (0.5 ≤ Drate < 0.6)

  24. Periodicity (individual node) • Bluetooth Encounter • Daily encounter pattern is observed. Figure. Individual Bluetooth encounter pattern for the encountered pairs at UF Bluetooth trace (hourly encounter rate)

  25. Contents • Introduction • Data sets • Methodology • Time Series Representation • Auto Correlation • Spectral Analysis • Periodicity in Nodal Encounter • Regular Encounters • Applications & Related Work • Conclusions & Future Work

  26. Regularity • Preliminary investigation. • Utilize periodic properties of encounter pairs. • Regular encounter pattern is stable and consistent pattern over the period of observed time. • E.g. consistent repetition of certain pattern over time. • Discover the pairs showing regular encounter pattern from the periodicity analysis. • Trace analyzed: USC 2006 spring 1 0 d (days) 128

  27. Regularity • Knee appears in the 0.8 area • Approaches to find regularly encountering pairs: • If peak frequency magnitude is in the top 20% in the group. • Regularly encountering pairs show distinctly stronger periodicity with higher frequency magnitude for their top frequency component. USC 2006 trace • Figure. Empirical CDF of the top peaks by daily encounter rate

  28. Regularity • Empirical heuristic approaches (preliminary) • Approach #1: Extracting regularly encountering pairs. • Choose the pairs whose peak frequency magnitude (top peak) is in the top 20% for peak frequency magnitude of all the pairs. • Max1 = max( ) ≥ θ, where θ is threshold for the top 20 % peak frequency magnitude where • Approach #2: Extracting regularly encountering pairs. • Pick top three magnitudes whose sum of frequency magnitudes takes over 30% of overall sum of frequency magnitudes. • Max1 + Max2+Max3 ≥ 0.3 * sum( ), where

  29. Regularity • Behavioral pattern of regularly encountering pairs (on-going investigation ) • Different location access pattern is observed among regularly encounter pairs and normal pairs. • Each of approach #1 and #2 show similar location access pattern. • Figure. Location (AP) access preference by general pairs vs regular pairs (approach #1 = top 20 percent, approach #2 = top 3 frequency magnitudes)

  30. Contents • Introduction • Data sets • Methodology • Time Series Representation • Auto Correlation • Spectral Analysis • Periodicity in Nodal Encounter • Regular Encounters • Applications & Related Work • Conclusions & Future Work

  31. Application • Develop realistic encounter model. • Profiling mobile nodes based on periodic property and embed profile to simulated node or robot node to emulate human behavior. * * Sungwook Moon and Ahmed Helmy. Mobile Testbeds with an Attitude. IEEE GlobeCom, Demo session, Dec 2010.

  32. Application • Classify the mobile users by regularity to create a stable overlay networks. ε (BD) D B ε (CD) ε (AB) ε (BE) ε: regularity metric A ε (BC) E ε (AC) ε (EF) ε (CF) F C Regular encounter Non-Regular encounter

  33. Related Work • Periodicity • Spectral analysis is used in network traffic analysis to discover similar footprints of DDOS attack. [4] • Periodicity study for activities at APs discovers strong periodicity from aggregate APs access pattern and mobility diameter of mobile nodes. [5] Our work is unique in that we use spectral analysis to analyze encounter pairs and individual encounter pattern. [4] AlefiyaHussain, John Heidemann, and Christos Papadopoulos. Identification of repeated denial of service attacks. In Proc. IEEE INFOCOM, Apr 2006. [5] Minkyong Kim and David F. Kotz. Periodic properties of user mobility and access-point popularity. Personal and Ubiquitous Computing, Springer-Verlag, 11, Aug 2007.

  34. Related Work • Encounter: Inter-contact time follows power-law distribution from an analysis of 200 mobile users. [2] • Regularity: Researchers indicate that discovering regular pattern can be useful in predicting behaviors to help routing decision. [6] We analyze the extensive network trace with diverse set of mobile users. Our regularity analysis can help to make an informed decision in predicting encounter behavior. [2] Thomas Karagiannis, Jean-Yves Le Boudec, and Milan Vojnovic. Power law and exponential decay of inter contact times between mobile devices. In Proc. ACM MobiCom, Sep 2007. [6] Pan Hui and Jon Crowcroft. Human mobility models and opportunistic communication system design. Royal Society Philosophical Transactions, (366):1872, Jun 2008.

  35. Contents • Introduction • Data sets • Methodology • Time Series Representation • Auto Correlation • Spectral Analysis • Periodicity in Nodal Encounter • Regular Encounters • Applications & Related Work • Conclusions & Future Work

  36. Conclusions • Contribution • Analyze the encounter pattern for extensive network traces for more than 50,000 mobile users and find mathematical methodology to study periodicity of encounter pattern. • Observe strong periodicity, particularly weekly encounter pattern, for rarely encountering pairs and individual encounter pattern. • Propose two empirical heuristic approaches to discover regularly encounter pattern, and discover regularly encountering pairs show different location visiting behavior than normal pairs.

  37. Future Work • Analyze periodicity of inter-contact time and location access pattern. • Investigation and validation of the methods to discover regular encounter pattern on the diverse set of traces. • Classifying the encountered pairs by periodicity to use in profiling and modeling encounter pattern.

  38. References • [1] AugustinChaintreau, Pan Hui, Jon Crowcroft, Christophe Diot, Richard Gass, and James Scott. Impact of human mobility on the design of opportunistic forwarding algorithms. In Proc. IEEE INFOCOM, Apr 2006. • [2] Thomas Karagiannis, Jean-Yves Le Boudec, and Milan Vojnovic. Power law and exponential decay of inter contact times between mobile devices. In Proc. ACM MobiCom, Sep 2007. • [3] W. Hsu, D. Dutta, and A. Helmy. Mining behavioral groups in large wireless lans. In Proc. ACM MobiCom, Sep 2007. • [4] AlefiyaHussain, John Heidemann, and Christos Papadopoulos. Identification of repeated denial of service attacks. In Proc. IEEE INFOCOM, Apr 2006. • [5] Minkyong Kim and David F. Kotz. Periodic properties of user mobility and access-point popularity. Personal and Ubiquitous Computing, Springer-Verlag, 11, Aug 2007. • [6] Pan Hui and Jon Crowcroft. Human mobility models and opportunistic communication system design. Royal Society Philosophical Transactions, (366):1872, Jun 2008. • [7] C. Chatfield. Analysis of Time Series, pages 18–24, 105–134. Chapman and Hall, • 1989. • [8] Sungwook Moon and Ahmed Helmy. Understanding periodicity and regularity of nodal encounters in mobile networks. Technical report, Aug 2010, arxXv:1004.4437. • [9]Sungwook Moon and Ahmed Helmy. Mobile Testbeds with an Attitude. IEEE GlobeCom, Demo session, Dec 2010.

  39. Questions • Thank you.

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