finding self-similarity in people opportunistic networks ling-jyh chen, yung-chih chen, paruvelli...
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Finding Self-similarity in Finding Self-similarity in People Opportunistic People Opportunistic
NetworksNetworksLing-Jyh Chen, Yung-Chih Chen, Paruvelli Sr
eedevi, Kuan-Ta ChenChen-Hung Yu, Hao Chu
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MotivationMotivation
• Fundamental properties of opportunistic networks are still under investigation.
• Observe inter-contact time distribution to better understand network connectivity.
• The long been ignored censorship issue• Regular people mobility
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ContributionContribution
• Point out and recover censorship existing in opportunistic traces– Propose Censorship Removal Algorithm
(CRA)– Recover censored measurements
• Prove the inter-contact time process as self-similar for future research on opportunistic networks
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OutlineOutline
• Trace Description • Censorship Issue• Survival Analysis• Censorship Removal Algorithm • Self-similarity
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Trace DescriptionTrace Description• UCSD campus trace
– 77 days, 273 nodes involved– Client-based trace using PDAs
• Dartmouth College trace – 1777 days, 5148 nodes involved – 77 days extracted for comparison – Interface-based trace using Wi-Fi adapters
• Basic assumption for a contact – Two nodes are associated to the same AP at the sa
me time period.
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Inter-contact time Inter-contact time
• Time period between 2 consecutive contacts
• Simplest way to observe network connectivity– Disconnection duration– Reconnection/ disconnection frequency – Distribution of inter-contact time
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CensorshipCensorship• Inter-contact time samples end after
the termination of the measurement• Inevitable to have censored data
UCSD Trace
Dartmouth College Trace
Censored Data Censored Data
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Survival AnalysisSurvival Analysis
• Important study in biostatistics, medicine, … – Estimate censored patients’ time to live or d
eath– Map to censored inter-contact time samples
• Censored samples should have the same likelihood distribution as the uncensored’s. – Kaplan-Meier’s Estimator – Survivorship’s Function
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Survival Analysis (Con’t)Survival Analysis (Con’t)• Suppose there are ni events,
di uncensored data at time Ti
• The survival function is
• Survival curve will terminate at the percentage of censored data (UCSD: 7%, Dartmouth: 1.3%)
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Survival Analysis (Con’t)Survival Analysis (Con’t)• Inter-contact time dist power-law dist.
– Ignoring censored data leads to heavy-tail.
Power-law dist.
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Censorship Removal Censorship Removal AlgorithmAlgorithm
• An effective way to recover censored data.– As time goes, uniformly distribute
censored points to their estimated value • Based on survivorship function calculated
– Iteratively mark censored points as uncensored.
– Terminate when all censored measurements are removed.
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Censorship Removal Algorithm (CoCensorship Removal Algorithm (Con’t)n’t)
• Suppose at Tic=Ti, Ci: censored, Di: complete
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Censorship Removal Algorithm (CoCensorship Removal Algorithm (Con’t)n’t)
• Recovered inter-contact time measurements UCSD Trace Dartmouth Trace
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Censorship Removal Algorithm (CoCensorship Removal Algorithm (Con’t)n’t)
• Using extracted trace from Dartmouth College– 77 days with censorship– Compare with 1,777
days
• Compare censored sample’s recovered value to its actual value in 1777 days.
• 80.4% are recovered • Almost identical dist.
as the complete trace
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Self-SimilaritySelf-Similarity
• What is self-similarity? – By definition, a self-similar object is exactly or
approximately similar to part of itself.
• In opportunistic network, we focus on the network connectivity: inter-contact time
• With recovered measurements, we prove inter-contact time series as self-similar process– Periodical reconnection/disconnection – Regular pattern in people opportunistic networks
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Self-SimilaritySelf-Similarity
• A self-similar series– Distribution should be heavy-tailed– Should satisfy three statistical analyses
• Estimated by a specific parameter : Hurst Parameter
• Variance Plot, R/S Plot, Periodogram Plot• H should be in the range of 0.5~1
– Results of three methods should be in the 95% confidence interval of Whittle estimator
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Self-Similarity (Con’t)Self-Similarity (Con’t)• Previous works show inter-contact
time dist. as power-law dist. with heavy-tail
• A random variable X is called heavy-tailed – If , with – Alpha can be found by log-log plot– Survival curves show the alpha for
• UCSD: 0.26• Dartmouth: 0.47
– Both are heavy-tailed distributions
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Self-Similarity (Con’t)Self-Similarity (Con’t)• Variance-Time
Method• For self-similar
processes, the variance decreases very slowly, even when the size grows large
• Using a least square line to fit different aggregation levels (m)
• The Hurst estimates are– UCSD: 0.801– Dartmouth: 0.7973
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Self-Similarity (Con’t)Self-Similarity (Con’t)
• Rescaled Adjusted Range (R/S) method
• A self-similar process should keep similar properties when the dataset is divided into several sub-sets
• The Hurst estimates are– UCSD:0.7472– Dartmouth:0.7493
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Self-Similarity (Con’t)Self-Similarity (Con’t)• Periodogram Method• Use the slope of power spec
trum of the series as frequency approaches zero
• Scattered around a negative slope rather than randomly around a constant – Processes should have non-su
mmable correlations• The Hurst estimates are
– UCSD: 0.7924– Dartmouth: 0.7655
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Self-Similarity (Con’t)Self-Similarity (Con’t)
• Whittle estimator • Usually being considered as
a more robust method• Provide a confidence interval • Results of the three
graphical methods are in the 95% confidence interval.
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ConclusionConclusion
• Two major properties exist in modern people opportunistic networks – Censorship– Self-similarity
• CRA helps recover more accurate datasets• Finding self-similarity helps us design
routing algorithm via specific mobility patterns and discover queuing properties in the opportunistic networks