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Helmy-Mobility-Tutorial-mswim-2

Helmy-Mobility-Tutorial-mswim-2 - Paradigm Shift in...

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Unformatted text preview: Paradigm Shift in Protocol Design – May end up with suboptimal performance or failures due to lack of context in the design Design general purpose protocols Evaluate using models (random mobility, traffic, …) Deployment context: Modify to improve performance and failures for specific context Analyze, model deployment context Design ‘application class’-specific parameterized protocols Utilize insights from context analysis to fine-tune protocol parameters Used to: Propose to: Problem Statement • How to gain insight into deployment context? • How to utilize insight to design future services? Approach • Extensive trace-based analysis to identify dominant trends & characteristics • Analyze user behavioral patterns – Individual user behavior and mobility – Collective user behavior: grouping, encounters • Integrate findings in modeling and protocol design – I. User mobility modeling – II. Behavioral grouping – III. Information dissemination in mobile societies, profile-cast The TRACE framework T race A nalyze E mploy (Modeling & Protocol Design) C haracterize ( C luster) R epresent n t t n x x x x , 1 , , 1 1 , 1 MobiLib Vision: Community-wide Wireless/Mobility Library • Library of – Measurements from Universities, vehicular networks – Realistic models of behavior (mobility, traffic, friendship, encounters) – Benchmarks for simulation and evaluation – Tools for trace data mining • Use insights to design future context-aware protocols? • http://nile.cise.ufl.edu/MobiLib T race Libraries of Wireless Traces • Multi-campus (community-wide) traces: – MobiLib (USC (04-06), now @ UFL) • nile.cise.ufl.edu/MobiLib • 25+ Traces from: USC, Dartmouth, MIT, UCSD, UCSB, UNC, UMass, GATech, Cambridge, UFL, … • Tools for mobility modeling (IMPORTANT, TVC), data mining – CRAWDAD (Dartmouth) • Types of traces: – University Campus (mainly WLANs) – Conference AP and encounter traces – Municipal (off-campus) wireless – Bus & vehicular wireless networks – Others … (on going) T race Wireless Networks and Mobility Measurements • In our case studies we use WLAN traces – From University campuses & corporate networks (4 universities, 1 corporate network) – The largest data sets about wireless network users available to date (# users / lengths) – No bias: not “special-purpose”, data from all users in the network • We also analyze – Vehicular movement trace (Cab-spotting) – Human encounter trace (at Infocom Conf) T race Case study I – Individual mobility Traces Individual user m obility O bservation A pplication U ser groups in the population Encounter patterns in the netw ork M obility m odel Profile-cast protocol Sm allW orld-based m essage dissem ination M icroscopic behavior M acroscopic behavior Case Study I: Goal • To understand the mobility/usage pattern of individual wireless network users • To observe how environments/user type/trace-collection techniques impact the observations...
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