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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This note was uploaded on 05/27/2011 for the course CIS 4930 taught by Professor Staff during the Spring '08 term at University of Florida.

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

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