Solar joy ghosh sumesh j philip chunming qiao joyghosh sumeshjp qiao @cse buffalo edu
Download
1 / 1

SOLAR Joy Ghosh, Sumesh J. Philip, Chunming Qiao {joyghosh, sumeshjp, qiao}@cse.buffalo - PowerPoint PPT Presentation


  • 93 Views
  • Uploaded on

Key Concepts Every user periodically visits a list of places of social interests (i.e., hubs) Can utilize such mobility information for location approximation and routing Examples (at right): User 1 ( green ), User 2 ( blue ) and User 3 ( red ) attending a conference

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' SOLAR Joy Ghosh, Sumesh J. Philip, Chunming Qiao {joyghosh, sumeshjp, qiao}@cse.buffalo' - anthony-maynard


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
Solar joy ghosh sumesh j philip chunming qiao joyghosh sumeshjp qiao @cse buffalo edu

  • Key Concepts

  • Every user periodically visits a list of places of social interests (i.e., hubs)

  • Can utilize such mobility information for location approximation and routing

  • Examples (at right):

    • User 1 (green), User 2 (blue) and User 3 (red) attending a conference

    • User 3 queries User 2 for the hub list of User 1

    • User 3 sends data to User 1

  • Advantage of Macro-level (hub-based) sociological orbital mobility profile

    • does not require continuous location monitoring

    • does not depend on exact movement in time or space

    • acquaintance-based soft location management

    • captures probabilistic routing in MANET & other networks (e.g., ICN)

  • SOLAR Variations: Ongoing Research

  • Non-probabilistic – Geographic forwarding to hubs

    • SOLAR Sequential – to all hubs in sequence

    • SOLAR Simulcast – to all hubs simultaneously

    • SOLAR Multicast – to a multicast tree of hubs

  • Probabilistic – Intermittently connected networks

    • SOLAR-P – forward to hubs in probabilistic order

    • SOLAR-KSP – K-shortest paths; store & forward routing

SOLAR Simulcast: Location Query and Routing

Conference Track 2

  • Research Issues:

  • Routing Objectives:

    • Maximize data throughput (under energy and memory constraints)

    • Minimize control overhead (number of location queries/updates)

    • Minimize number of logical hops required for each location query

    • Minimize number of acquaintances maintaining throughput

    • Minimize the end-to-end delay (location query + data delivery)

  • Routing Variable:

    • Cache size (number of acquaintances)

    • Logical hop threshold (acquaintance to acquaintance lookup)

    • Hub list discovery probability (reliability of location approximation)

  • Optimization problems:

    • What is the minimum cache size required to achieve a desired

    • discovery probability within a fixed number of search steps?

    • Given a fixed cache size, what is the minimum number of search

    • steps required to achieve desired reliability?

    • What is the probability of Hub list discovery within a fixed number

    • of search steps given a fixed cache size?

Sociological Orbits

City 2

Friends

Level 3

Level 2

Home

Town

City 3

Relatives

Outdoors

Level 1

School

Home

Intermittently

Connected

Networks

Cafe

Cubicle

Kitchen

Porch

Conf. Room

Living

MANET

Conference Track 2

Conference Track 1

Exhibits

  • Query Optimization – Subset of Acquaintances to query

  • Acquaintance Ai has a Hub list Hi = {h1, h2, …, hm} where hi is a hub

  • H = {H1, H2, …, Hn} is the set of hub lists covered by A1, A2, …, An

  • C = H1 U H2 U … U Hn is the set of all hubs covered by A1, A2, …, An

  • Objective: find a minimum subset H’ of H such that:

  • This is a minimum set cover problem – NP Complete

  • Possible solutions: Greedy Set Cover, Primal-Dual Schema, etc.

  • Minimizes the number of queries and optimizes the cache size

Exhibits

Conference Track 1

Conference Track 2

Conference Track 1

Exhibits

Hub A

Performance of SOLAR vs. conventional protocols

Lounge

Conference Track 3

Lounge

Hub B

Lounge

Conference Track 3

Conference Track 3

Registration

Posters

Registration

Registration

Hub E

Posters

Hub D

Posters

Conference Track 4

Conference Track 4

Conference Track 4

Cafeteria

Cafeteria

Cafeteria

Hub C

Hub F

Hub Centers

Green’s IHO: Hubs A, B, C

IHM of individual nodes

SOLAR achieves high throughput, low control (signaling) overhead, and reasonable delay (even for destinations far away)

(a) Geographic forwarding of location query to acquaintance

(b) Geographic forwarding of data to destination

Blue’s IHO: Hubs D, F

IHM: Random Waypoint; IHO: P2P Linear

Red’s IHO: Hubs E, F

SOLARJoy Ghosh, Sumesh J. Philip, Chunming Qiao{joyghosh, sumeshjp, qiao}@cse.buffalo.edu

A Random Orbit Model and its Parameters

Sociological Orbit aware Location Approximation and Routing

Laboratory for Advanced Network Design, Evaluation and Research (LANDER)