WiFi-Reports: PowerPoint Presentation

WiFi-Reports: PowerPoint Presentation

2016-07-02 52K 52 0 0

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Improving Wireless Network Selection with Collaboration. Presented. . By Tim McDowell. WiFi-Reports. Authors: . Jeffrey Pang, Carnegie Mellon University. Srinivasan. . Seshan. , Carnegie Mellon . University. ID: 387337

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Presentations text content in WiFi-Reports:

Slide1

WiFi-Reports:

Improving Wireless Network Selection with Collaboration

Presented

By Tim McDowell

Slide2

WiFi-Reports

Authors:

Jeffrey Pang, Carnegie Mellon University

Srinivasan

Seshan

, Carnegie Mellon

University

Michael

Kaminsky

, Intel Research Pittsburgh

Ben Greenstein, Intel Research Seattle

Damon McCoy, University of Colorado

Slide3

Overview

Background

Setup

Experimental Results

Discussion

Future Work

Conclusions

Slide4

Background: The Problem

Figure 1: Measured Hotspots Near the University of Washington

Slide5

Background: The Problem

Commercial Access

Points

May block certain applications

(e.g. SSH, File-sharing, Online Games)

Often exhibit poorer than advertised

performance

‘Official AP’ i

s not always best!

Slide6

Background: The Problem

Figure 2: (a) AP Connection Success Rate, (b) TCP Download Speed, (c) google.com Fetch Time

Slide7

Setup

Official AP is shown to have the lowest median latency ~70% of the time.

Why should we care about the remaining 30%?

Slide8

Setup

Performance depends on many factors

Ranking depends on application requirements

Reports include estimated back-haul capacity, ports blocked, and connectivity failures

Historical information,

{AP, SNR, date, connectivity

}, is collected and sent to a central database

Slide9

Setup

Figure 3:

Wifi

-Reports Components & Procedure

Slide10

Setup: Key Challenges

Maintain user privacy

Limit Fraudulent Reports

Slide11

Setup: Privacy Issues

Want to report on AP without revealing user’s location

‘Blind signature’ allows users to remain anonymous while preventing fraud.

Anonymous data is matched to location using existing war-driving databases.

Introduces processing overhead

Slide12

Setup: Privacy Issues

Table 1: Blind Signature Method Processing Times (ms)

Slide13

Setup: Preventing Fraud

Client must contact account authority to obtain credentials (email verification, text message, etc.) before reporting on an AP

Prevents a single user from submitting many fraudulent reports

Using median performance values allows reports to be robust to a small fraction of outliers

Slide14

Experimental Results

Figure 4: Box Plots of (a) TCP Download Speed & (b) google.com Fetch Time for 5 Different AP selection methods

Slide15

Experimental Results: Handling Fraudulent Reports

Figure 5: CDF of AP Prediction Accuracy for Varying Percentages of Fraudulent Reports

Robust enough to handle some frauds

Users lured to poor APs will submit correct reports

Slide16

Discussion: Limitations

AP performance still varies over

time

Blind signature introduces significant computation overhead in order to maintain privacy & prevent fraud

Persistent attackers can join together and submit fraudulent reports independently

Slide17

Discussion: Limitations

Selection is subjective (depends on cost, venue, service providers’ reputations)

Historical information looses accuracy over time, does not account for new APs

Not beneficial in regions with sparse

Wifi

coverage

Heavily reliant on war-driving

Slide18

Future Work

Geo-locating moving access points to deal with mobile users.

Improve robustness to fraud

More accurate location determination

Slide19

Conclusions

Wifi

Reports allows users to make informed decisions in areas of dense AP coverage

Users must decide for themselves if freedom of AP choice is worth the extra overhead

Slide20

Questions?

Slide21

Slide22

Slide23

Slide24


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