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Analytics for mobile users Dmitry Namiot Lomonosov Moscow State University [email protected]

Analytics for mobile users

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Analytics for mobile users. Analytics for visitors based on the monitoring for mobile phones. Google Adsense for real places and real visitors.

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Page 1: Analytics for mobile users

Analytics for mobile users

Dmitry Namiot Lomonosov Moscow State University

[email protected]

Page 2: Analytics for mobile users

• Analytics for visitors based on the monitoring for mobile phones• The final goal – an analogue for web statistics (a-la Google Analytics), but for the real places and real visitors• Additional services based on the detection of physical presence

About

Page 3: Analytics for mobile users

Contents

Introduction

Passive Wi-Fi monitoring

Players

Directions for research

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Common tasks

• How can we detect the presence for mobile phone?

• Key moment: it should be done without any additional activity from mobile users

• Network proximity: the detection is based on the wireless sensors. Mostly: Wi-Fi. Technically: we can use Bluetooth too.

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Passive Wi-Fi monitoring

• Wi-Fi probe request• Client (even not

connected) can send requests to AP

• AP can analyze requests

• We can collect MAC-addresses for clients

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Advantages and disadvantages

• Does not require special mobile applications • For mobile users it works automatically and transparently• It is anonymous monitoring. MAC address is used for re-identification only. It could be replaced with some hash-code (privacy)• It is not 100% reliable. There is no warranty that Wi-Fi client will send probe request

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Passive monitoring examplesNavizon

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Passive monitoring examples.Cisco MSE

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Passive monitoring examples.Libelium

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Meraki (Cisco)

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Walkbase

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Y-Find

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More players

• AirTight

• Euclid

• Nomi

• Swarm

• Nearbuy

• Boldstreet Wireless

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Spotique

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Spotique

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Future development

• At the end of the day, we expect mobile analytics to be included into almost every Wi-Fi router

• The key moment – additional services based on the physical presence detection.

• For example: detect routes indoor, behavior patterns and unusual behavior, local area messaging