MIT Media Lab - Viral CommunicationsP. Ypodimatopoulos Parallel Internets and Ultra-Local Economies...

MIT Media Lab - Viral CommunicationsP. Ypodimatopoulos <[email protected]> Parallel Internets and Ultra-Local Economies

Parallel Internets and Ultra-Local Economies

Polychronis Ypodimatopoulos

Viral Communications group

MIT Media Laboratory

CFP Bi-Annual Meeting

San Jose

January 2008


Project goal

• Organize the presence, profile and social interaction of humans and objects in physical proximity and make it accessible and useful


Social Networking

• Not really new: “The Network Nation”, S. Hiltz, M. Turoff (Addison-Wesley, 1978, 1993)

• Others followed:– USENET– classmates.com– sixdegrees.com– myspace.com– facebook.com– linkedin.com– …


How social really is Facebook ?

• Ypod: 98 friends, 108 total messages

Facebook messages per friend

0

5

10

15

20

25

30

35

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 97

friend ID

# m

essa

ges

Colleague in MOTOROLA

friend in Greece

friend in UK

others: None of people thatI interact with on daily basis

72 friends in MIT:Where is my social

interaction?!?


How social really is Facebook ?

• Facebook usage maximizes between 9pm-12am and plummets between Friday afternoon and Sunday afternoon

• Traffic also increases during summer/winter breaks

“Rhythms of social interaction: messaging within a massive online network”, Golder, Wilkinson, Huberman

Fraction of messages sent to recipients in the same school in 2005

Is Facebook really a tool for initiating social interaction,or for merely maintaining it?


Bottom-up approach to Soc. Networking

• Primary characteristic of social interaction for vast majority of humans?

• Location of participants to most popular social networking tools/platforms?

• Examples:– You can find the location of a building in a city, but have no idea

on which side its entrance is– You look for people with common interests, but fail to discover

those sitting next to you on the train– You live in large apartment building, but have no means of

establishing social interaction with neighbors (other than door2door, if you dare)

– Two strangers at the airport take separate taxis to go to the same location, etc.

Common physical location

Virtual location


Bottom-up approach to Soc. Networking

1. We build a mesh network of humans and objects in physical proximity

2. Each entity participates by means of a device that carries a public profile about its owner (human’s interests, location of a door, etc)

3. The confederation of all profiles in the network yields a new type of data that is specific to the profiles and the location of the entities

4. We can query this data to draw useful information, discover entities based on their location and help establish social interaction


Proposed solution: Cerebro

• Suppose there is a number of users and/or objects in physical proximity



• Cerebro discovers the presence of all other entities and offers asymmetric information resolution about the layout of the network (boosts scalability)



• A profile is stored at each entity and it is accessible throughout the network

• We have organized data that was previously unavailable into useful and accessible information



• Multiple mesh networks tunneled together form a “Parallel Internet”

MIT Media Lab - Viral Communications

Assumptions

• User carries some WiFi device that is (almost) always on• User regularly updates her profile to match her day-to-

day needs/mood/interests

P. Ypodimatopoulos <[email protected]> Parallel Internets and Ultra-Local Economies


The Result

• On the Street– Potential clients are literally

declaring products/services they need

– Discover your peers, combine your (buying) power

– Express any of multiple identities based on different contexts

– Communication in emergency situations



The Result

• At Home– Discover neighbors with

similar interests, share playlists, integrate into TV set

– Integrate into alarm systems and communicate emergency situations to neighbors

• At the Workplace– Organize and search for

know-how by physical location



The Challenges

• Extreme scalability• Efficient search for information• Reflect users social norms onto the behavior of the

device• Security



Progress so far

• Achieving scalability “on a diet”: Connected 100 nodes in mesh network using a single frame per node, per 10 seconds (15kb/sec in the worst case)

• Portability: Cerebro runs on x86, OLPC XO, Nokia N800 and ARM-based embedded computers (python)



Next steps

• Introduce a multi-radio device and demonstrate communication symmetry between humans and objects:– Discover some object (your door at office, your car or your

scooter)– Express one of your identities (by means of RFID)– Establish communication and exchange profiles:

• Get statistics from your home entrance (who’s inside?)

• Sync your MP3s with your car/scooter

• Customize your car/scooter settings



Questions?


MIT Media Lab - Viral CommunicationsP. Ypodimatopoulos Parallel Internets and Ultra-Local Economies...

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