Wireless Gossiping for Social Networking

Maarten van Steen

VU University AmsterdamDepartment of Informatics

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Overview• Background

• Wireless gossiping:– from point-to-point to broadcasting

• Social computing– detection of social interactions– dissemination of observed interactions

• Social games • Some experiments• Stimulating social mingling

• Crowd management• Measuring densities• A hybrid approach

• Outlook

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Wireless gossiping• We’ve been working a lot on point-to-point gossiping

protocols in wired systems• Wireless systems:

• broadcasting is the default, not point-to-point• links are often unreliable and asymmetric• no random selection across all the nodes

QuestionCan we mimic the behavior of p2p gossip while using broadcasting?

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From p2p to broadcast

Inputbuffer

Datastore

Outputbuffer

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From p2p to broadcast

Inputbuffer

Datastore

Outputbuffer

Receivebroadcasts

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From p2p to broadcast

Inputbuffer

Datastore

Outputbuffer

Updateinput buffer

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From p2p to broadcast

Inputbuffer

Datastore

Outputbuffer

Updatedata store

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From p2p to broadcast

Inputbuffer

Datastore

Outputbuffer

Broadcast

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Main operations1. Handle incoming messages

• Remove duplicates• Keep latest

2. Update data store• If repeated, remove item• Else if space available, then store• Else replace randomly chosen item• Replaced items are moved to output buffer, if room

3. Select items to broadcast• Output buffer contains items removed from store• If space, publisher can insert own item

NoteGoal is to reduce correlation between store[t] and store[t+1]

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Chalcedony nodes

• LED display• microphone• accelerometer• XMEGA128 processor• 8 kB SRAM/128 kB flash• slot for 8 MB SD card

• logging!

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Social games: detecting interactions• Simply measure and display social interactions:

• Ranges are kept short, such that only face-to-face encounters will lead to mutual detection of nodes

• Measure minimal duration and interpret as interaction• Nodes broadcast detections, which are sniffed and

forwarded to visualizer

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Social games: detecting interaction

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Social games: stimulating mingling• Assume people belong to different groups• If Alice from group 1 interacts with Bob from group 2,

Alice and Bob will earn credits for their group.• Group credits are disseminated to be seen by every

member.• Goal: earn as many credits for your group as possible.• Technical issues:

• Efficient dissemination schemes• Accurate interaction measurements

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Speculative: crowd management• Measure social interactions

• Can we identify social (sub)groups?• To what extent can we detect movements?

• Pick up and process audio and other signals• Can we pick up sounds that indicate potentially dangerous

situations (screams, gunfire, etc)• Measure (local) densities

• High density areas may indicate dangerous situations

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Speculative: crowd management• Combine in-network and offline processing

W ireless distributed network

Applications

S ensors/ actuators

Externalfeedbacksystem

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Outlook• Coming months:

• Turning the prototype testbed into a robust system• Implementing a few social games• Concentrate on reliable sampling of network traffic

• In 2011:• Large-scale trace-driven simulations• Concentrate on:

• supporting and realizing ultra-low duty cycling• automatic reintegration of partitioned networks

• Speculative:• Real-world experiments on 5000+ node networks