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FleaNet : A Virtual Market Place on Vehicular Networks. Uichin Lee, Joon-Sang Park Eyal Amir, Mario Gerla Network Research Lab, Computer Science Dept., UCLA. Advent of VANETs. Emerging VANET applications Safety driving (e.g., TrafficView) Content distribution (e.g., CarTorrent/AdTorrent)

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Fleanet a virtual market place on vehicular networks

FleaNet : A Virtual Market Place on Vehicular Networks

Uichin Lee, Joon-Sang Park Eyal Amir, Mario Gerla

Network Research Lab, Computer Science Dept., UCLA

Advent of vanets
Advent of VANETs

  • Emerging VANET applications

    • Safety driving (e.g., TrafficView)

    • Content distribution (e.g., CarTorrent/AdTorrent)

    • Vehicular sensors (e.g., MobEyes)

  • What about commerce “on wheels”?

Flea market on vanets
Flea Market on VANETs

  • Examples

    • A mobile user wants to sell “iPod Mini, 4G”

    • A road side store wants to advertise a special offer

  • How to form a “virtual” market place using wireless communications among mobile users as well as pedestrians (including roadside stores)?


  • FleaNet architecture

  • FleaNet protocol design

  • Feasibility analysis

  • Simulation

  • Conclusions

Fleanet architecture system components
FleaNet Architecture-- System Components

  • Vehicle-to-vehicle communications

  • Vehicle-to-infrastructure (ad-station) communications

* Roadside stores (e.g., a gas station)

Fleanet architecture query formats and management
FleaNet Architecture -- Query Formats and Management

  • Users express their interests using formatted queries

    • eBay-like category is provided

      • E.g., Consumer Electronics/Mp3 Player/Apple iPod

  • Query management

    • Query storage using a light weight DB (e.g., Berkeley DB)

    • Spatial/temporal queries

    • Process an incoming query to find matched queries (i.e., exact or approximate match)

      • E.g. Query(buy an iPod)  Query(sell an iPod)

Fleanet protocol design
FleaNet Protocol Design

  • FleaNet building blocks

    • Query dissemination

    • Distributed query processing

    • Transaction notification

      • Seller and buyer are notified

      • This requires routing in the VANET

  • VANET challenges

    • Large scale, dense, and highly mobile

  • Goal: designing “efficient, scalable, and non-interfering protocols” for VANETs

Query dissemination
Query Dissemination

  • Query dissemination exploiting vehicle mobility

  • Query “originator” periodically advertises its query to 1-hop neighbors

    • Vehicles “carry” received queries w/o further relaying

Yellow Car w/ Q1



Red Car w/ Q2



Distributed query processing



Distributed Query Processing

  • Received query is processed to find a match of interests

    • Eg. Q1 – buy iPod / QM – sell iPod / Q2 – buy Car

(1) Find a matching query for Q2 No match found



Cyan car w/ QM

(2) Send a match notification msg to the originator of query QM



(1) Find a matching query for QM

 Found query Q1


Red car w/ Q2& carries Q1

Transaction notification



Transaction Notification

  • After seeing a match, use Last Encounter Routing (LER) to notify seller/buyer

    • Forward a packet to the node with more “recent” encounter




Green car

Cyan car

Yellow car



Blue car





Originator of Q1

Red car


Current Time: T

Encounter timestamp

Fleanet latency
FleaNet Latency

  • Restricted mobility patterns are harmful to opportunistic data dissemination

  • However, latency can be greatly improved by the popularity of queries

  • Popularity distribution of 16,862 posting (make+model) in the vehicle ad section of Craigslist (Mar. 2006)

Frequency (log)

Items (log)

Fleanet scalability
FleaNet Scalability

  • Assume that only the query originator can “periodically” advertise a query to its neighbors

  • We are interested in link load

  • Load depends only on average number of neighbors and advertisement period (not on network size)

  • Example:

    • Parameter setting : R=250m, 1500B packet size, BW=11Mbps

    • N=1,000 nodes in 2,400m x 2,400m (i.e., 90 nodes within one’s communication range)

    • Advertisement period: 2 seconds

    • Worst case link utilization: < 4%


  • Ns-2 network simulator

  • 802.11b - 2Mbps, 250M radio range

  • Two-ray ground reflection model

  • “Track” mobility model

    • Vehicles move in the 2400mx2400m Westwood area in the vicinity of the UCLA campus

  • Metric

    • Average latency: time to find a matched query of interest

Westwood area, 2400mx2400m

Simulation results
Simulation Results

  • Impact of density and speed

Simulation results1
Simulation Results

  • Impact of query popularity

    • Popularity: the fraction of users with the same interest

    • For a single buyer, increase the number of sellers (e.g., N=200/0.1 = 20 sellers)

Simulation results2

avg. stationary

avg. mobile

Simulation Results

  • Impact of ad-station location

    • Given N=100, fix each node in its initial location, and set it as a “stationary” ad-station (as a buyer)

    • measure the average latency to the remaining 99 mobile nodes (run 99 times, by taking turns as a seller: 1 buyer  1 seller)


Latency rank


  • Proposed a virtual market concept in VANETs:

    • A mix of mobile and stationary users carry out buy/sell transactions (or any other matching of common interests) using vehicular networks

  • Mobility-assisted query dissemination and resolution (scalable and non-interfering)

    • Node density/speed are closely related to the performance

    • Popularity of a query greatly improves the performance

    • Location of an ad-station is important to the performance

  • Future work

    • Query aggregation to improve the performance

      • Unpopular queries/queries from ad-stations

    • How to enforce cooperativeness of users?

    • Security: false query injection and spamming?