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Mobility of agents and its impact on data harvesting in VANET

Mobility of agents and its impact on data harvesting in VANET. Kang-Won Lee IBM T. J. Watson Research. Urban sensing in VANETs. MobEyes : VSN-based urban monitoring Traffic reporting, relief to environmental monitoring, distributed surveillance, etc.

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Mobility of agents and its impact on data harvesting in VANET

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  1. Mobility of agents and its impact on data harvesting in VANET Kang-Won Lee IBM T. J. Watson Research NSF Workshop – Mobility in Wireless Networks

  2. Urban sensing in VANETs • MobEyes: VSN-based urban monitoring • Traffic reporting, relief to environmental monitoring, distributed surveillance, etc. • Multiple agents harvest meta-data from regular VSN-enabled vehicles. • Agents collaborate in harvesting and processing data, and searching for key information. • How to coordinate the mobility of multiple agents to collect data effectively ? NSF Workshop – Mobility in Wireless Networks

  3. Multi-agent harvesting problem • Challenges • Dynamic nature of the environment: continuous creation and movement of data • Scale of operation: harvesting region may range over multiple city blocks • Location and the nature of the critical information not known a priori • Approach • Social animals solve a similar problem – foraging to find reliable food sources • Animals solve the foraging problem quite efficiently using simple communications NSF Workshop – Mobility in Wireless Networks

  4. Algorithm design • Data-taxis • Similar to the chemotactic behavior of E. coli • Modes of locomotion: tumble, swim, search • Algorithmic view: greedy approach with random search • Three modes of agent operation • Collision avoidance • Avoids collecting the same data by different agents • Implicit detection vs. pheromone trail • Move in a direction to minimize collision (Levy jump) NSF Workshop – Mobility in Wireless Networks

  5. Evaluation framework • Simulation setup • NS-2 simulation • IEEE 802.11 (11Mbps, 250m) • Manhattan mobility model • Map of 7x7 grid (streets 2 and 6 with valuable information) • Up to 4 agents • Candidate algorithms • RWF (Random Walk Foraging) • BRWF (Biased RWF) • PPF (Preset Pattern Foraging) • DTF (Data-taxis Foraging) 7x7 Manhattan grid NSF Workshop – Mobility in Wireless Networks

  6. Performance results Aggregate number of harvested data NSF Workshop – Mobility in Wireless Networks

  7. Performance results Impact of vehicle speed NSF Workshop – Mobility in Wireless Networks

  8. Performance results Insensitivity of DTF performance to parameters NSF Workshop – Mobility in Wireless Networks

  9. Next Step • Evaluate with different mobility patterns • Map-based mobility, group mobility (e.g. military scenario) • Incorporate road-side infrastructure • Study the reactive query (compared to proactive harvesting) over VSN • Credits • UCLA: Uichin Lee, Mario Gerla • U of Cambridge: Pietro Lio • U of Bologna: Eugenio Magistrett, Paolo Bellavista • This research was sponsored in part by the U.S. ARL and the U.K. MOD under Agreement Number W911NF-06-3-0001. NSF Workshop – Mobility in Wireless Networks

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