Mobility of agents and its impact on data harvesting in VANET Kang-Won Lee IBM T. J. Watson Research 7/31/20071 NSF Workshop – Mobility in Wireless Networks

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 ? 7/31/20072 NSF Workshop – Mobility in Wireless Networks

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 7/31/20073 NSF Workshop – Mobility in Wireless Networks

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) 7/31/20074 NSF Workshop – Mobility in Wireless Networks

Evaluation framework Simulation setup – NS-2 simulation – IEEE (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) 7/31/20075 NSF Workshop – Mobility in Wireless Networks 7x7 Manhattan grid

Performance results Aggregate number of harvested data 7/31/20076 NSF Workshop – Mobility in Wireless Networks

Performance results Impact of vehicle speed 7/31/20077 NSF Workshop – Mobility in Wireless Networks

Performance results Insensitivity of DTF performance to parameters 7/31/20078 NSF Workshop – Mobility in Wireless Networks

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 /31/20079 NSF Workshop – Mobility in Wireless Networks