4.7 Article

Exploiting Trajectory-Based Coverage for Geocast in Vehicular Networks

Journal

IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
Volume 25, Issue 12, Pages 3177-3189

Publisher

IEEE COMPUTER SOC
DOI: 10.1109/TPDS.2013.2295808

Keywords

Vehicular networks; geocast; trajectory-based; encounter prediction

Funding

  1. China 973 Program [2014CB340303]
  2. NSFC [61170238, 60903190, 61027009, 60933011, 61202375]
  3. Doctoral Fund of Ministry of Education of China [20100073120021]
  4. National 863 Program [2009AA012201, 2011AA010500, 2013AA01A601]
  5. HP IRP [CW267311]
  6. SJTU SMC Project [201120]
  7. STCSM [08dz1501600, 12ZR1414900]
  8. Shanghai Chen Guang Program [10CG11]
  9. Singapore NRF [CREATE E2S2]
  10. MSRA
  11. Program for Changjiang Scholars and Innovative Research Team in University(PCSIRT), China [IRT1158]
  12. Direct For Computer & Info Scie & Enginr
  13. Division Of Computer and Network Systems [1239483] Funding Source: National Science Foundation

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Geocast in vehicular networks aims to deliver a message to a target geographical region, which is useful for many applications such as geographic advertising. This is a highly challenging task in vehicular network environments due to the rare encounter opportunities and uncertainty caused by vehicular mobility. As more vehicles are equipped with on-board navigation systems, vehicle trajectories are ready for exploitation. We observe that a vehicle has a higher capability of delivering a message to the target region if its own future trajectory or trajectories of those vehicles to be encountered overlap the target region. Motivated by this observation, we develop a message forwarding metric, called coverage capability, to characterize the capability of a vehicle to successfully geocast the message. When calculating the coverage capability, we are facing the major challenge raised by the absence of accurate vehicle arrival time. Through an empirical study using real vehicular GPS traces of 2,600 taxis, we verify that the travel time of a vehicle, which is modeled as a random variable, follows the Gamma distribution. The travel time modeling helps us to make accurate predictions for inter-vehicle encounters. We perform extensive trace-driven simulations and the results show that our approach achieves 37.4 percent higher delivery ratio and 43.1 percent lower transmission overhead comparing with GPSR which is a representative geographic routing protocol.

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