Performance Evaluation of Vehicular DTN Routing under Realistic

							  Performance Evaluation of
   Vehicular DTN Routing
under Realistic Mobility Models
          Pei’en LUO
                             Abstract
•   In performance studies of vehicular ad hoc networks (VANETs), the
    underlying mobility model plays an important role. Since conventional
    mobile ad hoc network (MANET) routing protocols do not work efficiently in
    vehicular environments due to the rapid topology changes, the Delay-
    Tolerant Network (DTN) model is often applied.
•   In this paper, we construct a new mobility model, the Shanghai Urban
    Vehicular Network (SUVnet) model by using the GPS data from more than
    4,000 taxis we have collected, and then investigate the performance of two
    kinds of DTN routing, the non-geographic pure epidemic routing and our
    newly-proposed geographic DTN routing, the Distance-Aware Epidemic
    Routing (DAER).
•   We use the popular random waypoint mobility model and a more complex
    microscopic traffic simulator generated model for performance comparison.
    With the two considered DTN routing protocols, conventional mobility
    models tend to give higher performance results than SUVnet model, the
    presumably more realistic mobility model.
        Contents
1. Issue with mobility models
    2. The SUMO project
       3. The SUVNet
       4. DTN Routing
 5. Performance simulation
        6. Conclusion
    Issue with mobility models
• Most previous works generate traces by
  probabilistic methods: RWP, RD…speed
  of mobile nodes is chosen randomly and
  constantly
  – A vehicle does NOT move freely in a specified
    area
  – Vehicles are NOT always moving at a
    constant speed
  – Traffic rules also limit the movements of
    vehicles
    Issue with mobility models
• More complex mobility models are
  developed
  – Limit nodes' movements in defined grids
  – Car-to-car interaction
  – Several tools & projects: BonnMotion tool,
    GEMM tool, MONARCH project
         The SUMO project
• The Simulation of Urban MObility (SUMO)
  is an open source, microscopic, space-
  continuous traffic simulator designed to
  handle large road networks.
• The car microscopic movement model in
  SUMO is a car following model and
  includes a stochastic traffic assignment
  modeled by a probabilistic route choice
  according to driver models.
         The SUMO project
• Generate traces from SUMO:
  – Build our road network description with the
    city map data
  – Describe explicit vehicle routes
  – Perform the traffic simulation and dump the
    mobility output trace
            The SUVNet
We try to obtain a trace data set from the
        live real GPS data received
     The SUVNet -Methodology
• Data collection
   – ~4,000 taxis
   – 20-40 sec interval
   – Coordinate, Timestamp, Direction, ID
     The SUVNet -Methodology
• Map-matching
   – Locating GPS data onto a road network
     map
   – Choose the road segment with least
     distance to a taxi and must comply with the
     headway direction
   – Error may occur
    The SUVNet -Methodology
• Route-finding
   – Find the route between two data points
   – Choose the suitable (possible) route
   – Reasonable accuracy: 92%
                DTN Routing
• Conventional Epidemic Routing
  – a source node tries to transfer its bundles to as many
    neighbors as possible in order to increase the
    probability of a successful delivery.
• Distance-Aware Epidemic Routing considers…
  – The order of bundles to be forwarded
  – The number of duplications
  – The buffer replacement policy
DTN Routing
          Performance simulation
•   Simulation Setup
    1. We examine the performance of two DTN routing, pure epidemic and
       DAER, in three mobility models: random waypoint, SUVnet and SUMO-
       generated model.
    2. Since we find there are about 600 vehicles that stay in the inner-loop
       area for 1,200 seconds, so we generate the same number nodes'
       traces for all three models.
    3. The average speed of nodes in random waypoint trace is 25km/h,
       similar to those in SUVnet.
    4. Network traffic is generated as follows: for light load, 100 bundles are
       generated and each node has 16MB buffer; for heavy load, 500
       bundles are generated and each node has 2MB buffer, bundle size is
       256kB.
    5. All bundles are sent at the beginning. The transmission rate is 1Mbps
       and communication range is set to 150m for all cases. Simulation
       endures 1,200 seconds and is done with our own built DTN simulator.
     Performance simulation
• Light Load Scenario
      Performance simulation
• High Load Scenario
        Performance simulation

1.   Difference is not significant for pure epidemic routing.
     Its delivery ratio is quite close for different mobility
     models.
2.   DAER performs much better in random waypoint
     model than the other two.
3.   Random waypoint model tends to give lowest average
     bundle delay and number of hops.


      Choice of mobility models will play a significant
      role in simulation based study for DTN routing.
                  Conclusion
1. We study the performance of DTN epidemic routing
   under three mobility models, the random waypoint,
   SUVnet and SUMO-generated models.
2. To construct a more realistic model, the SUVnet, we
   collected the GPS data from more than 4,000 taxis in
   Shanghai and generated their traces.
3. Our evaluation shows the performance of DTN routing
   in VANET depends highly on the underlying mobility
   models.
4. We argue from our results, that even by means of a
   complex map-based microscopic traffic simulator, care
   should be taken as the results obtained with these
   models might not be as close to reality as expected.
                 Resources
• SUMO project:
  – http://sumo.sourceforge.net/
• Shanghai digital map:
  – http://itis.grid.sjtu.edu.cn/Run.htm
• Shanghai taxi trace data:
  – http://itis.grid.sjtu.edu.cn/wiki/download.htm