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DTN Interworking

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					Transportation-aware Routing in
Delay Tolerant Networks (DTNs)



         Asia Future Internet 2008


             Taekyoung Kwon
          Seoul National University
         outline


1 Introduction

2 Scenario Model

3 Our Approaches

4 Summary




                 2
                          Introduction

 DTN
      Delay (or Disruption) Tolerant Networks


 Delay? Disruption?
      Interplanetary networks
      Sensor networks
           Nodes sleep to save power
      Vehicular networks
           Mobile devices get out of other devices’ radio ranges
      Opportunistic networks
         a sender and a receiver make contact at an unscheduled time

      Underwater networks
                     Introduction

 Motivation
      DTNs may have to be accommodated in future networks
         Intermittent connectivity

         Long or variable delay

         Asymmetric data rates

         Heterogeneous links

         High packet error rates

         Limited node uptime
            Research Issues in DTNs

 Delay Tolerant Network Architecture
      Overall redesign
         E.g. Bundle Protocol

 Routing Protocols
      Delivery ratio
      Reducing delay
 Congestion control
 Distributed Caching
 Multicast/Anycast
          IP routing may not work
 E2e connectivity may not exist at the same time
 Routing (e.g. MANET) performs poorly in DTN
  environments
 Some assumptions for routing will not work
      E.g. BGP leverages TCP




                                    Source: Kevin Fall, IRTF DTN RG


                                6
          Related Work (mobility)

 Mobility model
                     DTN



      No Mobility              Mobility



                    Routine               Random



      Predictable             Tendency-
                                based
               Related work (routing)

 Some Routing Strategies
      Epidemic routing
         Flooding

      Spray and wait (S&W)
         Limited number of copies of a message




 Important Metrics
      delivery probability
      delivery latency
      overhead ratio
                         Motivation

 Existing routing protocols use only past information like
  contact history, etc.

 DTN Routing can leverage additional information in the
  future
      speed, direction, destination of mobile node, etc.


 We want to propose routing protocol using these
  additional information
                    Scenario Model

 When to use DTN?
      DTNs can be used for delay tolerant applications
         environmental monitoring, some publish/subscribe

          applications


 We assume that each node has location information
      E.g. GPS, Navigation, localization techniques
               Potential Approaches

 Leveraging mobility information
      Direction of mobile host
      Speed of mobile host
      Location of mobile host’s destination
      Location of message’s destination
         Message’s destination can be fixed or mobile




 Our approaches
      Direction-based
      Destination-based
      Transportation info-based
                   Our Approach 1
 Direction-Based routing protocol
      Spray & Wait based
      Number of tokens: n
      Number of split tokens depends on direction difference

  sender’s direction
                          0°                 hand over                    -90°
                                    -n*angle/180° tokens


                           hand over
                       n*angle/180° tokens

                                                   hand over n/2 tokens



                          90 °
                                        receiver’s direction

                                              12
                 Our Approach 2
 Destination-Based routing protocol
      Spray and wait based
      Number of tokens for handover
         n/2*( distance / max diameter )




                  MAP
                                                Receiver’s destination




                                  Sender’s destination


                                   13
            Hybrid of approaches 1 and 2
 Direction-Distance-Hybrid (DDH)
  Direction                 Destination               Handed over tokens
  similar                   close                     few
  similar                   far                       medium
  different                 close                     medium
  different                 far                       n/2

 n/2*Direction(d1)*Distance(d2)*Speed(s)
      Direction(): function ranged [0,1]
      Distance(): function ranged [0,1]
      Speed(): function ranged [0,1]
      d1: direction difference of two nodes
      d2: distance difference of two nodes’ destinations
      s: difference of nodes’ speeds


                                        14
           Simulation results (1/2)
 Simulator
      The Opportunistic Network Environment (ONE) simulator
      http://www.netlab.tkk.fi/~jo/dtn/
 Parameter settings
   Parameters                               Value

   Area size (m*m)                       4500 X 3400

   Number of nodes                100 (mobile), 10 (static)

   Transmission range (m)                    100

   Speed (m/s)                              0~18

   Buffer size (GB)               1 (mobile), 200 (static)

   Message size (MB)                       0.01 ~ 3

   Transmission rate (KB)                    250

   Movement model                      Random waypoint

                                  15
        Simulation results (2/2)
 Comparison btw. S&W and DDH
   DDH can deliver 18% more packets than S&W
   When destination is fixed




          * : # of delivered packets per 1000 relayed packets

                                        16
       Problem of Previous Approaches

 Randomization effect problem
      It is caused by local view of tendency
      As number of contacts is increased, direction or distance is
       randomized
      Effect of our proposal gets reduced

                                                          Angle = 90°
                                                       ∴ handover n/2 tokens
 An illustration                              1st contact

      Some tokens can be carried in the
       same direction                                         2nd contact

      movement information that decides
       the number of copies relayed                  Angle = 90°
       becomes meaningless                               ∴ handover n/4 tokens
                 Scenario Model

 A DTN area consists of a certain number of subareas or
  regions
 There is a need of DTN between regions due to poor
  infrastructure or delay tolerant application
 How to dissemination messages between regions
  efficiently




      Region 1                              Region 2
                   Our Approach 3
 Prevention of randomization problem using history
      Area is divided into several sub areas with non uniform distribution
      Token handover policy
         When a source creates the message, it reserves a fixed number of

          tokens for each sub-area
         If the source meets a mobile host toward other regions, it sends the

          message to the host with pre-reserved tokens




 Tokens can be distributed more evenly across the area


                                       19
                Simulation Settings
 Simulator: Opportunistic Network Environment (ONE)

 Area size: 45 X 34km2
      4 sub-areas (20x15km2 each)
 # of nodes: 500
      Intra-area node & Inter-area node

 Tx range: 100m
 Speed: 100km/h, 4~60km/h
 S&W copies: 32
 Packet
      # of packets: 1000 (2 packets per each node)
      Packet size: ~ 30KB
 Buffer size big enough
                 Simulation Results
 Destination is mobile
 Delivery ratio
   = # of delivered packets / # of originated packets

                                                                            Delivery Probability (20% Inter-area Mobile Nodes)
                                                                      0.6




                                               Delivery Probability
                                                                      0.5

                                                                      0.4
                                                                                                                                 epi_20
                                                                      0.3                                                        snw_20
                                                                                                                                 our_20
                                                                      0.2

                                                                      0.1

                                                                       0
                                                                                0.5          1          1.5          2

                                                                                                 Days
                                     Simulation Results
 Overhead ratio                                                     Average number of relay
                       = (# of relayed - # of delivered) /            nodes
                          # of delivered

                 400                                                                     4.5


                 350                                                                      4

                                                                                         3.5
                 300
                                                                                          3




                                                                    # of relayed nodes
Overhead Ratio




                 250
                                                                                         2.5
                 200
                                                                                          2

                 150
                                                                                         1.5

                 100                                                                      1

                  50                                                                     0.5


                   0                                                                      0
                          Epidemic    SprayAndWait   Region-based                              Epidemic   SprayAndWait   Region-based

                                                                                                           10%   20%
                                       10%   20%
                                   Simulation Results
          Avg. latency                                                  Med. latency


               120000                                                            115000



                                                                                 110000
               115000



                                                                                 105000
               110000
Latency Avg.




                                                                  Latency Med.
                                                                                 100000

               105000

                                                                                  95000


               100000
                                                                                  90000



                95000                                                             85000
                        Epidemic   SprayAndW ait   Region-based                           Epidemic   SprayAndW ait   Region-based

                                    10%    20%                                                        10%    20%
                       Conclusions

 DTNs may play a vital role in future
 Routing is a key player in DTNs
 We proposed
      Direction-based
      Distance-based
      Transportation info-based
 Destination’s mobility affects the routing performance
 The more information, the better routing

				
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