1. Transportation-aware Routing in Delay Tolerant Networks (DTNs) Asia Future Internet 2008 Taekyoung Kwon Seoul National University

2. MMLAB 2 Introduction1 outline Scenario Model2 Our Approaches3 Summary4

3. MMLAB 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

4. MMLAB 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

5. MMLAB 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

6. MMLAB 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 6 Source: Kevin Fall, IRTF DTN RG

7. MMLAB Related Work (mobility) Mobility model DTN No MobilityMobility RoutineRandom PredictableTendency- based

8. MMLAB 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

9. MMLAB 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

10. MMLAB 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

11. MMLAB Potential Approaches Leveraging mobility information Direction of mobile host Speed of mobile host Location of mobile hosts destination Location of messages destination Messages destination can be fixed or mobile Our approaches Direction-based Destination-based Transportation info-based

12. MMLAB 12 Our Approach 1 Direction-Based routing protocol Spray & Wait based Number of tokens: n Number of split tokens depends on direction difference

13. MMLAB 13 Our Approach 2 Destination-Based routing protocol Spray and wait based Number of tokens for handover n/2*( distance / max diameter ) Maximum diameter MAP Senders destination Receivers destination distance

14. MMLAB 14 Hybrid of approaches 1 and 2 Direction-Distance-Hybrid (DDH) 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 DirectionDestinationHanded over tokens similarclosefew similarfarmedium differentclosemedium differentfarn/2

15. MMLAB 15 Simulation results (1/2) Simulator The Opportunistic Network Environment (ONE) simulator http://www.netlab.tkk.fi/~jo/dtn/ Parameter settings ParametersValue 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 modelRandom waypoint

16. MMLAB 16 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

17. MMLAB 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 Angle = 90° handover n/4 tokens An illustration Some tokens can be carried in the same direction movement information that decides the number of copies relayed becomes meaningless 2 nd contact 1 st contact

18. MMLAB 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

19. MMLAB 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

20. MMLAB Simulation Settings Simulator: Opportunistic Network Environment (ONE) Area size: 45 X 34km 2 4 sub-areas (20x15km 2 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

21. MMLAB Simulation Results Destination is mobile Delivery ratio = # of delivered packets / # of originated packets

22. MMLAB Simulation Results Overhead ratio = (# of relayed - # of delivered) / # of delivered Average number of relay nodes

23. MMLAB Simulation Results Avg. latency Med. latency

24. MMLAB 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 Destinations mobility affects the routing performance The more information, the better routing