MDDV: A Mobility-Centric Data Dissemination Algorithm for Vehicular Networks H. Wu, R. Fujimoto, R. Guensler and M. Hunter (gatech) VANET 2004: First ACM Int’l Workshop on Vehicular Ad Hoc Networks Presented by: Zakhia Abichar (Zak) Nov 3, 2004

2 Overview Mobility-centric approach for data dissemination Efficient, reliable operation in highly-mobile, partitioned networks Exploiting vehicle mobility for data dissemination –Opportunistic forwarding –Trajectory-based forwarding –Geographical forwarding Operation through localized algorithms

3 Introduction Current ITS are infrastructure heavy Moving towards mobile infrastructure –Shift of maintenance cost from government to drivers –In-vehicle sensors, much more powerful than out-of-vehicle equipment

4 Networks Architectures Pure wireless v2v ad hoc network (V2V) Wired backbone with wireless last-hop Hybrid architecture –Using v2v communications without relying on a fixed infrastructure –Exploiting infrastructure when available for improved functionality

5 Data Dissemination Applications require data dissemination with high delivery ratio The architectures “pure ad- hoc” (V2V) and “hybrid” require vehicle forwarding to achieve data dissemination The architecture “wireless last- hop” can rely on established wired protocols

6 Vehicular Networks Characteristics Predictable high mobility –Can be exploited for system optimization Dynamic rapidly changing topology Mainly one-directional movement Potentially large-scale Partitioned –Decreased end-to-end connectivity No significant power constraints

7 Mobile Computing Approach Partitioned, highly dynamic: –Large-scale structures are undesirable (e.g. trees) –Localized algorithms instead Each node operates based on its local information Behavior of nodes achieves a global goal Partitioned, highly mobile, unreliable channels, critical applications: –Data replication and diversity

8 Data Dissemination Services Subject to design objectives –Low delay –High reliability –Low memory occupancy –Low message passing overhead Four services defined –Unicast –Multicast –Anycast –Scan

9 Unicast Service Unicast with precise location –Delivering message to node i, in location l, before time t Unicast with approximate location –Delivering a message to node i, before time t1 –Node i, was at location l at time t2 and was moving with mobility m

10 Multicast, Anycast and Scan Delivering a message to all (any) nodes in region r before time t Scan: letting a message traverse a region r once before time t

11 Use of Services: An Example Pull approach –A vehicle desires information about a remote region –Query vehicles in proximity (multicast) –Reply (unicast with approximate/precise location) –If no answer, (anycast to remote region) –Reply (unicast with approximate/precise location) Push approach –Vehicle reporting a crash (multicast)

12 Data Delivery Mechanisms Def: defines the rules for passing information around the network Conventional data delivery mechanisms assume a connected network Node-centric approach –Specifying the routing path as a sequence of connected nodes –Not suitable for V2V Location-centric approach –Message sent to next-hop closer to the destination –Approach may fail when the network is partitioned Broadcast protocols cannot ensure reliable delivery in partitioned networks

13 Data Delivery Mechanisms (cont’d) Opportunistic forwarding –Employed when end-to-end path cannot be assumed to exist –Messages are stored and forwarded when opportunities present themselves Trajectory-based forwarding –Directing messages along pre-defined trajectories –Help limiting data propagation along specific paths –Suitable for V2V despite network sparseness Vehicles move along a pre-defined direction, i.e., the road graph

14 MDDV Approach Mobility-centric approach based on: –Opportunistic forwarding –Geographical forwarding –Trajectory forwarding A trajectory is specified, extending from the source to the destination A trajectory routes packets closer to the destination (geographical) With an opportunistic forwarding approach, rules are defined to determine: –Who is eligible to pass a message and when –When a message should be passed –When a vehicle should hold/drop a message

15 MDDV Assumptions A vehicle is aware of its location and holds a road map A vehicle knows the existence of its neighbors but not their locations Single-channel communication

16 Forwarding Trajectory A path is specified: extending from source to destination Road network: abstracted as a directed graph –Nodes: intersections –Edges: road segments Different from general ad- hoc models

17 Data Dissemination Process Forwarding phase –Message is passed along the forwarding trajectory until reaching the destination region Propagation phase –Message is propagated to every vehicle in the destination region Terminology: –Message head: message holder closest to the destination region –Message head pair: message head location and generation time

18 Data Dissemination Procedure A group of vehicles near the message head forward the message –The message head may become inoperative This group of vehicles is called message head candidates

19 Becoming a Message Head Candidate Non-MHC  MHC Passing L, before T+T1 MHC  non-MHC Leaving the trajectory Receives the same message with, Ln is closer to destination than Lc Tc : current time Lc : current location Message head pair:

20 Dissemination State Active state Transmission triggered –New messages –New message versions –Older message versions received –New neighbors appear Active propagation of messages Passive state Transmission triggered –Older message version received Eliminate obsolete messages

21 Dissemination State (cont’d) Installed head pair Tc : current time Lc : current location Active state: if (Tc < T+T2) & (|L,Lc|< L2) Passive state: if (Tc<T+T3) & (|L,Lc|<L3) –T2<T3, L2<L3 Otherwise, a station does not transmit at all

22 Performance Evaluation Transportation simulation by CORSIM –Adopts vehicle and driver behavior models Communication network by QualNet Vehicles in CORSIM are mapped to nodes in QualNet Comparison against two ideal protocols –Central intelligence –P2P

23 Evaluation: Central Intelligence Workload: 40 geographical- temporal multicast Message size: 512 bytes Average path length: 6.5 km IEEE DCF, 2 Mbps Expiration time: 480 s

24 Evaluation: MDDV Overhead normalized against that of P2P

25 Comments?