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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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)

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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

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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

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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

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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

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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

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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

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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

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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, Tn>, Ln is closer to destination than Lc

Tc: current time

Lc: current location

Message head pair: <L,T>

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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

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Dissemination State (cont’d)

• Installed head pair <L, T>• 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

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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

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Evaluation: Central Intelligence

• Workload: 40 geographical-temporal multicast

• Message size: 512 bytes

• Average path length: 6.5 km

• IEEE 802.11 DCF, 2 Mbps

• Expiration time: 480 s

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Evaluation: MDDV

• Overhead normalized against that of P2P

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