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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
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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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