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UFlood: High-Throughput Wireless Flooding

Jayashree SubramanianCollaborators: Robert Morris, Ramakrishna Gummadi,

and Hari Balakrishnan

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Goal of this work• To design a flooding protocol for wireless

multi-hop networks

• Application: Real-time video distribution

• What is a good flooding protocol?

• Use least #transmissions

• Provide high-throughput for the nodes

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Every transmission is broadcast (Opportunistic Receptions!)

Reception is probabilistic

Challenge in Wireless Flooding

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3

4 6

2

5

2 3

6

2 src

A B

D

C

23456

Delivery probability from Src to B

0.1

4

Challenge: Who should transmit next?What packet to transmit?

UFlood’s claim: Select best sender – to minimize total #transmissions to complete flooding

Challenge in Wireless Flooding …contd

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Contribution of this work1. UFlood – Choosing best sender for every

transmission maximizes throughput

2. UFlood performance:

• Achieves 2x higher throughput than controlled flooding

• Performs close to the benchmark - ExOR unicast routing (multihop transfer to a single receiver)

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• Existing Solutions

• Key Idea of UFlood

• Design of UFlood protocol

• Evaluation

Outline of this talk

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Related Work

1. Flooding in routing

• Controlled flooding (AODV, DSR)

×Does not maximize throughput2. Tree-based flooding

• MCDS, LESS

×Does not consider opportunism3. Gossip-based flooding

B

C

A

S

0.1

Wasted transmission!

2.Tree-based flooding (static decision)

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Related Work … contd3. Gossip-based flooding

(dynamic decision)

• Trickle, Deluge

× Does not choose best sender

B

C

A

S

0.90.1

Bad sender choice means more transmissions!

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• Existing Solutions

• Key Idea of UFlood

• Design of UFlood protocol

• Evaluation

Outline of this talk

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6

Select the best sender - to maximize

throughput

Select the best sender - to maximize

Useful Receptions

Key Idea

Packet availability

src

A B

D

C

12345

1 1

1

1

4 6

6

3

32 5

Delivery probability

0.9

0.1

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Calculating Useful Receptions

6 src

A B

D

C

12345

1 1

1

1

4 6

6

3

32 5

U(A,4)=

0.9+0.4+0.6=1.8 0.9

0.4

0.6

U(B,1)=0

12

0.9 0.5

0.2 0.1

B

DC

A

S

U(S)=1.7

0.5

0.2

U(S)=0.7

U(A)=1.5

1

0.5

0.3

0.5U(D)=0.8

S

A

D

B

C

To Flood a Single Packet using UFlood

To flood multiple packets calculate utility for every packet!s

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UFlood is a Local Heuristic

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• Difficulty: Knowing the packet availability at all nodes

• Solution: Local heuristic- Every node knows only about its neighbors (nodes whose packets it can hear!)

• Good news: Possibility of spatial reuse!

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• Existing Solutions

• Key Idea of UFlood

• Design of protocol

• Evaluation

Outline of this talk

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Design of UFlood

Information required:1. Node states - packets available with

the neighbors

2. Delivery probabilities of all node pairs in the network

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Node states

• Ni- number of neighbors of i

• P – # packets flooded

• node-state matrix – [NixP]

• Method:

• Maintain local version

• Gossip packet availability using periodic

status packets

0003

0102

0111

321

003

102

111

321

Knowing Node States

Packet #

Node #

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Delivery probabilities

• For all N nodes in the network

• Delivery prob matrix – [NxN]

• Method – offline experiment

• Each node broadcasts continuous

probes and rest of the nodes compute:

Computing Delivery Probabilities

10.60.93

110.42

0.50.311

321N

N

sentprobesTotal

receivedprobesTotalyprobabilitDelivery

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N

1ip))B(i,(1i)P(n,p)Utility(n,

Computing Packet Utility

Delivery probabilities

Node states

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Pseudo code of UFlood• Source Node floods all packets

• All nodes periodically broadcast status packet

• On reception of a new data or status packet:

1.Update node-state matrix

2.Calculate utility

3.Transmit in burst – all packets whose utility is greater than neighbor’s utility

• CSMA handles contention (Listen then send or back off)

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Batching in UFlood

The packets are sent in batches

• To reduce overhead

• To limit the size of the status packet

Current design considers single batch!

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• Existing Solutions

• Key Idea of UFlood

• Design of UFlood protocol

• Evaluation

Outline of this Talk

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250

feet

200 feet20-Node Indoor Test-bed

Source Node

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Implementation

Meraki mini, 802.11b/g

2dbi Omni-directional antenna

Transmit power = 60mW

Bit rate = 24Mbps

CLICK software router toolkit

Carrier Sense on

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Performance Comparison

Method: Flood a single batch of 5000-

1500B packet

Comparison:UFlood Vs. Controlled Flooding

UFlood Vs. Unicast routing

Time(sec)

packet(Mb)a ofsize *received packets# (Mbps)Throughput

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UFlood Vs. Controlled Flooding

Used in routing protocols like AODV and DSR

Method:• Source broadcasts all packet• Every node rebroadcasts only once

Why we used?• Aim: to quickly send the route information

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Throughput of UFlood = 2x Throughput of Controlled Flooding

No Choice of Sender!

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UFlood Vs. Estimated Unicast

Why unicast?• Multihop transfer to

one receiver Vs many receivers

Method• Setup independent unicast sessions to send a batch of packets from source (node 4) to each node

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packet

packetpacketpacketpacketpacket

ExOR

src

A B

dst

C

packetpacketpacket

• Decide who forwards after reception

• Goal: only closest receiver should forward

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Throughput of UFlood = Throughput of Estimated ExOR

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Second best node transmits!

Why does UFlood Perform good?

Best node transmits!

UFlood is a local heuristic –

Occasional errors!

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Future WorkImplement network-coding, bit-rate

adaptation, and batching UFlood vs. existing high-throughput

flooding protocols

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Conclusion1. UFlood’s Key Idea – Choosing best

sender for every transmission maximizes throughput

2. UFlood’s Performance:

• Achieves 2x higher throughput than controlled flooding

• Performs close to the benchmark - ExOR unicast routing