1Adapted from Ni et al

Wireless Networking & Mobile Computing

ECE 299.02 Spring 2007

Ian Wong

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The Broadcast Storm Problem in a Mobile Ad-Hoc Network

Sze-Yao Ni, Yu-Chee Tseng, Yuh-Shyan Chen, Jang-Ping Sheu

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Background

4Adapted from Ni et al

What are we looking at?

Mobile Ad-hoc networks No dedicated servers/base stations for the

entire network Units can move freely Utilizes CSMA without CD

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If you don’t know where they are…

What do you do?

6Adapted from Ni et al

Broadcast!

7Adapted from Ni et al

Broadcast!

Hi!!!

8Adapted from Ni et al

Broadcast!

9Adapted from Ni et al

So, what’s the problem?

Wireless CSMA inherently without CD, so a transmitter cannot inherently be aware of collisions

Broadcasts are spontaneous They happen whenever they need to

Broadcasts aren’t reliable A RTS/CTS and even an ACK are too much to

ask for!

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We’ve lost our reliable transport!

11Adapted from Ni et al

How would it happen?

In a very nice, linear system…it works…

12Adapted from Ni et al

But…?

Seven transmissions when only three are required!It’s like a flood! Hence….flooding!

13Adapted from Ni et al

So, the problem ends up being…

Redundant rebroadcasts Propagating (rebroadcasting) an old packet to

a node is pointless!

Increased contention Spending time propagating an old packet

consumes unnecessary bandwidth

Increased collisions Without backoff mechanisms and RTS/CTS,

collisions occur more frequently

14Adapted from Ni et al

So, about rebroadcasts…

They can be expensive! Use with caution!

•Where INTC(d) is the intersection area, where d є {0,r}

If d = r, then πr2 – INTC(r) ≈ 0.61πr2

Maximal improvement of at most 61% Average Improvements

•≈ 0.41πr2 for the first

•≈ 0.19πr2 for the second

•< 0.05πr2 for the fifth…

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Besides sheer area, once we’ve heard the first broadcast…

16Adapted from Ni et al

…who’s the first to speak?An analysis of Contention The probability of contention can be

calculated by:

In the simplest case, when two receive the same broadcast, the chance of contention is ≈ 59% This probability increases with increasing local

density

17Adapted from Ni et al

…Can you hear me now? Collisions!

CSMA/CA backs off if the carrier is busy But,

Overly quiet channels may lead many nodes to expend their backoff and transmit at the same time

No RTS/CTS dialogue precludes forewarning Without CD (collision detection), the host will

waste bandwidth until packet transmission completes

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So, given these problems…

…how could we solve them?

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What if…

…only a few need to yell?

An exercise in probability…

20Adapted from Ni et al

A Probabilistic Approach

What does it mean? Always yelling once you’ve heard something

•Probability of P = 1

Maybe yelling once you’ve heard something•Probability of P < 1

Assumptions Assumes that the topology of the network is

fairly dense, or that the probabilities are selected based on the network topology

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So, since it’s probabilistic…

…what are the chances that it’ll be effective?

22Adapted from Ni et al

First…what is effective?

Performance metrics Reachability

• Total # of reachable nodes/# of initially reachable nodes

Saved ReBroadcast•SRB = (r-t)/r

Average latency• tlast rebroadcast – tfirst broadcast

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Now that we’ve got metrics…

…how does our theory fare?

24Adapted from Ni et al

Analysis of Probabilistic Propagation

SRB decreases by ~(1-P) as P increases Broadcast latency increases as P increases, but

more sparse networks complete broadcasting faster Why?

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One Mississippi, Two Mississippi…

Using Counters!

26Adapted from Ni et al

Counting sheep…

Why count? Similar to deterministic probability

How do we do it? After hearing a message for the first time, start

a counter and count the number of overheard repeats

If after a random backoff the number of counts does not exceed threshold, rebroadcast the message

If the number of repeats exceeds the threshold before the time has elapsed, then do not propagate the message

27Adapted from Ni et al

I count one sheep, two sheep,…

High RE in C ≥ 3 SRB decreases with decreasing density

Why? 27% to 67% savings for higher density maps

Low latency

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Why transmit purely at random…

…when you can transmit only if you gain an advantage?

29Adapted from Ni et al

Leveraging distances!

Instead of simply counting, let’s improve that…why not look at additional coverage? Define minimum amount of extra coverage

calculated by πr2 – INTC(r) •Define a minimum distance D that provides at least a

certain amount of additional coverage

Out of all overheard transmissions, determine the distance dmin to the closest node.

If distance dmin < D, don’t transmit…

If distance dmin > D, propagate!

30Adapted from Ni et al

Do levers work?

Ds selected as effective comparisons for Counter schemes Equally high RE as counter SRB significantly lower (10% to 37%) Higher latency

If counter and distance are so similar, why all these issues? At higher data rates, SRB and RE drops. Why?

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More area?

Is there a better way to estimate extra coverage?

32Adapted from Ni et al

Location, location, location!

Given that we know relative distances, what about absolute distances? Acquire the location of broadcasting hosts to

precisely estimate coverage•Use external positioning devices, like GPS

Improves Distance-based topology Recalculate effective area when you hear each

new retransmission

33Adapted from Ni et al

Absolute location locates absolutely…but does it help absolutely…?

High RE High SRB Lowest latency of four statistical/geometrical

methods

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Aside from statistics and geometry…

…how else can you maximize your throughput?

35Adapted from Ni et al

Clusters

Go on…make little groups and talk to who’s around you… Each host knows who’s around it One card, low draw to see who gets to be the

local cluster head Local heads draw between one another to

figure out who is a global head How does this help?

Only the cluster heads need to retransmit to the cluster

Gateways need to retransmit between cluster heads

Members just sit and listen

36Adapted from Ni et al

This ain’t no cluster…

Highest consistent SRB Lowest latency Significant drop in RE at low densities

37Adapted from Ni et al

So…

One problem. Five approaches… V(aries), H(igh), M(edium), L(ow)

EffectivenessRE SRB Latency

Probabilistic V V M Counting H M L Distance H L M Location H H L Clustering V H L

38Adapted from Ni et al

Not just probabilistic, but better!

Gossiping (Probabilistic Flooding) Difference from ideal situations and packet

collision issues due to phase transitions – small changes can cause large changes [3]

Hypergossiping [2] Partition nodes

•Efficient intra-partition forwarding

•Retransmit an adequate subset of messages on partition joins

Adapt gossiping probability to node density to reduce broadcast storms

39Adapted from Ni et al

References

[1] Sze-Yao Ni, Yu-Chee Tseng, Yuh-Shyan Chen, Jang-Ping Sheu. The Broadcast Storm Problem in a Mobile Ad-Hoc Network

[2] Abdelmajid Khelil, Pedro Jose Marron, Christian Becker, Kurt Rothermel Hypergossiping: A Generalized Broadcast Strategy for Mobile Ad Hoc Networks

[3] Yoav Sasson David Cavin Andr´e Schiper. Probabilistic Broadcast for Flooding in Wireless Mobile Ad hoc Networks