1Adapted from Ni et al
Wireless Networking & Mobile Computing
ECE 299.02 Spring 2007
Ian Wong
2
The Broadcast Storm Problem in aMobile 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 atransmitter cannot inherently be aware ofcollisions
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…itworks…
12Adapted from Ni et al
But…?
Seven transmissions when only threeare 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 consumesunnecessary 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 heardthe 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 thesame 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 toexpend their backoff and transmit at the sametime
No RTS/CTS dialogue precludes forewarning Without CD (collision detection), the host will
waste bandwidth until packet transmissioncompletes
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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 basedon the network topology
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So, since it’s probabilistic…
…what are the chances that it’ll beeffective?
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 overheardrepeats
If after a random backoff the number of countsdoes not exceed threshold, rebroadcast themessage
If the number of repeats exceeds the thresholdbefore the time has elapsed, then do notpropagate 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 yougain an advantage?
29Adapted from Ni et al
Leveraging distances!
Instead of simply counting, let’s improvethat…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 estimateextra coverage?
32Adapted from Ni et al
Location, location, location!
Given that we know relative distances, whatabout 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…butdoes 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 yourthroughput?
35Adapted from Ni et al
Clusters
Go on…make little groups and talk to who’saround 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 thecluster
Gateways need to retransmit between clusterheads
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 – smallchanges 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 HocNetworks
[3] Yoav Sasson David Cavin Andr´e Schiper. Probabilistic Broadcast for Flooding inWireless Mobile Ad hoc Networks