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1 Adapted from Ni et al Wireless Networking & Mobile Computing ECE 299.02 Spring 2007 Ian Wong.

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Presentation on theme: "1 Adapted from Ni et al Wireless Networking & Mobile Computing ECE 299.02 Spring 2007 Ian Wong."— Presentation transcript:

1 1 Adapted from Ni et al Wireless Networking & Mobile Computing ECE Spring 2007 Ian Wong

2 2 The Broadcast Storm Problem in a Mobile Ad-Hoc Network Sze-Yao Ni, Yu-Chee Tseng, Yuh- Shyan Chen, Jang-Ping Sheu

3 3 Background

4 4 Adapted 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

5 5 If you don’t know where they are… What do you do?

6 6 Adapted from Ni et al Broadcast!

7 7 Adapted from Ni et al Broadcast! Hi!!!

8 8 Adapted from Ni et al Broadcast!

9 9 Adapted 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!

10 10 We’ve lost our reliable transport!

11 11 Adapted from Ni et al How would it happen? In a very nice, linear system … it works …

12 12 Adapted from Ni et al But…? Seven transmissions when only three are required! It ’ s like a flood! Hence ….flooding!

13 13 Adapted 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

14 14 Adapted 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 πr 2 – INTC(r) ≈ 0.61πr 2  Maximal improvement of at most 61%  Average Improvements ≈ 0.41πr 2 for the first ≈ 0.19πr 2 for the second < 0.05πr 2 for the fifth…

15 15 Besides sheer area, once we’ve heard the first broadcast…

16 16 Adapted 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

17 17 Adapted 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

18 18 So, given these problems… …how could we solve them?

19 19 What if… …only a few need to yell? An exercise in probability…

20 20 Adapted 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

21 21 So, since it’s probabilistic… …what are the chances that it’ll be effective?

22 22 Adapted 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 t last rebroadcast – t first broadcast

23 23 Now that we’ve got metrics… …how does our theory fare?

24 24 Adapted 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?

25 25 One Mississippi, Two Mississippi… Using Counters!

26 26 Adapted 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

27 27 Adapted 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

28 28 Why transmit purely at random… …when you can transmit only if you gain an advantage?

29 29 Adapted 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 πr 2 – 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 d min to the closest node.  If distance d min < D, don’t transmit…  If distance d min > D, propagate!

30 30 Adapted 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?

31 31 More area? Is there a better way to estimate extra coverage?

32 32 Adapted 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

33 33 Adapted from Ni et al Absolute location locates absolutely…but does it help absolutely…? High RE High SRB Lowest latency of four statistical/geometrical methods

34 34 Aside from statistics and geometry… …how else can you maximize your throughput?

35 35 Adapted 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

36 36 Adapted from Ni et al This ain’t no cluster… Highest consistent SRB Lowest latency Significant drop in RE at low densities

37 37 Adapted from Ni et al So… One problem. Five approaches…  V(aries), H(igh), M(edium), L(ow) Effectiveness RESRBLatency  ProbabilisticVVM  CountingHML  DistanceHLM  LocationHHL  ClusteringVHL

38 38 Adapted 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

39 39 Adapted 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


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