# Gossip-Based Ad Hoc Routing - PowerPoint PPT Presentation

PPT – Gossip-Based Ad Hoc Routing PowerPoint presentation | free to view - id: 1fdb8c-ZDc1Z

The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
Title:

Description:

### Many routing messages are propagated unnecessarily. Gossip ... Optimization of Gossip (GOSSIP2 and GOSSIP3). Integrate Gossip with AODV. 25. Thank you! ... – PowerPoint PPT presentation

Number of Views:13
Avg rating:3.0/5.0
Slides: 24
Provided by: csg3
Category:
Tags:
Transcript and Presenter's Notes

1
• Zygmunt J. Haas, Joseph Halpern, LiLi
• Cornell University
• Presented By Charuka Silva

2
Contents
• Introduction
• Pure Gossip
• Optimization of Gossip
• Summary

3
• Ad Hoc Network is a multi-hop wireless network
with no fixed infrastructure.
• Robust routing protocols must be developed. Some
variant of flooding is usually used.

4
Flooding and Gossiping
• Flooding
• Every node that receives a packet retransmits the
packet to all of its neighbors.
• Many routing messages are propagated
unnecessarily.
• Gossip
• Each node forwards a message with some
probability.

5
Gossip Bimodal Behavior
• Let the gossip probability be p. Then, in
sufficiently large nice graphs, there are
fractions ?S(p) and ?R(p) such that the gossip
quickly dies out in 1 - ?S(p) of the executions
and, in almost all of the fraction ?S(p) of the
executions where the gossip does not die out, a
fraction ?R(p) of the nodes get the message.
Moreover, in many cases of interest, ?R(p) is
close to 1.

6
Gossip Bimodal Behavior (cont.)
• In almost all executions of the algorithm, either
hardly any nodes receive the message, or most of
them do.
• By making the fraction of executions where the
gossip dies out relatively low while also keeping
the gossip probability low, we can reduce the

7
Contents
• Introduction
• Pure Gossip
• Optimization of Gossip
• Summary

8
GOSSIP1(p)
• A source sends the route request with probability
1. When a node first receives a route request,
with probability p it broadcasts the request to
its neighbors and with probability 1 p it
same request again, it is discarded.
• Problem with initial condition of the source
having very few neighbors.

9
GOSSIP1(p, k)
• For the first k hops, we gossip with probability
1. From the hop k 1, the gossip probability is
p.
• GOSSIP1(1, 1) is equivalent to flooding.
• GOSSIP1(p, 1) is equivalent to GOSSIP1(p).

10
Theorem II.1
• For all p 0, for almost all infinite graphs,
if GOSSIP1(p,0) is used by every node to spread a
message, then there is a well-defined probability
?0S(p) lt 1 that the message reaches infinitely
many nodes. Moreover, the probability ?0F (p)
that a node receives the message and forwards it
in an execution where the message reaches
infinitely many nodes is equal to ?0S (p).

11
Cont.
• ?0S(p) ?0F (p) def ?0(p)
• In an execution where the message does not die
out, the probability that a random node receives
the message is ?0(p)/p.

12
Experiment Probability varies
Gossiping on a random network of average degree
8. The higher the probability, the higher the
fraction of nodes receive the message.
13
Experiment - Probability varies
Gossiping on a random network of average degree
8. The higher the probability, the higher the
fraction of nodes receive the message.
14
Experiment Degree of network
• In a 20 50 regular network of degree 6,
gossiping with probability .65 ensure that almost
all nodes get the message in almost all
executions.
• for a 20 50 regular network of degree 3, we
need to gossip with probability .86 to ensure
that almost all nodes get the message in all
executions.
• Conclusion the higher the degree, the better
the gossiping effect.

15
GOSSIP1(p, k) - Conclusion
• With p sufficiently high, we can guarantee that
almost all nodes will receive the message in
almost all executions.
• Practically, we can guarantee that the
destination node receives the message, while
saving a fraction of 1 p of messages.
• The higher the degree, the better the gossiping
effect

16
Contents
• Introduction
• Pure Gossip
• Optimization of Gossip
• Summary

17
A two-threshold scheme
• Why?
• In a random network, a node may have very few
neighbors, thus the probability that none of the
nodes neighbors will propagate the gossip is
high. We hope that nodes with lower degree can
gossip with higher probability.

18
GOSSIP2(p1, k, p2, n)
• p1 typical gossip probability.
• k number of hops with which we gossip with
probability 1.
• n number of neighbors of a node.
• p2 probability for which p2 gt p1. Neighbors of
a node with fewer than n neighbors gossip with

19
Comparison of GOSSIP2 with GOSSIP1
GOSSIP2 vs. GOSSIP1 on a random network of
average degree 8 GOSSIP2(0.6,4,1,6) has better
performance than GOSSIP1(0.75,4), while using
4 fewer messages than GOSSIP1(0.75,4).
20
Prevent premature gossip death
• The idea behind
• If a node has n neighbors and the gossip
probability is p, for each message, the node
should get roughly pn copies from its neighbors.
If the node gets significantly fewer than pn
copies within a reasonable time interval, then
this is a clue that the message is dying out.

21
GOSSIP3(p, k, m)
• Same as GOSSIP1(p, k) except for the following
modification
• If a node originally did not broadcast a
received message, but then did not get the
message from at least m other nodes within some
timeout period, then the node will broadcast the
message immediately after the timeout period.
• Usually m 1.

22
Comparison of GOSSIP3 with GOSSIP1
GOSSIP3 vs. GOSSIP1 on a random network of
average degree 8 GOSSIP3(0.65,4,1) has better
performance than GOSSIP1(0.75,4), while using
8 fewer messages than GOSSIP1(0.75,4).
23
Contents
• Introduction
• Pure Gossip
• Optimization of Gossip
• Summary

24
Summary
• Pure Gossip (GOSSIP1).
• Optimization of Gossip (GOSSIP2 and GOSSIP3).
• Integrate Gossip with AODV.

25
Thank you!