Partition Detection
EDIFY Overview
Brian D. Davison
Partition Detection Papers
 Localized algorithms for detection of critical
nodes and links for connectivity in ad hoc
networks
 Jorgic, Stojmenovic, Hauspie, and Simplot-Ryl,
3rd IFIP MED-HOC-NET Workshop, 2004
 Partition Detection in Mobile Ad-Hoc
Networks Using Multiple Disjoint Paths Set
 Hauspie, Carle, and Simplot, Objects, Models &
Multimedia Tech. Workshop, 2003
 A Partition Detection System for Mobile AdHoc Networks
 Ritter, Winder, and Schiller, IEEE SECON 2004
Localized algorithms for detection
of critical nodes and links …






Jorgic, Stojmenovic, Hauspie, and Simplot-Ryl, IEEE MASS, 2004


services or data may be replicated before a partition occurs
Node trajectories could be changed to delay or prevent partitioning
(perhaps by re-inforcing the critical link) which will directly increase
delivery rates
Recognizes that before partitioning, there are critical nodes and
links
A node or link is critical if the subgraph of k-hop neighbors of the
node is disconnected without the node
Once critical nodes/edges are known
DFS was used to detect critical links



Global algorithm
Can be centralized or distributed
Communication costs are expensive


Reduced communication overhead
Increased detection speed
Possible partition can be detected with high probability with
localized algorithms
Literature
 Shah, Chen, and Nahrstedt (SCI 2001)
 Used GPS to monitor position, computes velocity, can
predict when partitioning will occur
 Wang and Li (INFOCOM, ICC 2002)
 Detected future partitions, replicated services
 Strong centralized approach
 Hauspie, Simplot and Carle (Med-Hoc 2003
Workshop)
 Evaluated stability of path from source to destination
with function of disjoint path between them and hop
distances
 Significant communication overhead to evaluate
function
Localized partition detection
 Localized algorithms
 More scalable, robust, and energy efficient
 May (falsely) detect some nodes as critical
 May still be better to replicate data/services when c/s is
far apart anyway
 Definitions
 Local (k-hop) knowledge
 k-hop neighbors: shortest route is k or less hops
 Collected by sending hello messages to neighbors
containing graph of their k-1 hop neighbors
 Positional vs. topological information
 (positional tells about connections between nodes 2
hops away, while topological does not)
 Topological is never better than Positional
Localized algorithms

Critical node detection



Critical link detection


Link AB is critical if the sets of k-hop neighbors of A and B are
disjoint
Detection of critical links based on loop length


A node is k-critical if the subgraph of k-hop neighbors is
disconnected
If globally critical, it will be detected
Link UV is k-loop-critical if hop distance between U and V is >
k (after UV is removed)
Detection of critical links based on critical nodes


Link AB is k-critical if both A and B are declared as k-critical
nodes
Much less communication required (just k-critical status)
Performance evaluation
 Test algorithms on connected random
graphs with n nodes, average degree d
 N=100, 500
 Densities of 3-15
 Measure is detection ratio
 Probability that a node/link is declared as critical
by local algorithm is indeed globally critical
 Also measured average number of critical
nodes and links detected
Detection ratios
Ave. Degree
15
11
10
9
8
7
6
5
4
3
2-hop alg.
50%
64.2
73.0
70.4
73.3
64.8
67.9
71.5
73.5
66.7
3-hop alg.
71.4%
79.5
90.2
97.7
88.7
82.5
83.6
78.6
91.5
86.5
Ave number of critical nodes
Ave. Deg.
15
11
10
9
8
7
6
5
4
3
Global
3.0
6.8
9.2
8.8
11.0
9.4
11.2
14.3
17.2
19.2
2-hop alg.
6.2
10.6
12.6
12.5
15.0
14.5
16.5
20.0
23.4
28.8
3-hop alg.
4.2
7.8
10.2
9.1
12.4
11.4
13.4
18.2
18.8
22.2
Conclusions
 Localized algorithms give excellent
results
 Detection of critical nodes is about as
accurate as detection of critical links
 Difficulty is in detection of ring
structures longer than k
 Little relation to density
A Partition Detection System for
Mobile Ad-Hoc Networks
 Ritter, Winder, and Schiller, IEEE SECON
2004
 Proposes two partition detection
schemes
 Centralized
 Distributed
 Motivating example: mobile
multiplayer games
 Group moves and partitions occur
Related work
 Babaoglu et al., Bacon et al. – partitionaware apps
 can reconfigure themselves after a merge
 Uses simple ping/ack mechanism with timeout
 Cannot distinguish between node failure and
partitioning
 Killijian et al.
 Failure anticipation, movement planner, etc.
 Expensive
Partition detection
 Two sets of nodes – either active participants in
monitoring system, or not
 Active (probing) nodes
 must be chosen carefully to ensure most of the
topology is monitored
 Have relatively degree (closer to the border of the
network)
 Periodic keep-alive messages between active nodes
(far apart)
 When not heard after some time period, partition
suspected
 Local validation – also use one-hop neighbor to watch
buddy, and notify other monitoring node if buddy fails
Active (border) node identification
 Nodes with small enough degree
 Static approach uses fixed threshold
(degree <= threshold)
 Dynamic approach sets threshold as
last set of neighbor counts received
from neighbor
 Because of fluctuations in mobile
networks, use a hysteresis with
different thresholds to change state
Centralized Partition Detection
 One node sends beacon messages
 First active elects itself, broadcast to rest
 Other nodes are recipients
 Disadvantages
 partition containing server does not
detect the partition
 Partitionings containing no active nodes
will not be detected
Distributed Partition Detection
 Every active node sends beacons
 Broadcast on becoming active
 Every node caches a set of recent
(and/or far away) node
announcements, uses them as
partner nodes
 All active nodes need a buddy node
 Buddy is told of partner node list
changes
Experimental Results
 Implemented both in ns2
 50 nodes, simulated 50 times for each
parameter combination
 Centralized approach
 Low message overhead
 Network unmonitored after server failure but
before active nodes register at new server
 Distributed approach
 required 7x the number of messages messages
spread across many nodes
 much more resilient to node failure