Clustering in Mobile Ad-Hoc Networks...• Mobility-aware clustering – Cluster based on the mobility behavior of the mobile nodes • Energy-efficient clustering – Consider the

09.03.2011

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Clustering in Mobile Ad-Hoc Networks

Ovidiu Valentin, DRUGAN

Department of Informatics, University of Oslo, Norway

Outline

• Clustering in MANETs

• Routing Protocol Clustering in MANETs

– Issues for clustering in routing

– Clustering approaches for routing

• Dynamic clustering in the overlay

– Communication non-intrusive clustering

– Evaluation

• Conclusions & References

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Motivation• Application Scenario for Mobile Ad-Hoc Network (MANET): Rescue operations

and emergency interventions – Properties:

• Network without a fixed infrastructure and topological structure that allows mobile nodes to create a temporary communication network

– Information sources:• Mobile devices, wireless sensors, stationary devices, Internet, …

– Important information to be shared:• Medical records, layout of buildings, installations, dangerous goods, collected evidence, …

– Cooperation is necessary …

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Clustering

• Definition: division of the network into different virtual groups, based on rules in order to discriminate the nodes allocated to different sub-networks

• Goal: achieve scalability in presence of large networks and high mobility

• Information sources: routing and higher level

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Properties:Geographically allocatedBalance resource useService localization

Nodes roles in a cluster

• Roles of nodes in a cluster– Cluster-Head: local coordinator of a

cluster

– Cluster-Member: ordinary node

– Cluster-Gateway: node with inter-cluster links, forwards information between clusters

– Cluster-guests: a node associated to a cluster

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Cluster-HeadCluster

Cluster-Member

Cluster-Gateway

Graphs

• A network is an undirected graph– G(V,E) Graph G with a set V of nodes (vertices) and a

set E of links (edges)

• Graphs specific measures– Node degree: number of edges incident to the node– Paths in the graph

• Diameter: length of the longest path in the graph• Shortest path: between 2 nodes in the network

– Centrality measures• Closeness: measures how many steps is required to access

every other node from a given node• Betweenness: number of shortest paths going through a node

or an link6

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Outline







– Evaluation


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Routing and Communication

• Routing: Nodes perform route discovery and maintenance

– Flat: works fine for small networks but might not work in large MANETs

• Proactive: messages communication overhead

• Reactive: high overhead just from route discovery

– Hierarchical: may work fine for large networks

• Localized route search and information dissemination

• Communication flows: follow hierarchical structures (i.e., social and organizational) 8

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Routing Protocol Clustering in MANET

• Clustering Goals

– Achieve communication scalability for a large number of nodes and high mobility

– Spatial reuse and coordination of resources• Increase system capacity• Reduce retransmissions and collisions • Balance the use of resources in the network

– Virtual communication backbone• Inter-cluster communication can be restricted to cluster-heads and cluster-gateways

– Local changes• Update and maintain cluster information only locally• Minimize information stored and propagated in the network 9

Advantages and Disadvantages• Advantages

– Reusability: spatial reuse of resources at nodes– Simplification: of addressing– Stability and Localization: smaller and potentially mode stabile sub-network

structures

• Disadvantages– Explicit control messaging: clustering related information exchange– Ripple effect: rebuild of cluster structure in case of network structure

changes– Stationary period: collect and exchange information for cluster formation – Computation rounds: number of rounds to complete the cluster election– Communication complexity: amount of control messages exchanged– No common solution

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Classification

• DS-based clustering– Route maintenance actions to the nodes from the dominating set

• Mobility-aware clustering – Cluster based on the mobility behavior of the mobile nodes

• Energy-efficient clustering – Consider the energy available at the nodes

• Load-balancing clustering – Limit the number of nodes in a cluster in order to distribute the workload.

• Combined-metrics clustering– Considers multiple metrics

• Low-maintenance clustering– Perform clustering for upper-layers and reduce the maintenance cost 11

DS-Clustering• Idea: Dominating Set (DS): in a graph G =(V, E) is a subset D

of V such that every node not in D is joined to at least one member of D by an edge from E– Agglomerative methods: each node assumes at the beginning a

cluster-head role and connected cluster can be merged

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Example1: Connected DS▪ A node announces in the set of connected nodes

▪ Inspects its neighborhood for complete inclusion into D, if true it removes itself from D

▪ Moving nodes send beacons at periodic intervals to inform the CDS about movement

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DS-Clustering (2)

• Summary– Clusters:

• 1-hop non-overlapping clusters

– Communication complexity in case of mobility: • |V| moving nodes cost O(2|V|) (i.e., two messages for each cluster related status

claim)

– Ripple effect• Recomputed the entire DS on local re-election and global re-clustering 13

Example2: Weak CDS▪ DS includes dominating and non-dominating

(i.e., connect 2 dominating nodes) ▪ Favors the nodes with high degree (i.e., nodes

with many links) for inclusion in WCDS▪ Merges the coverage zones of the nodes in DS

until the entire network is covered

Mobility-aware clustering• Idea: cluster nodes with similar moving patterns are clustered

together.

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Example1: MOBIC Nodes disseminate their mobility

information (speed and direction) Cluster-head:

▪ The node with the lowest relative mobility in a neighborhood is elected

▪ Cluster-Heads encounter: timers and lowest id cluster policy

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Mobility-aware clustering (2)

• Summary:

– MOBIC: 1-hop, high communication complexity (absolute and relative speed is distributed in the neighborhood of a node)

– DDCA: multi-hop, larger clusters, overlapping clusters 15

Example2: DDCA (α,t)-every mobile node in a cluster has a path to every other

node that will be available for some time period for a time period t with a probability ≥α▪ Independent of the hop count between nodes

Cluster-Member: ▪ Bidirectional path to the Cluster-Head which satisfy the clusters (α,t)▪ Favor the highest availability path cluster

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Energy-efficient clustering

• Idea: balance energy consumption on nodes by moving the cluster-heads

• Example1: IDLBC– Limit the time a node can be cluster-head based on time counters

• The counter is decremented while a node is cluster head• The cluster head relinquish its cluster-head role when counter is 0 and a new cluster-head the

node with higher counter

• Example2: Energy based DS– Limits the size of the DS by removing the nodes with low residual energy than direct

neighbor nodes in DS

• Summary– Active clustering schemes with stationary assumption– Affected by ripple effect– High communication complexity

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Load-balancing clustering• Idea: limit the minimum and maximum number of clusters in a cluster

• Example1: AMC– Cluster-Members and Cluster-Heads: Periodic broadcast of clustering

information– Cluster-Gateways: Periodic exchange own cluster info with neighbor clusters– Tries to maintain for each cluster

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Load-balancing clustering (2)• Example2: DLBC

– Optimal number of nodes for each cluster head– Increase the stability: variation interval around the optimal number of nodes

• Summary– Multi-hop clusters– AMC localizes the ripple effect, but DLBC is affected by it– The communication complexity is high

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Combined-metrics clustering

• Idea: use multiple metrics to elect a cluster-head

• Example: On-demand WCA– Parameters: degree-difference (difference node degree with the optimal number of

cluster-members), distance to neighbor nodes, average moving speed and cluster-head serving time

– Cluster-head: local area minimum for the combined weighted factor, where the sum of weights is 1

• Summary:– High communication complexity– High overhead – Longer frozen periods – Ripple effect on re-clustering

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Low-maintenance clustering

• Idea: increase the tolerance to topology changes – Reduce Re-affiliation and Re-clustering lower the communication overhead

• Re-affiliation: change the affiliation cluster for a node• Re-clustering: change the structure of a cluster

• Mechanisms: – Cluster head election: Lowest ID or Highest Connectivity

• Periodically check in a node’s neighborhood• Nodes which satisfy the condition in a neighborhood is elected as cluster head

• Example1: Least Cluster Change– A cluster-head has the lowest ID in a neighborhood– In range cluster-heads the one with the lowest id gives up

• Example2: 3-hop Between Adjacent Cluster-Heads– Role of Cluster-Guest which allows a higher stability for the clusters– Require a stationary period

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Low-maintenance clustering (2)• Example3: Passive Clustering

– Nodes states: • Initial, Cluster-Head, Gateway, and Ordinary• Timers to reset the states to “Initial”

– Initial Cluster-head: a node that has something to send• Piggybacking the cluster-head claim

– Initial Ordinary: node receiving one cluster-head claim– Initial Gateway: node receiving multiple cluster-head claim

• The number of gateways in an area is controlled (a constant based on the difference between no of cluster-heads and gateways in an area)

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Low-maintenance clustering (3)

• Summary– 1-hop clusters– Motion frozen period– Neighborhood Lowest ID or Highest Degree– Non-constant number of rounds– Time complexity is equal to the number of clusters– Nodes are wiling to renounce their Cluster-Head position

• PC – clustering when there is data to send

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Outline







– Evaluation


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Overlay Clustering in MANET

• Goal– Achieve service scalability and improved information dissemination in the

network

– Service placement

• Reduce the distance between providers and consumers

• Balance load on service providers

– Adaptation to application needs

• Allow applications define own clustering objectives

• Provide concomitantly different clusterings for different objectives

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Resources & Service Placement – Example

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Communication non-intrusive clustering

• Question: Where to place services in the network?

• Issue: Minimize the distance to resources in order to balance the use of resources

• Requirements:– Management overhead independence zero dedicated

cluster management– Position independence

• Solution: clustering with routing table information

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Usability

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• Replication of data

– 3 Replicas

– 4 Writers

– 4 Readers

Usability (2)

• Replica placement– Influences the accessibility and availability of the data – Well placed replicas Reduced bandwidth consumption

• 10% nodes with replica Close to optimal traffic for replica maintenance• Random placement increases the traffic by 380 KB/s while clustering increases the

traffic by 216 KB/s considering an ideal placement of replica

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Usability (3)

• Replica reconfiguration– Needs to replicate the data to the new node– Adding and removing data replica in the network can cost more in terms of transmitted data

• Delaying the reconfiguration can help …

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Point 10500: 1.207 MB/s

Point 4400: -2.072 MB/s

Temporary services

• Solution: Temporary Clustering with dynamic clustering methods (i.e., consider the dynamic in the network)– Clustering which adapts to the current network layout

• Adaptive number of clusters• Unconstrained number of nodes in a cluster

– Temporary service positioning: • Number of data replica and services, • Data and service placement, • Network partitioning

• Problem: No methods to handle dynamics issues of MANETs

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A non-intrusive information source

• Information Source: – Routing table in the routing protocol

• Information type: Topology information vs. Position of nodes

– Advantages• Updated view of the network, • Location independent

– Disadvantages• Sensitive to existence of communication• Sensitive to mobility and communication patterns• Partial topology of the network

– Issues to investigate • Accuracy• Consistency

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Topology Data Consistency

• Issue: Topology consistency (i.e., nodes may have different topology information)

• Question: How consistent is the topology information at the different nodes?

• Solution: Compare topology information on all the node in the network– Compute the Hamming Distance between topologies (count the differences between

the adjacency matrixes of different nodes)

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• Issue: Topology consistency

• Result: Similar topologies, if node are connected. 35

Ground truth

Non-intrusive Clustering• Method 1: Physical Position of Nodes

– Clustering based on the position of nodes

• Method 2: Community Detection– Separate the regions with dense network connections, and sparse connections

outside the groups– Clustering based on the network topology in the route table– Divide or agglomerate to detect the groups of nodes in the network with dense

network connections, and sparse connections outside the groups– Types:

• Modularity based method• Random walk method• Potts based method

• Evaluation– Cluster head placement: Cluster head election based on centrality measures – Measurements: quality, stability, similarity, consistency, and significance 36

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Non-intrusive Clustering (2)• PAM: Clustering based on the

position of nodes– Map a distance matrix of

objects into k number of clusters

– Finds k nodes which have the smallest distance to the nodes around them

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Non-intrusive Clustering (3)• Community Detection:

Modularity based method– NG [Newman and Grivan

2004]: recursively finds and deletes the links with high weight in the network

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Non-intrusive Clustering (4)

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• Community Detection: Random walk method– vD [van Dongen 2008] simulates

flow diffusion in a graph by random walks, a dense region in a graph will easily trap a random walker

Non-intrusive Clustering (5)

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• Community Detection: Clustering based on the network topology in the route table– RB [Reichard and Bornholdt 2006]

where community membership of a node is determined by its neighborhood (i.e., number of neighbors and neighbors’ membership)

Clustering Evaluation: Quality• Question: Is the clustering valid?• Measure: Silhouette index

– How well is a node clustered considering its distance to the center and of the center of the closest cluster

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Clustering Evaluation: Quality (3)• Question: Is the clustering valid?• Measure: Silhouette index

• Results: The created clusters are good. 43

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Clustering Evaluation: Stability• Question:

– Is the clustering stabile?

• Measure: – Stability quantifies the changes of the clustering with respect to the new network structure

• Measure the cluster-head time

– Delay the clustering

• Results: Delayed clustering can improve the stability of the clusters. 44

Clustering Evaluation: Consistency• Question: Are the clusters consistent in the network?• Measure: Damerau-Levenshtein Distance between detected communities at different

nodes – Counts the number of insertions, deletions, substitutions of single characters, and transpositions

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Clustering Evaluation: Consistency (2)• Question: Are the clusters consistent in the network?• Measure: Damerau-Levenshtein Distance between detected cluster-heads at different nodes

• Results: There are differences in the detected cluster-heads at different nodes.46

Cluster Head – Central NodeCluster Head – Marginal Node

Clustering Evaluation: Consistency (3)• Question: Are the clusters consistent in the network?• Measure: Damerau-Levenshtein Distance between detected communities at different nodes

• Results: Communities are similar at different nodes.47

Community – Marginal Node Community – Central Node

Clustering Evaluation: Similarity• Question: What is the difference between different clusterings?• Measure: The similarity measures the variation of information between clustering over

the same network– Variation of Information:

• Result: The NG, RB, and vD clustering techniques produce similar results.48

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Clustering Evaluation: Significance• Question: Is it a relevant clustering?• Measure: The significance measures the resilience of a clustering to changes in the

structure of the graph.– c-score: the probability of the node with the lowest internal degree in a community is in the same

community in a equivalent random graph (≤ 5%)

• Result: Most of the clusters represent relevant structures in the graph. 49

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Passive Clustering Discussion

• Topology information– Does not apply for reactive routing protocols– Requires a consistent view of the topologies at the nodes

• Clustering measures – Not for dynamic networks– Quality

• Not a general accepted metrics• Different metrics may give contradicting conclusions

– Stability• Does not consider the changes in the number of clusters• Does not consider the changes in the number of nodes in the network

– Consistency• Detected communities are more consistent than elected cluster heads

– Similarity: • Not applicable to clustering from different nodes in the network

– Significance:• Not all clusters represent significant structures in the network 50

Outline







– Evaluation


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Conclusions

• Clustering in routing – Clustering schemes have different focus and objectives

• Cluster structure stability, reduce overhead in cluster construction and maintenance, limit energy consumption, balance traffic load, or cluster-head balancing

• Different metrics hard to compare

– Communication overhead and complexity• Explicit control messages high overhead• PC piggybacks messages

– Cluster diameter and Ripple effect• Multi-hop clusters are less affected by ripple effect in re-clustering

– Localize the cluster management

• 1-hop clustering schemes usually create highly overlapping structures

• Clustering in overlay– Dependent on the performance of the routing protocol– Dependent on the objectives of the application

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References

• Jane Y. Yu and Peter H. J. Chong, “A survey of Clustering Schemes for Mobile Ad Hoc Networks”, IEEE Communications Surveys, vol. 7, no. 1, 2005

• Matija Puzar, Thomas Plagemann, “Evaluation of Replica Placement Strategies for Mobile Ad-Hoc Networks”, The 13th International Conference on Network-Based Information Systems (NBiS-2010), Takayama, Gifu, Japan

• Ovidiu V. Drugan, Thomas Plagemann, and Ellen Munthe-Kaas, “Detecting Communities in Sparse MANETs”, Accepted for publication in IEEE/ACM Transactions on Networking, 2011

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Documents

Clustering in Mobile Ad-Hoc Networks...• Mobility-aware clustering – Cluster based on the mobility behavior of the mobile nodes • Energy-efficient clustering – Consider the