Clustering in Mobile Ad-Hoc Networks Ovidiu Valentin, DRUGAN - PowerPoint PPT Presentation

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 2

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 … 3

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 Properties : Geographically allocated Balance resource use 4 Service localization

Nodes roles in a cluster • Roles of nodes in a cluster – Cluster-Head : local coordinator of a cluster Cluster Cluster-Head – Cluster-Member : ordinary node – Cluster-Gateway : node with inter- cluster links, forwards information between clusters – Cluster-guests : a node associated to a Cluster-Gateway cluster 5 Cluster-Member

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) 1 2 9 0 3 6 7 • Graphs specific measures 5 4 8 – Node degree : number of edges incident to the node – Paths in the graph • Diameter : length of the longest path in the graph    degree(G) 2, 2, 2, 3, 2, 2, 4, 3, 2, 2 • Shortest path : between 2 nodes in the network  Diam ( G ) 5 – Centrality measures    SP ( G , ( 5 , 9 )) 5 , 4 , 3 , 6 , 9 • Closeness : measures how many steps is required to access  Clos ( G , 5 ) 0 . 45 every other node from a given node  Betw ( G , 5 ) 8 • Betweenness : number of shortest paths going through a node or an link 6

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 7

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 2 ( ) O n 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 8 structures (i.e., social and organizational)

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 10

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

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  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 12

DS-Clustering (2)  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 • 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

Mobility-aware clustering • Idea : cluster nodes with similar moving patterns are clustered together.  Example1: MOBIC  m s , s , s [ 4 , 7 ] n n n s  Nodes disseminate their mobility 1 2 4 information (speed and direction)  m s , s , s [ 0 , 1 ] n n n s 3 5 6  Cluster-head:  m ▪ The node with the lowest relative mobility in a s , s , s [ 2 , 3 ] n n n s 7 8 9 neighborhood is elected ▪ Cluster-Heads encounter: timers and lowest id C 2 3 cluster policy 2 5 6 7 9 1 8 C 4 C 3 1 14

Mobility-aware clustering (2)  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) C    0 . 5 , t 60 2 ▪ Favor the highest availability path cluster 3     0 . 5 , 60 0 . 75 , 120   2 5 6 7 9 0 . 8 , 180   1   0 . 8 , 180 0 . 75 , 120   8 C    0 . 75 , t 150 4 1 • 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

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 16

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  i  L C U     • L C C and C merge if C C U i i j i j •     ' ' ' ' ' ' C U C splits into C and C such that L C , C U i i i i i i  L 3 C  C C 3 U 5 C 3 C 3 2 2 6 7 9 6 7 9 6 7 9 3 3 3 C 1 8 8 8 2 5 17 2 5 2 5 C C 1 1 1 1 1 4 4 4

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 1 1 1 1 C C  3 C opt 5 C 2 2 3 4 2 4 var  6 7 9 6 7 9 C 1 3 6 7 9 3 3 C C 8 C 1 1 8 8 1 2 5 2 2 5 5 1 1 1 1 1 1 1 4 1 4 1 0 4 0 2 2 1 C 1 3 • Summary – Multi-hop clusters – AMC localizes the ripple effect, but DLBC is affected by it – The communication complexity is high 18