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On the Effect of Group Mobility to Data Replication in Ad Hoc Networks Jiun-Long Huang and Ming-Syan ChenIEEE Transactions On Mobile Computing, May 2006Presented by Manu ShuklaCS 6204Fall 2006

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Agenda The Problem DRAM Algorithm Allocation unit construction phase VectorCluster Replica allocation phase Experiments and Evaluations Conclusions and Critique

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Introduction Mobile Ad Hoc Network (MANET) is a self-

organizing, rapidly deployable network of wireless nodes without infrastructure

Mobile nodes of a MANET also function as routers Disconnection often occurs due to mobility and

causes frequent network division Disconnected partitions decrease data accessibility Data replication can greatly improve the accessibility

for a partitioned network

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Introduction (2)

DCG and E-DCG are two previously proposed replica allocation schemes in MANET

The two drawbacks of the schemes are: Generation of large amounts of traffic Negligence of group mobility

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Introduction (3) Authors address the problem by exploring group mobility Propose Scheme DRAM to allocate replicas by considering

group mobility Underlying group mobility model is assumed to be Reference

Point Group Mobility model (RPGM)

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Description of symbols Symbols used in

formulae and equations

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Mobility Models RPGM models team collaboration where mobile

nodes collaborate and move as a group In RPGM, all mobile nodes are divided into several

mobility groups Each node is assigned to virtual reference node and

movement of a reference node in a time slot is called global motion vector

The vector from the position of corresponding reference node to mobile node position is random motion vector

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RPGM Example We have and

where Pi

N(k) and PiR(k) are positions of the mobile node and

reference node in time T(k)

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System Model m mobile nodes M1, M2,…,Mm and n data items

D1,D2,…,Dn

Each data item is updated by its original host periodically with period τi

Each node is equipped with GPS device so its location is always known

Movement of each group follows a waypoint model which breaks movement of mobile node into repeating pause and motion periods

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DRAM Design DRAM (Decentralized Replica Allocation with

group Mobility) is decentralized algorithm to produce effective replica allocation efficiently

Executed periodically with relocation period r time slots to adapt according to the network connectivity

Two phases in relocation period Allocation unit construction phase Replica allocation phase

In allocation unit construction phase, all mobile nodes in network are divided into several disjoint allocation units

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DRAM Design (2)

In replication allocation phase, the replicas of all data items are allocated according to access frequencies of the data items

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Allocation Unit Construction Phase Three mobile nodes states

INITIAL state ZONE-MASTER and ZONE-MEMBER states CLUSTER-MASTER and CLUSTER-MEMBER states

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INITIAL State Mobile node broadcast info

message to all mobile nodes in broadcast zone with a TTL

When a node receives the info message, it forwards it to all nodes that are at TTL or lesser distance from it

Each node maintains a list of its historical locations called a position list to track its pause and motion periods

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ZONE-MASTER and ZONE-MEMBER states In ZONE-MASTER and ZONE-MEMBER states

Mobile nodes are classified into two groups by the lowest-id clustering algorithm Ones with lowest host id are selected as master of their

broadcast zone enter ZONE-MASTER state Other nodes enter ZONE-MEMBER state

Node Mi in ZONE-MEMBER state joins node Mj in ZONE-MASTER state with lowest host id within broadcast zone of Mi

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ZONE-MASTER and ZONE-MEMBER states (2)

Each node in ZONE-MASTER state then clusters its member nodes

All nodes within a cluster are expected to have similar motion behavior

Master node re-clusters resulting clusters again by considering motion vectors

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Lemmas With help of

lemmas, we have two heuristics

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Lemmas (2)

In a mobility group, an actual motion vector is close to the global motion vector if it has the maximal number of neighbors in angle with maximal

difference θ Maximal number of neighbors in length with maximal

difference 2ε

Develop algorithm VectorCluster in accordance with above heuristics

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VectorCluster VectorCluster consists of two major procedures

ClusterByAngle ClusterByLength

After executing VectorCluster, each zone master will select one cluster master for each resulting cluster

The selected mobile nodes will enter the CLUSTER-MASTER state, and other nodes will enter CLUSTER-MEMBER

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VectorCluster (2) Result of VectorCluster

in given example

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CLUSTER-MASTER and CLUSTER-MEMBER states CLUSTER-MASTER and CLUSTER-MEMBER

states Tasks of nodes in this state consist of two steps

Cluster maintenance Cluster merge

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Cluster Maintenance Cluster member sends a status message to its

cluster master Cluster master checks if the moving behaviors

similar to one another It clusters motion behaviors in status messages Dominating cluster is one with most nodes It sends reject messages to nodes not in

dominating cluster and they return to INITIAL state

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Cluster Merge Merging clusters which tend

to be connected in the near future improves data accessibility

Two allocation units Ci and Cj can be merged into a new allocation unit if they are cluster wise connected in T(k) and potentially cluster wise connected in T(k+r)

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Cluster Merge (2) Here cluster-wise

connected and potentially cluster-wise connected are defined as shown

In replica allocation construction, each cluster master will broadcast a merge message containing cluster master id and current and estimate bounding rectangles

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ClusterMerge Procedure Cluster Merge can be performed by following

process below

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Replica Allocation Phase Objective is to

identify data items to be replicated locations to replicate them for each allocation unit in order to

maximize data accessibility Allocation weight of data item Dj in allocation unit Cx in T(k)

is All data items are allocated in Cx according to their allocation

weights in Cx in descendent order If the candidate set of Dj in Cx is not empty, Dj will be

allocated to Mi, where fij is the largest in allocation candidate set of Dj

Allocation process completes if all mobile hosts in Cx is full

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Procedure ReplicaAllocation Each master unit then executes ReplicaAllocation

procedure

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Complexity Complexity of VectorCluster is O(|V|log|V|)

where |V| is the number of input vectors Complexity of ReplicaAllocation is O(m/|c|

+n)

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Integration with other algorithms Li and Wang proposed RVGM (Reference Velocity

Group Mobility) Yin and Cao proposed scheme RN to balance the

tradeoff between data accessibility and query delay Each mobile node shares only part of its storage with

neighbors A mobile node Mi only cooperates with neighbors which

tend to be directly connected to it in future Easy to integrate these concepts into scheme DRAM

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Performance Evaluation Compare DRAM with E-DCG Use event driven simulator in C++ with SIM

Evaluated the performance of DRAM based on several parameters

Assume 120 mobile nodes in a 50mx50m flatland and each node owns 20 data items

Use data accessibility as measure of performance Accessibility=Number of successful requests/Number of

issued requests

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Performance Evaluation (2) Use produced network traffic to evaluate cost of schemes Effect of relocation period below Shorter relocation period means more executions of relocation

schemes making both schemes adapt quickly to relocation behavior of mobile nodes

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Performance Evaluation (3) Comparison based on effect of number of Mobility Nodes and number of

Mobility Groups More nodes for same number of mobility groups means more nodes can

share their storage by constructing larger allocation units

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Performance Evaluation (4) Effect of Number

of Replicas per Node

Effect of Update Period

Effect of Precision of Location Information

Effect of Packet Loss Rate

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Performance Evaluation (5) Effect of Value of Time-to-Live

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Conclusions Partitions in MANET frequent problem Mobility of nodes important consideration for

data replication DRAM algorithm efficient in allocating

replicas by considering group mobility DRAM also produces less network traffic

than prior algorithms along with producing higher data accessibility

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Critique Introduction to MANET and few examples of

disruptive nature of partitioning not adequate Experiments performed only on simulated data Lack of real world applications of DRAM and no

complexity and performance analysis on real application data a drawback

Number of nodes in simulation relatively small Consider clustering of moving object techniques

similar to ones used in spatial moving objects

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Q/A?

Thank You!