On the Effect of Group Mobility to Data Replication in Ad Hoc Networks

1
On the Effect of Group Mobility to Data
Replication in Ad Hoc Networks

• Jiun-Long Huang and Ming-Syan Chen
• IEEE Transactions On Mobile Computing, May 2006
• Presented by Manu Shukla
• CS 6204
• Fall 2006

2
Agenda

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

3
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

4
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

5
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)

6
Description of symbols

• Symbols used in formulae and equations

7
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

8
RPGM Example

• We have and
  
  
• where PiN(k) and PiR(k) are positions of the
  mobile node and reference node in time T(k)

9
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 ti
• 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

10
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

11
DRAM Design (2)

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

12
Allocation Unit Construction Phase

• Three mobile nodes states
• INITIAL state
• ZONE-MASTER and ZONE-MEMBER states
• CLUSTER-MASTER and CLUSTER-MEMBER states

13
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

14
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

15
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

16
Lemmas

• With help of lemmas, we have two heuristics

17
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 ?
• Maximal number of neighbors in length with
  maximal difference 2e
• Develop algorithm VectorCluster in accordance
  with above heuristics

18
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

19
VectorCluster (2)

• Result of VectorCluster in given example

20
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

21
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

22
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(kr)

23
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

24
ClusterMerge Procedure

• Cluster Merge can be performed by following
  process below

25
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

26
Procedure ReplicaAllocation

• Each master unit then executes ReplicaAllocation
  procedure

27
Complexity

• Complexity of VectorCluster is O(VlogV) where
  V is the number of input vectors
• Complexity of ReplicaAllocation is O(m/cn)

28
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

29
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
• AccessibilityNumber of successful
  requests/Number of issued requests

30
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

31
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

32
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

33
Performance Evaluation (5)

• Effect of Value of Time-to-Live

34
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

35
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

36

• Q/A?
• Thank You!